When humans respond to sensory stimulation, their reaction times tend to be long and variable relative to neural transduction and transmission times. The neural processes responsible for the duration and variability of reaction times are not understood. Single-cell recordings in a motor area of the cerebral cortex in behaving rhesus monkeys (Macaca mulatta) were used to evaluate two alternative mathematical models of the processes that underlie reaction times. Movements were initiated if and only if the neural activity reached a specific and constant threshold activation level. Stochastic variability in the rate at which neural activity grew toward that threshold resulted in the distribution of reaction times. This finding elucidates a specific link between motor behavior and activation of neurons in the cerebral cortex.

Response inhibition is essential for navigating everyday life. Its derailment is considered integral to numerous neurological and psychiatric disorders, and more generally, to a wide range of behavioral and health problems. Response-inhibition efficiency furthermore correlates with treatment outcome in some of these conditions. The stop-signal task is an essential tool to determine how quickly response inhibition is implemented. Despite its apparent simplicity, there are many features (ranging from task design to data analysis) that vary across studies in ways that can easily compromise the validity of the obtained results. Our goal is to facilitate a more accurate use of the stop-signal task. To this end, we provide 12 easy-to-implement consensus recommendations and point out the problems that can arise when they are not followed. Furthermore, we provide user-friendly open-source resources intended to inform statistical-power considerations, facilitate the correct implementation of the task, and assist in proper data analysis.

The onset latencies of single-unit responses evoked by flashing visual stimuli were measured in the parvocellular (P) and magnocellular (M) layers of the dorsal lateral geniculate nucleus (LGNd) and in cortical visual areas V1, V2, V3, V4, middle temporal area (MT), medial superior temporal area (MST), and in the frontal eye field (FEF) in individual anesthetized monkeys. Identical procedures were carried out to assess latencies in each area, often in the same monkey, thereby permitting direct comparisons of timing across areas. This study presents the visual flash-evoked latencies for cells in areas where such data are common (V1 and V2), and are therefore a good standard, and also in areas where such data are sparse (LGNd M and P layers, MT, V4) or entirely lacking (V3, MST, and FEF in anesthetized preparation). Visual-evoked onset latencies were, on average, 17 ms shorter in the LGNd M layers than in the LGNd P layers. Visual responses occurred in V1 before any other cortical area. The next wave of activation occurred concurrently in areas V3, MT, MST, and FEF. Visual response latencies in areas V2 and V4 were progressively later and more broadly distributed. These differences in the time course of activation across the dorsal and ventral streams provide important temporal constraints on theories of visual processing.

The stop-signal task has been used to study normal cognitive control and clinical dysfunction. Its utility is derived from a race model that accounts for performance and provides an estimate of the time it takes to stop a movement. This model posits a race between go and stop processes with stochastically independent finish times. However, neurophysiological studies demonstrate that the neural correlates of the go and stop processes produce movements through a network of interacting neurons. The juxtaposition of the computational model with the neural data exposes a paradox-how can a network of interacting units produce behavior that appears to be the outcome of an independent race? The authors report how a simple, competitive network can solve this paradox and provide an account of what is measured by stop-signal reaction time.

1. The latency between the appearance of a popout search display and the eye movement to the oddball target of the display varies from trial to trial in both humans and monkeys. The source of the delay and variability of reaction time is unknown but has been attributed to as yet poorly defined decision processes. 2. We recorded neural activity in the frontal eye field (FEF), an area regarded as playing a central role in producing purposeful eye movements, of monkeys (Macaca mulatta) performing a popout visual search task. Eighty-four neurons with visually evoked activity were analyzed. Twelve of these neurons had a phasic response associated with the presentation of the visual stimulus. The remaining neurons had more tonic responses that persisted through the saccade. Many of the neurons with more tonic responses resembled visuomovement cells in that they had activity that increased before a saccade into their response field. 3. The visual response latencies of FEF neurons were determined with the use of a Poisson spike train analysis. The mean visual latency was 67 ms (minimum = 35 ms, maximum = 138 ms). The visual response latencies to the target presented alone, to the target presented with distractors, or to the distractors did not differ significantly. 4. The initial visual activation of FEF neurons does not discriminate the target from the distractors of a popout visual search stimulus array, but the activity evolves to a state that discriminates whether the target of the search display is within the receptive field. We tested the hypothesis that the source of variability of saccade latency is the time taken by neurons involved in saccade programming to select the target for the gaze shift. 5. With the use of an analysis adapted from signal detection theory, we determined when the activity of single FEF neurons can reliably indicate whether the target or distractors are present within their response fields. The time of target discrimination partitions the reaction time into a perceptual stage in which target discrimination takes place, and a motor stage in which saccade programming and generation take place. The time of target discrimination occurred most often between 120 and 150 ms after stimulus presentation. 6. We analyzed the time course of target discrimination in the activity of single cells after separating trials into short, medium, and long saccade latency groups. Saccade latency was not correlated with the duration of the perceptual stage but was correlated with the duration of the motor stage. This result is inconsistent with the hypothesis that the time taken for target discrimination, as indexed by FEF neurons, accounts for the wide variability in the time of movement initiation. 7. We conclude that the variability observed in saccade latencies during a simple visual search task is largely due to postperceptual motor processing following target discrimination. Signatures of both perceptual and postperceptual processing are evident in FEF. Procrastination in the output stage may prevent stereot...