The ability to make efficient decisions is critical in a dynamic and changing environment, governing behavior ranging from the simple to the complex. Furthermore, altered decision-making is a hallmark of a number of diseases, such as epilepsy, major depression, and schizophrenia. In particular, value-guided decisions can be altered by aberrant processing of the history of recent rewards and the ability to use past experience to guide future decisions. While emerging work has outlined many brain areas involved in decision-making, how neural circuits decide is unknown. Theorists in psychology, economics, and ecology have outlined standard models of rational choice behavior, defining how optimal choosers should behave to maximize outcomes. In contrast to these theoretical predictions, empirical choice behavior in animals and humans often deviates markedly from optimality. Such inefficiencies likely reflect the constraints of a biological decision system, and studying rationality violations offers potential insight into the neurobiological basis of decision making. In this application, we examine the effect of previous history on the decision process and its underlying circuits. We hypothesize that temporal context-dependence in both value-coding neural activity and choice behavior arises from the way neural circuits represent value. Specifically, we hypothesize that adaptive value- coding is implemented using a standard computation widely found in sensory cortical circuits, divisive normalization, and that adaptation in perceptual processing provides a framework for understanding adaptation in valuation and decision-making. To test this hypothesis, we propose to undertake three aims addressing adaptation in value coding at the neural, computational, and behavioral levels.
In Aim 1, we propose electrophysiological recording experiments to test whether the neural representation of value adapts to prior reward history, and whether this computation matches the divisive normalization algorithm.
In Aim 2, we propose computational modeling experiments which will test the generality of the normalization model in explaining various, different value adaptation effects, and make specific predictions about the effect of adaptive value coding on choice behavior.
In Aim 3, we propose choice behavior experiments, in both an animal model and human subjects, to test the prediction that adaptive value coding can selectively enhance the efficiency of choice. Understanding adaptation in value coding is crucial for understanding both standard and pathological choice behavior. Temporal history effects in decision-making are suboptimal in terms of rational theories of choice, but may reflect a more global optimality that balances choice efficiency and the constraints of operating a biological decision process. Such temporal-dependence may be particularly important for understanding affective disorders, such as depression and bipolar disorder, where prolonged periods of low or high reward states may significantly impede the decision process.

Public Health Relevance

Human decision-making often deviates from the predictions of optimal choice theories, and these violations offer insight into how the brain operates given necessary biological limitations. In this application, we examine how the brain uses recent experience to adjust the neural representation of value information in decision circuits, a process known as temporal adaptation. Characterizing adaptive value coding in decision-making will offer insight in particular to affective disorders such as major depression and bipolar disorder, whose in which altered decision-making may be linked to adaptation to prolonged periods of low or high reward environments.

Agency
National Institute of Health (NIH)
Institute
National Institute of Mental Health (NIMH)
Type
Research Project (R01)
Project #
5R01MH104251-04
Application #
9418620
Study Section
Mechanisms of Sensory, Perceptual, and Cognitive Processes Study Section (SPC)
Program Officer
Rossi, Andrew
Project Start
2015-04-22
Project End
2020-01-31
Budget Start
2018-02-01
Budget End
2019-01-31
Support Year
4
Fiscal Year
2018
Total Cost
Indirect Cost
Name
New York University
Department
Neurosciences
Type
Schools of Arts and Sciences
DUNS #
041968306
City
New York
State
NY
Country
United States
Zip Code
10012
Khaw, Mel W; Glimcher, Paul W; Louie, Kenway (2017) Normalized value coding explains dynamic adaptation in the human valuation process. Proc Natl Acad Sci U S A 114:12696-12701
Zimmermann, Jan; Vazquez, Yuriria; Glimcher, Paul W et al. (2016) Oculomatic: High speed, reliable, and accurate open-source eye tracking for humans and non-human primates. J Neurosci Methods 270:138-146
Louie, Kenway; Glimcher, Paul W; Webb, Ryan (2015) Adaptive neural coding: from biological to behavioral decision-making. Curr Opin Behav Sci 5:91-99