Both the speed and the accuracy of a perceptual judgment depend on the strength of the sensory stimulation. When stimulus strength is high, accuracy is high and response time is fast; when stimulus strength is low, accuracy is low and response time is slow. Although the psychometric function is well established as a tool for analyzing the relationship between accuracy and stimulus strength, the corresponding chronometric function for the relationship between response time and stimulus strength has not received as much consideration. In this article, we describe a theory of perceptual decision making based on a diffusion model. In it, a decision is based on the additive accumulation of sensory evidence over time to a bound. Combined with simple scaling assumptions, the proportional-rate and power-rate diffusion models predict simple analytic expressions for both the chronometric and psychometric functions. In a series of psychophysical experiments, we show that this theory accounts for response time and accuracy as a function of both stimulus strength and speed-accuracy instructions. In particular, the results demonstrate a close coupling between response time and accuracy. The theory is also shown to subsume the predictions of Piéron's Law, a power function dependence of response time on stimulus strength. The theory's analytic chronometric function allows one to extend theories of accuracy to response time.

We performed a series of functional magnetic resonance imaging experiments to divide the human MT+ complex into subregions that may be identified as homologs to a pair of macaque motion-responsive visual areas: the middle temporal area (MT) and the medial superior temporal area (MST). Using stimuli designed to tease apart differences in retinotopic organization and receptive field size, we established a double dissociation between two distinct MT+ subregions in 8 of the 10 hemispheres studied. The first subregion exhibited retinotopic organization but did not respond to peripheral ipsilateral stimulation, indicative of smaller receptive fields. Conversely, the second subregion within MT+ did not demonstrate retinotopic organization but did respond to peripheral stimuli in both the ipsilateral and contralateral visual hemifields, indicative of larger receptive fields. We tentatively identify these subregions as the human homologues of macaque MT and MST, respectively. Putative human MT and MST were typically located on the posterior/ventral and anterior/dorsal banks of a dorsal/posterior limb of the inferior temporal sulcus, similar to their relative positions in the macaque superior temporal sulcus.

Decision-making often requires the accumulation and maintenance of evidence over time. Although the neural signals underlying sensory processing have been studied extensively, little is known about how the brain accrues and holds these sensory signals to guide later actions. Previous work has suggested that neural activity in the lateral intraparietal area (LIP) of the monkey brain reflects the formation of perceptual decisions in a random dot direction-discrimination task in which monkeys communicate their decisions with eye-movement responses. We tested the hypothesis that decision-related neural activity in LIP represents the time integral of the momentary motion "evidence." By briefly perturbing the strength of the visual motion stimulus during the formation of perceptual decisions, we tested whether this LIP activity reflected a persistent, integrated "memory" of these brief sensory events. We found that the responses of LIP neurons reflected substantial temporal integration. Brief pulses had persistent effects on both the monkeys' choices and the responses of neurons in LIP, lasting up to 800 ms after appearance. These results demonstrate that LIP is involved in neural time integration underlying the accumulation of evidence in this task. Additional analyses suggest that decision-related LIP responses, as well as behavioral choices and reaction times, can be explained by near-perfect time integration that stops when a criterion amount of evidence has been accumulated. Temporal integration may be a fundamental computation underlying higher cognitive functions that are dissociated from immediate sensory inputs or motor outputs.

Neurons in the macaque lateral intraparietal (LIP) area exhibit firing rates that appear to ramp upwards or downwards during decision-making. These ramps are commonly assumed to reflect the gradual accumulation of evidence towards a decision threshold. However, the ramping in trial-averaged responses could instead arise from instantaneous jumps at different times on different trials. We examined single-trial responses in LIP using statistical methods for fitting and comparing latent dynamical spike train models. We compared models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete “stepping” dynamics. Roughly three-quarters of the choice-selective neurons we recorded were better described by the stepping model. Moreover, the inferred steps carried more information about the animal’s choice than spike counts.

During decision-making, neurons in multiple brain regions exhibit responses that are correlated with decisions1-6. However, it remains uncertain whether or not various forms of decision-related activity are causally related to decision-making7-9. Here we address this question by recording and reversibly inactivating the lateral intraparietal (LIP) and middle temporal (MT) areas of rhesus macaques performing a motion direction discrimination task. Neurons in area LIP exhibited firing rate patterns that directly resembled the evidence accumulation process posited to govern decision making2,10, with strong correlations between their response fluctuations and the animal's choices. Neurons in area MT, in contrast, exhibited weak correlations between their response fluctuations and animal choices, and had firing rate patterns consistent with their sensory role in motion encoding1. The behavioral impact of pharmacological inactivation of each area was inversely related to their degree of decision-related activity: while inactivation of neurons in MT profoundly impaired psychophysical performance, inactivation in LIP had no measurable impact on decision-making performance, despite having silenced the very clusters that exhibited strong decision-related activity. Although LIP inactivation did not impair psychophysical behavior, it did influence spatial selection and oculomotor metrics in a free-choice control task. The absence of an effect on perceptual decision-making was stable over trials and sessions, arguing against several forms of compensation, and was robust to changes in stimulus type and task geometry. Thus, decision-related signals in LIP do not appear to be necessary for computing perceptual decisions. Our findings highlight a dissociation between decision correlation and causation, showing that strong neuron-decision correlations may reflect secondary or epiphenomenal signals, and do not necessarily offer direct access to the neural computations underlying decisions.

How do neurons in a decision circuit integrate time-varying signals, in favor of or against alternative choice options? To address this question, we used a recurrent neural circuit model to simulate an experiment in which monkeys performed a direction-discrimination task on a visual motion stimulus. In a recent study, it was found that brief pulses of motion perturbed neural activity in the lateral intraparietal area (LIP), and exerted corresponding effects on the monkey's choices and response times. Our model reproduces the behavioral observations and replicates LIP activity which, depending on whether the direction of the pulse is the same or opposite to that of a preferred motion stimulus, increases or decreases persistently over a few hundred milliseconds. Furthermore, our model accounts for the observation that the pulse exerts a weaker influence on LIP neuronal responses when the pulse is late relative to motion stimulus onset. We show that this violation of time-shift invariance (TSI) is consistent with a recurrent circuit mechanism of time integration. We further examine time integration using two consecutive pulses of the same or opposite motion directions. The induced changes in the performance are not additive, and the second of the paired pulses is less effective than its standalone impact, a prediction that is experimentally testable. Taken together, these findings lend further support for an attractor network model of time integration in perceptual decision making.

The lateral intraparietal area (LIP) of macaques has been asserted to play a fundamental role in sensorimotor decision-making. Here we dissect the neural code in LIP at the level of individual trial spike trains using a statistical approach based on generalized linear models. We show that LIP responses reflect a combination of temporally-overlapping task and decision-related signals. Our model accounts for the detailed statistics of LIP spike trains, and accurately predicts spike trains from task events on single trials. Moreover, we derive an optimal decoder for heterogeneous, multiplexed LIP responses that could be implemented in biologically plausible circuits. In contrast to interpretations of LIP as providing an instantaneous code for decision variables, we show that optimal decoding requires integrating LIP spikes over two timescales. These analyses provide a detailed understanding of the neural code in LIP, and a framework for studying the coding of multiplexed signals in higher brain areas.

Several fMRI studies have reported MT+ response increases correlated with perception of the motion aftereffect (MAE). However, attention can strongly affect MT+ responses, and subjects may naturally attend more to the MAE than control trials without MAE. We found that requiring subjects to attend to motion on both MAE and control trials produced equal levels of MT+ response, suggesting that attention may have confounded the interpretation of previous experiments; in our data, attention accounts for the entire effect. After eliminating this confound, we observed that direction-selective motion adaptation produced a direction-selective imbalance in MT+ responses (and earlier visual areas), and yielded a corresponding asymmetry in speed discrimination thresholds. These findings provide physiological evidence that population level response imbalances underlie the MAE, and quantify the relative proportions of direction-selective neurons across human visual areas.