TY - GEN A1 - Glasauer, Stefan A1 - Shi, Zhuanghua T1 - Central Tendency as Consequence of Experimental Protocol T2 - 2019 Conference on Cognitive Computational Neuroscience, 13-16 September 2019, Berlin, Germany N2 - Perceptual biases found experimentally are often taken to indicate that we should be cautious about the veridicality of our perception in everyday life. Here we show, to the contrary, that such biases may be a consequence of the experimental protocol that cannot be generalized to other situations. We show that the central tendency, an overestimation of small magnitudes and underestimation of large ones, strongly depends on stimulus order. If the same set of stimuli is, rather than being presented in the usual randomized order, is applied in an order that displays only small changes from one trial to the next, the central tendency decreases significantly. This decrease is predicted by a probabilistic model that assumes iterative trial-wise updating of a prior of the stimulus distribution. We conclude that the commonly used randomization of stimuli introduces systematic perceptual biases that may not relevant in everyday life. Y1 - 2019 U6 - https://doi.org/10.32470/CCN.2019.1148-0 SP - 268 EP - 271 CY - Berlin ER - TY - GEN A1 - Glasauer, Stefan A1 - Shi, Zhuanghua T1 - The origin of Vierordt's law: The experimental protocol matters T2 - PsyCH Journal N2 - In 1868, Karl Vierordt discovered one type of errors in time perception—an overestimation of short duration and underestimation of long durations, known as Vierordt's law. Here we reviewed the original study in its historical context and asked whether Vierordt's law is a result of an unnatural experimental randomization protocol. Using iterative Bayesian updating, we simulated the original results with high accuracy. Importantly, the model also predicted that a slowly changing random-walk sequence produces less central tendency than a random sequence with the same durations. This was validated by a duration reproduction experiment from two sequences (random and random walk) with the same sampled distribution. The results showed that trial-wise variation influenced the magnitude of Vierordt's law. We concluded that Vierordt's law is caused by an unnatural yet widely used experimental protocol. Y1 - 2021 UR - https://onlinelibrary.wiley.com/doi/10.1002/pchj.464 U6 - https://doi.org/10.1002/pchj.464 SN - 2046-0260 SN - 2046-0252 VL - 10 IS - 5 SP - 732 EP - 741 ER - TY - GEN A1 - Glasauer, Stefan A1 - Shi, Zhuanghua T1 - Differences in beliefs about stimulus generation explain individual perceptual biases T2 - Bernstein Conference 2021, September 21 - 23, 2021 Y1 - 2021 U6 - https://doi.org/10.12751/nncn.bc2021.p052 VL - 2021 ER - TY - GEN A1 - Glasauer, Stefan A1 - Shi, Zhuanghua T1 - Individual beliefs about temporal continuity explain variation of perceptual biases T2 - Scientific Reports N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1038/s41598-022-14939-8 SN - 2045-2322 VL - 12 ER - TY - GEN A1 - Cheng, Si A1 - Chen, Siyi A1 - Glasauer, Stefan A1 - Keeser, Daniel A1 - Shi, Zhuanghua T1 - Neural mechanisms of sequential dependence in time perception: the impact of prior task and memory processing T2 - Cerebral Cortex N2 - Our perception and decision-making are susceptible to prior context. Such sequential dependence has been extensively studied in the visual domain, but less is known about its impact on time perception. Moreover, there are ongoing debates about whether these sequential biases occur at the perceptual stage or during subsequent post-perceptual processing. Using functional magnetic resonance imaging, we investigated neural mechanisms underlying temporal sequential dependence and the role of action in time judgments across trials. Participants performed a timing task where they had to remember the duration of green coherent motion and were cued to either actively reproduce its duration or simply view it passively. We found that sequential biases in time perception were only evident when the preceding task involved active duration reproduction. Merely encoding a prior duration without reproduction failed to induce such biases. Neurally, we observed activation in networks associated with timing, such as striato-thalamo-cortical circuits, and performance monitoring networks, particularly when a “Response” trial was anticipated. Importantly, the hippocampus showed sensitivity to these sequential biases, and its activation negatively correlated with the individual’s sequential bias following active reproduction trials. These findings highlight the significant role of memory networks in shaping time-related sequential biases at the post-perceptual stages. Y1 - 2023 U6 - https://doi.org/10.1093/cercor/bhad453 SN - 1460-2199 VL - 34(2024) IS - 1 SP - 1 EP - 14 ER - TY - GEN A1 - Shi, Zhuanghua A1 - Gu, Bon-Mi A1 - Glasauer, Stefan A1 - Meck, Warren H. T1 - Beyond Scalar Timing Theory: Integrating Neural Oscillators with Computational Accessibility in Memory T2 - Timing & Time Perception N2 - 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. Y1 - 2022 U6 - https://doi.org/10.1163/22134468-bja10059 SN - 2213-4468 SP - 1 EP - 22 ER -