John M. Pearson

Posterior cingulate cortex: adapting behavior to a changing world.

When has the world changed enough to warrant a new approach? The answer depends on current needs, behavioral flexibility and prior knowledge about the environment. Formal approaches solve the problem by integrating the recent history of rewards, errors, uncertainty and context via Bayesian inference to detect changes in the world and alter behavioral policy. Neuronal activity in posterior cingulate cortex - a key node in the default network - is known to vary with learning, memory, reward and task engagement. We propose that these modulations reflect the underlying process of change detection and motivate subsequent shifts in behavior.

Neuronal basis of sequential foraging decisions in a patchy environment

Deciding when to leave a depleting resource to exploit another is a fundamental problem for all decision makers. The neuronal mechanisms mediating patch-leaving decisions remain unknown. We found that neurons in primate (Macaca mulatta) dorsal anterior cingulate cortex, an area that is linked to reward monitoring and executive control, encode a decision variable signaling the relative value of leaving a depleting resource for a new one. Neurons fired during each sequential decision to stay in a patch and, for each travel time, these responses reached a fixed threshold for patch-leaving. Longer travel times reduced the gain of neural responses for choosing to stay in a patch and increased the firing rate threshold mandating patch-leaving. These modulations more closely matched behavioral decisions than any single task variable. These findings portend an understanding of the neural basis of foraging decisions and endorse the unification of theoretical and experimental work in ecology and neuroscience.

Surprise Signals in Anterior Cingulate Cortex: Neuronal Encoding of Unsigned Reward Prediction Errors Driving Adjustment in Behavior

In attentional models of learning, associations between actions and subsequent rewards are stronger when outcomes are surprising, regardless of their valence. Despite the behavioral evidence that surprising outcomes drive learning, neural correlates of unsigned reward prediction errors remain elusive. Here we show that in a probabilistic choice task, trial-to-trial variations in preference track outcome surprisingness. Concordant with this behavioral pattern, responses of neurons in macaque (Macaca mulatta) dorsal anterior cingulate cortex (dACC) to both large and small rewards were enhanced when the outcome was surprising. Moreover, when, on some trials, probabilities were hidden, neuronal responses to rewards were reduced, consistent with the idea that the absence of clear expectations diminishes surprise. These patterns are inconsistent with the idea that dACC neurons track signed errors in reward prediction, as dopamine neurons do. Our results also indicate that dACC neurons do not signal conflict. In the context of other studies of dACC function, these results suggest a link between reward-related modulations in dACC activity and attention and motor control processes involved in behavioral adjustment. More speculatively, these data point to a harmonious integration between reward and learning accounts of ACC function on one hand, and attention and cognitive control accounts on the other.

A Physiologically-Inspired Model of Numerical Classification Based on Graded Stimulus Coding

In most natural decision contexts, the process of selecting among competing actions takes place in the presence of informative, but potentially ambiguous, stimuli. Decisions about magnitudes – quantities like time, length, and brightness that are linearly ordered – constitute an important subclass of such decisions. It has long been known that perceptual judgments about such quantities obey Webers Law, wherein the just-noticeable difference in a magnitude is proportional to the magnitude itself. Current physiologically inspired models of numerical classification assume discriminations are made via a labeled line code of neurons selectively tuned for numerosity, a pattern observed in the firing rates of neurons in the ventral intraparietal area (VIP) of the macaque. By contrast, neurons in the contiguous lateral intraparietal area (LIP) signal numerosity in a graded fashion, suggesting the possibility that numerical classification could be achieved in the absence of neurons tuned for number. Here, we consider the performance of a decision model based on this analog coding scheme in a paradigmatic discrimination task – numerosity bisection. We demonstrate that a basic two-neuron classifier model, derived from experimentally measured monotonic responses of LIP neurons, is sufficient to reproduce the numerosity bisection behavior of monkeys, and that the threshold of the classifier can be set by reward maximization via a simple learning rule. In addition, our model predicts deviations from Weber Law scaling of choice behavior at high numerosity. Together, these results suggest both a generic neuronal framework for magnitude-based decisions and a role for reward contingency in the classification of such stimuli.

Neurons in Posterior Cingulate Cortex Signal Exploratory Decisions in a Dynamic Multioption Choice Task

In dynamic environments, adaptive behavior requires striking a balance between harvesting currently available rewards (exploitation) and gathering information about alternative options (exploration). Such strategic decisions should incorporate not only recent reward history, but also opportunity costs and environmental statistics. Previous neuroimaging and neurophysiological studies have implicated orbitofrontal cortex, anterior cingulate cortex, and ventral striatum in distinguishing between bouts of exploration and exploitation. Nonetheless, the neuronal mechanisms that underlie strategy selection remain poorly understood. We hypothesized that posterior cingulate cortex (CGp), an area linking reward processing, attention, memory, and motor control systems, mediates the integration of variables such as reward, uncertainty, and target location that underlie this dynamic balance. Here we show that CGp neurons distinguish between exploratory and exploitative decisions made by monkeys in a dynamic foraging task. Moreover, firing rates of these neurons predict in graded fashion the strategy most likely to be selected on upcoming trials. This encoding is distinct from switching between targets and is independent of the absolute magnitudes of rewards. These observations implicate CGp in the integration of individual outcomes across decision making and the modification of strategy in dynamic environments.

Tracing the string: BMN correspondence at finite J2/N

Employing the string bit formalism of [18], we identify the basis transformation that relates BMN operators in = 4 gauge theory to string states in the dual string field theory at finite g2 = J2/N. In this basis, the supercharge truncates at linear order in g2, and the mixing amplitude between 1 and 2-string states precisely matches with the (corrected) answer of [5] for the 3-string amplitude in light-cone string field theory. Supersymmetry then predicts the order g22 contact term in the string bit hamiltonian. The resulting leading order mass renormalization of string states agrees with the recently computed shift in conformal dimension of BMN operators in the gauge theory.

Neuroethology of primate social behavior

A neuroethological approach to human and nonhuman primate behavior and cognition predicts biological specializations for social life. Evidence reviewed here indicates that ancestral mechanisms are often duplicated, repurposed, and differentially regulated to support social behavior. Focusing on recent research from nonhuman primates, we describe how the primate brain might implement social functions by coopting and extending preexisting mechanisms that previously supported nonsocial functions. This approach reveals that highly specialized mechanisms have evolved to decipher the immediate social context, and parallel circuits have evolved to translate social perceptual signals and nonsocial perceptual signals into partially integrated social and nonsocial motivational signals, which together inform general-purpose mechanisms that command behavior. Differences in social behavior between species, as well as between individuals within a species, result in part from neuromodulatory regulation of these neural circuits, which itself appears to be under partial genetic control. Ultimately, intraspecific variation in social behavior has differential fitness consequences, providing fundamental building blocks of natural selection. Our review suggests that the neuroethological approach to primate behavior may provide unique insights into human psychopathology.

Decision making: the neuroethological turn.

Neuroeconomics applies models from economics and psychology to inform neurobiological studies of choice. This approach has revealed neural signatures of concepts like value, risk, and ambiguity, which are known to influence decision making. Such observations have led theorists to hypothesize a single, unified decision process that mediates choice behavior via a common neural currency for outcomes like food, money, or social praise. In parallel, recent neuroethological studies of decision making have focused on natural behaviors like foraging, mate choice, and social interactions. These decisions strongly impact evolutionary fitness and thus are likely to have played a key role in shaping the neural circuits that mediate decision making. This approach has revealed a suite of computational motifs that appear to be shared across a wide variety of organisms. We argue that the existence of deep homologies in the neural circuits mediating choice may have profound implications for understanding human decision making in health and disease.

Through their eyes: selective attention in peahens during courtship

SUMMARY Conspicuous, multicomponent ornamentation in male animals can be favored by female mate choice but we know little about the cognitive processes females use to evaluate these traits. Sexual selection may favor attention mechanisms allowing the choosing females to selectively and efficiently acquire relevant information from complex male display traits and, in turn, may favor male display traits that effectively capture and hold female attention. Using a miniaturized telemetric gaze-tracker, we show that peahens (Pavo cristatus) selectively attend to specific components of peacock courtship displays and virtually ignore other, highly conspicuous components. Females gazed at the lower train but largely ignored the head, crest and upper train. When the lower train was obscured, however, females spent more time gazing at the upper train and approached the upper train from a distance. Our results suggest that peahens mainly evaluate the lower train during close-up courtship but use the upper train as a long-distance attraction signal. Furthermore, we found that behavioral display components (train rattling and wing shaking) captured and maintained female attention, indicating that interactions between display components may promote the evolution of multicomponent displays. Taken together, these findings suggest that selective attention plays a crucial role in sexual selection and likely influences the evolution of male display traits.

Neural mechanisms of social decision-making in the primate amygdala

Significance Making social decisions requires evaluation of benefits and costs to self and others. Long associated with emotion and vigilance, neurons in primate amygdala also signal reward and punishment as well as information about the faces and eyes of others. Here we show that neurons in the basolateral amygdala signal the value of rewards for self and others when monkeys make social decisions. These value-mirroring neurons reflected monkeys’ tendency to make prosocial decisions on a momentary as well as long-term basis. We also found that delivering the social peptide oxytocin into basolateral amygdala enhances both prosocial tendencies and attention to the recipients of prosocial decisions. Our findings endorse the amygdala as a critical neural nexus regulating social decisions. Social decisions require evaluation of costs and benefits to oneself and others. Long associated with emotion and vigilance, the amygdala has recently been implicated in both decision-making and social behavior. The amygdala signals reward and punishment, as well as facial expressions and the gaze of others. Amygdala damage impairs social interactions, and the social neuropeptide oxytocin (OT) influences human social decisions, in part, by altering amygdala function. Here we show in monkeys playing a modified dictator game, in which one individual can donate or withhold rewards from another, that basolateral amygdala (BLA) neurons signaled social preferences both across trials and across days. BLA neurons mirrored the value of rewards delivered to self and others when monkeys were free to choose but not when the computer made choices for them. We also found that focal infusion of OT unilaterally into BLA weakly but significantly increased both the frequency of prosocial decisions and attention to recipients for context-specific prosocial decisions, endorsing the hypothesis that OT regulates social behavior, in part, via amygdala neuromodulation. Our findings demonstrate both neurophysiological and neuroendocrinological connections between primate amygdala and social decisions.