Contrasting predictive and causal values of predictors and causes

Contrasting predictive and causal values of predictors and causes (2005)

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by Oskar Pineño , James C. Denniston , Tom Beckers , Ralph R. Miller

Venue:	Learning & Behavior
Citations:	3 - 3 self

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Datum	Value	Source
TITLE	Contrasting predictive and causal values of predictors and causes	INFERENCE
AUTHOR NAME	Oskar Pineño	SVM HeaderParse 0.2
AUTHOR AFFIL	State University of New York, Binghamton	SVM HeaderParse 0.2
AUTHOR ADDR	New York	SVM HeaderParse 0.2
AUTHOR NAME	James C. Denniston	SVM HeaderParse 0.2
AUTHOR AFFIL	; State University of New York, Binghamton; and Appalachian State University, Boone	SVM HeaderParse 0.2
AUTHOR ADDR	; New York; North Carolina	SVM HeaderParse 0.2
AUTHOR NAME	Tom Beckers	SVM HeaderParse 0.2
AUTHOR AFFIL	; State University of New York, Binghamton; and University of Leuven; HELENA MATUTE; University of Deusto, Bilbao; and	SVM HeaderParse 0.2
AUTHOR ADDR	; New York; Leuven, Belgium; Spain	SVM HeaderParse 0.2
AUTHOR NAME	Ralph R. Miller	SVM HeaderParse 0.2
AUTHOR AFFIL	State University of New York, Binghamton	SVM HeaderParse 0.2
AUTHOR ADDR	New York	SVM HeaderParse 0.2
ABSTRACT	Three experiments examined human processing of stimuli as predictors and causes. In Experiments 1A and 1B, two serial events that both preceded a third were assessed as predictors and as causes of the third event. Instructions successfully provided scenarios in which one of the serial (target) stimuli was viewed as a strong predictor but as a weak cause of the third event. In Experiment 2, participants ’ preexperimental knowledge was drawn upon in such a way that two simultaneous antecedent events were processed as predictors or causes, which strongly influenced the occurrence of overshadowing between the antecedent events. Although a tendency toward overshadowing was found between predictors, reliable overshadowing was observed only between causes, and then only when the test question was causal. Together with other evidence in the human learning literature, the present results suggest that predictive and causal learning obey similar laws, but there is a greater susceptibility to cue competition in causal than predictive attribution. This paper examines differences between predictive and causal learning in humans. Events often occur in our environment according to a consistent temporal distribution. Some events occur simultaneously (e.g., the sound and sight of water running out of the tap), whereas other events occur sequentially (e.g., hunger dissipates after the intake of food). When the events repeatedly take place following a sequential distribution in time, the first event (i.e., the antecedent event) can become a signal for the occurrence of the second event (i.e., the subsequent event). Learning to predict the occurrence of an event on O.P. was supported by a postdoctoral fellowship from the Spanish	SVM HeaderParse 0.2
YEAR	2005	INFERENCE
VENUE	Learning & Behavior	INFERENCE
VENUE TYPE	JOURNAL	INFERENCE
PAGES	184196	INFERENCE
CITATIONS	37 found	ParsCit 1.0