Value-based decision making via sequential sampling with hierarchical competition and attentional modulation
- Creators
- Colas, Jaron T.
Abstract
In principle, formal dynamical models of decision making hold the potential to represent fundamental computations underpinning value-based (i.e., preferential) decisions in addition to perceptual decisions. Sequential-sampling models such as the race model and the drift-diffusion model that are grounded in simplicity, analytical tractability, and optimality remain popular, but some of their more recent counterparts have instead been designed with an aim for more feasibility as architectures to be implemented by actual neural systems. Connectionist models are proposed herein at an intermediate level of analysis that bridges mental phenomena and underlying neurophysiological mechanisms. Several such models drawing elements from the established race, drift-diffusion, feedforward-inhibition, divisive-normalization, and competing-accumulator models were tested with respect to fitting empirical data from human participants making choices between foods on the basis of hedonic value rather than a traditional perceptual attribute. Even when considering performance at emulating behavior alone, more neurally plausible models were set apart from more normative race or drift-diffusion models both quantitatively and qualitatively despite remaining parsimonious. To best capture the paradigm, a novel six-parameter computational model was formulated with features including hierarchical levels of competition via mutual inhibition as well as a static approximation of attentional modulation, which promotes "winner-take-all" processing. Moreover, a meta-analysis encompassing several related experiments validated the robustness of model-predicted trends in humans' value-based choices and concomitant reaction times. These findings have yet further implications for analysis of neurophysiological data in accordance with computational modeling, which is also discussed in this new light.
Additional Information
© 2017 Jaron T. Colas. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Received: June 14, 2017; Accepted: October 9, 2017; Published: October 27, 2017. Data Availability Statement: All relevant data are within the paper and its Supporting Information files. I gratefully acknowledge the financial support provided by the National Science Foundation (https://www.nsf.gov/) Graduate Research Fellowship Program, the Rose Hills Foundation (https://www.rosehillsfoundation.org/), and the Gordon and Betty Moore Foundation (https://www.moore.org/). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. The author has declared that no competing interests exist. I thank Cendri Hutcherson, Ian Krajbich, Seung-Lark Lim, Joy Lu, Antonio Rangel, and Nicolette Sullivan for contributing data. I thank Antonio Rangel and John O'Doherty for useful comments.Attached Files
Published - journal.pone.0186822.pdf
Supplemental Material - S1File.zip
Erratum - journal.pone.0203093.pdf
Files
Additional details
- PMCID
- PMC5659783
- Eprint ID
- 83078
- Resolver ID
- CaltechAUTHORS:20171108-142846583
- NSF Graduate Research Fellowship
- Rose Hills Foundation
- Gordon and Betty Moore Foundation
- Created
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2017-11-08Created from EPrint's datestamp field
- Updated
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2023-06-01Created from EPrint's last_modified field