Developmental psychology and cultural evolution are concerned with the same research questions but rarely interact. Collaboration between these fields could lead to substantial progress. Developmental psychology and related fields such as educational science and linguistics explore how behavior and cognition develop through combinations of social and individual experiences and efforts. Human developmental processes display remarkable plasticity, allowing children to master complex tasks, many which are of recent origin and not part of our biological history, such as mental arithmetic or pottery. It is this potency of human developmental mechanisms that allow humans to have culture on a grand scale. Biological evolution would only establish such plasticity if the combinatorial problems associated with flexibility could be solved, biological goals be reasonably safeguarded, and cultural transmission faithful. We suggest that cultural information can guide development in similar way as genes, provided that cultural evolution can establish productive transmission/teaching trajectories that allow for incremental acquisition of complex tasks. We construct a principle model of development that fulfills the needs of both subjects that we refer to as Incremental Functional Development. This process is driven by an error-correcting mechanism that attempts to fulfill combinations of cultural and inborn goals, using cultural information about structure. It supports the acquisition of complex skills. Over generations, it maintains function rather than structure, and this may solve outstanding issues about cultural transmission. The presence of cultural goals gives the mechanisms an open architecture that become an engine for cultural evolution.

We introduce a mathematical model of cultural evolution to study cultural traits that shape how individuals exchange information. Current theory focuses on traits that influence the reception of information (receiver traits), such as evaluating whether information represents the majority or stems from a trusted source. Our model shifts the focus from the receiver to the sender of cultural information and emphasizes the role of sender traits, such as communicability or persuasiveness. Here, we show that sender traits are probably a stronger driving force in cultural evolution than receiver traits. While receiver traits evolve to curb cultural transmission, sender traits can amplify it and fuel the self-organization of systems of mutually supporting cultural traits, including traits that cannot be maintained on their own. Such systems can reach arbitrary complexity, potentially explaining uniquely human practical and mental skills, goals, knowledge and creativity, independent of innate factors. Our model incorporates social and individual learning throughout the lifespan, thus connecting cultural evolutionary theory with developmental psychology. This approach provides fresh insights into the trait-individual duality, that is, how cultural transmission of single traits is influenced by individuals, who are each represented as an acquired system of cultural traits.

In this paper, we introduce a minimal cognitive architecture designed to explore the mechanisms underlying human language learning abilities. Our model inspired by research in artificial intelligence incorporates sequence memory, chunking and schematizing as key domain-general cognitive mechanisms. It combines an emergentist approach with the generativist theory of type systems. By modifying the type system to operationalize theories on usage-based learning and emergent grammar, we build a bridge between theoretical paradigms that are usually considered incompatible. Using a minimal error-correction reinforcement learning approach, we show that our model is able to extract functional grammatical systems from limited exposure to small artificial languages. Our results challenge the need for complex predispositions for language and offer a promising path for further development in understanding cognitive prerequisites for language and the emergence of grammar during learning.

In this paper, we address the question of what minimal cognitive features are necessary for learning to process and extract grammatical structure from language. We build a minimalistic computational model containing only the two core features chunking and sequence memory and test its capacity to identify sentence borders and parse sentences in two artificial languages. The model has no prior linguistic knowledge and learns only by reinforcement of the identification of meaningful units. In simulations, the model turns out to be successful at its tasks, indicating that it is a good starting point for an extended model with ability to process and extract grammatical structure from larger corpora of natural language. We conclude that a model with the features chunking and sequence memory, that should in the future be complemented with the ability to establish hierarchical schemas, has the potential of describing the emergence of grammatical categories through language learning.

In a recent study, Wasserman, Kain, and O'Donoghue (Current Biology, 33(6), 1112–1116, 2023) set out to resolve the associative learning paradox by showing that pigeons can solve a complex category learning task through associative learning. The present Outlook paper presents their findings, expands on this paradox, and discusses implications of their results.

Abnormal behaviours are common in captive animals, and despite a lot of research, the development, maintenance and alleviation of these behaviours are not fully understood. Here, we suggest that conditioned reinforcement can induce sequential dependencies in behaviour that are difficult to infer from direct observation. We develop this hypothesis using recent models of associative learning that include conditioned reinforcement and inborn facets of behaviour, such as predisposed responses and motivational systems. We explore three scenarios in which abnormal behaviour emerges from a combination of associative learning and a mismatch between the captive environment and inborn predispositions. The first model considers how abnormal behaviours, such as locomotor stereotypies, may arise from certain spatial locations acquiring conditioned reinforcement value. The second model shows that conditioned reinforcement can give rise to abnormal behaviour in response to stimuli that regularly precede food or other reinforcers. The third model shows that abnormal behaviour can result from motivational systems being adapted to natural environments that have different temporal structures than the captive environment. We conclude that models including conditioned reinforcement offer an important theoretical insight regarding the complex relationships between captive environments, inborn predispositions, and learning. In the future, this general framework could allow us to further understand and possibly alleviate abnormal behaviours.