Potential confounds in estimating trial-to-trial correlations between neuronal response and behavior using choice probabilities

Decision-related activity and movement selection in primate visual cortex

Fluctuations in the activity of sensory neurons often predict perceptual decisions. This connection can be quantified with a metric called choice probability (CP), and there is a longstanding debate about whether CP reflects a causal influence on decisions or an echo of decision-making activity elsewhere in the brain. Here, we show that CP can reflect a third variable, namely, the movement used to indicate the decision. In a standard visual motion discrimination task, neurons in the middle temporal (MT) area of primate cortex responded more strongly during trials that involved a saccade toward their receptive fields. This variability accounted for much of the CP observed across the neuronal population, and it arose through training. Moreover, pharmacological inactivation of MT biased behavioral responses away from the corresponding visual field locations. These results demonstrate that training on a task with fixed sensorimotor contingencies introduces movement-related activity in sensory brain regions and that this plasticity can shape the neural circuitry of perceptual decision-making.

Behavioral read-out from population value signals in primate orbitofrontal cortex

The primate orbitofrontal cortex (OFC) has long been recognized for its role in value-based decisions; however, the exact mechanism linking value representations in the OFC to decision outcomes has remained elusive. Here, to address this question, we show, in non-human primates, that trial-wise variability in choices can be explained by variability in value signals decoded from many simultaneously recorded OFC neurons. Mechanistically, this relationship is consistent with the projection of activity within a low-dimensional value-encoding subspace onto a potentially higher-dimensional, behaviorally potent output subspace. Identifying this neural-behavioral link answers longstanding questions about the role of the OFC in economic decision-making and suggests population-level read-out mechanisms for the OFC similar to those recently identified in sensory and motor cortex.

Hierarchical differences in the encoding of sound and choice in the subcortical auditory system

Detection of sounds is a fundamental function of the auditory system. Although studies of auditory cortex have gained substantial insight into detection performance using behaving animals, previous subcortical studies have mostly taken place under anesthesia, in passively listening animals, or have not measured performance at threshold. These limitations preclude direct comparisons between neuronal responses and behavior. To address this, we simultaneously measured auditory detection performance and single-unit activity in the inferior colliculus (IC) and cochlear nucleus (CN) in macaques. The spontaneous activity and response variability of CN neurons were higher than those observed for IC neurons. Signal detection theoretic methods revealed that the magnitude of responses of IC neurons provided more reliable estimates of psychometric threshold and slope compared with the responses of single CN neurons. However, pooling small populations of CN neurons provided reliable estimates of psychometric threshold and slope, suggesting sufficient information in CN population activity. Trial-by-trial correlations between spike count and behavioral response emerged 50-75 ms after sound onset for most IC neurons, but for few neurons in the CN. These results highlight hierarchical differences between neurometric-psychometric correlations in CN and IC and have important implications for how subcortical information could be decoded.NEW & NOTEWORTHY The cerebral cortex is widely recognized to play a role in sensory processing and decision-making. Accounts of the neural basis of auditory perception and its dysfunction are based on this idea. However, significantly less attention has been paid to midbrain and brainstem structures in this regard. Here, we find that subcortical auditory neurons represent stimulus information sufficient for detection and predict behavioral choice on a trial-by-trial basis.

Perceptual enhancement and suppression correlate with V1 neural activity during active sensing

Perception in multiple sensory modalities is an active process that involves exploratory behaviors. In humans and other primates, vision results from sensory sampling guided by saccadic eye movements. Saccades are known to modulate visual perception, and a corollary discharge signal associated with saccades appears to establish a sense of visual stability. Neural recordings have shown that saccades also modulate activity widely across the brain. To investigate the neural basis of saccadic effects on perception, simultaneous recordings from multiple neurons in area V1 were made as animals performed a contrast detection task. Perceptual and neural measures were compared when the animal made real saccades that brought a stimulus into V1 receptive fields and when simulated saccades were made (identical retinal stimulation but no eye movement). When real saccades were made and low spatial frequency stimuli were presented, we observed a reduction in both perceptual sensitivity and neural activity compared with simulated saccades; conversely, with higher spatial frequency stimuli, saccades increased visual sensitivity and neural activity. The performance of neural decoders, which used the activity of the population of simultaneously recorded neurons, showed saccade effects on sensitivity that mirrored the frequency-dependent perceptual changes, suggesting that the V1 population activity could support the perceptual effects. A minority of V1 neurons had significant choice probabilities, and the saccades decreased both average choice probability and pairwise noise correlations. Taken together, the findings suggest that a signal related to saccadic eye movements alters V1 spiking to increase the independence of spiking neurons and bias the system toward processing higher spatial frequencies, presumably to enhance object recognition. The effects of saccades on visual perception and noise correlations appear to parallel effects observed in other sensory modalities, suggesting a general principle of active sensory processing.

Task-induced neural covariability as a signature of approximate Bayesian learning and inference

Perception is often characterized computationally as an inference process in which uncertain or ambiguous sensory inputs are combined with prior expectations. Although behavioral studies have shown that observers can change their prior expectations in the context of a task, robust neural signatures of task-specific priors have been elusive. Here, we analytically derive such signatures under the general assumption that the responses of sensory neurons encode posterior beliefs that combine sensory inputs with task-specific expectations. Specifically, we derive predictions for the task-dependence of correlated neural variability and decision-related signals in sensory neurons. The qualitative aspects of our results are parameter-free and specific to the statistics of each task. The predictions for correlated variability also differ from predictions of classic feedforward models of sensory processing and are therefore a strong test of theories of hierarchical Bayesian inference in the brain. Importantly, we find that Bayesian learning predicts an increase in so-called “differential correlations” as the observer’s internal model learns the stimulus distribution, and the observer’s behavioral performance improves. This stands in contrast to classic feedforward encoding/decoding models of sensory processing, since such correlations are fundamentally information-limiting. We find support for our predictions in data from existing neurophysiological studies across a variety of tasks and brain areas. Finally, we show in simulation how measurements of sensory neural responses can reveal information about a subject’s internal beliefs about the task. Taken together, our results reinterpret task-dependent sources of neural covariability as signatures of Bayesian inference and provide new insights into their cause and their function.

Perceptual decision-making has classically been studied in the context of feedforward encoding/ decoding models. Here, we derive predictions for the responses of sensory neurons under the assumption that the brain performs hierarchical Bayesian inference, including feedback signals that communicate task-specific prior expectations. Interestingly, those predictions stand in contrast to some of the conclusions drawn in the classic framework. In particular, we find that Bayesian learning predicts the increase of a type of correlated variability called “differential correlations” over the course of learning. Differential correlations limit information, and hence are seen as harmful in feedforward models. Since our results are also specific to the statistics of a given task, and since they hold under a wide class of theories about how Bayesian probabilities may be represented by neural responses, they constitute a strong test of the Bayesian Brain hypothesis. Our results can explain the task-dependence of correlated variability in prior studies and suggest a reason why these kinds of correlations are surprisingly common in empirical data. Interpreted in a probabilistic framework, correlated variability provides a window into an observer’s task-related beliefs.

Neuronal origins of reduced accuracy and biases in economic choices under sequential offers

Economic choices are characterized by a variety of biases. Understanding their origins is a long-term goal for neuroeconomics, but progress on this front has been limited. Here, we examined choice biases observed when two goods are offered sequentially. In the experiments, rhesus monkeys chose between different juices offered simultaneously or in sequence. Choices under sequential offers were less accurate (higher variability). They were also biased in favor of the second offer (order bias) and in favor of the preferred juice (preference bias). Analysis of neuronal activity recorded in the orbitofrontal cortex revealed that these phenomena emerged at different computational stages. Lower choice accuracy reflected weaker offer value signals (valuation stage), the order bias emerged during value comparison (decision stage), and the preference bias emerged late in the trial (post-comparison). By neuronal measures, each phenomenon reduced the value obtained on average in each trial and was thus costly to the monkey.

Correlations enhance the behavioral readout of neural population activity in association cortex

Noise correlations (that is, trial-to-trial covariations in neural activity for a given stimulus) limit the stimulus information encoded by neural populations, leading to the widely held prediction that they impair perceptual discrimination behaviors. However, this prediction neglects the effects of correlations on information readout. We studied how correlations affect both encoding and readout of sensory information. We analyzed calcium imaging data from mouse posterior parietal cortex during two perceptual discrimination tasks. Correlations reduced the encoded stimulus information, but, seemingly paradoxically, were higher when mice made correct rather than incorrect choices. Single-trial behavioral choices depended not only on the stimulus information encoded by the whole population, but unexpectedly also on the consistency of information across neurons and time. Because correlations increased information consistency, they enhanced the conversion of sensory information into behavioral choices, overcoming their detrimental information-limiting effects. Thus, correlations in association cortex can benefit task performance even if they decrease sensory information.

Stimulus-dependent relationships between behavioral choice and sensory neural responses

Understanding perceptual decision-making requires linking sensory neural responses to behavioral choices. In two-choice tasks, activity-choice covariations are commonly quantified with a single measure of choice probability (CP), without characterizing their changes across stimulus levels. We provide theoretical conditions for stimulus dependencies of activity-choice covariations. Assuming a general decision-threshold model, which comprises both feedforward and feedback processing and allows for a stimulus-modulated neural population covariance, we analytically predict a very general and previously unreported stimulus dependence of CPs. We develop new tools, including refined analyses of CPs and generalized linear models with stimulus-choice interactions, which accurately assess the stimulus- or choice-driven signals of each neuron, characterizing stimulus-dependent patterns of choice-related signals. With these tools, we analyze CPs of macaque MT neurons during a motion discrimination task. Our analysis provides preliminary empirical evidence for the promise of studying stimulus dependencies of choice-related signals, encouraging further assessment in wider data sets.

Choice-related activity and neural encoding in primary auditory cortex and lateral belt during feature-selective attention

Selective attention is necessary to sift through, form a coherent percept of, and make behavioral decisions on the vast amount of information present in most sensory environments. How and where selective attention is employed in cortex and how this perceptual information then informs the relevant behavioral decisions is still not well understood. Studies probing selective attention and decision-making in visual cortex have been enlightening as to how sensory attention might work in that modality; whether or not similar mechanisms are employed in auditory attention is not yet clear. Therefore, we trained rhesus macaques on a feature-selective attention task, where they switched between reporting changes in temporal (amplitude modulation, AM) and spectral (carrier bandwidth) features of a broadband noise stimulus. We investigated how the encoding of these features by single neurons in primary (A1) and secondary (middle lateral belt, ML) auditory cortex was affected by the different attention conditions. We found that neurons in A1 and ML showed mixed selectivity to the sound and task features. We found no difference in AM encoding between the attention conditions. We found that choice-related activity in both A1 and ML neurons shifts between attentional conditions. This finding suggests that choice-related activity in auditory cortex does not simply reflect motor preparation or action and supports the relationship between reported choice-related activity and the decision and perceptual process.NEW & NOTEWORTHY We recorded from primary and secondary auditory cortex while monkeys performed a nonspatial feature attention task. Both areas exhibited rate-based choice-related activity. The manifestation of choice-related activity was attention dependent, suggesting that choice-related activity in auditory cortex does not simply reflect arousal or motor influences but relates to the specific perceptual choice.

Processing of motion boundary orientation in macaque V2

Human and nonhuman primates are good at identifying an object based on its motion, a task that is believed to be carried out by the ventral visual pathway. However, the neural mechanisms underlying such ability remains unclear. We trained macaque monkeys to do orientation discrimination for motion boundaries (MBs) and recorded neuronal response in area V2 with microelectrode arrays. We found 10.9% of V2 neurons exhibited robust orientation selectivity to MBs, and their responses correlated with monkeys' orientation-discrimination performances. Furthermore, the responses of V2 direction-selective neurons recorded at the same time showed correlated activity with MB neurons for particular MB stimuli, suggesting that these motion-sensitive neurons made specific functional contributions to MB discrimination tasks. Our findings support the view that V2 plays a critical role in MB analysis and may achieve this through a neural circuit within area V2.

The Correlation of Neuronal Signals with Behavior at Different Levels of Visual Cortex and Their Relative Reliability for Behavioral Decisions

Behavior can be guided by neuronal activity in visual, auditory, or somatosensory cerebral cortex, depending on task requirements. In contrast to this flexible access of cortical signals, several observations suggest that behaviors depend more on neurons in later areas of visual cortex than those in earlier areas, although neurons in earlier areas would provide more reliable signals for many tasks. We recorded from neurons in different levels of visual cortex of 2 male rhesus monkeys while the animals did a visual discrimination task and examined trial-to-trial correlations between neuronal and behavioral responses. These correlations became stronger in primary visual cortex as neuronal signals in that area became more reliable relative to the other areas. The results suggest that the mechanisms that read signals from cortex might access any cortical area depending on the relative value of those signals for the task at hand.SIGNIFICANCE STATEMENT Information is encoded by the action potentials of neurons in various cortical areas in a hierarchical manner such that increasingly complex stimulus features are encoded in successive stages. The brain must extract information from the response of appropriate neurons to drive optimal behavior. A widely held view of this decoding process is that the brain relies on the output of later cortical areas to make decisions, although neurons in earlier areas can provide more reliable signals. We examined correlations between perceptual decisions and the responses of neurons in different levels of monkey visual cortex. The results suggest that the brain may access signals in any cortical area depending on the relative value of those signals for the task at hand.

Economic Decisions through Circuit Inhibition

Economic choices between goods are thought to rely on the orbitofrontal cortex (OFC), but the decision mechanisms remain poorly understood. To shed light on this fundamental issue, we recorded from the OFC of monkeys choosing between two juices offered sequentially. An analysis of firing rates across time windows revealed the presence of different groups of neurons similar to those previously identified under simultaneous offers. This observation suggested that economic decisions in the two modalities are formed in the same neural circuit. We then examined several hypotheses on the decision mechanisms. OFC neurons encoded good identities and values in a juice-based representation (labeled lines). Contrary to previous assessments, our data argued against the idea that decisions rely on mutual inhibition at the level of offer values. In fact, we showed that previous arguments for mutual inhibition were confounded by differences in value ranges. Instead, decisions seemed to involve mechanisms of circuit inhibition, whereby each offer value indirectly inhibited neurons encoding the opposite choice outcome. Our results reconcile a variety of previous findings and provide a general account for the neuronal underpinnings of economic choices.

Routing information flow by separate neural synchrony frequencies allows for "functionally labeled lines" in higher primate cortex

Dynamical coordination of the neural activity between individual neurons is known to have a key role in the efficient transfer of sensory information to associative areas. Here, we report a role of interneuronal synchrony within the high-gamma (180 to 220 Hz) frequency range of activity in macaque area MT (a visual area in the dorsal visual pathway) in determining behavioral performance. This is, however, in contrast to previous reports for the ventral visual pathway (such as area V4), where only gamma range (40 to 70 Hz) was observed to play a role. We propose that such a difference between the functional coordination in different visual pathways might be used to unambiguously identify the source of input to the higher areas.

Efficient transfer of sensory information to higher (motor or associative) areas in primate visual cortical areas is crucial for transforming sensory input into behavioral actions. Dynamically increasing the level of coordination between single neurons has been suggested as an important contributor to this efficiency. We propose that differences between the functional coordination in different visual pathways might be used to unambiguously identify the source of input to the higher areas, ensuring a proper routing of the information flow. Here we determined the level of coordination between neurons in area MT in macaque visual cortex in a visual attention task via the strength of synchronization between the neurons’ spike timing relative to the phase of oscillatory activities in local field potentials. In contrast to reports on the ventral visual pathway, we observed the synchrony of spikes only in the range of high gamma (180 to 220 Hz), rather than gamma (40 to 70 Hz) (as reported previously) to predict the animal’s reaction speed. This supports a mechanistic role of the phase of high-gamma oscillatory activity in dynamically modulating the efficiency of neuronal information transfer. In addition, for inputs to higher cortical areas converging from the dorsal and ventral pathway, the distinct frequency bands of these inputs can be leveraged to preserve the identity of the input source. In this way source-specific oscillatory activity in primate cortex can serve to establish and maintain “functionally labeled lines” for dynamically adjusting cortical information transfer and multiplexing converging sensory signals.

Contribution of Sensory Encoding to Measured Bias

Signal detection theory (SDT) is a widely used theoretical framework that describes how variable sensory signals are integrated with a decision criterion to support perceptual decision-making. SDT provides two key measurements: sensitivity (d') and bias (c), which reflect the separability of decision variable distributions (signal and noise) and the position of the decision criterion relative to optimal, respectively. Although changes in the subject's decision criterion can be reflected in changes in bias, decision criterion placement is not the sole contributor to measured bias. Indeed, neuronal representations of bias have been observed in sensory areas, suggesting that some changes in bias are because of effects on sensory encoding. To directly test whether the sensory encoding process can influence bias, we optogenetically manipulated neuronal excitability in primary visual cortex (V1) in mice of both sexes during either an orientation discrimination or a contrast detection task. Increasing excitability in V1 significantly decreased behavioral bias, whereas decreasing excitability had the opposite effect. To determine whether this change in bias is consistent with effects on sensory encoding, we made extracellular recordings from V1 neurons in passively viewing mice. Indeed, we found that optogenetic manipulation of excitability shifted the neuronal bias in the same direction as the behavioral bias. Moreover, manipulating the quality of V1 encoding by changing stimulus contrast or interstimulus interval also resulted in consistent changes in both behavioral and neuronal bias. Thus, changes in sensory encoding are sufficient to drive changes in bias measured using SDT.SIGNIFICANCE STATEMENT Perceptual decision-making involves sensory integration followed by application of a cognitive criterion. Using signal detection theory, one can extract features of the underlying decision variables and rule: sensitivity (d') and bias (c). Because bias is measured as the difference between the optimal and actual criterion, it is sensitive to both the sensory encoding processes and the placement of the decision criterion. Here, we use behavioral and electrophysiological approaches to demonstrate that measures of bias depend on sensory processes. Optogenetic manipulations of V1 in mice bidirectionally affect both behavioral and neuronal measures of bias with little effect on sensitivity. Thus, changes in sensory encoding influence bias, and the absence of changes in sensitivity do not preclude changes in sensory encoding.

Choice-Related Activity during Visual Slant Discrimination in Macaque CIP But Not V3A

Creating three-dimensional (3D) representations of the world from two-dimensional retinal images is fundamental to visually guided behaviors including reaching and grasping. A critical component of this process is determining the 3D orientation of objects. Previous studies have shown that neurons in the caudal intraparietal area (CIP) of the macaque monkey represent 3D planar surface orientation (i.e., slant and tilt). Here we compare the responses of neurons in areas V3A (which is implicated in 3D visual processing and precedes CIP in the visual hierarchy) and CIP to 3D-oriented planar surfaces. We then examine whether activity in these areas correlates with perception during a fine slant discrimination task in which the monkeys report if the top of a surface is slanted toward or away from them. Although we find that V3A and CIP neurons show similar sensitivity to planar surface orientation, significant choice-related activity during the slant discrimination task is rare in V3A but prominent in CIP. These results implicate both V3A and CIP in the representation of 3D surface orientation, and suggest a functional dissociation between the areas based on slant-related choice signals.

Neuronal evidence for good-based economic decisions under variable action costs

Previous work showed that economic decisions can be made independently of spatial contingencies. However, when goods available for choice bear different action costs, the decision necessarily reflects aspects of the action. One possibility is that "stimulus values" are combined with the corresponding action costs in a motor representation, and decisions are then made in actions space. Alternatively, action costs could be integrated with other determinants of value in a non-spatial representation. If so, decisions under variable action costs could take place in goods space. Here, we recorded from orbitofrontal cortex while monkeys chose between different juices offered in variable amounts. We manipulated action costs by varying the saccade amplitude, and we dissociated in time and space offer presentation from action planning. Neurons encoding the binary choice outcome did so well before the presentation of saccade targets, indicating that decisions were made in goods space.

Inferring decoding strategies for multiple correlated neural populations

Studies of neuron-behaviour correlation and causal manipulation have long been used separately to understand the neural basis of perception. Yet these approaches sometimes lead to drastically conflicting conclusions about the functional role of brain areas. Theories that focus only on choice-related neuronal activity cannot reconcile those findings without additional experiments involving large-scale recordings to measure interneuronal correlations. By expanding current theories of neural coding and incorporating results from inactivation experiments, we demonstrate here that it is possible to infer decoding weights of different brain areas at a coarse scale without precise knowledge of the correlation structure. We apply this technique to neural data collected from two different cortical areas in macaque monkeys trained to perform a heading discrimination task. We identify two opposing decoding schemes, each consistent with data depending on the nature of correlated noise. Our theory makes specific testable predictions to distinguish these scenarios experimentally without requiring measurement of the underlying noise correlations.

The neocortex is structurally organized into distinct brain areas. The role of specific brain areas in sensory perception is typically studied using two kinds of laboratory experiments: those that measure correlations between neural activity and reported percepts, and those that inactivate a brain region and measure the resulting changes in percepts. The two types of experiments have generally been interpreted in isolation, in part because no theory has been able combine their outcomes. Here, we describe a mathematical framework that synthesizes both kinds of results, giving us a new way to assess how different brain areas contribute to perception. When we apply our framework to experiments on behaving monkeys, we discover two models that can explain the perplexing finding that one brain area can predict an animal’s reported percepts, even though the percepts are not affected when that brain area is inactivated. The two models ascribe dramatically different efficiencies to brain computation. We show that these two models could be distinguished by a proposed experiment that measures correlations while inactivating different brain areas.

Clustering of heading selectivity and perception-related activity in the ventral intraparietal area

The ventral intraparietal area (VIP) of the macaque brain is a multimodal cortical region, with many cells tuned to both optic flow and vestibular stimuli. Responses of many VIP neurons also show robust correlations with perceptual judgments during a fine heading discrimination task. Previous studies have shown that heading tuning based on optic flow is represented in a clustered fashion in VIP. However, it is unknown whether vestibular self-motion selectivity is clustered in VIP. Moreover, it is not known whether stimulus- and choice-related signals in VIP show clustering in the context of a heading discrimination task. To address these issues, we compared the response characteristics of isolated single units (SUs) with those of the undifferentiated multiunit (MU) activity corresponding to several neighboring neurons recorded from the same microelectrode. We find that MU activity typically shows selectivity similar to that of simultaneously recorded SUs, for both the vestibular and visual stimulus conditions. In addition, the choice-related activity of MU signals, as quantified using choice probabilities, is correlated with the choice-related activity of SUs. Overall, these findings suggest that both sensory and choice-related signals regarding self-motion are clustered in VIP. NEW & NOTEWORTHY We demonstrate, for the first time, that the vestibular tuning of ventral intraparietal area (VIP) neurons in response to both translational and rotational motion is clustered. In addition, heading discriminability and choice-related activity are also weakly clustered in VIP.

Feedback determines the structure of correlated variability in primary visual cortex

The variable responses of sensory neurons tend to be weakly correlated (spike-count correlation, r_sc). This is widely thought to reflect noise in shared afferents, in which case r_sc can limit the reliability of sensory coding. However, it could also be due to feedback from higher-order brain regions. Currently, the relative contributions of these sources are unknown. We addressed this by recording from populations of V1 neurons in macaques performing different discrimination tasks involving the same visual input. We found that the structure of r_sc (the way r_sc varied with neuronal stimulus preference) changed systematically with task instruction. Therefore, even at the earliest stage in the cortical visual hierarchy, r_sc structure during task performance primarily reflects feedback dynamics. Consequently, previous proposals for how r_sc constrains sensory processing need not apply. Furthermore, we show that correlations between the activity of single neurons and choice depend on feedback engaged by the task.

Decoupled choice-driven and stimulus-related activity in parietal neurons may be misrepresented by choice probabilities

Trial-by-trial correlations between neural responses and choices (choice probabilities) are often interpreted to reflect a causal contribution of neurons to task performance. However, choice probabilities may arise from top-down, rather than bottom-up, signals. We isolated distinct sensory and decision contributions to single-unit activity recorded from the dorsal medial superior temporal (MSTd) and ventral intraparietal (VIP) areas of monkeys during perception of self-motion. Superficially, neurons in both areas show similar tuning curves during task performance. However, tuning in MSTd neurons primarily reflects sensory inputs, whereas choice-related signals dominate tuning in VIP neurons. Importantly, the choice-related activity of VIP neurons is not predictable from their stimulus tuning, and these factors are often confounded in choice probability measurements. This finding was confirmed in a subset of neurons for which stimulus tuning was measured during passive fixation. Our findings reveal decoupled stimulus and choice signals in the VIP area, and challenge our understanding of choice signals in the brain.Choice-related signals in neuronal activity may reflect bottom-up sensory processes, top-down decision-related influences, or a combination of the two. Here the authors report that choice-related activity in VIP neurons is not predictable from their stimulus tuning, and that dominant choice signals can bias the standard metric of choice preference (choice probability).

Defining the V5/MT neuronal pool for perceptual decisions in a visual stereo-motion task

In the primate visual cortex, neurons signal differences in the appearance of objects with high precision. However, not all activated neurons contribute directly to perception. We defined the perceptual pool in extrastriate visual area V5/MT for a stereo-motion task, based on trial-by-trial co-variation between perceptual decisions and neuronal firing (choice probability (CP)). Macaque monkeys were trained to discriminate the direction of rotation of a cylinder, using the binocular depth between the moving dots that form its front and rear surfaces. We manipulated the activity of single neurons trial-to-trial by introducing task-irrelevant stimulus changes: dot motion in cylinders was aligned with neuronal preference on only half the trials, so that neurons were strongly activated with high firing rates on some trials and considerably less activated on others. We show that single neurons maintain high neurometric sensitivity for binocular depth in the face of substantial changes in firing rate. CP was correlated with neurometric sensitivity, not level of activation. In contrast, for individual neurons, the correlation between perceptual choice and neuronal activity may be fundamentally different when responding to different stimulus versions. Therefore, neuronal pools supporting sensory discrimination must be structured flexibly and independently for each stimulus configuration to be discriminated.

This article is part of the themed issue ‘Vision in our three-dimensional world'.

The Contribution of Area MT to Visual Motion Perception Depends on Training

Perceptual decisions require the transformation of raw sensory inputs into cortical representations suitable for stimulus discrimination. One of the best-known examples of this transformation involves the middle temporal area (MT) of the primate visual cortex. Area MT provides a robust representation of stimulus motion, and previous work has shown that it contributes causally to performance on motion discrimination tasks. Here we report that the strength of this contribution can be highly plastic: depending on the recent training history, pharmacological inactivation of MT can severely impair motion discrimination, or it can have little detectable influence. Further analysis of neural and behavioral data suggests that training moves the readout of motion information between MT and lower-level cortical areas. These results show that the contribution of individual brain regions to conscious perception can shift flexibly depending on sensory experience.

Sequential sensory and decision processing in posterior parietal cortex

Decisions about the behavioral significance of sensory stimuli often require comparing sensory inference of what we are looking at to internal models of what we are looking for. Here, we test how neuronal selectivity for visual features is transformed into decision-related signals in posterior parietal cortex (area LIP). Monkeys performed a visual matching task that required them to detect target stimuli composed of conjunctions of color and motion-direction. Neuronal recordings from area LIP revealed two main findings. First, the sequential processing of visual features and the selection of target-stimuli suggest that LIP is involved in transforming sensory information into decision-related signals. Second, the patterns of color and motion selectivity and their impact on decision-related encoding suggest that LIP plays a role in detecting target stimuli by comparing bottom-up sensory inputs (what the monkeys were looking at) and top-down cognitive encoding inputs (what the monkeys were looking for).

DOI:http://dx.doi.org/10.7554/eLife.23743.001

A neural basis for the spatial suppression of visual motion perception

In theory, sensory perception should be more accurate when more neurons contribute to the representation of a stimulus. However, psychophysical experiments that use larger stimuli to activate larger pools of neurons sometimes report impoverished perceptual performance. To determine the neural mechanisms underlying these paradoxical findings, we trained monkeys to discriminate the direction of motion of visual stimuli that varied in size across trials, while simultaneously recording from populations of motion-sensitive neurons in cortical area MT. We used the resulting data to constrain a computational model that explained the behavioral data as an interaction of three main mechanisms: noise correlations, which prevented stimulus information from growing with stimulus size; neural surround suppression, which decreased sensitivity for large stimuli; and a read-out strategy that emphasized neurons with receptive fields near the stimulus center. These results suggest that paradoxical percepts reflect tradeoffs between sensitivity and noise in neuronal populations.

DOI:http://dx.doi.org/10.7554/eLife.16167.001

People usually find it easier to see things when they are big and bright, but there are occasionally exceptions. One example concerns moving objects: when they are small, we can identify their direction of motion easily, but this becomes much more difficult for larger objects. This decreased perceptual sensitivity appears to be linked to other mental processes. For example, studies have suggested that people with high IQs have more difficulty perceiving the motion of large objects, whereas people with various psychiatric disorders, such as schizophrenia, are better able to see such movement. Although several theories have been proposed, there is currently no good explanation for these findings.

Liu et al. set out to determine why the part of the brain that is responsible for vision (the visual cortex) fails to register the direction of large moving objects and how this failure might relate to mental function in general. To do this, Liu et al. trained monkeys to report which direction different sized stimuli were moving on a screen. The electrical activity of nerve cells in the part of the visual cortex that deals with movement was recorded while the monkeys performed this task. The results of the experiments revealed that, on average, these cells responded strongly to large moving stimuli, even though the monkeys had trouble seeing their motion. However, nerve cells are “noisy” – they respond a bit differently every time they are presented with the same stimulus – and this noise was stronger for larger stimuli.

By studying the mathematical relationship between the noise and what the animals perceived, Liu et al. found that the visual cortex attempts to suppress the noise and in the process often shuts off the responses to large stimuli entirely. This suppression is likely to cause the movement of large stimuli to be poorly perceived.

If suppressing this kind of noise is really responsible for failures in perceiving motion, then this mechanism could also explain the connection between motion perception and other mental processes. Liu et al. are currently testing this idea.

DOI:http://dx.doi.org/10.7554/eLife.16167.002

A Modality-Specific Feedforward Component of Choice-Related Activity in MT

The activity of individual sensory neurons can be predictive of an animal's choices. These decision signals arise from network properties dependent on feedforward and feedback inputs; however, the relative contributions of these inputs are poorly understood. We determined the role of feedforward pathways to decision signals in MT by recording neuronal activity while monkeys performed motion and depth tasks. During each session, we reversibly inactivated V2 and V3, which provide feedforward input to MT that conveys more information about depth than motion. We thus monitored the choice-related activity of the same neuron both before and during V2/V3 inactivation. During inactivation, MT neurons became less predictive of decisions for the depth task but not the motion task, indicating that a feedforward pathway that gives rise to tuning preferences also contributes to decision signals. We show that our data are consistent with V2/V3 input conferring structured noise correlations onto the MT population.

How Can Single Sensory Neurons Predict Behavior?

Single sensory neurons can be surprisingly predictive of behavior in discrimination tasks. We propose this is possible because sensory information extracted from neural populations is severely restricted, either by near-optimal decoding of a population with information-limiting correlations or by suboptimal decoding that is blind to correlations. These have different consequences for choice correlations, the correlations between neural responses and behavioral choices. In the vestibular and cerebellar nuclei and the dorsal medial superior temporal area, we found that choice correlations during heading discrimination are consistent with near-optimal decoding of neuronal responses corrupted by information-limiting correlations. In the ventral intraparietal area, the choice correlations are also consistent with the presence of information-limiting correlations, but this area does not appear to influence behavior, although the choice correlations are particularly large. These findings demonstrate how choice correlations can be used to assess the efficiency of the downstream readout and detect the presence of information-limiting correlations.

NEURONAL MODELING. Single-trial spike trains in parietal cortex reveal discrete steps during decision-making

Neurons in the macaque lateral intraparietal (LIP) area exhibit firing rates that appear to ramp upward or downward during decision-making. These ramps are commonly assumed to reflect the gradual accumulation of evidence toward a decision threshold. However, the ramping in trial-averaged responses could instead arise from instantaneous jumps at different times on different trials. We examined single-trial responses in LIP using statistical methods for fitting and comparing latent dynamical spike-train models. We compared models with latent spike rates governed by either continuous diffusion-to-bound dynamics or discrete "stepping" dynamics. Roughly three-quarters of the choice-selective neurons we recorded were better described by the stepping model. Moreover, the inferred steps carried more information about the animal's choice than spike counts.

Perceptual decision related activity in the lateral geniculate nucleus

Fundamental to neuroscience is the understanding of how the language of neurons relates to behavior. In the lateral geniculate nucleus (LGN), cells show distinct properties such as selectivity for particular wavelengths, increments or decrements in contrast, or preference for fine detail versus rapid motion. No studies, however, have measured how LGN cells respond when an animal is challenged to make a perceptual decision using information within the receptive fields of those LGN cells. In this study we measured neural activity in the macaque LGN during a two-alternative, forced-choice (2AFC) contrast detection task or during a passive fixation task and found that a small proportion (13.5%) of single LGN parvocellular (P) and magnocellular (M) neurons matched the psychophysical performance of the monkey. The majority of LGN neurons measured in both tasks were not as sensitive as the monkey. The covariation between neural response and behavior (quantified as choice probability) was significantly above chance during active detection, even when there was no external stimulus. Interneuronal correlations and task-related gain modulations were negligible under the same condition. A bottom-up pooling model that used sensory neural responses to compute perceptual choices in the absence of interneuronal correlations could fully explain these results at the level of the LGN, supporting the hypothesis that the perceptual decision pool consists of multiple sensory neurons and that response fluctuations in these neurons can influence perception.

A functional link between MT neurons and depth perception based on motion parallax

As an observer translates, objects lying at different distances from the observer have differential image motion on the retina (motion parallax). It is well established psychophysically that humans perceive depth rather precisely from motion parallax and that extraretinal signals may be used to correctly perceive the sign of depth (near vs far) when binocular and pictorial depth cues are absent or weak. However, the neural basis for this capacity remains poorly understood. We have shown previously that neurons in the macaque middle temporal (MT) area combine retinal image motion with smooth eye movement command signals to signal depth sign from motion parallax. However, those studies were performed in animals that were required simply to track a visual target, thus precluding direct comparisons between neural activity and behavior. Here, we examine the activity of MT neurons in rhesus monkeys that were trained to discriminate depth sign based on motion parallax, in the absence of binocular disparity and pictorial depth cues. We find that the most sensitive MT neurons approach behavioral sensitivity, whereas the average neuron is twofold to threefold less sensitive than the animal. We also find that MT responses are predictive of perceptual decisions (independent of the visual stimulus), consistent with a role for MT in providing sensory signals for this behavior. Our findings suggest that, in addition to its established roles in processing stereoscopic depth, area MT is well suited to contribute to perception of depth based on motion parallax.

Sensory integration dynamics in a hierarchical network explains choice probabilities in cortical area MT

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

Choice-related Activity in the Anterior Intraparietal Area during 3-D Structure Categorization

The anterior intraparietal area (AIP) of macaques contains neurons that signal the depth structure of disparity-defined 3-D shapes. Previous studies have suggested that AIP's depth information is used for sensorimotor transformations related to the efficient grasping of 3-D objects. We trained monkeys to categorize disparity-defined 3-D shapes and examined whether neuronal activity in AIP may also underlie pure perceptual categorization behavior. We first show that neurons with a similar 3-D shape preference cluster in AIP. We then demonstrate that the monkeys' 3-D shape discrimination performance depends on the position in depth of the stimulus and that this performance difference is reflected in the activity of AIP neurons. We further reveal correlations between the neuronal activity in AIP and the subject's subsequent choices and RTs during 3-D shape categorization. Our findings propose AIP as an important processing stage for 3-D shape perception.

ROC-based estimates of neural-behavioral covariations using matched filters

Correlations between responses in visual cortex and perceptual performance help draw a functional link between neural activity and visually guided behavior. These correlations are commonly derived with ROC-based neural-behavioral covariances (referred to as choice or detect probability) using boxcar analysis windows. Although boxcar windows capture the covariation between neural activity and behavior during steady-state stimulus presentations, they are not optimized to capture these correlations during short time-varying visual inputs. In this study, we implemented a matched-filter technique, combined with cross-validation, to improve the estimation of ROC-based neural-behavioral covariance under short and dynamic stimulus conditions. We show that this approach maximizes the area under the ROC curve and converges to the true neural-behavioral covariance using a Poisson spiking model. We also demonstrate that the matched filter, combined with cross-validation, reveals the dynamics of the neural-behavioral covariations of individual MT neurons during the detection of a brief motion stimulus.

Perceptual learning of simple stimuli modifies stimulus representations in posterior inferior temporal cortex

Practicing simple visual detection and discrimination tasks improves performance, a signature of adult brain plasticity. The neural mechanisms that underlie these changes in performance are still unclear. Previously, we reported that practice in discriminating the orientation of noisy gratings (coarse orientation discrimination) increased the ability of single neurons in the early visual area V4 to discriminate the trained stimuli. Here, we ask whether practice in this task also changes the stimulus tuning properties of later visual cortical areas, despite the use of simple grating stimuli. To identify candidate areas, we used fMRI to map activations to noisy gratings in trained rhesus monkeys, revealing a region in the posterior inferior temporal (PIT) cortex. Subsequent single unit recordings in PIT showed that the degree of orientation selectivity was similar to that of area V4 and that the PIT neurons discriminated the trained orientations better than the untrained orientations. Unlike in previous single unit studies of perceptual learning in early visual cortex, more PIT neurons preferred trained compared with untrained orientations. The effects of training on the responses to the grating stimuli were also present when the animals were performing a difficult orthogonal task in which the grating stimuli were task-irrelevant, suggesting that the training effect does not need attention to be expressed. The PIT neurons could support orientation discrimination at low signal-to-noise levels. These findings suggest that extensive practice in discriminating simple grating stimuli not only affects early visual cortex but also changes the stimulus tuning of a late visual cortical area.

Contributions of orbitofrontal and lateral prefrontal cortices to economic choice and the good-to-action transformation

Previous work indicates that economic decisions can be made independently of the visuomotor contingencies of the choice task (space of goods). However, the neuronal mechanisms through which the choice outcome (the chosen good) is transformed into a suitable action plan remain poorly understood. Here we show that neurons in lateral prefrontal cortex reflect the early stages of this good-to-action transformation. Monkeys chose between different juices. The experimental design dissociated in space and time the presentation of the offers and the saccade targets associated with them. We recorded from the orbital, ventrolateral, and dorsolateral prefrontal cortices (OFC, LPFCv, and LPFCd, respectively). Prior to target presentation, neurons in both LPFCv and LPFCd encoded the choice outcome in goods space. After target presentation, they gradually came to encode the location of the targets and the upcoming action plan. Consistent with the anatomical connectivity, all spatial and action-related signals emerged in LPFCv before LPFCd.

Neuronal origins of choice variability in economic decisions

To investigate the mechanisms through which economic decisions are formed, I examined the activity of neurons in the orbitofrontal cortex while monkeys chose between different juice types. Different classes of cells encoded the value of individual offers (offer value), the value of the chosen option (chosen value), or the identity of the chosen juice (chosen juice). Choice variability was partly explained by the tendency to repeat choices (choice hysteresis). Surprisingly, near-indifference decisions did not reflect fluctuations in the activity of offer value cells. In contrast, near-indifference decisions correlated with fluctuations in the preoffer activity of chosen juice cells. After the offer, the activity of chosen juice cells reflected the decision difficulty but did not resemble a race-to-threshold. Finally, chosen value cells presented an "activity overshooting" closely related to the decision difficulty and possibly due to fluctuations in the relative value of the juices. This overshooting was independent of choice hysteresis.

V1 mechanisms underlying chromatic contrast detection

To elucidate the cortical mechanisms of color vision, we recorded from individual primary visual cortex (V1) neurons in macaque monkeys performing a chromatic detection task. Roughly 30% of the neurons that we encountered were unresponsive at the monkeys' psychophysical detection threshold (PT). The other 70% were responsive at threshold but on average, were slightly less sensitive than the monkey. For these neurons, the relationship between neurometric threshold (NT) and PT was consistent across the four isoluminant color directions tested. A corollary of this result is that NTs were roughly four times lower for stimuli that modulated the long- and middle-wavelength sensitive cones out of phase. Nearly one-half of the neurons that responded to chromatic stimuli at the monkeys' detection threshold also responded to high-contrast luminance modulations, suggesting a role for neurons that are jointly tuned to color and luminance in chromatic detection. Analysis of neuronal contrast-response functions and signal-to-noise ratios yielded no evidence for a special set of "cardinal color directions," for which V1 neurons are particularly sensitive. We conclude that at detection threshold--as shown previously with high-contrast stimuli-V1 neurons are tuned for a diverse set of color directions and do not segregate naturally into red-green and blue-yellow categories.