The impact of emotion on temporal prediction ability in different timing contexts

Our ability to predict temporal events (TP) is dynamically modulated by contextual factors (i.e., different predictive contexts) and closely intertwined with emotional states, shaping our adaptive responses within the environment. While studies have extensively probed how emotions distort time perception, their impact on predictive ability remains unexplored. Here, we investigated emotions' impact on temporal prediction. Participants (N = 23) completed a standard implicit TP task and its emotional version (TP-E), using positive (i.e., joy), negative (i.e., fear), and neutral faces as visual stimuli. Reaction times (RTs) to the target were recorded in two predictive contexts: rhythmic (i.e., interstimulus intervals (ISIs) were constant, 900 ms) and single-interval condition (i.e., target's timing estimation was based on the prior exposure of the train of stimuli) and random (no-time context). We found a specific decrease in RTs, in the single-interval context, when fearful stimuli were used, compared to neutral stimuli. This suggests that negative emotion influences temporal prediction, aligning with emotional adjustments in processing threatening situations, including modulation of physiological arousal, cognitive appraisal, and time estimation. Indeed, such modulation of RTs, specifically in the single-interval condition, may be attributed to improved memory and attention, essential cognitive abilities for single-based predictions, enhanced by the exposure to fearful stimuli.

Authors

Institutions

Publication Details

Journal
Scientific Reports
Published
2025-03-22
DOI
https://doi.org/10.1038/s41598-025-87887-8
Primary Topic
Neuroscience and Music Perception
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The impact of emotion on temporal prediction ability in different timing contexts

Susanna Mezzarobba, Elisa Pelosin, Sara Terranova, Alessandro Botta et al.
Scientific Reports
Neuroscience and Music Perception
article

The impact of emotion on temporal prediction ability in different timing contexts

Susanna Mezzarobba, Elisa Pelosin, Sara Terranova, Alessandro Botta, Giovanna Lagravinese, Carola Cosentino, Martina Putzolu, Elisa Ravizzotti, Gaia Bonassi, Laura Avanzino
article en

Abstract

Our ability to predict temporal events (TP) is dynamically modulated by contextual factors (i.e., different predictive contexts) and closely intertwined with emotional states, shaping our adaptive responses within the environment. While studies have extensively probed how emotions distort time perception, their impact on predictive ability remains unexplored. Here, we investigated emotions' impact on temporal prediction. Participants (N = 23) completed a standard implicit TP task and its emotional version (TP-E), using positive (i.e., joy), negative (i.e., fear), and neutral faces as visual stimuli. Reaction times (RTs) to the target were recorded in two predictive contexts: rhythmic (i.e., interstimulus intervals (ISIs) were constant, 900 ms) and single-interval condition (i.e., target's timing estimation was based on the prior exposure of the train of stimuli) and random (no-time context). We found a specific decrease in RTs, in the single-interval context, when fearful stimuli were used, compared to neutral stimuli. This suggests that negative emotion influences temporal prediction, aligning with emotional adjustments in processing threatening situations, including modulation of physiological arousal, cognitive appraisal, and time estimation. Indeed, such modulation of RTs, specifically in the single-interval condition, may be attributed to improved memory and attention, essential cognitive abilities for single-based predictions, enhanced by the exposure to fearful stimuli.

Scientific ReportsVol. 15(1)
Ospedale Policlinico San Martino (IT), University of Genoa (IT)
Ministero dell'Università e della Ricerca
Openalex Percentile: Top 95%
Neuroscience and Music Perception
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.