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Breaking the cardinal rule: The impact of interitem interaction and attentional priority on the cardinal biases in orientation working memory

Abstract

Although it is not typically assumed in influential models of visual working memory (WM), representations in WM are systematically biased by multiple factors. Orientation representations are biased away from the cardinal axis (i.e., cardinal bias) and they are biased away from or toward the other orientation simultaneously held in WM (i.e., interitem interaction). The present study investigated the extent to which these two bias mechanisms interact in WM. In Experiment 1, participants remembered two sequentially presented orientations and reproduced both orientations after a short delay. Cardinal biases were assessed separately for the trials where the two mechanisms produce biases in the same direction (i.e., congruent trials) and the trials where they produce biases in the opposite direction (i.e., incongruent trials). Whereas congruent trials exhibited a typical cardinal bias, incongruent trials exhibited no cardinal bias, demonstrating that the cardinal bias was canceled out by the interitem interaction. Follow-up experiments extended these results by manipulating attentional priority for the two orientations by means of precue (Experiment 2) and postcue (Experiment 3). In both experiments, attentionally prioritized items exhibited a typical cardinal bias irrespective of the congruency whereas attentionally unprioritized items exhibited a reversal of the cardinal bias in the incongruent trials, demonstrating that selective attention modulates the influence of the interitem interaction. Together, these results suggest that WM leverages information about specific stimuli and their relationship to support a given behavioral goal.

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Data availability

The data and materials will be made available at https://osf.io/vygr3 upon acceptance of the manuscript.

Notes

  1. 1.

    Although excluding errors larger than the smallest orientation difference does not completely remove swap errors, they can only produce attraction biases, which should decrease the overall repulsion bias observed in the present study.

References

  1. Bae, G.-Y. (2021). Neural evidence for categorical biases in location and orientation representations in a working memory task. NeuroImage, 240(15), 118–366. https://doi.org/10.1016/j.neuroimage.2021.118366

    Article  Google Scholar 

  2. Bae, G.-Y., & Luck, S. J. (2017). Interactions between visual working memory representations. Attention, Perception, & Psychophysics, 79(8), 2376–2395. https://doi.org/10.3758/s13414-017-1404-8

    Article  Google Scholar 

  3. Bae, G.-Y., & Luck, S. J. (2018). Dissociable decoding of spatial attention and working memory from EEG oscillations and sustained potentials. The Journal of Neuroscience, 38(2), 409–422. https://doi.org/10.1523/JNEUROSCI.2860-17.2017

    Article  PubMed  PubMed Central  Google Scholar 

  4. Bae, G.-Y., & Luck, S. J. (2019). What happens to an individual visual working memory representation when it is interrupted? British Journal of Psychology, 110(2), 268–287. https://doi.org/10.1111/bjop.12339

    Article  PubMed  Google Scholar 

  5. Bae, G.-Y., Olkkonen, M., Allred, S. R., & Flombaum, J. I. (2015). Why some colors appear more memorable than others: A model combining categories and particulars in color working memory. Journal of Experimental Psychology: General, 144(4), 744–763. https://doi.org/10.1037/xge0000076

    Article  Google Scholar 

  6. Bays, P. M., Catalao, R. F. G., & Husain, M. (2009). The precision of visual working memory is set by allocation of a shared resource. Journal of Vision, 9(10), 7–7. https://doi.org/10.1167/9.10.7

    Article  PubMed  Google Scholar 

  7. Benjamini, Y., & Hochberg, Y. (1995). Controlling the false discovery rate: A practical and powerful approach to multiple testing. Journal of the Royal Statistical Society: Series B (Methodological), 57(1), 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x

    Article  Google Scholar 

  8. Blakemore, C., Carpenter, R. H., & Georgeson, M. A. (1970). Lateral inhibition between orientation detectors in the human visual system. Nature, 228(5266), 37–39. https://doi.org/10.1038/228037a0

    Article  PubMed  Google Scholar 

  9. Brady, T., & Alvarez, G. (2011). Hierarchical encoding in visual working memory: Ensemble statistics bias memory for individual items. Psychological Science, 22(3), 384–392.

    Article  Google Scholar 

  10. Ester, E. F., Sprague, T. C., & Serences, J. T. (2020). Categorical biases in human occipitoparietal cortex. Journal of Neuroscience, 40(4), 917–931. https://doi.org/10.1523/JNEUROSCI.2700-19.2019

    Article  PubMed  Google Scholar 

  11. Foster, J. J., Sutterer, D. W., Serences, J. T., Vogel, E. K., & Awh, E. (2016). The topography of alpha-band activity tracks the content of spatial working memory. Journal of Neurophysiology, 115(1), 168–177. https://doi.org/10.1152/jn.00860.2015

    Article  PubMed  Google Scholar 

  12. Fukuda, K., Pereira, A. E., Saito, J. M., Tang, T. Y., Tsubomi, H., & Bae, G.Y. (in press). Working memory content is distorted by its use in perceptual comparisons. Psychological science. https://doi.org/10.1177/09567976211055375

  13. Gibson, J. J., & Radner, M. (1937). Adaptation, after-effect and contrast in the perception of tilted lines. I: Quantitative studies. Journal of Experimental Psychology, 20(5), 453–467. https://doi.org/10.1037/h0059826

    Article  Google Scholar 

  14. Girshick, A. R., Landy, M. S., & Simoncelli, E. P. (2011). Cardinal rules: Visual orientation perception reflects knowledge of environmental statistics. Nature Neuroscience, 14(7), 926–932. https://doi.org/10.1038/nn.2831

    Article  PubMed  PubMed Central  Google Scholar 

  15. Golomb, J. D. (2015). Divided spatial attention and feature-mixing errors. Attention, Perception, & Psychophysics, 77(8), 2562–2569. https://doi.org/10.3758/s13414-015-0951-0

    Article  Google Scholar 

  16. Huang, J., & Sekuler, R. (2010a). Attention protects the fidelity of visual memory: Behavioral and electrophysiological evidence. Journal of Neuroscience, 30(40), 13461–13471.

    Article  Google Scholar 

  17. Huang, J., & Sekuler, R. (2010b). Distortions in recall from visual memory: Two classes of attractors at work. Journal of Vision, 10(2), 1–27.

    Article  Google Scholar 

  18. Kiyonaga, A., & Egner, T. (2016). Center-surround inhibition in working memory. Current Biology : CB, 26(1), 64–68. https://doi.org/10.1016/j.cub.2015.11.013

    Article  PubMed  Google Scholar 

  19. Matsukura, M., Luck, S. J., & Vecera, S. P. (2007). Attention effects during visual short-term memory maintenance: Protection or prioritization? Perception & Psychophysics, 69(8), 1422–1434. https://doi.org/10.3758/BF03192957

    Article  Google Scholar 

  20. Nelder, J. A., & Mead, R. (1965). A simplex method for function minimization. The Computer Journal, 7(4), 308–313.

    Article  Google Scholar 

  21. Pratte, M. S., Park, Y. E., Rademaker, R. L., & Tong, F. (2017). Accounting for stimulus-specific variation in precision reveals a discrete capacity limit in visual working memory. Journal of Experimental Psychology: Human Perception and Performance, 43(1), 6–17. https://doi.org/10.1037/xhp0000302

    Article  PubMed  Google Scholar 

  22. van den Berg, R., Shin, H., Chou, W.-C., George, R., & Ma, W. J. (2012). Variability in encoding precision accounts for visual short-term memory limitations. Proceedings of the National Academy of Sciences of the United States of America, 109(22), 8780–8785. https://doi.org/10.1073/pnas.1117465109

    Article  PubMed  PubMed Central  Google Scholar 

  23. Wei, X.-X., & Stocker, A. A. (2015). A Bayesian observer model constrained by efficient coding can explain “anti-Bayesian” percepts. Nature Neuroscience, 18(10), 1509–1517. https://doi.org/10.1038/nn.4105

    Article  PubMed  Google Scholar 

  24. Zhang, W., & Luck, S. J. (2008). Discrete fixed-resolution representations in visual working memory. Nature, 453(7192), 233–235. https://doi.org/10.1038/nature06860

    Article  PubMed  PubMed Central  Google Scholar 

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Funding

No funding was received to assist with the preparation of this manuscript.

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G.B. designed the study, collected and analyzed data, and wrote the manuscript.

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Correspondence to Gi-Yeul Bae.

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The author has no conflicts of interest to declare that are relevant to the content of this article.

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Approval was obtained from the ethics committee of Arizona State University. The procedures used in this study adhere to the tenets of the Declaration of Helsinki.

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Freely available statistical software (R) was used for the data analyses.

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Bae, GY. Breaking the cardinal rule: The impact of interitem interaction and attentional priority on the cardinal biases in orientation working memory. Atten Percept Psychophys (2021). https://doi.org/10.3758/s13414-021-02374-2

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Keywords

  • Cardinal bias
  • Interitem interaction
  • Selective attention
  • Visual working memory