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Large Language Models have taken the cognitive science world by storm.
“Subjective probability: A judgment of representativeness”
Daniel Kahneman and Amos Tversky · 1972
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“Subjective probability: A judgment of representativeness”
Daniel Kahneman and Amos Tversky · 1972
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“The weirdest people in the world?”
Joseph Henrich, Steven Heine and Ara Norenzayan · 2010
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“The weirdest people in the world?”
Joseph Henrich, Steven Heine and Ara Norenzayan · 2010
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“Performance-optimized hierarchical models predict neural responses in higher visual cortex”
Daniel Yamins et al · 2014
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“Performance-optimized hierarchical models predict neural responses in higher visual cortex”
Daniel Yamins et al · 2014
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“A primer in BERTology: What we know about how BERT works”
Anna Rogers, Olga Kovaleva and Anna Rumshisky · 2021
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“A primer in BERTology: What we know about how BERT works”
Anna Rogers, Olga Kovaleva and Anna Rumshisky · 2021
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“Probing classifiers: Promises, shortcomings, and advances”
Yonatan Belinkov · 2022
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“When to make exceptions: Exploring language models as accounts of human moral judgment”
Zhijing Jin et al · 2022
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“Reconstructing the cascade of language processing in the brain using the internal computations of a transformer-based language model”
Sreejan Kumar et al · 2022
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“Neural theory-of-mind? on the limits of social intelligence in large lms”
Maarten Sap, Ronan LeBras, Daniel Fried and Yejin Choi · 2022
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“Chain-of-thought prompting elicits reasoning in large language models”
Jason Wei et al · 2022
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“Probing classifiers: Promises, shortcomings, and advances”
Yonatan Belinkov · 2022
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“When to make exceptions: Exploring language models as accounts of human moral judgment”
Zhijing Jin et al · 2022
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“Reconstructing the cascade of language processing in the brain using the internal computations of a transformer-based language model”
Sreejan Kumar et al · 2022
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“Neural theory-of-mind? on the limits of social intelligence in large lms”
Maarten Sap, Ronan LeBras, Daniel Fried and Yejin Choi · 2022
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“Chain-of-thought prompting elicits reasoning in large language models”
Jason Wei et al · 2022
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“Perils and Opportunities in Using Large Language Models in Psychological Research”
Suhaib Abdurahman et al · 2023
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“Using large language models to simulate multiple humans and replicate human subject studies”
Gati Aher, Rosa Arriaga and Adam Kalai · 2023
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“Out of one, many: Using language models to simulate human samples”
Lisa Argyle et al · 2023
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“Using cognitive psychology to understand GPT-3”
Marcel Binz and Eric Schulz · 2023
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“What are large language models supposed to model?”
Idan Blank · 2023
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“A categorical archive of chatgpt failures”
Ali Borji · 2023
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“Large language models demonstrate the potential of statistical learning in language”
Pablo Contreras, Ross Kristensen-McLachlan and Morten Christiansen · 2023
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“Using large language models in psychology”
Dorottya Demszky et al · 2023
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“Can AI language models replace human participants?”
Danica Dillion, Niket Tandon, Yuling Gu and Kurt Gray · 2023
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Ronald Fischer, Markus Luczak-Roesch and Johannes Karl · 2023
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“Baby steps in evaluating the capacities of large language models”
Michael Frank · 2023
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“Openly accessible LLMs can help us to understand human cognition”
Michael Frank · 2023
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“Understanding social reasoning in language models with language models”
Kanishk Gandhi, Jan-Philipp Fränken, Tobias Gerstenberg and Noah Goodman · 2023
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“Relational reasoning and generalization using nonsymbolic neural networks.”
Atticus Geiger, Alexandra Carstensen, Michael Frank and Christopher Potts · 2023
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“AI and the transformation of social science research”
Igor Grossmann et al · 2023
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“Human-like intuitive behavior and reasoning biases emerged in large language models but disappeared in ChatGPT”
Thilo Hagendorff, Sarah Fabi and Michal Kosinski · 2023
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“Large language models meet cognitive science: LLMs as tools, models, and participants”
Mathew Hardy, Ilia Sucholutsky, Bill Thompson and Tom Griffiths · 2023
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“A cultural species and its cognitive phenotypes: implications for philosophy”
Joseph Henrich et al · 2023
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“Large language models as simulated economic agents: What can we learn from homo silicus?”, 2023
John Horton · 2023
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“Generative language models exhibit social identity biases”
Tiancheng Hu et al · 2023
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“Event knowledge in large language models: the gap between the impossible and the unlikely”
Carina Kauf et al · 2023
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“Theory of mind may have spontaneously emerged in large language models”
Michal Kosinski · 2023
Cited alongside, same era.
“Neuro-Symbolic Models of Human Moral Judgment: LLMs as Automatic Feature Extractors”
Joe Kwon, Sydney Levine and Joshua Tenenbaum · 2023
Cited alongside, same era.
“Language models show human-like content effects on reasoning tasks”
Andrew Lampinen et al · 2023
Cited alongside, same era.
“Generative agents: Interactive simulacra of human behavior”
Joon Park et al · 2023
Cited alongside, same era.
“Modern language models refute Chomsky’s approach to language”
Steven Piantadosi · 2023
Cited alongside, same era.
“Neuro-Symbolic Models of Human Moral Judgment: LLMs as Automatic Feature Extractors”
Joe Kwon, Sydney Levine and Joshua Tenenbaum · 2023
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“Language models show human-like content effects on reasoning tasks”
Andrew Lampinen et al · 2023
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“Generative agents: Interactive simulacra of human behavior”
Joon Park et al · 2023
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“Modern language models refute Chomsky’s approach to language”
Steven Piantadosi · 2023
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“GPT is an effective tool for multilingual psychological text analysis”
Steve Rathje et al · 2023
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“Exploring ChatGPT’s Empathic Abilities”
Kristina Schaaff, Caroline Reinig and Tim Schlippe · 2023
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Steve Rathje et al · 2023
Cited alongside, same era.
“Exploring ChatGPT’s Empathic Abilities”
Kristina Schaaff, Caroline Reinig and Tim Schlippe · 2023
Cited alongside, same era.
“Personality traits in large language models”
Greg Serapio-García et al · 2023
Cited alongside, same era.
“Auditing and Mitigating Cultural Bias in LLMs”
Yan Tao, Olga Viberg, Ryan Baker and Rene Kizilcec · 2023
Cited alongside, same era.
“Do large language models know what humans know?”
Sean Trott et al · 2023
Cited alongside, same era.
“Large language models fail on trivial alterations to theory-of-mind tasks”
Tomer Ullman · 2023
Cited alongside, same era.
“Emergent analogical reasoning in large language models”
Taylor Webb, Keith Holyoak and Hongjing Lu · 2023
Cited alongside, same era.
Later among the works it cites.
“Personality traits in large language models”
Greg Serapio-García et al · 2023
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“Auditing and Mitigating Cultural Bias in LLMs”
Yan Tao, Olga Viberg, Ryan Baker and Rene Kizilcec · 2023
Later among the works it cites.
“Do large language models know what humans know?”
Sean Trott et al · 2023
Later among the works it cites.
“Large language models fail on trivial alterations to theory-of-mind tasks”
Tomer Ullman · 2023
Later among the works it cites.
“Emergent analogical reasoning in large language models”
Taylor Webb, Keith Holyoak and Hongjing Lu · 2023
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“Causal interventions expose implicit situation models for commonsense language understanding”
Takateru Yamakoshi, James McClelland, Adele Goldberg and Robert Hawkins · 2023
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“Tree of thoughts: Deliberate problem solving with large language models”
Shunyu Yao et al · 2023
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“Transmission versus truth, imitation versus innovation: What children can do that large language and language-and-vision models cannot (yet)”
Eunice Yiu, Eliza Kosoy and Alison Gopnik · 2023
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“Evaluating Subjective Cognitive Appraisals of Emotions from Large Language Models”
Hongli Zhan, Desmond Ong and Junyi Li · 2023
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“Can large language models transform computational social science?”
Caleb Ziems et al · 2023
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“Inductive reasoning in humans and large language models”
Simon Han, Keith Ransom, Andrew Perfors and Charles Kemp · 2024
Closest in time.
“Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training”
Eghbal Hosseini et al · 2024
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“Large Language Models Produce Responses Perceived to be Empathic”
Yoon Lee et al · 2024
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“Skill but not Effort Drive GPT Overperformance over Humans in Cognitive Reframing of Negative Scenarios”
Joanna Li, Alina Herderich and Amit Goldenberg · 2024
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“Dissociating language and thought in large language models: a cognitive perspective”
Kyle Mahowald et al · 2024
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“AI Psychometrics: Assessing the psychological profiles of large language models through psychometric inventories”
Max Pellert et al · 2024
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“Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines”
Marko Sarstedt, Susanne Adler, Lea Rau and Bernd Schmitt · 2024
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“Grounded language acquisition through the eyes and ears of a single child”
Wai Vong, Wentao Wang, A. Orhan and Brenden. Lake · 2024
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“Interpretability at scale: Identifying causal mechanisms in alpaca”
Zhengxuan Wu et al · 2024
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“AI can help people feel heard, but an AI label diminishes this impact”
Yidan Yin, Nan Jia and Cheryl Wakslak · 2024
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“Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided”
Hongli Zhan et al · 2024
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“Inductive reasoning in humans and large language models”
Simon Han, Keith Ransom, Andrew Perfors and Charles Kemp · 2024
Closest in time.
“Artificial neural network language models predict human brain responses to language even after a developmentally realistic amount of training”
Eghbal Hosseini et al · 2024
Closest in time.
“Large Language Models Produce Responses Perceived to be Empathic”
Yoon Lee et al · 2024
Closest in time.
“Skill but not Effort Drive GPT Overperformance over Humans in Cognitive Reframing of Negative Scenarios”
Joanna Li, Alina Herderich and Amit Goldenberg · 2024
Closest in time.
“Dissociating language and thought in large language models: a cognitive perspective”
Kyle Mahowald et al · 2024
Closest in time.
“AI Psychometrics: Assessing the psychological profiles of large language models through psychometric inventories”
Max Pellert et al · 2024
Closest in time.
“Using large language models to generate silicon samples in consumer and marketing research: Challenges, opportunities, and guidelines”
Marko Sarstedt, Susanne Adler, Lea Rau and Bernd Schmitt · 2024
Closest in time.
“Grounded language acquisition through the eyes and ears of a single child”
Wai Vong, Wentao Wang, A. Orhan and Brenden. Lake · 2024
Closest in time.
“Interpretability at scale: Identifying causal mechanisms in alpaca”
Zhengxuan Wu et al · 2024
Closest in time.
“AI can help people feel heard, but an AI label diminishes this impact”
Yidan Yin, Nan Jia and Cheryl Wakslak · 2024
Closest in time.
“Large Language Models are Capable of Offering Cognitive Reappraisal, if Guided”
Hongli Zhan et al · 2024
Closest in time.