Central tendency effects override and generalize across illusions in time estimation

摘要

Illusions and central tendency effects strongly modulate temporal interval estimations. First, interval estimations are subject to distortions during active and passive observation: Interval compression occurs when an action produces a stimulus or when one of the interval markers is masked. Second, central tendency effects consist in an overestimation of short and an underestimation of long intervals. To understand the functional role of both phenomena, I asked which effect would dominate if both are set into direct competition. To this end, I tested two temporal illusions: active intentional compression and passive mask-induced compression. Both illusions produced systematic underestimations when several intervals durations were presented in blockwise fashion. However, strong central tendency effects occurred when interval duration was randomized. I presented an interval of 112 ms intermixed either in a context of 5 shorter (32-96 ms) or 5 longer (128-192 ms) intervals. The 112 ms interval compressed to about half of its duration when presented only with shorter intervals and dilated by a factor of 1.5 when presented only with shorter intervals. Central tendency effects thus clearly dominated interval estimations. Next, I asked about the generality of central tendency effects by testing their transfer between the illusions. I presented the active illusion with a duration of 112 ms either with 5 shorter or with 5 longer passive illusion intervals. In separate sessions, I presented the passive illusion for 112 ms intermixed into either shorter or longer active illusion intervals. Central tendency effects induced in either the active or the passive illusion intervals transferred to the other illusion. These results demonstrate that the immediate context of sensory stimulation determines whether intervals appear compressed or dilates, irrespective of whether the interval is actively produced or passively observed. This is consistent with recent bayesian explanations of time estimation.