Models of causation and the semantics of causal verbs

This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb cause itself. According to focal set models of causation ( Cheng, 1997; Cheng & Novick, 1991, 1992), the concept of C...

Full description

Saved in:
Bibliographic Details
Published in:Cognitive psychology 2003-11, Vol.47 (3), p.276-332
Main Authors: Wolff, Phillip, Song, Grace
Format: Article
Language:English
Subjects:
Biological and medical sciences
Causal models
Causation
Experiments
Fundamental and applied biological sciences. Psychology
Humans
Inferences
Language
Language acquisition
Lexical semantics
Linguistics
Logical Thinking
Miscellaneous
Prediction
Psychology. Psychoanalysis. Psychiatry
Psychology. Psychophysiology
Random Allocation
Semantics
Verbal Behavior
Verbs
Citations: Items that this one cites
Items that cite this one
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:This research examines the relationship between the concept of CAUSE as it is characterized in psychological models of causation and the meaning of causal verbs, such as the verb cause itself. According to focal set models of causation ( Cheng, 1997; Cheng & Novick, 1991, 1992), the concept of CAUSE should be more similar to the concepts of ENABLE and PREVENT than either is to each other. According to a model based on Talmy’s (1988) theory of force dynamics, the force dynamic model, the concepts of CAUSE, ENABLE, and PREVENT should be roughly equally similar to one another. The relationship between these predictions and the meaning of causal verbs was examined by having participants sort causal verbs and rate them with respect to the dimensions specified by the two models. The results from five experiments indicated that the force dynamic model provides a better account of the meaning of causal verbs than do focal set models of causation. Implications for causal inference and induction are discussed.
ISSN:0010-0285
1095-5623
DOI:10.1016/S0010-0285(03)00036-7