Inseparability of Go and Stop in Inhibitory Control: Go Stimulus Discriminability Affects Stopping Behavior.

Ning Ma,Angela J Yu

doi:10.3389/fnins.2016.00054

Abstract

Inhibitory control, the ability to stop or modify preplanned actions under changing task conditions, is an important component of cognitive functions. Two lines of models of inhibitory control have previously been proposed for human response in the classical stop-signal task, in which subjects must inhibit a default go response upon presentation of an infrequent stop signal: (1) the race model, which posits two independent go and stop processes that race to determine the behavioral outcome, go or stop; and (2) an optimal decision-making model, which posits that observers decides whether and when to go based on continually (Bayesian) updated information about both the go and stop stimuli. In this work, we probe the relationship between go and stop processing by explicitly manipulating the discrimination difficulty of the go stimulus. While the race model assumes the go and stop processes are independent, and therefore go stimulus discriminability should not affect the stop stimulus processing, we simulate the optimal model to show that it predicts harder go discrimination should result in longer go reaction time (RT), lower stop error rate, as well as faster stop-signal RT. We then present novel behavioral data that validate these model predictions. The results thus favor a fundamentally inseparable account of go and stop processing, in a manner consistent with the optimal model, and contradicting the independence assumption of the race model. More broadly, our findings contribute to the growing evidence that the computations underlying inhibitory control are systematically modulated by cognitive influences in a Bayes-optimal manner, thus opening new avenues for interpreting neural responses underlying inhibitory control.

Highlights

The ability to cancel or modify planned actions according to changing task conditions is known as inhibitory control, and thought to be an important aspect of human cognitive function
To incentivize the subjects to be engaged in the task, and to help standardize the relative costs of the different kind of errors across individuals, subjects are compensated proportional to points they earn in the task, whereby they lose 50 points for a go discrimination or omission error, 50 points for a stop error, and 3 points for each 100ms of response delay
Classical behavioral results in the stop signal task, such as increases in stop error rate as a function of stop-signal delay (SSD) and the generally faster SE reaction time (RT) compared to go RT, have been shown to be natural consequences of such a rational decision-making process (Shenoy and Yu, 2011), these effects are captured by the race model (Logan and Cowan, 1984)

Summary

Introduction

The ability to cancel or modify planned actions according to changing task conditions is known as inhibitory control, and thought to be an important aspect of human cognitive function. Problematic for the simple race model, various cognitive contextual factors have been shown to systematically modulate stopping behavior, such as the reward structure of the task (Leotti and Wager, 2009) and the statistical frequency of stop signals (Emeric et al, 2007) In response to these and other observed cognitive influences, we previously proposed an alternative model of inhibitory control, a Bayes-optimal decision-making model positing that subjects choose when and whether to initiate a go response according to continually (Bayesian) updated sensory beliefs about both the go and stop stimuli, and relative to a behavioral objective function that penalize go and stop errors as well as response delay. This optimal model can capture cognitives influences on stopping behavior as a function of sensory statistics at multiple timescales (Ide et al, 2013; Ma and Yu, 2015a,b) and the reward structure of the task (Shenoy and Yu, 2011)

Methods

Results

Conclusion