Shrunken Social Brains? A Minimal Model of the Role of Social Interaction in Neural Complexity

Unshared Minds, Decaying Worlds: Towards a Pathology of Chronic Loneliness

The moment when a person's actual relationships fall short of desired relationships is commonly identified as the etiological moment of chronic loneliness, which can lead to physical and psychological effects like depression, worse recovery from illness and increased mortality. But, this etiology fails to explain the nature and severe impact of loneliness. Here, we use philosophical analysis and neuroscience to show that human beings develop and maintain our world-picture (our sense of what is true, important, and good) through joint attention and action, motivated by friendship, in the Aristotelian sense of "other selves" who share a sense of the true and the good, and desire the good for each other as much as for themselves. The true etiological event of loneliness is the moment one's world-picture becomes unshared. The pathogenesis is a resultant decay of our world-picture, with brain and behavior changes following as sequelae.

The Dynamics of Social Interaction Among Evolved Model Agents

We offer three advances to the perceptual crossing simulation studies, which are aimed at challenging methodological individualism in the analysis of social cognition. First, we evolve and systematically test agents in rigorous conditions, identifying a set of 26 "robust circuits" with consistently high and generalizing performance. Next, we transform the sensor from discrete to continuous, facilitating a bifurcation analysis of the dynamics that shows that nonequilibrium dynamics are key to the mutual maintenance of interaction. Finally, we examine agents' performance with partners whose neural controllers are different from their own and with decoy objects of fixed frequency and amplitude. Nonclonal performance varies and is not predicted by genotypic distance. Frequency-amplitude values that fool the focal agent do not include the agent's own values. Altogether, our findings accentuate the importance of dynamical and nonclonal analyses for simulated sociality, emphasize the role of dialogue between artificial and human studies, and highlight the contributions of simulation studies to understanding social interactions.

How can we apply decision-making theories to wild animal behavior? Predictions arising from dual process theory and Bayesian decision theory

Our understanding of decision-making processes and cognitive biases is ever increasing, thanks to an accumulation of testable models and a large body of research over the last several decades. The vast majority of this work has been done in humans and laboratory animals because these study subjects and situations allow for tightly controlled experiments. However, it raises questions about how this knowledge can be applied to wild animals in their complex environments. Here, we review two prominent decision-making theories, dual process theory and Bayesian decision theory, to assess the similarities in these approaches and consider how they may apply to wild animals living in heterogenous environments within complicated social groupings. In particular, we wanted to assess when wild animals are likely to respond to a situation with a quick heuristic decision and when they are likely to spend more time and energy on the decision-making process. Based on the literature and evidence from our multi-destination routing experiments on primates, we find that individuals are likely to make quick, heuristic decisions when they encounter routine situations, or signals/cues that accurately predict a certain outcome, or easy problems that experience or evolutionary history has prepared them for. Conversely, effortful decision-making is likely in novel or surprising situations, when signals and cues have unpredictable or uncertain relationships to an outcome, and when problems are computationally complex. Though if problems are overly complex, satisficing via heuristics is likely, to avoid costly mental effort. We present hypotheses for how animals with different socio-ecologies may have to distribute their cognitive effort. Finally, we examine the conservation implications and potential cognitive overload for animals experiencing increasingly novel situations caused by current human-induced rapid environmental change.

PCE simulation toolkit: a platform for perceptual crossing experiment research

The Perceptual Crossing Experiment (PCE) has been the object of study for over a decade, and aims at explaining how we perceive, interact with, and understand each other in real-time. In addition to human participant studies, a number of computational models have investigated how virtual agents can solve this task. However, the set of implementation choices that has been explored to date is rather limited, and the large number of variables that can be used make it very difficult to replicate the results. The main objective of this paper is to describe the PCE Simulation Toolkit we have developed and published as an open-source repository on GitHub. We hope that this effort will help make future PCE simulation results reproducible and advance research in the understanding of possible behaviors in this experimental paradigm. At the end of this paper, we present two case studies of evolved agents that demonstrate how parameter choices affect the simulations.

Social complexity and brain evolution: insights from ant neuroarchitecture and genomics

Brain evolution is hypothesized to be driven by requirements to adaptively respond to environmental cues and social signals. Diverse models describe how sociality may have influenced eusocial insect-brain evolution, but specific impacts of social organization and other selective forces on brain architecture have been difficult to distinguish. Here, we evaluate predictions derived from and/or inferences made by models of social organization concerning the effects of individual and collective behavior on brain size, structure, and function using results of neuroanatomical and genomic studies. In contrast to the predictions of some models, we find that worker brains in socially complex species have great behavioral and cognitive capacity. We also find that colony size, the evolution of worker physical castes, and task specialization affect brain size and mosaicism, supporting the idea that sensory, processing and motor requirements for behavioral performance select for adaptive allometries of functionally specialized brain centers. We review available transcriptomic and comparative genomic studies seeking to elucidate the molecular pathways functionally associated with social life and the genetic changes that occurred during the evolution of social complexity. We discuss ways forward, using comparative neuroanatomy, transcriptomics, and comparative genomics, to distinguish among multiple alternative explanations for the relationship between the evolution of neural systems and social complexity.

Social Brain Energetics: Ergonomic Efficiency, Neurometabolic Scaling, and Metabolic Polyphenism in Ants

Metabolism, a metric of the energy cost of behavior, plays a significant role in social evolution. Body size and metabolic scaling are coupled, and a socioecological pattern of increased body size is associated with dietary change and the formation of larger and more complex groups. These consequences of the adaptive radiation of animal societies beg questions concerning energy expenses, a substantial portion of which may involve the metabolic rates of brains that process social information. Brain size scales with body size, but little is understood about brain metabolic scaling. Social insects such as ants show wide variation in worker body size and morphology that correlates with brain size, structure, and worker task performance, which is dependent on sensory inputs and information-processing ability to generate behavior. Elevated production and maintenance costs in workers may impose energetic constraints on body size and brain size that are reflected in patterns of metabolic scaling. Models of brain evolution do not clearly predict patterns of brain metabolic scaling, nor do they specify its relationship to task performance and worker ergonomic efficiency, two key elements of social evolution in ants. Brain metabolic rate is rarely recorded and therefore the conditions under which brain metabolism influences the evolution of brain size are unclear. We propose that studies of morphological evolution, colony social organization, and worker ergonomic efficiency should be integrated with analyses of species-specific patterns of brain metabolic scaling to advance our understanding of brain evolution in ants.

Behavioral performance and division of labor influence brain mosaicism in the leafcutter ant Atta cephalotes

Brain evolution is hypothesized to be driven by behavioral selection on neuroarchitecture. We developed a novel metric of relative neuroanatomical investments involved in performing tasks varying in sensorimotor and processing demands across polymorphic task-specialized workers of the leafcutter ant Atta cephalotes and quantified brain size and structure to examine their correlation with our computational approximations. Investment in multisensory and motor integration for task performance was estimated to be greatest for media workers, whose highly diverse repertoire includes leaf-quality discrimination and leaf-harvesting tasks that likely involve demanding sensory and motor processes. Confocal imaging revealed that absolute brain volume increased with worker size and functionally specialized compartmental scaling differed among workers. The mushroom bodies, centers of sensory integration and learning and memory, and the antennal lobes, olfactory input sites, were larger in medias than in minims (gardeners) and significantly larger than in majors ("soldiers"), both of which had lower scores for involvement of olfactory processing in the performance of their characteristic tasks. Minims had a proportionally larger central complex compared to other workers. These results support the hypothesis that variation in task performance influences selection for mosaic brain structure, the independent evolution of proportions of the brain composed of different neuropils.

When and Why Did Human Brains Decrease in Size? A New Change-Point Analysis and Insights From Brain Evolution in Ants

Human brain size nearly quadrupled in the six million years since Homo last shared a common ancestor with chimpanzees, but human brains are thought to have decreased in volume since the end of the last Ice Age. The timing and reason for this decrease is enigmatic. Here we use change-point analysis to estimate the timing of changes in the rate of hominin brain evolution. We find that hominin brains experienced positive rate changes at 2.1 and 1.5 million years ago, coincident with the early evolution of Homo and technological innovations evident in the archeological record. But we also find that human brain size reduction was surprisingly recent, occurring in the last 3,000 years. Our dating does not support hypotheses concerning brain size reduction as a by-product of body size reduction, a result of a shift to an agricultural diet, or a consequence of self-domestication. We suggest our analysis supports the hypothesis that the recent decrease in brain size may instead result from the externalization of knowledge and advantages of group-level decision-making due in part to the advent of social systems of distributed cognition and the storage and sharing of information. Humans live in social groups in which multiple brains contribute to the emergence of collective intelligence. Although difficult to study in the deep history of Homo, the impacts of group size, social organization, collective intelligence and other potential selective forces on brain evolution can be elucidated using ants as models. The remarkable ecological diversity of ants and their species richness encompasses forms convergent in aspects of human sociality, including large group size, agrarian life histories, division of labor, and collective cognition. Ants provide a wide range of social systems to generate and test hypotheses concerning brain size enlargement or reduction and aid in interpreting patterns of brain evolution identified in humans. Although humans and ants represent very different routes in social and cognitive evolution, the insights ants offer can broadly inform us of the selective forces that influence brain size.

Intelligence as Information Processing: Brains, Swarms, and Computers

There is no agreed definition of intelligence, so it is problematic to simply ask whether brains, swarms, computers, or other systems are intelligent or not. To compare the potential intelligence exhibited by different cognitive systems, I use the common approach used by artificial intelligence and artificial life: Instead of studying the substrate of systems, let us focus on their organization. This organization can be measured with information. Thus, I apply an informationist epistemology to describe cognitive systems, including brains and computers. This allows me to frame the usefulness and limitations of the brain-computer analogy in different contexts. I also use this perspective to discuss the evolution and ecology of intelligence.