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Combining hierarchical latent-mixture and evidence accumulation-based models with fMRI data

Authors
Dr. Percy Mistry
Stanford University
Dr. Kaustubh Supekar
Stanford University
Vinod Menon
Stanford University, United States of America
Abstract

We show how the functional resolution of mapping neural circuit features to distinct cognitive and behavioral components of decision-making process can be improved, by combining hierarchical latent-mixture and evidence-accumulation based models with neural (fMRI) data. These models are implemented within a Bayesian inference framework. Theory based and structural assumptions are used to develop evidence accumulation models whose parameters are hierarchically governed by cognitive subsystems, such as performance monitoring, belief updating, error-feedback, and executive control. Such models explicitly account for the temporal dynamics and learning associated with repeated decision making. They dissociate between different potential cognitive processes and strategies that may be used on a trial-by-trial basis, and account for the hierarchical process of strategy switching. The hierarchical cognitive processes and subsystems are characterized by cognitive parameters that potentially capture and mediate the relationship between neural (fMRI) and behavioral data. Such neuro-cognitive modeling allows us to differentiate between theories, provide insights into the developmental maturation of brain networks, and improve the identification of differential brain feature characteristics associated with different cognitive processes in clinical and neuro-diverse populations. Applications include identifying the joint neural and behavioral basis of individual differences in mathematical decision making, response inhibition, and perceptual decision making tasks.

Tags

Keywords

cognitive model
fMRI
diffusion model
Bayesian inference

Topics

Cognitive Modeling
Reaction Times
Bayesian Modeling
Accumulator/Diffusion models
Cognitive Neuromodeling
Discussion
New
is everything an accumulator? Last updated 2 months ago

Very cool work! I was wondering about the following: does your approach assume that every strategy is an accumulation process? I would assume that the different strategies affect the duration of different task stages but not all of the task stages are necessarily an accumulation process. As such, it may be interesting to compare your work to a HMM ...

Marieke Van Vugt 1 comment
stop failure Last updated 3 months ago

thanks for the great talk! I had a question referring to the stop task results, using your methods, can you comment on the neural bases of those fast-go responses leading to errors?

Dr. Aline Bompas 1 comment
Audio issues? Last updated 3 months ago

Great talk Percy! Thank you! I look forward to seeing the code online and any associated paper. I watched the entire presentation without audio but the subtitles were fine. Am I the only one that couldn't get the audio to work?

Dr. Michael D. Nunez 7 comments