Contextualizing predictive minds

2024/11/22 by Martin V. Butz, Maximilian Mittenbühler, Sarah Schwöbel +4 · 1 voice · 10 citations
Neuroscience · Psychology · #Action Observation and Synchronization #Child and Animal Learning Development #Cognitive psychology #Embodied and Extended Cognition #Predictive coding #Psychoanalysis #Psychology #Sociology

paper · doi:10.1016/j.neubiorev.2024.105948

published in Neuroscience & Biobehavioral Reviews 168, 105948 (Elsevier BV)

openalex publication_date 2024/11/22 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/05

Abstract

The structure of human memory seems to be optimized for efficient prediction, planning, and behavior. We propose that these capacities rely on a tripartite structure of memory that includes concepts, events, and contexts-three layers that constitute the mental world model. We suggest that the mechanism that critically increases adaptivity and flexibility is the tendency to contextualize. This tendency promotes local, context-encoding abstractions, which focus event- and concept-based planning and inference processes on the task and situation at hand. As a result, cognitive contextualization offers a solution to the frame problem-the need to select relevant features of the environment from the rich stream of sensorimotor signals. We draw evidence for our proposal from developmental psychology and neuroscience. Adopting a computational stance, we present evidence from cognitive modeling research which suggests that context sensitivity is a feature that is critical for maximizing the efficiency of cognitive processes. Finally, we turn to recent deep-learning architectures which independently demonstrate how context-sensitive memory can emerge in a self-organized learning system constrained by cognitively-inspired inductive biases.

Citations

• I.—COMPUTING MACHINERY AND INTELLIGENCE
• Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning
• Mastering Atari, Go, Chess and Shogi by Planning with a Learned Model
• On the Dangers of Stochastic Parrots
• Toward Causal Representation Learning
• Making sense of abstract events: Building event schemas

Cited by

• Schema-based active inference supports rapid generalization of experience and frontal cortical coding of abstract structure
• Analogy making as amortised model construction
• Causal Process Models: Reframing Dynamic Causal Graph Discovery as a Reinforcement Learning Problem
• Cognitive biases as Bayesian probability weighting in context. [europepmc]
• The active construction of past episodes. [europepmc]

Discussions

• What role does #context play in human #cognition? In this review, we argue that the context-sensitivity of human memory and its hierarchical structure are the features that enable flexible planning, g [bsky, 37 points, 0 comments]

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