Decisions about sensory stimuli are often based on an accumulation of evidence in time. When subjects control stimulus duration, the decision terminates when the accumulated evidence reaches a criterion level. Under many natural circumstances and in many laboratory settings, the environment, rather than the subject, controls the stimulus duration. In these settings, it is generally assumed that subjects commit to a choice at the end of the stimulus stream. Indeed, failure to benefit from the full stream of information is interpreted as a sign of imperfect accumulation or memory leak. Contrary to these assumptions, we show that monkeys performing a direction discrimination task commit to a choice when the accumulated evidence reaches a threshold level (or bound), sometimes long before the end of stimulus. This bounded accumulation of evidence is reflected in the activity of neurons in the lateral intraparietal cortex. Thus, the readout of visual cortex embraces a termination rule to limit processing even when potentially useful information is available.

Gradual accumulation of evidence is thought to be fundamental for decision-making, and its neural correlates have been found in multiple brain regions1–8. Here we develop a generalizable method to measure tuning curves that specify the relationship between neural responses and mentally-accumulated evidence, and apply it to distinguish the encoding of decision variables in posterior parietal cortex (PPC) and prefrontal cortex (frontal orienting fields, FOF). We recorded the firing rates of neurons in PPC and FOF from rats performing a perceptual decision-making task. Classical analyses uncovered correlates of accumulating evidence, similar to previous observations in primates and also similar across the two regions. However, tuning curve assays revealed that while the PPC encodes a graded value of the accumulating evidence, the FOF has a more categorical encoding that indicates, throughout the trial, the decision provisionally favored by the evidence accumulated so far. Contrary to current views3,5,7–9, this suggests that premotor activity in frontal cortex does not play a role in the accumulation process, but instead has a more categorical function, such as transforming accumulated evidence into a discrete choice. To causally probe the role of FOF activity, we optogenetically silenced it during different timepoints of the trial. Consistent with a role in committing to a categorical choice at the end of the evidence accumulation process, but not consistent with a role during the accumulation itself, a behavioral effect was observed only when FOF silencing occurred at the end of the perceptual stimulus. Our results place important constraints on the circuit logic of brain regions involved in decision-making.

When making a decision, one must first accumulate evidence, often over time, and then select the appropriate action. Here, we present a neural model of decision making that can perform both evidence accumulation and action selection optimally. More specifically, we show that, given a Poisson-like distribution of spike counts, biological neural networks can accumulate evidence without loss of information through linear integration of neural activity, and can select the most likely action through attractor dynamics. This holds for arbitrary correlations, any tuning curves, continuous and discrete variables, and sensory evidence whose reliability varies over time. Our model predicts that the neurons in the lateral intraparietal cortex involved in evidence accumulation encode, on every trial, a probability distribution which predicts the animal’s performance. We present experimental evidence consistent with this prediction, and discuss other predictions applicable to more general settings.

Decisions are often based on a combination of new evidence with prior knowledge of the probable best choice. Optimal combination requires knowledge about the reliability of evidence, but in many realistic situations, this is unknown. Here we propose and test a novel theory: the brain exploits elapsed time during decision formation to combine sensory evidence with prior probability. Elapsed time is useful because (i) decisions that linger tend to arise from less reliable evidence, and (ii) the expected accuracy at a given decision time depends on the reliability of the evidence gathered up to that point. These regularities allow the brain to combine prior information with sensory evidence by weighting the latter in accordance with reliability. To test this theory, we manipulated the prior probability of the rewarded choice while subjects performed a reaction-time discrimination of motion direction using a range of stimulus reliabilities that varied from trial to trial. The theory explains the effect of prior probability on choice and reaction time over a wide range of stimulus strengths. We found that prior probability was incorporated into the decision process as a dynamic bias signal that increases as a function of decision time. This bias signal depends on the speed-accuracy setting of human subjects, and it is reflected in the firing rates of neurons in the lateral intraparietal cortex (LIP) of rhesus monkeys performing this task.