Contingency learning as binding? Testing an exemplar view of the colour-word contingency learning effect


Journal Article


Schmidt, J. R., Giesen, C. G., Rothermund, K.




Contingency learning as binding? Testing an exemplar view of the colour-word contingency learning effect

Journal / Livre / Conférence

Quarterly Journal of Experimental Psychology


The learning of contingent regularities between events is fundamental for interacting with our world. We are also heavily influenced by recent experiences, as frequently studied in the stimulus-response (S-R) binding literature. According to one view (“unitary view”), the learning of regularities across many events and the influence of recent events on current performance can coherently be explained with one high-learning rate memory mechanism. That is, contingency learning effects and binding effects are essentially the same thing, only studied at different time scales. On the other hand, there may be more to a contingency effect than just the summation of the influence of past events (e.g., an additional impact of learned regularities). To test these possibilities, the current report reanalyzes a number of datasets from the colour-word contingency learning paradigm. It is shown that the weighted sum of binding effects accumulated across many previous trials (with especially strong influence of very recent events) does explain a large chunk of the contingency effect, but not all of it. In particular, the asymptote toward which the contingency effect decreases by accounting for an increasing number of previous-trial binding effects is robustly above zero. On the other hand, we also observe evidence for higher-order interactions between binding effects at differing lags, suggesting that a mere linear accumulation of binding episodes might underestimate their influence on contingency learning. Accordingly, focusing only on episodic S-R binding effects that are due to the last occurrence of a stimulus rendered contingency learning effects non-significant. Implications for memory models are discussed.






contingency learning, binding, exemplars, episodic memory, mixed effect models, multicollinearity

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