Duration Is Not a Reliable Indicator for Anticipating Event Boundaries

Abstract Research in event cognition has shown that how people segment events plays an important role in how they are perceived and remembered. One potential source of information for anticipating upcoming event boundaries is the typical duration of the event. The small amount of prior work that exists suggests that the durations of daily events have normal distributions and that people have accurate prior beliefs about these durations (Griffiths & Tenenbaum, 2006). This research study aims to further explore the temporal distribution of self-reported events from daily life. Forty-eight participants provided information regarding the duration of events from their daily life for 14 days. Event durations for various activity types were characterised using duration modelling with truncated normal, exponential, and gamma models. Predictions based on mean and median values, including ± 5% and ± 10% error margins, were obtained, and the cumulative hazard functions were derived. Exponential and gamma distributions fit most event categories better than the truncated normal distribution. Mean-based predictions of boundaries based on duration showed only 4–5% accuracy, indicating limited usefulness. This aligns with the cumulative hazard results, where most events followed the exponential distribution, suggesting no optimal time for predicting the end of the event. This suggests that when daily events are studied in an ecological context, most of them have little sign of a typical duration. Consequently, it is unlikely that duration estimation plays a large role in the anticipation of event boundaries.

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Publication Details

Journal
Computational Brain & Behavior
Published
2025-04-08
DOI
https://doi.org/10.1007/s42113-025-00243-x
Primary Topic
Simulation Techniques and Applications
Type
article
Field-Weighted Citation Impact
0.00

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article

Duration Is Not a Reliable Indicator for Anticipating Event Boundaries

Rebecca Defina, Luz Viviana Sastre-Gómez, Jeffrey M. Zacks, Simon Dennis et al.
Computational Brain & Behavior
Simulation Techniques and Applications
article

Duration Is Not a Reliable Indicator for Anticipating Event Boundaries

Rebecca Defina, Luz Viviana Sastre-Gómez, Jeffrey M. Zacks, Simon Dennis, Paul Michael Garrett
article en

Abstract

Abstract Research in event cognition has shown that how people segment events plays an important role in how they are perceived and remembered. One potential source of information for anticipating upcoming event boundaries is the typical duration of the event. The small amount of prior work that exists suggests that the durations of daily events have normal distributions and that people have accurate prior beliefs about these durations (Griffiths & Tenenbaum, 2006). This research study aims to further explore the temporal distribution of self-reported events from daily life. Forty-eight participants provided information regarding the duration of events from their daily life for 14 days. Event durations for various activity types were characterised using duration modelling with truncated normal, exponential, and gamma models. Predictions based on mean and median values, including ± 5% and ± 10% error margins, were obtained, and the cumulative hazard functions were derived. Exponential and gamma distributions fit most event categories better than the truncated normal distribution. Mean-based predictions of boundaries based on duration showed only 4–5% accuracy, indicating limited usefulness. This aligns with the cumulative hazard results, where most events followed the exponential distribution, suggesting no optimal time for predicting the end of the event. This suggests that when daily events are studied in an ecological context, most of them have little sign of a typical duration. Consequently, it is unlikely that duration estimation plays a large role in the anticipation of event boundaries.

Computational Brain & BehaviorVol. 8(4)
Australian Psychological Society (AU), Hologic (Germany) (DE)
University of Melbourne
Openalex Percentile: Top 92%
Simulation Techniques and Applications
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