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Agents interacting with their environments, machine or otherwise, arrive at decisions based on their incomplete access to data and their particular cognitive architecture, including data sampling frequency and memory storage limitations. In particular, the same data streams, sampled and stored differently, may cause agents to arrive at different conclusions and to take different actions. This phenomenon has a drastic impact on polities-populations of agents predicated on the sharing of information. We show that, even under ideal conditions, polities consisting of epistemic agents with heterogeneous cognitive architectures might not achieve consensus concerning what conclusions to draw from datastreams. Transfer entropy applied to a toy model of a polity is analyzed to showcase this effect when the dynamics of the environment is known. As an illustration where the dynamics is not known, we examine empirical data streams relevant to climate and show the manifest.
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http://dx.doi.org/10.3390/e24101378 | DOI Listing |
Psychol Rev
September 2025
Neural Computation Group, Max-Planck Institute for Human Cognitive and Brain Sciences.
It has been suggested that episodic memory relies on the well-studied machinery of spatial memory. This influential notion faces hurdles that become evident with dynamically changing spatial scenes and an immobile agent. Here I propose a model of episodic memory that can accommodate such episodes via temporal indexing.
View Article and Find Full Text PDFHum Brain Mapp
September 2025
Cognitive Neurophysiology, Department of Child and Adolescent Psychiatry, Faculty of Medicine, TU Dresden, Dresden, Germany.
Acting intentionally is a major aspect of human cognitive development and depends on the ability to link actions with their consequences. Action-effect binding (AEB) is a fundamental mechanism enabling this. While AEB has been well-characterized in adults, its neurophysiological underpinnings during adolescence remain unclear.
View Article and Find Full Text PDFActa Psychol (Amst)
September 2025
Shanghai Jiao Tong University, China. Electronic address:
This study investigates fundamental differences in the acquisition of morphological patterns by humans and large language models (LLMs) within an artificial language learning paradigm. Specifically, it compares how each system responds to variations in input structure-blocked versus interleaved sequences and juxtaposed versus spaced presentation-across verb classification and inflection tasks. While LLMs (GPT4mini, DeepSeek_V3, Llama3.
View Article and Find Full Text PDFAgeing Res Rev
September 2025
Fondazione Policlinico Universitario "A. Gemelli" IRCCS, Rome, Italy; Department of Medicine and Surgery, LUM University, Casamassima, Italy. Electronic address:
Neuroscience
September 2025
School of Computer Science and Technology, Hangzhou Dianzi University, Hangzhou, Zhejiang 310018, China; Key Laboratory of Intelligent Image Analysis for Sensory and Cognitive Health, Ministry of Industry and Information Technology of China, Hangzhou, Zhejiang 310018, China.
Achieving a deep understanding of brain mechanisms requires multi-scale perspectives to capture the architecture of complex networks. In this study, we focused on patients with cognitive impairment and constructed individual brain networks from neuroimaging data. We introduced a Significant Edges Selection (SES) method, which effectively extracts the most informative connections while suppressing noise.
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