Hierarchical organization of rhesus macaque behavior.

Primatologists, psychologists and neuroscientists have long hypothesized that primate behavior is highly structured. However, delineating that structure has been impossible due to the difficulties of precision behavioral tracking. Here we analyzed a dataset consisting of continuous measures of the 3D position of two male rhesus macaques (Macaca mulatta) performing three different tasks in a large unrestrained environment over several hours. Using an unsupervised embedding approach on the tracked joints, we identified commonly repeated pose patterns, which we call postures. We found that macaques' behavior is characterized by 49 distinct postures, lasting an average of 0.6 seconds. We found evidence that behavior is hierarchically organized, in that transitions between poses tend to occur within larger modules, which correspond to identifiable actions; these actions are further organized hierarchically. Our behavioral decomposition allows us to identify universal (cross-individual and cross-task) and unique (specific to each individual and task) principles of behavior. These results demonstrate the hierarchical nature of primate behavior, provide a method for the automated ethogramming of primate behavior, and provide important constraints on neural models of pose generation.

Automated markerless pose estimation in freely moving macaques with OpenMonkeyStudio.

The rhesus macaque is an important model species in several branches of science, including neuroscience, psychology, ethology, and medicine. The utility of the macaque model would be greatly enhanced by the ability to precisely measure behavior in freely moving conditions. Existing approaches do not provide sufficient tracking. Here, we describe OpenMonkeyStudio, a deep learning-based markerless motion capture system for estimating 3D pose in freely moving macaques in large unconstrained environments. Our system makes use of 62 machine vision cameras that encircle an open 2.45 m × 2.45 m × 2.75 m enclosure. The resulting multiview image streams allow for data augmentation via 3D-reconstruction of annotated images to train a robust view-invariant deep neural network. This view invariance represents an important advance over previous markerless 2D tracking approaches, and allows fully automatic pose inference on unconstrained natural motion. We show that OpenMonkeyStudio can be used to accurately recognize actions and track social interactions.

Macaques are risk-averse in a freely moving foraging task.

Rhesus macaques (Macaca mulatta) appear to be robustly risk-seeking in computerized gambling tasks typically used for electrophysiology. This behavior distinguishes them from many other animals, which are risk-averse, albeit measured in more naturalistic contexts. We wondered whether macaques' risk preferences reflect their evolutionary history or derive from the less naturalistic elements of task design associated with the demands of physiological recording. We assessed macaques' risk attitudes in a task that is somewhat more naturalistic than many that have previously been used: subjects foraged at four feeding stations in a large enclosure. Patches (i.e., stations), provided either stochastically or non-stochastically depleting rewards. Subjects' patch residence times were longer at safe than at risky stations, indicating a preference for safe options. This preference was not attributable to a win-stay-lose-shift heuristic and reversed as the environmental richness increased. These findings highlight the lability of risk attitudes in macaques and support the hypothesis that the ecological validity of a task can influence the expression of risk preference.

Cognitive Science: Persistent Apes Are Intelligent Apes.

In humans, self-control is correlated with general intelligence; a new study finds that this correlation extends to chimpanzees as well. The new results highlight the cognitive bases of self-control and suggest a common evolutionary history for human and primate self-control.

Control without Controllers: Toward a Distributed Neuroscience of Executive Control.

Executive control refers to the regulation of cognition and behavior by mental processes and is a hallmark of higher cognition. Most approaches to understanding its mechanisms begin with the assumption that our brains have anatomically segregated and functionally specialized control modules. The modular approach is intuitive: Control is conceptually distinct from basic mental processing, so an organization that reifies that distinction makes sense. An alternative approach sees executive control as self-organizing principles of a distributed organization. In distributed systems, control and controlled processes are colocalized within large numbers of dispersed computational agents. Control then is often an emergent consequence of simple rules governing the interaction between agents. Because these systems are unfamiliar and unintuitive, here we review several well-understood examples of distributed control systems, group living insects and social animals, and emphasize their parallels with neural systems. We then reexamine the cognitive neuroscience literature on executive control for evidence that its neural control systems may be distributed.

Of fish and mirrors: Fluoxetine disrupts aggression and learning for social rewards.

Aggressive signaling is a key social behavior of male Siamese fighting fish (Betta splendens). Successfully establishing a territory and defending it from intruders has direct fitness effects, making Betta splendens a prime model for studies examining the biological underpinnings of aggressive behavior. Current research has outlined serotonin transporter pathways as one key component for the engagement and coordination of aggressive behavior in Betta splendens. Using the selective serotonin reuptake inhibitor fluoxetine, we examined the impact of 10µmol exposures on associative learning and aggression between mirror and conspecific social reinforcers. Our results provide clear evidence that exposure to fluoxetine reduces aggression and impairs learning independent of social reinforce type. In addition, our results provide support for motor inhibition of aggressive behavior as the main behavioral mechanism of action for fluoxetine. Placed within the broader context of behavioral syndromes, our results, along with others, implicate serotonergic pathways as a key biological correlate of the bold-aggressive phenotype.

Behavioral effects of fluoxetine on aggression and associative learning in Siamese fighting fish (Betta splendens).

Past research has implicated serotonin as an important neurotransmitter in the facilitation of aggressive behavior. In Siamese fighting fish (Betta splendens), the SSRI fluoxetine has been demonstrated to reduce both frequency and duration of aggressive displays across a variety of concentration exposure procedures. While this multi-method approach has provided strong evidence for fluoxetine's impact on aggression, no study has sought to examine the behavioral mechanism by which fluoxetine exerts its anti-aggressive effect. To address this question, a Go-No Go discrimination task utilizing mirror presentations as a reinforcer was designed. Consistent with previous reports, the results indicated that fluoxetine may exert a sedative effect upon aggressive behavior via decreased arousal to external stimuli.