Structural awareness mitigates the effect of delay in human causal learning
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Updated: May 12, 2026

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
Published on: June 30, 2020
W James Greville1, Adam A Cassar, Mark K Johansen
1College of Medicine, Swansea University, Swansea, UK. w.j.s.greville@swansea.ac.uk
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 "structural-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.
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