Cognitive Science Commons^™

Understanding Behavioral And Representational Divergences Of Humans And Machines, Thomas Lasman Botch

Dartmouth College Ph.D Dissertations

Human behavior and cognition are strikingly variable: people differ from one another in their preferences and abilities, and even from themselves across situations. Yet this diversity arises from common neural machinery shaped by the complex environments humans inhabit. A central objective of cognitive neuroscience is to understand how this varied experience emerges from interactions between brains, agents, and environments. In this dissertation, I argue that comparing behavior and neural computation across humans, artificial systems, and contexts is essential for understanding the flexibility of human cognition. Across three chapters, I use this comparative approach to examine how the rich, multimodal contexts …

A Novel Approach To Creativity Assessment: Forced Pairwise Ranking With Large Language Models, Phillip R. Gregory Jr

Master's Theses

Assessing creativity at scale remains a persistent challenge in cognitive science, as human raters are costly, slow, and often inconsistent in their judgments. This thesis introduced a novel framework for automated scientific creativity assessment using forced pairwise ranking, in which fine-tuned large language models compared response pairs and determined which was more creative. Five empirical studies were conducted using Llama-2-7B and Llama-2-13B models adapted via LoRA fine-tuning and benchmarked against human scored responses from the Scientific Creative Thinking Test. A regression baseline achieved Pearson �� = .74 on the test set, matching the human inter-rater ceiling reported in the literature. …

Why General Ai Inherited The Body: A Structural Account Of Embodied Modulation Under Monolithic Imitation, Griselda Poe

Publications and Research

General AI has pursued the replication of human-level intelligence without first decomposing human cognition into structurally distinct components. Human cognition, however, is shaped by embodied constraints such as mortality, survival pressures, finite lifespan, and physiological states. This paper argues that when cognition is treated as a single undifferentiated whole, embodied modulation is not accidentally introduced into AI systems but structurally entailed. Any attempt to imitate “human intelligence” under a monolithic model necessarily incorporates variability shaped by mortal embodiment. The tensions observed in contemporary AI systems are better understood as consequences of copying an undecomposed target rather than isolated implementation errors. …

The Illusion Of Causality In Llms: A Developmentally Grounded Analysis Of Semantic Scaffolding And Benchmark–Capability Mismatches, Daisuke Akiba

Publications and Research

Recent benchmarks increasingly report that large language models (LLMs) exhibit human-like causal reasoning abilities, including counterfactual inference and intervention planning. However, many such evaluations rely on domains that are heavily represented in training data and embed strong semantic cues, raising the possibility that apparent causal competence may reflect semantic pattern recombination rather than structure-sensitive causal reasoning. Drawing on human developmental theories of causal induction, this perspective argues that genuine causal understanding requires robustness to novelty and reliance on conditional structure rather than semantic familiarity. To illustrate the testability of this claim, the paper includes a pilot demonstration using synthetic causal …

Cerebral Documents And Algorithmic Sensemaking: Searching For Expressions In Human And Artificial Cognitive Collaborations, Rebekah L. Cowell

Proceedings from the Document Academy

Generative Artificial Intelligences (AIs) and current advanced large language models (LLMs) are algorithmically designed to generate text-based conversations as conversational agents (CAs), by replicating human language and conversational communication. Pairing human cognition with generative computationally coded cognition. We have never been here before: cerebral and artificial information collaborations and processing producing expressions that may or may not become visible as second-hand/secondary source documents.

Sensemaking or sense(un)making is a unique autonomous human drive cognitively, our information processing is sensemaking in action and expressions and articulations are evidence of the sensemaking cycle. Documentation [expressed or articulated through various mediums] are a product …

Large Language Models As Machines Of Beauty: Cognitive Averaging, Latent Space Geometry, And The Entropic Foundations Of Aesthetic Preference, Daniel Plate, James Hutson

Faculty Scholarship

This study advances the position that large language models (LLMs) and human perceptual systems are governed by a shared computational drive toward prototypicality, entropy reduction, and aesthetic coherence. Drawing on developmental evidence that infants exhibit early preferences for facial symmetry and averageness, the analysis situates aesthetic preference within broader research on processing fluency and predictive coding, emphasizing that biological perception rewards stimuli that reduce uncertainty and support efficient information compression. This foundation is used to examine how LLMs, through cross-entropy optimization, perplexity minimization, and latent space clustering, converge on high-density representational regions that operate as statistical prototypes of linguistic and …

Examining Differences In Concept Representation Across Similarity Spaces Between Humans And Large Language Models, Krishnachandra Nair

Computer Science Senior Theses

The replication of human concept representation is a critical task for the pursuit of artificial general intelligence. With the recent influx of large language models that demonstrate text-generation capabilities nearly on par with humans, the question stands on whether these large language models can capture concepts within language. We examine this question by exploring differences in concept representation across similarity spaces between humans and LLMs. We find that, while concept representation within LLMs does partially mimic human concept representation, LLMs are greatly limited by their dependence on semantic information and cannot therefore develop an understanding of human social code or …