Contrastive Constraints Guide Explanation-Based Category Learning

By Seth Chin-Parker, Julie A Cantelon. 2017

25 citations recorded by the source corpus.

Subfields: Artificial Intelligence, History and Philosophy of Science, Molecular Biology, Cognitive Science & Psychology

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Ana Marasovic
2. Matthew E. Peters
3. Alexis Ross
4. Martin Pereira-Fariña
5. Tim Miller
6. Ilia Stepin
7. Alejandro Catalá
8. J. Alonso

In History and Philosophy of Science

1. Tim Miller

In Molecular Biology

1. Tim Miller
2. Ana Marasovic
3. Matthew E. Peters

In Cognitive Science & Psychology

1. Tim Miller

Citations in the corpus

• Explanation in Artificial Intelligence: Insights from the Social Sciences (2017, background): “Chin-Parker and Cantelon [28] provide support for the contrastive account of explanation (see Section 2.3) in categorisation/classification tasks.”
• A Survey of Contrastive and Counterfactual Explanation Generation Methods for Explainable Artificial Intelligence (2021, background): “Chin-Parker and Bradner [17] as well as Chin-Parker and Cantelon [76] provide a unified theoretical framework for causal and non-causal contrastive explanation for category learning.”
• Explaining NLP Models via Minimal Contrastive Editing (MiCE) (2020, background): “First, they cite contrastive features : features that result in the contrast case when they are changed in a particular way (Chin-Parker and Cantelon, 2017).”