Recent experiences bias the perception of following stimuli,as has been verified in various kinds of experiments in visual perception.This phenomenon,known as serial dependence,may reflect mechanisms to maintain perce...Recent experiences bias the perception of following stimuli,as has been verified in various kinds of experiments in visual perception.This phenomenon,known as serial dependence,may reflect mechanisms to maintain perceptual stability.In the current study,we examined several key properties of serial dependence in temporal perception.Firstly,we examined the source of the serial dependence effect in temporal perception.We found that perception without motor reproduction is sufficient to induce the sequential effect;motor reproduction caused a stronger effect and is achieved by biasing the perception of the future target duration rather than directly influencing the subsequent movement.Secondly,we ask how working memory influences serial dependence in a temporal reproduction task.By varying the delay time between standard duration and the reproduction,we showed that the strength of serial dependence is enhanced as the delay increased.Those features of serial dependence are consistent with what has been observed in visual perceptual tasks,for example,orientation perception or location perception.The similarities between the visual and the timing tasks may suggest a similar neural coding mechanism of magnitude between the visual stimuli and the duration.展开更多
In the area of time series modelling, several applications are encountered in real-life that involve analysis of count time series data. The distribution characteristics and dependence structure are the major issues t...In the area of time series modelling, several applications are encountered in real-life that involve analysis of count time series data. The distribution characteristics and dependence structure are the major issues that arise while specifying a modelling strategy to handle the analysis of those kinds of data. Owing to the numerous applications there is a need to develop models that can capture these features. However, accounting for both aspects simultaneously presents complexities while specifying a modeling strategy. In this paper, an alternative statistical model able to deal with issues of discreteness, overdispersion, serial correlation over time is proposed. In particular, we adopt a branching mechanism to develop a first-order stationary negative binomial autoregressive model. Inference is based on maximum likelihood estimation and a simulation study is conducted to evaluate the performance of the proposed approach. As an illustration, the model is applied to a real-life dataset in crime analysis.展开更多
基金National Natural Science Foundation of China,Grant/Award Numbers:Projects 31771213,31371018。
文摘Recent experiences bias the perception of following stimuli,as has been verified in various kinds of experiments in visual perception.This phenomenon,known as serial dependence,may reflect mechanisms to maintain perceptual stability.In the current study,we examined several key properties of serial dependence in temporal perception.Firstly,we examined the source of the serial dependence effect in temporal perception.We found that perception without motor reproduction is sufficient to induce the sequential effect;motor reproduction caused a stronger effect and is achieved by biasing the perception of the future target duration rather than directly influencing the subsequent movement.Secondly,we ask how working memory influences serial dependence in a temporal reproduction task.By varying the delay time between standard duration and the reproduction,we showed that the strength of serial dependence is enhanced as the delay increased.Those features of serial dependence are consistent with what has been observed in visual perceptual tasks,for example,orientation perception or location perception.The similarities between the visual and the timing tasks may suggest a similar neural coding mechanism of magnitude between the visual stimuli and the duration.
文摘In the area of time series modelling, several applications are encountered in real-life that involve analysis of count time series data. The distribution characteristics and dependence structure are the major issues that arise while specifying a modelling strategy to handle the analysis of those kinds of data. Owing to the numerous applications there is a need to develop models that can capture these features. However, accounting for both aspects simultaneously presents complexities while specifying a modeling strategy. In this paper, an alternative statistical model able to deal with issues of discreteness, overdispersion, serial correlation over time is proposed. In particular, we adopt a branching mechanism to develop a first-order stationary negative binomial autoregressive model. Inference is based on maximum likelihood estimation and a simulation study is conducted to evaluate the performance of the proposed approach. As an illustration, the model is applied to a real-life dataset in crime analysis.
