Abstract
Our behavior is guided by the statistical regularities in the environment. Prior research on temporal context effects has demonstrated the dynamic processes through which3 humans adapt to the environment’s temporal regularities. However, learning temporal4 regularities not only entails dynamic adaptation to traces of previous individual events5 but also often requires the extraction and retention of summary statistics (e.g., the mean)6 of temporal distributions. To investigate these summary representations for temporal7 distributions and to test their sensitivity to distributional changes, we explicitly asked8 participants to extract the mean of different distributions of time intervals, which shared9 the same mean but varied in their variability specifically operationalized by the width10 and presentation frequency of the intervals. Our findings showed that the variability of11 the estimated mean increased with the distributions’ variability, even though the actual12 mean remained constant. We further examined how such learning of temporal13 distributions modulates EEG signals during subsequent temporal judgments. Analysis14 revealed that the contingent negative variation (CNV), predictive of single-trial RTs,15 was correlated with how much individuals’ estimates of the mean were affected by the16 distributions’ variability. Conversely, the post-interval P2 was not modulated by the17 distributions but predicted participants’ responses, suggesting that P2 reflects the18 perceived duration of an interval. Taken together, our results demonstrate not only that19 humans can accurately estimate the mean of a temporal distribution, but also that the20 representation of the mean becomes more uncertain as the variability of the distribution21 increases, as reflected neurally in the preparation-related CNV during temporal22 decisions.23 24
Authors
Taku Otsuka, Hakan Karsilar, and Hedderik van Rijn
https://doi.org/10.1162/jocn_a_02327