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A new algorithm of human attention

Published online by Cambridge University Press:  26 November 2025

Mario Belledonne  and
Ilker Yildirim Open the ORCID record for Ilker Yildirim [Opens in a new window]
Mario Belledonne*
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA mario.belledonne@yale.edu ilker.yildirim@yale.edu
Ilker Yildirim*
Affiliation:
Department of Psychology, Yale University, New Haven, CT, USA mario.belledonne@yale.edu ilker.yildirim@yale.edu
*
*Corresponding author.
*Corresponding author.
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Abstract

How do our goals continually impact perceptual processing? The answer could arise from a computational specification of perception in terms of visual tasks, or perhaps several mechanisms operating over specific contexts. Here, we suggest an alternative: adaptive computation, a new algorithmic account of attention that rations the general resource of perceptual computations according to their impact on decision making.

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Type
Open Peer Commentary
Information
Behavioral and Brain Sciences , Volume 48 , 2025 , e131
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Copyright
© The Author(s), 2025. Published by Cambridge University Press

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Footnotes

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Article updated 10 December 2025

References

Belledonne, M., Butkus, E., Scholl, B.J., & Yildirim, I. (2025). Adaptive computation as a new mechanism of dynamic human attention. Psychological Review. Advanced online publication. https://psycnet.apa.org/doi/10.1037/rev0000572.Google Scholar
Bruce, N. D., & Tsotsos, J. K. (2005). Saliency based on information maximization. Advances in Neural Information Processing Systems, 18, 155–162.Google Scholar
He, S., Cavanagh, P., & Intriligator, J. (1997). Attentional resolution. Trends in Cognitive Sciences, 1(3), 115–121.CrossRefGoogle ScholarPubMed
Huang, L., Mo, L., & Liu, Y. (2012). Measuring the interrelations among multiple paradigms of visual attention: an individual differences approach. Journal of Experimental Psychology: Human Perception and Performance, 38 (2), 414–428.Google ScholarPubMed
Neisser, U. (1967). Cognitive psychology. Appleton-Century-Crofts.Google Scholar
Nobre, A. C., & Ede, F. van. (2023). Attention in flux. Neuron, 111 (7), 971–986.CrossRefGoogle ScholarPubMed
Pashler, H. (1997). The psychology of attention. The MIT Press. https://doi.org/10.7551/mitpress/5677.001.0001 CrossRefGoogle Scholar
Treviño, M., Zhu, X., Lu, Y. Y., Scheuer, L. S., Passell, E., Huang, G. C., Germine, L. T., & Horowitz, T. S. (2021). How do we measure attention? Using factor analysis to establish construct validity of neuropsychological tests. Cognitive Research: Principles and Implications, 6, 1–26.Google ScholarPubMed
Tsotsos, J. K., Liu, Y., Martinez-Trujillo, J. C., Pomplun, M., Simine, E., & Zhou, K. (2005). Attending to visual motion. Computer Vision and Image Understanding, 100 (12), 3–40.CrossRefGoogle Scholar