ReviewPsychonomic bulletin & review2021
Artificial cognition: How experimental psychology can help generate explainable artificial intelligence.
Review in Psychonomic bulletin & review, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
21 citing papers in PubMed.
- Comparability between AI and human cognition and its role in psychological research and AI ethics.British journal of psychology (London, England : 1953) · 2026Review
- A large-scale comparison of divergent creativity in humans and large language models.Nature human behaviour · 2026Article
- Cognitive models facilitate real-time inference of latent motives.Scientific reports · 2026Article
- The Potential of Cognitive-Inspired Neural Network Modeling Framework for Computer Vision.Advanced science (Weinheim, Baden-Wurttemberg, Germany) · 2025Article
- Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.Advances in health sciences education : theory and practice · 2025Article
- Article
- Leveraging machine learning models in evaluating ADMET properties for drug discovery and development.ADMET & DMPK · 2025Review
- Principled Limitations on Self-Representation for Generic Physical Systems.Entropy (Basel, Switzerland) · 2024Article
- Enhancing surgical performance in cardiothoracic surgery with innovations from computer vision and artificial intelligence: a narrative review.Journal of cardiothoracic surgery · 2024Review
- Artificial cognition vs. artificial intelligence for next-generation autonomous robotic agents.Frontiers in computational neuroscience · 2024Article
- A deep learning MRI-based signature may provide risk-stratification strategies for nasopharyngeal carcinoma.European archives of oto-rhino-laryngology : official journal of the European Federation of Oto-Rhino-Laryngological Societies (EUFOS) : affiliated with the German Society for Oto-Rhino-Laryngology - Head and Neck Surgery · 2023Article
- Diverse types of expertise in facial recognition.Scientific reports · 2023Article
- Endoscopic Image Classification Based on Explainable Deep Learning.Sensors (Basel, Switzerland) · 2023Article
- Computational Scientific Discovery in Psychology.Perspectives on psychological science : a journal of the Association for Psychological Science · 2023Article
- Modeling Sleep Quality Depending on Objective Actigraphic Indicators Based on Machine Learning Methods.International journal of environmental research and public health · 2022Article
- Competency in Navigating Arbitrary Spaces as an Invariant for Analyzing Cognition in Diverse Embodiments.Entropy (Basel, Switzerland) · 2022Article
- Cognitive psychology-based artificial intelligence review.Frontiers in neuroscience · 2022Review
- Three levels at which the user's cognition can be represented in artificial intelligence.Frontiers in artificial intelligence · 2022Article
- What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach.Brain sciences · 2021Article
- Overview of Explainable Artificial Intelligence for Prognostic and Health Management of Industrial Assets Based on Preferred Reporting Items for Systematic Reviews and Meta-Analyses.Sensors (Basel, Switzerland) · 2021Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence powered by deep neural networks has reached a level of complexity where it can be difficult or impossible to express how a model makes its decisions. This black-box problem is especially concerning when the model makes decisions with consequences for human well-being. In response, an emerging field called explainable artificial intelligence (XAI) aims to increase the interpretability, fairness, and transparency of machine learning. In this paper, we describe how cognitive psychologists can make contributions to XAI. The human mind is also a black box, and cognitive psychologists have over 150 years of experience modeling it through experimentation. We ought to translate the methods and rigor of cognitive psychology to the study of artificial black boxes in the service of explainability. We provide a review of XAI for psychologists, arguing that current methods possess a blind spot that can be complemented by the experimental cognitive tradition. We also provide a framework for research in XAI, highlight exemplary cases of experimentation within XAI inspired by psychological science, and provide a tutorial on experimenting with machines. We end by noting the advantages of an experimental approach and invite other psychologists to conduct research in this exciting new field.
Indexed as
Identifiers
33159244What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.