Neuronal variability in sensory cortex predicts perceptual decisions. This relationship, termed choice probability (CP), can arise from sensory variability biasing behaviour and from top-down signals reflecting behaviour. To investigate the interaction of these mechanisms during the decision-making process, we use a hierarchical network model composed of reciprocally connected sensory and integration circuits. Consistent with monkey behaviour in a fixed-duration motion discrimination task, the model integrates sensory evidence transiently, giving rise to a decaying bottom-up CP component. However, the dynamics of the hierarchical loop recruits a concurrently rising top-down component, resulting in sustained CP. We compute the CP time-course of neurons in the medial temporal area (MT) and find an early transient component and a separate late contribution reflecting decision build-up. The stability of individual CPs and the dynamics of noise correlations further support this decomposition. Our model provides a unified understanding of the circuit dynamics linking neural and behavioural variability.

Dorsal premotor cortex is implicated in somatomotor decisions. However, we do not understand the temporal patterns and laminar organization of decision-related firing rates in dorsal premotor cortex. We recorded neurons from dorsal premotor cortex of monkeys performing a visual discrimination task with reaches as the behavioral report. We show that these neurons can be organized along a bidirectional visuomotor continuum based on task-related firing rates. “Increased” neurons at one end of the continuum increased their firing rates ~150 ms after stimulus onset and these firing rates covaried systematically with choice, stimulus difficulty, and reaction time—characteristics of a candidate decision variable. “Decreased” neurons at the other end of the continuum reduced their firing rate after stimulus onset, while “perimovement” neurons at the center of the continuum responded only ~150 ms before movement initiation. These neurons did not show decision variable-like characteristics. “Increased” neurons were more prevalent in superficial layers of dorsal premotor cortex; deeper layers contained more “decreased” and “perimovement” neurons. These results suggest a laminar organization for decision-related responses in dorsal premotor cortex.

Summary A fundamental challenge in studying the frontal lobe is to parcellate this cortex into ‘natural’ functional modules despite the absence of topographic maps, which are so helpful in primary sensory areas. Here we show that unsupervised clustering algorithms, applied to 96-channel array recordings from prearcuate gyrus, reveal spatially segregated sub-networks that remain stable across behavioral contexts. Looking for natural groupings of neurons based on response similarities, we discovered that the recorded area includes at least two spatially segregated sub-networks that differentially represent behavioral choice and reaction time. Importantly, these sub-networks are detectable during different behavioral states, and surprisingly, are defined better by ‘common noise’ than task-evoked responses. Our parcellation process works well on ‘spontaneous’ neural activity, and thus bears strong resemblance to the identification of ‘resting state’ networks in fMRI datasets. Our results demonstrate a powerful new tool for identifying cortical sub-networks by objective classification of simultaneously recorded electrophysiological activity.

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How deliberation on sensory cues and action selection interact in decision-related brain areas is still not well understood. Here, monkeys reached to one of two targets, whose colors alternated randomly between trials, by discriminating the dominant color of a checkerboard cue composed of different numbers of squares of the two target colors in different trials. In a Targets First task the colored targets appeared first, followed by the checkerboard; in a Checkerboard First task, this order was reversed. After both cues appeared in both tasks, responses of dorsal premotor cortex (PMd) units covaried with action choices, strength of evidence for action choices, and RTs— hallmarks of decision-related activity. However, very few units were modulated by checkerboard color composition or the color of the chosen target, even during the checkerboard deliberation epoch of the Checkerboard First task. These findings implicate PMd in the action-selection but not the perceptual components of the decision-making process in these tasks.

SummaryStudies in multiple species have revealed the existence of neural signals that lawfully co-vary with different aspects of the decision-making process, including choice, sensory evidence that supports the choice, and reaction time. These signals, often interpreted as the representation of a decision variable (DV), have been identified in several motor preparation circuits and provide insight about mechanisms underlying the decision-making process. However, single-trial dynamics of this process or its representation at the neural population level remain poorly understood. Here, we examine the representation of the DV in simultaneously recorded neural populations of dorsal premotor (PMd) and primary motor (M1) cortices of monkeys performing a random dots direction discrimination task with arm movements as the behavioral report. We show that single-trial DVs covary with stimulus difficulty in both areas but are stronger and appear earlier in PMd compared to M1 when the stimulus duration is fixed and predictable. When temporal uncertainty is introduced by making the stimulus duration variable, single-trial DV dynamics are accelerated across the board and the two areas become largely indistinguishable throughout the entire trial. These effects are not trivially explained by the faster emergence of motor kinematic signals in PMd and M1. All key aspects of the data were replicated by a computational model that relies on progressive recruitment of units with stable choice-related modulation of neural population activity. In contrast with several recent results in rodents, decision signals in PMd and M1 are not carried by short sequences of activity in non-overlapping groups of neurons but are instead distributed across many neurons, which once recruited, represent the decision stably during individual behavioral epochs of the trial.

22When making a categorical decision about a noisy stimulus, it is common to fluctuate between 44 levels of commitment to a choice before reporting a decision. In some instances the fluctuations 45 are sufficiently strong to lead to a "change of mind" (CoM) while deliberating [1][2][3][4][5][6] or even while the 46 reporting action is being executed 7 . Because these within-trial fluctuations are different from trial 47 to trial and not necessarily tied to an external event or stimulus feature, they can only be captured 48 using a moment-to-moment neural readout of the decision state on single trials. 49To obtain this readout, we decoded a decision variable (DV) from neural population activity in 50 PMd and M1 in real time to continuously estimate the decision state while two monkeys performed 51 a motion discrimination task 8,9 (Fig. 1a, see Methods). The DV was estimated by applying a linear 52 decoder, trained on data from a previous experimental session, to spiking data (from 96 to 192 53 electrodes) from the preceding 50 ms, updated every 10 ms throughout each trial ( Fig. 1b, see 54 Methods). The sign of the DV indicated which choice was predicted by the decoder, which allowed 55 us to calculate the decoder's prediction accuracy. The DV magnitude reflected the confidence of 56 the model's prediction in units of log-odds for one vs. the other decision (see Methods). Note that 57 the decision variable as defined here encompasses all choice predictive signals that can be decoded 58 from neural activity 10 , including but not limited to moment-to-moment value of accumulated 59 evidence as posited in classical sequential sampling models. 60We have previously demonstrated with offline analysis that this decision variable (DV) can predict 61 choices on single trials up to seconds before initiation of the operant response, and that the 62 accuracy of these predictions increases on average throughout the course of the trial 10 . 63Here, we employed closed-loop, neurally-contingent control over stimulus timing to directly probe 64 the relationship of within-trial DV fluctuations to behaviorally meaningful decision states. For the 65 4 first time, we quantified the behavioral effects of previously covert DV variations (i) as a function 66 of time and for different virtual DV boundaries imposed during the trial, (ii) when large, CoM-like 67 fluctuations were detected during deliberation on noisy visual evidence, and (iii) when 68 subthreshold stimulus pulses were added during the trial. 69Having a nearly instantaneous real-time estimate of the decision state read-out enabled us to 70 terminate the visual stimulus based on the current value (or history) of the DV and validate the 71 behavioral relevance of DV fluctuations using the monkey's behavioral reports following stimulus 72 termination. 73Decisions on perceived stimulus motion can be reliably decoded in real time based on 50 ms 74 of PMd/M1 neural activity 75 76 Two monkeys performed a variable duration variant of the classical random dot motion 77 discriminatio...

In dynamic environments, subjects often integrate multiple samples of a signal and combine them to reach a categorical judgment. The process of deliberation on the evidence can be described by a time-varying decision variable (DV), decoded from neural activity, that predicts a subject's decision at the end of a trial. However, within trials, large moment-to-moment fluctuations of the DV are observed. The behavioral significance of these fluctuations and their role in the decision process remain unclear. Here we show that within-trial DV fluctuations decoded in real time from motor cortex are tightly linked to choice behavior, and that robust changes in DV sign have the statistical regularities expected from behavioral studies of changes-of-mind. Furthermore, we find single-trial evidence for absorbing decision bounds. As the DV builds up, heavily favoring one or the other choice, moment-to-moment variability in the DV is reduced, and both neural DV and behavioral decisions become resistant to additional pulses of sensory evidence as predicted by diffusion-to-bound and attractor models of the decision process.