1. Introduction to cognitive modeling Marieke van Vugt University of Groningen The Netherlands

2. Who am I? PhD University of Pennsylvania (Philadelphia, US) – Models of visual working memory – Brain oscillations Advisor: Michael Kahana

3. Who am I? Postdoctoral work Princeton University – Models of decision making – Brain oscillations

4. Who am I Assistant professor University of Groningen, Netherlands – Models of decision making, meditation (!) Also: student of Sogyal Rinpoche

5. Modeling cognition? https://www.youtube.com/watch?v=fDOuuqk eWrs https://www.youtube.com/watch?v=fDOuuqk eWrs

6. Model predicts performance on cognitive tasks See Katherine Shephard’s lectures Measure response times and accuracies in response to different stimuli Change the conditions to infer something about how a person perceives or processes the world

7. How do we make decisions? 1.Perception of information (e.g., visual cortex, motion perception area)

8. How do we make decisions? 1.Perception of information (e.g., visual cortex, motion perception area) 2.Accumulating evidence over time (parietal cortex)

9. How do we make decisions? 1.Perception of information (e.g., visual cortex, motion perception area) 2.Accumulating evidence over time (parietal cortex) 3.Motor response

10. Models of decision making Accumulator Models Reinforcement learning

11. Accumulator model in action

12. A model you can try out Data from perceptual decision making task Linear ballistic accumulator model: specific version of accumulator model right left

13. What does data look like?

14. Input the data into Rstudio

15. Next steps… Tell the computer to: – Read in the data – Clean 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 left right source(‘fitDotsBias.R’)

16. Model results Comparison of observed and fitted response times for correct (top) and incorrect (bottom) responses

17. Model results Estimates 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)

18. Model results Parameters say something about cognition: – s = variability in drift -> fluctuations in attention – A = starting point -> bias for a choice option left right

19. Model results – Ter = non-decision time -> fixed perceptual/motor latencies – B = threshold -> how conservative are you? – V = drift -> how strong is your attention and/or evidence? right left (larger drift -> higher slope of accumulation process)

20. What do we do with that? Comparing different individuals Predictions for new situations (experiments) Adjust the model van Vugt & Jha (2011)

21. Summary Making models of cognition = writing computer code that (we think) simulates what a human does Then comparing the model’s predictions to actual human behavior And starting again! Next lecture: how can we model shamatha with a visual object? left right