A generative joint model for spike trains and saccades during perceptual decision-making

Theory development in both psychology and neuroscience can benefit by consideration of both behavioral and neural data sets. However, the development of appropriate methods for linking these data sets is a difficult statistical and conceptual problem. Over the past decades, different linking approac...

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Bibliographic Details
Published in:Psychonomic bulletin & review 2016-12, Vol.23 (6), p.1757-1778
Main Authors: Cassey, Peter J., Gaut, Garren, Steyvers, Mark, Brown, Scott D.
Format: Article
Language:English
Subjects:
Animals
Behavior
Behavioral Science and Psychology
Cognitive models
Cognitive Psychology
Decision making
Decision Making - physiology
Electrophysiological Phenomena
Humans
Models, Theoretical
Neurons
Neurons - physiology
Perception - physiology
Psychology
Researchers
Saccades - physiology
Theoretical Review
Theory
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Summary:Theory development in both psychology and neuroscience can benefit by consideration of both behavioral and neural data sets. However, the development of appropriate methods for linking these data sets is a difficult statistical and conceptual problem. Over the past decades, different linking approaches have been employed in the study of perceptual decision-making, beginning with rudimentary linking of the data sets at a qualitative, structural level, culminating in sophisticated statistical approaches with quantitative links. We outline a new approach, in which a single model is developed that jointly addresses neural and behavioral data. This approach allows for specification and testing of quantitative links between neural and behavioral aspects of the model. Estimating the model in a Bayesian framework allows both data sets to equally inform the estimation of all model parameters. The use of a hierarchical model architecture allows for a model, which accounts for and measures the variability between neurons. We demonstrate the approach by re-analysis of a classic data set containing behavioral recordings of decision-making with accompanying single-cell neural recordings. The joint model is able to capture most aspects of both data sets, and also supports the analysis of interesting questions about prediction, including predicting the times at which responses are made, and the corresponding neural firing rates.
ISSN:1069-9384
1531-5320
DOI:10.3758/s13423-016-1056-z