Bridging Neural and Computational Viewpoints on Perceptual Decision-Making

Abstract

Sequential sampling models have provided a dominant theoretical framework guiding computational and neurophysiological investigations of perceptual decision-making. While these models share the basic principle that decisions are formed by accumulating sensory evidence to a bound, they come in many forms that can make similar predictions of choice behaviour despite invoking fundamentally different mechanisms. The identification of neural signals that reflect some of the core computations underpinning decision formation offers new avenues for empirically testing and refining key model assumptions. Here, we highlight recent efforts to explore these avenues and, in so doing, consider the conceptual and methodological challenges that arise when seeking to infer decision computations from complex neural data.

Keywords: computational modelling; lateral intraparietal area (LIP); perceptual decision-making; sequential sampling.

Figure 1

Alternative Mechanisms to Explain Why Choice Accuracy Reduces over Time within a Trial. (A) Schematic illustrating how drift rate variability with static bounds can produce slow errors. Solid lines indicate the path taken by a diffusion decision variable on each of two example single trials, one resulting in a correct response (green) and one resulting in an erroneous (orange) choice. Drift rate variability tends to produce response times that are longer, on average, for erroneous choices than for correct choices. Dotted lines mark the drift rate for each of those two trials. (B) Schematic illustrating how collapsing bounds without drift rate variability can alternatively produce slow errors. Again, two example single trials are shown, in this case arising from the same, fixed drift rate. (C) Conditional accuracy functions illustrating the decrease in accuracy as a function of response time (RT). Blue and red lines represent data from two different task conditions emphasising accuracy and speed, respectively. (D) Lateral intraparietal area (LIP) firing rate data highlighting that speed emphasis leads to an increase in the starting level of activity at trial onset and also an evidence-independent acceleration of signal build-up over time, reflecting a dynamic urgency component, the impact of which is equivalent to a collapsing bound (B). Panels C and D adapted from and , respectively.

Figure 2

A Multiplicity of Decision Signals. (A) (i) When monkeys indicate motion direction discrimination decisions via saccade, neurons in the lateral intraparietal area (LIP) exhibit accumulation-to-bound dynamics that are highly sensitive to variations in sensory evidence. Here, LIP neuron firing rates increase more rapidly when coherent motion more strongly favours a saccade to a target located within the response field of the neuron (Tin). Although many intracranial recording studies of perceptual decision-making have targeted the LIP, similar neural decision signals have been observed in a variety of other regions of the monkey brain. (ii) When monkeys make reach movements to indicate their decisions, instead of saccades, reach-related neurons in the medial intraparietal area (MIP) exhibit similar accumulate-to-bound dynamics (unbroken traces). (iii) Movement neurons in frontal eye field (FEF) exhibit evidence accumulation dynamics during visual search decisions reported via saccade. Thin lines represent trials on which a distractor appeared within the response field of a neuron (Tout). (B) When rodents performed an auditory decision task, evidence accumulation dynamics are observed in (i) posterior parietal cortex (PPC) and (ii) frontal orienting fields (FOF). However, tuning curve analyses (iii) indicate that, while PPC provides a graded representation of incoming evidence, momentary FOF activity reflects the currently favoured alternative in a more categorical fashion. This pattern accords with the general observation from multisite recording studies that neural activity becomes progressively more closely linked to the observer’s action choices as one proceeds toward the motor end of the sensorimotor hierarchy. (C) When humans make motion discrimination decisions, highly similar accumulate-to-threshold signals are observed in non-invasive electrophysiological recordings. This work has uncovered two functionally distinct classes of decision signal: (i) when observers indicate their decisions via hand movement, contralateral motor preparation signals trace decision formation. These signals cease to trace decision formation if the stimulus-to-response mapping is withheld or when hand movements are not required. (ii) A centroparietal-positive (CPP) component in the event-related potential also traces evidence accumulation but does so irrespective of the sensory or motor requirements of the task. (iii) When participants withheld motion direction decision reports until the appearance of a response cue (1600 ms after stimulus onset), the CPP traced decision formation irrespective of whether the participant had foreknowledge of the stimulus-to-response mapping (fixed mapping) or not (variable mapping) and fell silent only when dot motion was rendered irrelevant to the task (ignore motion). Figures adapted from (A.i), (A.ii), (A.iii), (B.i-iii), (C.i-ii), and (C.iii).