AIMC Topic: Cognition

Clear Filters Showing 1 to 10 of 768 articles

Token-splitting improves GPT-4.1 performance on plastic surgery exams: implications for AI-Assisted medical education.

Medical education online
Large language models (LLMs), such as ChatGPT, have demonstrated impressive performance on general medical examinations; however, their effectiveness significantly declines in specialized board examinations due to limited domain-specific training dat...

Similarity as likelihood ratio: Coupling representations from machine learning (and other sources) with cognitive models.

Psychonomic bulletin & review
Similarity lies at the core of theories of memory and perception. To understand similarity relations among complex items like text and images, researchers often rely on machine learning to derive high-dimensional vector representations of those items...

Shared human-machine control of an intelligent bionic hand improves grasping and decreases cognitive burden for transradial amputees.

Nature communications
Bionic hands can replicate many movements of the human hand, but our ability to intuitively control these bionic hands is limited. Humans' manual dexterity is partly due to control loops driven by sensory feedback. Here, we describe the integration o...

Association of the endothelial activation and stress index with cognitive function in older adults: a cross-sectional study with machine learning.

European journal of medical research
BACKGROUND: Age-associated memory impairment (AAMI) is a predementia state linked to endothelial dysfunction. The endothelial activation and stress index (EASIX) quantifies endothelial injury, yet its association with cognitive function remains unval...

Addressing Autonomy Risks in Generative Chatbots with the Socratic Method.

Science and engineering ethics
Autonomy is a fundamental ethical principle in artificial intelligence (AI) ethics. Current discussions regarding autonomy-related risks in human-AI interaction, as well as potential mitigation strategies, have mainly focused on recommendation system...

Deep learning and whole-brain networks for biomarker discovery: modeling the dynamics of brain fluctuations in resting-state and cognitive tasks.

Scientific reports
Brain network models offer insights into brain dynamics, but the utility of model-derived bifurcation parameters as biomarkers remains underexplored. This study evaluates bifurcation parameters from a whole-brain network model as biomarkers for disti...

A dual recurrent neural network model of human-like motion for artificial agents and its evaluation in a VR mirror game turing test.

Scientific reports
Action-oriented approaches to cognition which emphasize the constitutive role of sensorimotor patterns for perception are gaining importance for the study of cognitive processes in the human brain as well as for endowing artificial agents with cognit...

Generating synthetic task-based brain fingerprints for population neuroscience using deep learning.

Communications biology
Task-based functional magnetic resonance imaging (fMRI) reveals individual differences in neural correlates of cognition but faces scalability challenges due to cognitive demands, protocol variability, and limited task coverage in large datasets. Her...

Relationship between cognitive abilities and mental health as represented by cognitive abilities at the neural and genetic levels of analysis.

eLife
Cognitive abilities are closely tied to mental health from early childhood. This study explores how neurobiological units of analysis of cognitive abilities-multimodal neuroimaging and polygenic scores (PGS)-represent this connection. Using data from...

Modeling public trust in AI cognitive capabilities using statistical and machine learning approaches.

Scientific reports
As artificial intelligence (AI) systems increasingly perform cognitive functions, assessing public trust in these capabilities is critical. This study investigates the impact of age, gender, and familiarity with AI on confidence in AI's ability to ma...