Neural correlates of perceptual decision making before, during, and after decision commitment in monkey frontal eye field

Neuronal Correlates of Many-To-One Sensorimotor Mapping in Lateral Intraparietal Cortex

Efficiently mapping sensory stimuli onto motor programs is crucial for rapidly choosing appropriate behavioral responses. While neuronal mechanisms underlying simple, one-to-one sensorimotor mapping have been extensively studied, how the brain achieves complex, many-to-one sensorimotor mapping remains unclear. Here, we recorded single neuron activity from the lateral intraparietal (LIP) cortex of monkeys trained to map multiple spatial positions of visual cue onto two opposite saccades. We found that LIP neurons' activity was consistent with directly mapping multiple cue positions to the associated saccadic direction (SDir) regardless of whether the visual cue appeared in or outside neurons' receptive fields. Unlike the explicit encoding of the visual categories, such cue-target mapping (CTM)-related activity covaried with the associated SDirs. Furthermore, the CTM was preferentially mediated by visual neurons identified by memory-guided saccade. These results indicate that LIP plays a crucial role in the early stage of many-to-one sensorimotor transformation.

Computational and neurophysiological principles underlying auditory perceptual decisions

A fundamental scientific goal in auditory neuroscience is identifying what mechanisms allow the brain to transform an unlabeled mixture of auditory stimuli into distinct perceptual representations. This transformation is accomplished by a complex interaction of multiple neurocomputational processes, including Gestalt grouping mechanisms, categorization, attention, and perceptual decision-making. Despite a great deal of scientific energy devoted to understanding these principles of hearing, we still do not understand either how auditory perception arises from neural activity or the causal relationship between neural activity and auditory perception. Here, we review the contributions of cortical and subcortical regions to auditory perceptual decisions with an emphasis on those studies that simultaneously measure behavior and neural activity. We also put forth challenges to the field that must be faced if we are to further our understanding of the relationship between neural activity and auditory perception.

Coding Perceptual Decisions: From Single Units to Emergent Signaling Properties in Cortical Circuits

Spiking activity in single neurons of the primate visual cortex has been tightly linked to perceptual decisions. Any mechanism that reads out these perceptual signals to support behavior must respect the underlying neuroanatomy that shapes the functional properties of sensory neurons. Spatial distribution and timing of inputs to the next processing levels are critical, as conjoint activity of precursor neurons increases the spiking rate of downstream neurons and ultimately drives behavior. I set out how correlated activity might coalesce into a micropool of task-sensitive neurons signaling a particular percept to determine perceptual decision signals locally and for flexible interarea transmission depending on the task context. As data from more and more neurons and their complex interactions are analyzed, the space of computational mechanisms must be constrained based on what is plausible within neurobiological limits. This review outlines experiments to test the new perspectives offered by these extended methods.

Aging impairs perceptual decision-making in mice: integrating computational and neurobiological approaches

Decision-making is one of the cognitive domains which has been under-investigated in animal models of cognitive aging along with its neurobiological correlates. This study investigated the latent variables of the decision process using the hierarchical drift-diffusion model (HDDM). Neurobiological correlates of these processes were examined via immunohistochemistry. Young (n = 11, 4 months old), adult (n = 10, 10 months old), and old (n = 10, 18 months old) mice were tested in a perceptual decision-making task (i.e. two-alternative forced-choice; 2AFC). Observed data showed that there was an age-dependent decrease in the accuracy rate of old mice while response times were comparable between age groups. HDDM results revealed that age-dependent accuracy difference was a result of a decrease in the quality of evidence integration during decision-making. Significant positive correlations observed between evidence integration rate and the number of tyrosine hydroxylase positive (TH+) neurons in the ventral tegmental area (VTA) and axon terminals in dorsomedial striatum (DMS) suggest that decrease in the quality of evidence integration in aging is related to decreased function of mesocortical and nigrostriatal dopamine.

The caudate nucleus contributes causally to decisions that balance reward and uncertain visual information

Our decisions often balance what we observe and what we desire. A prime candidate for implementing this complex balancing act is the basal ganglia pathway, but its roles have not yet been examined experimentally in detail. Here, we show that a major input station of the basal ganglia, the caudate nucleus, plays a causal role in integrating uncertain visual evidence and reward context to guide adaptive decision-making. In monkeys making saccadic decisions based on motion cues and asymmetric reward-choice associations, single caudate neurons encoded both sources of information. Electrical microstimulation at caudate sites during motion viewing affected the monkeys’ decisions. These microstimulation effects included coordinated changes in multiple computational components of the decision process that mimicked the monkeys’ similarly coordinated voluntary strategies for balancing visual and reward information. These results imply that the caudate nucleus plays causal roles in coordinating decision processes that balance external evidence and internal preferences.

Nonlinear neural network dynamics accounts for human confidence in a sequence of perceptual decisions

Electrophysiological recordings during perceptual decision tasks in monkeys suggest that the degree of confidence in a decision is based on a simple neural signal produced by the neural decision process. Attractor neural networks provide an appropriate biophysical modeling framework, and account for the experimental results very well. However, it remains unclear whether attractor neural networks can account for confidence reports in humans. We present the results from an experiment in which participants are asked to perform an orientation discrimination task, followed by a confidence judgment. Here we show that an attractor neural network model quantitatively reproduces, for each participant, the relations between accuracy, response times and confidence. We show that the attractor neural network also accounts for confidence-specific sequential effects observed in the experiment (participants are faster on trials following high confidence trials). Remarkably, this is obtained as an inevitable outcome of the network dynamics, without any feedback specific to the previous decision (that would result in, e.g., a change in the model parameters before the onset of the next trial). Our results thus suggest that a metacognitive process such as confidence in one's decision is linked to the intrinsically nonlinear dynamics of the decision-making neural network.

Causal contributions of human frontal eye fields to distinct aspects of decision formation

Several theories propose that perceptual decision making depends on the gradual accumulation of information that provides evidence in favour of one of the choice-options. The outcome of this temporally extended integration process is thought to be categorized into the 'winning' and 'losing' choice-options for action. Neural correlates of corresponding decision formation processes have been observed in various frontal and parietal brain areas, among them the frontal eye-fields (FEF). However, the specific functional role of the FEFs is debated. Recent studies in humans and rodents provide conflicting accounts, proposing that the FEF either accumulate the choice-relevant information or categorize the outcome of such evidence integration into discrete actions. Here, we used transcranial magnetic stimulation (TMS) on humans to interfere with either left or right FEF activity during different timepoints of perceptual decision-formation. Stimulation of either FEF affected performance only when delivered during information integration but not during subsequent categorical choice. However, the patterns of behavioural changes suggest that the left-FEF contributes to general evidence integration, whereas right-FEF may direct spatial attention to the contralateral hemifield. Taken together, our results indicate an FEF involvement in evidence accumulation but not categorization, and suggest hemispheric lateralization for this function in the human brain.

Evidence accumulation during perceptual decisions in humans varies as a function of dorsal frontoparietal organization

Animal neurophysiological studies have identified neural signals within dorsal frontoparietal areas that trace a perceptual decision by accumulating sensory evidence over time and trigger action upon reaching a threshold. Although analogous accumulation-to-bound signals are identifiable on extracranial human electroencephalography, their cortical origins remain unknown. Here neural metrics of human evidence accumulation, predictive of the speed of perceptual reports, were isolated using electroencephalography and related to dorsal frontoparietal network (dFPN) connectivity using diffusion and resting-state functional magnetic resonance imaging. The build-up rate of evidence accumulation mediated the relationship between the white matter macrostructure of dFPN pathways and the efficiency of perceptual reports. This association between steeper build-up rates of evidence accumulation and the dFPN was recapitulated in the resting-state networks. Stronger connectivity between dFPN regions is thus associated with faster evidence accumulation and speeded perceptual decisions. Our findings identify an integrated network for perceptual decisions that may be targeted for neurorehabilitation in cognitive disorders.

From Prior Information to Saccade Selection: Evolution of Frontal Eye Field Activity during Natural Scene Search

Prior knowledge about our environment influences our actions. How does this knowledge evolve into a final action plan and how does the brain represent this? Here, we investigated this question in the monkey oculomotor system during self-guided search of natural scenes. In the frontal eye field (FEF), we found a subset of neurons, "Early neurons," that contain information about the upcoming saccade long before it is executed, often before the previous saccade had even ended. Crucially, much of this early information did not relate to the actual saccade that would eventually be selected. Rather, it related to prior information about the probabilities of possible upcoming saccades based on the presaccade fixation location. Nearer to the time of saccade onset, a greater proportion of these neurons' activities related to the saccade selection, although prior information continued to influence activity throughout. A separate subset of FEF neurons, "Late neurons," only represented the final action plan near saccade onset and not prior information. Our results demonstrate how, across the population of FEF neurons, prior information evolves into definitive saccade plans.

Smooth Pursuit and Saccades after Sport-Related Concussion

Smooth pursuit eye movements (SPEMs) and saccadic eye movements are both commonly impaired following sport-related concussion (SRC). Typical oculomotor assessments measure individual eye movements in a series of restrictive tests designed to isolate features such as response times. These measures lack ecological validity for athletes because athletes are adept at simple tasks designed for the general population. Yet, because eye movement metrics are sensitive and well-characterized neuroanatomically, it would be valuable to test whether athletes exhibit abnormal eye movements with more challenging tasks. To address this gap in knowledge, we collected eye-tracking data during a sport-like task to gain insight on gaze behavior during active self-motion. SPEMs and saccadic eye movements were recorded during a sport-like visual task within 24-48 h following SRC. Thirty-six Division I student-athletes were divided into SRC and control (CON) groups. All participants completed two blocks of the Wii Fit© soccer heading game (WF) while wearing a monocular infrared eye tracker. Eye movement classification systems quantified saccadic amplitude (SA), velocity (SV), and count (SC); as well as SPEM velocity (SPV) and amplitude (SPA). Separate Mann-Whitney U tests evaluated SPA and SC and found no significant effects (SPA, p = 0.11; SC, p = 0.10). A multi-variate analysis of variance (MANOVA) for remaining variables revealed SPV was significantly greater in CON (p < 0.05), but the SRC group had greater SA and SV (p < 0.05). These findings suggest that during a sport-like task, to maintain foveation SRC subjects used larger amplitude, faster saccades, but exhibited slower SPEMs. Measuring oculomotor function during ecologically valid, sport-like tasks may serve as a concussion biomarker and provide insights into eye movement control after SRC.

Right frontal eye field has perceptual and oculomotor functions during optokinetic stimulation and nystagmus

The right frontal eye field (rFEF) is associated with visual perception and eye movements. rFEF is activated during optokinetic nystagmus (OKN), a reflex that moves the eye in response to visual motion (optokinetic stimulation, OKS). It remains unclear whether rFEF plays causal perceptual and/or oculomotor roles during OKS and OKN. To test this, participants viewed a leftward-moving visual scene of vertical bars and judged whether a flashed dot was moving. Single pulses of transcranial magnetic stimulation (TMS) were applied to rFEF on half of trials. In half of blocks, to explore oculomotor control, participants performed an OKN in response to the OKS. rFEF TMS, during OKN, made participants more accurate on trials when the dot was still, and it slowed eye movements. In separate blocks, participants fixated during OKS. This not only controlled for eye movements but also allowed the use of EEG to explore the FEF's role in visual motion discrimination. In these blocks, by contrast, leftward dot motion discrimination was impaired, associated with a disruption of the frontal-posterior balance in alpha-band oscillations. None of these effects occurred in a control site (M1) experiment. These results demonstrate multiple related yet dissociable causal roles of the right FEF during optokinetic stimulation.NEW & NOTEWORTHY This study demonstrates causal roles of the right frontal eye field (FEF) in motion discrimination and eye movement control during visual scene motion: previous work had only examined other stimuli and eye movements such as saccades. Using combined transcranial magnetic stimulation and EEG and a novel optokinetic stimulation motion-discrimination task, we find evidence for multiple related yet dissociable causal roles within the FEF: perceptual processing during optokinetic stimulation, generation of the optokinetic nystagmus, and the maintenance of alpha oscillations.

Countermanding Perceptual Decision-Making

We investigated whether a task requiring concurrent perceptual decision-making and response control can be performed concurrently, whether evidence accumulation and response control are accomplished by the same neurons, and whether perceptual decision-making and countermanding can be unified computationally. Based on neural recordings in a prefrontal area of macaque monkeys, we present behavioral, neural, and computational results demonstrating that perceptual decision-making of varying difficulty can be countermanded efficiently, that single prefrontal neurons instantiate both evidence accumulation and response control, and that an interactive race between stochastic GO evidence accumulators for each alternative and a distinct STOP accumulator fits countermanding choice behavior and replicates neural trajectories. Thus, perceptual decision-making and response control, previously regarded as distinct mechanisms, are actually aspects of a common neuro-computational mechanism supporting flexible behavior.

ChaRTr: An R toolbox for modeling choices and response times in decision-making tasks

BACKGROUND

Decision-making is the process of choosing and performing actions in response to sensory cues to achieve behavioral goals. Many mathematical models have been developed to describe the choice behavior and response time (RT) distributions of observers performing decision-making tasks. However, relatively few researchers use these models because it demands expertise in various numerical, statistical, and software techniques.

NEW METHOD

We present a toolbox - Choices and Response Times in R, or ChaRTr - that provides the user the ability to implement and test a wide variety of decision-making models ranging from classic through to modern versions of the diffusion decision model, to models with urgency signals, or collapsing boundaries.

RESULTS

In three different case studies, we demonstrate how ChaRTr can be used to effortlessly discriminate between multiple models of decision-making behavior. We also provide guidance on how to extend the toolbox to incorporate future developments in decision-making models.

COMPARISON WITH EXISTING METHOD(S)

Existing software packages surmounted some of the numerical issues but have often focused on the classical decision-making model, the diffusion decision model. Recent models that posit roles for urgency, time-varying decision thresholds, noise in various aspects of the decision-formation process or low pass filtering of sensory evidence have proven to be challenging to incorporate in a coherent software framework that permits quantitative evaluation among these competing classes of decision-making models.

CONCLUSION

ChaRTr can be used to make insightful statements about the cognitive processes underlying observed decision-making behavior and ultimately for deeper insights into decision mechanisms.

Accumulators, Neurons, and Response Time

The marriage of cognitive neurophysiology and mathematical psychology to understand decision-making has been exceptionally productive. This interdisciplinary area is based on the proposition that particular neurons or circuits instantiate the accumulation of evidence specified by mathematical models of sequential sampling and stochastic accumulation. This linking proposition has earned widespread endorsement. Here, a brief survey of the history of the proposition precedes a review of multiple conundrums and paradoxes concerning the accuracy, precision, and transparency of that linking proposition. Correctly establishing how abstract models of decision-making are instantiated by particular neural circuits would represent a remarkable accomplishment in mapping mind to brain. Failing would reveal challenging limits for cognitive neuroscience. This is such a vigorous area of research because so much is at stake.

Activity in LIP, But not V4, Matches Performance When Attention is Spread

The enhancement of neuronal responses in many visual areas while animals perform spatial attention tasks has widely been thought to be the neural correlate of visual attention, but it is unclear whether the presence or absence of this modulation contributes to our striking inability to notice changes in change blindness examples. We asked whether neuronal responses in visual area V4 and the lateral intraparietal area (LIP) in posterior parietal cortex could explain the limited ability of subjects to attend multiple items in a display. We trained animals to perform a change detection task in which they had to compare 2 arrays of stimuli separated briefly in time and found that each animal's performance decreased as function of set-size. Neuronal discriminability in V4 was consistent across set-sizes, but decreased for higher set-sizes in LIP. The introduction of a reward bias produced attentional enhancement in V4, but this could not explain the vast improvement in performance, whereas the enhancement in LIP responses could. We suggest that behavioral set-size effects and the marked improvement in performance with focused attention may not be related to response enhancement in V4 but, instead, may occur in or on the way to LIP.

Foraging as an evidence accumulation process

The patch-leaving problem is a canonical foraging task, in which a forager must decide to leave a current resource in search for another. Theoretical work has derived optimal strategies for when to leave a patch, and experiments have tested for conditions where animals do or do not follow an optimal strategy. Nevertheless, models of patch-leaving decisions do not consider the imperfect and noisy sampling process through which an animal gathers information, and how this process is constrained by neurobiological mechanisms. In this theoretical study, we formulate an evidence accumulation model of patch-leaving decisions where the animal averages over noisy measurements to estimate the state of the current patch and the overall environment. We solve the model for conditions where foraging decisions are optimal and equivalent to the marginal value theorem, and perform simulations to analyze deviations from optimal when these conditions are not met. By adjusting the drift rate and decision threshold, the model can represent different “strategies”, for example an incremental, decremental, or counting strategy. These strategies yield identical decisions in the limiting case but differ in how patch residence times adapt when the foraging environment is uncertain. To describe sub-optimal decisions, we introduce an energy-dependent marginal utility function that predicts longer than optimal patch residence times when food is plentiful. Our model provides a quantitative connection between ecological models of foraging behavior and evidence accumulation models of decision making. Moreover, it provides a theoretical framework for potential experiments which seek to identify neural circuits underlying patch-leaving decisions.

Foraging is a ubiquitous animal behavior, performed by organisms as different as worms, birds, rats, and humans. Although the behavior has been extensively studied, it is not known how the brain processes information obtained during foraging activity to make subsequent foraging decisions. We form an evidence accumulation model of foraging decisions that describes the process through which an animal gathers information and uses it to make foraging decisions. By building on studies of the neural decision mechanisms within systems neuroscience, this model connects the foraging decision process with ecological models of patch-leaving decisions, such as the marginal value theorem. The model suggests the existence of different foraging strategies, which optimize for different environmental conditions and their potential implementation by neural decision making circuits. The model also shows how state-dependence, such as satiation level, can affect evidence accumulation to lead to sub-optimal foraging decisions. Our model provides a framework for future experimental studies which seek to elucidate how neural decision making mechanisms have been shaped by evolutionary forces in an animal’s surrounding environment.

Asymmetric smooth pursuit eye movements and visual motion reaction time

Smooth pursuit eye movements often show directional asymmetry in pursuit initiation or steady-state pursuit in both humans and monkeys. It has been demonstrated that the initial part of smooth pursuit is driven by visual motion related signals in cortical areas. Parietal cortex such as middle temporal (MT) and medial superior temporal (MST) areas are known to be involved in visual motion perception as well as pursuit initiation. Therefore, the purpose of this study is to determine whether directional asymmetry in pursuit initiation is associated with visual motion perception. We used a step-ramp paradigm to induce horizontal smooth pursuit eye movements and then tested visual motion reaction time (RT). Visual motion RT was measured to the visual motion stimuli that moved leftward or rightward, which is an important parameter of our sensory motor processing based on visual motion perception. Nineteen healthy male subjects participated in the study. We found that some of our subjects showed directional asymmetries in initial pursuit acceleration between the leftward and rightward directions, which were consistent with an asymmetric bias in visual motion RT. Therefore, our results suggest that asymmetric pursuit initiation is associated with, at least in part, a bias of visual motion perception. These results could be due to a common neuronal pathway involved in both pursuit initiation and visual motion RT.

Post-decision processing in primate prefrontal cortex influences subsequent choices on an auditory decision-making task

Perceptual decisions do not occur in isolation but instead reflect ongoing evaluation and adjustment processes that can affect future decisions. However, the neuronal substrates of these across-decision processes are not well understood, particularly for auditory decisions. We measured and manipulated the activity of choice-selective neurons in the ventrolateral prefrontal cortex (vlPFC) while monkeys made decisions about the frequency content of noisy auditory stimuli. As the decision was being formed, vlPFC activity was not modulated strongly by the task. However, after decision commitment, vlPFC population activity encoded the sensory evidence, choice, and outcome of the current trial and predicted subject-specific choice biases on the subsequent trial. Consistent with these patterns of neuronal activity, electrical microstimulation in vlPFC tended to affect the subsequent, but not current, decision. Thus, distributed post-commitment representations of graded decision-related information in prefrontal cortex can play a causal role in evaluating past decisions and biasing subsequent ones.

Distinct Sources of Variability Affect Eye Movement Preparation

The sequence of events leading to an eye movement to a target begins the moment visual information has reached the brain, well in advance of the eye movement itself. The process by which visual information is encoded and used to generate a motor plan has been the focus of substantial interest partly because of the rapid and reproducible nature of saccadic eye movements, and the key role that they play in primate behavior. Signals related to eye movements are present in much of the primate brain, yet most neurophysiological studies of the transition from vision to eye movements have measured the activity of one neuron at a time. Less is known about how the coordinated action of populations of neurons contribute to the initiation of eye movements. One cortical area of particular interest in this process is the frontal eye fields, a region of prefrontal cortex that has descending projections to oculomotor control centers. We recorded from populations of frontal eye field neurons in macaque monkeys engaged in a memory-guided saccade task. We found a variety of neurons with visually evoked responses, saccade-aligned responses, and mixtures of both. We took advantage of the simultaneous nature of the recordings to measure variability in individual neurons and pairs of neurons from trial-to-trial, as well as the moment-to-moment population activity structure. We found that these measures were related to saccadic reaction times, suggesting that the population-level organization of frontal eye field activity is important for the transition from perception to movement.SIGNIFICANCE STATEMENT The transition from perception to action involves coordination among neurons across the brain. In the case of eye movements, visual and motor signals coexist in individual neurons as well as in neighboring neurons. We used a task designed to compartmentalize the visual and motor aspects of this transition and studied populations of neurons in the frontal eye fields, a key cortical area containing neurons that are implicated in the transition from vision to eye movements. We found that the time required for subjects to produce an eye movement could be predicted from the statistics of the neuronal response of populations of frontal eye field neurons, suggesting that these neurons coordinate their activity to optimize the transition from perception to action.

Corollary Discharge for Action and Cognition

In motor systems, a copy of the movement command known as corollary discharge is broadcast to other regions of the brain to warn them of the impending movement. The premise of this review is that the concept of corollary discharge may generalize in revealing ways to the brain's cognitive systems. An oculomotor pathway from the brain stem to frontal cortex provides a well-established example of how corollary discharge is instantiated for sensorimotor processing. Building on causal evidence from inactivation of the pathway, we motivate forward models as a tool for understanding the contributions of corollary discharge to perception and movement. Finally, we extend the definition of corollary discharge to account for signals that may be used for cognitive forward models of decision making. This framework may provide new insights into signals and circuits that contribute to sequential decision processes, the breakdown of which may account for some symptoms of psychiatric disorders.

The Neuro-Computational Architecture of Value-Based Selection in the Human Brain

Current neural models of value-based decision-making consider choices as a 2-stage process, proceeding from the "valuation" of each option under consideration to the "selection" of the best option on the basis of their subjective values. However, little is known about the computational mechanisms at play at the selection stage and its implementation in the human brain. Here, we used drift-diffusion models combined with model-based functional magnetic resonance imaging, effective connectivity, and multivariate pattern analysis to characterize the neuro-computational architecture of value-based decisions. We found that 2 key drift-diffusion computations at the selection stage, namely integration and choice readout, engage distinct brain regions, with the dorsolateral prefrontal cortex integrating a decision value signal computed in the ventromedial prefrontal cortex, and the posterior parietal cortex reading out choice outcomes. Our findings suggest that this prefronto-parietal network acts as a hub implementing behavioral selection through a distributed drift-diffusion process.

Macaque dorsal premotor cortex exhibits decision-related activity only when specific stimulus-response associations are known

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.

Neuronal signatures of a random-dot motion comparison task

The study of perceptual decision making has made significant progress owing to major contributions from two experimental paradigms: the sequential vibrotactile frequency comparison task for the somatosensory domain requiring working memory, and the random-dot motion task in the visual domain requiring evidence accumulation over time. On the one hand, electrophysiological recordings in nonhuman primates and humans have identified changes in firing rates and power modulations of beta band oscillations with the vibrotactile frequencies held in working memory, as well as with the mental operation required for decision making. On the other hand, firing rates and centro-parietal potentials were found to increase to a fixed level at the time of responding during the random-dot motion task, possibly reflecting an underlying evidence accumulation mechanism until a decision threshold is met. Here, to bridge these two paradigms, we presented two visual random-dot motion stimuli in a sequential comparison task while recording EEG from human volunteers. We identified a modulation of prefrontal beta band power that scaled with the level of dot motion coherence of the first stimulus during a short retention interval. Furthermore, beta power in premotor areas was modulated by participants' choices approximately 700 ms before responses were given via button press. At the same time, dot motion patches of the second stimulus evoked a pattern of broadband centro-parietal signal build-up till responses were made, whose peak varied with trial difficulty. Hence, we show that known modulations of beta power during working memory and decision making extend from the vibrotactile to the visual domain and provide support for the notion of evidence accumulation as an unconfined decision-making mechanism generalizing over distinct decision types.

Modeling Saccadic Action Selection: Cortical and Basal Ganglia Signals Coalesce in the Superior Colliculus

The distributed nature of information processing in the brain creates a complex variety of decision making behavior. Likewise, computational models of saccadic decision making behavior are numerous and diverse. Here we present a generative model of saccadic action selection in the context of competitive decision making in the superior colliculus (SC) in order to investigate how independent neural signals may converge to interact and guide saccade selection, and to test if systematic variations can better replicate the variability in responses that are part of normal human behavior. The model was tasked with performing pro- and anti-saccades in order to replicate specific attributes of healthy human saccade behavior. Participants (ages 18-39) were instructed to either look toward (pro-saccade, well-practiced automated response) or away from (anti-saccade, combination of inhibitory and voluntary responses) a peripheral visual stimulus. They generated express and regular latency saccades in the pro-saccade task. In the anti-saccade task, correct reaction times were longer and participants occasionally looked at the stimulus (direction error) at either express or regular latencies. To gain a better understanding of the underlying neural processes that lead to saccadic action selection and response inhibition, we implemented 8 inputs inspired by systems neuroscience. These inputs reflected known sensory, automated, voluntary, and inhibitory components of cortical and basal ganglia activity that coalesces in the intermediate layers of the SC (SCi). The model produced bimodal reaction time distributions, where express and regular latency saccades had distinct modes, for both correct pro-saccades and direction errors in the anti-saccade task. Importantly, express and regular latency direction errors resulted from interactions of different inputs in the model. Express latency direction errors were due to a lack of pre-emptive fixation and inhibitory activity, which aloud sensory and automated inputs to initiate a stimulus-driven saccade. Regular latency errors occurred when the automated motor signals were stronger than the voluntary motor signals. While previous models have emulated fewer aspects of these behavioral findings, the focus of the simulations here is on the interaction of a wide variety of physiologically-based information integration producing a richer set of natural behavioral variability.

Decoding vibrotactile choice independent of stimulus order and saccade selection during sequential comparisons

Decision-making in the somatosensory domain has been intensively studied using vibrotactile frequency discrimination tasks. Results from human and monkey electrophysiological studies from this line of research suggest that perceptual choices are encoded within a sensorimotor network. These findings, however, rely on experimental settings in which perceptual choices are inextricably linked to sensory and motor components of the task. Here, we devised a novel version of the vibrotactile frequency discrimination task with saccade responses which has the crucial advantage of decoupling perceptual choices from sensory and motor processes. We recorded human fMRI data from 32 participants while they performed the task. Using a whole-brain searchlight multivariate classification technique, we identify the left lateral prefrontal cortex and the oculomotor system, including the bilateral frontal eye fields (FEF) and intraparietal sulci, as representing vibrotactile choices. Moreover, we show that the decoding accuracy of choice information in the right FEF correlates with behavioral performance. Not only are these findings in remarkable agreement with previous work, they also provide novel fMRI evidence for choice coding in human oculomotor regions, which is not limited to saccadic decisions, but pertains to contexts where choices are made in a more abstract form.

Vestibular System and Self-Motion

Detection of the state of self-motion, such as the instantaneous heading direction, the traveled trajectory and traveled distance or time, is critical for efficient spatial navigation. Numerous psychophysical studies have indicated that the vestibular system, originating from the otolith and semicircular canals in our inner ears, provides robust signals for different aspects of self-motion perception. In addition, vestibular signals interact with other sensory signals such as visual optic flow to facilitate natural navigation. These behavioral results are consistent with recent findings in neurophysiological studies. In particular, vestibular activity in response to the translation or rotation of the head/body in darkness is revealed in a growing number of cortical regions, many of which are also sensitive to visual motion stimuli. The temporal dynamics of the vestibular activity in the central nervous system can vary widely, ranging from acceleration-dominant to velocity-dominant. Different temporal dynamic signals may be decoded by higher level areas for different functions. For example, the acceleration signals during the translation of body in the horizontal plane may be used by the brain to estimate the heading directions. Although translation and rotation signals arise from independent peripheral organs, that is, otolith and canals, respectively, they frequently converge onto single neurons in the central nervous system including both the brainstem and the cerebral cortex. The convergent neurons typically exhibit stronger responses during a combined curved motion trajectory which may serve as the neural correlate for complex path perception. During spatial navigation, traveled distance or time may be encoded by different population of neurons in multiple regions including hippocampal-entorhinal system, posterior parietal cortex, or frontal cortex.

More than Just a "Motor": Recent Surprises from the Frontal Cortex

Motor and premotor cortices are crucial for the control of movements. However, we still know little about how these areas contribute to higher-order motor control, such as deciding which movements to make and when to make them. Here we focus on rodent studies and review recent findings, which suggest that-in addition to motor control-neurons in motor cortices play a role in sensory integration, behavioral strategizing, working memory, and decision-making. We suggest that these seemingly disparate functions may subserve an evolutionarily conserved role in sensorimotor cognition and that further study of rodent motor cortices could make a major contribution to our understanding of the evolution and function of the mammalian frontal cortex.

Ongoing, rational calibration of reward-driven perceptual biases

Decision-making is often interpreted in terms of normative computations that maximize a particular reward function for stable, average behaviors. Aberrations from the reward-maximizing solutions, either across subjects or across different sessions for the same subject, are often interpreted as reflecting poor learning or physical limitations. Here we show that such aberrations may instead reflect the involvement of additional satisficing and heuristic principles. For an asymmetric-reward perceptual decision-making task, three monkeys produced adaptive biases in response to changes in reward asymmetries and perceptual sensitivity. Their choices and response times were consistent with a normative accumulate-to-bound process. However, their context-dependent adjustments to this process deviated slightly but systematically from the reward-maximizing solutions. These adjustments were instead consistent with a rational process to find satisficing solutions based on the gradient of each monkey's reward-rate function. These results suggest new dimensions for assessing the rational and idiosyncratic aspects of flexible decision-making.

Saccadic inhibition interrupts ongoing oculomotor activity to enable the rapid deployment of alternate movement plans

Diverse psychophysical and neurophysiological results show that oculomotor networks are continuously active, such that plans for making the next eye movement are always ongoing. So, when new visual information arrives unexpectedly, how are those plans affected? At what point can the new information start guiding an eye movement, and how? Here, based on modeling and simulation results, we make two observations that are relevant to these questions. First, we note that many experiments, including those investigating the phenomenon known as "saccadic inhibition", are consistent with the idea that sudden-onset stimuli briefly interrupt the gradual rise in neural activity associated with the preparation of an impending saccade. And second, we show that this stimulus-driven interruption is functionally adaptive, but only if perception is fast. In that case, putting on hold an ongoing saccade plan toward location A allows the oculomotor system to initiate a concurrent, alternative plan toward location B (where a stimulus just appeared), deliberate (briefly) on the priority of each target, and determine which plan should continue. Based on physiological data, we estimate that the advantage of this strategy, relative to one in which any plan once initiated must be completed, is of several tens of milliseconds per saccade.

Functional Categories of Visuomotor Neurons in Macaque Frontal Eye Field

Frontal eye field (FEF) in macaque monkeys contributes to visual attention, visual-motor transformations and production of eye movements. Traditionally, neurons in FEF have been classified by the magnitude of increased discharge rates following visual stimulus presentation, during a waiting period, and associated with eye movement production. However, considerable heterogeneity remains within the traditional visual, visuomovement, and movement categories. Cluster analysis is a data-driven method of identifying self-segregating groups within a dataset. Because many cluster analysis techniques exist and outcomes vary with analysis assumptions, consensus clustering aggregates over multiple analyses, identifying robust groups. To describe more comprehensively the neuronal composition of FEF, we applied a consensus clustering technique for unsupervised categorization of patterns of spike rate modulation measured during a memory-guided saccade task. We report 10 functional categories, expanding on the traditional 3 categories. Categories were distinguished by latency, magnitude, and sign of visual response; the presence of sustained activity; and the dynamics, magnitude and sign of saccade-related modulation. Consensus clustering can include other metrics and can be applied to datasets from other brain regions to provide better information guiding microcircuit models of cortical function.

Causal contribution and dynamical encoding in the striatum during evidence accumulation

A broad range of decision-making processes involve gradual accumulation of evidence over time, but the neural circuits responsible for this computation are not yet established. Recent data indicate that cortical regions that are prominently associated with accumulating evidence, such as the posterior parietal cortex and the frontal orienting fields, may not be directly involved in this computation. Which, then, are the regions involved? Regions that are directly involved in evidence accumulation should directly influence the accumulation-based decision-making behavior, have a graded neural encoding of accumulated evidence and contribute throughout the accumulation process. Here, we investigated the role of the anterior dorsal striatum (ADS) in a rodent auditory evidence accumulation task using a combination of behavioral, pharmacological, optogenetic, electrophysiological and computational approaches. We find that the ADS is the first brain region known to satisfy the three criteria. Thus, the ADS may be the first identified node in the network responsible for evidence accumulation.

Cerebellar involvement in an evidence-accumulation decision-making task

To make successful evidence-based decisions, the brain must rapidly and accurately transform sensory inputs into specific goal-directed behaviors. Most experimental work on this subject has focused on forebrain mechanisms. Using a novel evidence-accumulation task for mice, we performed recording and perturbation studies of crus I of the lateral posterior cerebellum, which communicates bidirectionally with numerous forebrain regions. Cerebellar inactivation led to a reduction in the fraction of correct trials. Using two-photon fluorescence imaging of calcium, we found that Purkinje cell somatic activity contained choice/evidence-related information. Decision errors were represented by dendritic calcium spikes, which in other contexts are known to drive cerebellar plasticity. We propose that cerebellar circuitry may contribute to computations that support accurate performance in this perceptual decision-making task.

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.

Neural mechanisms of speed-accuracy tradeoff of visual search: saccade vigor, the origin of targeting errors, and comparison of the superior colliculus and frontal eye field

Balancing the speed-accuracy tradeoff (SAT) is necessary for successful behavior. Using a visual search task with interleaved cues emphasizing speed or accuracy, we recently reported diverse contributions of frontal eye field (FEF) neurons instantiating salience evidence and response preparation. Here, we report replication of visual search SAT performance in two macaque monkeys, new information about variation of saccade dynamics with SAT, extension of the neurophysiological investigation to describe processes in the superior colliculus (SC), and a description of the origin of search errors in this task. Saccade vigor varied idiosyncratically across SAT conditions and monkeys but tended to decrease with response time. As observed in the FEF, speed-accuracy tradeoff was accomplished through several distinct adjustments in the superior colliculus. In "Accurate" relative to "Fast" trials, visually responsive neurons in SC as in FEF had lower baseline firing rates and later target selection. The magnitude of these adjustments in SC was indistinguishable from that in FEF. Search errors occurred when visual salience neurons in the FEF and the SC treated distractors as targets, even in the Accurate condition. Unlike FEF, the magnitude of visual responses in the SC did not vary across SAT conditions. Also unlike FEF, the activity of SC movement neurons when saccades were initiated was equivalent in Fast and Accurate trials. Saccade-related neural activity in SC, but not FEF, varied with saccade peak velocity. These results extend our understanding of the cortical and subcortical contributions to SAT. NEW & NOTEWORTHY Neurophysiological mechanisms of speed-accuracy tradeoff (SAT) have only recently been investigated. This article reports the first replication of SAT performance in nonhuman primates, the first report of variation of saccade dynamics with SAT, the first description of superior colliculus contributions to SAT, and the first description of the origin of errors during SAT. These results inform and constrain new models of distributed decision making.

Motor selection dynamics in FEF explain the reaction time variance of saccades to single targets

In studies of voluntary movement, a most elemental quantity is the reaction time (RT) between the onset of a visual stimulus and a saccade toward it. However, this RT demonstrates extremely high variability which, in spite of extensive research, remains unexplained. It is well established that, when a visual target appears, oculomotor activity gradually builds up until a critical level is reached, at which point a saccade is triggered. Here, based on computational work and single-neuron recordings from monkey frontal eye field (FEF), we show that this rise-to-threshold process starts from a dynamic initial state that already contains other incipient, internally driven motor plans, which compete with the target-driven activity to varying degrees. The ensuing conflict resolution process, which manifests in subtle covariations between baseline activity, build-up rate, and threshold, consists of fundamentally deterministic interactions, and explains the observed RT distributions while invoking only a small amount of intrinsic randomness.

As we examine the space around us our eyes move in short steps, looking toward a new location about four times a second. Neurons in a region of the brain called the frontal eye field help initiate these eye movements, which are known as saccades. Each neuron contributes to a saccade with a specific direction and size. Before a saccade, the relevant neurons in the frontal eye field steadily increase their activity. When this activity reaches a critical threshold, the visual system issues a command to move the eyes in the appropriate direction. So a saccade that moves the eyes to the right requires a specific group of neurons to be strongly activated – but, at the same time, the neurons responsible for movement to the left need to be less active.

Imagine that you have to move your eyes as quickly as possible to look at a spot of light that appears on a screen. Some of the time your eyes will start to move about 100 milliseconds after the light appears. But on other attempts, your eyes will not start moving until 300 milliseconds after the light came on. What causes this variability?

To find out, Hauser et al. recorded from neurons in monkeys trained to perform such a task. When the spot of light appeared many different neurons were active, suggesting there is conflict between the plan that would move the eyes toward the target and plans to look at other locations. That is, when the target appears, the monkey is already thinking of looking somewhere. The time required to resolve this conflict depends on how far apart the target and the competing locations are from one another, and on how much the competing neurons have increased their activity before the target appears.

Similar mechanisms are likely to operate when we sit at the dinner table and look for the salt shaker, for example, and so the results presented by Hauser et al. will help us to understand how we direct our attention to different points in space. Understanding how these processes work in more detail will help us to discern what happens when they go wrong, as occurs in attention deficit disorders like ADHD.

A probabilistic, distributed, recursive mechanism for decision-making in the brain

Decision formation recruits many brain regions, but the procedure they jointly execute is unknown. Here we characterize its essential composition, using as a framework a novel recursive Bayesian algorithm that makes decisions based on spike-trains with the statistics of those in sensory cortex (MT). Using it to simulate the random-dot-motion task, we demonstrate it quantitatively replicates the choice behaviour of monkeys, whilst predicting losses of otherwise usable information from MT. Its architecture maps to the recurrent cortico-basal-ganglia-thalamo-cortical loops, whose components are all implicated in decision-making. We show that the dynamics of its mapped computations match those of neural activity in the sensorimotor cortex and striatum during decisions, and forecast those of basal ganglia output and thalamus. This also predicts which aspects of neural dynamics are and are not part of inference. Our single-equation algorithm is probabilistic, distributed, recursive, and parallel. Its success at capturing anatomy, behaviour, and electrophysiology suggests that the mechanism implemented by the brain has these same characteristics.

Decision-making is central to cognition. Abnormally-formed decisions characterize disorders like over-eating, Parkinson’s and Huntington’s diseases, OCD, addiction, and compulsive gambling. Yet, a unified account of decision-making has, hitherto, remained elusive. Here we show the essential composition of the brain’s decision mechanism by matching experimental data from monkeys making decisions, to the knowable function of a novel statistical inference algorithm. Our algorithm maps onto the large-scale architecture of decision circuits in the primate brain, replicating the monkeys’ choice behaviour and the dynamics of the neural activity that accompany it. Validated in this way, our algorithm establishes a basic framework for understanding the mechanistic ingredients of decision-making in the brain, and thereby, a basic platform for understanding how pathologies arise from abnormal function.

The Role of the Lateral Intraparietal Area in (the Study of) Decision Making

Over the past two decades, neurophysiological responses in the lateral intraparietal area (LIP) have received extensive study for insight into decision making. In a parallel manner, inferred cognitive processes have enriched interpretations of LIP activity. Because of this bidirectional interplay between physiology and cognition, LIP has served as fertile ground for developing quantitative models that link neural activity with decision making. These models stand as some of the most important frameworks for linking brain and mind, and they are now mature enough to be evaluated in finer detail and integrated with other lines of investigation of LIP function. Here, we focus on the relationship between LIP responses and known sensory and motor events in perceptual decision-making tasks, as assessed by correlative and causal methods. The resulting sensorimotor-focused approach offers an account of LIP activity as a multiplexed amalgam of sensory, cognitive, and motor-related activity, with a complex and often indirect relationship to decision processes. Our data-driven focus on multiplexing (and de-multiplexing) of various response components can complement decision-focused models and provides more detailed insight into how neural signals might relate to cognitive processes such as decision making.

Area PEc Neurons Use a Multiphasic Pattern of Activity to Signal the Spatial Properties of Optic Flow

The cortical representation of visual perception requires the integration of several-signal processing distributed across many cortical areas, but the neural substrates of such perception are largely unknown. The type of firing pattern exhibited by single neurons is an important indicator of dynamic circuitry within or across cortical areas. Neurons in area PEc are involved in the spatial mapping of the visual field; thus, we sought to analyze the firing pattern of activity of PEc optic flow neurons to shed some light on the cortical processing of visual signals. We quantified the firing activity of 152 optic flow neurons using a spline interpolation function, which allowed determining onset, end, and latency of each neuronal response. We found that many PEc neurons showed multiphasic activity, which is strictly related to the position of the eye and to the position of the focus of expansion (FOE) of the flow field. PEc neurons showed a multiphasic activity comprised of excitatory phases interspersed with inhibitory pauses. This phasic pattern seems to be a very efficient way to signal the spatial location of visual stimuli, given that the same neuron sends different firing patterns according to a specific combination of FOE/eye position.

Frontal Eye Field Inactivation Diminishes Superior Colliculus Activity, But Delayed Saccadic Accumulation Governs Reaction Time Increases

Stochastic accumulator models provide a comprehensive framework for how neural activity could produce behavior. Neural activity within the frontal eye fields (FEFs) and intermediate layers of the superior colliculus (iSC) support such models for saccade initiation by relating variations in saccade reaction time (SRT) to variations in such parameters as baseline, rate of accumulation of activity, and threshold. Here, by recording iSC activity during reversible cryogenic inactivation of the FEF in four male nonhuman primates, we causally tested which parameter(s) best explains concomitant increases in SRT. While FEF inactivation decreased all aspects of ipsilesional iSC activity, decreases in accumulation rate and threshold poorly predicted accompanying increases in SRT. Instead, SRT increases best correlated with delays in the onset of saccade-related accumulation. We conclude that FEF signals govern the onset of saccade-related accumulation within the iSC, and that the onset of accumulation is a relevant parameter for stochastic accumulation models of saccade initiation.SIGNIFICANCE STATEMENT The superior colliculus (SC) and frontal eye fields (FEFs) are two of the best-studied areas in the primate brain. Surprisingly, little is known about what happens in the SC when the FEF is temporarily inactivated. Here, we show that temporary FEF inactivation decreases all aspects of functionally related activity in the SC. This combination of techniques also enabled us to relate changes in SC activity to concomitant increases in saccadic reaction time (SRT). Although stochastic accumulator models relate SRT increases to reduced rates of accumulation or increases in threshold, such changes were not observed in the SC. Instead, FEF inactivation delayed the onset of saccade-related accumulation, emphasizing the importance of this parameter in biologically plausible models of saccade initiation.

Laminar differences in decision-related neural activity in dorsal premotor cortex

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.

Dorsal premotor cortex (PMd) is thought to be involved in making somatomotor decisions. Chandrasekaran et al. investigated the temporal response dynamics of PMd neurons across cortical layers and show stronger and earlier decision-related responses in the superficial layers and more action execution-related signals in the deeper layers.

Prefrontal Contribution to Decision-Making under Free-Choice Conditions

Executive function is thought to be the coordinated operation of multiple neural processes and allows to accomplish a current goal flexibly. The most important function of the prefrontal cortex is the executive function. Among a variety of executive functions in which the prefrontal cortex participates, decision-making is one of the most important. Although the prefrontal contribution to decision-making has been examined using a variety of behavioral tasks, recent studies using fMRI have shown that the prefrontal cortex participates in decision-making under free-choice conditions. Since decision-making under free-choice conditions represents the very first stage for any kind of decision-making process, it is important that we understand its neural mechanism. Although few studies have examined this issue while a monkey performed a free-choice task, those studies showed that, when the monkey made a decision to subsequently choose one particular option, prefrontal neurons showing selectivity to that option exhibited transient activation just before presentation of the imperative cue. Further studies have suggested that this transient increase is caused by the irregular fluctuation of spontaneous firing just before cue presentation, which enhances the response to the cue and biases the strength of the neuron's selectivity to the option. In addition, this biasing effect was observed only in neurons that exhibited sustained delay-period activity, indicating that this biasing effect not only influences the animal's decision for an upcoming choice, but also is linked to working memory mechanisms in the prefrontal cortex.

A Two-Stage Process Model of Sensory Discrimination: An Alternative to Drift-Diffusion

Discrimination of the direction of motion of a noisy stimulus is an example of sensory discrimination under uncertainty. For stimuli that are extended in time, reaction time is quicker for larger signal values (e.g., discrimination of opposite directions of motion compared with neighboring orientations) and larger signal strength (e.g., stimuli with higher contrast or motion coherence, that is, lower noise). The standard model of neural responses (e.g., in lateral intraparietal cortex) and reaction time for discrimination is drift-diffusion. This model makes two clear predictions. (1) The effects of signal strength and value on reaction time should interact multiplicatively because the diffusion process depends on the signal-to-noise ratio. (2) If the diffusion process is interrupted, as in a cued-response task, the time to decision after the cue should be independent of the strength of accumulated sensory evidence. In two experiments with human participants, we show that neither prediction holds. A simple alternative model is developed that is consistent with the results. In this estimate-then-decide model, evidence is accumulated until estimation precision reaches a threshold value. Then, a decision is made with duration that depends on the signal-to-noise ratio achieved by the first stage.

SIGNIFICANCE STATEMENT

Sensory decision-making under uncertainty is usually modeled as the slow accumulation of noisy sensory evidence until a threshold amount of evidence supporting one of the possible decision outcomes is reached. Furthermore, it has been suggested that this accumulation process is reflected in neural responses, e.g., in lateral intraparietal cortex. We derive two behavioral predictions of this model and show that neither prediction holds. We introduce a simple alternative model in which evidence is accumulated until a sufficiently precise estimate of the stimulus is achieved, and then that estimate is used to guide the discrimination decision. This model is consistent with the behavioral data.

Decision Making and Sequential Sampling from Memory

Decisions take time, and as a rule more difficult decisions take more time. But this only raises the question of what consumes the time. For decisions informed by a sequence of samples of evidence, the answer is straightforward: more samples are available with more time. Indeed, the speed and accuracy of such decisions are explained by the accumulation of evidence to a threshold or bound. However, the same framework seems to apply to decisions that are not obviously informed by sequences of evidence samples. Here, we proffer the hypothesis that the sequential character of such tasks involves retrieval of evidence from memory. We explore this hypothesis by focusing on value-based decisions and argue that mnemonic processes can account for regularities in choice and decision time. We speculate on the neural mechanisms that link sampling of evidence from memory to circuits that represent the accumulated evidence bearing on a choice. We propose that memory processes may contribute to a wider class of decisions that conform to the regularities of choice-reaction time predicted by the sequential sampling framework.

Two different mechanisms support selective attention at different phases of training

Selective attention supports the prioritized processing of relevant sensory information to facilitate goal-directed behavior. Studies in human subjects demonstrate that attentional gain of cortical responses can sufficiently account for attention-related improvements in behavior. On the other hand, studies using highly trained nonhuman primates suggest that reductions in neural noise can better explain attentional facilitation of behavior. Given the importance of selective information processing in nearly all domains of cognition, we sought to reconcile these competing accounts by testing the hypothesis that extensive behavioral training alters the neural mechanisms that support selective attention. We tested this hypothesis using electroencephalography (EEG) to measure stimulus-evoked visual responses from human subjects while they performed a selective spatial attention task over the course of ~1 month. Early in training, spatial attention led to an increase in the gain of stimulus-evoked visual responses. Gain was apparent within ~100 ms of stimulus onset, and a quantitative model based on signal detection theory (SDT) successfully linked the magnitude of this gain modulation to attention-related improvements in behavior. However, after extensive training, this early attentional gain was eliminated even though there were still substantial attention-related improvements in behavior. Accordingly, the SDT-based model required noise reduction to account for the link between the stimulus-evoked visual responses and attentional modulations of behavior. These findings suggest that training can lead to fundamental changes in the way attention alters the early cortical responses that support selective information processing. Moreover, these data facilitate the translation of results across different species and across experimental procedures that employ different behavioral training regimes.

Coupled Decision Processes Update and Maintain Saccadic Priors in a Dynamic Environment

Much of what we know about how the brain forms decisions comes from studies of saccadic eye movements. However, saccadic decisions are often studied in isolation, which limits the insights that they can provide about real-world decisions with complex interdependencies. Here, we used a serial reaction time (RT) task to show that prior expectations affect RTs via interdependent, normative decision processes that operate within and across saccades. We found that human subjects performing the task generated saccades that were governed by a rise-to-threshold decision process with a starting point that reflected expected state-dependent transition probabilities. These probabilities depended on decisions about the current state (the correct target) that, under some conditions, required the accumulation of information across saccades. Without additional feedback, this information was provided by each saccadic decision threshold, which represented the total evidence in favor of the chosen target. Therefore, the output of the within-saccade process was used, not only to generate the saccade, but also to provide input to the across-saccade process. This across-saccade process, in turn, helped to set the starting point of the next within-saccade process. These results imply a novel role for functional information-processing loops in optimizing saccade generation in dynamic environments.SIGNIFICANCE STATEMENT Saccades are the rapid, ballistic eye movements that we make approximately three times every second to scan the visual scene for interesting things to look at. The apparent ease with which we produce saccades belies their computational sophistication, which can be studied quantitatively in the laboratory to provide insights into how our brain manages the interplay between sensory input and motor output. The present work is important because we show for the first time how this interplay operates both within and across saccades to ensure that these eye movements are guided effectively by learned expectations in dynamic environments. More generally, this study shows how sensory-motor decision processes, typically studied in isolation, interact via functional information-processing loops in the brain to produce complex, adaptive behaviors.

RELATING ACCUMULATOR MODEL PARAMETERS AND NEURAL DYNAMICS

Accumulator models explain decision-making as an accumulation of evidence to a response threshold. Specific model parameters are associated with specific model mechanisms, such as the time when accumulation begins, the average rate of evidence accumulation, and the threshold. These mechanisms determine both the within-trial dynamics of evidence accumulation and the predicted behavior. Cognitive modelers usually infer what mechanisms vary during decision-making by seeing what parameters vary when a model is fitted to observed behavior. The recent identification of neural activity with evidence accumulation suggests that it may be possible to directly infer what mechanisms vary from an analysis of how neural dynamics vary. However, evidence accumulation is often noisy, and noise complicates the relationship between accumulator dynamics and the underlying mechanisms leading to those dynamics. To understand what kinds of inferences can be made about decision-making mechanisms based on measures of neural dynamics, we measured simulated accumulator model dynamics while systematically varying model parameters. In some cases, decision- making mechanisms can be directly inferred from dynamics, allowing us to distinguish between models that make identical behavioral predictions. In other cases, however, different parameterized mechanisms produce surprisingly similar dynamics, limiting the inferences that can be made based on measuring dynamics alone. Analyzing neural dynamics can provide a powerful tool to resolve model mimicry at the behavioral level, but we caution against drawing inferences based solely on neural analyses. Instead, simultaneous modeling of behavior and neural dynamics provides the most powerful approach to understand decision-making and likely other aspects of cognition and perception.

Comparing fixed and collapsing boundary versions of the diffusion model

Optimality studies and studies of decision-making in monkeys have been used to support a model in which the decision boundaries used to evaluate evidence collapse over time. This article investigates whether a diffusion model with collapsing boundaries provides a better account of human data than a model with fixed boundaries. We compared the models using data from four new numerosity discrimination experiments and two previously published motion discrimination experiments. When model selection was based on BIC values, the fixed boundary model was preferred over the collapsing boundary model for all of the experiments. When model selection was carried out using a parametric bootstrap cross-fitting method (PBCM), which takes into account the flexibility of the alternative models and the ability of one model to account for data from another model, data from 5 of 6 experiments favored either fixed boundaries or boundaries with only negligible collapse. We found that the collapsing boundary model produces response times distributions with the same shape as those produced by the fixed boundary model and that its parameters were not well-identified and were difficult to recover from data. Furthermore, the estimated boundaries of the best-fitting collapsing boundary model were relatively flat and very similar to those of the fixed-boundary model. Overall, a diffusion model with decision boundaries that converge over time does not provide an improvement over the standard diffusion model for our tasks with human data.

Learning a New Selection Rule in Visual and Frontal Cortex

How do you make a decision if you do not know the rules of the game? Models of sensory decision-making suggest that choices are slow if evidence is weak, but they may only apply if the subject knows the task rules. Here, we asked how the learning of a new rule influences neuronal activity in the visual (area V1) and frontal cortex (area FEF) of monkeys. We devised a new icon-selection task. On each day, the monkeys saw 2 new icons (small pictures) and learned which one was relevant. We rewarded eye movements to a saccade target connected to the relevant icon with a curve. Neurons in visual and frontal cortex coded the monkey's choice, because the representation of the selected curve was enhanced. Learning delayed the neuronal selection signals and we uncovered the cause of this delay in V1, where learning to select the relevant icon caused an early suppression of surrounding image elements. These results demonstrate that the learning of a new rule causes a transition from fast and random decisions to a more considerate strategy that takes additional time and they reveal the contribution of visual and frontal cortex to the learning process.

Neural underpinnings of the evidence accumulator

Gradual accumulation of evidence favoring one or another choice is considered a core component of many different types of decisions, and has been the subject of many neurophysiological studies in non-human primates. But its neural circuit mechanisms remain mysterious. Investigating it in rodents has recently become possible, facilitating perturbation experiments to delineate the relevant causal circuit, as well as the application of other tools more readily available in rodents. In addition, advances in stimulus design and analysis have aided studying the relevant neural encoding. In complement to ongoing non-human primate studies, these newly available model systems and tools place the field at an exciting time that suggests that the dynamical circuit mechanisms underlying accumulation of evidence could soon be revealed.