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Artificial phantasia: Evidence for propositional reasoning-based mental imagery in large language models https://arxiv.org/abs/2509.23108 on the representation of visual imagery in humans; more information in the Bluesky thread

Artificial Phantasia: Evidence for Propositional Reasoning-Based Mental Imagery in Large Language Models
This study offers a novel approach for benchmarking complex cognitive behavior in artificial systems. Almost universally, Large Language Models (LLMs) perform best on tasks which may be included in their training data and can be accomplished solely using natural language, limiting our understanding of their emergent sophisticated cognitive capacities. In this work, we created dozens of novel items of a classic mental imagery task from cognitive psychology. A task which, traditionally, cognitive…

Exploring Artificial Intelligence and Culture: Methodology for a comparative study of AI's impact on norms, trust, and problem-solving across academic and business environments
Matthias Huemmer, Theophile Shyiramunda, Michelle J. Cummings-Koether
https://arxiv.org/abs/2510.11530

Exploring Artificial Intelligence and Culture: Methodology for a comparative study of AI's impact on norms, trust, and problem-solving across academic and business environments
This paper proposes a rigorous framework to examine the two-way relationship between artificial intelligence (AI), human cognition, problem-solving, and cultural adaptation across academic and business settings. It addresses a key gap by asking how AI reshapes cognitive processes and organizational norms, and how cultural values and institutional contexts shape AI adoption, trust, and use over time. We employ a three-wave longitudinal design that tracks AI knowledge, perceived competence, trust…

A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
E. A. Dzhivelikian, A. I. Panov
https://arxiv.org/abs/2510.03286

A Biologically Interpretable Cognitive Architecture for Online Structuring of Episodic Memories into Cognitive Maps
Cognitive maps provide a powerful framework for understanding spatial and abstract reasoning in biological and artificial agents. While recent computational models link cognitive maps to hippocampal-entorhinal mechanisms, they often rely on global optimization rules (e.g., backpropagation) that lack biological plausibility. In this work, we propose a novel cognitive architecture for structuring episodic memories into cognitive maps using local, Hebbian-like learning rules, compatible with neura…

Lightweight Prompt Engineering for Cognitive Alignment in Educational AI: A OneClickQuiz Case Study
Antoun Yaacoub, Zainab Assaghir, J\'er\^ome Da-Rugna
https://arxiv.org/abs/2510.03374

Lightweight Prompt Engineering for Cognitive Alignment in Educational AI: A OneClickQuiz Case Study
The rapid integration of Artificial Intelligence (AI) into educational technology promises to revolutionize content creation and assessment. However, the quality and pedagogical alignment of AI-generated content remain critical challenges. This paper investigates the impact of lightweight prompt engineering strategies on the cognitive alignment of AI-generated questions within OneClickQuiz, a Moodle plugin leveraging generative AI. We evaluate three prompt variants-a detailed baseline, a simple…

Maybe AI Was Never a Tool
They can deliver conclusions that feel complete but skip the struggle that gives thought its humanity. This is what I call anti-intelligence—not stupidity, but perhaps better expressed as a kind of counterfeit cognition. It's intelligence without friction that results in output—built in that shared cognitive dynamic—that looks like insight but has bypassed the work that makes insight truly yours.

Maybe AI Was Never a Tool
Using artificial intelligence "responsibly" assumes it is just another tool—it isn't.

This long work from @… and @…, among many other interesting ideas,
calls the new kind of "AI" by a new name to distinguish it from older, often more ethical kinds of stats models:
"Displacement AI"
I lov…

Olivia Guest · Ολίβια Γκεστ (@olivia@scholar.social)
Attached: 3 images New preprint 🌟 Psychology is core to cognitive science, and so it is vital we preserve it from harmful frames. @Iris & I use our psych and computer science expertise to analyse and craft: Critical Artificial Intelligence Literacy for Psychologists. https://doi.org/10.31234/osf.io/dkrgj_v1 🧵 1/

A Study of Rule Omission in Raven's Progressive Matrices
Binze Li
https://arxiv.org/abs/2510.03127 https://arxiv.org/pdf/2510.03127

A Study of Rule Omission in Raven's Progressive Matrices
Analogical reasoning lies at the core of human cognition and remains a fundamental challenge for artificial intelligence. Raven's Progressive Matrices (RPM) serve as a widely used benchmark to assess abstract reasoning by requiring the inference of underlying structural rules. While many vision-based and language-based models have achieved success on RPM tasks, it remains unclear whether their performance reflects genuine reasoning ability or reliance on statistical shortcuts. This study invest…

A Modular Theory of Subjective Consciousness for Natural and Artificial Minds
Micha\"el Gillon
https://arxiv.org/abs/2510.01864 https://arxiv.org/pdf/…

A Modular Theory of Subjective Consciousness for Natural and Artificial Minds
Understanding how subjective experience arises from information processing remains a central challenge in neuroscience, cognitive science, and AI research. The Modular Consciousness Theory (MCT) proposes a biologically grounded and computationally explicit framework in which consciousness is a discrete sequence of Integrated Informational States (IISs). Each IIS is a packet of integrated information tagged with a multidimensional density vector that quantifies informational richness. Its magnit…

Position: Human Factors Reshape Adversarial Analysis in Human-AI Decision-Making Systems
Shutong Fan, Lan Zhang, Xiaoyong Yuan
https://arxiv.org/abs/2509.21436 https://

Position: Human Factors Reshape Adversarial Analysis in Human-AI Decision-Making Systems
As Artificial Intelligence (AI) increasingly supports human decision-making, its vulnerability to adversarial attacks grows. However, the existing adversarial analysis predominantly focuses on fully autonomous AI systems, where decisions are executed without human intervention. This narrow focus overlooks the complexities of human-AI collaboration, where humans interpret, adjust, and act upon AI-generated decisions. Trust, expectations, and cognitive behaviors influence how humans interact with…

Multi state neurons
Robert Worden
https://arxiv.org/abs/2512.08815 https://arxiv.org/pdf/2512.08815 https://arxiv.org/html/2512.08815
arXiv:2512.08815v1 Announce Type: new
Abstract: Neurons, as eukaryotic cells, have powerful internal computation capabilities. One neuron can have many distinct states, and brains can use this capability. Processes of neuron growth and maintenance use chemical signalling between cell bodies and synapses, ferrying chemical messengers over microtubules and actin fibres within cells. These processes are computations which, while slower than neural electrical signalling, could allow any neuron to change its state over intervals of seconds or minutes. Based on its state, a single neuron can selectively de-activate some of its synapses, sculpting a dynamic neural net from the static neural connections of the brain. Without this dynamic selection, the static neural networks in brains are too amorphous and dilute to do the computations of neural cognitive models. The use of multi-state neurons in animal brains is illustrated in hierarchical Bayesian object recognition. Multi-state neurons may support a design which is more efficient than two-state neurons, and scales better as object complexity increases. Brains could have evolved to use multi-state neurons. Multi-state neurons could be used in artificial neural networks, to use a kind of non-Hebbian learning which is faster and more focused and controllable than traditional neural net learning. This possibility has not yet been explored in computational models.
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