Abstract

Associative models of causal learning predict recency effects. Judgments at the end of a trial series should be strongly biased by recently presented information. Prior research, however, presents a contrasting picture of human performance. López, Shanks, Almaraz, and Fernández (1998) observed recency, whereas Dennis and Ahn (2001) found the opposite, primacy. Here we replicate both of these effects and provide an explanation for this paradox. Four experiments show that the effect of trial order on judgments is a function of judgment frequency, where incremental judgments lead to recency while single final judgments abolish recency and lead instead to integration of information across trials (i.e., primacy). These results challenge almost all existing accounts of causal judgment. We propose a modified associative account in which participants can base their causal judgments either on current associative strength (momentary strategy) or on the cumulative change in associative strength since the previous judgment (integrative strategy).

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