Academic journal article Memory & Cognition

Structural Awareness Mitigates the Effect of Delay in Human Causal Learning

Academic journal article Memory & Cognition

Structural Awareness Mitigates the Effect of Delay in Human Causal Learning

Article excerpt

Published online: 12 April 2013

© Psychonomic Society, Inc. 2013

Abstract Many studies have demonstrated that reinforcement delays exert a detrimental influence on human judgments of causality. In a free-operant procedure, the trial structure is usually only implicit, and delays are typically manipulated via trial duration, with longer trials tending to produce both longer experienced delays and also lower objective contingencies. If, however, a learner can become aware of this trial structure, this may mitigate the effects of delay on causal judgments. Here we tested this "structura-awareness" hypothesis by manipulating whether response-outcome contingencies were clearly identifiable as such, providing structural information in real time using an auditory tone to delineate consecutive trials. A first experiment demonstrated that providing cues to indicate trial structure, but without an explicit indication of their meaning, significantly increased the accuracy of causal judgments in the presence of delays. This effect was not mediated by changes in response frequency or timing, and a second experiment demonstrated that it cannot be attributed to the alternative explanation of enhanced outcome salience. In a third experiment, making trial structure explicit and unambiguous, by telling participants that the tones indicated trial structure, completely abolished the effect of delays. We concluded that, with sufficient information, a continuous stream of causes and effects can be perceived as a series of discrete trials, the contingency nature of the input may be exploited, and the effects of delay may be eliminated. These results have important implications for human contingency learning and in the characterization of temporal influences on causal inference.

Keywords Causality · Contiguity · Reinforcement delay · Trial structure · Free-operant procedure · Associative learning · Decision making · Reasoning · Judgment


Causal learning is a core cognitive competency that enables us to impose structure on the world and to intervene on the environment to achieve desired outcomes. The principles underlying causal learning are still debated (Dickinson, 2001b; Griffiths & Tenenbaum, 2005, 2009; Holyoak & Cheng, 2011). In most cases, a causal relationship between one event and another cannot be directly perceived. Rather, the connection must be inferred by detecting patterns in the occurrence of these events. Most contemporary theories of causal learning acknowledge three crucial cues to causality, first described by David Hume (1739/1888): temporal order (i.e., causes must precede their effects), contingency (the causes must reliably and repeatedly produce their effects), and contiguity (the causes and effects must occur closely together in time).

Temporal order is almost unanimously accepted as a necessity for causal learning. Most researchers also agree that in order for two events to be classified, respectively, as cause and effect, some form of statistical dependence (i.e., contingency) of the latter on the former is necessary. Broadly, the stronger the contingency between cause and effect, the stronger the inferred relationship between them. In the case of two binary variables, at any given point there are four possibilities: Both the cause and the effect may be either present or absent, which can be represented using a 2x2 contingency table. Table 1 illustrates the four possible outcomes relevant to a simple binary causal relation, where the cause c is either present or absent (τ), and the effect e likewise is also either present or absent (_,e). The key debate is how exactly this information is used to obtain a metric of causality. One of the most well-known and longstanding models is the AP statistic (Jenkins & Ward, 1965), which calculates contingency using the A, B, C, and D cells from the 2x2 matrix as: A/(A + B) C/(C + D) = P(e \ c) P(e | -c). Though more recent models have been developed that more accurately reflect human judgments than A Ρ (Cheng, 1997; Griffiths & Tenenbaum, 2005), this metric can provide a useful estimate of causality in many cases. …

Search by... Author
Show... All Results Primary Sources Peer-reviewed


An unknown error has occurred. Please click the button below to reload the page. If the problem persists, please try again in a little while.