AINeutralarXiv – CS AI · Jun 95/10
🧠Researchers have developed a formal decision-theoretic framework that quantifies the value of perception, prediction, communication, and common sense in autonomous decision-making systems. The work reveals that perception alone can have negative value, while combined perception-prediction and standalone prediction always yield non-negative returns, with applications to autonomous systems design and cognitive science.
AINeutralarXiv – CS AI · Jun 96/10
🧠Researchers have developed Brain2Text, a deep learning model that decodes fMRI brain signals directly into textual descriptions of viewed images without requiring visual training data. The breakthrough reveals that higher-level visual cortices like MT+ complex and ventral stream regions are critical for semantic processing, advancing neuroscience understanding of how the brain represents and processes visual meaning.
AINeutralarXiv – CS AI · Jun 85/10
🧠Research comparing human adults and large language models on causal learning tasks reveals that active exploration significantly improves humans' ability to identify conjunctive causal rules (where multiple causes must occur simultaneously), though conjunctive reasoning remains harder than disjunctive reasoning. State-of-the-art LLMs approach human performance on accuracy but demonstrate less efficient exploration strategies and similar reasoning gaps.
AINeutralarXiv – CS AI · Jun 56/10
🧠Researchers introduce Learning-to-Theorize, a new AI paradigm that builds explicit explanatory theories of the world from observations rather than simply predicting future states. The Neural Theorizer (NEO) model represents understanding as executable, compositional programs whose learned primitives can be recombined to explain novel phenomena, enabling explanation-driven generalization.
AINeutralarXiv – CS AI · Jun 26/10
🧠A new academic framework proposes interaction as the primary unit of analysis for understanding intelligence in human-AI systems, shifting focus from isolated computation within individual models to the relational dynamics that emerge through collaborative engagement. The paper synthesizes decades of research across distributed cognition, embodied cognition, and computational creativity to argue that intelligence, creativity, and meaning arise from evolving interaction patterns rather than internal computation alone.
AINeutralarXiv – CS AI · Jun 26/10
🧠Researchers compared how human children and large language models approach inductive reasoning tasks under uncertainty, finding both similarities and critical differences in their information-seeking strategies. While LLMs replicate children's adaptive responses to environmental structure, they exhibit distinct biases toward over-observation and instruction compliance, suggesting fundamentally different underlying computational principles govern their decision-making.
AINeutralarXiv – CS AI · May 296/10
🧠Researchers introduce the Cognitive Categorical Transformer (CCT), a 306M-parameter language model that applies category-theoretic principles to improve upon GPT-2 Small, achieving 12% relative perplexity reduction on WikiText-103. The work provides empirical validation that simplicial message passing enhances language modeling performance and identifies a distinction between topology-adding versus consistency-enforcing categorical priors.
🏢 Perplexity
AINeutralarXiv – CS AI · May 286/10
🧠Researchers propose a Cognitive Taxonomy framework to measure progress toward AGI by evaluating systems against 10 key cognitive faculties derived from psychology and neuroscience research. The framework aims to address the lack of standardized metrics for AGI advancement and provide empirical evaluation methods to support responsible AI governance.
AINeutralarXiv – CS AI · May 286/10
🧠Researchers introduce Contextual Alternative Choice (CAC), a new evaluation method that measures both syntactic and functional properties of language models using metrics derived from child language acquisition studies. While some large language models approach human-level performance on these benchmarks, none trained on comparable data volumes simultaneously meet both formal and functional standards that children achieve early in development.
AINeutralarXiv – CS AI · May 275/10
🧠This arXiv paper proposes the Sensation Modulating Network (SMN), a theoretical cognitive architecture that attempts to bridge the long-standing divide between cognitivism and embodied cognition approaches. The framework grounds meaning-making in the body's opponent dynamics and hierarchical action patterns, offering a novel perspective on how agents achieve intentional directedness without requiring additional computational modules.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers present Bounded Pragmatic Listener (BPL), a Bayesian framework that models how cognitive limitations affect susceptibility to misinformation. The framework incorporates three cognitively grounded constraints—working memory limits, information bottlenecks, and saliency-weighted sampling—to predict vulnerability to disinformation across benchmark datasets.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers introduce the Developmental Sentence Completion Test (DSCT), a 20-item assessment tool that evaluates how large language models understand and reflect human developmental cognition based on Kegan's constructive-developmental theory. The study finds that frontier LLMs accurately identify developmental stages in simulated personas but show only fair agreement with real human responses, revealing that developmental signal is cleaner in synthetic data than human-generated text.
🏢 Meta
AINeutralarXiv – CS AI · May 126/10
🧠Researchers demonstrate that humans learn abstractions prospectively rather than retrospectively when facing non-stationary task environments. Using a visual program synthesis experiment called Pattern Builder Task, they show that human library learning anticipates future task structures rather than merely compressing past experience, a capability that existing algorithmic approaches and LLM-based models fail to replicate.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers propose a computational model that evaluates explanations by converting them into executable action plans through large language models and planning agents. Across four experiments with 1,200 explanations, higher-scored explanations correlate with improved navigation performance and user helpfulness judgments, demonstrating that explanation quality can be measured by practical outcomes under uncertainty.
AINeutralarXiv – CS AI · May 126/10
🧠Researchers studying 21 large language models found a significant 'grounding gap' in how LLMs understand abstract concepts compared to humans. While LLMs rely heavily on word associations, they systematically underreproduce emotional and internal-state properties, achieving maximum correlation of r=0.37 versus human-to-human baselines above r=0.9. The findings suggest current models can identify grounding dimensions when explicitly queried but fail to recruit them naturally during free generation.
AINeutralarXiv – CS AI · May 125/10
🧠Researchers extend the COLIBRI fuzzy color model to reveal that human color categories exhibit significant perceptual asymmetry, with yellow forming a narrow, sharply-defined region while green spans a broader interval. This finding challenges computational models that assume uniformly distributed color representations and suggests color naming follows non-uniform geometric organization in perceptual space.
AIBullisharXiv – CS AI · May 116/10
🧠Researchers compared frontier Large Reasoning Models (LRMs) with traditional AI systems using human gameplay data paired with fMRI brain recordings. LRMs demonstrated superior alignment with human learning behavior and predicted brain activity an order of magnitude better than reinforcement learning alternatives, suggesting they more closely mirror human cognition during complex decision-making.
AINeutralarXiv – CS AI · May 116/10
🧠Researchers propose a Multi-Memory Segment System (MMS) that improves how AI agents generate and store long-term memories by moving beyond simple summarization. The system creates structured retrieval and contextual memory units inspired by cognitive psychology, enabling more effective historical data utilization and response quality in agent interactions.
AINeutralarXiv – CS AI · May 46/10
🧠Researchers compared how large language models, humans, and algorithms approach the exploration-exploitation tradeoff in multi-armed bandit decision-making tasks. The study finds that enabling thinking processes in LLMs makes them behave more like humans in simple environments, but LLMs fail to match human adaptability in complex, non-stationary settings despite similar regret outcomes.
AINeutralarXiv – CS AI · May 16/10
🧠Researchers introduce MEDS (Math Education Digital Shadows), a dataset of 28,000 personas from 14 LLMs designed to evaluate how language models reason about mathematics and report their confidence levels. The dataset integrates math proficiency with psychological measures like anxiety and self-efficacy, revealing that LLMs exhibit human-like biases including negative attitudes and overconfidence in mathematical reasoning.
🧠 Grok
AINeutralarXiv – CS AI · Apr 206/10
🧠Researchers introduced 'Mind's Eye,' a benchmark that tests multimodal large language models (MLLMs) on visual reasoning tasks inspired by human intelligence tests. The evaluation reveals a significant gap between human performance (80% accuracy) and leading MLLMs (below 50%), exposing limitations in visuospatial reasoning, visual attention, and conceptual abstraction.
AINeutralarXiv – CS AI · Apr 206/10
🧠Researchers challenge the Uniform Information Density hypothesis in LLM reasoning, finding that high-quality reasoning exhibits locally smooth but globally non-uniform information flow. This counter-intuitive pattern suggests LLMs optimize differently than human communication, with entropy-based metrics effectively predicting reasoning quality across seven benchmarks.
AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers discovered that large language models exhibit working memory limitations similar to humans, encoding multiple memory items in entangled representations that require interference control rather than direct retrieval. This finding reveals a shared computational constraint between biological and artificial systems, suggesting that working memory capacity may be a fundamental bottleneck in intelligent systems rather than a limitation unique to biological brains.
AINeutralarXiv – CS AI · Apr 146/10
🧠Researchers propose a human-centered framework for evaluating whether AI systems fail in ways similar to humans by measuring out-of-distribution performance across a spectrum of perceptual difficulty rather than arbitrary distortion levels. Testing this approach on vision models reveals that vision-language models show the most consistent human alignment, while CNNs and ViTs demonstrate regime-dependent performance differences depending on task difficulty.
AINeutralarXiv – CS AI · Apr 136/10
🧠Researchers formalize how agents can use environmental artifacts as external memory to reduce computational requirements in reinforcement learning tasks. The study demonstrates that spatial observations can implicitly serve as memory substitutes, allowing agents to learn effective policies with less internal memory capacity than previously thought necessary.
