Introduction to cognitive modeling

Introduction to cognitive modelingMarieke van VugtUniversity of GroningenThe NetherlandsWho am I?PhD University of Pennsylvania (Philadelphia, US)Models of visual working memoryBrain oscillations

Advisor: Michael Kahana

Who am I?Postdoctoral work Princeton UniversityModels of decision makingBrain oscillations

Who am IAssistant professor University of Groningen, NetherlandsModels of decision making, meditation (!)

Also: student of Sogyal Rinpoche

Modeling cognition?https://www.youtube.com/watch?v=fDOuuqkeWrsThis video explains what modeling of cognition is: to understand cognition we try to build it. We give a computer the same task as a human being and then try to see whether they actually do the same things. When they do not do the same thing, we adapt our model and try again. In this movie you see how we use different simulated cognitive components (simulated eyes, simulated ears, simulated working memory, simulated long term memory ) to put together how we behave in a cognitive task. We can for example see what happens when youre multitasking: texting while driving. This is likely to see to problems because when youre using your eyes to text, you cannot use them to watch the road, and youll crash.5Model predicts performance on cognitive tasksSee Katherine Shephards lecturesMeasure response times and accuracies in response to different stimuli

Change the conditions to infer something about how a person perceives or processes the world

Point: cognitive models are controlled simplifications of the world which are simple enough so we can actually write a computer program to do it. An example of such a task, which I have used a lot is the random dot motion task. The participant sees a cloud of randomly moving dots on the screen and has to tell me what direction the dots are moving in. They should do that as quickly and accurately as possible because they will receive a penny for every correct response. I measure the speed by which they respond as well as the actual responses. This leads to a distribution of accuracies and response times (which will be discussed later in the lecture).6How do we make decisions?Perception of information (e.g., visual cortex, motion perception area)

Before we go deeper into the exact task, lets take a step back and consider theories about how we decide.7How do we make decisions?Perception of information (e.g., visual cortex, motion perception area)Accumulating evidence over time (parietal cortex)

How do we make decisions?Perception of information (e.g., visual cortex, motion perception area)Accumulating evidence over time (parietal cortex)Motor response

Models of decision makingAccumulator Models

Reinforcement learning

Accumulator model in action

11A model you can try outData from perceptual decision making taskLinear ballistic accumulator model: specific version of accumulator model

rightleftHow do cognitive models work? If you want, you can try out a specific model after class on your own laptop. Here well walk through the steps of using such a model. Note: this requires a bit of math and computation skill! The linear ballistic accumulator simplifies the accumulation process by saying that the evidence accumulation is a linear trajectory, of which the speed differs between trials. 12What does data look like?

When participants do a task such as the random dot motion task, they produce a set of responses and response times in the different task conditions. These are then stored in a datafile that you can store on your computer. You can see in the first column a number indicating the task condition (e.g, 1= easy trials; 2=difficult trials), in the second column a number indicating a correct (1) or incorrect (0) response, and in the third column the response time in milliseconds. These form the input of the model13Input the data into Rstudio

Next stepsTell the computer to:Read in the dataClean up the data if necessary (participants are not always doing the task we want them to)Computer tries out different parameters and finds the ones that reproduce the data best

Result: predicted data + measure of discrepancy

leftrightsource(fitDotsBias.R)Here I explain that a parameter is like a knob that you can turn to produce a certain pattern of results. The particular knobs you have to turn to create a persons data says something about the underlying cognitive process. Types of knobs that this model has are the attentional fluctuations (variability in the slope of the line), decision threshold (how much information you accumulate before deciding, this says something about your level of caution)15Model results

Comparison of observed and fitted response times for correct (top) and incorrect (bottom) responsesYou can graph these data in terms of a histogram: a response time distribution. Here Ill spend some time explaining how you actually construct a RT distribution.16Model resultsEstimates of parameters for an individual: s A Ter b1 b2 b3 b4 v 0.306 409 302 645 636 620 560 0.744

(s = variability in drift; A = bias; Ter = non-decision time; b=decision threshold; v=drift)

Image shows a normal distribution, which has a variance and a mean. When you draw random numbers many times and make a histogram, this is what you get.17Model resultsParameters say something about cognition:s = variability in drift -> fluctuations in attentionA = starting point -> bias for a choice option

leftright18Model resultsTer = non-decision time -> fixed perceptual/motor latenciesB = threshold -> how conservative are you?V = drift -> how strong is your attention and/or evidence?

rightleft(larger drift -> higher slope of accumulation process)What do we do with that?Comparing different individualsPredictions for new situations (experiments)Adjust the model

van Vugt & Jha (2011)

Figure shows a comparison of meditators and controls in accumulator model parameters decision threshold (a) and drift (b)20SummaryMaking models of cognition = writing computer code that (we think) simulates what a human doesThen comparing the models predictions to actual human behaviorAnd starting again!Next lecture: how can we model shamatha with a visual object?

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