tag:blogger.com,1999:blog-18265018945380860902024-03-13T10:57:10.464+00:00BehaviourBlogIntermittent musings on all things behaviour-analytic, behavioural and neuroscientific.BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.comBlogger19125tag:blogger.com,1999:blog-1826501894538086090.post-32825599544237662002015-01-29T11:47:00.000+00:002015-02-26T13:11:49.668+00:00The Contingencies of Smoking Cessation Interventions in Pregnancy<div class="separator" style="clear: both; text-align: center;">
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A study has just been published in the BMJ on the effects of financial incentives on smoking cessation in pregnancy. <a href="http://www.bmj.com/content/350/bmj.h134">Tappin et al. (2015</a>) conducted one of the first studies of voucher-based reinforcement for health related behaviour change in the UK, and the findings have generated considerable media coverage.<br />
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Tappin et al. found that more than twice the number of pregnant women in an intervention group given vouchers for stopping smoking managed to quit (69 or 22.5%) compared to a group given standard smoking cessation services (26 or 8.6%). This is a large improvement over existing smoking cessation treatments like nicotine replacement therapy, etc.<br />
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I've always been interested in the applications of the principles of reinforcement, derived from operant conditioning, to the treatment of health related behaviour change. For several years, I've taught undergraduate and postgraduate classes on the topics of "incentive therapy" or "voucher based reinforcement" or "contingency management" (CM) or a host of other terms used to describe the same approach (I'll use these terms interchangeably). And, I've often wondered why it has taken UK researchers so long to adopt these protocols, which have been proven to be effective at reducing problematic behaviour, such as substance abuse (e.g., <a href="http://www.ncbi.nlm.nih.gov/pubmed/17034434">here</a>, <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1360-0443.2006.01311.x/abstract?deniedAccessCustomisedMessage=&userIsAuthenticated=false">here</a> and <a href="http://www.ncbi.nlm.nih.gov/pubmed/24750232">here</a>), increasing medication adherence (e.g., <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3432682/">here</a> and <a href="http://onlinelibrary.wiley.com/doi/10.1111/jch.12441/full">here</a>), attendance (e.g., <a href="http://www.sciencedirect.com/science/article/pii/S0740547210001042">here</a>), and for increasing healthy behaviour (e.g., <a href="http://www.bmj.com/content/338/bmj.b1415">here</a>) in other countries around the world such as the USA. So, the present trial is to be welcomed. It joins a handful of other such studies from UK based researchers on contingency management for vaccination adherence/substance abuse (<a href="http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(14)60196-3/abstract">Weaver et al., 2014</a>) and breastfeeding (<a href="http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(14)62131-0/abstract">Relton et al., 2014</a>).<br />
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There are, however, some unique features to this latest UK based research which I think warrant further consideration, and hopefully, funding.</div>
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<b>(1) Contingencies and Escalating Schedules</b></div>
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According to <a href="http://www.drugandalcoholdependence.com/article/S0376-8716(99)00071-X/abstract">Petry (2002)</a>, CM interventions have three basic tenets:<br />
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"<i>First, the clinician arranges the environment such that target behaviors (e.g. drug abstinence, clinic attendance, medication compliance, other behaviors) are readily detected. Second, tangible reinforcers are provided when the target behavior is demonstrated, and third, incentives are withheld when the target behavior does not occur</i>." (p.9).<br />
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These three steps must be present for a study to constitute an example of contingency management incentives. This isn't just an academic point; these tenets describe how operant conditioning procedures, and in particular reinforcement, are implemented. If one or more step is missing or changed, then it's not an instance of voucher reinforcement CM.<br />
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Another defining feature of voucher based reinforcement studies is the use of escalating schedules of reinforcement. These schedules initially provide a fixed sum, say a £10 voucher, contingent on the presence of the target behaviour (in this case, the absence of objectively verified breath carbon monoxide, CO). The next time the criterion for reinforcement is met (e.g., CO levels under a certain parts per million threshold, for instance) it is followed by an increased quantity of the reinforcer (£15, for example). This quantity increases as the criterion for reinforcement is met again across successive sessions, but crucially it resets the moment criterion is not met. So, if CO levels above the set threshold are detected (i.e., the target behaviour is absent), the schedule resets to the original value and the participant has to once again meet criterion to start earning vouchers again.<br />
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The escalating schedule requires multiple opportunities to earn reinforcement, so repeated measurement of the target behaviour is crucial (e.g., <a href="http://www.sciencedirect.com/science/article/pii/S0165178105003884">here</a>). On (re-)reading the procedures of Tappin et al., it appears that, strictly speaking, they did not employ an escalating schedule and did not undertake multiple, repeated assessments (other than just 3 sessions).<br />
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The intervention group received £50 if they attended their first face to face consultation and set a date to quit at the outset of the trial. Note that there were no contingencies in place for abstinence at this stage. Four weeks later, smoking status was confirmed with a CO breath test. If the test was < 10 ppm CO, then a further £50 voucher was earned. Twelve weeks later, those who has quit smoking at four weeks were again contacted and provided a CO2 test. If abstinent, a £100 voucher was earned. Then, between 34 and 38 weeks’ gestation, self-reported quitters were once again asked to provide a CO2 recording, and if abstinent they earned a £200 voucher. </div>
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Although the magnitude of reinforcement remained the same from session one to two, it increased in the third and fourth assessments but it did not reset to zero contingent on a positive breath test (i.e., > 10 ppm). There were also only three opportunities available to earn vouchers (apart from the first session, where £50 was available but it was not contingent on any objective health related behaviour change, just showing up at the session). Moreover, the three CO measures were obtained from only those participants who self-reported quitting, and we all know how unreliable self-reports can be.<br />
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<span style="color: red;">***!!STOP PRESS!! This study used an escalating schedule of financial incentives with pregnant women seeking to stop smoking; it worked. See <a href="http://onlinelibrary.wiley.com/doi/10.1111/add.12817/abstract">here</a> for a full text version of the study.***</span><br />
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<b>(2) Objective Measures of Smoking Abstinence</b></div>
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In the intervention group, vouchers were contingent on CO readings of <10 ppm. At the outset of the study, however, inclusion criteria consisted of CO levels of at least 7 ppm, which “indicates current smoking” status. No further justification is given for the use of <10 ppm or 7 ppm, and only participants' baseline ppm levels are reported.<br />
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Let's look at the ppm readings: not surprisingly, levels did not differ between the intervention and control groups (13.65 and 13.14 ppm, respectively). These are high levels and suggest that all participants were heavy smokers. Subsequent analyses of CO ppm are not provided, other than numbers of participants who were ‘CO negative’.<br />
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Tappin et al. do cite a previous study from their group (<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2265678/">Unman et al., 2008</a>) that tested the conventional standard of 8 ppm, which others had claimed was too high and should be reduced to 2 or 3 ppm in the general population and to 4 ppm in post-partum women. Unman et al. concluded that “<i>The current paper concurs with cut-off levels of 2 ppm and 3 ppm in the antenatal clinic setting to detect, with reasonable accuracy and discrimination, women who are smokers</i>.”</div>
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So, why not target similar levels in the study? Or why not justify < 10 ppm? Clearly, participants in the intervention group could meet criterion for reinforcement with CO levels of 9 ppm, which is an obvious improvement but perhaps not worthy of such concerted attention? Why not differentially shape a reduction in breath CO levels? A study from the applied behaviour analysis literature did precisely this in a changing-criterion design with individuals with developmental disabilities (see <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC1284405/pdf/12555914.pdf">here</a>). But, I digress.<br />
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Previously, <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1360-0443.2008.02237.x/abstract?deniedAccessCustomisedMessage=&userIsAuthenticated=false">Heil et al. (2008)</a> have used a threshold of CO < 6 ppm from week 3 of their study (with breath CO levels the key criterion for reinforcement during the initial 5 days of the smoking cessation intervention) and, critically, vouchers were contingent on urine-cotinine levels of <80ng/ml, which required “a longer duration of smoking abstinence than breath CO” (p.1011). <a href="http://onlinelibrary.wiley.com/doi/10.1111/j.1360-0443.2010.03073.x/abstract?deniedAccessCustomisedMessage=&userIsAuthenticated=false">Higgins et al. (2010)</a> used the same levels.<br />
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In the Tappin et al. study, urine-cotinine was also measured but just once at the end of the study, which was at 34-38 weeks’ gestation and there no contingencies were in place for high or low levels. That is, voucher reinforcement was only in effect for CO ppm, not for urine-cotinine, and that was at three fixed points separated by extended periods of time. The urine-cotinine data of Tapping et al. showed however that 80% of the self-reported quitters (only 14 women) had in fact quit during pregnancy.<br />
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This is an interesting secondary measure, but it remains unclear why urine-cotinine was targeted more often during the study and why a threshold of <10 ppm was adopted.<br />
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<b>(3) Secondary Measures: Birth weight and miscarriages</b></div>
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Self-report measures constituted several of the secondary outcome measures, but other, objective measures were also reported. For instance, the birth weight of babies born to mothers in the different groups was recorded because previous research (Heil et al., 2008) has shown this to be a sensitive measure of intervention effectiveness.<br />
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In Tappin et al., the mean birth weight (3140; <i>SD</i> = 600) in the intervention group versus a mean of 3102 (<i>SD</i>=590) in the control group did not differ statistically, and nor did the number of miscarriages (intervention group (<i>M</i>=2, <i>SD</i>=.7) compared to the control group (<i>M</i>=5, <i>SD</i>=1.7).<br />
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This is disappointing, particularly as other studies (e.g., Heil et al., 2008, Higgins et al., 2010) have found positive effects on birth outcomes and reductions in miscarriages. It is possible that these differences were due to the delayed contingencies, infrequent assessment and partial escalating schedule employed (although further research and lot of grant funding is needed to test this!).<br />
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<b>(4) Citations: Smoking Cessation vs. Breastfeeding</b></div>
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In the Discussion section of their article, Tappin et al. mention the "four studies (three in the United States, one in the United Kingdom) included in the BIBS (benefits of incentives for breastfeeding and smoking cessation in pregnancy) systematic review 2014 of financial incentives for smoking cessation in pregnancy included 332 participants in total." (p.4)</div>
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The review cited was in fact a one-page meeting abstract published in the Lancet:</div>
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<a href="http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(13)60404-3/abstract">Hoddinott P, Hislop J, Morgan H, Stewart F, Farrar S, Rothnie K, et al. Incentive interventions for smoking cessation in pregnancy: a mixed methods evidence synthesis. Lancet 2012;380:S48.</a> <br />
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I couldn't find a References section for this article so with a little more detective work I unearthed a survey, also by Hoddinott and colleagues, of <a href="http://bmjopen.bmj.com/content/4/7/e005524.full?sid=b5bde67f-6a2e-486d-bb7c-cd1d16971d2b">"public acceptability of financial incentives for smoking cessation in pregnancy and breastfeeding"</a> published in BMJ Open in 2014.</div>
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In this paper, Hoddinott et al. cited, the following studies as providing "promising evidence supporting financial incentives for smoking cessation in pregnancy":</div>
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<a href="http://www.ncbi.nlm.nih.gov/pubmed/24154953">Chamberlain C, O'Mara-Eves A, Oliver S, et al. Psychosocial interventions for supporting women to stop smoking in pregnancy. Cochrane Database Syst Rev 2013. (10):CD001055.</a></div>
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<a href="http://www.ncbi.nlm.nih.gov/pubmed/22227223">Higgins ST, Washio Y, Heil SH, et al. Financial incentives for smoking cessation among pregnant and newly postpartum women. Prev Med 2012;55(Suppl):S33–40.</a><br />
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Hoddinott P, Hislop J, Morgan H, et al. Incentive interventions for smoking cessation in pregnancy: a mixed methods evidence synthesis. Lancet 2012;380(Suppl 30):S48<br />
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Two of these are review articles (Chamberlain et al. and Higgins et al.) while the other article is the meeting abstract I mentioned previously.<br />
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When I teach this topic, the Heil et al. (2008) and Higgins et al. (2011) articles are the first I refer to, so it's odd that they were not cited here. It also remains unclear what the one UK study of voucher incentives for smoking cessation is.</div>
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There is much to commend in this study, however closer adherence to published reinforcement protocols in further studies can only serve to boost the potential impact and effectiveness of this unique approach to behaviour change.<br />
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BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-43570167968364388282014-02-26T19:07:00.003+00:002014-03-13T16:22:33.400+00:00Research Briefing: How "Almost Winning" on Slot Machines Evokes Unique Neural Signatures in Gamblers <div style="text-align: justify;">
My colleagues and I have just had a multi-methods imaging study on slot machine gambling published in <i>NeuroImage</i>. The article describes the findings of two studies, one conducted with <a href="http://en.wikipedia.org/wiki/Functional_magnetic_resonance_imaging">fMRI</a> and the other with magnetoencephalography <a href="http://en.wikipedia.org/wiki/Magnetoencephalography">MEG</a>, which were part of a large project funded by the <a href="http://www.wicn.ac.uk/">Wales Institute of Cognitive Neuroscience</a> (WICN). The <i>NeuroImage</i>
article is the culmination of several years' hard work from everyone
involved. I can't tell you how relieved I am to finally see it in print!
It's certainly been one of the longest duration projects I have ever
worked on, and also perhaps the most rewarding because it has taught me
the value of interdisciplinary collaboration and how finding answers to
the really interesting scientific questions really does require the
input of a wide range of subject experts. </div>
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<a href="http://3.bp.blogspot.com/-JQc3lYCeZQ4/UwtAFgyUW_I/AAAAAAAAAII/uTMPrKLELxc/s1600/AlmostWinning_coverart.jpg" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://3.bp.blogspot.com/-JQc3lYCeZQ4/UwtAFgyUW_I/AAAAAAAAAII/uTMPrKLELxc/s1600/AlmostWinning_coverart.jpg" height="320" width="303" /></a> </div>
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<span style="font-size: large;"><b><i>Background: The Near-Miss Effect</i></b></span></div>
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At the time we started this project, a lot was known about the neurobehavioural mechanisms of the "near-miss effect" in simulated slot machine gambling, thanks mainly to <a href="http://www.large.psychol.cam.ac.uk/">Luke Clark's lab</a> at Cambridge and <a href="http://ehs.siu.edu/rehab/faculty-staff/tenure/dixon.php">Mark Dixon</a>'s pioneering behavioural work at Southern Illinois (Mike Dixon at Calgary has since down some fantastic work in this regard). The near-miss effect refers to a cluster of findings concerning the neurobehavioural effects of "almost winning". Typically studied using simulated, multi-reel slot machines, near-misses may be defined as a losing display physically resembling a winning display (i.e., two out of three matching symbols on the payout line of three-reel slot). Now, you need three matching symbols to win on a given spin, but 2/3 is deemed closer to a win than, say, a 1/3 display. They are both losses, but the near-miss is subjective and objectively deemed to be almost a win, perhaps due to physical resemblance/generalization.<br />
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<a href="http://2.bp.blogspot.com/-iCPkXOeho2k/Uwu6Y2y_5ZI/AAAAAAAAAIY/WkyNmA513ds/s1600/FruitMachineIcons.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-iCPkXOeho2k/Uwu6Y2y_5ZI/AAAAAAAAAIY/WkyNmA513ds/s1600/FruitMachineIcons.jpg" height="217" width="320" /></a> </div>
We know that the proportion of near-misses influences gambling persistence (around a third of trials presenting near-misses is generally thought best), that people (gamblers and non-gamblers alike) rate their chances of winning on the next spin higher than following losses, that spins following near-misses are usually initiated faster than after other outcomes, and that physiological arousal to near-misses resembles that of actual wins. Studies by Luke Clark, Mark Dixon and colleagues have individually shown that near-misses evoke activation in ventral striatum, rostral anterior cingulate cortex (rACC), and in reward-related neurocircuitry, such as the midbrain (substantia nigra/ventral tegmental). Even the extent of activation is predicted by gambling severity: indeed, the more severe your gambling problem or history is, the greater the activiation seen in anterior insula (bilaterally) in non-gamblers (Clark et al., 2009) and in the midbrain of gamblers (Chase & Clark, 2010).<br />
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Of course, there's only so much that fMRI research, with its reliance on the slow haemodynamic BOLD response, can tell us about the brain systems underlying gambling behaviour (I will gladly leave it to others with far better expertise than me to fully explain the issues involved - <a href="http://neurochambers.blogspot.co.uk/2014/01/tough-love-for-fmri-questions-and.html">here</a> and in terms of fMRI vs. MEG, <a href="http://www.sciencedirect.com/science/article/pii/S1053811912000456">here</a>). Instead, we wished to examine the temporal dynamics and oscillatory changes involved in the near-miss effect and hence chose magnetoencephalography (MEG) as the method to do so.<br />
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<span style="font-size: large;"><i><b>Our Methods and Predictions</b></i></span></div>
We first measured BOLD-fMRI in a mixed sample of gamblers and non-gamblers and focused on the overlap between activation related to wins and near-misses, and examined the associations with gambling severity and a trait measure of cognitive distortions. We predicted that similar activation patterns would be seen when near-misses were contrasted with losses and when wins were contrasrted with losses. Also, we expected that activitation of the insula to near-misses would be predicted by gambling cognitions.<br />
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We designed a simulated, three-reel slot machine task based on Mark Dixon's previous fMRI work. Here's an illustration: <br />
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" width="400" /></div>
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Participants spun the reels, viewed the outcomes and made ratings while lying in the MRI scanner or sitting in the MEG device. We compared separate groups of gamblers and non-gamblers using MEG to investigate induced oscillatory power changes associated with win and near-miss, relative to loss, outcomes. Our focus was on the orbitofrontal cortex (OFC)/ACC and insula regions previously identified in fMRI studies. <br />
<br />
By using MEG, we were also able to record EEG at different frequency ranges as well. So, on the basis of previous EEG/ERP work, we were particularly interested in whether near-misses would induce greater theta power, in the 4-7Hz range, in gamblers compared to non-gamblers in our frontal and insula regions of interest. <br />
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All participants were administered the South Oaks Gambling Screen (SOGS) and, in the MEG study, their scores determined which group they were assigned to (gamblers or non-gamblers).<br />
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" width="320" /> </div>
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<span style="font-size: large;"><i><b>Our Findings</b></i></span></div>
Generally, near-misses recruited similar brain regions to wins, including right inferior frontal gyrus and insula. Behaviourally, both gamblers and non-gamblers rated near-misses as closer to a win than a loss (replicating this intriguing effect once again):<br />
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<img alt="" height="320" 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" width="266" /></div>
<br />
BOLD-fMRI responses in the insula to near-misses were associated with the extent to which participants thought about gambling (GRCS) - this neatly replicates an earlier finding from Luke Clark's lab.<br />
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/3e7/3e4YcfDnjYwx72qEc9apNNNrnuuuskkeCMM87YaKONHv7wh0P4yEc+8oUvfOE3vvGNSIqXvOQlpmkSRIhsv/32pk2vfe1rZSyiJNLHTOpFL3rRIx7xiOB/4hOfePHFF/ckqQYOhQN1DjUUNlYk/ThgxkQuMP2SL+edd96//du/GfZkRFOnkL/IAvCNN95ID3r3u9/9kIc8hGy65ZZbzLBOPfXU00477Wtf+9r+++9PVdlggw0e85jHXHnllZdeeulOO+109dVXP+hBD7rpppvM0UzEIIfw1ltvlZcPZ0IyS0LDzjvvTHjJQhh997vfNb16/etfD+hXkxq3AA5UWbMA5tWsg3GAUfaCCy7IbEgOIoASQWEBC6RlCIkgCD5/Y2Ehjy6//HLiZsMNN/zXf/1XBhfJCI6gOuyww3bZZZdrrrnmve99L4SiZI8fPE1YCLQJ599zzz3PeMYz/uM//uPss88GvOlNb3r/+9+viJKgAkPnQJ1DDZ2lFeH9OGDAmwGRLKZRIgz4/fbb76qrrjKlSroiAqJx+CtLDDQHHnjg1NTUb/3Wb1FhhK9cuVIWakhQveY1r3noQx966KGHCtljjz0e+MAHkkE9jbsQcqU4yUyd3vOe97zvfe87//zz6USnn356SZBk1R86B6qsGTpLK8L7cYCMOOSQQy655JKPfexjIsgCAIUiibpFw7RYWO3JWMQTcSOEjJDL6hUMhJe/pkKXXXbZO9/5zic84QnWsO5XcOMPVFwj4BdMslh5ttlmm5NPPvnaa6+lQDVjKzwKDlRZMwquVpyrORCpASAUyJRtt912hx12EGic77XXXsKbIqDIggIEg4xcETRQ/dd//RfJcuKJJ1500UWmUZQaKf/93/+dmSbJ+EKkhIqfv/wAooSbmv3t3/4tzPBYEXvjG98oPFFi4xJS/WFxoMqaYXGy4unkQBmxZZAb2E9/+tP9/exnP8uC05FB+o6Q8rdEkQ7WmFhnrEMxEm+55ZYW0QVKGZHEkAw+88wzGYmYlr/5zW/6WwgILGVMP/7+xm/8xl//9V+fcMIJYK4U1MySqOovkAPVNrxABtbss3OgDGBTITuG119//bvvvtv+YJrOL//yLxvVXFMBaWKUV2zT33HHHY899tgf/ehHRx11VFKKffGLX/ykJz0JYHXJnMhy0qte9SpGH3mlCf6SWLItttiCFem2225j6BEuhAAys/ve97736Ec/OlmSvvrD4kCVNcPiZMXTgwPWd/7lX/7lWc96lsEs2nj+7d/+7Q9/+MPEAeFC4vibbMTB7rvv7kzDpptuKvFb3vIW2//8FeuvqD/4gz+w4wZsO4wVcTKLzkKaWLF6ylOe8spXvjIi6aCDDlprrbWyE8eWHHLtwgsvtIwllkpFCQI4CaFcNDDWWJACm9x9/vOfR6r5XWSNgqTsUaUaNF8OrP5izDdvzVc5MCMHip6SDlbGbcIzkuMTQMHiLxdYYIGFBIakAAW/2CBvFiFZE20yluwdsQVDkOdvM42Q6hbOgarXLJyHFUMPDmTkG7GAplyIECmCIMma+RNSsmT8B0/8krjk7Q5PVPImvZAkiyokMLEJLzgBCSkUNqMqvBAOVL1mIdyreWfkQHOcNxNlwMdPeOQCeKYszex94MHxNEvviTAJCsKeaWrgXDlQZc1cOVbTVw5UDsyHA3XNez5cq3kqByoH5sqBKmvmyrGavnKgcmA+HKiyZj5cq3kqByoH5sqBKmvmyrGavnKgcmA+HKiyZj5cq3kqByoH5sqBKmvmyrGavnKgcmA+HKiyZj5cq3kqByoH5sqBKmvmyrGavnKgcmA+HKiyZj5cq3kqByoH5sqBSTwPlbM2fBegOHO89957O0Nczr/Yn86dddZZzgE7Q+yqfVfzl9i58remrxyoHAgHJvGMQmTN3/zN37z85S93nbV7m9x+ksu0MYWgWbFixZ577gl2IsZ7ry5DyeVvtdNUDlQOzJsDEzqHcm8b+eK+pWklpvOmEldee/LZxXFvfvObvTTihv1587dmrByoHAgHJlGvIV+22mor12J7G8jzY5/+9Ke33nrrMksS7vIkN9r+/d//vVtsPWzkKiZ304ZfkU1vfetbf/KTn5Q+RP0RbiLmzbN6OLiwpQKVA00OTKK9hlygubix7QMf+ABZQ8o0BYSbaN2enRv8E57nnwvXZP+zP/uzCJ0SKKX7/fO+YgmsQOVA5UDhwCTKGpWnthAWBATf38BhSkIKg7oBssmF2PQa2YPBPEsuIU083RlrSOXAJHNgEmVNBIRWz93XERARMaJ+8zd/81d+5Ve8BiskgWw3HV3kH/7hH8iXBLI0P/vZz2Zj9i6a7B0p69/KgcqBcGASZY2aRyg0RQOt5JxzzrHq5DVFd2hff/31pldf/epXTaC8wVq6S7JMTU2VEPJILr4nAUpgBSoHKgc6ODChsgYXVist992kDXCHvgfJKCl203gtyL387MeSuXP/4IMP7uBa/Vs5UDkwVw5MrqyhoVj2tp79tKc9DdesN4HNngBeXPWChyfKhL/iFa8Y/AHWpqI015ao6SsHljcH2l7zpjhgqEe/7HD50pe+5DUy75M9//nPZ+wQbqwuueFKJ/Ki0O233/685z3v0ksvXXL0L+/+XWs3PhxoW68xMq+55prtt9/eA4O4QGX4zGc+Y57ifeVf//VfL5t3x4dBg1BS5mJV0AzCrppmMjnQ9r5hC8ZeJrzjjju8ZLj22muzp/7hH/6hx9vJGoKmDNrJbIxa68qBZcyBtmWNORSN5jnPeY4X4E09iJ7TTz/d/l3vpYrydxnzulatcmCSOdD22Ka50GW+853vfO5zn7PMzMxB1jDf/O7v/q4JSNVrJrkv1rovbw60ba8hUPbbb79DDjnkZS97GS3mhhtuIF8YhvO8/BK1dyxRspd3z661GzcOtK3XGJa2sey6665RYfj26drP8qhHPWrcWFPpqRyoHBgiB9rWa5DOUmPS5ESSLblUm4c97GGXXHKJ22TcEbPLLrvkphgiiRtiPUeKqk79RsreESEvXzs9LbCDKWXz93QHXDI9cEQsGi7aRZA1JMsnP/lJrZuFp5tuuskqeBrb/hQu8HDrWbFVDvTkAJliUeLmm28+8sgjr7zyysc97nEuLdp88817Jq6BC+HAIsgaGo0bG17/+tc//elP/+EPf3jMMceowHunnWtiGIzFEjdLSK9ZSAPUvIvLAT3NSVr7MO+55x7wqlWrLrvssrPPPnvHHXesPXC4TbMIsuYTn/iEwwHveMc71CTNue+++zruuP/++++222633HJLLnwYbj0rtnY4YLhm70LzazHmg/bCCy90KRoiQ7OZ1GmnnbbTTju1w7F5l4JaeUNz0x9bbrdtG3aNw5133una8Ntuuw1T9MscO/qnf/qnv/qrv8I7Rxb4Y8uvnj1jaVHbswpDDAw39H6NC22GxBDxDx0V5RrNTTrpOEMvZegIS68rgibA0AsaFsK29Ro2Guenzz33XDdmrrvuuq59sbWPfHEV3sc//vFHPOIRTMVp9cLKYVV1dHia3XR0pSwJzD4hH/nIR/i2a2633Xbjzxnd7Pd///cLnQE22GCD8ec2UY74H/zgB77cbq196lOfaqUF/eM7cBDXpnPFlEUoc6hsEcYXwoUS69JfEofuioNxbVK1kLJQS0Tqmub8C8GzpPNiAo3VDc05Q5uByiTXWlOmIDRwpdACC+nJ3iTwnXPWX1fkUK4frly5cqYsPfGMLjB16enban/AAQestdZahhL3jW98I9UZHTELxHzvNS4LxDJ4drIGR/iOXJ544olOe7s4Bit/+tOfAmbtHIMX1GbKKmvSps997nOb6oAB4JhbO4NWKToVUwtAp3LIjmJlTVOgEOT16Q8SmDS9+93vfsELXnDggQdaGO2fvg+q4UY1h0MqwjcVYPG099UUAYcjH53yYYVI+uHSMERs95umNjvKiGDt6rkld4Nr0VQjDCJ3nvzkJ4dxKboJj4iYYaF1Wv373/8+vcYSxrBwLiE8aUefinXWWcdoD+Waj6PavOc97zEkRl2d0pesM3hHMMW5vPWUU06x4OBvHxrk1S1RKxnf3wAJAS+WMzRCNpIcVya4Xatky0juSBDouqVNN93UkhnBuuGGG5YqLBbBs5SL4jadRvX1I5KbDQn2IFxTikvWJlULLCuXaU3sHCoNZ2r8mMc8ptnbjJMPfvCDYhfI3gGzK+i8885DQLqW0gE++HZR9O9OYlMFfoAoEQOWO9Jk6HELgvc5zJXKkFG1xz72sXbbm7QivuhuIX6k9CwEedu2YXy57rrrfHCseeMX9qEevzJQ/eWa/XUUsCZRitd1teKDH/zgvfbaywU6pdxw0/cwixFo88VweiuUiG2S1PzbhJtplj2s62OpNt1nn32OOuoo9Q0rcJV5uJ3qK1ELOsqLGE2mUL4QEtCZOypzHzIk4ySID/At7JN+iFHNPoOHkY/wC0c/FYYFk7VByDSNq4k0UtzK8tKXvrSEhNpC/BDJGzIqtWrT0bEZzD2SjbMdDnO5FohR7mGHHaZt3EmudRmnib9SLhrM8sigNB7fDcTNWPA0pT//GEavoa+1Q38hZkwAtS5N+dGPftRgmJqaevWrX22rVMJboDMtctFFFzlkUBoOoHGJm7Ftl5DN9w3GK76dPq5A+OM//mNKYkSPWgC22GIL2111VJbsJB7bSs3U3G3bhjHIeSiS2HMFlp90Dg5zbboJ32cidIjhmkpDeuPJ2Qgb0rXlH/3RHxX8mtyqrUCGBqZ+uo+vZYlFpATxBQJITxfx+ALY2O5vSTlRQBiCsVzGTLhUGDVqbqQghWrQzNC1oA3oVAOB3KgJmB/+wjcUereDectRZPRHygDohsceeyypHd6GvRK3xtj51atnrrbnUIhwzTBmeSDFNxAQPe0v//Iv7bvRP4astvVCpxTG/E022cRqhX1cDNUUmZJQLEMvOtnhvv71r6MqGriQkubtb3+7p3j9DU8JSrnuvvvukmCigMIZY0PFDQNAAvmttWnKspxkswk7EUvN4Ycf7ipr9Bi6i94ihUsoabIFfxw8fte73mUvKzozIgTa9WN2/6pXveohD3lIeNjMBck4VGpOXG1b1uBaRjgqwWkAXDMNSR+dE/XzS0xPoYg2X5iz4l5QIUmro+2qq65CmB2GtJucqEgaUc5w6RNiQ3/8JbHZtFRziACGwBYfkDHQETjE4mZClXLFUm04ABq4SMCZcrUZrp+kqyjUzO5rX/ua1WvmmGz7CKmbbbaZpXfTdq/IS1wqleq0Se3wy5qufnseJVBhBirJAuCDBQKiH7ZAiuV27WoCrFDKi+bMCawULZCs8eA3wigs7P/2p1llDKmhlmbrsmTOJ5QPm4Z54hOfKE0L9Nci+nNAK6RH9U/Wcmyo0n9ozSwvTFq6DUcU8q2TvPOd77TfWrKma5nIkRbXtl5jTGKlfSg/+tGPYpr19+qrr95hhx2cWsD0/tIUL/okmDV78tKhzOQZ4fTI7P6ibwczDAAqj60iElNfiZJ//Md/tJFUroLf0oa8EkiM/sc//vE+TcyQfWirUW1yIOqA1ilN1k7pSuSUzm+WqJN8+ctf1s+/8IUveEyVUpNYR47/37Qzo2ep6cjVxLAM4LZljbYn1N/ylrekExTmPvOZz4zS2KdzJLFm625LLZHuNUiTkHGuN6bBsrppeFmU/uMf//hNb3qTVYzjjz9+6623FsicRKDceOONOsRGG20UzMjjPC9TCkJVtHQEiCrhFVgsDixWK+gJnNID8HGAqkK+MPCx7xbCdBgbf48++mgzpsIlsSVBCVxWQOFLOwBJQabgKX0BH139adxaEmIToV6K7UOGBEnTBGQpf/vkbUZJz+CSduX7nugHJlbUGRsx//mf//mtb30r2kTpEyQIoRP1R1lcExVYSM4oMEN2RNW/E8WB6d7xc4+q+4Y3vMHpqvIVBNCg7UIyc9cJfcnSdfnd/Wr5sa5tvYZR1h1o9tfQKQh1jUGn2GabbbKs01+KG/zXX3+9ZaPjjjvuiiuuOPTQQ1lSPLa98847R3D0z96Mdbm6WZLlJ/KFtZ/Io9b+yZ/8CZVH5/AVIhCtdsti18wLX/hC+LX9XEtplljhZc8B3cM3ybkkC+32wTMUECJqLZxqzEqgq9t8ZP5O6KQ7pUcRNBPRtVoWn2Q56wZbrLPw5qiEDqlBr2EY0zD9idEk7PMa7y/+4i8Yd/K5IB2yU7v9L0NKRHY5e9k+Df05trRicTIOGwF8vYUfQMiiVyfENIkMjE7LCPaa+3xGaqRz+pI5XmCpIdVZdPoXl4BF2HdguwqFwkY+gL02tsA5sWJlR0PiRf+vn4PzdBANCQMJRS3Sis3dMf2zjyJW30r3GgXyicLZwUn9QYguAcjQHRNupLkzbsEsMiwvFpJs9mUATi10TrtVbRa1c9UWLZQn15hUYVHIaHsOheOOdFM1rRBrA0oNnfPZz352udGmDxd8Pe666y6mFiYVO2K0JTXH4vSqVasipFpuzlJcSu9DeY0ahAPYSKzYpmRPE8u9uYYTzM4xh8+F24OgaiGNLxyjrz2oX/nKV7wIgngCkcRh9GW5o7DHIikc5bWHaJFFkDVEvjnU2972Ng3w8Ic/nH2EbsI6a6Nk/y5iusRAQ1O1Q1en1KiQyEJs8cfq09e/IosSW7r7TF1/HAYzY/zrXvc6xviwSFs7zJlWbpm8wq7SWCFAx3MXOqoIGp9MscL1PYchGf623HJL8yZpBHIAUVCNqHMGPxoUkVL4+RsCCvFjAUwT2Z6HO2XBGzvCAoDvQ6a+fUiRN69lajkHmphpZFxzzTVvvfXWRZwPo2pJ2GvQGQ7zjZYPfOADjqFRFcM6sX04304UGnxFqDPNgaF9mfbab1/EECX4E4cAqjQh6IuYYczXD5G3/fbbOxIpPdcOo1KK4tCGMGu47ES2a9gC9sUvflFs++wapOJt6zW44CQU1cb1hbkvUpvh2pOe9CTkzir+nXYxsO2FcYzYpbAuu/fRw+Jm72wfVoX2C51riRkJZijmnr7JFgSR7Wt8/vnnUxhn5fxci5tfems3CGvmNWv2+vui7JPEEyyybEooO0wAsH1cL0Wermt18uUvf7nzBBRzIcLb7wYotFpqbcvGVDTYkfyKV7zi4osvturS5OG4wIMIpCGmIWus/zlXres3nXBCWkifssRKZpzsvffejg4w79t3Z/+xwKDqk3ekURF2Y76/JtyjzhgScbogwKk/DBwpfwZEjkKmt4zwMjxMSVj02m9fJhgvfBAotlzhEqo4QtkqKo7ZO4NpXLha/AFrOpRk4UmZJYRjSHVcM4QNpZQhImlJr0GxRUEyWIPR9y644AL75axAaUg8wjWSmLJTelhPAB8tjR9xxBGwScA4Z0eMJzTtjPJtEdszVwuBoSetsohk9K9piDR7ClAS2wliw7TAcaDcciSrx+mnn66fIEmXoLdOTU3pIQlBtrmM07Pke1HHQvxc6W/yoeCnIDBLOw9pXwUbYtI4qkJ3cM8mE2FUBmWV4gKUv4WxLQDIy7FhpYcJCqX1t1D0fIpAYgtOW1rk1l0wpaf78z//80H0mvXWW49G86EPfUhXMxfz2WFNpN8uriCPXqM7qmYLzJxfEWjjbFMM/0tfsY2VYULU/NAOMddqPWHaucrT/kxLyM6d3Rd276/PlYUFyg6DnWRCUc6fHxkyxuQhu2tG7Au1WJFeGt/czefNnvIkSxedtaPOj5i55lJxlDB0alCtyUczt+jDYaaK3GvBnil6WOEa1dyH2c+JhDifrPx94AMfCDDJTL/pU6KZvHmyLb9Y7FvnEk/6EVlDu8F3rk/ekUYVWTPSUhaIHH9wmBUzumRkjVagHs7K+QUWPWB2ZHAat8P3F/Emy1QwBBtOGV1gc67EzqP1ZUlZvnO77rqrbllGLPwegLY93Xb24OdHIifLgDUaabLQb67HdolgDv32kYRCBI+09Hkgb2kOhRGWA50DQGJaNLROs2jQ3Qd0ZmeXqDNUGyz2+Hfs//YEwqYLZvy07KciKRScJm+ZhgGLQ5tlFDvovTRgV6TrBL1r7BjqgNlHnax0BgBOKi4A4wg6rbDEAppwzc3O7cNjM05CuskLkjSKkRn8kgkhODxLwOhL0OQmB7G+W8+YdiysZI2QghOs+8koDZ9rxpZkLQOIwQevj9sowILum4d8IWNCXic3pvnWnkfoanVGFt8ootdynWVsE2NwXB9SZKTlYqVmLs7mmmy3Edsn7+iilFv0msWiYU61Q2RYDSjfwLGlHKlMexlUpe+W1mdDSXV60i+QSx0BqTVLMwlFmy5I9CiH45xX+ta3vpU0fOnnxNXFTRyCx5zsluZQpSU0oRsP3Rxu1UYncFejJncmjZ4yK6fkdWWnAyZ6nm7H19VcLiOck72U0iag6KUrazAtTi3aZNrgZSHPtnLioAgagD4jhPM9L6KkG+fqbnGfrIHHja42IlONI2WCwcE6Lz7TuGk6QRVf+m6EYxuimgieru6YtiPWtTSHKh1FkT5TmtOGGk1OpeeuvPJKCq2L8oSUlD0B603Ek6PeJtJ0Wtfi6TFSQjtr3p4IFxJYCgUsBE/7eQuvAJw+2j4Ng5fITtfB4fy1JMTUnQ4AW0ea4LdMw7LLXGppImqLZHrOxhtvbBnU3gsH+pMSH0Tx87egzd/x9wvl40sq/rbpHLNk37WFjLjJN8cuBlseWfvzPelDDMlt6k5U0W4uvfRSOx08w+x4m/BZ8/ZBO+8o9Mur9CWxb3je1VzcjNgbvcbgN5ziXGa2YsWKyKCQl76kG6QnyGUnhFuH7eu9L9NqVcjHyTOYbiZJwy1u1Sat9EWYQzmf5qy2r41vji5ic4dOYPe3/pHRO1MbiKUzS8wqbL8mgAg3f7FdUl5upowjDUdVmUONtKDJRI69zCuZNUdqWPBeuXKl5u7oLat7wHQf8PlhIs1DUekkMhI6lCAbc+TipORPJksXq9ZtyxptTDHR9oxzNm7ZvwC2ruSqTV+k/s0vLzlFiWBXNuu25OmMguyf+tSnFrfrVFkzuu6rVzgP5TSADXUkjg7jQGaam1/KZe8zDfe+klc6yBe9Is7XCGC3TvSdImUAXMlegRY40Las0cC6yJ577unrlN5goH76059OeP8Km3+RUO9///sd4ZF3l1120b10LFf8wakz9c9eYpVl9dQyqvsKrMTbStvsdmCoHLwSy/Zsyawj1l8uHT0A8Yee5z3veaWIAYFpTEusx4dm1Q9QqlAAdS9wAWZlSElZgI4s9te4gcSao6LZcdPiEgOaRyIjXIo6o12EsMto056Ym4HgFNoMLNVJYHyFdpA3j78dpZSi+6Pqmat/lo7YJoYCdwD5Gz+VBXfgmevfRZA1+cLYEnrZZZe5W8jhuvQbfv/6iLU2afxb59aHbBJxxBZAr0lPGrDyEhN2uiN9yjZCUo/NqORFBuORWHN7t8OKpZOX2ML9AHzpyRrpyRp/S8pBgIKkAyh5m+H9A0vsTEBBNVOCAcMLnm6gYEiUvwUoUd1AR5qOvyU9PjfdqlWrPExq61peQ8b/OCtNbMbgSBmCBuyzJC/M/ILQ38BNIKWXkJKghBeg4OkDNPGUjCUwIR3ZS6zwJlySJVfPvCVNAXpiaGIueGYCwrcSWzDPA2hb1oT0iAYwF9ETeCbWpGJiI1z0HvuG3ZilJzH93HHHHXPihcRUcc6036MOsLnZqPDON9MtRzR2qnvs1h0v8IZgSFILvv3ykFjFV4uCpz8ge58E/WP7ZGwnCnmcyhaglJsQfwtQomYCpCxRTTiBJQSQTkLT9L2JBQfbyZSIlQC2RNieBxYVHQdMEYYNhriC2d8SXgIDNP378t3vt5mgGw7m7vCEBFEzthkya95mxjnBPUspxXXE5i8/DT14356JpJbWvFFc2j6fGj6X/hEYif4Gnsln3rMXizH4T//0T61/M9+w3VBPCv6ZMjbDSRNzKKsbsruYQl53BZQEZmpULXuR6TVMQkyMzNglNhRaR4NBjdBcsnv+xbI90VMSdwOlgjKKLX87UvaMTWBS9sw4a4KOUpp/m3lLeM9SxEqs1g7TArCI81e49MHTDQRnQdhdXKKElzTJUkIAFhN8AHxjCpOVW8oKzjPPPNOWaHD+xvdFOemkk0oRTVJTSiEv6QvOEitEt9ExNHq03aQsCQqGAIPEhp6O9KXoZmyzFJiTppmgZ3FydaQsyRJeEjTxJzC+nSV4bleKWWrGb0fKOf1tSdaom/m2bdS2NqCv1Dm06jGmRWwx4WOfCuRdp3BKt3Mk1wq6XBn2wvvkLVEy4qDEZYSUviuNY7J6VRLDzJHoJS9AiP3yyZu/oYeQMitsplyWcJPJWJG6D6umBXkwh70FecoqaYQHzjCQJen1ikQlBGwDF38hrhQ69CovhKqR5s2YUoSNJqX6CymxJVmjhVzLuu2225bm7yDaEX5z747Ajr8qvGrVKvsjPvaxjxntbttyXs4ny4UdRn6U6o4s8/tb+lM3i0VpAzoRI2VihbAuK8iZRtpQkVPzK3pJ5MIBEpmCiQNM+1yRvAunXztiKbVRF/d9ajaBcC3ue0PZTEHKtVkm6r2QJKar5tKZhRNTMMDsI2+TurozGpqAD7HKpZTxAdRXTSkHao2qZivMn0jt14LTMCY7PSkWyDkFnmlhH2IkYPmTmBnFLr6wgALfc7dFfzx0KNtGUaVcePLkZrJQT3RlS6eK0+N1breWFGyyUIJCKpgjdML9xz3ucQlZ3r66c5aEVjfbGmt4QmCI9YWZ9c3t98Zz8McnOxyGdKwf87lSovQIkKYMANv8SuwQAQX5HKbLlT0WQ8Q/bqjUl/P8L966ZyPklVEwP6AlvUYjoTsyEqGlc4DTSwaZQElpF5/Zls+dE96+LXQcixE05HXXXZcYDqpBfJYaetY73vEOa0zSM+uailvnRpjtP17UdU8KjcmhUJtT7RssOCXoUKDKN1Z4qVdJv/wAddRq0T7CjXnUOp0Vc4LNX4e2yX0aq6VJ7ZuOYYLsMIE3Nui8G264YTczJXN3mvUBu4TJI0uBttLMg55uzN0h8AtUYhp6RKV0l7uIIaqsvtxwaAiuUftFbPsokZcmGjRkxkWqOL3AMsEgt6gQVUSSWyzdlaWlzaHYRwBuAp2T3FX6aaedJiNHQpE7Dj2YnWX9m+mRRLOSir9irTHRbvrwp7wDv/766/dJtmyiwmoHaHEPi3J50DxqB086A3HvDoccJsBwjvjGTPcT6SEpTp8BdJeScH4T6E62wJDQEBVYla1z9SRmgaWMW3Z13G677dR36ek1iOZ0UBNs9+npRnpVAukObl10D3H+zuSbLll7+shHPmKnOTwmPpRtiV3UNlOWnuHyurHY5Oiqq67SxT1ox7ePxstTerki7JdxVI+qTK5bQ5WyJ56JDcTADAzA/JiQsWpaTZHh6wBBJdwWYdt/rWrDnEBlgUV1aJSl6NCTv0lcooYOhKT4Q0c+bggxs9S0APMmsqU5FEJLJ6CPmJNTj6nKdrIQOkY4NVhnKtKnWZ+SEeCcCykgdmpqygTeljwnMAFNpjTz9oRTiqvYXGhWkBNkroku6a1ec+VvN1Ayqpox4LsK7ZzI6MY5tiGlsqHQXzqgOaxa24gksCQoPTIhafe0bEKsAGpBFjfmD5dUJb3VSeZ2cyVbmUyHBYaZKS5pevaNkixp+AEWzknUcrDFh5BOrcpCdBWBPYsYVuk9kY8osFQz+Jt/DU+fYbUu9U1sk5I5VFnmNh3Lq+0w7CM5XmBCRCO1YcHdIlGDu4kRbvZkT425j6eg7LtjuKXQ2hTDQmwzhQTRxrvzji7E+IGcr/TsG37uc587uuIWF7NqcjHKqi8XOOH+FqDQOZ1qdXhS8hnIrBhSFdM7Izuw7vDDDzdLLTiDreBZLADl+ltqEZKa1U9IfCmTbLFIXXi5ab74ah0glWrWsdQUUDrA4KW3pNcUQegL5rPAEGjlkrXP0rWL8uk1TCQWjEuyDsChAW/puv3TDfueoNNNLUipMIXC99B3klFwDvK1A/t8/+KyrCkXMQAh7ZMxX/Lnli9VU82oEqlsUKTn5YBbQVoS2OLoc6KhXR+jg8KjuX0krO5RZEyEc8gg+MeKgYYZerIjTL1UHP38NLEogXyB0gDCnMKBpQKgPI0FcISVxgoI8SU8f5MSW/zFB8BMs9qedZ/D2k3P/HMNRJxOZreVq17NyZ26tNBgGkVPmwmVJqT4mHNRYh3vjgUXIEQ419HLZ8IzuvB0vtHhX3TM6WTkhWZiz6KZcj4PKm6fbv66/IzWmW6qycyPLBGyu2tlC3y+ED6YFHJm4GuvvdYcikXf6z25JS/4YdODR1RZRXS77rJQzknpfQI1dQMOWLJvf/vbzuLqcip75JFHZhC66lgalWIxzG6jboRLIiScYVJQwbI3Rcv6q8U5dbQaoC4WZ/3VahQFzT232nU3wEhDfASs+LC2mAQxEmsqzWYzBQuOqDRzBwHEJyeqQ6dd3Smm1VcZk6YjY2t/bWbDdLpVayW2XFD47KYh1dRecUajNbi0YEK22WYbDeFIh3VoPPFdSbhceq3j+BYf01gQtlmF0J9OUuCeNEiDQiqYEYV478aE4HJVRfQaqwoMT9YopQlPfEGlbLNSwyorFTzjjDPSWFZpcEYgu2ppbgD72pe+9KVUP76bEnrycCbC2p5DIZo10eIO0inPVGhd034WFhlfDLE9HZnqwygqTasyJRkDECtARnsJrMAoOECvwX93sLqjV9uZBVsTpJP6+LvOymAjfSwm2jRgxOqFaPBUg3Ule17sijJ3FpIWHAV5/XEqV1ehnpjvUEOyWaZnFh92zxAzIcmSnkYNt9/Kx5zJybkqHDAg9Vu6m9WJE044gZC1LejSSy91RUlPnOMcqI7OwVt1BahyGg6gyuYQF110ke8EdrHZ2yIn1plE9TX/xYRyymygCiqgTUdeultPTSIaAXEaOB20mxhZ8pBoUqoVoFk30zFpujO2FjIJeg0Vxj6Le1trjTUOO+ww7cXXjrvvvjsRU6KEmO1aUbLfUhpOB20C/rbWNClIieY4NnaHSHfpe3muJxkCWQbtyTABVJF9991XiNecZXRVBdgpRLDBSU3TCannquYLL7FJR8v1GkpxpgvsGL4WprRqROioJudbEnYRNHZRGWJmxEJIVVme+tSnMl/4rgxOQ9t6jYYhDlFs9Rrp6hZafWoAwptCJHAqzJdAiNVxglZtKbFCmJmjxs+UvRthDZkrBzDfUSA7HmWklvrmO2dPVfHUFLafddZZaTg+5cVUyzs8dJ8EpqwCF2CuNMwjfbpEfOoVcQOGx0bz/fbbLw9LdaPdY4897EdnUTLeQq3OJpm/fBNDSHTjHIhLAr5Arhvb+IeokWs6qC05kJha2Ftr5RfxLDWam4nK5CNf9DCBjwlzas22ZQ3xbxnCmrcPAkI5dYvfp1VIE/IlJ/GyhsUSaf8LBjnwnlUASPpgqFEL4YA28mHwGgEmM64x6/pg2KCU0ahNNYT9ePZY+rbTaBZS1hDzlt5lLYyShc6CHP1mRjlVVwIBakpcdvSlDL9gM94kg4oTnqjwoSNXE+2Yw9o0deenFu7n9cSdcNqcmzB9P8gjVU5sU+wOXrWfc3/wPAtJiVzGGisR5oEOKNBNNB79PG0WvwO/QBaBm2++WV+3oxQG5hsbc3ypMIK4ZcpZus3cUdnx/KsJKDUWnswsKKQGKjozwERRvx1/3XzzzQWy4wjxxRurirDU6CEI4xAGRryPVjeR6UhJllghJgtg3dWScGrt47feeuuJIl5FJX0MUt04xz9ERVQhdUctgNZmxFELwCQOn/pDx5HM9FMaTewbTwOYQ+1kbtNpKjIytTIbIj61GZ/KivqelBBGcomNT6E1pWRIhiSOvSaxPbOPOhBVNqTh+JJeh1KLmZzdklnpTC8sviOpuSyVxLeDRkfU8ywypi1gGzXnB8EfMhiwc9Dkvi6zBjXN1KAbQ2ECA6KvWpZaLEtF2fEwWYw+VvFtRjUUbVvX/VyrxkTFmNqNcPxDVDlNxrCtynarCTHENLTVN5XyIii+nXzyybQEgazCZtBEj0l02DtgHe81lwyYeuHJEEf31nLapumsccxEN1njdiuV91F1YwDZlA01auumEiG5wWSm7AunuT8G5S4bWaPPxeE5VYXyyEoabpdRCtAjCRrfN4nx31+BPv4MNzIKzOehP9/ajNVG7Ls+aejUbUzAadb9OwwJIjGzThjirFy2AgmkSlNnhL/yla/01/DTk9/1rncJabNSwyoLHzhNxiihOuZKKkLpY3TzV+NyuoFdKT4kdksJ5DS3w7EyDk7Gat0Js1pzqoFQxcVPuWjwF91q1QxPLC4wTWnLVExfYTbfa6+9KPO5Z0CWYOjO20K9FE1BY0ij1zh33kKJoygCb8NGDeRuGmsx6mLKkO4hiont0EMPVTQRo5mcFMmnnpnGUjdl0wffp97U2JqUO2XI30Vpjp7MUTvOJIiNRv+xK89qbs/OVrLbhEHXVinLMVLiA+F79dVXmyT6/luaEajW7rJgAZiamqLcqe/4VLlUZFZA1TjJmP85G/FJ1QSqHaGjoV/ykpdIgIe4ZwZtTsr27yy+v4NXuW1Zow40FAYXk73CBXWg4lLYWMJ9Ikp4AFlYBHK5jBCaW1QbvVxGY4OGT5VV7Y6M7fxF3vKQNS7r0TQ+VsyohfOmRbaTWpfRtwSmY6kyOCMQHICC7RCJBELMKRjRxmfTE5K4VCoUpi6pTsI7/GThp3ZiAwfgN/Hoh/5K2YFkSfxNTUuNUk3DKtVRL3AGV6pZUgbow8PO6qek1nx026WuGkiMCxy6LWFEfEhWSKLXHHzwwfclv/e31NN/2wIXUWlHcNlfAy5kjxuAtji8jfMX3775zW+anDo4StwXJps3aSZ7SWygmJW38DAbd3zi7LgtpSyEFU2aA/MXgrDmXSwO/Fy56BRCI/tPG2d/sopkfsjWbXJETXWJDEOjSbKelB7fLJ+sseuhGa5z024o6kQVOx9fxsVSbQqpKCzw2AKI1NuowWxkmG9e4G+opWzao2UDm7m6CXlSltg+NXKujWRpVp/qmr+yN8P7IJkpKhi0bxIsENtMpdTwkXNAQ7bp9EjnuUkZX0vOXxdxkhHsu7Zm6EY2XAlsfrv8TUgAvom0LLnSvEQ1s7RTIyXGLQnbMFLxihnCCstBBx3EElGGLo3GrnOW3WJlT+I0UDjch6US+0gU/TRorQAku9g+eWeNUjpDCZptyLJyNCsxsyKsCRaLA22vQ/mc2upucEam6D16ORFz7rnn0ncsIlpymqk/6bWcWAdPdGjXSoDnwTi53H3DtGyXBFPLihUrmngUgUjmw6zH26VmMaKUEgKk58DCAeZQqoD+kmzRgZAXH52svFZemGwz3UMtRzrQDV2HumrVqoUQrBRihbU4UgZaOhErrPBEBQi7BilIyjjbr5iQmCrhhNzWO4ZYUYMgqWnGjQNtyxqd0pjUdVgEHLzMERtWfRfTUOAZ+Uvv7OaUTiaWb4nEUPEG5vy6HSTER5lwQeWmrlIcnEwYbO86tyjJGOGbsaogTXzhsGXNxTrU/OgpyIcFIANVceh0nTNzjLpwqRTAwi2es68n2UIol5dzEJH0J7ycNnJcaKeddqI9OTuT2PiDVzBUEYIaQm+Zpn21d+CBB0I1OJ6acnw40JKsKR1OH3JA1rAkWdKBfGltgjbhd+HgcccdJ0EZxt1sssoogWdrmG/s/pCyO82sITAo1KUBtq67/UBXZp4oucRmPcXGLdduS9P87Ic8PpdKISmyxngek2GADEuVRIwzq9lUgtUZsZZmXU6mUhKEzxKnLoUDcwXCh2mW/IzwUigBneJ8RdwJXQoaEHNIgjC3shZZA8BqG/MGxFOTjRUH2rMN2wtk7mMnu2NN1lDtwnCbCe3GB9ZExqq+NVddE3d0qWjjHb4om/fMfez+8JSC2MJKcM9cEiSZwRaAb5ihgWhgB9Vxhdxwww38OCntMgDr6LLbxWR3k79BFTxOikICNh6MK5trwDaeJPZeRCP7KZQE6CjHw3g2vFhaStUSi2/77LOPDaAUSSHobCJZONmwBQnG2mCKLWkOk6BTTz3VmlQHkf3/BhWc5HhIDbV8riik/ZHU2LHjQNpv1L7OR3PR/2zf0OPTEdOl+P5at5bGB60PJTquSZYPNSElZflaygjmd+fNJzffyfiSWfyiVdmRJcRuJU1iKJa8ArMvSxo7vvhmBAW/2AwAZMelLpDY2iS24BkdoBQOSXEFdjYXqVm6RhWHQtsdGX1tYwuXprOOkEiaYNiSjg52LpwUnlO5SYxgG+SL/guhGlmJFzs63lbMo+NAS3qNCjjB5a4dy9vm8yx8+k26oyg90hVnARLY09ftPJngviIbWF/60pc6gCOjHkk02AEI7pkrgXaa2dWqm1KL7P0DSK9EsQB/m3ktjVGyWB8YiZ0jpyYQTywRSe+7yg4qShYhnDsKkEFHayIZKaxQ+C0qWfu3Vdcajb/M7cJVB6OQzZ7FNBa5mfThedKMiDztyJSLsJTI1+55a2EeJdpA6FEXOx40h0o5GOGeqnngqVnGggN6QwvOUOxwhndxvs9gCfh9iBFL1kSmxA8H+9yzlSLg98E31WcSsq9HIIsjoSOcHIHEZKqUK1agdbFoDfQCvdy2/aBKLZqwZFncYYQqSEYKhAZExhZTpDaekL/ur2LqRlX4mVqEYP6sTF4g5YpjD2q2jtluClX6gMhDZ3Lx4VQjE9XAg+MZsLiarB0OtKTXlPGQWhUpa53CIreFHpp/CewDWKXmYNPhdGjYAFZYAc3+3YFBeiu70uisMQmxENnDxgYcjcDxCKvvzEAW3S1R0QjIFxYlls5Vq1YZ0tSEFKGUUlAA4VxHiSP9q1xzInT62oPVjraoRIqYY2K5BLcQ2WHdKOEjohB+wo5e49SMNkXk7rvvXlq/Z6FN7oFDYcmSv4x6PfPWwKXEgdUDpUVntNtRykJpKu5kupGvMxnqBo/PV/9PVj5r+VDz2U34CeTPlFd400nJnXLKKWwxitanLb0btGSKEPTYeiOW/BJF9FiucmFCcgVPB7cEtq/XqPhRRx3lil/TPSePCnkBOihs829HiwxCDwZKVvyeTG6zCrWsEXHgXptFa9JRT7IAxMpgkkLZXrlypQ1a9n2ZEeT6jHzHetKDBTqlBW+2RlLAF9sWtRUrVrjwnbyQsX/egjOsJPLcdO3b62YA2Egun2LqzLbbbgsPUcjMiVqH6E2jhMgV/B2lCHeDt0sGzKFs/CmljA7IaFT9VIRMFKI4wExEjo6YDswaqMkrsf5yHcmaf9Gcv4DUZdYszewVXjIcSH9tzTcrodQYz4SLLsX4R144FmwzSz5u/SmxepqOW7qj3gnVrHkzPoM88Pz8bvLgYQnS3q3Za2alvJvI1kJCm+IAxe9fuuOde+yxh2kgke0BAzqm1uyfpcYuRQ60ZK8potfX2PTkrrvusn+PvLAmdfHFF7OVuGdP7+wwLpRcASRgBjbTsb3FZXFWtSwtWf3JdQcdiTv+RkIlsAl3JFvI3xGh7SaptYK6i541pNAWoPztk9G9rvZDJKVHYFw2agNEn/Q1aolyoPeuuZFWxqk/JmHvWimFjdY9ElQbt9LQUPqXa5pj9sT8ydBrkds7Eo6GC/ElJOb7562xY8uBSy65pAgm7WjJr7bm2DbWQgibZXgvBHXPvHqVW68YgwkLpy5te6GYMLgImbWHsarYy8eSYhZm2mUx1dZYyg7DyqxyqicxCw9s0gxu/l048gnBYKeSXsHhHt+OTXruhNR9oqrZ9hwKc6kwnijVq4gJMsKu9gCz8l1iO9NcHGuzrytj7YWXBR6L0+mms2IYeoKMkILW3wJXYEAO2E9QHjXVjm4jMsseMG9NtoQ40LZegzVFLhTrDCFC3AjvzzgJHJVkQWSvYUTMQoy9pJZ+g7Z/9ho7nhxwTZqWTWsCcqvxeJJaqVoIB9pe845A4UcjCFAqIJArfzsAiTkKtjsf2IanpqbYmC1pZXte/7wdqIb71/6apX63+XAZMids2tS82BKBkyt2ddrDvYhNOSfKa+I5caClOdS0lFjtFcMKOIRGuJS//al3GQIrD3uwZGeeeaZvoD0yXlnon2uksSjvIx9HWvTyQI57rHXc8qhOrcVMHGhpDpUB2RyTYAvYDgeartvUxyI4iLhhV2bfSUqHG9iGXXnjFgXVayKfqbY1vHKgcmCxONCSrFG9DlHijn6r3bbb2rlrI59XWQZhgQ3Htsw50OzUtQPE9BqGHsesu/EPgm2BadSoo1ILRFizVw4sYw60JGsoHVSSXBaT8ek+KvMpt8m6qJxq47aqWRUT+2tsOyabWGocJtAqDIpkTS5zmDX7cFuxSpnh8rNiW/YcaM9e43Cj44Lu36POcAAywho2qeHAHkk0K6/tr/HipTVvOwBZhb0Lznc7hBNV8hr8LYsbxVWJM2ur1QSVA+FAS6MlS9oMNG6iZNklLI4//niCwxNF6LBBxoPkGbrFeNzdQgY29Ydt2InNxLpfwqVzDiv4KzvXnWvUIahq+ezlqGtU8VcOjIID7cka1JMFbMDuJHcTgns8TYXOOOMMNxA3K9ZfXpBZ7kZxa7eVb2qOI+MOYRNPUWr6522WMiw4ek2VNcPiZ8WzjDnQnqwhCGyj4MgLJwzYd71+acMo7cZuLtflGbc9lRqnfu+++27TpQgUjZERXlrFE07J2DN7SdYNwFNQlbwlJOn9RTnXnb0koKbZX2NNrRztmSlxDa8cmFwOrB5to3csMu6LoYlk3LLR5DJz53odbjLO3Vk9070Qwj1pkIylncrglzeo5lEJUi+FZooXDOC4RKHc3z7IxeauLLKmf8o+SGpU5cCy50BL61BkhCUnYsW+O85lJSeccIJA1wBTcNwU6V6+Ikc6gEiZ6B2B+f4G1kJJX4CO7P3/ykVAzJRGVMqdKUFHOJI6QurfyoHKgXCgpXUoI9a124ZuDvW6RMLdmqHAQtJJJ51UDvt2NwxxwKLsLEKkifHMouz9IyYbpxM8I5dd7d0ZZwqhsKAHNpM41+jY3ePK9GZi8zWma3t56F9bb721KV4RInJxzb8lo/ACV6ByoHKgkwPTY2fknuF96623OpydUWrhyVpSpiezzjskyHSG7wJQm3EgISzcSuFGPkiEzzrTadYw6W04NqeDp2MKJjYvAbi/Itchu22nZC/EAAIrmr6GrTYZCikpK1A5UDnQ5EB7cygGVNv2vCFpMmWnr42/xnmn5Ov13+AnXBBt+cmuHAcvpXKh34033rjVVluBxcb1yt0jDCoGIAIFZjBhIXtJJ+Tss8+2Y5Dd+hOf+AQiLZaVWIDEkSmB/bWlcE4ENLFVuHJgQjjQ0hwq3PQm2XbbbWcw5y+gOchn4rjRTja97nWvu+6660gHT53ZxceWbPGbM+zdeJIbCXpiK8XBL0EKtbHQrRSkjKMS8EMCQwhgOXLSyr2ipKGLhG04dLyzgzYv4WXSBxtn/V4CB7U6ktW/lQOVA4UDLcmaosIY1RnwhYJBAK+mOGBpVEvMTGN7jgtA85dvJuXVbVHdskYsJ5wJxhEHpZMdD33oQyksJlB2+lgIk6CQB4mbDWwCkoWL4iNjB5F5VUoCCJMXErk6ktW/lQOVA4UDLcmaUl4BDFTwakkwwGvw5l9eI5NSFhkDBBXYvej8ICz4mwCJ4EU0m18kYwZ21TFBAxYehM3EZEecBIntwCzcddxEUnJRjrwDm4NaTTwVrhyoHGhyoG1ZY5eNVSTvKNme50UEZhdrTG4FbdLUDRvbXMZ8USWayQR2SITEkgucKBeq224n2QYbbJConulFkVwIMy2ipyCSalOWzApOq2AlO/xqdNttt5khlsCkrH7lQOVA4UBLsmZ6yK827u62226mJPlrZNpG7MorT3ZsscUWAikU3cNVYMiVAOBvsrOwEASxsxRrS6lYgJK3ebYzeCRQFgcJmLXFCw3SMwxbRLf2dPTRR998880kjgWmYEuWUhY8spNfQRiqhJTEFagcqBwoHGhP1vjyv/GNbzR0PTjtqkfHFExDTGesKx100EGevqRNFLJmAqK/ZDwXkTFT4lnD4YmDihkoV16wDZ944ok77LCDJ4CJlWc84xlUqj6oYOgTW6MqByoHwoGWZI3Crr322hwacimEPXIZokSPs5du9nSdzaxPykUukFAsL14ss/nFEvjU1NS823LPPfdkVLbeBAMJ6A5jdhybZdZee20vPVx//fXoRFXux+kuJVVoypom3J2+hlQOTDIHWpI1BmHUEI9nkxHhOCXFMLZ6bUk7Vwj3bwkYrEZbNb/66qul9JfxheRy9hJcpkv9kTRjSRanJRICA7HFRypU1qoIMn/F9pcg0iRZE3OFKwcqBzo4MNBuuo488/sbcWNWksHJN6T5TKoQOrUwCNo3v/nNZlskFH2EaLCN+EUvepEHoYNzEAzSoKTbIUZg/KQpKQdEW5NVDlQO9OFAS3pNoeB973uf3cNGdWQN1cYOPbFl8JeUPQELWOY4FrNMfORlVbH1xhTMlRQ907cTiPh2CqqlVA4sXQ60LWvYRBiDCZqwrIzSEtKHlfawMNy6H4tGQ9BQkSyZkzU2AffJVaMqByoHxoED7ckaVpVdd93VJCXVJlwiaEgNq+D0lIibIn26uWOhyg14jl+fcsop1qGZh8855xzJnvzkJ/P7ZOxGNdwQVQjCUqnh4q/YKgeWAQdakjUGIYlg60pT1oR911xzjS02sdr0H6tid9xxx7e//e0HHHAAyeIvZx8w7SbwYomblMsPsAy6Ra1C5cDQOdCebZg4aA7FwAKthTtIPch7crLY6mIOlRMG/tpwfMEFF7hcAswNnTtzQqguc0pfE1cOTBQHWtJrCk+7B6Q9LGUOUpI1gcRSiACOXLvQz/VabvNzvwyhY4Pvlltu6UGFDlnWxNAaPA40tFbZWlDlwJw40J6s6dY7jMy4QnF3mkQ538hJ7N4Z9zw4pc2+I/H++++/YsUKZ7V33nlnsTNlL/hHBCxWuTCMCsUAAA6ASURBVCOqTkVbOTAKDrQkawgC1PM7DhMJJERSsT4j9itf+Yrn6+S15Y/PQpzE7q+R1wnJRRQ0qRcfSaEq1al+5UDlQJMDLckag9CdL7QP991F7oQI4Q4iNQnqhqWx6sQA7LJOsWZSLqPJxEqUMwQMxnAWq3M3hnZC0MAhqZ3iaimVA0uLA+3JmlzRkPcSSIrYX4zMjM/+Q1QaN4fSgDxxae3cmndkDR3HQngw1HG+tHpepXbSONCSrCEaXGx+7LHHkg5NoQDmMN2KUgRQt9BJAhmJJzv3AM4oRItJVATWIuo1heYCTFo3qvWtHJiVAy3JGoPQZt/DDjuMdPjwhz98xRVXUHOsQOWuLIHcTMIiokRNJHjQgx4EkJjfMbAFdoTMWvmFJ1iUQhdOdsVQOdA+B9qTNepmofrFL36xIwVliLpa/NRTT3VxjNWlmSpPgnQIkfK3G5gJSc9wZIQSKhV1KWkSCC7Im7EdeKSRvgQGW/k7IgA9hSQlgiOmZxLWIyKjoq0cmBMHWpI1hoQ1I0ZcgsYMaJNNNiFc3LPpXj7Ho7x+SdxQcxZltHQIiP5/58Tc0SW29ObiUXcJPutZz5r13p/RkVExVw4MzoGWZA2CjA2bg91Sft555xkhNAKXb9o07PbPyy+/3Fnt5zznOYPTPayUkSxFhYG2W1WRpqcQ7JBKyTsswvrg8TCWW0pjZZfs9NNPf+1rX9snfY2qHBgHDrR3RuG73/2uCrtYk0wxno1ezmmmQw45BPC9733P0G2HIwqKcwMpizXjUUe5Frx22mknQtCdgZwnYpoJ7su9mlpwR1Tz73DhlAuni5AjaBJy1FFHIZgbbnGtYQsPU5fiK73ASWCqWAILbYkqfkeW7r8lYwXa50B7eo0LQIkYt8/oNID4eoPJFF9sa5VXnAkdE/WRRx5pMnLPPfcIQVIhwEkIe3lQyBRN+aKLlSgpOX9L+oSUvyXlKIAUbWej4goZWPf1r39dca5GHkWhLeDEatXxyUmllBgggaksXzJRHaxOR0pUk1QhEApJgiAEF8NcM3GFW+BAS7JGS+exFE8pveAFL0jDq57VKOZhwOMf/3j9gMmmhTrrrA43fPzjH0935HcUaui6J9DtojM9cdfR3fO3hU6sIKxj8CoMRLnAnJLvqMVS+asuqsCFYEBCCv35myqXZIktUQlPGlH+comNxCnpC9oKtMyBluZQ2ttkhJpw1llnucfXJRKf/exnGW7c4OnwJMWBtdhYLX1lWFyAsOmIFX/5Tm+aiXirN+ImxZWUuSrQXX9uHUb2ypUrS5SUstgN5GFfdxXH3XHHHcLjD4vymfAYQq4BEoskMGDTTTf1+hU3U5YxDC/8BJDs++67L/pJTM77xR59F061fNrTnmZXBOselQ3snIr5rMvtk9334IMf/KCprly6lm+YHqWyYYtlh+23396hXGd0HWrxRrMXeJpyZwzZssxJarb66GDWBJMRR7Q1Nqc3xAUmgMRykQVDJANCRfM9iuDmUNePuhgwgcJPPvlkrUvqCeFSLjI23nhjuoNr2IlCdPqbBMUXiPJ0azApyV9//fWHSHkfVCj03BV5Z0K67bbbuu99Ne9+9rM+WcYtKgTjp7VIk2jc42i+jrCEsZqGHBHoL1HiEffcHOKvl5Fl13y5xkga3ypfBVHgY445Riy5T8T4i0U+Y9n8+bKXvUyucWPF5NBzPwvc6KqdvsVnG/aZchdEBirTrFmVPudqPrGA4dIAIbR62NZbb22CxtGqBArhn3TSSU1ZIyRu1apVLjYOvM466zgGQVTdF7n6d5tttnGNjlNaHISpjsvARA2X/p7YwkwcY6kJHL9n4rENTBNYQSMR1lxzTRLkJz/5CSWXDNVMv/M7vyNE6xAZbrO/6aab7I2wOUtip+rwmZWNzkK+aFkKC91zs80285dUou9cfPHFkExNTbn3HnM8+CXKszyixpYhy56wNuwjegynl+CmaQt366236lgu8XSFMOVZeGL5STxcH1o9OLp3LgzV8xTKbxakB0vJp8MbCfqu2LyQV9Z9QqeHOkWFWul1YsPegGliGymsaM57fikFPNLiho48LW5v52c+8xmtcOGFF+K2Wpgx2dtJslMSv/WtbymXzvjIaYfP6623nhBNI/vnPvc5R3CZ+SjF4bzpFdWVcgShd4Gkod287W1v8/ChaaZPmreSCaCh16UiHJADrbLePpo3vOEN7MG6xZ133rn33ntneFN2PIk5IMVzSrZ6RE6Pw+OPP75kTKHCA+iUnJlIZkwWwj3XTbe3DC+NR/L01CzxBFX8gq35twmXBEMHIiINwqFjbg1hGGUbhO2I5k2UREX7Al100UVMaaZLt9xySy4AsFC41VZbCSFZyCbJGOy1V27IJ2siaCDUTERV2pTuzFjD/O/aWcIIr5h7qLHtNFBrbFxiBU0PtJF7vkVGL8U4jU0xpi8YM/5yugvRE6V65KRMF6BHUq3T+Zwg95eWblbPeQtYHy33iqIt3XomwuRlcdDq5lMzpanhHRzANI5uovWzsUBz2JTor17BaYg99tjDX4zlR7DSVijFvlUSO1sn3KxKt/E3rglL5mY1KlHePoTzKU95Sp1DdTREm3/vN4kYnZjULcy6faxMYQ4++GAGDrMnXyFKBJXBJ8tQT8caHQ0FM/6mB1tpImiyX5kR0dXFzpHTtH0SPcVJ4rgA0LMN9PmStwJD5ACek+nGf/Z5Uj2ovcQHMURqkA5aiiTSRq9+9auVq9tstNFGkTvZ9ETrIWXSc+xBP+KII8gvliywNSmGc6Ycl+ezNMtredHrzEOkv6KaGwfaEWw6hJk5ys4//3ywNU7wueeeC87XzGYWsE7WAj2rP6nTBQXgF5UqIaGkCc9ElTSiil6TvzMlruGFA4W35kckxYYbbuhjk0CvFeobBEr0GgtMzC4aaLfddhPO4OKjpYEc1IgpzQQ87eVhQqgIL6ZAJ+9g8GEzERNLayazZLfmWGioQMscaE+v0dKctQO+xQI+1Ubn4NRZPwOsTjF6N13mvcp5YP0ypeevL2oAfuCZiCq5ZkpQw/twQKN7gYfGYaM2hYVW4i40qk14Ljz8T99473vfS+UEs7/oMK5Mc8BFgkMPPXTPPfe0GsWi7+9+++0noyMmYIqM7VFMdczDVgaIrawM9CGpRo2QA+3INl2EXqP5nQxYtWqVbxT4uOOOs1RpQ4Tq5UxAO8QMvZToNc9//vOHjnkZI6Ru0FY4+2gcVdcfOFLGd8jc1nw2Ci/54pLpaC6WqCSgudinJ0RHsruPZUdGXUhK0/MkhvZDH/oQeRSxJcHaa69NSAlfxiwd86qt1ilGKMnuQ60U6qvNVDQIzoxajH7AF0USkTU+Puk092VaMr8MkNa82YYvvfTSJVqFlnldRgV2hWMYaK5kWsQQYykKPaQJQx7JYqHKX8n0E5sPhEc7TqAQ8y+dysqDNQexkS+KYB421eJDst5669F3BKbXtVzfWhwOtLTmrY19f7KVU6mZaTcbQG/Qk9JLmuFLC86wWVo0Lwq1qwXM/afM2UTTJEZnKBuIEk6gZIW7mcyu4ubMqOyggV83YwlqJu4otBlV4VFzoCVZo41ti7Bq0KxPafh8i8ijZuwSgktFlhDNldTKgZY50JKsic4ykwZbx2rLrV6LqxxonwMtrUNlkjyTTBFODPG59lmwwBJRzi0QSc1eObDsOdCSXtNfiEwLmaUnZUrn6F+7kqwClQOTzIGW9JrlzeKq1yzv9q21GwoHqqxZEBurlFkQ+2rmSeJAlTULau06e1oQ+2rmSeJAlTWT1Nq1rpUDi8eBKmsWj/e15MqBSeJAS+tQY8XSYmSx68ckyF9+2breDAzZEpTY7orIy3WH15DKgcqBJgcmV6+JiOmWFEISxcepwE2WVbhyoHJgHhyYUFlDgnAutXAKlCIDLryLkpJYfkRPia1A5UDlwPw4MImyhnBx257Dfm4hcJbPjScu5S/sE+uype22285hYhcsHXTQQSUKQPo0XUL4RUI1E1e4cqByoHBgEmUNufCa17zGM7teL/OKg4tO/C0cARx++OGukvQslItpXB/pzsASWwQNkSSw/AWUNBWoHKgc6ObAJMoa1yndfvvt3ld0fZdH3TDlO9/5TmGN65RcqkTr+eIXv+iZIZeenHbaaSW2TKkCkDh5CsZfML+krEDlQOVAkwOTuA611lpr5T0QosEzmHy35xSmkB1u23afuSuaXObmzhSCqcQG8CCUZDJSZ/iuawIQYfnbkbj+rRyoHMCBZS5rMtNRT++uuODaXwYaj20SEALPOeccr3qTKW69jVZCWJhblZsiXazT3UvkdYNcR5TA3CbXnb6GVA5UDuDAMpc1REB0DTfRfuELX/DXEzEechW4//77n3HGGa5u8+jilltuiRcC+SZN5E421OSStyKw0mMk8zhRNKOSS6B3o5Og+pUDlQPdHFjmsiYVJiwe/ehHT01NkQhupRX40Y9+lEnYX/erEzRkUFIC3LZP08kVgq6qlabj3klpjjrqKH7Z4BdhJGU3f2tI5UDlQDiwzGVNhAifChNYtZljPN5ANFBhbrjhhgMOOMB92vvss4+nP1hnVqxY4dEiZmMhXn21B8dl/c3uAk9QwRCg+AGaiStcOVA5EA6snmJMGi+uuOIKzwal4pEXLMGf//znn/CEJ5g0eYOROmM53BX8ZIenpq1GFRVm0nhV61s5MCwOTKKsYWq5/PLLyRFzHz45YpbkYTMPsNJr8syTt4euuuoqoscMy4tFMdwMi+kVT+XABHJgEmXNBDZzrXLlwKJzYBL38i060ysBlQMTyIEqayaw0WuVKwcWgQNV1iwC02uRlQMTyIEqayaw0WuVKwcWgQNV1iwC02uRlQMTyIEqayaw0WuVKwcWgQNV1iwC02uRlQMTyIEqayaw0WuVKwcWgQNV1iwC02uRlQMTyIH/Dw5eqrnSnPaAAAAAAElFTkSuQmCC" width="320" /></div>
<br />
Increased theta-band oscillations to near-misses were observed in the
insula and right OFC, which were positively associated with gambling
severity. The MEG data for R OFC and cognitive distortions related to gambling are shown below:<br />
<br />
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<img alt="" height="341" 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" 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Taken together, these data show that the near-miss effect in slot machine gambling is underpinned by increases in theta power in R OFC and insula, and which gamblers are particularly sensitive to, and that such increases are associated with the extent to which someone reports cognitive distortions related to gambling and with gambling history/severity. The hope is that the diagnostic and therapeutic implications of these findings might one day be realised by, for instance, devising interventions to attenuate the neural signatures of this near-miss effect.</div>
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<i>Reference: </i></div>
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Dymond, S., Lawrence, N.S., Dunkley, B., Yuen, S.L.K., Hinton, E.C.,
Dixon, M.R., Hoon, A.E., Munnelly, A., Muthukumaraswamy, S.D., &
Singh, K.S. (2014). <a href="http://www.sciencedirect.com/science/article/pii/S1053811914000408">Almost winning: Induced MEG theta power in insula and orbitofrontal cortex increases during gambling near-misses and is associated with BOLD signal and gambling severity.</a> <i>NeuroImage</i>, <i>91</i>, 210-219. <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/DymondLawrenceEtAl2014_AlmostWinning.pdf">PDF</a></div>
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<b>Some links to media coverage of our research </b></div>
<a href="http://www.swansea.ac.uk/media-centre/latest-research/almostwinninghownearmissesforgamblersfireoffawinresponseinthebrain.php">Swansea University press release</a><br />
<span style="font-size: x-small;"></span><a href="http://press-news.org/122288-study-uncovers-why-almost-winning-is-just-as-good-for-some-gamblers.html">Press-News.org (Poland)</a><br />
<a href="http://www.dailykos.com/story/2014/02/26/1280468/-Overnight-News-Digest-Down-On-The-Corner-Edition">Daily Kos (US)</a><br />
<a href="http://www.allvoices.com/news/16613861-near-misses-just-as-good-as-winning-for-some-gamblers">AllVoices</a><br />
<a href="http://www.sciencecodex.com/study_uncovers_why_almost_winning_is_just_as_good_for_some_gamblers-128575">Science Codex</a><br />
<a href="http://www.m2.com/m2/web/story.php/20143703687%20;%20http://www.electronicsweekly.com/news/research/government-fights-criminal-apps-2014-02">M2</a><br />
<a href="http://www.retailtechnology.co.uk/news/5134/funding-fights-rise-of-mobile-app-attacks">Electronics Weekly</a><br />
<a href="http://medicalxpress.com/news/2014-02-uncovers-good-gamblers.html">Medical Xpress </a><br />
<a href="http://www.theexeterdaily.co.uk/news/education/almost-winning-just-good-some-gamblers">The Exeter Daily</a><br />
<a href="http://www.domain-b.com/technology/Health_Medicine/20140305_almost_winning.html">domain-b.com </a><br />
<a href="http://medicalboox.com/why-almost-winning-is-just-as-good-for-some-gamblers/#.Ux2N6V78ExE">Medical Boox</a><br />
<a href="http://www.upi.com/Health_News/2014/02/25/Near-misses-just-as-good-as-winning-for-some-gamblers/UPI-64841393372010/">UPI</a> <br />
<a href="http://www.breitbart.com/Big-Peace/2014/02/26/Near-misses-just-as-good-as-winning-for-some-gamblers">Breitbart</a><br />
<a href="http://gamingzion.com/gamblingnews/nearly-winning-is-the-same-as-winning-for-some-bettors-according-to-university-research-4401">Gaming Zion</a><br />
<a href="http://www.business-standard.com/article/news-ani/why-near-misses-are-just-as-good-as-winning-for-gamblers-114022600597_1.html">Business Standard</a><br />
<a href="http://www.aninews.in/newsdetail9/story157039/why-near-misses-are-just-as-good-as-winning-for-gamblers.html">AniNews</a><br />
<a href="http://news.webindia123.com/news/Articles/India/20140226/2347641.html">WebIndia123</a><br />
<a href="http://www.sciencedaily.com/releases/2014/02/140225112902.htm">Science Daily</a> <br />
<a href="http://news.nationalgeographic.com/news/2014/02/140228-gambling-brain-win-slot-machines/">National Geographic </a><br />
<a href="http://www.economist.com/news/science-and-technology/21598948-neural-seat-compulsive-gambling-may-have-been-found-slotting">The Economist</a> BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com1tag:blogger.com,1999:blog-1826501894538086090.post-21381356871226292882014-01-17T14:38:00.000+00:002014-01-24T11:37:06.898+00:00Keep Calm: Here's Today's Misrepresentation of Radical Behaviourism (and What You Can Do To Protect Yourself From It)<div style="text-align: left;">
In what someone, somewhere clearly thought would be a good way of filling time during the quiet news month of January, many influential writers and thinkers were asked, <a href="http://www.edge.org/annual-question/what-scientific-idea-is-ready-for-retirement">"what scientific idea is due for retirement in 2014"?</a></div>
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Among the 174 responses, which include a range of topics such as quantum physics, statistics, and evolutionary biology, is a contribution from leading autism expert, Professor Simon Baron-Cohen. <a href="http://www.edge.org/response-detail/25473">Prof. Baron-Cohen thinks that "Radical Behaviourism" should be retired in 2014</a>. He claims that radical behaviourism:<br />
<ul>
<li>"was displaced by the cognitive revolution, because it was deeply flawed scientifically".</li>
<li>that Chomsky's (1959) now infamous review of B.F. Skinner's (1957) book, <i>Verbal Behavior</i>, essentially killed-off an entire field of study before it had even begun.</li>
<li>that "there is more to behavior than just learned associations. There are evolved neurocognitive mechanisms".</li>
<li>that radical behaviourism is "scientifically uninformative".</li>
<li>and that it should cease to be implemented via "'behavior modification' programs, in
which a trainer aims to shape another person's or an animal's behaviour". </li>
<li>Baron-Cohen then mentions a researcher who conducts research at "the interface of neuroscience and ethics" who examined the life of a captured orca, or killer whale, "involved" in the deaths of three people, which "may have been a reaction to the Radical Behaviorists who were
training this orca to show new behaviors". </li>
</ul>
There is so much wrong with this argument that it is hard to know where to start. Professor Richard Hastings has delivered <a href="http://profhastings.blogspot.co.uk/2014/01/expunging-radical-behaviourism-from.html">an excellent riposte </a>to Baron-Cohen's "retirement anti-eulogy" by describing a dystopian vision of what the world might actually look like without radical behaviourism. In fact, the influence of behavioural thinking is far deeper ingrained in our scientific and cultural lives than Baron-Cohen might think. Lee Hulbert-Williams has also <a href="http://leehw.com/beh101/">just responded.</a><br />
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Here, I wish to briefly address the stated reasons for why Baron-Cohen thinks radical behaviourism should be wiped from the annals of scientific history. In so doing, I will revisit many of the same issues and mistakes made by undergraduates as they grapple with the history of psychology and with behaviorism's place within it.<br />
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<i><b>Point 1. Do Amoeba Dream of Blackfish?</b></i></div>
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First, it's unclear whether Baron-Cohen is aware of the difference between J.B. Watson's <i>methodological behaviourism</i> and <a href="http://www.amazon.com/Perspectives-Contemporary-Behaviorism-International-Contributions/dp/0313296014">B.F. Skinner's <i>radical behaviourism</i>.</a> The latter, radical behaviourism, was so named because Skinner was determined to emphasise the differences between his account and Watson's. These differences included the consideration of "private events" or thoughts, feeling and attitudes, etc. in a science of behaviour.<br />
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Now, <i>that's radical</i> compared to <a href="http://www.questia.com/library/652574/psychology-from-the-standpoint-of-a-behaviorist">Watson's behaviorism</a> in which all unobservable events were excluded from scientific analysis. Watson emphasised overt, observable dimensions of behaviour as his sole subject matter. He wasn't concerned with what may be going on "under the hood".<br />
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<a href="http://2.bp.blogspot.com/-Ge-ASqG4quE/UtkixI4mOiI/AAAAAAAAAHM/0axT_viR458/s1600/farside_ameo1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" src="http://2.bp.blogspot.com/-Ge-ASqG4quE/UtkixI4mOiI/AAAAAAAAAHM/0axT_viR458/s1600/farside_ameo1.jpg" height="400" width="307" /></a></div>
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So, thinking and feeling <i>do</i> play a role in modern radical behaviorism (and are far more complex than simple stimulus-response associations); it's just that such events are not given causal status in behavioural analysis. It's a complex, philosophical argument to fully describe but private events are basically deemed "behaviour" (as a mass noun, not a count noun) and all such behaviour is the product of environmental events working in tandem with evolutionary (phylogenic) and individual (ontogenic) variables. Change the environment and behaviour changes (including thoughts).<br />
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<i><b>Point 2: We're Not Talking About A Revolution!</b></i></div>
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Talking in terms of a "cognitive revolution" is now old hat and a woeful over-simplification of how the self-corrective processes of science actually work. As Henry Roediger has written ("<a href="http://www.psychologicalscience.org/index.php/uncategorized/what-happened-to-behaviorism.html">What Happened to Behaviorism</a>?"), it is a "cartoon view of the history of psychology" and behaviourism has today "won the intellectual debate". <br />
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<i><b>Point 3: Dear Reader, Please Don't Believe the Hype.</b></i><br />
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When someone critiques a field of enquiry outside of their own and in ways that illustrate they have little or no expertise in what they are commenting on (I am unclear as to Baron-Cohen's training in behaviourism and his ability to keep up with modern developments in the literature), then Chomsky is usually never far behind. Wow, never has a single<a href="http://www.chomsky.info/articles/1967----.htm"> book review</a> been said to exert so much power, and for so long! That's because it's not credible.<br />
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But, if we were to believe the hype, then Chomsky not only trashed Skinner's functional account of language but also torpedoed a research agenda of an entire discipline before it even had a chance to gain momentum. This is inaccurate on several levels.<br />
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Skinner's account, while limited, <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/DymondEtAl2006.pdf">has been cited countless times</a> and <a href="http://www.google.co.uk/url?sa=t&rct=j&q=&esrc=s&source=web&cd=1&cad=rja&ved=0CC4QFjAA&url=http%3A%2F%2Fwww.tidewaterasa.org%2Fdocuments%2FPOSTSundbergMichael200pdf.pdf&ei=UCHZUoqkDcWqhQfs8ICwCA&usg=AFQjCNGHwDpFoxX0eNKlYg2SRAdwJ6TW0Q&sig2=iuDdiksEpRDDmSThCjUo4g&bvm=bv.59568121,d.ZG4">forms the basis</a> of numerous <a href="http://www.researchgate.net/publication/41909906_Applied_behavior_analytic_intervention_for_autism_in_early_childhood_meta-analysis_meta-regression_and_dose-response_meta-analysis_of_multiple_outcomes">effective and well-validated</a> intervention strategies designed to get people with developmental disorders, brain injury, <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3196209/">and other conditions to speak</a> (or once lost, to regain the ability to speak). For sure, it may not have had quite the impact outside of behaviour analysis <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/TPRVol60No2-Dymond.pdf">that many would have liked</a>, particularly in the domain of complex human language and cognition, but if Baron-Cohen had kept up with his reading over the past few decades he would have encountered more than a fair share of exciting modern developments in this domain. <a href="http://contextualscience.org/rft">Relational frame theory</a> (RFT), for instance, is a post-Skinnerian behavioural account of language and cognition that has <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/bhan-33-01-97.pdf">generated lots of research</a> and aims to <a href="http://www.amazon.co.uk/Advances-Relational-Frame-Theory-Application/dp/1608824470/ref=sr_1_2?ie=UTF8&qid=1355339022&sr=8-2/">move beyond </a>some of the limitations of Skinner's conceptual account, which is, let's face it is more than 50 years old!<br />
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<i><b>Point 4. Is this the Psych 101 Class?</b></i></div>
<br />
No one doubts the role of evolved abilities and neurocognitive
systems in language, certainly not in behaviourism. Most behaviourists
agree with Baron-Cohen that "there is more to behavior than just learned
associations". Indeed, discriminative stimuli, conditional stimuli,
contextual cues, motivating operations, reinforcement history, and other
current and situational demands all exert an influence over behaviour.<br />
<br />
But anyone with even a passing knowledge of behaviour principles would
know that. Sometimes, all of the answers to life's questions can't be
answered on the basis of a Psych 101 class.<br />
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<i><b> Point 5. The Road to Nowhere.</b></i><br />
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How does one evaluate the claim that radical behaviourism is "scientifically uninformative"? With a Talking Heads video, of course! <i><b><br /></b></i></div>
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Radical behaviourism, the philosophy of the science of behavior, is elegant, practical and parsimonious. It rejects hypothetical constructs and mediational variables. They simply impede progress towards the stated <a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2741891/">goals of science</a>: prediction and influence (with precision, scope and depth). It rejects the hypothetico-deductive model, emphasises inductive research methods (including single case or small-<i>n</i> designs), acknowledges a clear role for theory, particularly what is known as analytic-abstractive theory, in science and forms the basis of modern, <a href="http://contextualscience.org/functional_contextualism_0">functional contextual</a> approaches to human psychology. Uninformative? I think not.</div>
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<div style="text-align: center;">
<i><b>Point 6: Has Anyone Seen Blackfish?</b></i> </div>
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Perhaps the most striking aspect of Baron-Cohen's misinformed tract against radical behaviourism, is his failure to acknowledge the empirical support for treatments based on radical behaviourism, such as applied behaviour analysis for autism spectrum disorders. Instead, Baron-Cohen concludes his piece with an anecdote about animal training, such as that undertaken with killer whales at places like Seaworld in the USA (and the subject of a compelling recent documentary called <a href="http://www.imdb.com/title/tt2545118/">Blackfish</a>).<br />
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If we focus on animal training for a moment, behavioural principles have been used<a href="http://www.bbc.co.uk/news/world-africa-19584422"> to train rats to detect landmines and tuberculosis (TB)</a>, <a href="http://www.youtube.com/watch?v=_T7d0bze4kM">honey bees to detect explosives</a>, <a href="http://en.wikipedia.org/wiki/United_States_Navy_Marine_Mammal_Program">dolphins and other marine mammals in naval operations</a>, and in training "seeing-eye" or guide dogs (without having to worry about "who or what the animal really is"). There are numerous <a href="http://www.clickertraining.com/karen">other examples</a>. <br />
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Putting animal training aside, Baron-Cohen must be aware of the effectiveness of applied behaviour analysis for autism. After all, he chaired the committee responsible for drawing up the <a href="http://www.nice.org.uk/CG142">NICE clinical guidelines on autism in adults</a>. Quoting from the guidelines (thanks to Richie May for this), we find:<br />
<br />
"1.5.3 When deciding on the nature and content of a psychosocial
intervention to address challenging behaviour, use a functional
analysis. The functional analysis should facilitate the targeting of
interventions that address the function(s) of problem behaviour(s) by:<br />
<ul>
<li>providing information, from a range of environments, on:</li>
<li>factors that appear to trigger the behaviour</li>
<li>the consequences of the behaviour (that is, the reinforcement received as a result of their behaviour[14])</li>
<li>identifying
trends in behaviour occurrence, factors that may be evoking that
behaviour, and the needs that the person is attempting to meet by
performing the behaviour."</li>
</ul>
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Now, that sounds very behavioural to me.<br />
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Perhaps Baron-Cohen prefers to think of radical behaviourism as being synonymous with 'behavior modification', in which the underlying causes of the behaviour in question were often overlooked at the expense of conducting a reductive intervention, and not with '<a href="http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2755377/">applied behaviour analysis'</a>, where the function of behaviour is matched to (individualised) treatment, then so be it.<br />
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I hope I have shown that this is an historically inaccurate stance which only serves to reinforce the impression that he is a little too far removed from what he is critiquing.<br />
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Coming next: my take on quantum mechanics! <br />
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**Update: 24/1/14: More reaction and comment here - <a href="https://www.blogger.com/(https://www.facebook.com/note.php?note_id=582322825178868">Hank Schlinger and Bob Remington give Baron-Cohen a C+</a> and <a href="http://theskepticaladvisor.wordpress.com/2014/01/21/simon-baron-cohens-fantastically-false-article-on-radical-behavior-an-example-of-valid-but-false-premises/">Simon Baron-Cohen’s Fantastically False Article on Radical Behavior: An Example of Valid, but False Premises***</a><br />
<br />BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com5tag:blogger.com,1999:blog-1826501894538086090.post-71576694935508637762014-01-10T11:04:00.001+00:002014-02-10T10:31:32.558+00:00Research briefing - Relational Memory Generalization and Integration: The Role of Task Awareness<a href="http://www.ucd.ie/psychology/staff/">Anita Munnelly</a>, a former PhD student, and I have just had a paper published in <i>Neurobiology of Learning & Memory</i> about which we are very excited and want to share!<br />
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The paper describes the results of two experiments from Anita's PhD thesis investigating the effects of prior instructed task awareness, and a couple of other methodological factors, on humans' transitive inference (TI) ability, which is considered a hallmark of human and nonhuman reasoning.<br />
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In a typical TI task, overlapping pairs of simultaneous discriminations are trained, such as A+B-, B+C-, C+D-, and D+E- (where “+” indicates reinforced choices and “–” non-reinforced choices), before novel combinations of stimulus pairs (e.g., AE, BD) are presented in a test phase in the absence of feedback. Now, the AE endpoint test pair may be solved without reference to the intervening pairs, and A chosen over E, since during training A is always reinforced and E is never reinforced. On the other hand, the BD non-endpoint pair have comparable training histories since reinforcement is made available equally often for choices of B and D during training. Despite this, selection of B over D is predicted because D was paired with E during training, which was never reinforced. We used Kanji script stimuli, as shown in the figure below:<br />
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Anita and I were interested in the role of instructed awareness in facilitating TI, so we explicitly informed one group of participants that they should pay close attention during training as the stimulus pairs reflected an underlying hierarchy (i.e, A+B-, B+C-, C+D-, and D+E-), while another group were not given this instruction. We also employed repeated training and testing exposures, so if someone didn't meet criterion at test, he or she was re-exposed to the training and testing sequence a maximum of two more times. This allows for consistent correct (predicted) and incorrect (unpredicted) performances to emerge; it's also an approached based on functional-accounts of relational memory which emphasise that a prior relational learning history may be both necessary and sufficient for novel behaviour, such as that seen on tests for TI, to emerge. Another factor we manipulated was presenting the critical BD probe trials at an early stage of learning to chart the time-course of emergence of this relational ability.<br />
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Results demonstrated that prior instructional task awareness led to a minor, but non-significant, performance advantage (in Experiment 1, 80% of the Instructed group, and 63% of the Uninstructed group, met criterion at test. Similarly, in Experiment 2, 86% of the Instructed group and 69% of the Uninstructed group met test criterion). Informing participants about the underlying stimulus hierarchy, combined with repeated training and testing (as necessary), did not lead to better TI performance. Moreover, presenting BD trials throughout Experiment 2 did not faciliate performance, and behavioural accuracy was not correlated with a post-experimental measure of task awareness.<br />
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So, a mixed bag. Instructing participants that the stimuli formed an underlying hierarchy didn't boost test performance, although any instructional effects may have been mitigated by the predetermined exposure and mastery criteria we employed. Also, the present findings were obtained with a relatively small sample size; further research with larger samples sizes is therefore necessary to replicate the findings and determine the size of any possible effects.<br />
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Here's the references and a link to the PDF: <br />
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<a href="http://psy.swan.ac.uk/staff/dymond/Pubs/Munnelly&Dymond2014_NLM.pdf">Munnelly, A., & Dymond, S. (2014). Relational memory generalization and integration in a transitive inference task with and without instructed awareness. <i>Neurobiology of Learning and Memory</i>, 109, 169-177.</a> <br />
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<br />BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-43494303750441398342014-01-01T14:59:00.000+00:002014-01-01T14:59:45.340+00:00Yet Another End/Start of Year List: Some of My Favourite Records of 2013<div class="separator" style="clear: both; text-align: center;">
<a href="http://2.bp.blogspot.com/-76_4lXH4UTM/UsQq5YaoNNI/AAAAAAAAAFk/WG-N0W5VhSs/s1600/delta-machine-album.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="311" src="http://2.bp.blogspot.com/-76_4lXH4UTM/UsQq5YaoNNI/AAAAAAAAAFk/WG-N0W5VhSs/s320/delta-machine-album.jpg" width="320" /></a></div>
Depeche Mode - Delta Machine<br />
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DJ Koze - Amygdala<br />
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Kid Cudi - Indicud<br />
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Daniel Avery - Drone Logic<br />
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Trentemoller - Lost<br />
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James Blake - Overgrown<br />
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Mano Le Tough - Changing Days<i><i> </i></i><br />
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Jon Hopkins - Immunity<br />
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Bonobo -Northern Borders<br />
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Foals - Holy Fire <br />
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Blood Orange - Cupid Deluxe<br />
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Onehotrix Point Never - R Plus Seven<br />
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Kaito- Until The End of Time<br />
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Sebastian Tellier - Confection<br />
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The National - Trouble Will Find Me<br />
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Tim Hecker - Virgins<br />
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Darkside - Psychic<br />
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Burial - Rival Dealer EP<br />
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Blue Hawaii - Untogether<br />
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The Field - Cupid's Head<br />
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Mazzy Star - Seasons of Your Day<br />
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FKA Twigs - EP2<br />
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Forest Swords - Engravings<br />
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Machinedrum - Vapor City<br />
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Apparat - Krieg und Frieden (Music For Theatre)<br />
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Cliff Martinez - Only God Forgives OST <br />
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<b><span style="font-size: large;">Mixes, Compilations & Podcasts</span></b></div>
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Danny Howells - Balance 24<br />
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John Talabot - DJ Kicks <br />
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Bonobo - Late Night Tales<br />
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Apollonia - Fabric 70<br />
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Adam Freeland -Kazantip 118 BPM @ 5AM<br />
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DJ Koze - XLR8R podcast<br />
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Dave DK - Datatransmission podcast <br />
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Anton Zap - Resident Advisor podcast<br />
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Merveille & Crosson - Resident Advisor podcast <br />
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<a href="https://soundcloud.com/pamparecords/santa-kos-xmas-goodie">Santa Kos Xmas-Goodie</a><br />
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<br />BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-44847198998106590742013-10-04T16:41:00.000+01:002013-10-04T16:48:48.238+01:00A couple of new publications to shareWow, where has the time gone? In particular, what's happened to summer?!<br />
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It's Autumn and I'm already busy with the start of semester, grant and paper writing, and welcoming new students (PhD and UG) to the lab. So I just wanted to update the blog and share details of two recently published studies.<br />
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The <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/GrevilleEtAl_13.pdf">first study </a>is from a BIAL Foundation project and was published in <i>Computers in Human Behavior</i>. It's on <a href="http://psychology.wikia.com/wiki/Conditioned_suppression">conditioned suppression</a>, which is a behavioural model of anxiety where a previously conditioned aversive cue (CS+) interupts or supresses appetitive, approach-oriented operant behaviour. We received funding to develop a virtual reality task with which to investigate conditioned suppression in humans. And the results are very promising! Here's the abstract of the study:<br />
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<blockquote class="tr_bq">
<div style="text-align: justify;">
Virtual environments (VEs) provide an inexpensive way of conducting ecologically valid psychological research. The present study used a VE to demonstrate conditioned suppression, a behavioral model of anxiety, in a first-person perspective video game. During operant training, participants learned to shoot crates to find gold bars and thus score points in the game. Next, during Pavlovian conditioning, a colored light (i.e., conditioned stimulus: CS+) was followed by a white noise unconditioned stimulus (US) while a different colored light (CS ) was not paired with the US. Probe trials in a final testing phase were then used to assess suppression. We found significant suppression of accurate responding (shots hitting the designated targets) during the presence of the CS+ relative to the CS , both in terms of total hits and hits as a proportion of total shots. Importantly, this effect emerged despite the overall level of operant responding being undiminished during the CS+. Our findings are consistent with related studies examining human behavior in real environments, and demonstrate the potential of VEs in combination with a modestly aversive CS to allow a detailed behavioral profile of anxiety to emerge.</div>
</blockquote>
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Greville, W. J., Newton, P. M., Roche, B., & Dymond, S. (2013). <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/GrevilleEtAl_13.pdf">Conditioned suppression in a virtual environment.</a> <i>Computers in Human Behavior, 29</i>, 552-558. <br />
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We currently have another paper under submission and another, multi-experiment paper in the works, so watch this space!<br />
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The <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/DymondEtAl_spiderfearQJEP.pdf">other study </a>is due out in the <i>Quarterly Journal of Experimental Psychology </i>and was conducted with people screened for spider fear and divided into high vs. low groups. Both groups were given the same symbolic generalization of avoidance task and each performed differently. Basically, the high spider fearful individuals showed greater behavioural avoidance and lower cognitive expectancy, whereas the opposite was the case for the low spider fearful people.<br />
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This could mean that, for someone with a fear of spiders, the world is a place where spiders could, potentially, be found, and that their avoidance behaviour modulates expectancies of spiders. Much, much more to be done on this important topic! Anyway, here's the abstract:<br />
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<blockquote class="tr_bq">
<div style="text-align: justify;">
Overgeneralization of fear and threat-avoidance represents a formidable barrier to successful clinical treatment of anxiety disorders. While stimulus generalization along quantifiable physical dimensions has been studied extensively, less consideration has been given to symbolic generalization, in which stimuli are indirectly and arbitrarily related. The present study examined whether the magnitude and extent of symbolic generalization of threat-avoidance and threat-beliefs differed between spider-phobic and non-phobic individuals. Initially, participants learned two sets of stimulus equivalence relations (A1 = B1 = C1; A2 = B2 = C2). Next, one cue (B1) was established as a conditioned stimulus (CS +; threat) that signalled onset of spider images and prompted avoidance, and another cue (B2) was established as a CS– (safety cue) that signalled the absence of such images. Subsequent testing showed that phobics compared to non-phobics exhibited greater symbolic generalization of threat avoidance to threat cues A1 and C1 (indirect CS+ threat cues related via symmetry and equivalence, respectively), while all individuals showed non-avoidance to indirect safety cues A2 and C2. The enhanced symbolic generalization of threat-beliefs and avoidance behaviour observed in spider phobics warrants further investigation.</div>
</blockquote>
Dymond, S., Schlund, M. W., Roche, B., & Whelan, R. (in press). <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/DymondEtAl_spiderfearQJEP.pdf"> The spread of fear: Symbolic generalization mediates graded threat-avoidance in specific phobia.</a> <i> Quarterly Journal of Experimental Psychology.</i> DOI:10.1080/17470218.2013.800124BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-85693332986026835102013-04-01T12:20:00.000+01:002013-04-01T14:42:38.825+01:00Advances in Relational Frame Theory: Research and ApplicationBryan Roche and I have an edited volume, <i>Advances in Relational Frame Theory: Research and Application,</i> due out on May 1st, and published by New Harbinger.<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="http://3.bp.blogspot.com/-JGZ0Ls9RtMA/UVlsp0rmIVI/AAAAAAAAAEs/rs2QphI1hhY/s1600/518eiiyvzCL._SY450_.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="320" src="http://3.bp.blogspot.com/-JGZ0Ls9RtMA/UVlsp0rmIVI/AAAAAAAAAEs/rs2QphI1hhY/s320/518eiiyvzCL._SY450_.jpg" width="213" /></a></div>
<br />
<br />
For ONE WEEK ONLY (from April 1-7), New Harbinger are offering a 30% discount on orders placed via their website. The discount, which is only available for US orders, can be obtained by visiting the <a href="http://www.newharbinger.com/advances-relational-frame-theory">New Harbinger website</a> and entering the following discount code: <b>SDRFT13</b><br />
<br />
Those outside the US wishing to pre-order the book, can still do so via <a href="http://www.newharbinger.com/advances-relational-frame-theory">New Harbinger</a> or <a href="http://www.amazon.co.uk/Advances-Relational-Frame-Theory-Application/dp/1608824470/ref=sr_1_3?s=books&ie=UTF8&qid=1364814870&sr=1-3">Amazon</a> (for £42.50 or £32.44 for the Kindle edition).<br />
<br />
Here are some details about the book and endorsements from Niklas Törneke, John Forsyth, Mike Dougher, and David Sloan Wilson: <br />
<br />
<b><span style="background-color: white;"><span style="color: blue;">Full Book Description</span>:</span></b> As acceptance and commitment therapy (ACT) increases in popularity among clinicians, it becomes more and more vital to understand its theoretical basis, relational frame theory (RFT). RFT is a psychological theory of human language and cognition, developed by Steven C. Hayes. It focuses on how humans learn language and how language connects them to their environment. In essence, our thoughts, feelings, and behaviors are dependent on our experiences and the context that these experiences provide.<br />
<br />
Edited by leading relational frame theory (RFT) scholars, Simon Dymond, PhD, and Bryan Roche, PhD, Advances in Relational Frame Theory presents advances in all aspects of RFT research over the last decade, and provides a greater understanding of the core principals of acceptance and commitment therapy (ACT). The book also contains chapters written by Steven C. Hayes and Kelly Wilson, both research-active experts from the RFT community around the world.<br />
Because ACT is focused largely on accepting one’s thoughts, it is important to understand where these thoughts come from. And while many books on RFT are abstract and require extensive knowledge of behavior analysis, this is the first book to comprehensively but accessibly introduce RFT to ACT mental health professionals.<br />
<br />
Gaining a deeper knowledge of the relational concepts of RFT can help you understand why a person's behavior does not always match up with their self-professed values. Whether you are a mental health professional, or simply someone who is interested in the connection between language and experience, this book is an invaluable resource. <br />
<br />
<b><span style="background-color: white;"><span style="color: blue;">Brief Book Description:</span></span></b> Edited by leading relational frame theory (RFT) scholars, Simon Dymond, PhD, and Bryan Roche, PhD, <i>Advances in Relational Frame Theory</i> presents advances in all aspects of RFT research over the last decade, and provides mental health professionals a greater understanding of the core principals of acceptance and commitment therapy (ACT). A must-read for anyone interested in ACT, the book contains chapters written by Steven C. Hayes and Kelly Wilson, both research-active experts from the RFT community around the world. <br />
<br />
<b><span style="background-color: white;"><span style="color: blue;">Endorsements:</span></span></b> “The interest in relational frame theory is growing within different fields of psychology. For anyone who wants to keep up-to-date with basic research in this area, this is the book to read.” —<b>Niklas Törneke MD</b>, author of Learning RFT <br />
<br />
“Psychology is full of theories of mind, but relational frame theory (RFT) differs from all the rest in many ways. You see that when you open up this book. This lucid and engaging volume brings together the latest cutting-edge research and theory on RFT. It will challenge you in many ways, and also surprise you. It is a must-read for anyone interested in language and cognition, and especially researchers and practitioners of mindfulness and acceptance-based interventions.” —<b>John P. Forsyth, PhD</b>, professor of psychology director, Anxiety Disorders Research Program University at Albany, State University of New York<br />
<br />
“Dymond and Roche have put together an outstanding volume that not only provides an excellent and accessible overview of relational frame theory and its rapidly accumulating empirical evidence, but also elegantly situates RFT in its proper philosophical context, makes contact with other contextually-based sciences, and elucidates nicely the many applied extensions of the theory. This book is a must and enjoyable read for anyone interested in RFT as a powerful new approach to language and cognition as well as its compelling applications.” —<b>Michael J. Dougher, PhD</b>, senior vice-provost for academic affairs, University of New Mexico<br />
<br />
“Relational frame theory addresses the fundamental nature of symbolic thought in addition to its practical applications. It therefore deserves to be known among a large interdisciplinary audience, including my own field of evolutionary science. Advances in Relational Frame Theory reports on the current state of the art.” —<b>David Sloan Wilson</b>, <b>PhD</b>, president of the Evolution Institute and State University of New York distinguished professor of biology and anthropology, Binghamton University.<br />
<br />
So, remember, for ONE WEEK ONLY (from April 1-7), enter the discount code SDRFT13 to receive a 30% discount on all US orders placed via the <a href="http://www.newharbinger.com/advances-relational-frame-theory">New Harbinger website</a>. <br />
<b></b><br />
<br />
*commercial ends*<br />
<br />
<br />BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-63195548281855434392013-02-07T15:58:00.001+00:002013-02-11T09:35:58.228+00:00Le Parsimonious Crow: Part Deux<span style="font-size: large;">Our <span style="font-size: large;">l</span>etter (<i>Clever crows or unbalanced birds?</i>) on the Taylor et al. paper claiming to have shown causal reasoning in crows has been published in <i>PNAS</i>, along with the author's reply. </span><br />
<br />
<span style="font-size: large;">You can read our letter <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/Dymond,%20Haselgrove%20&%20McGregor%20(2013).pdf">here</a> and the reply <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/TaylorEtAl13_reply.pdf">here</a>.</span><br />
<br />
<span style="font-size: large;">This
particular scientific journey started on Twitter, where Mark
Haselgrove, Anthony McGregor and I discussed the paper<span style="color: red;"><span style="font-size: large;"><span style="color: black;">. </span><span style="color: red;"><span style="font-size: large;"><span style="color: black;">I then <a href="http://behaviourblog.blogspot.co.uk/2012/09/the-parsimonious-crow.html">blogged about </a><span style="font-size: large;"><a href="http://behaviourblog.blogspot.co.uk/2012/09/the-parsimonious-crow.html">it</a>, <span style="font-size: large;">whi<span style="font-size: large;">ch <span style="font-size: large;">lead to <span style="font-size: large;">us firing off a brief, 500-word commentar<span style="font-size: large;">y to <i>P</i><span style="font-size: large;"><i>NAS</i>.</span></span></span></span></span></span></span></span></span></span></span></span></span><span style="font-size: large;"> </span><br />
<br />
<span style="font-size: large;"><span style="font-size: large;">Interesti<span style="font-size: large;">ngly, <span style="color: black;"><span style="font-size: large;"><a href="http://www.pnas.org/content/110/4/E273">another commentar</a><span style="font-size: large;"><a href="http://www.pnas.org/content/110/4/E273">y</a> on the paper <span style="font-size: large;">has also <span style="font-size: large;">since </span>been <span style="color: black;"></span>published</span></span></span></span></span></span></span><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;">.</span></span></span></span><br />
<span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><br /></span></span></span></span></span></span></span>
<span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;"><span style="font-size: large;">Both commentaries <span style="font-size: large;">make the po<span style="font-size: large;">int that <span style="font-size: large;">more empirical work, with appropriate <span style="font-size: large;">co<span style="font-size: large;">ntrols<span style="font-size: large;">,</span></span></span> is needed be<span style="font-size: large;">fore it can be concluded that cr<span style="font-size: large;">ows engage in causa<span style="font-size: large;">l </span>reasoning<span style="font-size: large;">.</span> </span></span></span></span></span></span></span></span></span></span></span></span><br />
<br />
<span style="font-size: large;"><br /></span>BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-43322163833763772602012-12-28T15:12:00.002+00:002012-12-28T15:13:16.856+00:00My Top Albums of 2012By far the best thing I heard all year was Nicholas Jaar's Essential mix. Sheer downtempo genius:<br />
<a href="https://soundcloud.com/r_co/nicolas-jaar-essential-mix-19">Nicholas Jaar - Essential Mix 19-05-2012</a><br />
<br />
Other albums that stood out:<br />
<br />
Silversun Pickups - Neck of the woods<br />
<br />
Matthew Dear - Beams<br />
<br />
Andy Stott - Luxury problems<br />
<br />
Michael Mayer - Mantasy<br />
<br />
Luke Abbott - Modern driveway<br />
<br />
Christian Loffler - A forest<br />
<br />
The xx - Coexist<br />
<br />
Grizzly Bear - Shields<br />
<br />
Dan Deacon - America<br />
<br />
The Gaslamp Killer - Breakthrough<br />
<br />
Death in Vegas - Trans-love energies<br />
<br />
Halls - Ark<br />
<br />
Actress -R.I.P<br />
<br />
Kendrick Lamar - good kid, m.A.A.d city<br />
<br />
Nas - Life is good <br />
<br />
Efterkland - Piramida<br />
<br />
Sam Willis - Winterval<br />
<br />
Melody's Echo Chamber - Melody's echo chamber<br />
<br />
How to dress well - Total loss<br />
<br />
Grimes - Visions<br />
<br />
Light Asylum - Light asylum<br />
<br />
John Talabot - fin<br />
<br />
Cloud Nothings - Attack on memory<br />
<br />
Beach House - Bloom<br />
<br />
Liars - WIXIW<br />
<b> </b><br />
<b>Notable mixes, podcasts and compilations</b>:<br />
<br />
<a href="https://soundcloud.com/adamfreeland/after-burning-man-i-took-the">Adam Freeland - After Burning Man I took the train from Paris to Berlin and did this mixtape )'(</a><br />
<a href="https://soundcloud.com/mixfeed/apollonia-ra-podcast-338-11-12">Appolonia - RA podcast</a><br />
<a href="http://www.mixcloud.com/Route12/art-department-resident-advisor-podcast-340-03-12-2012/">Art Department - RA podcas</a>t<br />
<a href="http://www.amazon.co.uk/Masterpiece-Andrew-Weatherall-Various-Artists/dp/B007CGFVCM">Andrew Weatherall - Masterpiece</a><br />
<a href="http://www.mixcloud.com/_Reina_/ra322-the-sight-below-rafael-anton-irisarri-30-july-2012/">The Sight Below - RA podcast </a><br />
<a href="http://www.residentadvisor.net/podcast-episode.aspx?id=319">James Zabiela - RA podcast</a><br />
<a href="https://soundcloud.com/faciendo/fac-rad-031-faciendo">FAC-RAD-031 (Faciendo Soundsystem - John Digweed Transitions mix)</a><br />
<a href="http://www.xlr8r.com/podcast/2011/05/robag-wruhme">XLR8R Podcast: Robag Wruhme - June 7, 2011 </a><br />
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BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-27800626560146172322012-09-25T15:35:00.003+01:002013-02-07T15:35:59.753+00:00The Parsimonious Crow?<a href="http://www.blogger.com/blogger.g?blogID=1826501894538086090" imageanchor="1" style="clear: right; float: right; margin-bottom: 1em; margin-left: 1em;"></a>Recently, <a href="http://www.pnas.org/content/early/2012/09/10/1208724109.abstract">a study</a> was published by Alex H. Taylor, Rachael Miller, and Russell D. Gray in <i>Proceedings of the National Academy of Sciences</i> (PNAS) claiming that New Caledonian crows are capable of reasoning about "hidden causal agents".<br />
<br />
The study has generated considerable press interest (e.g., on the <a href="http://www.bbc.co.uk/nature/19626912">BBC website</a>, and see <a href="http://alexhtaylor.com/">here</a> for a list of links to press coverage of the article), several blog posts (for example, <a href="http://whyevolutionistrue.wordpress.com/2012/09/23/do-crows-have-a-concept-of-hidden-causal-agents/">here</a> and <a href="http://10000birds.com/new-caledonian-crows-understand-unknown-causal-agents.htm">here</a>), and also a wide-ranging Reddit discussion (<a href="http://www.reddit.com/r/science/comments/105nvg/caws_and_effect_iam_alex_taylor_evolutionary/">here</a>). As far as I can tell, absent from all of this media attention has been an informed critique of the study's procedures and the explanation provided by the authors for their findings. Until now.<br />
<br />
Thanks to the encouraging words of Anthony McGregor, Mark Haselgrove, and Graham Davey on Twitter, I wrote this blog to try and offer a critical evaluation of the study and to take issue with the authors' explanation. As you will see, there are several potential methodological limitations that may detract from the study's findings and which suggest a simpler, more parsimonious explanation of the crows' behaviour. Before we get to what that explanation might be, let's first consider the procedures in detail.<br />
<br />
The authors sought to develop an ecologically valid task that resembled the following scenario:<br />
<br />
<div style="text-align: center;">
"<i>Imagine a bird looking down on a monkey moving through<br />a forest canopy. Generally, the bird will be able to observe both<br />the monkey moving and the canopy shaking at the same time.<br />Sometimes, however, when the canopy is thick overhead, the bird<br />may only observe that, against a background of stationery leaves,<br />there are waves of moving leaves that can start and stop abruptly.<br />Humans are able to imagine why the leaves are moving when<br />the monkey is out of sight. They can hypothesize that there is<br />a hidden causal agent that must be moving the leaves because<br />when the wind is not blowing, the canopy does not shake on its own</i>. </div>
<br />
Essentially, the authors investigated what would happen when crows were presented with two conditions similar to those described above: one, where a human agent is observed to enter a setting, (presumably) engage in a behaviour, and then leave the setting; and, two, where the same behaviour occurs but without observing a human exit the setting. Would crows' food-seeking behaviour be influenced by the assumed absence of the human agent in the second condition?<br />
<br />
<div style="text-align: center;">
<h2>
<b>Methods</b></h2>
</div>
Eight crows (five adults), wild caught in <a href="http://en.wikipedia.org/wiki/New_Caledonia">New Caledonia</a> (a series of islands in the south west Pacific), were tested in an outdoor aviary containing a table, a food source (obscured by what looked to be a hollowed-out brick), a small stick tool, and a hide through which a long stick protruded. Here's a diagram from the study:<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="http://4.bp.blogspot.com/--1VoJLDd2lk/UGGrnpyIwEI/AAAAAAAAAEA/tJ9g4bUTYrs/s1600/Slide1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="300" src="http://4.bp.blogspot.com/--1VoJLDd2lk/UGGrnpyIwEI/AAAAAAAAAEA/tJ9g4bUTYrs/s400/Slide1.jpg" width="400" /></a></div>
<div style="text-align: center;">
<br /></div>
<br />
In the condition on the left, called the human causal agent (HCA) condition, the crows "<i>observed two humans walk into the aviary. One, the agent, walked into the hide and so became hidden from the crows. A wooden stick was then probed in and out of a hole in the hide wall 15 times toward the baited hole. The agent then exited the hide and left the room. At this point the second human, who had stood 1.5 m from the hide in the corner of the room with closed eyes and hands held in front of the body, also left the room. The crow was now free to come down to the table, pick up a tool, and use it to extract the food from the box."</i><br />
<br />
In the condition on the right, called the unknown causal agent (UCA) condition, the crows observed one human enter the cage and stand<i> "with closed eyes and hands held in front of the body as before. The tool was then probed through the hole in the hide 15 times. The human then left, and the crows again were free to come down to extract food</i>."<br />
<br />
So, the question was: would crows respond differently under the two conditions? In other words, would they show less caution during the HCA condition, in which they had seen the human enter the hide, poke the stick and leave the hide? Could they reason that they were unlikely to get poked in the back of the head by the stick? (The stick was, in fact, moved by a researcher in both conditions who was out of sight of the birds.) Or would they be less likely to try and extract the food in the UCA condition when that pesky human might be hidden, waiting to pounce? Remember, only 1 human entered and left during the UCA condition, whereas 2 entered and left in the HCA condition.<br />
<br />
Now, before the authors could undertake these tests they needed to ensure that the crows could extract food using the tool and do so in the presence of the hide. "Habituation" sessions were conducted in which the crows were taught to extract food at progressively decreasing distances from the hide, starting when the hide was 100 cm away and ending when it was 20 cm distance. <br />
<br />
By now, the crows could reliably extract food within 20 cm of the hide so the researchers were ready to begin testing the HCA and UCA conditions. Recall that the two conditions were intended to test whether crows could infer
what had moved an inanimate object and whether it was likely to occur
again. This was measured by the (mean) number of hide inspections & the
number of abandoned
searches (when crows stopped trying to use the tool to extract food). <br />
<br />
<div style="text-align: center;">
<h3>
<b>Predictions</b></h3>
</div>
<br />
The authors contrasted the predictions of two hypotheses. First, the "habituation hypothesis" predicts that, because the movement of the stick is a novel, sudden stimulus event, the crows should seek to minimise chances of being struck by it while they tried to extract the food. If this hypothesis is correct, then the <i>"level of caution would then be progressively reduced each time the crows used a tool in the box and the stick did not appear</i>." Their cautious behaviour would, therefore, habituate. So, we should see a decrease in the number of inspections during the UCA condition relative to HCA. The second hypothesis, the "causal reasoning hypothesis", predicts that crows should "<i>predict the stick’s movement by reasoning about why the stick was moving</i>." In other words, if the crows are capable of attributing causal agency to a hidden human they should demonstrate a high level of caution (i.e., number of inspections) during the UCA condition. The two hypotheses therefore make opposite predictions.<br />
<br />
<br />
<div style="text-align: center;">
<h3>
<b>Findings</b></h3>
</div>
<div style="text-align: center;">
</div>
<div style="text-align: left;">
The number of inspections was significantly
higher during the UCA condition than the HCA condition (see the diagram above). While there was an upwards trend evident between the second and third HCA trials, the first UCA trial resulted in the highest mean number of inspections which, by the third and final trial, resembled that of the first condition. </div>
<div style="text-align: left;">
<br /></div>
<div style="text-align: left;">
The crows also
abandoned more searches in the UCA condition (and none at all during
HCA), but this trend decreased from the first trial and stabilised during the second and third trials (see diagram below).<b> </b>They gave up more often because they remained highly cautious: any moment now, that pesky stick could start moving again and get in the way of their food.</div>
<br />
<div class="separator" style="clear: both; text-align: center;">
<a href="http://1.bp.blogspot.com/-MhxU8eG_F50/UGGrRmP7syI/AAAAAAAAAD4/ReeMyjGbFKE/s1600/Slide1.jpg" imageanchor="1" style="margin-left: 1em; margin-right: 1em;"><img border="0" height="240" src="http://1.bp.blogspot.com/-MhxU8eG_F50/UGGrRmP7syI/AAAAAAAAAD4/ReeMyjGbFKE/s320/Slide1.jpg" width="320" /></a></div>
<div style="text-align: center;">
<br /></div>
Recall that the "causal
reasoning hypothesis" makes opposite predictions to a simple account
based on habituation - but the data showed that across the 3 UCA trials,
the number of inspections decreased, not increased. Thus, the authors predicted "high level of caution" does, it seems, revolve around data from the first UCA trial <i>only</i>. <br />
<br />
At this stage, it may help to view a short movie showing the first author introducing the study and with some examples of trials taken from the experiment (another video is available <a href="http://www.pnas.org/content/suppl/2012/09/11/1208724109.DCSupplemental/sm01.mp4">here</a>):<br />
<br />
<div class="separator" style="clear: both; text-align: center;">
</div>
<div style="text-align: center;">
<iframe allowfullscreen='allowfullscreen' webkitallowfullscreen='webkitallowfullscreen' mozallowfullscreen='mozallowfullscreen' width='320' height='266' src='https://www.youtube.com/embed/ZnqUAsyOTv4?feature=player_embedded' frameborder='0'></iframe></div>
<br />
<h2>
</h2>
<div style="text-align: center;">
<h2>
<i><b>Analysis & Alternatives</b></i></h2>
</div>
<br />
Right, so is this definitive evidence for "causal reasoning" then? Consider the following methodological issues and theoretical challenges.<br />
<br />
<h3>
<b> 1. Absence of counterbalancing.</b></h3>
<br />
<div style="text-align: left;">
This is perhaps the most startling feature of the procedure.....the conditions were not counterbalanced. <i>All eight crows received the HCA trials followed by the UCA trials</i>.</div>
<br />
It is a standard requirement in research design to balance the order of
administration of two or more independent variables. This is done to
prevent the effects of one condition carrying over and influencing
subsequent conditions. Counterbalancing ensures that potential carryover
effects are balanced across the different orders (i.e., for instance,
half of the crows could have been administered HCA followed by UCA, and
the other half administered UCA followed by HCA).<br />
<br />
For some unexplained
reason, the authors chose not to counterbalance the conditions. Both the
HCA and UCA conditions are relatively distinct and easily
operationalised, so it's unclear why counterbalancing was not employed.<br />
<br />
<h3>
<b>2. "Blind" observers?</b></h3>
<br />
Let's consider another
troublesome aspect of the procedure. The Methods section reveals that
the data were scored by two observers who had an inter-reliability score
of 91%.<br />
<br />
Presumably, the two observers were not blind as
to which condition was in effect (i.e., if it's the first half of the
study, then it must be HCA; if not, it must be UCA.) but that does not
mean that blind observation was impossible. For instance, the observers
could have been presented with video-recorded trials starting with when
the crows left their perch and landed on the table. That would avoid
having to show the human(s) entering and exiting the aviary and biasing
the resulting observations. <br />
<br />
Regardless of whether the
observers were blind or not, the 91% inter-reliability score could refer
to one or both of the dependent measures employed. Without this
information and details of what inter-observer agreement procedure and
calculation method was used, it is difficult to evaluate the reliability
score. Moreover, we must assume from the list of author contributions
and the absence of acknowledged assistance with data collection that the
"two observers" were also those who carried out the study.<br />
<br />
<h3>
<b>3. "Habituation" training.</b></h3>
<br />
Although no data are presented on the number of trials needed to
complete this training, these sessions amounted to what is commonly
called "shaping by differential reinforcement". In other words,
successive approximations to the goal or target behaviour - extracting
food in the presence of the hide - were reinforced (with food), while
those behaviours that did not approximate the goal were not reinforced.<br />
<br />
Shaping is required to get the behaviour of interest to occur before you can introduce variables of interest (such as response requirements, etc.). Rats are not born with the ability to lever-press, you know! Shaping may also be used to increase desirable behaviour, such as a study conducted by Charlotte Slater and I on <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/Slater&Dymond_2011.pdf">trailer loading in horses</a>.<br />
<br />
Presumably, the crows did not need to be taught how to use the tool to
extract the food, but without any data we are left wondering about the nature and extent of their tool-using behaviour prior to the critical test trials.<br />
<br />
<br />
<h3>
<b>4. Stimulus control analysis of each condition. </b></h3>
<br />
From the perspective of the crows, the HCA condition consisted of the following sequence of events: two humans enter the aviary, one disappears from sight, the stick moves, one human reappears and exits, and, finally, the second human leaves the aviary. From the perspective of the crows, the UCA condition consisted of the following sequence of changes: one human enters the aviary, the stick moves, and the human leaves.<br />
<br />
Both conditions were, therefore, readily discriminable from one another (which, of course, was the whole idea). Both were followed by food; presumably, the crows successfully used the tool to extract food, although no data are provided on the consumption per trial. Strictly speaking, then, the two conditions, and the order in which they were administered, amounted to a discrimination learning task that was always followed by food.<br />
<br />
In terms of stimulus control, there are two main types of discrimination learning procedures: simple and conditional. Simple discrimination involves a history of differential reinforcement for selecting one stimulus over another (e.g., given X and Y, selecting X is reinforced with food, whereas selecting Y is not) and involve three terms of analysis: discriminative stimulus (i.e., X) - response (i.e., pointing or pecking) - consequence (i.e., food).<br />
<br />
Conditional discrimination involves a history of differential reinforcement in which selecting one stimulus is conditional upon the presence or absence of another stimulus. For instance, given X, selecting Y might be followed by food (S+), but given Z, selecting Y would be not be followed by food (S-). Conditional discriminations add an extra term to the analysis: conditional stimulus (i.e., X) - discriminative stimulus (i.e., Y) - response - consequence. <br />
<br />
Do the HCA and UCA conditions resemble simple or conditional discriminations? The response of tool-use was the same and was always followed by food, the stick moved in both conditions and was always followed by one of the humans leaving the aviary. No humans were present prior to the crows leaving their perch and landing on the table, but the sequence of events that lead up to this differed in each condition, as I have already outlined. The conditions could have resembled a form of conditional discrimination (i.e., two humans enter, stick moves, both leave [HCA] and one human enters, stick moves, and human leaves), but the response (which, remember was very well established in the crows' repertoires by this point) was the same and was always consequated by food.<br />
<br />
Moreover, because HCA was followed by UCA, generalization may have occurred in which the effects of the (directly reinforced) first three trials could have influenced the three UCA trials. Crows may have discriminated the partial similarity of the sequence of events and generalized their tool-using behaviour. The fact that caution decreased over trials supports this interpretation.<br />
<br />
I offer this preliminary analysis, not as an exhaustive explanation of what was going on in the study, but to suggest that a parsimonious account is possible and that such an account need only refer to a few behavioural principles (in this case, discrimination and reinforcement).<br />
<br />
This analysis does, however, lead to testable predictions. For instance, if the crows were engaging in conditional discrimination, then it should be possible to differentially reinforce greater numbers of inspections in one condition over another by following HCA (or UCA) with food and withholding food during the other. We would expect the number of inspections to decrease on trials followed by food but not in its absence. Indeed, you would probably see an increase in novelty-seeking behaviour (extinction induced) in no-food conditions.<br />
<br />
<h3>
</h3>
<h3>
<b>5. Parsimony</b></h3>
<br />
The etymology of "parsimony" means "to spare", or, to be "stingy". This is a great way of remembering that, in any scientific analysis, it is incumbent on the scientist to pitch an explanation of the phenomena under study with as few terms as possible. Complex terms, such as "causal reasoning" require further definition, and thus lower level, simple, parsimonious explanations that adhere to <a href="http://en.wikipedia.org/wiki/Occam%27s_razor">Occam's razor</a> should be sought at all times.<br />
<br />
Essentially, it is non-parsimonious to propose that the difference between observed data obtained from a total of six trials from two conditions administered in a fixed order represents evidence "that crows can reason about a hidden causal agent." As I have suggested above, a more parsimonious account of the data is possible.<br />
<br />
<h3>
</h3>
<h3>
<b>6. Experimental control: Why more trials are better than some</b>.</h3>
<br />
In non-human research, it is not always possible to collect data in the natural environment because of the myriad of potential disrupting influences that exist there. Taylor and colleagues sought to minimise many of these influences by designing an outdoor aviary and a hide. Another way of conducting research like this might involve adapting an operant conditioning chamber, which are widely used in behavioural, neuroscience and pharmacology research with pigeons, rats, and even <a href="http://www.jeabjaba.org/abstracts/JeabAbstracts/82/_82-001.Htm">crows</a>. Using an operant chamber permits precise experimental control over the animal's history with the stimulus and response dimensions of interest and the inclusion of automated recording means that the data are unlikely to influenced from potential experimenter cuing effects or observer bias.<br />
<br />
Operant chambers also allow a greater number of training and test trials to be presented. Why should that matter? Well, I think this represents the main theoretical difference between Taylor et al's approach and mine. The authors were not necessarily interested in the experience that might have lead crows to show evidence of "causal reasoning" (as the absence of details concerning the nature and extent of "habituation" training testifies). Instead, they were concerned with what might happen when the crows were suddenly presented with the HCA and UCA test trials.<br />
<br />
This approach, in which the role played by learning histories are either completely ignored or just downplayed, is indicative of the <a href="http://en.wikipedia.org/wiki/World_Hypotheses">mechanism world view</a> where, literally, science is a process of discovery. "Testing" is intended to reveal already-existing responses and capabilities of the participant that just required the actions of the scientist to uncover ("the truth is 'out there'"). Mechanistic viewpoints may be contrasted with contextualist or pragmatic perspectives that emphasise findings merely reflect the current and historical context, including the scientist's history and experience, and are always tentative and open to disconfirmation.<br />
<br />
Behavioural psychology or <a href="http://contextualpsychology.org/functional_contextualism_0">behaviour analysis shares many features with contextualism</a> and also emphasises the importance of <a href="http://books.google.co.uk/books?id=gGOX-0fzMbsC&pg=PA196&source=gbs_toc_r&cad=4#v=onepage&q&f=false">steady-state or stable responding</a>. Ensuring data paths are stable, and the effects not just transient, usually requires presenting multiple training and testing trials and employing a predetermined stability criterion. So, more trials could have been conducted using an adapted operant chamber, the training and testing details fully described, and greater emphasis placed on the steady state demonstration of complex behaviours deemed to illustrate 'causal reasoning'. <br />
<br />
Some ingenious examples of adapting operant chambers and incorporating automated presentation of trials may be found here: for instance, follow the link to this <a href="http://news.sciencemag.org/sciencenow/2012/04/monkey-see-monkey-do-monkey.html">video</a> of baboons performing a matching to sample task in a chamber housed within their living quarters, while <a href="http://www.ncbi.nlm.nih.gov/pubmed/8308488">Wright and Delius (1994)</a> had pigeons "scratch and match" different coloured gravel as a means of training conditional discriminations (the pigeons learned by 11 trials and met criterion within 27 trials. It often takes thousands of trials in a standard operant chamber). In 1996, I also attempted something similar with <a href="http://contextualpsychology.org/publications/dymond_gomezmartin_amp_barnes_1996">rats</a>.<br />
<br />
<h3>
</h3>
<h3>
<b>7. Further questions.</b></h3>
<br />
Hide inspections were only counted as such if the crows had first looked at the hole containing the food. This is a complex observation protocol requiring extensive coding and observer training, but what were the rates of other behaviours engaged in by the crows? How many times, for instance, did they inspect the hide without looking at the hole?<br />
<br />
Abandoned probes were "defined as a crow inserting the tool into the hole and then leaving the testing area without extracting the food." Were trials in which a crow returned to the testing areas included in the analysis? Or was the next trial initiated if/when the crows returned to the testing area? What was the
intertrial interval and how was recorded?<br />
<br />
<br />
<h3>
</h3>
<div style="text-align: center;">
<h3>
<b>Final comments</b></h3>
</div>
<br />
Notwithstanding the absence of counterbalancing and the non-parsimonious basis of the explanation, the
study's findings are based on a total of three trials per condition. For me, this amounts to a
rather weak basis on which to attach claims about the evolution of
comparative cognition and scientific and religious thought...<br />
<br />
Hopefully, someone who reads this blog will undertake a systematic replication of the study.<br />
<br />
<div style="text-align: center;">
<span style="color: red;"><b>UPDATE (13/12/2012):</b> </span></div>
<div style="text-align: center;">
<span style="color: red;">A commentary, written with Mark Haselgrove and Anthony McGregor, which summarised a few key points made in this blog has just been accepted for publication in <i>Proceedings of the National Academy of Sciences of the USA</i>. </span></div>
<div style="text-align: center;">
<br />
<span style="color: red;">More soon!</span><br />
<br />
<span style="color: red;"> </span></div>
BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com10tag:blogger.com,1999:blog-1826501894538086090.post-49321305009042889732011-12-16T16:41:00.000+00:002011-12-16T16:41:35.437+00:00My Favourite Albums of 2011 (in no particular order)It's that time of year again, and here's a list of some of the albums I enjoyed listening (and listening) to during 2011. They're in no particular order, well after the first one anyway. The first is, at a push probably my favourite.<br />
<a href="http://www.blogger.com/goog_1217498192"><br />
</a><br />
<a href="http://www.joanaspolicewoman.com/site/">Joan as Police Woman - The Deep Field</a><br />
Kurt Vile & The Violators - Smoke Ring for my Halo<br />
Josh T. Pearson - The Last of The Country Gentlemen<br />
The Antlers - Burst Apart<br />
Nicholas Jaar - Space is Only Noise <br />
James Blake - James Blake<br />
SBTRKT - SBTRKT<br />
King Creosote & Jon Hopkins - Diamond Mine<br />
The Radio Department - Clinging to a Scheme<br />
Death Grips - Exmilitary<br />
Girls - Father, Son & Holy Ghost<br />
Austra - Feel it break<br />
Holy Fuck - Latin (+Ghost)<br />
Ghostpoet - Peanut Butter Blues & Melancholy Jam<br />
Wild Beasts - Smother<br />
Africa Hitech - 93 million miles<br />
Death in Vegas - Trans-Love Energies<br />
Walls - Coracle<br />
DJ Shadow - The Less You Know, The Better<br />
Bon Iver - Bon Iver<br />
Thievery Corporation - Culture of Fear<br />
Apparat - The Devil's Walk<br />
Salem - King's Night<br />
The Field - Looping State of Mind<br />
Kinshasha One Two - DRC Music<br />
Yelawolf - Radioactive<br />
Kate Bush - 50 Words for Snow<br />
Pinch & Shackleton - Pinch & Shackleton<br />
<br />
Have I missed any?!BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-29968137033182543522011-09-09T15:37:00.000+01:002011-10-05T16:09:13.140+01:00Threat Avoidance and Fear of Fear<div style="font-family: "Trebuchet MS",sans-serif;">
When I was a undergrad student of psychology at University College Cork, my future PhD supervisor, <a href="http://psychology.nuim.ie/Holmes.shtml">Dermot Barnes-Holmes</a>, I think, assigned me a paper to read as part of one of my classes in "abnormal" psychology. It was <a href="http://www.sciencedirect.com/science/article/pii/S0005789478800537">A Re-Analysis of Agoraphobia</a> by Goldstein and Chambless, which was published in Behavior Therapy, 1978. </div>
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In it, the authors introduced the concept of "fear of fear" in which people diagnosed with agoraphobia become fearful, and show avoidance of, the very thought of going outside. That is, it's not the actual going outside that is fearful, it's the thought of it. As a result, people with this diagnosis develop elaborate ways of reducing the impact of this fear, largely through cognitive avoidance and distraction. What becomes a priority for such individuals is the removal of fear, or the avoidance of fear; hence, <a href="http://www.springerlink.com/content/q0414574l8012r04/">fear of fear</a>.</div>
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That paper made quite an impact on me, but it wasn't until many years later that I was able to undertake an experimental investigation of some of these fear and avoidance processes. </div>
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My colleagues and students and I have just published a paper in Behaviour Research & Therapy that, we believe, gets at some of these processes. In it, we showed how a relatively simple response that avoids or prevents something bad from happening may spread or transfer through indirectly related stimuli. </div>
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This, we argue, resembles a lot of what goes on when we avoid things, people and events that we have never previously had anything bad happen in the presence of. I say "we" because such processes are the staple diet of language-able humans; no diagnosis is needed to show this behaviour. We all do it, so much so that one leading account of human psychopathology, one of the "third wave" behaviour therapies, <a href="http://contextualpsychology.org/about_act">Acceptance & Commitment Therapy (ACT)</a>, claims that it is an integral part of being human.</div>
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Here's the Abstract:</div>
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<span style="font-size: x-small;">S<i><span style="font-size: small;">ymbolic generalization of avoidance may underlie the aetiology and maintenance of anxiety disorders. The aim of the present study was to demonstrate inferred threat-avoidance and safety (non-avoidance) behaviours that occur in the presence of stimuli indirectly related to learned threat and safety cues. A laboratory experiment was conducted involving two symbolic stimulus equivalence relations consisting of three physically dissimilar stimuli (avoidance cues: AV1-AV2-AV3 and neutral cues: N1-N2-N3). During avoidance learning involving aversive images and sounds, a key-press avoidance response was trained for one member of one of the relations (AV2) and non-avoidance for another (N2). Inferred threat and safety behaviour and ratings of the likelihood of aversive events were tested with presentations of all remaining stimuli. Findings showed a significantly high percentage of avoidance to both the learned and inferred threat cues and less avoidance to both the learned and inferred safety cues. Ratings in the absence of avoidance were high during training and testing to threat cues and low to safety cues and were generally lower in the presence of avoidance. Implications for associative and behavioural accounts of avoidance, and modern therapies for anxiety disorders are discussed.</span></i></span> </div>
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You can read the pdf <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/DymondEtAl_BRAT_2011.pdf">here</a>.</div>
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<b>Reference</b>:</div>
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Dymond, S., Schlund, M. W., Roche, B., Whelan, R., Richards, J., & Davies, C. (2011). <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/DymondEtAl_BRAT_2011.pdf"> Inferred threat and safety: Symbolic generalization of human avoidance learning. </a> Behaviour Research and Therapy, 49, 614-621. doi:10.1016/j.brat.2011.06.007 </div>
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BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-23310046920898059902011-07-13T17:34:00.001+01:002011-07-14T11:25:30.464+01:00It's that graduation/job-hunting/masters-applying time of the year againThe sun is shining (at least, it is in Swansea). Graduation ceremonies are beginning. This is the cue for lots of forced/shocked/concerned/drunk smiles in small and large group photos of people you'll never see or hear from again. That's if my experience is anything to go by.<br />
<br />
Recently, I stumbled upon my BA and PhD graduation photos. Of my 30 or so peers at my BA graduation in 1993, I've only kept in touch with just one. And by 'kept in touch' I mean, 'have her email address' and 'we catch up at conferences from time to time'. All of the others didn't even make it to being Facebook friends, mainly because FB wasn't invented way back in 1993. At my PhD graduation in 1996 it was easier to keep in touch with everyone because, well, I was the only one from my faculty at that particular ceremony. Either that or everyone was mysteriously doing laundry that day ...<br />
<br />
In any event, it got me thinking: what must it be like nowadays to be leaving the safe surrounds of academe and to be embarking on a new career? Perhaps a new career in research? What words of advice might I humbly proffer someone considering a career in behavioural research?<br />
<br />
Well, here's a link (PDF) to some great pearls of wisdom from Prof. Steve Hayes, co-founder of <a href="http://contextualpsychology.org/rft">RFT</a> and <a href="http://contextualpsychology.org/act">ACT,</a> on "<a href="http://www.cs.dal.ca/%7Eeem/gradResources/13Rules.pdf%20">13 rules of success for graduate students</a>".<br />
<br />
And here's a link to the wonderful <a href="http://deevybee.blogspot.com/">blog of Prof. Dorothy Bishop</a>, developmental neuropsychologist at Oxford, in which she summarises how to survive in psychological research. <br />
<br />
<br />
At a time when the world is producing more PhDs than ever before ("<a href="http://www.nature.com/news/2011/110420/full/472276a.html">The PhD Factory</a>"), we all need as much help and advice as we can get...BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-75194049189157802532011-03-15T10:27:00.000+00:002011-03-15T10:27:12.776+00:00They Shape Horses, Don't They?<table align="center" cellpadding="0" cellspacing="0" class="tr-caption-container" style="margin-left: auto; margin-right: auto; text-align: center;"><tbody>
<tr><td style="text-align: center;"><a href="https://lh4.googleusercontent.com/-NkkFxG-o6g8/TX89Pynh2_I/AAAAAAAAABM/JXAKstG57eo/s1600/horse-photo-trailer-load.jpg" imageanchor="1" style="margin-left: auto; margin-right: auto;"><img border="0" height="240" src="https://lh4.googleusercontent.com/-NkkFxG-o6g8/TX89Pynh2_I/AAAAAAAAABM/JXAKstG57eo/s320/horse-photo-trailer-load.jpg" width="320" /></a></td></tr>
<tr><td class="tr-caption" style="text-align: center;">For many horse owners, this only happens in an ideal world.</td></tr>
</tbody></table><div style="text-align: center;"><br />
</div>Every so often, behaviour analysis is able to take an important problem, operationalise it in objective terms, and implement a reinforcement procedure that yields spectacular results. This is one of those occasions.<br />
<br />
Charlotte Slater, a former masters student, and I have just had published a two-study article that investigated the use of differential positive <b><a href="http://en.wikipedia.org/wiki/Reinforcement">conditioned reinforcement </a></b>for reducing undesirable behaviour and increasing desirable behaviour in horses. It was based on a previous study by Ferguson and Rosales-Ruiz (2001). In our study, the horses were all referred to as "problem loaders" - they resisted being loaded into trailers and were often subject to aversive methods, such as shouting, clapping, tightening of ropes around the rear quarters, and whipping. In addition, one horse had been struck from behind with a large plastic tube during previous loading attempts. Not surprisingly, the horses' owners were desperate for a non-aversive solution to their problem, and one that they could continue to implement, once the intervention had ended.<br />
<br />
The first stage involved conducting a task analysis - a detailed breakdown of the sequence of steps involved in fully loading (obtained by watching a horse that could fully load). <b><a href="http://www.clickertraining.com/">Clicker training</a></b> was then conducted, in which the sound of the clicker was paired with food until the horse could 'associate' the sound with a nice, tasty treat (Polo mint, anyone?). This meant that we could use the clicker as a conditioned reinforcer, and save us a fortune on Polo mints! It also allowed us to deliver reinforcement immediately and contingently following the behaviours we were interested in shaping up. Gradually, approximations of the goal behaviour (fully loading) were reinforced, bit by bit, starting with touching a target, which was moved closer and closer to the inside of the trailer. <br />
<br />
We used a <b><a href="http://en.wikipedia.org/wiki/Multiple_baseline_design">multiple baseline </a></b>across horses design to illustrate the effects of the shaping procedure. All four horses showed immediate improvements when the intervention was introduced and all readily loaded, without protest or complaint, on several consecutive days. As if this wasn't impressive enough, all horses showed that they would now fully load when their owners conducted the intervention and when a novel trailer was used. Once learned, these shaped behaviours showed generalization, an important training target for a study like this as horses are often required to load into unfamiliar trailers by unfamiliar handlers. <br />
<br />
A further study was conducted with one of the horses that had never had shoes fitted because of the problematic behaviour he showed every time his feet were handled. The target behaviour for this study was to have the horse allow his feet to be held for 1 minute. At the outset, he would not allow his feet to be held for more than 5 seconds, but during the intervention, we reinforced progressively longer periods of feet-handling (in steps of 10s). After 20 sessions, the horse met the training criterion: he allowed his feet to be handled for 1 minute, across consecutive sessions. His problem behaviour decreased, and it was now possible to have shoes fitted for the first time.<br />
<br />
The horses owners rated the intervention highly and all considered the program to be completed either “quickly” or “very quickly”. Each owner felt able to continue using the techniques and reported that they would be “very likely’ to use the techniques to train other horses and would “definitely” recommend the procedure.<br />
<br />
These encouraging findings show that is possible to overcome seemingly intractable problem behaviour without recourse to aversive treatment by shaping desirable behaviour. They also illustrate the wide range of challenges that are amenable to "the power of positive reinforcement".<br />
<br />
<b>Reference (</b>PDF from the link below): <br />
<br />
Slater, C., & Dymond, S. (2011). <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/Slater&Dymond_2011.pdf">Improving equine welfare: Using differential reinforcement to shape appropriate equine behaviour during truck loading and feet handling</a>. <i>Behavioural Processes</i>, <i>86</i>, 329-339.<br />
<br />
See also:<br />
<br />
Ferguson, D. L., & Rosales-Ruiz, J. (2001). <a href="http://seab.envmed.rochester.edu/abstracts/JabaAbstracts/34/_34-409.HTM">Loading the problem loader: The effects of target training and shaping on trailer- loading behavior of horses.</a> <i>Journal of Applied Behavior Analysis</i>, <i>34</i>, 409-424.BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com2tag:blogger.com,1999:blog-1826501894538086090.post-13006784169655467262010-12-19T17:26:00.000+00:002010-12-19T17:26:27.268+00:00Reviewers say the darnedest things ...Lately, I've been revising several manuscripts that have received largely postitive reviews, but which the Associate Editor (AE) deems require "further reconsideration" before they are accepted. Interesting choice of terms, "further reconsideration". Does this mean that the revised ms. will be sent out for review again? Or, will the AE merely glance over the 'response to reviewers' cover letter and check that everything tallies with the requests made by the original reviewers? Worse still, will the AE send it to the reviewer who wrote the longest review because he/she clearly has lots of time on his/her hands? If so, what is the right tone to adopt when writing the cover letter? Smarmy-meets-fair? Defiant-meets-desperate?! We all know AEs don't actually read the manuscripts they've been sent, preferring instead to get the reviewers to do their dirty work.<br />
<br />
Anyway, in writing these cover letters and revising the manusripts, I've been keeping myself sane by writing a "real" letter and a "not so real" letter. Here are a few choice terms and phrases from the latter, translated into honest-speak.<br />
<br />
1. "<i>We thank the reviewer for raising this important issue</i>."<br />
<br />
<b>Translated: </b>"Hate to say it, but you're wrong. You seem to have misread the entire paper. Re-read it. This time, with the light on. Dummy."<br />
<br />
2. "<i>The reviewer makes an interesting suggestion for future research</i>."<br />
<br />
<b>Translated: </b>"Are you reviewing an imaginary paper or the one that we submitted that reports the findings of 3 huge experiments? What more do you want? One multi-experiment paper on a topic you yourself seem to be quite excited about can only do so much! I shall file this comments under "Future research..." and pad-out the final paragraph with some vague statements about "empirical questions", 'needs further research attention", and "important empirical issues", etc."<br />
<br />
3. "<i>The reviewer requests that we comment...</i>."<br />
<br />
<b>Translated: </b>In order to pander to the whims of this clearly-deluded reviewer, I have included a few, vague comments as requested, you know, just in case I ever bump into him/her at a conference. Actually, it's probably best if we don't ever meet. I may say/do something I later regret. <br />
<br />
4. "<i>The reviewer request that we address these, and other, suggestions, in a revised manuscript</i>."<br />
<br />
<b>Translated: </b>"Er, how? How about by writing an entirely new paper on an entirely new topic and submitting to an entirely new journal where, hopefully, the paper may be assigned reviewers who actually know what they are talking about?! Yeah, right. Instead, I shall blow most of his/her arguments out of the water in the cover letter and save the text of the paper for material that actually makes sense and doesn't contravene all known behavioural laws. Sheesh."<br />
<br />
5. "<i>We thank the reviewers for their helpful comments</i>."<br />
<br />
<b>Translated</b>: "How do I go about requesting that Reviewer 1/2/3 (delete as applicable) is never asked to review any of my work in the future? I can nominate, but how do I black-list?"<br />
<br />
6. "<i>The manuscript has been considerably strengthened as a result</i>."<br />
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<b>Translated:</b> "If this journal didn't have such a high impact factor, I would have pulled it long ago. Instead, I've learned the art of patience because, crazy maybe, I dream of one day getting promoted...."<br />
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<a href="http://bit.ly/8C4j5B">This video</a> pretty much sums it up.<br />
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Okay, that's enough procrastination. I've got a cover letter to write ....BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-46500108159585076962010-11-18T13:37:00.000+00:002010-11-18T13:37:59.443+00:00Gambling on the Near-MissRecent studies from the growing literature on the neuroscience of gambling have focused on the "near miss" effect in slot machine gambling. Briefly, "near miss" is a term used to describe an effect resembling 'almost winning' on a slot machine when, for instance, two of three matching symbols appear on the payout line. This outcome is still a loss though (a win would be three matching symbols). <br />
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It has been speculated that near misses serve to maintain persistent gambling. There are several strands of data that support this assertion. For instance, gamblers and non-gamblers initiate trials following a trial with a near miss faster than trials with either wins or losses (Dixon & Schrieber, 2004). Animals also show similar effects: Weatherly and Derenne (2007) and Peters et al. (2010) developed an animal model of slot machine gambling and found that the pause durations of rats was shortest after losses and longest after large "wins".<br />
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Gamblers also rate their chances of winning on the next trial far higher than on trials following other outcomes. These effects serve to suggest that gamblers ignore the "independence of turns" rule and act in way that implies that outcomes of the next trial will, in some way, be related to the outcome of the previous trial. They are not. Slot machines operate according to complex random ratio schedules, where each response is independent of the last. This is what makes gambling, generally, and the near miss effect, in particular, so interesting to study.<br />
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The proportion and physical form of the near misses that a gambler experiences has also been shown to effect persistence. A well-cited experiment by Kassinove and Schare (2001) found that a proportion of 30% of near miss trials resulted in greatest persistence (as measured by the number of trials played in the absence of feedback), while Ghezzi and colleagues (2006) showed that near misses on three-reel slot machines of the form X-X-Y were preferred over near misses of the form X-Y-X or Y-Y-X. The findings on the effects of proportion of near misses suggest that they do not resemble direct losses to the gambler. They still lose on these trials. The fact that the form of the near misses exerts an influence highlights how seemingly irrelevant, structural features of the stimuli involved in slot machine gambling may come to exert an influence over the behaviour of seemingly "free", language-able humans. <br />
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B.F. Skinner argued that near misses may function as conditioned reinforcers: <br />
<i>Gambling devices make an effective use of conditioned reinforcers which are set up by pairing certain stimuli with the economic reinforcers which occasionally appear. For example, the standard slot machine reinforces the player when certain arrangements of three pictures appear in a window on the front of the machine. By paying off very generously—with the jack pot—for "three bars," the device<br />
eventually makes two bars plus any other figure strongly reinforcing. Almost hitting the jack pot" increases the probability that the individual will play the machine, although this reinforcer costs the owner of the device nothing.</i> (1953, p. 397).<br />
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Recent neuroscience evidence supports this view (see the excellent <a href="http://sciblogs.co.nz/skepticon/2010/10/22/gamblers-rewarded-by-near-misses/">Skepticon posting</a>, for instance). fMRI studies by Chase and Clark (2010) and Habib and Dixon (2010) have each shown that near-misses activated the same brain regions as wins for gamblers and regions related to losses in non-gamblers. This suggests that near misses have conditioned reinforcing functions for gamblers (i.e., they behave "as if" they are wins). Interestingly, the extent of brain activation in mid-brain regions declines as a function of gambling severity. The more severe the gambling problem, the less activation. This implies that a degree of tolerance has occurred: repeated experience with near-misses leads to habituation-like effects on activation, and suggests that gambling persistence must be, at least in part, maintained in other ways. There is an awful lot more work to be on the behavioural and neural correlates of gambling, but the handful of studies just described have already made quite an impact on our understanding of this fascinating behaviour.<br />
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<b>References</b><br />
Chase, H., & Clark, L. (2010). Gambling severity predicts midbrain response to near-miss outcomes <i>Journal of Neuroscience, 30</i> (18), 6180-6187 DOI: 10.1523/JNEUROSCI.5758-09.2010<br />
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Dixon, M. R., & Schreiber, J. (2004). Near-miss effects on response latencies and win estimations of slot machine players. <i>The Psychological Record, 54</i>, 335–348.<br />
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Ghezzi, P. M., Wilson, G. R., & Porter, J. C. K. (2006). The near-miss effect in simulated slot machine play. In P. M. Ghezzi, C. A. Lyons, M. R. Dixon, & G. R. Wilson (Eds.), <i>Gambling: Behavior theory, research and application</i> (pp. 155-170). Reno, NV: Context Press.<br />
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Habib, R. & Dixon, M.R. (2010). Neurobehavioral evidence for the “near-miss” effect in pathological gamblers. <i>Journal of the Experimental Analysis of Behavior, 93</i> (3), 313-328. DOI: 10.1901/jeab.2010.93-313<br />
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Kassinove, J. I., & Schare, M. L. (2001). Effects of the ‘near miss’ and the ‘big win’ on persistence at slot machine gambling. <i>Psychology of Addictive Behaviors, 15</i>, 155–158.<br />
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Peters, H., Hunt, M., & Harper, D. (2010). An animal model of slot machine gambling: The effect of structural characteristics on response latency and persistence. <i>Journal of Gambling Studies, 26</i>, 521-531. <br />
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Weatherly, J.N., & Derenne, A. (2007). Rats playing a slot machine: A preliminary attempt at an animal gambling model. <i><a href="http://analysisofgamblingbehavior.org/">Analysis of Gambling Behavi</a>or, 2</i>, 79-89.BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com3tag:blogger.com,1999:blog-1826501894538086090.post-67515040681468045452010-11-12T15:46:00.000+00:002010-11-12T15:46:21.899+00:00Children's "Erroneous Beliefs" About GamblingThe empirical literature on gambling behaviour is small but growing, and has tended to be dominated by qualitative/survey, cognitive, and behavioral approaches. Qualitative approaches to gambling focus on the prevalence, nature, and extent of different forms of gambling. Largely demographic or survey-based, these studies provide important details about changing social trends in gambling and gambling related problems. When writing an empirical article on gambling one has to cite at least one gambling prevalence survey in the opening paragraphs; you know, to set the scene, to introduce 'the problem'. And, let's face it, that's all these studies allow us to do: to get a feel for what's happening "out there", in the "real world". <br />
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Cognitive approaches emphasise the role played by a gambler's thoughts, feelings, attitudes or beliefs in initiating and maintaining gambling. Gamblers may be deemed to suffer from "erroneous beliefs" or to engage in "cognitive switching" whenever the outcomes of games of chance go against their "expectations". This is all well and good as a description of behaviour, but it is not a complete explanation of the causes of the behaviour observed. Woaahh, did I lose you? Let me back up.<br />
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In behaviour analysis, saying that a gambler persists in betting his/her final dollar on "red 16" because of faulty cognitive processes or beliefs etc. is to offer a mentalistic explanation, which is a mediational-based account that says, "the behavior you observe (in this case, betting on "red 16") is caused or initiated by a mysterious, nonphysical realm called 'beliefs'". We can't directly manipulate "beliefs". All we can manipulate are environmental events, like peoples' histories with "red 16" and money, and with instructions to behave in certain ways, etc. Mentalism seduces us into thinking that a complete explanation has been provided, when in fact it hasn't. If we can't manipulate it (i.e., can't predict and influence it's occurrence), then it's of no use to a behaviour analyst. We are arch-pragmatists after all.<br />
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Faced with this, and arriving under-dressed, grumpy and late to the party on empirically analyzing gambling behaviour, behaviour analysts have sought to "behaviouralize" common cognitive findings, while seeking to preserve their philosophical integrity. For instance, an article from my lab published in the <a href="http://seab.envmed.rochester.edu/jeab/index.html"><i>Journal of the Experimental Analysis of Behavior</i></a> set about studying 'erroneous beliefs' from a behavioural perspective in a gambling-like task with young children. The article kicked off with the now-customary critique of all things non-behavioural, which, if you don't mind, I think is worth repeating here:<br />
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"<i>A limitation of mediational explanations is that they tend to treat mediational "responses" (e.g., self-stated rules, beliefs, etc.) as primary causes. Individual differences with regard to mediational responses is purported to explain observed differences in gambling situations. Unfortunately, such an approach places the causes of gambling in the thoughts and other behaviors of the participants, and we are left unable to determine the conditions that cause those thoughts. Information about the mediational variables is important in that they allow us to predict gambling. However, they do not allow us to control the occurrence of gambling</i>." (Dymond, Bateman, & Dixon, 2010, pp. 353-354). <br />
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Key here are "the conditions that cause those thoughts". What kind of thoughts might young kids have about a gambling-like task? Well, a previous study showed that 7-10 year olds could be taught to believe that one of two dice was "luckier" or "hotter" than another. The procedures were as follows. Kids chose between a red die and a blue die that 'rolled' a random number between 1 and 6, and a preselected game piece moved the predetermined number of spaces along a racetrack. Kids "raced" against the computer.<br />
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They love stuff like this, so the task was very reinforcing and they played it until it was clear that they did not have a preference for either the red or blue die. After all, why should they? Each die randomly rolled between 1 and 6, so kids distributed their choices relatively easily between them, as expected. Next, the kids were taught to associate one color (red) with "greater than" and another color (blue) with "less than". Then, they were re-exposed to the dice task and, guess what, they showed an increased preference for the red die, despite the identical outcomes of the two die! Red still rolled as it did (as indeed blue did) during the pretest phase, but now, after a relational intervention, kids behaved "as if" red was more likely to roll higher numbers than blue. Cool.<br />
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We wanted to extend these promising findings by examining whether or not we could first attach fixed, high and low roll outcomes to two die labelled with meaningless nonsense words (let's call them Bill and Ben) and then testing selections of indirectly related die (let's call them Dave and Nick). Right, I may have lost you again, which is bad, particularly as you have read this far. So, let me summarise the design of the study as follows:<br />
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1. Die labelled "Bill" always roll high numbers (4, 5, 6) and die labelled "Ben" always rolls low numbers (1, 2, 3).<br />
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2. Imagine if Mary was related to Bill, and Mary was related Dave; also, that Anne was related to Ben, and that Anne was related to Nick. <br />
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3. Now, if I asked you, would you prefer Bill or Ben, you'd probably opt for Bill. After all, it's loaded; it always rolls high numbers. Okay, but what if I asked you to choose between Dave and Nick, and you had no prior experience of these labelled die? Which one would you choose? <br />
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We predicted that young kids, given this history, would choose Dave over Nick. Why? Because Dave is indirectly related to the directly-experienced Bill die. And boy do I like that Bill die! So, despite never have "played" the Dave or Nick dice before, and the fact that, in reality, each rolled the same, I'm going to choose that there Dave more than Nick. And that's what the kids did, well, all but one (there's always one!). The kids also rated the Dave die more as more likable than the Nick die.<br />
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What does this all mean? What's it all got to do with knocking cognitive-based explanations of seemingly irrational gambling behavior? Well, it suggests a behavioural process whereby novel, indirectly experienced stimuli may come to control behavior. Direct experience with all stimuli and the good/bad things attached to them (such as high and low rolls) may not be necessary for complex gambling behavior to develop. In this way, "beliefs" about outcomes, and the behavior based upon these beliefs, can be said to be mediated by words. This allows us to remove ourselves from the spatio-temporal present and 'imagine' how good a nice Dave would be right now... <br />
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Gaining an empirical handle on these word-webs, and the behavioural processes responsible for them, is what my students, colleagues and I spend a lot of time doing. I'll blog about this on an ongoing basis. <br />
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<a href="http://psy.swan.ac.uk/staff/dymond/Pubs/jeab-94-03-0353.pdf">Here's</a> a link to a PDF of the paper described above.BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-79065893105990197132010-11-03T17:36:00.000+00:002010-11-03T17:36:12.828+00:00Prompting Therapists to Deliver More Accurate & Varied Discrete TrialsA student from my lab, Richard J. May, has just published a paper in <i><a href="http://www.elsevier.com/wps/find/journaldescription.cws_home/709651/description#description">Research on Autism Spectrum Disorders</a> </i>on a novel method of training therapists to deliver discrete trial teaching (DTT). DTT is probably the most common instructional strategy used in early intervention work with children with autism. It involves breaking complex skills down into component parts and arranging for clearly specifiable antecedents to precede the behaviour (such as request or instruction) and consequences to follow the desired behaviour (such as praise). Progress is often slow on DTT programs, for a host of reasons, and applied behaviour analysts have recently made great headway in developing new and exciting ways of boosting outcomes and of designing easy-to-use programs and instructional materials for use with therapists working on home intervention programs with children with autism.<br />
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Richard's clinical experience lead him to design a simple prompting system to facilitate delivery of DTT across entire teaching sessions. Teaching therapists to implement DTT effectively and efficiently has obvious implications for educational intervention with children with autism. Thus, "VB-TiPS" was born: the <i>Verbal Behaviour Teaching integrity Prompting System</i> (although it isn't actually called this in the paper).<br />
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Richard evaluated VB-TiPS with and without existing behavioural skills training (BST) procedures and found that all three therapists studied delivered a higher rate of accurate teaching trials and a greater variety of trials when VB-TiPS was present. The prompting system helped therapists to provide greater numbers of different verbal operant trials: tacts, intraverbals, and listener relations were all presented more often compared to traditional BST procedures.<br />
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These encouraging preliminary findings highlight the need for continued research on developing facilitative DTT procedures that are applicable to a host of different participant populations (not just children with autism) and are sensitive to the varied qualifications and background of therapists working on early intervention programs.<br />
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As the paper concludes: "<i>Behavior analytic consultants are often faced with the challenge of training a large number of therapists in a relatively short amount of time, particularly when starting a program for a new client or when therapist turnover is high. The continued identification of training strategies that are both efficient and effective is imperative not only for therapists, but for the children they endeavor to teach</i>." (May, Austin, & Dymond, 2011, p. 315).<br />
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You can get a (PDF) copy of the paper <a href="http://psy.swan.ac.uk/staff/dymond/Pubs/RASD296.pdf">here</a>.BehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0tag:blogger.com,1999:blog-1826501894538086090.post-44615882812204410832010-10-01T17:27:00.000+01:002010-10-01T17:27:43.649+01:00Endtroducing: BehaviourBlog"Insight, foresight, the clock on the wall reads a quarter past midnight" - DJ Shadow<br />
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Right, I have succumbed to the need to set-up a blog. Not sure quite what the content will be, or how often it will be updated, but the aim is to provide more word-space than Twitter currently permits.<br />
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Expect vaguely informed musings on behaviour analysis, behavioural and cognitive neuroscience, clinical psychology, experimental psychopathology, and science in general. Some on music too. Also, expect a few postings on how great it is to be an atheist-extremist-vegetarian.<br />
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All thoughts and opinions are my own (with due acknowledgment to my phylogeny and ontogeny), as well as current situational context, and do not in any way reflect the views of Swansea University, etc etc.<br />
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Now, you may be seated.<br />
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BehaviourBlogBehaviourBloghttp://www.blogger.com/profile/04481669290622708125noreply@blogger.com0