Abstract
Speed-accuracy tradeoff (SAT) is an adaptive process balancing urgency and caution when making decisions. Computational cognitive theories, known as “evidence accumulation models”, have explained SATs via a manipulation of the amount of evidence necessary to trigger response selection. New light has been shed on these processes by single-cell recordings from monkeys who were adjusting their SAT settings. Those data have been interpreted as inconsistent with existing evidence accumulation theories, prompting the addition of new mechanisms to the models. We show that this interpretation was wrong, by demonstrating that the neural spiking data, and the behavioural data are consistent with existing evidence accumulation theories, without positing additional mechanisms. Our approach succeeds by using the neural data to provide constraints on the cognitive model. Open questions remain about the locus of the link between certain elements of the cognitive models and the neurophysiology, and about the relationship between activity in cortical neurons identified with decision-making vs. activity in downstream areas more closely linked with motor effectors.
Highlights
The speed-accuracy tradeoff (SAT) is an important element of day-to-day functioning for humans, managing the balance between making decisions correctly while not wasting time
The data responsible for this broadening correspond with the response time distributions observed for the speed-emphasis condition, which suggests an explanation for these unusual results: on some decisions when the monkeys were given an accuracy-emphasis cue they instead reverted to behavior more consistent with speed emphasis
We have shown that a standard cognitive model can fit both the behavioral and neural data recorded by Heitz and Schall
Summary
The speed-accuracy tradeoff (SAT) is an important element of day-to-day functioning for humans, managing the balance between making decisions correctly while not wasting time This balance of caution with urgency has been studied for decades in humans (e.g., [1]) and has been observed in the behavior of many other animals, from rats to bees and even slime mould [2,3,4,5]. When a low threshold is set decisions are made quickly, but are more often wrong because they are based on too little evidence Using this conventional parameterization, accumulator models have a long and successful history of providing detailed, quantitative accounts of many different aspects of decision-making, including the SAT [6,7,8,9,10]. Accumulator models underpin hundreds of applied studies, where the decision-making theory is used as a tool to understand important problems including clinical disorders [11], alcohol intoxication [12], and sleep deprivation [13]
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