A test of multiple correlation temporal window characteristic of non-Markov processes
We introduce a sensitive test of memory effects in successive events. The test consists of a combination K of binary correlations at successive times. K decays monotonically from K = 1 for uncorrelated events as a Markov process; whereas memory effects provide a temporal window with K > 1. For a monotonic memory fading, K < 1 always. Here we report evidence of a K > 1 temporal window in cognitive tasks consisting of the visual identification of the front face of the Necker cube after a previous presentation of the same. The K > 1 behaviour is maximal at an inter-measurement time {\tau} around 2 sec with inter-subject differences. The K > 1 persists over a time window of 1 sec around {\tau}; outside this window the K < 1 behaviour is recovered. The universal occurrence of a K > 1 window in pairs of successive perceptions suggests that, at variance with single visual stimuli eliciting a suitable response, a pair of stimuli shortly separated in time displays mutual correlations.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
LC-Mamba: Local and Continuous Mamba with Shifted Windows for Frame Interpolation
In this paper, we propose LC-Mamba, a Mamba-based model that captures fine-grained spatiotemporal information in video frames, addressing limitations in current interpolation methods and enhancing performance. The ma…
MambaPerformance Modeling for Correlation-based Neural Decoding of Auditory Attention to Speech
Correlation-based auditory attention decoding (AAD) algorithms exploit neural tracking mechanisms to determine listener attention among competing speech sources via, e.g., electroencephalography signals. The correlation …
Wind Power Scenario Generation based on the Generalized Dynamic Factor Model and Generative Adversarial Network
For conducting resource adequacy studies, we synthesize multiple long-term wind power scenarios of distributed wind farms simultaneously by using the spatio-temporal features: spatial and temporal correlation, waveforms,…
HTGN-BTW: Heterogeneous Temporal Graph Network with Bi-Time-Window Training Strategy for Temporal Link Prediction
With the development of temporal networks such as E-commerce networks and social networks, the issue of temporal link prediction has attracted increasing attention in recent years. The Temporal Link Prediction task of WS…
Link PredictionPredictionInformation integration from distributed threshold-based interactions
We consider a collection of distributed units that interact with one another through the sending of messages. Each message carries a positive ($+1$) or negative ($-1$) tag and causes the receiving unit to send out messag…
TAG