Symposia

Deep Learning is a neural network approach where a network of multiple layers is trained to process complex data. Applications for deep learning include recognizing complex patterns in pictures, text, and sounds to, in some cases, produce insights into how human process information. Deep learning has garnered widescale interest, fostered by advances in, for instance, natural language processing and the release of innovative tools like Chat GPT. But does deep learning have implications for theory in the cognitive sciences; that is, is deep learning the answer for understanding human cognitive dynamics?

Reducing inequality, mitigating climate change, and responding to public health crises are large-scale goals that require the cooperation and coordination of many individuals. These goals cannot be achieved by one individual alone, and contributing is not always beneficial to each individual. And yet, individuals must contribute in order to make a difference. How do we hold individuals and groups responsible for collective action?

Large language models (LLMs) like OpenAI’s GPT-4 (OpenAI et al., 2023), or Google’s PaLM (Chowdhery et al., 2022) generate text responses to user-generated text prompts.  In contrast to work that evaluates  the extent to which model-generated text coheres with linguistic rules (i.e., formal competence) (Chomsky et al., 2023; Piantadosi, 2023), the present symposium discusses the  work of cognitive scientists aimed at assessing the extent and manner in which LLMs show effective understanding, reasoning and decision making, capacities associated with human higher cognition (i.e., functional competence) (Binz & Schulz, 2023; Mahowald et al., 2023; Webb et al., 2023). Given both their expertise and their interest in clarifying the nature of human thinking, cognitive scientists are in a unique position both to carefully evaluate LLMs’ capacity for thought (Bhatia, 2023; Han et al., 2024; Mitchell, 2023) and to benefit from them as methodological and theoretical tools. This symposium will thus be of interest not only to cognitive scientists concerned with machine intelligence, but also to those looking to incorporate advances in artificial intelligence with their study of human intelligence.

Predicting the actions and reactions of others is crucial to suc- cessful social interaction. When deciding whether to bluff in a game of poker, we consider the chances that the other players will fold or continue to play and unmask our bluff. When deciding whether to tell our boss that their plans are likely to have adverse effects, we consider a range of reac- tions, from being grateful for our honesty to being dismissed out of spite. Such predictions are highly uncertain and com- plex, not least because the other’s (re)actions usually result from them making equally complex and uncertain inferences about us. Nevertheless, we are often remarkably successful – although sometimes utterly wrong – in our social inferences. How do we explain these successes and failures?

In this Symposium we link different perspectives and traditions of research on language and concepts in the cognitive sciences to better understand the process of abstraction from the social-interactive standpoint. In contrast to the usual benefits of abstraction – such as the ability to categorize and generalize – we underscore the possibility of abstract concepts and words to remain underspecified, “open to the context, the suggestions of others” (Borghi, 2023) or to processes of real-time negotiations (Christiansen & Chater, 2022). This is enabled by their largely non-perceptual rela-tional character (Gentner, 2005) and by the fact that they arise in cognitive systems continually embedded and active in their environments (Cisek 1999; Mannella & Tummolini 2023).

“To communicate is to move. There is no way around that. If we pick up comprehensive handbooks or introductory texts in movement science (Hong and Bartlett (2008)) we see that there is very rich knowledge and tractable mathematical models about different aspects of movements. Yet, we find no chapter on communicative movements. While the field of speech motor control is a developed area on its own (Parrell and Lammert (2019)), there is no movement science of communication proper, which would include whole-body-, hand-gestural-, signed-, and inter-bodily actions.”