A probabilistic contrast model of causal induction

Dados Bibliográficos

ID6205912
AutoresP Wenjie Cheng (0000-0001-5047-5467, University of California, Los Angeles), Laura R Novick (0009-0008-3383-1923)
Ano1990
Volume58
Fascículo4
Páginas545-567
Data de publicação1990-01-01
Peer ReviewedSim
Open AccessNão
TipoARTICLE
PeriódicoJournal of Personality and Social Psychology (JOURNAL)
Identificadores do periódicoISSN: 0022-3514 • E-ISSN: 1939-1315
EditoraAmerican Psychological Association (APA) (PUBLISHER)
DOI10.1037//0022-3514.58.4.545
PMID2348358
OpenAlexW2025454278
IdiomaEN
Citações recebidas71
Referências citadas6

Deviations from the predictions of covariational models of causal attribution have often been reported in the literature. These include a bias against using consensus information, a bias toward attributing effects to a person, and a tendency to make a variety of unpredicted conjunctive attributions. It is contended that these deviations, rather than representing irrational biases, could be due to (a) unspecified information over which causal inferences are computed and (b) the questionable normativeness of the models against which these deviations have been measured. A probabilistic extension of Kelley's analysis-of-variance analogy is proposed. An experiment was performed to assess the above biases and evaluate the proposed model against competing ones. The results indicate that the inference process is unbiased.

Analogy · Attribution · Causal inference · Causal model · Cognitive psychology · Contrast (vision · Econometrics · Epistemology · Inference · Irrational number · Probabilistic logic · Statistics · Variance (accounting · Variety (cybernetics · Artificial Intelligence · Bayesian Modeling and Causal Inference · Cognitive Science and Mapping · Computer Science · Decision-Making and Behavioral Economics · Mathematics · Opinion Dynamics and Social Influence · Psychology · Social and Intergroup Psychology · Social Psychology