Abstract
Attentional engagement is known to vary on a moment-to-moment basis. However, few self-report methods can effectively capture dynamic fluctuations in attentional engagement over time. In the current paper, we evaluated the utility of stimulated recall, a method wherein individuals are asked to remember their subjective states while using a mnemonic cue, for the measurement of temporal changes in attentional engagement. Participants were asked to watch a video lecture, during which we assessed their in-the-moment levels of attentional engagement using intermittent thought probes. Then, we used stimulated recall by cueing participants with short video clips from the lecture to retrospectively assess the levels of attentional engagement they had experienced when they first watched those clips within the lecture. Experiment 1 assessed the statistical overlap between in-the-moment and video-stimulated ratings. Experiment 2 assessed the generalizability of video-stimulated recall across different types of lectures. Experiment 3 assessed the impact of presenting video-stimulated probe clips in non-chronological order. Experiment 4 assessed the effect of video-stimulated recall on its own. Across all experiments, we found statistically robust correspondence between in-the-moment and video-stimulated ratings of attentional engagement, illustrating a strong convergence between these two methods of assessment. Taken together, our findings indicate that stimulated recall provides a new and practical methodological approach that can accurately capture dynamic fluctuations in subjective attentional states over time.
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Data availability
Anonymized data for all studies in this paper are publicly available on the Open Science Framework at https://osf.io/r8ub7/. The experiments were not preregistered.
Code availability
Analysis scripts for this paper are publicly available on the Open Science Framework at https://osf.io/r8ub7/.
Notes
Given that a minimum of three probe responses are needed to construct the time series, subsampling the data at a proportion lower than 20% was not possible.
To avoid overparameterization within the model, only positive time lags were used in the analysis; however, analyzing the data using only negative time lags replicated the pattern of effects found. See Supplementary Materials for complete analyses.
To offer comparison to the video lecture used in Experiment 1, another group of independent raters (32 participants: 17 women, 15 men; Mage = 20.4 years, SDage = 1.3 years) were asked to assess the evolutionary psychology video on various engagement metrics on a scale of 0 to 10 (see Supplementary Materials). Compared to the video lectures used in Experiment 2, the evolutionary psychology video had significantly higher engagement metrics compared to the art history video [most ps < .036, most ds > .55, except for ratings of flow, appeal, and video quality where ps > .11, ds < .41], but was statistically similar to the computer science video [most ps > .05, most ds < .51, except for ratings of appeal and video quality where ps < .025, ds > .59].
When analyzing the data using only negative time lags, we replicated the pattern of effects found within positive time lags (see Supplementary Materials).
Negative time lags replicated the pattern of effects found within positive time lags (see Supplementary Materials).
Negative time lags replicated the pattern of effects found within positive time lags (see Supplementary Materials).
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This research was funded by the Natural Sciences and Engineering Research Council of Canada (NSERC) Banting and Postdoctoral Fellowships awarded to EJP, NSERC Masters- and Doctoral-level Canada Graduate Scholarships awarded to SAG, and NSERC Discovery Grants awarded to JDW and DS.
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Pereira, E.J., Ayers-Glassey, S., Wammes, J.D. et al. Attention in hindsight: Using stimulated recall to capture dynamic fluctuations in attentional engagement. Behav Res (2023). https://doi.org/10.3758/s13428-023-02273-4
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DOI: https://doi.org/10.3758/s13428-023-02273-4