Unifying the structures of language in a neural population code.

The compositionality continuum as a principle for studying the neural basis of intelligence.

The Genealogy of Large Language Models: From Auxiliary Tools in ASR to Foundational Transformers and Back Again

This paper traces the evolutionary trajectory of Large Language Models (LLMs), arguing that their origins lie in the practical need to correct transcription errors in Automatic Speech Recognition (ASR) systems. We delineate this development, starting with domain-specific grammars, progressing through statistical n-gram models, and then to Artificial Neural Network-based models (ANNs), specifically RNNs, LSTMs, and GRUs, until reaching the pivotal breakthrough of the Transformer architecture. This evolution, driven by the pursuit of better language modeling, enabled the massive scaling that defines modern LLMs, which exhibit unprecedented capabilities. We conclude that LLMs, which emerged as an auxiliary component to mitigate the deficiencies of ASR systems, have "closed the circle" by becoming the foundational technology that now redefines the state of the art in their progenitor systems, thereby establishing themselves as a unifying technology for Artificial Intelligence.

Large Language Models as Distributional Baselines for Language Tasks

In order to ask questions about the mechanisms underpinning human cognition, researchers must control for properties of stimuli that could confound detected effects. In experiments involving linguistic stimuli, this includes properties like frequency, length, and neighborhood size of those stimuli, which are known to affect behavioral and neural responses. With improvements in the performance and usability of language models, it is now possible to also control for how predictable stimuli and their parts are, on the basis of the distributions of words alone: their distributional predictability. This coincides with a resurgence of interest in the possibility that statistical language learning may underlie a broad range of human cognitive phenomena; indeed, there are both theoretical and empirical reasons to believe that humans rely on distributional information during certain cognitive tasks. This creates a confound, whereby experimental operationalizations of psychological constructs with linguistic stimuli may not in fact be testing what they are intended to test. Thus, the central contributions of this paper are twofold: first, we articulate the conditions under which distributional predictability threatens the internal validity of an experiment; and second, we provide concrete recommendations for how to control for this potential confound. Beyond these primary contributions, we survey techniques for measuring distributional predictability, review theoretical and empirical work supporting the role of distributional statistics in human cognition, and present several case studies illustrating the range of possible outcomes—from the “distributional baselines” only marginally affecting theoretical inferences to constituting fully deflationary confounds. We also enumerate and address potential objections to this approach. This paper is primarily intended for researchers in psychology, cognitive science, and linguistics who use linguistic stimuli but have not yet incorporated distributional baselines into their work.

AI Models Can Predict and Collaboratively Modulate Human Memory Search

Large language models (LLMs) exhibit unprecedented natural language generation and many text-based problem-solving capabilities. Indeed, in many language-based tasks, for example routine coding, these artificial intelligence models have reduced, or even eliminated, the need for human input. But rather than replacing human cognitive effort, LLMs may instead serve as cognitive tools to extend human abilities, particularly when they are engaged in a task requiring open-ended conceptual exploration and creative ideation. However, we are yet to understand how these models may enhance such generative human cognitive abilities in human--AI interactions. In this study, we explore and evaluate the ability of LLMs to follow and enhance human mental trajectories during semantic memory search. To test this, we use the semantic fluency task (SFT), a classic cognitive paradigm requiring generative semantic memory retrieval that has long served to characterize convergent and divergent thinking in humans. We demonstrate that an LLM's abilities to track and predict human memory trajectories in this task exceed those of other humans.

Cognitive Convergence: Deep Similarities Between Large Language Models and Human Cognition

LLMs are widely regarded as alien intelligences, systems whose cognitive operations are fundamentally unlike our own. Apparent similarities to human cognition are therefore often seen as the result of anthropomorphic projection. We argue that this framing is mistaken. LLMs clearly differ from humans in important respects, including their physical substrate, learning history, and the environments with which they interact. These differences make it all the more striking that contemporary LLM-based systems converge with human cognition on a number of principles of cognitive organization with longstanding support in cognitive science. We identify structural correspondences across five dimensions: inferential organization, computational architecture, representational structure, prediction-driven learning, and reinforcement-learning-like mechanisms supporting goal-directed action. These correspondences support a broader model of intelligent cognition in which core principles long used to explain human intelligence also characterize contemporary LLM-based systems.

Rethinking and formalising the state across languages: a unified computational learning theory account

It is suggested that the state is a systemic, context-dependent morphosyntactic mechanism that selects grammatical templates across synthetic languages and constitutes one instance of a broader class of syntactically conditioned dependencies that also includes agreement and grammatical case.