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Adaptation to average duration.

ABSTRACT: There has been a recent surge of research examining how the visual system compresses information by representing the average properties of sets of similar objects to circumvent strict capacity limitations. Efficient representation by perceptual averaging helps to maintain the balance between the needs to perceive salient events in the surrounding environment and sustain the illusion of stable and complete perception. Whereas there have been many demonstrations that the visual system encodes spatial average properties, such as average orientation, average size, and average numerosity along single dimensions, there has been no investigation of whether the fundamental nature of average representations extends to the temporal domain. Here, we used an adaptation paradigm to demonstrate that the average duration of a set of sequentially presented stimuli negatively biases the perceived duration of subsequently presented information. This negative adaptation aftereffect is indicative of a fundamental visual property, providing the first evidence that average duration is encoded along a single visual dimension. Our results not only have important implications for how the visual system efficiently encodes redundant information to evaluate salient events as they unfold within the dynamic context of the surrounding environment, but also contribute to the long-standing debate regarding the neural underpinnings of temporal encoding.

SUBMITTER: Corbett JE

PROVIDER: S-EPMC8049910 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:[1] The information contained in hyetographs and hydrographs is often synthesized by using key properties such as the peak or maximum value Xp , volume V, duration D, and average intensity I. These variables play a fundamental role in hydrologic engineering as they are used, for instance, to define design hyetographs and hydrographs as well as to model and simulate the rainfall and streamflow processes. Given their inherent variability and the empirical evidence of the presence of a significant degree of association, such quantities have been studied as correlated random variables suitable to be modeled by multivariate joint distribution functions. The advent of copulas in geosciences simplified the inference procedures allowing for splitting the analysis of the marginal distributions and the study of the so-called dependence structure or copula. However, the attention paid to the modeling task has overlooked a more thorough study of the true nature and origin of the relationships that link [Formula: see text], and I. In this study, we apply a set of ad hoc bootstrap algorithms to investigate these aspects by analyzing the hyetographs and hydrographs extracted from 282 daily rainfall series from central eastern Europe, three 5 min rainfall series from central Italy, 80 daily streamflow series from the continental United States, and two sets of 200 simulated universal multifractal time series. Our results show that all the pairwise dependence structures between [Formula: see text], and I exhibit some key properties that can be reproduced by simple bootstrap algorithms that rely on a standard univariate resampling without resort to multivariate techniques. Therefore, the strong similarities between the observed dependence structures and the agreement between the observed and bootstrap samples suggest the existence of a numerical generating mechanism based on the superposition of the effects of sampling data at finite time steps and the process of summing realizations of independent random variables over random durations. We also show that the pairwise dependence structures are weakly dependent on the internal patterns of the hyetographs and hydrographs, meaning that the temporal evolution of the rainfall and runoff events marginally influences the mutual relationships of [Formula: see text], and I. Finally, our findings point out that subtle and often overlooked deterministic relationships between the properties of the event hyetographs and hydrographs exist. Confusing these relationships with genuine stochastic relationships can lead to an incorrect application of multivariate distributions and copulas and to misleading results.

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Adaptation to average duration.

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