David Bourget (Western Ontario)
David Chalmers (ANU, NYU)
Rafael De Clercq
Jack Alan Reynolds
Learn more about PhilPapers
Cognitive Science 34 (8):1574-1593 (2010)
Judging similarities among objects, events, and experiences is one of the most basic cognitive abilities, allowing us to make predictions and generalizations. The main assumption in similarity judgment is that people selectively attend to salient features of stimuli and judge their similarities on the basis of the common and distinct features of the stimuli. However, it is unclear how people select features from stimuli and how they weigh features. Here, we present a computational method that helps address these questions. Our procedure combines image-processing techniques with a machine-learning algorithm and assesses feature weights that can account for both similarity and categorization judgment data. Our analysis suggests that a small number of local features are particularly important to explain our behavioral data
|Keywords||Similarity perception Feature selection Inductive generalization Machine learning|
|Categories||categorize this paper)|
Setup an account with your affiliations in order to access resources via your University's proxy server
Configure custom proxy (use this if your affiliation does not provide a proxy)
|Through your library|
References found in this work BETA
Cindy M. Bukach, Isabel Gauthier & Michael J. Tarr (2006). Beyond Faces and Modularity: The Power of an Expertise Framework. Trends in Cognitive Sciences 10 (4):159-166.
Susan A. Gelman & Ellen M. Markman (1986). Categories and Induction in Young Children. Cognition 23 (3):183-209.
Ulrike Hahn, Nick Chater & Lucy B. Richardson (2003). Similarity as Transformation. Cognition 87 (1):1-32.
D. Luce (ed.) (1963). Handbook of Mathematical Psychology. John Wiley & Sons..
David Marr (1982). Vison. W. H. Freeman.
Citations of this work BETA
No citations found.
Similar books and articles
Christopher D. Manning & Kristina Toutanova, Feature Selection for a Rich HPSG Grammar Using Decision Trees.
Daniel J. Navarro, Matthew J. Dry & Michael D. Lee (2012). Sampling Assumptions in Inductive Generalization. Cognitive Science 36 (2):187-223.
Emmanuel M. Pothos, Ulrike Hahn & Mercè Prat-Sala (2010). Contingent Necessity Versus Logical Necessity in Categorisation. Thinking and Reasoning 16 (1):45 – 65.
Shimon Edelman (1998). Things Are What They Seem. Behavioral and Brain Sciences 21 (1):25-25.
Joshua B. Tenenbaum & Thomas L. Griffiths (2001). Generalization, Similarity, and Bayesian Inference. Behavioral and Brain Sciences 24 (4):629-640.
Philippe G. Schyns, Robert L. Goldstone & Jean-Pierre Thibaut (1998). Ways of Featuring in Object Categorization. Behavioral and Brain Sciences 21 (1):41-54.
Stephen Grossberg (1998). Self-Organizing Features and Categories Through Attentive Resonance. Behavioral and Brain Sciences 21 (1):27-28.
Pepper Williams, Isabel Gauthier & Michael J. Tarr (1998). Feature Learning During the Acquisition of Perceptual Expertise. Behavioral and Brain Sciences 21 (1):40-41.
Philippe G. Schyns, Robert L. Goldstone & Jean-Pierre Thibaut (1998). The Development of Features in Object Concepts. Behavioral and Brain Sciences 21 (1):1-17.
Added to index2010-08-11
Total downloads12 ( #189,864 of 1,700,283 )
Recent downloads (6 months)3 ( #206,271 of 1,700,283 )
How can I increase my downloads?