Is Cognitive Dissonance Actually a Thing? In 1934, an 8.0-magnitude earthquake hit eastern India, killing thousands and devastating several cities. Curiously, in areas that were spared the worst destruction, stories soon spread that an even bigger disaster was on its way. Leon Festinger, a young American psychologist at the University of Minnesota, read about these rumors in the early nineteen-fifties and was puzzled. Festinger didn't think people would voluntarily adopt anxiety-inducing ideas. Instead, he reasoned, the rumors could better be described as "anxiety justifying." Some had felt the earth shake and were overwhelmed with fear. When the outcome--they were spared--didn't match their emotions, they embraced predictions that affirmed their fright.

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

This paper explores the design of a propaganda detection tool using Large Language Models (LLMs). Acknowledging the inherent biases in AI models, especially in political contexts, we investigate how these biases might be leveraged to enhance critical think ing in news consumption. Countering the typical view of AI biases as detrimental, our research proposes strategies of user choice and personalization in response to a user's political stance, applying psychological concepts of confirmation bias and cogniti ve dissonance.

DL based evidence prediction with uncertainty anchor sample identification.

We thank all reviewers for their valuable feedback and constructive suggestions. Major comments are addressed below. Several works (eg, [7] and [11]) follow a similar rationale. We thank the reviewer for suggesting these large-scale image datasets. Q1: What "evidence-based entropy" is when claiming entropy can be decomposed into vacuity and dissonance.

What uncertainties do we need in bayesian deep learning for computer vision?

Thanks to graph neural networks (GNNs), semi-supervised node classification has shown the state-of-the-art performance in graph data.

We thank all reviewers for their valuable feedback and constructive suggestions. Major comments are addressed below. Several works (eg, [7] and [11]) follow a similar rationale. We thank the reviewer for suggesting these large-scale image datasets. Q1: What "evidence-based entropy" is when claiming entropy can be decomposed into vacuity and dissonance.

DL based evidence prediction with uncertainty anchor sample identification.