Transient Oscillations of Neural Firing Rate Associated With Routing of Evidence in a Perceptual Decision.

To form a perceptual decision, the brain must acquire samples of evidence from the environment and incorporate them in computations that mediate choice behavior. While much is known about the neural circuits that process sensory information and those that form decisions, less is known about the mechanisms that establish the functional linkage between them. We trained monkeys of both sexes to make difficult decisions about the net direction of visual motion under conditions that required trial-by-trial control of functional connectivity. In one condition, the motion appeared at different locations on different trials. In the other, two motion patches appeared, only one of which was informative. Neurons in the parietal cortex produced brief oscillations in their firing rate at the time routing was established: upon onset of the motion display when its location was unpredictable across trials, and upon onset of an attention cue that indicated in which of two locations an informative patch of dots would appear. The oscillation was absent when the stimulus location was fixed across trials. We interpret the oscillation as a manifestation of the mechanism that establishes the source and destination of flexibly routed information, but not the transmission of the information per se Significance Statement It has often been suggested that oscillations in neural activity might serve a role in routing information appropriately. We observe an oscillation in neural firing rate in the lateral intraparietal area consistent with such a role. The oscillations are transient. They coincide with the establishment of routing, but they do not appear to play a role in the transmission (or conveyance) of the routed information itself.

Low-dimensional dynamics for working memory and time encoding.

Our decisions often depend on multiple sensory experiences separated by time delays. The brain can remember these experiences and, simultaneously, estimate the timing between events. To understand the mechanisms underlying working memory and time encoding, we analyze neural activity recorded during delays in four experiments on nonhuman primates. To disambiguate potential mechanisms, we propose two analyses, namely, decoding the passage of time from neural data and computing the cumulative dimensionality of the neural trajectory over time. Time can be decoded with high precision in tasks where timing information is relevant and with lower precision when irrelevant for performing the task. Neural trajectories are always observed to be low-dimensional. In addition, our results further constrain the mechanisms underlying time encoding as we find that the linear "ramping" component of each neuron's firing rate strongly contributes to the slow timescale variations that make decoding time possible. These constraints rule out working memory models that rely on constant, sustained activity and neural networks with high-dimensional trajectories, like reservoir networks. Instead, recurrent networks trained with backpropagation capture the time-encoding properties and the dimensionality observed in the data.

Comparison of Decision-Related Signals in Sensory and Motor Preparatory Responses of Neurons in Area LIP.

Neurons in the lateral intraparietal (LIP) area of Macaques exhibit both sensory and oculomotor preparatory responses. During perceptual decision making, the preparatory responses have been shown to track the state of the evolving evidence leading to the decision. The sensory responses are known to reflect categorical properties of visual stimuli, but it is not known whether these responses also track evolving evidence. We recorded neural responses from lateral intraparietal area of 2 female rhesus monkeys during a direction discrimination task. We compared sensory and oculomotor-preparatory responses in the same neurons when either the discriminandum (random dot motion) or an eye movement choice-target was in the neuron's response field. The neural responses in both configurations reflected the strength and direction of motion and were correlated with the animal's choice, albeit more prominently when the choice-target was in the response field. However, the variance and autocorrelation pattern of only the motor preparatory responses reflected the process of evidence accumulation. Simulations suggest that the task related activity of sensory responses could be inherited through lateral interactions with neurons that are carrying evidence accumulation signals in their motor-preparatory responses. The results are consistent with the proposal that evolving decision processes are supported by persistent neural activity in the service of actions or intentions, as opposed to high-order representations of stimulus properties.SIGNIFICANCE STATEMENT Perceptual decision making is the process of choosing an appropriate motor action based on perceived sensory information. Association areas of the cortex play an important role in this sensory-motor transformation. The neurons in these areas show both sensory- and motor-related activity. We show here that, in the macaque parietal association area LIP, signatures of the process of evidence accumulation that underlies the decisions are predominantly reflected in the motor-related activity. This finding supports the proposal that perceptual decision making is implemented in the brain as a process of choosing between available motor actions rather than as a process of representing the properties of the sensory stimulus.

Representation of accumulating evidence for a decision in two parietal areas.

Decisions are often made by accumulating evidence for and against the alternatives. The momentary evidence represented by sensory neurons is accumulated by downstream structures to form a decision variable, linking the evolving decision to the formation of a motor plan. When decisions are communicated by eye movements, neurons in the lateral intraparietal area (LIP) represent the accumulation of evidence bearing on the potential targets for saccades. We now show that reach-related neurons from the medial intraparietal area (MIP) exhibit a gradual modulation of their firing rates consistent with the representation of an evolving decision variable. When decisions were communicated by saccades instead of reaches, decision-related activity was attenuated in MIP, whereas LIP neurons were active while monkeys communicated decisions by saccades or reaches. Thus, for decisions communicated by a hand movement, a parallel flow of sensory information is directed to parietal areas MIP and LIP during decision formation.

Time in Cortical Circuits.

Time is central to cognition. However, the neural basis for time-dependent cognition remains poorly understood. We explore how the temporal features of neural activity in cortical circuits and their capacity for plasticity can contribute to time-dependent cognition over short time scales. This neural activity is linked to cognition that operates in the present or anticipates events or stimuli in the near future. We focus on deliberation and planning in the context of decision making as a cognitive process that integrates information across time. We progress to consider how temporal expectations of the future modulate perception. We propose that understanding the neural basis for how the brain tells time and operates in time will be necessary to develop general models of cognition. SIGNIFICANCE STATEMENT: Time is central to cognition. However, the neural basis for time-dependent cognition remains poorly understood. We explore how the temporal features of neural activity in cortical circuits and their capacity for plasticity can contribute to time-dependent cognition over short time scales. We propose that understanding the neural basis for how the brain tells time and operates in time will be necessary to develop general models of cognition.

Changes of mind in decision-making.

A decision is a commitment to a proposition or plan of action based on evidence and the expected costs and benefits associated with the outcome. Progress in a variety of fields has led to a quantitative understanding of the mechanisms that evaluate evidence and reach a decision. Several formalisms propose that a representation of noisy evidence is evaluated against a criterion to produce a decision. Without additional evidence, however, these formalisms fail to explain why a decision-maker would change their mind. Here we extend a model, developed to account for both the timing and the accuracy of the initial decision, to explain subsequent changes of mind. Subjects made decisions about a noisy visual stimulus, which they indicated by moving a handle. Although they received no additional information after initiating their movement, their hand trajectories betrayed a change of mind in some trials. We propose that noisy evidence is accumulated over time until it reaches a criterion level, or bound, which determines the initial decision, and that the brain exploits information that is in the processing pipeline when the initial decision is made to subsequently either reverse or reaffirm the initial decision. The model explains both the frequency of changes of mind as well as their dependence on both task difficulty and whether the initial decision was accurate or erroneous. The theoretical and experimental findings advance the understanding of decision-making to the highly flexible and cognitive acts of vacillation and self-correction.

Integration of direction cues is invariant to the temporal gap between them.

Many decisions involve integration of evidence conferred by discrete cues over time. However, the neural mechanism of this integration is poorly understood. Several decision-making models suggest that integration of evidence is implemented by a dynamic system whose state evolves toward a stable point representing the decision outcome. The internal dynamics of such point attractor models render them sensitive to the temporal gaps between cues because their internal forces push the state forward once it is dislodged from the initial stable point. We asked whether human subjects are as sensitive to such temporal gaps. Subjects reported the net direction of stochastic random dot motion, which was presented in one or two brief observation windows (pulses). Pulse strength and interpulse interval varied randomly from trial to trial. We found that subjects' performance was largely invariant to the interpulse intervals up to at least 1 s. The findings question the implementation of the integration process via mechanisms that rely on autonomous changes of network state. The mechanism should be capable of freezing the state of the network at a variety of firing rate levels during temporal gaps between the cues, compatible with a line of stable attractor states.

Incorporation of a cost of deliberation time in perceptual decision making.

Many decisions benefit from the accumulation of evidence obtained sequentially over time. In such circumstances, the decision maker must balance speed against accuracy, and the nature of this tradeoff mediates competing desiderata and costs, especially those associated with the passage of time. A neural mechanism to achieve this balance is to accumulate evidence in suitable units and to terminate the deliberation when enough evidence has accrued. To accommodate time costs, it has been hypothesized that the criterion to terminate a decision may become lax as a function of time. Here we tested this hypothesis by manipulating the cost of time in a perceptual choice-reaction time task. Participants discriminated the direction of motion in a dynamic random-dot display, which varied in difficulty across trials. After each trial, they received feedback in the form of points based on whether they made a correct or erroneous choice. They were instructed to maximize their points per unit of time. Unbeknownst to the participants, halfway through the experiment, we increased the time pressure by canceling a small fraction of trials if they had not made a decision by a provisional deadline. Although the manipulation canceled less than 5% of trials, it induced the participants to make faster decisions while lowering their decision accuracy. The pattern of choices and reaction times were explained by bounded drift-diffusion. In all phases of the experiment, stopping bounds were found to decline as a function of time, consistent with the optimal solution, and this decline was exaggerated in response to the time-cost manipulation.

Deliberation in the motor system: reflex gains track evolving evidence leading to a decision.

Both decision making and sensorimotor control require real-time processing of noisy information streams. Historically these processes were thought to operate sequentially: cognitive processing leads to a decision, and the outcome is passed to the motor system to be converted into action. Recently, it has been suggested that the decision process may provide a continuous flow of information to the motor system, allowing it to prepare in a graded fashion for the probable outcome. Such continuous flow is supported by electrophysiology in nonhuman primates. Here we provide direct evidence for the continuous flow of an evolving decision variable to the motor system in humans. Subjects viewed a dynamic random dot display and were asked to indicate their decision about direction by moving a handle to one of two targets. We probed the state of the motor system by perturbing the arm at random times during decision formation. Reflex gains were modulated by the strength and duration of motion, reflecting the accumulated evidence in support of the evolving decision. The magnitude and variance of these gains tracked a decision variable that explained the subject's decision accuracy. The findings support a continuous process linking the evolving computations associated with decision making and sensorimotor control.

The cost of accumulating evidence in perceptual decision making.

Decision making often involves the accumulation of information over time, but acquiring information typically comes at a cost. Little is known about the cost incurred by animals and humans for acquiring additional information from sensory variables due, for instance, to attentional efforts. Through a novel integration of diffusion models and dynamic programming, we were able to estimate the cost of making additional observations per unit of time from two monkeys and six humans in a reaction time (RT) random-dot motion discrimination task. Surprisingly, we find that the cost is neither zero nor constant over time, but for the animals and humans features a brief period in which it is constant but increases thereafter. In addition, we show that our theory accurately matches the observed reaction time distributions for each stimulus condition, the time-dependent choice accuracy both conditional on stimulus strength and independent of it, and choice accuracy and mean reaction times as a function of stimulus strength. The theory also correctly predicts that urgency signals in the brain should be independent of the difficulty, or stimulus strength, at each trial.

Neural integrators for decision making: a favorable tradeoff between robustness and sensitivity.

A key step in many perceptual decision tasks is the integration of sensory inputs over time, but a fundamental questions remain about how this is accomplished in neural circuits. One possibility is to balance decay modes of membranes and synapses with recurrent excitation. To allow integration over long timescales, however, this balance must be exceedingly precise. The need for fine tuning can be overcome via a "robust integrator" mechanism in which momentary inputs must be above a preset limit to be registered by the circuit. The degree of this limiting embodies a tradeoff between sensitivity to the input stream and robustness against parameter mistuning. Here, we analyze the consequences of this tradeoff for decision-making performance. For concreteness, we focus on the well-studied random dot motion discrimination task and constrain stimulus parameters by experimental data. We show that mistuning feedback in an integrator circuit decreases decision performance but that the robust integrator mechanism can limit this loss. Intriguingly, even for perfectly tuned circuits with no immediate need for a robustness mechanism, including one often does not impose a substantial penalty for decision-making performance. The implication is that robust integrators may be well suited to subserve the basic function of evidence integration in many cognitive tasks. We develop these ideas using simulations of coupled neural units and the mathematics of sequential analysis.

Probabilistic reasoning by neurons.

Our brains allow us to reason about alternatives and to make choices that are likely to pay off. Often there is no one correct answer, but instead one that is favoured simply because it is more likely to lead to reward. A variety of probabilistic classification tasks probe the covert strategies that humans use to decide among alternatives based on evidence that bears only probabilistically on outcome. Here we show that rhesus monkeys can also achieve such reasoning. We have trained two monkeys to choose between a pair of coloured targets after viewing four shapes, shown sequentially, that governed the probability that one of the targets would furnish reward. Monkeys learned to combine probabilistic information from the shape combinations. Moreover, neurons in the parietal cortex reveal the addition and subtraction of probabilistic quantities that underlie decision-making on this task.

Judging the difficulty of perceptual decisions.

Deciding how difficult it is going to be to perform a task allows us to choose between tasks, allocate appropriate resources, and predict future performance. To be useful for planning, difficulty judgments should not require completion of the task. Here we examine the processes underlying difficulty judgments in a perceptual decision making task. Participants viewed two patches of dynamic random dots, which were colored blue or yellow stochastically on each appearance. Stimulus coherence (the probability, pblue, of a dot being blue) varied across trials and patches thus establishing difficulty, pblue-0.5. Participants were asked to indicate for which patch it would be easier to decide the dominant color. Accuracy in difficulty decisions improved with the difference in the stimulus difficulties, whereas the reaction times were not determined solely by this quantity. For example, when the patches shared the same difficulty, reaction times were shorter for easier stimuli. A comparison of several models of difficulty judgment suggested that participants compare the absolute accumulated evidence from each stimulus and terminate their decision when they differed by a set amount. The model predicts that when the dominant color of each stimulus is known, reaction times should depend only on the difference in difficulty, which we confirm empirically. We also show that this model is preferred to one that compares the confidence one would have in making each decision. The results extend evidence accumulation models, used to explain choice, reaction time and confidence to prospective judgments of difficulty.

A neural mechanism for terminating decisions.

The brain makes decisions by accumulating evidence until there is enough to stop and choose. Neural mechanisms of evidence accumulation are established in association cortex, but the site and mechanism of termination are unknown. Here, we show that the superior colliculus (SC) plays a causal role in terminating decisions, and we provide evidence for a mechanism by which this occurs. We recorded simultaneously from neurons in the lateral intraparietal area (LIP) and SC while monkeys made perceptual decisions. Despite similar trial-averaged activity, we found distinct single-trial dynamics in the two areas: LIP displayed drift-diffusion dynamics and SC displayed bursting dynamics. We hypothesized that the bursts manifest a threshold mechanism applied to signals represented in LIP to terminate the decision. Consistent with this hypothesis, SC inactivation produced behavioral effects diagnostic of an impaired threshold sensor and prolonged the buildup of activity in LIP. The results reveal the transformation from deliberation to commitment.

Judging the difficulty of perceptual decisions.

Deciding how difficult it is going to be to perform a task allows us to choose between tasks, allocate appropriate resources, and predict future performance. To be useful for planning, difficulty judgments should not require completion of the task. Here, we examine the processes underlying difficulty judgments in a perceptual decision-making task. Participants viewed two patches of dynamic random dots, which were colored blue or yellow stochastically on each appearance. Stimulus coherence (the probability, pblue, of a dot being blue) varied across trials and patches thus establishing difficulty, |pblue -0.5|. Participants were asked to indicate for which patch it would be easier to decide the dominant color. Accuracy in difficulty decisions improved with the difference in the stimulus difficulties, whereas the reaction times were not determined solely by this quantity. For example, when the patches shared the same difficulty, reaction times were shorter for easier stimuli. A comparison of several models of difficulty judgment suggested that participants compare the absolute accumulated evidence from each stimulus and terminate their decision when they differed by a set amount. The model predicts that when the dominant color of each stimulus is known, reaction times should depend only on the difference in difficulty, which we confirm empirically. We also show that this model is preferred to one that compares the confidence one would have in making each decision. The results extend evidence accumulation models, used to explain choice, reaction time, and confidence to prospective judgments of difficulty.

Large-scale high-density brain-wide neural recording in nonhuman primates.

High-density, integrated silicon electrodes have begun to transform systems neuroscience, by enabling large-scale neural population recordings with single cell resolution. Existing technologies, however, have provided limited functionality in nonhuman primate species such as macaques, which offer close models of human cognition and behavior. Here, we report the design, fabrication, and performance of Neuropixels 1.0-NHP, a high channel count linear electrode array designed to enable large-scale simultaneous recording in superficial and deep structures within the macaque or other large animal brain. These devices were fabricated in two versions: 4416 electrodes along a 45 mm shank, and 2496 along a 25 mm shank. For both versions, users can programmatically select 384 channels, enabling simultaneous multi-area recording with a single probe. We demonstrate recording from over 3000 single neurons within a session, and simultaneous recordings from over 1000 neurons using multiple probes. This technology represents a significant increase in recording access and scalability relative to existing technologies, and enables new classes of experiments involving fine-grained electrophysiological characterization of brain areas, functional connectivity between cells, and simultaneous brain-wide recording at scale.

DREDge: robust motion correction for high-density extracellular recordings across species.

High-density microelectrode arrays (MEAs) have opened new possibilities for systems neuroscience in human and non-human animals, but brain tissue motion relative to the array poses a challenge for downstream analyses, particularly in human recordings. We introduce DREDge (Decentralized Registration of Electrophysiology Data), a robust algorithm which is well suited for the registration of noisy, nonstationary extracellular electrophysiology recordings. In addition to estimating motion from spikes in the action potential (AP) frequency band, DREDge enables automated tracking of motion at high temporal resolution in the local field potential (LFP) frequency band. In human intraoperative recordings, which often feature fast (period <1s) motion, DREDge correction in the LFP band enabled reliable recovery of evoked potentials, and significantly reduced single-unit spike shape variability and spike sorting error. Applying DREDge to recordings made during deep probe insertions in nonhuman primates demonstrated the possibility of tracking probe motion of centimeters across several brain regions while simultaneously mapping single unit electrophysiological features. DREDge reliably delivered improved motion correction in acute mouse recordings, especially in those made with an recent ultra-high density probe. We also implemented a procedure for applying DREDge to recordings made across tens of days in chronic implantations in mice, reliably yielding stable motion tracking despite changes in neural activity across experimental sessions. Together, these advances enable automated, scalable registration of electrophysiological data across multiple species, probe types, and drift cases, providing a stable foundation for downstream scientific analyses of these rich datasets.

Elapsed decision time affects the weighting of prior probability in a perceptual decision task.

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 (1) decisions that linger tend to arise from less reliable evidence, and (2) 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 area (LIP) of rhesus monkeys performing this task.

Decision-making with multiple alternatives.

Simple perceptual tasks have laid the groundwork for understanding the neurobiology of decision-making. Here, we examined this foundation to explain how decision-making circuitry adjusts in the face of a more difficult task. We measured behavioral and physiological responses of monkeys on a two- and four-choice direction-discrimination decision task. For both tasks, firing rates in the lateral intraparietal area appeared to reflect the accumulation of evidence for or against each choice. Evidence accumulation began at a lower firing rate for the four-choice task, but reached a common level by the end of the decision process. The larger excursion suggests that the subjects required more evidence before making a choice. Furthermore, on both tasks, we observed a time-dependent rise in firing rates that may impose a deadline for deciding. These physiological observations constitute an effective strategy for handling increased task difficulty. The differences appear to explain subjects' accuracy and reaction times.

Decision formation in parietal cortex transcends a fixed frame of reference.

Neurons in the lateral intraparietal cortex represent the formation of a decision when it is linked to a specific action, such as an eye movement to a choice target. However, these neurons should be unable to represent a decision that transpires across actions that would disrupt this linkage. We investigated this limitation by simultaneously recording many neurons from two rhesus monkeys. Although intervening actions disrupt the representation by single neurons, the ensemble achieves continuity of the decision process by passing information from currently active neurons to neurons that will become active after the action. In this way, the representation of an evolving decision can be generalized across actions and transcends the frame of reference that specifies the neural response fields. The finding extends previous observations of receptive field remapping, thought to support the stability of perception across eye movements, to the continuity of a thought process, such as a decision.