Dynamic Integration of Reward and Stimulus Information in Perceptual Decision-Making

Juan Gao,James L Mcclelland,Rebecca Tortell,Olaf Sporns

doi:10.1371/journal.pone.0016749

Abstract

In perceptual decision-making, ideal decision-makers should bias their choices toward alternatives associated with larger rewards, and the extent of the bias should decrease as stimulus sensitivity increases. When responses must be made at different times after stimulus onset, stimulus sensitivity grows with time from zero to a final asymptotic level. Are decision makers able to produce responses that are more biased if they are made soon after stimulus onset, but less biased if they are made after more evidence has been accumulated? If so, how close to optimal can they come in doing this, and how might their performance be achieved mechanistically? We report an experiment in which the payoff for each alternative is indicated before stimulus onset. Processing time is controlled by a “go” cue occurring at different times post stimulus onset, requiring a response within msec. Reward bias does start high when processing time is short and decreases as sensitivity increases, leveling off at a non-zero value. However, the degree of bias is sub-optimal for shorter processing times. We present a mechanistic account of participants' performance within the framework of the leaky competing accumulator model [1], in which accumulators for each alternative accumulate noisy information subject to leakage and mutual inhibition. The leveling off of accuracy is attributed to mutual inhibition between the accumulators, allowing the accumulator that gathers the most evidence early in a trial to suppress the alternative. Three ways reward might affect decision making in this framework are considered. One of the three, in which reward affects the starting point of the evidence accumulation process, is consistent with the qualitative pattern of the observed reward bias effect, while the other two are not. Incorporating this assumption into the leaky competing accumulator model, we are able to provide close quantitative fits to individual participant data.

Highlights

Imagine you are in a counter-terrorist fight
Dynamical Models Motivated by the dynamics of the stimulus sensitivity and reward bias, we explore a possible mechanism underlying the effect of reward on the decision-making process within the context of the leaky competing accumulator (LCA) model
We find that the detailed pattern of results can be captured by the inhibition-dominant leaky competing accumulator model (LCA), under the assumption that reward offsets the initial state of the accumulators before stimulus information begins to accumulate

Summary

Introduction

Imagine you are in a counter-terrorist fight. As a person approaches, you have to quickly identity whether he is a friend or foe and take an action: either you must protect him or kill him before he kills you. The answers to these questions tell us more than just how well people can do in such situations. How do we integrate vague stimulus information, such as that person’s body-figure, and the consequences of taking each of several possible actions, under time pressure? They may open a window to the underlying mechanism of the interaction between bottom-up stimulus information and higher-level factors such as payoffs. There has been less emphasis on the question: How do decision makers integrate differential payoffs for responses to the different alternatives? There were a few earlier theoretical investigations (See [11,12] and other papers cited in [12]), but there is only a small and very recent literature combining experimental and computational investigations [12,13,14,15]

Methods

Results

Discussion

Conclusion