Bayesian integration assumes that a current observation is integrated with previous observations. An example in the temporal domain is the central tendency effect: when a range of durations is presented,… Click to show full abstract
Bayesian integration assumes that a current observation is integrated with previous observations. An example in the temporal domain is the central tendency effect: when a range of durations is presented, a regression towards the mean is observed. Furthermore, a context effect emerges if a partially overlapping lower and a higher range of durations is presented in a blocked design, with the overlapping durations pulled towards the mean duration of the block. We determine under which conditions this context effect is observed, and whether explicit cues strengthen the effect. Each block contained either two or three durations, with one duration present in both blocks. We provided either no information at the start of each block about the nature of that block, provided written ("short" / "long" or "A" / "B") categorizations, or operationalized pitch (low vs high) to reflect the temporal context. We demonstrate that (1) the context effect emerges as long as sufficiently distinct durations are presented; (2) the effect is not modulated by explicit instructions or other cues; (3) just a single additional duration is sufficient to produce a context effect. Taken together, these results provide information on the most efficient operationalization to evoke the context effect, allowing for highly economical experimental designs, and highlights the automaticity by which priors are constructed.
               
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