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Chain-of-thought (CoT) prompting has become a widely used strategy for improving large language and multimodal model performance.
Age differences in short-term retention of rapidly changing information
Kirchner, W. K · 1958
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Implicit learning of synthetic languages: The role of instructional set
Reber, A. S · 1976
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Implicit learning: An analysis of the form and structure of a body of tacit knowledge
Reber, A. S. and Lewis, S · 1977
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Verbal overshadowing of visual memories: Some things are better left unsaid
Schooler, J. W. and Engstler-Schooler, T. Y · 1990
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Post-encoding verbalization impairs transfer on artificial grammar tasks
Fallshore, M. and Schooler, J. W · 1993
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Thoughts beyond words: When language overshadows insight
Schooler, J. W., Ohlsson, S., and Brooks, K · 1993
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Incidentally, things in general are particularly determined: An episodic-processing account of implicit learning
Whittlesea, B. W. and Dorken, M. D · 1993
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The misremembrance of wines past: Verbal and perceptual expertise differentially mediate verbal overshadowing of taste memory
Melcher, J. M. and Schooler, J. W · 1996
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Inferences through imagined actions: Knowing by simulated doing
Schwartz, D. L. and Black, T · 1999
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How did you get here from there? Verbal overshadowing of spatial mental models
Fiore, S. M. and Schooler, J. W · 2002
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Re-representing consciousness: Dissociations between experience and meta-consciousness
Schooler, J. W · 2002
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Think different: the merits of unconscious thought in preference development and decision making
Dijksterhuis, A · 2004
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Intentional artificial grammar learning: When does it work?
Van den Bos, E. and Poletiek, F. H · 2008
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Statistical learning and language acquisition
Romberg, A. R. and Saffran, J. R · 2010
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The role of explanation in discovery and generalization: Evidence from category learning
Williams, J. J. and Lombrozo, T · 2010
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Statistical learning: From acquiring specific items to forming general rules
Aslin, R. N. and Newport, E. L · 2012
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Hidden conflicts: Explanations make inconsistencies harder to detect
Khemlani, S. S. and Johnson-Laird, P. N · 2012
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Explanation and prior knowledge interact to guide learning
Williams, J. J. and Lombrozo, T · 2013
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The hazards of explanation: Overgeneralization in the face of exceptions
Williams, J. J., Lombrozo, T., and Rehder, B · 2013
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Explanatory preferences shape learning and inference
Lombrozo, T · 2016
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The time course of conflict on the cognitive reflection test
Travers, E., Rolison, J. J., and Feeney, A · 2016
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Explaining constrains causal learning in childhood
Walker, C. M., Lombrozo, T., Williams, J. J., Rafferty, A. N., and Gopnik, A · 2017
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Evaluating (and improving) the correspondence between deep neural networks and human representations
Peterson, J. C., Abbott, J. T., and Griffiths, T. L · 2018
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Explanation recruits comparison in a category-learning task
Edwards, B. J., Williams, J. J., Gentner, D., and Lombrozo, T · 2019
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‘Learning by thinking’ in science and in everyday life
Lombrozo, T · 2019
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Learning through simulation
Aronowitz, S. and Lombrozo, T · 2020
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Self-consistency improves chain of thought reasoning in language models
Wang, X., Wei, J., Schuurmans, D., Le, Q. V., Chi, E. H., Narang, S., Chowdhery, A., and Zhou, D · 2023
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Tree of thoughts: Deliberate problem solving with large language models
Yao, S., Yu, D., Zhao, J., Shafran, I., Griffiths, T. L., Cao, Y., and Narasimhan, K. R · 2023
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Automatic chain of thought prompting in large language models
Zhang, Z., Zhang, A., Li, M., and Smola, A · 2023
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Analyzing the roles of language and vision in learning from limited data
Chen, A., Sucholutsky, I., Russakovsky, O., and Griffiths, T. L · 2024
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Cogbench: a large language model walks into a psychology lab
Coda-Forno, J., Binz, M., Wang, J. X., and Schulz, E · 2024
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Position: LLMs can’t plan, but can help planning in LLM-modulo frameworks
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Measuring massive multitask language understanding
Hendrycks, D., Burns, C., Basart, S., Zou, A., Mazeika, M., Song, D., and Steinhardt, J · 2021
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Show your work: Scratchpads for intermediate computation with language models
Nye, M., Andreassen, A. J., Gur-Ari, G., Michalewski, H., Austin, J., Bieber, D., Dohan, D., Lewkowycz, A., Bosma, M., Luan, D., et al · 2021
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Chain-of-thought prompting elicits reasoning in large language models
Wei, J., Wang, X., Schuurmans, D., Bosma, M., Xia, F., Chi, E., Le, Q. V., Zhou, D., et al · 2022
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Using cognitive psychology to understand GPT-3
Binz, M. and Schulz, E · 2023
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The role of episodic memory in storytelling: Comparing large language models with humans
Cornell, C., Jin, S., and Zhang, Q · 2023
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Kambhampati, S., Valmeekam, K., Guan, L., Verma, M., Stechly, K., Bhambri, S., Saldyt, L. P., and Murthy, A. B · 2024
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How do large language models navigate conflicts between honesty and helpfulness?
Liu, R., Sumers, T., Dasgupta, I., and Griffiths, T. L · 2024
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Learning by thinking in natural and artificial minds
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Embers of autoregression show how large language models are shaped by the problem they are trained to solve
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DALL·E 2, 2024b
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Multimodal chain-of-thought reasoning in language models
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Incoherent probability judgments in large language models
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Claude’s extended thinking, 2025
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Explicitly unbiased large language models still form biased associations
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Large language models assume people are more rational than we really are
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To CoT or not to CoT? Chain-of-thought helps mainly on math and symbolic reasoning
Sprague, Z. R., Yin, F., Rodriguez, J. D., Jiang, D., Wadhwa, M., Singhal, P., Zhao, X., Ye, X., Mahowald, K., and Durrett, G · 2025
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