Faculty Opinions recommendation of A representation of the hazard rate of elapsed time in macaque area LIP.

Author(s):  
Wolfram Schultz
Keyword(s):  
2005 ◽  
Vol 8 (3) ◽  
pp. 396-396
Author(s):  
Peter Janssen ◽  
Michael N Shadlen
Keyword(s):  

2005 ◽  
Vol 8 (2) ◽  
pp. 234-241 ◽  
Author(s):  
Peter Janssen ◽  
Michael N Shadlen
Keyword(s):  

2017 ◽  
Author(s):  
Alessandro Tavano ◽  
Erich Schröger ◽  
Sonja A. Kotz

SummaryHumans tend to use elapsed time to increase the perceived probability that an impending event – e.g., the Go sign at a traffic light - will occur soon. This prompts faster reactions for longer waiting times (hazard rate effect). Which neural processes reflect instead the perceived probability of uncertain future events? We recorded behavioral and electroencephalographic (EEG) data while participants detected a target tone, rarely appearing at one of three successive positions of a repeating five-tone sequence with equal probability. Pre-stimulus oscillatory power in the low betaband range (Beta 1: 15-19 Hz) predicted the hazard rate of response times to the uncertain target, suggesting it encodes abstract estimates of a potential event onset. Informing participants about the target’s equiprobable distribution endogenously suppressed the hazard rate of response times. Beta 1 power still predicted behavior, validating its role in contextually estimating temporal probabilities for uncertain future events.HighlightsElapsed time to an uncertain future target increases response speed (Hazard rate).Pre-stimulus low beta-band (Beta 1: 15-19 Hz) power predicts the hazard rate to uncertain targets.Beta 1 power predicts response times even when elapsed time is factored out.eTOC BlurbTavano et al. show that pre-stimulus low beta band (15-19 Hz) power predicts response times to an uncertain future target, even before its occurrence and under different prior knowledge conditions, suggesting it reflects contextual, subjective estimates of potential future events.


2019 ◽  
Author(s):  
Matthias Grabenhorst ◽  
Georgios Michalareas ◽  
Laurence T. Maloney ◽  
David Poeppel

AbstractHumans use sensory input to anticipate events. The brain’s capacity to predict cues in time is commonly assumed to be modulated by two uncertainty parameters, the hazard rate (HR) of event probability and the uncertainty in time estimation, which increases with elapsed time. We investigate both assumptions by manipulating event probability density functions (PDF) in three sensory modalities. First we show, contrary to expectation, that perceptual systems use the reciprocal PDF – and not the HR – to model event probability density. Next we demonstrate that temporal uncertainty does not necessarily grow with elapsed time but also diminishes, depending on the event PDF. Finally we show that reaction time (RT) distributions comprise modality-specific and modality-independent components, the latter likely reflecting similarity in processing of probability density across sensory modalities. The results are consistent across vision, audition, and somatosensation, indicating that probability density is more fundamental than hazard rate in terms of the neural operations determining event anticipation and temporal uncertainty. Previous research identified neuronal activitity related to event probability in multiple levels of the cortical hierarchy such as early and higher sensory (V1, V4), association (LIP), motor and other areas. This work proposed that the elementary neuronal computation in estimation of probability across time is the HR. In contrast, our results suggest that the neurobiological implementation of probability estimation is based on a different, much simpler and more stable computation than HR: the reciprocal PDF of events in time.


2008 ◽  
Vol 47 (02) ◽  
pp. 167-173 ◽  
Author(s):  
A. Pfahlberg ◽  
O. Gefeller ◽  
R. Weißbach

Summary Objectives: In oncological studies, the hazard rate can be used to differentiate subgroups of the study population according to their patterns of survival risk over time. Nonparametric curve estimation has been suggested as an exploratory means of revealing such patterns. The decision about the type of smoothing parameter is critical for performance in practice. In this paper, we study data-adaptive smoothing. Methods: A decade ago, the nearest-neighbor bandwidth was introduced for censored data in survival analysis. It is specified by one parameter, namely the number of nearest neighbors. Bandwidth selection in this setting has rarely been investigated, although the heuristical advantages over the frequently-studied fixed bandwidth are quite obvious. The asymptotical relationship between the fixed and the nearest-neighbor bandwidth can be used to generate novel approaches. Results: We develop a new selection algorithm termed double-smoothing for the nearest-neighbor bandwidth in hazard rate estimation. Our approach uses a finite sample approximation of the asymptotical relationship between the fixed and nearest-neighbor bandwidth. By so doing, we identify the nearest-neighbor bandwidth as an additional smoothing step and achieve further data-adaption after fixed bandwidth smoothing. We illustrate the application of the new algorithm in a clinical study and compare the outcome to the traditional fixed bandwidth result, thus demonstrating the practical performance of the technique. Conclusion: The double-smoothing approach enlarges the methodological repertoire for selecting smoothing parameters in nonparametric hazard rate estimation. The slight increase in computational effort is rewarded with a substantial amount of estimation stability, thus demonstrating the benefit of the technique for biostatistical applications.


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