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Source Files for A Multiscale ε–δ Metric Framework for Phenomenological Time Perception

Zenodo2025-06-03 更新2026-04-07 收录

时序建模

计算认知科学

数据链接：

https://zenodo.org/doi/10.5281/zenodo.15579941 数据链接链接失效反馈

官方服务：

资源简介：

LaTeX source, figures, and Python notebooks for a theoretical model of time perception, as described in Braun (2025).

应用场景：

创建时间：

2025-06-03

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This structure is generated by the following equations: V^{(1)}_t = 0.2 * V^{(1)}_{t - 1} + 0.1 * \eps^1_t V^{(2)}_t = 0.2 * V^{(2)}_{t - 1} + 0.7 * V^{(1)}_{t - 1} + 0.1 * \eps^2_t V^{(3)}_t = 0.2 *

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Accounting for time series errors in partially linear model with single- or multiple-run

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This paper concerns statistical estimation of the partially linear model (PLM) for time course measurements, which are temporally correlated and allow multiple-run for repeated measurements to enhance

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Supplement 1. R code for simulation implementation and parameter estimation.

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File List EstimationComparison.R (MD5: 5055e25ebc61fe8dbf012b48198eadda) Simulations.R (MD5: 56eef6b34cb59a85d5072b0b20e645ec) CreateFigures.R (MD5: 2c19a14d1ac5887ba544c2937d8639ca

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Text-attributed Temporal Graph Benchmark Datasets

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Internal Representations of Temporal Statistics and Feedback Calibrate Motor-Sensory Interval Timing

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Humans have been shown to adapt to the temporal statistics of timing tasks so as to optimize the accuracy of their responses, in agreement with the predictions of Bayesian integration. This suggests t

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