On the relationship of arousal and attentional distraction by emotional novel sounds

Bonmassar, Carolina; Scharf, Florian; Widmann, Andreas; Wetzel, Nicole

doi:10.1016/j.cognition.2023.105470

Cited by 2 publications

(1 citation statement)

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“…Dynamic effects may be (4a) autoregressive effects acting on the level of the original values of the variables (e.g., the slower the raw RT value of trial i, the slower the raw RT in trial i + 1), (4b) residual autoregressive effects acting on the residuals (i.e., the difference from the model-predicted values, e.g., the slower the residual RT in trial i the faster residual RT in trial i +1), or (4c) moving average effects where the outcome value depends on previous residuals (e.g., the slower the residual RT in trial i the faster raw RT in trial i +1). Whereas all these effects are potentially interesting depending on the experimental setup and have been investigated occasionally (Bonmassar et al, 2023;Kristjansson et al, 2007;LoTemplio et al, 2021;Tremblay & Newman, 2015;Volkmer et al, 2022), we will focus on temporal trends in the following because the original analysis of Cui and Herrmann (2023) would have benefited from incorporating temporal trend.…”

Section: Theoretical Part: Consideration Of Temporal Trends In Data A...mentioning

confidence: 99%

Pupillometry is sensitive to speech masking during story listening: a commentary on the critical role of modeling temporal trends

Widmann,

Herrmann,

Scharf

2024

Preprint

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An increase in pupil size is an important index of listening effort, for example, when listening to speech masked by noise. Specifically, the pupil dilates as the signal-to- noise ratio decreases. A growing body of work aims to assess listening effort under naturalistic conditions using continuous speech, such as spoken stories. However, a recent study found that pupil size was sensitive to speech masking only when listening to sentences but not under such naturalistic conditions when listening to stories. The pupil typically constricts with increasing time on task during an experimental block or session, and it may be necessary to account for this temporal trend in experimental design and data analysis in paradigms using longer, continuous stimuli. In the current work, we re-analyze the previously published pupil data, taking into account a problematic constraint of randomization and time-on-task, and use the data to outline methodological solutions for accounting for temporal trends in physiological data using linear mixed models. The results show that, in contrast to the previous work, pupil size is indeed sensitive to speech masking even during continuous story listening. Furthermore, accounting for the temporal trend further allowed modeling the dynamic changes in the speech masking effect on pupil size over time as the continuous story unfolded. After demonstrating the importance of accounting for temporal trends in the analysis of empirical data, we provide simulations, methodological considerations, and user recommendations for the analysis of temporal trends in experimental data using linear mixed models.

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