Decoding subjective decisions from orbitofrontal cortex.

When making a subjective choice, the brain must compute a value for each option and compare those values to make a decision. The orbitofrontal cortex (OFC) is critically involved in this process, but the neural mechanisms remain obscure, in part due to limitations in our ability to measure and control the internal deliberations that can alter the dynamics of the decision process. Here we tracked these dynamics by recovering temporally precise neural states from multidimensional data in OFC. During individual choices, OFC alternated between states associated with the value of two available options, with dynamics that predicted whether a subject would decide quickly or vacillate between the two alternatives. Ensembles of value-encoding neurons contributed to these states, with individual neurons shifting activity patterns as the network evaluated each option. Thus, the mechanism of subjective decision-making involves the dynamic activation of OFC states associated with each choice alternative.

Capturing the temporal evolution of choice across prefrontal cortex.

Activity in prefrontal cortex (PFC) has been richly described using economic models of choice. Yet such descriptions fail to capture the dynamics of decision formation. Describing dynamic neural processes has proven challenging due to the problem of indexing the internal state of PFC and its trial-by-trial variation. Using primate neurophysiology and human magnetoencephalography, we here recover a single-trial index of PFC internal states from multiple simultaneously recorded PFC subregions. This index can explain the origins of neural representations of economic variables in PFC. It describes the relationship between neural dynamics and behaviour in both human and monkey PFC, directly bridging between human neuroimaging data and underlying neuronal activity. Moreover, it reveals a functionally dissociable interaction between orbitofrontal cortex, anterior cingulate cortex and dorsolateral PFC in guiding cost-benefit decisions. We cast our observations in terms of a recurrent neural network model of choice, providing formal links to mechanistic dynamical accounts of decision-making.

The Role of Prefrontal Cortex in Working Memory: A Mini Review.

A prominent account of prefrontal cortex (PFC) function is that single neurons within the PFC maintain representations of task-relevant stimuli in working memory. Evidence for this view comes from studies in which subjects hold a stimulus across a delay lasting up to several seconds. Persistent elevated activity in the PFC has been observed in animal models as well as in humans performing these tasks. This persistent activity has been interpreted as evidence for the encoding of the stimulus itself in working memory. However, recent findings have posed a challenge to this notion. A number of recent studies have examined neural data from the PFC and posterior sensory areas, both at the single neuron level in primates, and at a larger scale in humans, and have failed to find encoding of stimulus information in the PFC during tasks with a substantial working memory component. Strong stimulus related information, however, was seen in posterior sensory areas. These results suggest that delay period activity in the PFC might be better understood not as a signature of memory storage per se, but as a top down signal that influences posterior sensory areas where the actual working memory representations are maintained.

A hierarchy of intrinsic timescales across primate cortex.

Specialization and hierarchy are organizing principles for primate cortex, yet there is little direct evidence for how cortical areas are specialized in the temporal domain. We measured timescales of intrinsic fluctuations in spiking activity across areas and found a hierarchical ordering, with sensory and prefrontal areas exhibiting shorter and longer timescales, respectively. On the basis of our findings, we suggest that intrinsic timescales reflect areal specialization for task-relevant computations over multiple temporal ranges.

Executive control processes underlying multi-item working memory.

A dominant view of prefrontal cortex (PFC) function is that it stores task-relevant information in working memory. To examine this and determine how it applies when multiple pieces of information must be stored, we trained two subjects to perform a multi-item color change detection task and recorded activity of neurons in PFC. Few neurons encoded the color of the items. Instead, the predominant encoding was spatial: a static signal reflecting the item's position and a dynamic signal reflecting the subject's covert attention. These findings challenge the notion that PFC stores task-relevant information. Instead, we suggest that the contribution of PFC is in controlling the allocation of resources to support working memory. In support of this, we found that increased power in the alpha and theta bands of PFC local field potentials, which are thought to reflect long-range communication with other brain areas, was correlated with more precise color representations.

Medial-lateral organization of the orbitofrontal cortex.

Emerging evidence suggests that specific cognitive functions localize to different subregions of OFC, but the nature of these functional distinctions remains unclear. One prominent theory, derived from human neuroimaging, proposes that different stimulus valences are processed in separate orbital regions, with medial and lateral OFC processing positive and negative stimuli, respectively. Thus far, neurophysiology data have not supported this theory. We attempted to reconcile these accounts by recording neural activity from the full medial-lateral extent of the orbital surface in monkeys receiving rewards and punishments via gain or loss of secondary reinforcement. We found no convincing evidence for valence selectivity in any orbital region. Instead, we report differences between neurons in central OFC and those on the inferior-lateral orbital convexity, in that they encoded different sources of value information provided by the behavioral task. Neurons in inferior convexity encoded the value of external stimuli, whereas those in OFC encoded value information derived from the structure of the behavioral task. We interpret these results in light of recent theories of OFC function and propose that these distinctions, not valence selectivity, may shed light on a fundamental organizing principle for value processing in orbital cortex.

Prefrontal-amygdala interactions underlying value coding.

Dissociating the source and function of value-related signals is a major challenge for understanding the role of reward in neural processing. In this issue of Neuron, Rudebeck et al. (2013) provide insight into the neuroanatomical origins of a subset of these signals.

Single-neuron mechanisms underlying cost-benefit analysis in frontal cortex.

Effective decision-making requires consideration of costs and benefits. Previous studies have implicated orbitofrontal cortex (OFC), dorsolateral prefrontal cortex (DLPFC), and anterior cingulate cortex (ACC) in cost-benefit decision-making. Yet controversy remains about whether different decision costs are encoded by different brain areas, and whether single neurons integrate costs and benefits to derive a subjective value estimate for each choice alternative. To address these issues, we trained four subjects to perform delay- and effort-based cost-benefit decisions and recorded neuronal activity in OFC, ACC, DLPFC, and the cingulate motor area (CMA). Although some neurons, mainly in ACC, did exhibit integrated value signals as if performing cost-benefit computations, they were relatively few in number. Instead, the majority of neurons in all areas encoded the decision type; that is whether the subject was required to perform a delay- or effort-based decision. OFC and DLPFC neurons tended to show the largest changes in firing rate for delay- but not effort-based decisions; whereas, the reverse was true for CMA neurons. Only ACC contained neurons modulated by both effort- and delay-based decisions. These findings challenge the idea that OFC calculates an abstract value signal to guide decision-making. Instead, our results suggest that an important function of single PFC neurons is to categorize sensory stimuli based on the consequences predicted by those stimuli.

Choice coding in frontal cortex during stimulus-guided or action-guided decision-making.

To optimally obtain desirable outcomes, organisms must track outcomes predicted by stimuli in the environment (stimulus-outcome or SO associations) and outcomes predicted by their own actions (action-outcome or AO associations). Anterior cingulate cortex (ACC) and orbitofrontal cortex (OFC) are implicated in tracking outcomes, but anatomical and functional studies suggest a dissociation, with ACC and OFC responsible for encoding AO and SO associations, respectively. To examine whether this dissociation held at the single neuron level, we trained two subjects to perform choice tasks that required using AO or SO associations. OFC and ACC neurons encoded the action that the subject used to indicate its choice, but this encoding was stronger in OFC during the SO task and stronger in ACC during the AO task. These results are consistent with a division of labor between the two areas in terms of using rewards associated with either stimuli or actions to guide decision-making.

Inferring the value of the world.

A prominent view of the function of orbitofrontal cortex (OFC) is that it is responsible for calculating the value of things in the environment. A recent study shows that this role is restricted to cases in which the value must be inferred from our knowledge of the world.

Capacity and precision in an animal model of visual short-term memory.

Temporary storage of information in visual short-term memory (VSTM) is a key component of many complex cognitive abilities. However, it is highly limited in capacity. Understanding the neurophysiological nature of this capacity limit will require a valid animal model of VSTM. We used a multiple-item color change detection task to measure macaque monkeys' VSTM capacity. Subjects' performance deteriorated and reaction times increased as a function of the number of items in memory. Additionally, we measured the precision of the memory representations by varying the distance between sample and test colors. In trials with similar sample and test colors, subjects made more errors compared to trials with highly discriminable colors. We modeled the error distribution as a Gaussian function and used this to estimate the precision of VSTM representations. We found that as the number of items in memory increases the precision of the representations decreases dramatically. Additionally, we found that focusing attention on one of the objects increases the precision with which that object is stored and degrades the precision of the remaining. These results are in line with recent findings in human psychophysics and provide a solid foundation for understanding the neurophysiological nature of the capacity limit of VSTM.

Contrasting reward signals in the orbitofrontal cortex and anterior cingulate cortex.

Damage to the orbitofrontal cortex (OFC) and anterior cingulate cortex (ACC) impairs decision making, but the underlying value computations that cause such impairments remain unclear. Both the OFC and ACC encode a wide variety of signals correlated with decision making. The current challenge is to determine how these two different areas support decision-making processes. Here, we review a series of experiments that have helped define these roles. A special population of neurons in the ACC, but not the OFC, multiplex value information across decision parameters using a unified encoding scheme, and encode reward prediction errors. In contrast, neurons in the OFC, but not the ACC, encode the value of a choice relative to the recent history of choice values. Together, these results suggest complementary valuation processes: OFC neurons dynamically evaluate current choices relative to the value contexts recently experienced, while ACC neurons encode choice predictions and prediction errors using a common valuation currency reflecting the integration of multiple decision parameters.

Double dissociation of value computations in orbitofrontal and anterior cingulate neurons.

Damage to prefrontal cortex (PFC) impairs decision-making, but the underlying value computations that might cause such impairments remain unclear. Here we report that value computations are doubly dissociable among PFC neurons. Although many PFC neurons encoded chosen value, they used opponent encoding schemes such that averaging the neuronal population extinguished value coding. However, a special population of neurons in anterior cingulate cortex (ACC), but not in orbitofrontal cortex (OFC), multiplexed chosen value across decision parameters using a unified encoding scheme and encoded reward prediction errors. In contrast, neurons in OFC, but not ACC, encoded chosen value relative to the recent history of choice values. Together, these results suggest complementary valuation processes across PFC areas: OFC neurons dynamically evaluate current choices relative to recent choice values, whereas ACC neurons encode choice predictions and prediction errors using a common valuation currency reflecting the integration of multiple decision parameters.

Cross-species studies of orbitofrontal cortex and value-based decision-making.

Recent work has emphasized the role that orbitofrontal cortex (OFC) has in value-based decision-making. However, it is also clear that a number of discrepancies have arisen when comparing the findings from animal models to those from humans. Here, we examine several possibilities that might explain these discrepancies, including anatomical difference between species, the behavioral tasks used to probe decision-making and the methodologies used to assess neural function. Understanding how these differences affect the interpretation of experimental results will help us to better integrate future results from animal models. This will enable us to fully realize the benefits of using multiple approaches to understand OFC function.

Challenges of Interpreting Frontal Neurons during Value-Based Decision-Making.

The frontal cortex is crucial to sound decision-making, and the activity of frontal neurons correlates with many aspects of a choice, including the reward value of options and outcomes. However, rewards are of high motivational significance and have widespread effects on neural activity. As such, many neural signals not directly involved in the decision process can correlate with reward value. With correlative techniques such as electrophysiological recording or functional neuroimaging, it can be challenging to distinguish neural signals underlying value-based decision-making from other perceptual, cognitive, and motor processes. In the first part of the paper, we examine how different value-related computations can potentially be confused. In particular, error-related signals in the anterior cingulate cortex, generated when one discovers the consequences of an action, might actually represent violations of outcome expectation, rather than errors per se. Also, signals generated at the time of choice are typically interpreted as reflecting predictions regarding the outcomes associated with the different choice alternatives. However, these signals could instead reflect comparisons between the presented choice options and previously presented choice alternatives. In the second part of the paper, we examine how value signals have been successfully dissociated from saliency-related signals, such as attention, arousal, and motor preparation in studies employing outcomes with both positive and negative valence. We hope that highlighting these issues will prove useful for future studies aimed at disambiguating the contribution of different neuronal populations to choice behavior.

The dynamics of learning and behavioral flexibility.

Fundamental to behavior is the capacity to distinguish beneficial from detrimental environmental stimuli. In this issue of Neuron, a new study by Morrison et al. shows that underlying these processes are qualitatively different dynamical interactions between brain structures involved in processing the value of environmental stimuli.

Reversible large-scale modification of cortical networks during neuroprosthetic control.

Brain-machine interfaces (BMIs) provide a framework for studying cortical dynamics and the neural correlates of learning. Neuroprosthetic control has been associated with tuning changes in specific neurons directly projecting to the BMI (hereafter referred to as direct neurons). However, little is known about the larger network dynamics. By monitoring ensembles of neurons that were either causally linked to BMI control or indirectly involved, we found that proficient neuroprosthetic control is associated with large-scale modifications to the cortical network in macaque monkeys. Specifically, there were changes in the preferred direction of both direct and indirect neurons. Notably, with learning, there was a relative decrease in the net modulation of indirect neural activity in comparison with direct activity. These widespread differential changes in the direct and indirect population activity were markedly stable from one day to the next and readily coexisted with the long-standing cortical network for upper limb control. Thus, the process of learning BMI control is associated with differential modification of neural populations based on their specific relation to movement control.

Oscillatory phase coupling coordinates anatomically dispersed functional cell assemblies.

Hebb proposed that neuronal cell assemblies are critical for effective perception, cognition, and action. However, evidence for brain mechanisms that coordinate multiple coactive assemblies remains lacking. Neuronal oscillations have been suggested as one possible mechanism for cell assembly coordination. Prior studies have shown that spike timing depends upon local field potential (LFP) phase proximal to the cell body, but few studies have examined the dependence of spiking on distal LFP phases in other brain areas far from the neuron or the influence of LFP-LFP phase coupling between distal areas on spiking. We investigated these interactions by recording LFPs and single-unit activity using multiple microelectrode arrays in several brain areas and then used a unique probabilistic multivariate phase distribution to model the dependence of spike timing on the full pattern of proximal LFP phases, distal LFP phases, and LFP-LFP phase coupling between electrodes. Here we show that spiking activity in single neurons and neuronal ensembles depends on dynamic patterns of oscillatory phase coupling between multiple brain areas, in addition to the effects of proximal LFP phase. Neurons that prefer similar patterns of phase coupling exhibit similar changes in spike rates, whereas neurons with different preferences show divergent responses, providing a basic mechanism to bind different neurons together into coordinated cell assemblies. Surprisingly, phase-coupling-based rate correlations are independent of interneuron distance. Phase-coupling preferences correlate with behavior and neural function and remain stable over multiple days. These findings suggest that neuronal oscillations enable selective and dynamic control of distributed functional cell assemblies.

Heterogeneous reward signals in prefrontal cortex.

Neurons encode upcoming rewards throughout frontal cortex. Recent papers have helped to determine that these signals play different roles in different frontal regions. Neurons in orbitofrontal cortex (PFo) appear to be responsible for calculating the specific value of an expected reward, information that can help efficiently guide decision-making. Similar signals are also present in the cingulate sulcus (PFcs). By contrast, reward signals in lateral prefrontal cortex (PFl) are consistent with a role in using reward to guide other cognitive processes, such as the allocation of attentional resources and using value information to guide learning other relationships in the environment such as arbitrary stimulus-response mappings. A remaining issue for future work is to specify the precise roles of PFo and PFcs. These two areas show very different patterns of connectivity with other brain areas, and it is currently unclear how this effects their contribution to decision-making.