Dataset for: Inter-trial variability of context influences the binding structure in a stimulus-response episode

Dataset for: Qiu, R., Möller, M., Koch, I., & Mayr, S. (2022). Inter-Trial Variability of Context Influences the Binding Structure in a Stimulus-Response Episode. Journal of Cognition, 5(1), 25. DOI: http://doi.org/10.5334/joc.215 There is strong evidence that stimuli and responses are bound together in a direct (binary) fashion into an episodic representation called stimulus-response episode (or event file). However, in an auditory negative priming study in which participants were required to respond to the target stimulus and to ignore the distractor stimulus, context information (i.e., a completely task-irrelevant stimulus) was found to rather modulate the binding between the distractor stimulus and the response, instead of entering into a binary binding with the response itself (Mayr et al., 2018). The current study demonstrates that simply increasing the variability of the context across trials leads to a binary binding between the context and the response. The same auditory negative priming task was implemented, and participants were either assigned to the high-variability group (8 different context sounds) or the low-variability group (2 different context sounds). For the low-variability group, results replicated previous findings of contextual modulation of the binding between the distractor stimulus and the response. For the high-variability group, however, repetition of the context per se retrieved the prime response, indicating a binary binding between the context and the response. Together, the current findings provide evidence that the inter-trial variability of context information is a determinant of how context is bound in a stimulus-response episode. Possible underlying mechanisms are discussed. This work was funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation)–Project number 393269228. raw data sets of 11 participants (offline) and 92 participants (online); LiveCode data preprocessing program for offline data sets; Python data preprocessing program for online data sets; readme text for using the codes unknown