Decisions about the visual world can take time to form, especially when information is unreliable. We studied the neural correlate of gradual decision formation by recording activity from the lateral intraparietal cortex (area LIP) of rhesus monkeys during a combined motion-discrimination reaction-time task. Monkeys reported the direction of random-dot motion by making an eye movement to one of two peripheral choice targets, one of which was within the response field of the neuron. We varied the difficulty of the task and measured both the accuracy of direction discrimination and the time required to reach a decision. Both the accuracy and speed of decisions increased as a function of motion strength. During the period of decision formation, the epoch between onset of visual motion and the initiation of the eye movement response, LIP neurons underwent ramp-like changes in their discharge rate that predicted the monkey's decision. A steeper rise in spike rate was associated with stronger stimulus motion and shorter reaction times. The observations suggest that neurons in LIP integrate time-varying signals that originate in the extrastriate visual cortex, accumulating evidence for or against a specific behavioral response. A threshold level of LIP activity appears to mark the completion of the decision process and to govern the tradeoff between accuracy and speed of perception.

Decisions based on uncertain information may benefit from an accumulation of information over time. We asked whether such an accumulation process may underlie decisions about the direction of motion in a random dot kinetogram. To address this question we developed a computational model of the decision process using ensembles of neurons whose spiking activity mimics neurons recorded in the extrastriate visual cortex (area MT or V5) and a sensorimotor association area of the parietal lobe (area LIP). The model instantiates the hypothesis that neurons in sensorimotor association areas compute the time integral of sensory signals from the visual cortex, construed as evidence for or against a proposition, and that the decision is made when the integrated evidence reaches a threshold. The model explains a variety of behavioral and physiological measurements obtained from monkeys.

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.

As any child knows, the first step in counting is summing up individual elements, yet the brain mechanisms responsible for this process remain obscure. Here we show, for the first time, that a population of neurons in the lateral intraparietal area of monkeys encodes the total number of elements within their classical receptive fields in a graded fashion, across a wide range of numerical values (2–32). Moreover, modulation of neuronal activity by visual quantity developed rapidly, within 100 ms of stimulus onset, and was independent of attention, reward expectations, or stimulus attributes such as size, density, or color. The responses of these neurons resemble the outputs of “accumulator neurons” postulated in computational models of number processing. Numerical accumulator neurons may provide inputs to neurons encoding specific cardinal values, such as “4,” that have been described in previous work. Our findings may explain the frequent association of visuospatial and numerical deficits following damage to parietal cortex in humans.