Jove
Visualize
Contact Us

Related Concept Videos

Attribution Theory00:56

Attribution Theory

13.7K
Behavior is a product of both the situation (e.g., cultural influences, social roles, and the presence of bystanders) and of the person (e.g., personality characteristics). Subfields of psychology tend to focus on one influence or behavior over others. Situationism is the view that our behavior and actions are determined by our immediate environment and surroundings. In contrast, dispositionism holds that our behavior is determined by internal factors (Heider, 1958).
13.7K
Fundamental Attribution Error01:14

Fundamental Attribution Error

13.7K
According to some social psychologists, people tend to overemphasize internal factors as explanations—or attributions—for the behavior of other people. They tend to assume that the behavior of another person is a trait of that person, and to underestimate the power of the situation on the behavior of others. They tend to fail to recognize when the behavior of another is due to situational variables, and thus to the person’s state. This erroneous assumption is...
13.7K
Attribution01:26

Attribution

264
In social interactions, individuals frequently seek to understand the motivations and causes behind others' behaviors. This fundamental aspect of social perception, known as attribution, plays a crucial role in shaping interpersonal relationships and guiding future actions. Attribution refers to the cognitive process through which people infer the reasons behind others' behaviors, allowing them to assess character traits, intentions, and situational influences.Attribution Theory and Its...
264
Personal Choice and Fate Attributions01:19

Personal Choice and Fate Attributions

164
Some individuals interpret life events as a consequence of their personal choices and actions, while others believe that outcomes are dictated by fate or destiny. This divergence in perspective has been examined in psychological and cross-cultural studies, particularly in relation to religious faith and cultural beliefs about causality.Fate and Personal ResponsibilityPeople who emphasize personal responsibility view events as direct consequences of their decisions. For instance, breaking a leg...
164
Theory of Attribution I: Correspondent Inference Theory01:15

Theory of Attribution I: Correspondent Inference Theory

460
Correspondent inference theory, proposed by Jones and Davis in 1965, seeks to explain how individuals infer stable personality traits from observed behaviors. It suggests that people attribute actions to underlying dispositions rather than external circumstances, particularly when the behavior appears intentional and socially significant.Voluntary Behavior and Dispositional AttributionAccording to this theory, individuals are more likely to attribute behavior to personal traits when it appears...
460
Theory of Attribution II: Kelley's Covariation Theory01:29

Theory of Attribution II: Kelley's Covariation Theory

514
Attribution theory plays a crucial role in social psychology, helping to explain how individuals interpret the causes of behavior. One prominent model within this field is Harold Kelley's covariation theory, which provides a systematic approach to determining whether internal traits or external circumstances drive a person's actions. The model posits that individuals rely on three key types of information—consensus, consistency, and distinctiveness—to make these judgments.Consensus:...
514

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Methodology for Safe and Secure AI in Diabetes Management.

Journal of diabetes science and technology·2024
Same journal

Development of a Unified Cardiovascular-Pupillary Model for Interpreting Pupil Size Variability as an Autonomic Marker.

Biomedical engineering and computational biology·2026
Same journal

Fractional Soliton Dynamics in Coupled Myelinated Fibers: Comparative Modeling With Beta, Caputo, and Atangana-Baleanu Derivatives.

Biomedical engineering and computational biology·2026
Same journal

Accuracy and Functional Performance of Artificial Intelligence-Based Automated Crown Design Systems: A Systematic Review and Meta-Analysis.

Biomedical engineering and computational biology·2026
Same journal

Scalable HMO-CNN-SVM Framework for Skin Lesion Classification: A Metaheuristic-Driven Approach With Parallelizable Optimization for Cluster Deployment.

Biomedical engineering and computational biology·2026
Same journal

Computational Screening of Microbial Metabolites as Erythropoietin (EPO) Mimetics for the Treatment of Anemia.

Biomedical engineering and computational biology·2026
Same journal

Mechanistic Elucidation of Liujun Jiaoxian Tang in Management of Sepsis Through Metabolomics and Network Pharmacology.

Biomedical engineering and computational biology·2026
See all related articles
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Experiment Video

Updated: Jan 21, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K

Token-Level Attribution for Transparent Biomedical AI.

Remco Jan Geukes Foppen1, Alessio Zoccoli2, Vincenzo Gioia3

  • 1Independent Researcher, Anzio, Italy.

Biomedical Engineering and Computational Biology
|January 20, 2026
PubMed
Summary
This summary is machine-generated.

Small Language Models (SLMs) combined with explainable AI (xAI) methods offer technical traceability for clinical decision support. Token-level attribution in SLMs successfully identified key clinical features, demonstrating auditable AI within practical hardware limits.

Keywords:
AI in healthcareartificial intelligenceclinical decision supportclinical informaticsdataexplainable AIhealth informationhealthcare AIhealthcare innovationmedical decision-makingregulatory compliancesecuritysemantic explainabilitysmall language modelstoken attribution

More Related Videos

Generation of Alginate Microspheres for Biomedical Applications
10:33

Generation of Alginate Microspheres for Biomedical Applications

Published on: August 12, 2012

21.7K
Graphene Coatings for Biomedical Implants
13:21

Graphene Coatings for Biomedical Implants

Published on: March 1, 2013

21.7K

Related Experiment Videos

Last Updated: Jan 21, 2026

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems
05:47

Evidence-based Knowledge Synthesis and Hypothesis Validation: Navigating Biomedical Knowledge Bases via Explainable AI and Agentic Systems

Published on: June 13, 2025

1.3K
Generation of Alginate Microspheres for Biomedical Applications
10:33

Generation of Alginate Microspheres for Biomedical Applications

Published on: August 12, 2012

21.7K
Graphene Coatings for Biomedical Implants
13:21

Graphene Coatings for Biomedical Implants

Published on: March 1, 2013

21.7K

Area of Science:

  • Artificial Intelligence in Medicine
  • Computational Linguistics
  • Health Informatics

Background:

  • Explainable AI (xAI) is crucial for trust, safety, and compliance in healthcare AI.
  • Large Language Models (LLMs) present challenges due to their "black box" nature and high computational costs.
  • Small Language Models (SLMs) offer efficiency, interpretability, and privacy benefits for clinical applications.

Purpose of the Study:

  • To evaluate token-level attribution (TLA) methods for technical traceability in SLMs for clinical decision support.
  • To assess the feasibility of using SLMs with xAI for auditable healthcare AI.
  • To determine if SLMs can meet regulatory requirements for AI explainability.

Main Methods:

  • Applied the Captum 0.7 attribution library to a Qwen-2.5-1.5B SLM.
  • Utilized perturbation-based integrated gradients to analyze feature influence on token generation.
  • Tested on 20 breast cancer cases from a public dataset using consumer-grade GPUs.

Main Results:

  • Attribution heatmaps successfully identified clinically relevant input features.
  • High-attribution tokens corresponded to expected clinical factors in breast cancer cases.
  • The SLM required minimal storage, allowing local deployment without cloud infrastructure.

Conclusions:

  • SLMs combined with perturbation-based xAI methods can achieve auditable clinical AI.
  • This approach offers technical feasibility within practical hardware constraints.
  • Further research is needed to bridge statistical associations from TLA to causal clinical reasoning.