Abstract
The mechanisms of perceptual decision-making are frequently studied through measurements of reaction time (RT). Classical sequential-sampling models (SSMs) of decision-making posit RT as the sum of non-overlapping sensory, evidence accumulation, and motor delays. In contrast, recent empirical evidence hints at a continuous-flow paradigm in which multiple motor plans evolve concurrently with the accumulation of sensory evidence. Here we employ a trial-to-trial reliability-based component analysis of encephalographic data acquired during a random-dot motion task to directly image continuous flow in the human brain. We identify three topographically distinct neural sources whose dynamics exhibit contemporaneous ramping to time-of-response, with the rate and duration of ramping discriminating fast and slow responses. Only one of these sources, a parietal component, exhibits dependence on strength-of-evidence. The remaining two components possess topographies consistent with origins in the motor system, and their covariation with RT overlaps in time with the evidence accumulation process. After fitting the behavioral data to a popular SSM, we find that the model decision variable is more closely matched to the combined activity of the three components than to their individual activity. Our results emphasize the role of motor variability in shaping RT distributions on perceptual decision tasks, suggesting that physiologically plausible computational accounts of perceptual decision-making must model the concurrent nature of evidence accumulation and motor planning.
Highlights
Behavioral analyses of perceptual decision-making have been firmly grounded in the theoretical framework provided by sequential sampling models (SSMs) [1], whose hallmark is the decision variable (DV), an abstract entity quantifying the amount of evidence favoring one alternative versus the other [2, 3]
Accuracy on the random-dot motion task ranged from an average of 99% on easy discriminations (10.2° deviation from vertical) to 87% on difficult conditions (3.4° from vertical), with mean reaction time (RT) varying from 464 ms on the easy discriminations to 611 ms for vertical motion (Fig 1)
Reliable Components Analysis” (RCA) projects the sensor data onto a low-dimensional space in which trial-to-trial reliability is maximal, yielding component time courses and scalp topographies corresponding to the underlying neural sources
Summary
Behavioral analyses of perceptual decision-making have been firmly grounded in the theoretical framework provided by sequential sampling models (SSMs) [1], whose hallmark is the decision variable (DV), an abstract entity quantifying the amount of evidence favoring one alternative versus the other [2, 3]. The temporal evolution of the DV determines behavioral outcomes, such that SSMs make concrete predictions about both the accuracy and reaction time (RT) of a perceptual decision. The pursuit of the neural basis of perceptual decisions has been marked by attempts to match neural signals (for example, firing rates of single neurons or mass field potentials) to model-generated DVs [2, 3]. These efforts have focused on identifying the neural correlates of the evidence accumulation process. Increasing evidence suggests that decision formation is gated through the motor system in a concurrent fashion [5,6,7,8,9,10], and as such, activity in the motor system has been found to mimic the DV [11,12,13,14]
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