Formal Modeling as Theoretical Glue Between Laboratory and Naturalistic Studies of Memory

2026/06/15 by Qiong Zhang · 1 voice
Neuroscience · Computer Science · Engineering · #Cognitive Science and Education Research #Neural Networks and Applications #Slime Mold and Myxomycetes Research

paper · doi:10.1177/17456916261419428

openalex created_date 2025/10/10 · openalex publication_date 2026/06/15 · openalex updated_date 2026/07/16

Abstract

Memory research has evolved along two distinct traditions: well-controlled laboratory experiments emphasizing precision and tractability and naturalistic-memory experiments emphasizing generalization to real-world contexts. Although both have yielded important insights, we do not yet have a generalized theory of memory consistently interpreted across laboratory and naturalistic paradigms. By analyzing the strengths and limitations of the two traditions, I propose that formal modeling is the key to creating this theoretical link. A formal theory, instantiated in precise computational models that are developed over decades of laboratory-based experiments, needs naturalistic experiments to test its generalizability and reveal its limitations. Naturalistic experiments, in turn, better connect with existing laboratory paradigms when their results are explained by the same theoretical model. To achieve this, I propose a step-by-step procedure in which naturalistic settings are considered as all possible scenarios that could be realized in the real world, with laboratory settings forming a smaller subset that we have understood well. Our goal as memory researchers is to incrementally expand the scope of existing laboratory studies, theories, and models to account for increasingly naturalistic scenarios, ultimately achieving a generalized theory of memory. Together, the proposed framework no longer views laboratory versus naturalistic approaches as a trade-off to navigate, given their different priorities and methodologies, but considers them both essential in working toward the same goal.

Citations

• On the Rate of Gain of Information
• A Distributed Representation of Temporal Context
• Perceiving, remembering, and communicating structure in events.
• The magical number seven, plus or minus two: Some limits on our capacity for processing information.
• How Computational Modeling Can Force Theory Building in Psychological Science
• Toward Causal Representation Learning
• Attention, similarity, and the identification–categorization relationship.
• Using the environment to predict memory performance.
• Reconciling Shared versus Context-Specific Information in a Neural Network Model of Latent Causes
• Event segmentation and the temporal compression of experience in episodic memory
• Invariant Causal Prediction for Nonlinear Models
• Causal inference using invariant prediction: identification and confidence intervals
• When Does Semantic Similarity Help Episodic Retrieval?

Discussions

• [Review] "a framework to unify laboratory and naturalistic approaches in memory research, where naturalistic data serve to constrain and build a more generalized theory of memory, and formal modeling [bsky, 0 points, 0 comments]