Abstract
Response time (RT) is an oft-reported behavioral measure in psychological and neurocognitive experiments, but the high level of observed trial-to-trial variability in this measure has often limited its usefulness. Here, we combine computational modeling and psychophysics to examine the hypothesis that fluctuations in this noisy measure reflect dynamic computations in human statistical learning and corresponding cognitive adjustments. We present data from the stop-signal task (SST), in which subjects respond to a go stimulus on each trial, unless instructed not to by a subsequent, infrequently presented stop signal. We model across-trial learning of stop signal frequency, P(stop), and stop-signal onset time, SSD (stop-signal delay), with a Bayesian hidden Markov model, and within-trial decision-making with an optimal stochastic control model. The combined model predicts that RT should increase with both expected P(stop) and SSD. The human behavioral data (n = 20) bear out this prediction, showing P(stop) and SSD both to be significant, independent predictors of RT, with P(stop) being a more prominent predictor in 75% of the subjects, and SSD being more prominent in the remaining 25%. The results demonstrate that humans indeed readily internalize environmental statistics and adjust their cognitive/behavioral strategy accordingly, and that subtle patterns in RT variability can serve as a valuable tool for validating models of statistical learning and decision-making. More broadly, the modeling tools presented in this work can be generalized to a large body of behavioral paradigms, in order to extract insights about cognitive and neural processing from apparently quite noisy behavioral measures. We also discuss how this behaviorally validated model can then be used to conduct model-based analysis of neural data, in order to help identify specific brain areas for representing and encoding key computational quantities in learning and decision-making.
Highlights
Response time (RT) is an oft-reported behavioral measure in psychology and neuroscience studies
Systematic patterns of sequential effects have long been observed in human 2AFC tasks, in which subjects’ responses speed up when a new stimulus confirms to a recent run of repetitions or alternations, and slow down when these local patterns are violated (Soetens et al, 1985; Cho et al, 2002), as though humans maintain an expectancy of stimulus type based on experienced trial sequences and their RT is modulated by this expectancy
We presented a rational inference, learning, and decision-making model of inhibitory control, which can account for significant variability of human RT in the stop-signal task (SST)
Summary
Response time (RT) is an oft-reported behavioral measure in psychology and neuroscience studies. As RT can vary greatly across trials of apparently identical experimental conditions, average or median RT across many identical trials is typically used to examine how task performance or an Learning, decision-making, and RT variability internal speed-accuracy tradeoff might be affected by different experimental conditions. We approach RT modeling from a different angle, attempting to capture trial-to-trial variability in RT as a consequence of statistically normative learning about environmental statistics and corresponding adaptations within an internal decision-making strategy. We model trial-by-trial behavior in SST, using a Bayesian hidden Markov model to capture across-trial learning of stop signal frequency [P(stop)] and onset asynchrony (SSD), and a rational decision-making control policy for within-trial processing, which combines prior beliefs and sensory data to produce behavioral outputs under task-specific constraints/objectives
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