Modeling the effects of methylphenidate on interference and evidence accumulation processes using the conflict linear ballistic accumulator
Although methylphenidate and other stimulants have been demonstrated to improve task performance across a variety of domains, a computationally rigorous account of how these drugs alter cognitive processing remains elusive. Recent applications of mathematical models of cognitive processing and electrophysiological methods to this question have suggested that stimulants improve the integrity of evidence accumulation processes for relevant choices, potentially through catecholaminergic modulation of neural signal-to-noise ratios. However, this nascent line of work has thus far been limited to simple perceptual tasks and has largely omitted more complex conflict paradigms that contain experimental manipulations of specific top-down interference resolution processes.
Objectives and methods
To address this gap, this study applied the conflict linear ballistic accumulator (LBA), a newly proposed model designed for conflict tasks, to data from healthy adults who performed the Multi-Source Interference Task (MSIT) after acute methylphenidate or placebo challenge.
Model-based analyses revealed that methylphenidate improved performance by reducing individuals’ response thresholds and by enhancing evidence accumulation processes across all task conditions, either by improving the quality of evidence or by reducing variability in accumulation processes. In contrast, the drug did not reduce bottom-up interference or selectively facilitate top-down interference resolution processes probed by the experimental conflict manipulation.
Enhancement of evidence accumulation is a biologically plausible and task-general mechanism of stimulant effects on cognition. Moreover, the assumption that methylphenidate’s effects on behavior are only visible with complex executive tasks may be misguided.
KeywordsMethylphenidate Stimulants Evidence accumulation Conflict tasks Executive functions Cognitive modeling Computational psychiatry Bayesian
AW was supported by funding from the National Institute on Alcohol Abuse and Alcoholism (T32 AA007477). CS was supported by funding from the National Institute on Alcohol Abuse and Alcoholism (K23-AA-020297).
Compliance with ethical standards
All experimental procedures were in compliance with the laws of the country in which the experiment was performed (USA).
Conflict of interest
The authors declare that they have no conflict of interest.
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