Addressing Longstanding Challenges in Cognitive Science with Language Models
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Abstract (arXiv)
Cognitive science faces ongoing challenges in research integration, formalization, conceptual clarity, and other areas, in part due to its multifaceted and interdisciplinary nature. Recent advances in artificial intelligence, particularly the development of language models, offer tools that may help to address these longstanding issues. Specifically, they can help map fragmented literatures, formalize verbal theories, identify overlap among constructs and measures, generate predictions across tasks, and extract cultural or ecological structure from naturalistic data. However, these opportunities come with risks, including oversimplification, opacity, deskilling, and bias. Taken together, we conclude that language models could serve as tools for a more integrative and cumulative cognitive science when used judiciously to complement, rather than replace, human agency.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 33 of its 130 references that are in the map. First posted on arXiv 31 Oct 2025.
Builds on
33 of 130 references.
References · 33
- 2017Attention Is All You Needcited by 60,361
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
- 2020Language Models are Few-Shot Learnerscited by 32,419
- 2022Training language models to follow instructions with human feedbackcited by 15,971
- 2019Sentence-BERT: Sentence Embeddings using Siamese BERT-Networkscited by 9,170
- 2025DeepSeek-R1: Incentivizing Reasoning Capability in LLMs via Reinforcement Learningcited by 6,330
- 2023Large Language Modelscited by 780
- 2023Whose Opinions Do Language Models Reflect?cited by 773
- 2023Large Language Models Empowered Agent-based Modeling and Simulation: A Survey and Perspectivescited by 257
- 2024Leak, Cheat, Repeat: Data Contamination and Evaluation Malpractices in Closed-Source LLMscited by 229
- 2023LLMs4OL: Large Language Models for Ontology Learningcited by 77
- 2024Large language models surpass human experts in predicting neuroscience resultscited by 69
21 more
- 2025Language Model Fine-Tuning on Scaled Survey Data for Predicting Distributions of Public Opinionscited by 66
- 2025The Prompt Makes the Person(a): A Systematic Evaluation of Sociodemographic Persona Prompting for Large Language Modelscited by 42
- 2024Best Practices for Text Annotation with Large Language Modelscited by 36
- 2024The LLM Language Network: A Neuroscientific Approach for Identifying Causally Task-Relevant Unitscited by 34
- 2023OpinionGPT: Modelling Explicit Biases in Instruction-Tuned LLMscited by 29
- 2023Using Sequences of Life-events to Predict Human Livescited by 27
- 2025Persistent Instability in LLM's Personality Measurements: Effects of Scale, Reasoning, and Conversation Historycited by 26
- 2024LLMs4OM: Matching Ontologies with Large Language Modelscited by 22
- 2025Large Language Models Do Not Simulate Human Psychologycited by 19
- 2024Sparse Autoencoders Reveal Temporal Difference Learning in Large Language Modelscited by 15
- 2025The threat of analytic flexibility in using large language models to simulate human datacited by 15
- 2025Generating Computational Cognitive Models using Large Language Modelscited by 14
- 2024LLMs4Synthesis: Leveraging Large Language Models for Scientific Synthesiscited by 13
- 2024Building Knowledge-Guided Lexica to Model Cultural Variationcited by 11
- 2025LLMs4SchemaDiscovery: A Human-in-the-Loop Workflow for Scientific Schema Mining with Large Language Modelscited by 9
- 2025Using Reinforcement Learning to Train Large Language Models to Explain Human Decisionscited by 9
- 2025Emergent LLM behaviors are observationally equivalent to data leakagecited by 9
- 2025Missing the Margins: A Systematic Literature Review on the Demographic Representativeness of LLMscited by 9
- 2024Exploring the applicability of Large Language Models to citation context analysiscited by 6
- 2024Probing the contents of semantic representations from text, behavior, and brain data using the psychNorms metabasecited by 5
- 2025Reply to "Emergent LLM behaviors are observationally equivalent to data leakage"cited by 2
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