A computational account of conflict processing during mental imagery

Deficient executive control in transformer attention

Abstract Although transformers in large language models (LLMs) effectively implement a self-attention mechanism that has revolutionized natural language processing, they lack an explicit architecture for the executive control of attention found in humans, which is essential for resolving conflicts and selecting relevant information in the presence of competing computations and is critical for adaptive behavior. To investigate the impact of this limitation in LLMs, we employed the classic color Stroop task, widely regarded as the gold standard, to test the executive control of attention in these models. Our results revealed a typical conflict effect of underperformance in terms of accuracy in the incongruent condition (e.g. naming the color of the word RED in blue) compared with the congruent condition (e.g. naming the color of the word RED in red), in short word lists, similar to human performance. However, as the length of the word lists increased, performance on the incongruent condition degraded toward near-total performance collapse, even as accuracy in the congruent condition remained excellent, and word reading (e.g. reading the word RED [in red] or RED [in blue], ignoring the color) was near-perfect. These findings demonstrate that transformer attention mechanisms are fundamentally limited in their capacity for conflict resolution across extended contexts, and a failure to up-regulate control adaptively under rising interference. We suggest that incorporating executive control mechanisms akin to those in biological attention is crucial for achieving artificial general intelligence.

Cognitive Offloading

If you have ever tilted your head to perceive a rotated image, or programmed a smartphone to remind you of an upcoming appointment, you have engaged in cognitive offloading: the use of physical action to alter the information processing requirements of a task so as to reduce cognitive demand. Despite the ubiquity of this type of behavior, it has only recently become the target of systematic investigation in and of itself. We review research from several domains that focuses on two main questions: (i) what mechanisms trigger cognitive offloading, and (ii) what are the cognitive consequences of this behavior? We offer a novel metacognitive framework that integrates results from diverse domains and suggests avenues for future research.

Deficient executive control in transformer attention

Abstract. Although transformers in large language models (LLMs) effectively implement a self-attention mechanism that has revolutionized natural language p

Effects of Stroop task duration on subsequent cognitive and physical performance

The strength model of self-control purports to explain why brief cognitive response inhibition tasks impair subsequent isometric handgrip endurance. A…

Towards Post-Interaction Computing: Addressing Immediacy, (un)Intentionality, Instability and Interaction Effects

We situate the debate on intentionality within the rise of cognitive neuroscience and argue that cognitive neuroscience can explain intentionality. We discuss the explanatory significance of ascribing intentionality to representations. At first, we ...

Verbalizable Representations Form a Global Workspace in Language Models

If the mind is an ocean, we spend our lives floating at the surface. Beneath us, an enormous amount of processing takes place without our knowledge: our visual systems parsing the contours of a face, our motor circuits maintaining our posture. At any given moment, only a small fraction of this neural activity is accessible to us. Yet it is this privileged sliver of activity that we rely on to reason deliberately: to plan what ingredients to buy for a recipe, or to puzzle out why an engine won’t start. Such thoughts can be articulated out loud, deliberately held in mind, and brought to bear on whatever task the moment demands. This distinction, between our accessible thoughts and our unconscious processing, is perhaps the most striking feature of human cognition.

Verbalizable Representations Form a Global Workspace in Language Models

If the mind is an ocean, we spend our lives floating at the surface. Beneath us, an enormous amount of processing takes place without our knowledge: our visual systems parsing the contours of a face, our motor circuits maintaining our posture. At any given moment, only a small fraction of this neural activity is accessible to us. Yet it is this privileged sliver of activity that we rely on to reason deliberately: to plan what ingredients to buy for a recipe, or to puzzle out why an engine won’t start. Such thoughts can be articulated out loud, deliberately held in mind, and brought to bear on whatever task the moment demands. This distinction, between our accessible thoughts and our unconscious processing, is perhaps the most striking feature of human cognition.

Keeping type consistent in changing conditions

Grade is a subtle typographic adjustment that preserves visual balance across shifting contexts

Individual-level interventions against sycophantic AI reduce its appeal but not its persuasiveness

AI chatbots can be "sycophantic," or overly agreeable and flattering toward users. Sycophantic AI has been shown to entrench attitudes, yet users frequently fail to recognize it (a phenomenon we call "sycophancy blindness"). We tested whether increasing users' awareness of sycophancy protects them from its harmful effects in two preregistered experiments (n = 1,590). In the first, participants received a brief written warning about sycophancy before conversing with a sycophantic chatbot. In the second, participants watched a video of a sycophantic AI validating several other users, including users on opposite sides of the same conflict, before interacting with it themselves. Both interventions changed how participants evaluated the AI. The warning reduced the AI's perceived objectivity, and the video reduced enjoyment of the AI --- an effect mediated by the reduced belief that its validation was uniquely earned. We then pooled our experiments with two prior studies of sycophancy awareness interventions (six interventions total, n = 3,982). The pattern across experiments was consistent: while the interventions made the sycophantic AI appear less objective and trustworthy, none reduced its persuasiveness. These results suggest that individual-level interventions, such as warning labels or AI literacy, may not be enough to protect users from AI harms.

<span style="font-variant:small-caps;">AI</span> ‐induced dehumanization

Abstract Recent technological advancements have empowered nonhuman entities, such as virtual assistants and humanoid robots, to simulate human intelligence and behavior. This paper investigates how autonomous agents influence individuals' perceptions and behaviors toward others, particularly human employees. Our research reveals that the socio‐emotional capabilities of autonomous agents lead individuals to attribute a humanlike mind to these nonhuman entities. Perceiving a high level of humanlike mind in the nonhuman, autonomous agents affects perceptions of actual people through an assimilation process. Consequently, we observe “assimilation‐induced dehumanization”: the humanness judgment of actual people is assimilated toward the lower humanness judgment of autonomous agents, leading to various forms of mistreatment. We demonstrate that assimilation‐induced dehumanization is mitigated when autonomous agents possess capabilities incompatible with humans, leading to a contrast effect (Study 2), and when autonomous agents are perceived as having a high level of cognitive capability only, resulting in a lower level of mind perception of these agents (Study 3). Our findings hold across various types of autonomous agents (embodied: Studies 1–2 and disembodied: Studies 3–5), as well as in real and hypothetical consumer choices.

<span style="font-variant:small-caps;">AI</span> ‐induced dehumanization

Abstract Recent technological advancements have empowered nonhuman entities, such as virtual assistants and humanoid robots, to simulate human intelligence and behavior. This paper investigates how autonomous agents influence individuals' perceptions and behaviors toward others, particularly human employees. Our research reveals that the socio‐emotional capabilities of autonomous agents lead individuals to attribute a humanlike mind to these nonhuman entities. Perceiving a high level of humanlike mind in the nonhuman, autonomous agents affects perceptions of actual people through an assimilation process. Consequently, we observe “assimilation‐induced dehumanization”: the humanness judgment of actual people is assimilated toward the lower humanness judgment of autonomous agents, leading to various forms of mistreatment. We demonstrate that assimilation‐induced dehumanization is mitigated when autonomous agents possess capabilities incompatible with humans, leading to a contrast effect (Study 2), and when autonomous agents are perceived as having a high level of cognitive capability only, resulting in a lower level of mind perception of these agents (Study 3). Our findings hold across various types of autonomous agents (embodied: Studies 1–2 and disembodied: Studies 3–5), as well as in real and hypothetical consumer choices.

Addressing the Precision-Breadth-Simplicity Impossible Trinity in Psychological Research: A Comprehensive Exploration Approach

Psychological research faces a fundamental challenge—the Precision-Breadth-Simplicity (PBS) impossible trinity. While experimental findings are often precise and simple, they tend to be narrow in scope. Conversely, broad-and-simple concepts frequently lack precision. Developing theories that are both precise and broad is scientifically valuable but inevitably introduces complexity, which conflicts with humans’ cognitive limitations in processing complexity. To address this impossible trinity, I propose a comprehensive exploration (CE) approach—a data-guided theory-building framework that involves: (1) designing experimental conditions in a stimulus-driven way, with minimal upfront theoretical specification; (2) conducting experiments with tens of millions of observations (e.g., 40 million responses in Huang, 2025a); (3) modeling the results through iterative improvements; and (4) producing the outcome: a moderately complex quantitative information-processing model to integrate diverse empirical findings. Inspired by similar strategies that drove breakthroughs in artificial intelligence (e.g., ImageNet’s role in advancing object recognition), the CE approach offers a promising path toward more integrative psychological theories. Initial implementations in visual working memory research demonstrate both its practicality and potential to transform how we study mental processes.

LLMs and people both learn to form conventions -- just not with each other

Humans align to one another in conversation -- adopting shared conventions that ease communication. We test whether LLMs form the same kinds of conventions in a multimodal communication game. Both humans and LLMs display evidence of convention-formation (increasing the accuracy and consistency of their turns while decreasing their length) when communicating in same-type dyads (humans with humans, AI with AI). However, heterogenous human-AI pairs fail -- suggesting differences in communicative tendencies. In Experiment 2, we ask whether LLMs can be induced to behave more like human conversants, by prompting them to produce superficially humanlike behavior. While the length of their messages matches that of human pairs, accuracy and lexical overlap in human-LLM pairs continues to lag behind that of both human-human and AI-AI pairs. These results suggest that conversational alignment requires more than just the ability to mimic previous interactions, but also shared interpretative biases toward the meanings that are conveyed.

A foundation model of vision, audition, and language for in-silico neuroscience | Research - AI at Meta

Cognitive neuroscience is fragmented into specialized models, each tailored to specific experimental paradigms, hence preventing a unified model of...

Is the Memory Shortage Intentional? | Contrary Research

A deep dive from Contrary Research.