Don’t Make Decisions When You’re HungryI was recently listening to a freakonomics podcast called ‘How to Make a Bad Decision’, that suggested decisions made be judges in asylum courts aren’t as objective as we might hope. The podcast focusses on a study by Chen, Moskowitz, and Shue (2016), which argues that cognitive biases such as ‘the law of small numbers’ and the ‘gambler’s fallacy’ can affect judges’ decisions on whether to approve or deny asylum to a petitioner by as much as a 10% difference. For those seeking asylum, this is a pretty big number when the decision made could greatly affect, and even possibly save theirs and their family’s lives. It’s therefore important to understand these cognitive biases and seek to prevent their effect where possible.The law of small numbers is the tendency for us to overestimate the representativeness of samples to the wider population. For example, consider a scenario in which six babies have recently been born in hospital. When asked which sequence of gender is more likely out of; (a) GGGGGG and; (b) BGBGBG, most people intuitively answer (b), even though each individual event has no relation to the others and every time a baby is born the chance of it being a boy or girl is 50:50 (Kahneman, 2011). This demonstrates how intuitive the law of small numbers is as we assume that our sample of 6 will closely resemble the general population which does balance out as being around 50:50 girls and boys due to the size of the population. In contrast, our sample is very small and therefore it is just as likely for (a) to occur as it is (b), but we don’t intuitively think this.This closely links with the gambler’s fallacy, which is our misunderstanding of the nature of randomness and our intuitive tendency to assign patterns to random processes. This cognitive bias makes us prefer (b) as it has some regularity to it, whereas (a) seems unlikely due to our lack of understanding of random processes and the fact that each individual event is not casually related to the others. Anyway, enough of the technical terms, back to the effect this has on parole judges and consequently, asylum seekers.In Chen et al’s study, the researchers argue that the gambler’s fallacy causes judges to be more likely to deny asylum by up to 5%. This number is based on how many cases the judge had already approved or denied earlier that day. If a judge approved a case the previous day, they are almost 1% more likely the deny the current case they’re looking at the day after. If the previous case was approved on the same day, this number increases to 3%. If three cases are considered on the same day, with the previous two both approved, the judge becomes 5% less likely to approve the third case. This effect is also shown in reverse when a judge denies the previous two cases, they are then 5% more likely to approve the next. This results in a 10% difference between deciding to approve or deny a case, simply based on the approval rate of cases earlier in the day. This is a huge number for a decision affecting something as important as whether or not you will be allowed to remain in the country of your choice.Another study mentioned in the podcast, carried out by Danziger, Levav, and Avnaim-Pesso (2011), suggests that the time since your last meal break also leads to a cognitive bias in decision-making. This is related to Pavlovian classical conditioning, which states that we come to associate positive feelings with the stimulus present at the time after repeated co-occurrences. For example, Pavlov managed to train dogs to salivate at the sound of a bell after repeatedly presenting them with food when the bell sounded, causing them to associate the presentation of food (and the associated response of salivating) with the sound of a bell. Danzinger et al in their study found a similar effect with parole judges. This graph shows the percentage of cases approved by judges across the day, with the dotted line indicating a meal break:As you can see, at the start of the day and immediately after a meal break there was a 65% chance that the judges would approve a case. This gradually declined until it hit 0% just before a meal break, and then shot up to around 65% again straight after the break. This is arguably because the judges experience a good feeling when they have a break and eat some food which they then associate with whatever case they then come to look at, making them more likely to approve the cases close to their meal breaks. This is another cognitive bias that seems to have a great effect on the likelihood of asylum seekers having their case approved.Whilst parole judges are trained to consider the legal merits of cases presented to them, these cognitive biases are largely unknown and not included as part of their training. I think it’s important that courts become more aware of the existence and effects of these in order to attempt to counteract them in judges decisions. In the meantime, if you’re seeking asylum I suggest you somehow aim to get a hearing first thing in the morning before the judge has looked at any other cases, or immediately after they’ve had a meal break. Moskowitz suggests that perhaps a good lawyer would be able to lobby for this. Failing this, perhaps the best option left would be to inconspicuously give the judge a snack so that they come to associate the good feeling they receive from food with your asylum case. Make sure this snack isn’t Twiglets though, because Twiglets taste gross and definitely won’t leave a good taste in the judge’s mouth!References Cialdini, R. (2009) Influence: Science and Practice. 5th edn. Harlow: Pearson Education (US). Chen, D. L., Moskowitz, T. J. and Shue, K. (2016) ‘Decision-Making Under the Gambler’s Fallacy: Evidence from Asylum Judges, Loan Officers, and Baseball Umpires’, SSRN Electronic Journal. Danziger, S., Levav, J. and Avnaim-Pesso, L. (2011) ‘Extraneous Factors in Judicial Decisions’, Proceedings of the National Academy of Science. 108(17), pp. 6889-6892. Dubner, S. J. (2016) How to Make a Bad Decision. Freakonomics Radio. Kahneman, D. (2012) Thinking, Fast and Slow. London: Penguin Press/Classics.By Gemma Crook
Mapping Emotions in the Body
Feelings are often associated with physical reactions: terror can send chills down your spine, and love can leave you weak in the knees. A recent study has linked specific emotions to physical sensations. Researchers tested emotional responses in hundreds of subjects and then created maps identifying locations in the body where emotions cause physical changes.
Click here to view the embedded video.
The Problem of Lie Detection
Somewhat unsurprisingly, the movies have painted a highly inaccurate picture of the power of polygraphs, or so-called lie-detector tests. While many crime dramas showcase a seemingly miraculous technology for distinguishing truths from lies, this portrayal, itself, is far from true.
However, a new study published in the Journal of Clinical Psychiatry suggests that polygraph tests may yet have a role to play, alongside MRI machines and brain scans.
Subjects were asked to write down numbers and then lie to researchers about what they had written down. While being interrogated, each participant was subjected to both an MRI scan and a polygraph test, and the researchers attempted to evaluate when they were being lied to.
While the MRI test proved to be about 24 percent more effective than the polygraph, both tests employed in tandem were able to determine deception in almost every case: a remarkable achievement.
Previous studies on MRI testing found them to be up to 90 percent accurate, while the accuracy of polygraph tests ranged wildly from perfectly accurate to completely unreliable. Even 90 percent accuracy falls short of being reliable enough for criminal proceedings. However, with this study, the doors have opened towards justifying more research into lie detection testing.
As Dr. Daniel Langleben, a study author, said: “While the jury remains out on whether fMRI will ever become a forensic tool, these data certainly justify further investigation of its potential.”
In order to appreciate the significance of this study, it is important to understand the limitations that both MRI machines and polygraph tests face in detecting deception.
MRI machines generate images of the subjects’ brains. These images allow researchers to see any physical abnormalities or changes in blood flow, revealing which parts of the brain are currently active. Some of the earliest studies on MRIs as lie detectors had subjects select playing cards and then lie about which ones they had picked. This helped narrow down which parts of the brain light up when a person is being deceitful.
However, there may be confounding factors, as these MRI images often just reveal when the subject has to think quickly about how best to respond. While this does detect lies, it may also indicate uncertainty, or it could be easily misled by a well-rehearsed story that took no effort to recite.
Based on this uncertainty, every attempt to introduce MRI-based lie detection as evidence in court proceedings has failed. In fact, they often show false signs of deception, which would be a major flaw in court proceedings.
Polygraph tests, on the other hand, work by tracking the subject’s heart rate, blood pressure, respiration, and other physiological responses. Law enforcement personnel ask “control” questions that are only tangentially related to the investigation at hand, as well as “relevant” questions which probe for details on the subject’s involvement in the crime.
If the subject shows a higher heart rate when asked the “relevant” questions, this indicates that they are attempting to conceal their guilt. However, there are significant underlying problems with this approach. In fact, there is little evidence to show that these physiological responses are even unique to the practice of deception.
Between the theoretical flaws and the fact that polygraphs can be outsmarted, it is understandable that they are currently not admissible in court proceedings.
For more information on polygraph tests, read our blog here. Also, check out this post to learn how you can be a better lie-detector.
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