@misc{GlasauerShi, author = {Glasauer, Stefan and Shi, Zhuanghua}, title = {Individual beliefs about temporal continuity explain variation of perceptual biases}, series = {Scientific Reports}, volume = {12}, journal = {Scientific Reports}, issn = {2045-2322}, doi = {10.1038/s41598-022-14939-8}, abstract = {Perception of magnitudes such as duration or distance is often found to be systematically biased. The biases, which result from incorporating prior knowledge in the perceptual process, can vary considerably between individuals. The variations are commonly attributed to differences in sensory precision and reliance on priors. However, another factor not considered so far is the implicit belief about how successive sensory stimuli are generated: independently from each other or with certain temporal continuity. The main types of explanatory models proposed so far—static or iterative—mirror this distinction but cannot adequately explain individual biases. Here we propose a new unifying model that explains individual variation as combination of sensory precision and beliefs about temporal continuity and predicts the experimentally found changes in biases when altering temporal continuity. Thus, according to the model, individual differences in perception depend on beliefs about how stimuli are generated in the world.}, language = {en} } @misc{ShiGuGlasaueretal., author = {Shi, Zhuanghua and Gu, Bon-Mi and Glasauer, Stefan and Meck, Warren H.}, title = {Beyond Scalar Timing Theory: Integrating Neural Oscillators with Computational Accessibility in Memory}, series = {Timing \& Time Perception}, journal = {Timing \& Time Perception}, issn = {2213-4468}, doi = {10.1163/22134468-bja10059}, pages = {1 -- 22}, abstract = {One of the major challenges for computational models of timing and time perception is to identify a neurobiological plausible implementation that predicts various behavioral properties, including the scalar property and retrospective timing. The available timing models primarily focus on the scalar property and prospective timing, while virtually ignoring the computational accessibility. Here, we first selectively review timing models based on ramping activity, oscillatory pattern, and time cells, and discuss potential challenges for the existing models. We then propose a multifrequency oscilla- tory model that offers computational accessibility, which could account for a much broader range of timing features, including both retrospective and prospective timing.}, language = {en} } @misc{ZangZhuAllenmarketal., author = {Zang, Xuelian and Zhu, Xiuna and Allenmark, Fredrik and Wu, Jiao and M{\"u}ller, Hermann J. and Glasauer, Stefan and Shi, Zhuanghua}, title = {Duration reproduction under memory pressure : modeling the roles of visual memory load in duration encoding and reproduction}, series = {bioRxiv}, journal = {bioRxiv}, publisher = {Cold Spring Harbor Laboratory}, address = {Cold Spring Harbor}, doi = {10.1101/2022.02.10.479853}, pages = {1 -- 34}, abstract = {Duration estimates are often biased by the sampled statistical context, yielding the classical central-tendency effect, i.e., short durations are over- and long duration underestimated. Most studies of the central-tendency bias have primarily focused on the integration of the sensory measure and the prior information, without considering any cognitive limits. Here, we investigated the impact of cognitive (visual working-memory) load on duration estimation in the duration encoding and reproduction stages. In four experiments, observers had to perform a dual, attention-sharing task: reproducing a given duration (primary) and memorizing a variable set of color patches (secondary). We found an increase in memory load (i.e., set size) during the duration-encoding stage to increase the central-tendency bias, while shortening the reproduced duration in general; in contrast, increasing the load during the reproduction stage prolonged the reproduced duration, without influencing the central tendency. By integrating an attentional-sharing account into a hierarchical Bayesian model, we were able to predict both the general over- and underestimation and the central-tendency effects observed in all four experiments. The model suggests that memory pressure during the encoding stage increases the sensory noise, which elevates the central-tendency effect. In contrast, memory pressure during the reproduction stage only influences the monitoring of elapsed time, leading to a general duration over-reproduction without impacting the central tendency.}, language = {en} }