Competing neural representations of choice shape evidence accumulation in humans
- Carnegie Mellon University, United States
- University of Washington, United States
- University of Pittsburgh, United States
Abstract
Making adaptive choices in dynamic environments requires flexible decision policies. Previously, we showed how the evidence accumulation process that drives decisions shifts when outcome contingencies change (1). Using in silico experiments, here we show how the cortico-basal ganglia-thalamic (CBGT) circuits can feasibly implement shifts in the evidence accumulation process. When action contingencies change, dopaminergic plasticity redirects the balance of power, both within and between action representations, to divert the flow of evidence from one option to another. This model predicts that when competition between action representations is highest, the rate of evidence accumulation is lowest. This prediction was validated in in vivo experiments on human subjects, using fMRI, which showed that 1) evoked hemodynamic responses can reliably predict trialwise choices and 2) competition between action representations, measured using a classifier model, tracked with changes in the rate of evidence accumulation. These results paint a holistic picture of how CBGT circuits manage and adapt the evidence accumulation process in mammals.
Data availability
Behavioral data and computational derivatives are publically available here: https://github.com/kalexandriabond/competing-representations-shape-evidence-accumulation. Raw and preprocessed hemodynamic data, in addition to physiological measurements collected for quality control, are available here: https://openneuro.org/datasets/ds004283/versions/1.0.3.
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neurolokiOpenNeuro; doi:10.18112/openneuro.ds004283.v1.0.3.
Article and author information
Author details
Krista Alexandria Marie Bond
Funding
Air Force Research Laboratory (180119)
- Timothy Verstynen
National Institutes of Health (CRCNS: R01DA053014)
- Timothy Verstynen
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Ethics
Human subjects: All procedures were approved by the Carnegie Mellon University Institutional Review Board (Approval Code: 2018_00000195).All research participants provided informed consent to participate in the study and consent to publish any research findings based on their provided data.
Copyright
© 2023, Bond et al.
This article is distributed under the terms of the Creative Commons Attribution License permitting unrestricted use and redistribution provided that the original author and source are credited.
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Competing neural representations of choice shape evidence accumulation in humans
eLife 12:e85223.
https://doi.org/10.7554/eLife.85223
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Evidence accumulation models (EAMs) are the dominant framework for modeling response time (RT) data from speeded decision-making tasks. While providing a good quantitative description of RT data in terms of abstract perceptual representations, EAMs do not explain how the visual system extracts these representations in the first place. To address this limitation, we introduce the visual accumulator model (VAM), in which convolutional neural network models of visual processing and traditional EAMs are jointly fitted to trial-level RTs and raw (pixel-space) visual stimuli from individual subjects in a unified Bayesian framework. Models fitted to large-scale cognitive training data from a stylized flanker task captured individual differences in congruency effects, RTs, and accuracy. We find evidence that the selection of task-relevant information occurs through the orthogonalization of relevant and irrelevant representations, demonstrating how our framework can be used to relate visual representations to behavioral outputs. Together, our work provides a probabilistic framework for both constraining neural network models of vision with behavioral data and studying how the visual system extracts representations that guide decisions.
