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