Seminars

I present a graded, evolutionarily grounded view in which human mathematical cognition is built through the abstraction and layering of conserved neural systems. I show that numerosity provides a shared foundation across non-human primates, humans, and many other species, supported by homologous parietal brain systems that emerge early in development and continue to scaffold symbolic mathematics rather than being replaced by it. I then examine relational reasoning, where important continuities persist across species and development, alongside qualitative expansions in abstractions that recruit symbolic representations while preserving core analog relational codes. Finally, I turn to sequence learning and hierarchical representation, where clearer discontinuities emerge: although many species represent ordered and patterned sequences, humans uniquely support hierarchical thinking through the integration of ancient sequence-processing mechanisms with symbolic and semantic systems. Together, these findings suggest that human symbolic thought arises not from new cognitive machinery, but from the progressive elaboration and layering of shared evolutionary foundations.

Humans and other primates exhibit a range of vocal behaviors. This range arises from species-specific developmental processes which in turn are shaped by evolutionary pressures. We must therefore consider that all primate species do not follow the same developmental path to their vocal behaviors. Some, like humans, may learn their vocalizations during infancy whereas others do not. Why? We will use marmoset monkeys as a case study for the integrative biology of vocal learning. Marmoset contact call development exhibits a pattern of socially guided vocal production learning that is like the pattern exhibited by prelinguistic human infants and songbirds like the zebra finch. This socially guided contact call development and other vocal behaviors emerge in a landscape of anatomical changes that is modulated by a large-scale neural system. Comparing human and marmoset vocal development, we consider the possibility that their similar capacity for infant vocal production learning may be the result of similar (convergent) evolutionary changes to their developmental timing (e.g., altricial brains) and life history strategy (i.e., cooperative breeding).

Traditional prescriptive decision theories, from Pascal’s to modern Expected Utility Theory, have struggled to explain inconsistent human choices, leading to modern predictive models like Prospect Theory. This paper introduces a novel approach to understand why these behaviors occur, reconciling these observed "irrationalities" with a simple rational objective. Here we propose that the human nervous system, faced with limited cognitive capacity (finite representational precision), optimally selects a task-appropriate utility function to either maximize long-run average returns or to minimize errors. We argue that the measured utility function is a tool, sculpted by knowledge of the choice environment's probabilistic structure and the decision-maker's precision level, for optimally achieving a rational objective. Our results show that almost every experimentally observed utility function can be described as a near optimal tool for achieving a maximization of objective gains (a goal abandoned since Bernoulli) or for minimizing errors. We quantify the specific gains that result from adding cognitive capacity and find that these gains are quite small as long as the utility function is correctly adjusted to compensate for losses in cognitive capacity. Our research provides a novel framework that offers a testable and foundational explanation for why human and animal decision-makers evolved the puzzling range of utility functions and risk attitudes that have been widely observed.

How is it that we are able to produce sounds and marks to convey meanings to one another? How does language acquire meaning? This would seem to be the central, foundational problem of philosophical and psychological inquiry into linguistic meaning. I give a nontechnical overview of my answer to this question. I divide the problem into two parts, micro-semantics and macro-semantics. The first involves modeling a single conversation in detail assuming that the conventional meanings of words are given. I do this by using situation theory and game theory. The second involves relaxing the assumption and letting conventional meanings vary, and modeling how conversations interlock across society, again using situation theory and game theory. This combination of models of micro-semantics and macro-semantics results in showing how language acquires meaning assuming only the rationality of interacting agents.

All living things must move and act in space to survive. Spatial thinking is the foundation of thought, not the entire edifice, but the foundation. I will back this audacious claim with evidence from brain, language, gesture, and the cognitive tools we create to expand the mind, create, and communicate. I will bring it together in a new concept, spraction: actions in space create abstractions.