Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain

Teun van Gils; Rowan P. Sommers; Markus Ostarek; Peter Hagoort

q-bio.NCAug 29, 2026

Rate-Coding Bundle Memory: A Unified Model of Memory and Control for Symbolic Computation in the Brain

Authors: Teun van Gils, Rowan P. Sommers, Markus Ostarek, Peter Hagoort

Organizations: Neurobiology of Language Department, Max Planck Institute for Psycholinguistics · Donders Institute for Brain, Cognition and Behaviour, Radboud University

Abstract

We propose a neurobiologically plausible model of cognition that combines the advantages of connectionist and symbolic systems, and that can explain a wide range of cognitive phenomena. This model, called Rate-Coding Bundle Memory (RCBM), is based on the Symbolic Subsystem Hypothesis, which posits that the brain implements a symbolic subsystem within its fundamentally connectionist nature. RCBM is a hybrid model that uses rate coding to represent symbols in a continuous space, and it uses a bundle memory system to store and retrieve these symbols. The model is capable of solving a wide range of cognitive phenomena, including one-shot learning, pattern separation, and the binding problem. We argue that RCBM provides a promising framework for understanding the nature of cognition, and that it can be used to develop more sophisticated models of cognition in the future.

Computational Cognitive Modeling

Computational cognitive modeling uses formal models and simulations to explain how people perceive, learn, remember, reason, and choose.

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Thought without systematicity? Evaluating reasoning models on rule induction tasks

Simon Schug, Brenden M. Lake

Princeton University

Reasoning Consistency in Language Models Large Reasoning Models Inductive Inference

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A central tenet of human cognition is systematicity, the principle that understanding one concept is inherently tied to understanding close variations of that concept. Do reasoning models robustly exhibit such systematicity? If so, we would expect consistent performance on structurally equivalent variants of the same task. Here, we extend established rule induction tasks from cognitive science to assess the systematicity of thought in current reasoning models. Each task family has compositional structure that we use to create structurally equivalent task variations through task isomorphisms such as recombination and substitution. We find that despite being able to correctly solve a task, models often fail on structurally equivalent variants of the same task. These findings suggest that many model behaviors lack systematicity, rendering it difficult to robustly establish the cognitive abilities of reasoning models beyond the particular contexts they were evaluated in.

Abstract

Computational Cognitive Modeling

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