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Changes within neural population codes can be inferred from psychophysical threshold studies

Authors
Mr. Jason Hays
Florida International University ~ Psychology
Fabian Soto
Florida International University ~ Department of Psychology
Abstract

The use of population encoding models has come to dominate human visual neuroscience, serving as a primary tool that allows researchers to infer, through indirect measurements, how cognitive states (i.e., attentional shifts, learning, adaptation, etc) change neural stimulus representations. Inverted encoding modeling is commonly used to retrieve estimates of neural population responses from neuroimaging data, but recent results suggest that the approach may have identifiability problems, because multiple mechanisms of encoding change can produce similar neural responses. Psychophysical data might be able to provide additional constraints to infer the encoding change mechanism underlying some behavior of interest. Here, we explored how well eight different mechanisms of encoding change could be differentiated by comparing the relative change between psychophysical thresholds across states. The eight types (previously proposed in the literature as mechanisms for improved task performance) included specific and nonspecific gain, specific and nonspecific tuning, specific suppression, specific suppression plus gain, and inward and outward tuning shifts. For each of the eight types (plus a homogeneous baseline), Monte Carlo simulations were used to obtain thresholds along the stimulus domain (a threshold vs stimulus function, or TvS) or along levels of external noise (a threshold vs noise function, or TvN). With the exception of specific gain and specific tuning, all studied mechanisms produced qualitatively different patterns of change in the TvN and TvS curves, suggesting that psychophysical studies can be used as a complement to inverted encoding modeling and provide strong constraints on inferences based on the latter.

Tags

Keywords

encoding
decoding
Monte Carlo Simulations
population models

Topics

Cognitive Modeling
Cognitive Neuromodeling
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