Tag Archives: cognitive science

A Curated Reading List

Fact: I do not read enough of the literature any more. I don’t really read anything. I read manuscripts that I am reviewing, but that’s not really sufficient to stay abreast of the field. I assign readings for classes, to grad students, and trainees and we may discuss current trends. This is great for lab, but for me the effect is something like me saying to my lab “read this and tell me what happened”. And I read twitter.

But I always have a list of things I want to read. What better way to work through these papers than to blog about them, right?

So this the first instalment of “Paul’s Curated Reading List”. I’m going to focus on cognitive science approaches to categorization and classification behaviour. That is my primary field, and the one I most want to stay abreast of. In each instalment, I’ll pick a paper that was published in the last few months, a preprint, or a classic. I’ll read it, summarize it, and critique. I’m not looking to go after anyone or promote anyone. I just want to stay up to date. I’ll have a new instalment on a regular basis (once every other week, once a month, etc.). I’m doing this for me.

So without further introduction, here is Reading List Item #1…

Smith, J. D., Jamani, S., Boomer, J., & Church, B. A. (2018). One-back reinforcement dissociates implicit-procedural and explicit-declarative category learning. Memory & Cognition,46(2), 261–273.

Background

This paper was published on line last fall but was just published officially in Feb of 2018. I came across it this morning and I was looking at the “Table of Contents” email from Memory & Cognition. Full disclosure, the first author was my grad advisor from 1995-2000, though we have’t collaborated since then (save for a chapter). He’s now at Georgia State and has done a lot of fascinating work on metacognition in non-human primates.

The article describes a single study on classification/category learning. The authors are working within a multiple systems approach of category learning. According to this framework, a verbally-mediated, explicit system learns categories by trying to abstract use a rule, and a procedurally-mediated, implicit system learns categories by Stimulus Response (S-R) association. Both systems have well-specified neural underpinnings. These two systems work together but sometimes they are in competition. I know this theory well and have published quite of few papers on the topic. So of course, I wanted to read this one.

A common paradigm in this field is to introduce a manipulation that is predicted to impair or enhance one of the systems and leave the other unharmed in order to create a behavioural dissociation. The interference in this paper was a 1-back feedback manipulation. In one condition, participants received feedback right after their decision and in another, they received feedback about their decision on the previous trial. Smith et al. reasoned that the feedback delay would disrupt the S-R learning mechanism of the procedural/implicit system, because it would interfere with the temporal congruity stimulus and response. It should have less of an effect on the explicit system, since learners can use working memory to verbalize the rule they used and the response they made.

Methods

In the experiment, Smith et al. taught people to classify a large set (480) of visual stimuli that varied along two perceptual dimensions into two categories. You get 480 trials, and on each trial you see shape, make a decision, get feedback, and see another shape, and so on. The stimuli themselves are rectangles that vary in terms of size (dimension 1) and pixel density (dimension two). The figure below shows examples of range. There was no fixed set of exemplars, but “each participant received his or her own sample of randomly selected category exemplars appropriate to the assigned task”.

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They used a 2 by 2 design with two between-subject factors. The first factor was category set. Participants learned either a rule based category (RB) in which a single dimension (size or density) creates an easily-verbalized rule, or an information integration category (II) in which both dimensions need to be integrated at a pre decisional stage. This II category can’t be learned very easily by a verbal rule and many studies have suggested it’s being learned by the procedural system. The figure below shows how many hundreds of individual exemplars would be divided into two categories for each of the each of the category sets (RB and II).

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The second factor was feedback. After each decision, you either received feedback right after you made a decisions (0Back) or you received feedback one trial later (1Back). This creates a heavier task demand, so it should make it harder to learn the RB categories at first because the task creates a heavier working memory load. But it should interfere with II learning by the procedural system because the 1-Back disturbs the S-R association.

Results

So what did they find? The learning data are plotted below and suggest that the 1Back feedback made it harder to learn the RB categories at first, and seemed to hurt the II categories at the end. The 3-way ANOVA (Category X Feedback X Block) provided evidence to that effect, but it’s not an overwhelming effect. Smith et al.’s decision to focus a follow up analysis on the final block was not very convincing. Essentially, they compared means and 95% CIs for the final block for each of the four cells and found that performance in the two RB conditions did not differ, but performance in the two II conditions did. Does that mean that the feedback was disrupting the feedback? I’m not sure. Maybe participants in that condition (II-1Back) were just getting weary of a very demanding task. A visual inspection of the data seems to support that alternative conclusion as well.  Exploring the linear trends might have been a stronger approach.

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The second analysis was a bit more convincing. They fit each subject’s data with a rule model and an II model. Each model tried to account for each subject’s final 100 trials. This is pretty easy to do and you are just looking to see which model provides the most likely account of the data. You can then plot the best fitting model. For subjects who learned the RB category, the optimal rule should be the vertical partition and for the II category, the optimal model is the diagonal partition.

As seen the figure below, the feedback did not change the strategy very much for subjects who learned the RB categories. Panel (a) and (b) show that the best-fitting model was usually a rule based one (the vertical partition). The story is different for subjects learning II categories. First, there is way more variation in best fitting model. Second, very few subjects in the 1-back condition (d) show evidence of the using the optimal rule (the diagonal partition).

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Conclusions

Smith et al concluded: “We predicted that 1-Back reinforcement would disable associative, reinforcement-driven learning and the II category-learning processes that depend on it. This disabling seems to have been complete” But that’s a strong conclusion. Too strong. Based on the modelling, the more measured conclusion seems to be that about 7-8 of the 30 subjects in the II-0Back condition learned the optimal rule (the diagonal) compared to about 1 subject in the II-1Back. Maybe a just handful of keener’s ended up in the II-0Back and learned the complex structure? It’s not easy to say. There is some evidence in favour of Smith et al’s conclusion but its not at all clear.

I still enjoyed reading the paper. The task design is clever, and the predictions flow logically from the theory (which is very important).It’s incremental work. It adds to the literature on the multiple systems theory but does not (in my opinion) rule out a single-system approach. But I wish they had done a second study as an internal replication to explore the stability of the result. Or maybe a second study with the same category structure but different stimuli. It’s incremental work. It adds to the literature on the multiple systems theory but does not (in my opinion) rule out a single-system approach.

Tune in a few weeks for the next instalment. Follow my blog if you like and check the tag to see the full list. As the list grows, I may create a better structure for these, too.

 

Cognitive Bias and Guns in America

UPDATE Oct 3, 2017: I wrote this almost exactly two years ago, following an earlier mass shooting. I think it still applies and helps to explain why we have trouble even talking about this. 

I posed the following question this week (Oct. of 2015)  to the students in my 3rd year Psychology of Thinking class.

“How many of you think that the US is a dangerous place to visit?”

About 70% of the students raised their hands. This is surprising to me because although I live and work in Canada, and I’m a Canadian citizen, I grew up in the US; my family still lives there and I think it’s very safe place to visit. Most students justified their answer by referring to school shootings, gun violence, and problems with American police. So although none of these students had even actually encountered violence in the US, they were thinking about it because it has been in the news.

Cognitive Bias

This is a clear example of a cognitive bias known as the Availability Heuristic. The idea, originally proposed in the early 1970s by Daniel Kahneman and Amos Tversky is that people generally make judgments and decisions on the basis of relevant memories that they retrieve and that are thus available at the time that the assessment or judgement is made. In other words, when you make a judgment about a likelihood of occurrence, you search your memory and make your decision on the basis of the available evidence. Most of the time, this heuristic produces useful and correct evidence. But in other cases, the available evidence may not correspond exactly to evidence in the world. We typically overestimate the likelihood of shark attacks and random violence because these low probability events are highly salient and available in memory.

Another cognitive bias (also from Kahneman and Tversky) is known as the Representativeness Heuristic. This is the general tendency to treat individuals as representative of the the entire category. For example, if I have a concept of American gun owners as being violent (based on what I’ve read or seen in the news), I might infer that each individual American is a violent gun owner. I’d be making a generalization or a stereotype and this can lead to bias. As with Availability, the Representativeness Heuristic arrises out of the natural tendency of humans to generalize information. Most of the time, this heuristic produces useful and correct evidence. But in other cases, the representative evidence may not correspond exactly to individual evidences in the world.

The Gun Debate in the US

I’ve been thinking about this a great deal as the US engages in yet another debate about gun violence and gun control. It’s been reported widely that the US has the highest rate of private gun ownership in the world, and also has an extraordinary rate of gun violence relative to other counties. These are facts. Of course, we all know that “correlation does not equal causation” but many strong correlations often derive from a causal link.

So why to do we continue to argue about this? One problem that I rarely see being discussed is that many of us have limited experience with guns and/or violence and have to rely on what we know from memory and from external sources and we’re susceptible to cognitive biases as a result.

For example, I’m not a gun owner any more, but many of my friends and family are, and these people are some of the most responsible gun owners I know. They own firearms for sport and also for personal protection and in some cases, even run successful training courses for people to learn about gun safety. From the perspective of a responsible and passionate gun owner, it’s quite true that the problem is not guns but the bad people who use them to kill. After all, if you are safe with guns and all your friends and family are too, then you base your judgements on the available evidence: gun owners are safe and so gun violence is not a problem of guns and their owners, but a problem of criminals with bad intentions.

But what about non gun owners?  Although I do not own a gun, I feel safe at home. My personal freedoms are not infringed and I recognize that illegal guns are the problem. And so I generalize this experience and I may have difficulty understanding why people would need a gun in the first place whether for personal protection or for a vaguely defined “protection from tyranny”. From our perspective it’s far more sensible to focus on reducing the number of guns. After all we don’t have one, we don’t believe we need one, so we generalize to assume that anyone who owns firearms might be suspect or irrationally fearful.

In each case, we are relying on cognitive biases to infer things about others and about guns. These things and inferences may be stifling the debate and interfering with our ability to find a solution

How do we overcome this?

It’s not easy to overcome a bias, because these cognitive heuristics are deeply engrained and indeed arise as a necessary function of how the mind operates. They are adaptive and useful. But occasionally we need to override a bias.

Here are some proposals, but each involves taking the perspective of someone on the other side of this debate.

  1. Those of us on the left of the debate (liberals, proponents of gun regulations) should try to recognize that nearly all gun enthusiasts are safe, law abiding people who are responsible with their guns. Seen through their eyes, the problem is with irresponsible owners. An attempt to place restrictions on their legally guns activates another cognitive bias known as the endowment effect  in which people place high value on that which they already possess, and the prospect of losing this is aversive.
  2. Those on the right (gun owners) should consider the debate from the perspective of non gun owners and might also consider that proposals to regulate firearms are not attempt to seize or ban guns but rather attempts to address one aspect of the problem: the sheer number of guns in the US, all of which could potentially be used for illegal purposes. We’re not trying to ban guns, but rather to regulate the and encourage greater responsibility in their use.

I think these things are important to deal with. The US really does have a problem with gun violence. It’s disproportionally high. Solutions to this problem must recognize the reality of the large number of guns, the perspective of non gun owners, and the perspectives of gun owners. We’re not going to do this by simply arguing for one side. We’re only going to do this by first recognizing these cognitive biases and them attempting to overcome them in ways that search for common ground.

As always: comments are welcome.