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Main Authors:	Kundu, Akash, Goswami, Rishika
Format:	Preprint
Published:	2025
Subjects:	Artificial Intelligence
Online Access:	https://arxiv.org/abs/2506.18156
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author	Kundu, Akash Goswami, Rishika
author_facet	Kundu, Akash Goswami, Rishika
contents	We investigate whether Large Language Models (LLMs) exhibit human-like cognitive patterns under four established frameworks from psychology: Thematic Apperception Test (TAT), Framing Bias, Moral Foundations Theory (MFT), and Cognitive Dissonance. We evaluated several proprietary and open-source models using structured prompts and automated scoring. Our findings reveal that these models often produce coherent narratives, show susceptibility to positive framing, exhibit moral judgments aligned with Liberty/Oppression concerns, and demonstrate self-contradictions tempered by extensive rationalization. Such behaviors mirror human cognitive tendencies yet are shaped by their training data and alignment methods. We discuss the implications for AI transparency, ethical deployment, and future work that bridges cognitive psychology and AI safety
format	Preprint
id	arxiv_https___arxiv_org_abs_2506_18156
institution	arXiv
publishDate	2025
record_format	arxiv
spellingShingle	AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology Kundu, Akash Goswami, Rishika Artificial Intelligence We investigate whether Large Language Models (LLMs) exhibit human-like cognitive patterns under four established frameworks from psychology: Thematic Apperception Test (TAT), Framing Bias, Moral Foundations Theory (MFT), and Cognitive Dissonance. We evaluated several proprietary and open-source models using structured prompts and automated scoring. Our findings reveal that these models often produce coherent narratives, show susceptibility to positive framing, exhibit moral judgments aligned with Liberty/Oppression concerns, and demonstrate self-contradictions tempered by extensive rationalization. Such behaviors mirror human cognitive tendencies yet are shaped by their training data and alignment methods. We discuss the implications for AI transparency, ethical deployment, and future work that bridges cognitive psychology and AI safety
title	AI Through the Human Lens: Investigating Cognitive Theories in Machine Psychology
topic	Artificial Intelligence
url	https://arxiv.org/abs/2506.18156

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