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A dynamic normalization model of temporal attention

Abstract

Vision is dynamic, handling a continuously changing stream of input, yet most models of visual attention are static. Here, we develop a dynamic normalization model of visual temporal attention and constrain it with new psychophysical human data. We manipulated temporal attention—the prioritization of visual information at specific points in time—to a sequence of two stimuli separated by a variable time interval. Voluntary temporal attention improved perceptual sensitivity only over a specific interval range. To explain these data, we modelled voluntary and involuntary attentional gain dynamics. Voluntary gain enhancement took the form of a limited resource over short time intervals, which recovered over time. Taken together, our theoretical and experimental results formalize and generalize the idea of limited attentional resources across space at a single moment to limited resources across time at a single location.

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Fig. 1: Behavioural protocol and data.
Fig. 2: Static R&H normalization model of attention equation.
Fig. 3: Model architecture and example simulated time series.
Fig. 4: Voluntary attention as a limited but recoverable resource.
Fig. 5: Model fits to perceptual sensitivity data.
Fig. 6: Generalization to independent datasets.

Data availability

All behavioural data are publicly available on the Open Science Framework (OSF) (https://osf.io/dkx7n).

Code availability

All custom code for the model is publicly available on OSF (https://osf.io/dkx7n). Code for the behavioural experiments is available on GitHub (https://github.com/racheldenison/temporal-attention).

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Acknowledgements

This research was supported by National Institutes of Health National Eye Institute R01 EY019693 to M.C. and D.J.H., R01 EY027401 to M.C., F32 EY025533 to R.N.D. and T32 EY007136 to NYU. The funders had no role in study design, data collection and analysis, decision to publish or preparation of the manuscript. The authors thank H.-H. Li for consultation on the model and Carrasco Lab members, especially V. Peña for assistance with data collection and A. Fernández and M. Jigo for their comments on the manuscript.

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R.N.D., M.C. and D.J.H. conceived the project, designed the experiment and interpreted the behavioural results and model findings. R.N.D. and D.J.H. conceived the model. R.N.D. implemented the model, conducted the experiment and analysed the data. R.N.D. wrote and all three authors edited the manuscript.

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Correspondence to Rachel N. Denison.

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The authors declare no competing interests.

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Peer review information Nature Human Behaviour thanks Elkan Akyürek, Benjamin Morillon and Valentin Wyart and the other, anonymous, reviewer(s) for their contribution to the peer review of this work.

Publisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Extended data

Extended Data Fig. 1 Individual observer data.

Behavioural data for individual observers (data points) at each SOA (separate plots). Valid vs. invalid performance for T1 (purple) and T2 (green). For visualization, individual data across SOAs and cuing conditions were normalized separately for each target by adding a constant to equate the individual target mean with the group mean. This adjusts for individual differences in overall performance for a given target without changing the differences among cueing and SOA conditions, facilitating visualization of the pattern of data across these factors. (a) Perceptual sensitivity (d’). Data points lying above the unity line have a temporal cueing effect: higher d’ for valid than invalid trials. The improvement of d’ with temporal attention specifically for intermediate SOAs was consistent across individual observers. (b) Reaction time (RT). Data points lying below the unity line have a temporal cueing effect: faster RT for valid than invalid trials. Reaction time improvements were consistent across observers.

Extended Data Fig. 2 Behavioural statistics.

Repeated measures ANOVA table for behavioural data. SOA = stimulus onset asynchrony, dfn = degrees of freedom in the numerator, dfd = degrees of freedom in the denominator.

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Supplementary Results, Supplementary Figs. 1–4 and Supplementary Tables 1–4.

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Denison, R.N., Carrasco, M. & Heeger, D.J. A dynamic normalization model of temporal attention. Nat Hum Behav (2021). https://doi.org/10.1038/s41562-021-01129-1

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