Element contents
Series: Elements in Perception
Representing Variability
How Do We Process the Heterogeneity in the Visual Environment?
Published online by Cambridge University Press: 28 February 2024
Summary
The visual world is full of detail. This Element focuses on this variability in perception, asking how it affects performance in visual tasks and how the variability is represented by human observers. The authors highlight different methods for assessing representations of variability and suggest that understanding visual variability can be elusive when straightforward explicit methods are used, while more implicit methods may be better suited to uncovering such processing. The authors conclude that variability is represented in far more detail than previously thought and that this aspect of perception is vital for understanding the complexity of visual consciousness.
Keywords
Information
- Type
- Element
- Information
- Series: Elements in PerceptionOnline ISBN: 9781009396035Publisher: Cambridge University PressPrint publication: 21 March 2024
References
Acerbi, L., Wolpert, D. M., & Vijayakumar, S. (2012). Internal representations of temporal statistics and feedback calibrate motor-sensory interval timing. PLOS Computational Biology, 8(11), e1002771. https://doi.org/10.1371/journal.pcbi.1002771.CrossRefGoogle ScholarPubMed
Alvarez, G. A. (2011). Representing multiple objects as an ensemble enhances visual cognition. Trends in Cognitive Sciences, 15(3), 122–131. https://doi.org/10.1016/j.tics.2011.01.003.CrossRefGoogle ScholarPubMed
Arita, J. T., Carlisle, N. B., & Woodman, G. F. (2012). Templates for rejection: Configuring attention to ignore task-irrelevant features. Journal of Experimental Psychology: Human Perception and Performance, 38(3), 580–584. https://doi.org/10.1037/a0027885.Google ScholarPubMed
Atchley, P., & Andersen, G. J. (1995). Discrimination of speed distributions: Sensitivity to statistical properties. Vision Research, 35(22), 3131–3144. https://doi.org/10.1016/0042-6989(95)00057-7.CrossRefGoogle ScholarPubMed
Attarha, M., & Moore, C. M. (2015). The capacity limitations of orientation summary statistics. Attention, Perception, & Psychophysics, 77(4), 1116–1131. https://doi.org/10.3758/s13414-015-0870-0.CrossRefGoogle ScholarPubMed
Attarha, M., Moore, C. M., & Vecera, S. P. (2014). Summary statistics of size: Fixed processing capacity for multiple ensembles but unlimited processing capacity for single ensembles. Journal of Experimental Psychology: Human Perception and Performance, 40(4), 1440–1449. https://doi.org/10.1037/a0036206.Google ScholarPubMed
Avraham, T., Yeshurun, Y., Lindenbaum, M., Yeshurun, Y., & Lindenbaum, M. (2008). Predicting visual search performance by quantifying stimuli similarities. Journal of Vision, 8(2008), 9.1–22. https://doi.org/10.1167/8.4.9.CrossRefGoogle ScholarPubMed
Balas, B. J., Nakano, L., & Rosenholtz, R. (2009). A summary-statistic representation in peripheral vision explains visual crowding. Journal of Vision, 9(12), 13.1–18. https://doi.org/10.1167/9.12.13.CrossRefGoogle ScholarPubMed
Bauer, B. (2015). A selective summary of visual averaging research and issues up to 2000. Journal of Vision, 15(4), 1–15. https://doi.org/10.1167/15.4.14.CrossRefGoogle ScholarPubMed
Bays, P., Schneegans, S., Ma, W. J., & Brady, T. (2022). Representation and computation in working memory. PsyArXiv. https://doi.org/10.31234/osf.io/kubr9.CrossRefGoogle Scholar
Bertana, A., Chetverikov, A., van Bergen, R. S., Ling, S., & Jehee, J. F. M. (2021). Dual strategies in human confidence judgments. Journal of Vision, 21(5), 21. https://doi.org/10.1167/jov.21.5.21.CrossRefGoogle ScholarPubMed
Biederman, I. (1987). Recognition-by-components: A theory of human image understanding. Psychological Review, 94, 115–147. https://doi.org/10.1037/0033-295X.94.2.115.CrossRefGoogle ScholarPubMed
Block, N. (2011). Perceptual consciousness overflows cognitive access. Trends in Cognitive Sciences, 15(12), 1–9. https://doi.org/10.1016/j.tics.2011.11.001.CrossRefGoogle ScholarPubMed
Block, N. (2018). If perception is probabilistic, why does it not seem probabilistic? Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1755). https://doi.org/10.1098/rstb.2017.0341.CrossRefGoogle Scholar
Boldt, A., de Gardelle, V., & Yeung, N. (2017). The impact of evidence reliability on sensitivity and bias in decision confidence. Journal of Experimental Psychology: Human Perception and Performance, 43(8), 1520–1531. https://doi.org/10.1037/xhp0000404.Google ScholarPubMed
Braddick, O. J., O’Brien, J. M. D., Wattam-Bell, J. et al. (2001). Brain areas sensitive to coherent visual motion. Perception, 30(1), 61–72. https://doi.org/10.1068/p3048.CrossRefGoogle ScholarPubMed
Brady, T. F., & Alvarez, G. A. (2011). Hierarchical encoding in visual working memory ensemble statistics bias memory for individual items. Psychological Science, 22(3), 384–392. https://doi.org/10.1177/0956797610397956.CrossRefGoogle ScholarPubMed
Bravo, M. J., & Nakayama, K. (1992). The role of attention in different visual-search tasks. Perception & Psychophysics, 51(5), 465–472. https://doi.org/10.3758/BF03211642.CrossRefGoogle ScholarPubMed
Bronfman, Z. Z., Brezis, N., Jacobson, H., & Usher, M. (2014). We see more than we can report: ‘cost free’ color phenomenality outside focal attention. Psychological Science, 25(7), 1394–1403. https://doi.org/10.1177/0956797614532656.CrossRefGoogle ScholarPubMed
Calder-Travis, J., & Ma, W. J. (2020). Explaining the effects of distractor statistics in visual search. Journal of Vision, 20(13), 11. https://doi.org/10.1167/jov.20.13.11.CrossRefGoogle ScholarPubMed
Cha, O., Blake, R., & Gauthier, I. (2022). Contribution of a common ability in average and variability judgments. Psychonomic Bulletin & Review, 29(1), 108–115. https://doi.org/10.3758/s13423-021-01982-1.CrossRefGoogle ScholarPubMed
Chapman, A. F., Chunharas, C., & Störmer, V. S. (2023). Feature-based attention warps the perception of visual features. Scientific Reports, 13(1), Article 1. https://doi.org/10.1038/s41598-023-33488-2.CrossRefGoogle ScholarPubMed
Chetverikov, A. (2023). Demixing model: A normative explanation for inter-item biases in memory and perception. bioRxiv. https://doi.org/10.1101/2023.03.26.534226.CrossRefGoogle Scholar
Chetverikov, A., Campana, G., & Kristjánsson, Á. (2016). Building ensemble representations: How the shape of preceding distractor distributions affects visual search. Cognition, 153, 196–210. https://doi.org/10.1016/j.cognition.2016.04.018.CrossRefGoogle ScholarPubMed
Chetverikov, A., Campana, G., & Kristjánsson, Á. (2017a). Learning features in a complex and changing environment: A distribution-based framework for visual attention and vision in general. In Howard, Christina J. (Ed.), Progress in Brain Research (Vol. 236, pp. 97–120). Elsevier. https://doi.org/10.1016/bs.pbr.2017.07.001.Google Scholar
Chetverikov, A., Campana, G., & Kristjánsson, Á. (2017b). Rapid learning of visual ensembles. Journal of Vision, 17(21), 1–15. https://doi.org/10.1167/17.2.21.CrossRefGoogle ScholarPubMed
Chetverikov, A., Campana, G., & Kristjánsson, Á. (2017c). Representing color ensembles. Psychological Science, 28(10), 1–8. https://doi.org/10.1177/0956797617713787.CrossRefGoogle ScholarPubMed
Chetverikov, A., Campana, G., & Kristjánsson, Á. (2017d). Set size manipulations reveal the boundary conditions of distractor distribution learning. Vision Research, 140(November), 144–156. https://doi.org/10.1016/j.visres.2017.08.003.CrossRefGoogle Scholar
Chetverikov, A., Campana, G., & Kristjánsson, Á. (2020). Probabilistic rejection templates in visual working memory. Cognition, 196, 104075. https://doi.org/10.1016/j.cognition.2019.104075.CrossRefGoogle ScholarPubMed
Chetverikov, A., Hansmann-Roth, S., Tanrikulu, Ö. D., & Kristjánsson, Á. (2019). Feature distribution learning (FDL): A new method for studying visual ensembles perception with priming of attention shifts. In Pollmann, S. (Ed.), Neuromethods (pp. 1–21). Springer. https://doi.org/10.1007/7657_2019_20.Google Scholar
Chetverikov, A., & Jehee, J. F. M. (2023). Motion direction is represented as a bimodal probability distribution in the human visual cortex. Nature Communications, 14(7634), Article 1. https://doi.org/10.1038/s41467-023-43251-w.CrossRefGoogle ScholarPubMed
Chetverikov, A., & Kristjánsson, Á. (2015). History effects in visual search for monsters: Search times, choice biases, and liking. Attention, Perception, & Psychophysics, 77(2), 402–412. https://doi.org/10.3758/s13414-014-0782-4.CrossRefGoogle ScholarPubMed
Chetverikov, A., & Kristjánsson, A. (2022). Probabilistic representations as building blocks for higher-level vision. Neurons, Behavior, Data Analysis, and Theory, 1–32. https://doi.org/10.51628/001c.24910.CrossRefGoogle Scholar
Chong, S. C., & Treisman, A. (2003). Representation of statistical properties. Vision Research, 43(4), 393–404. https://doi.org/10.1016/S0042-6989(02)00596-5.CrossRefGoogle ScholarPubMed
Cohen, M. A., Dennett, D. C., & Kanwisher, N. (2016). What is the bandwidth of perceptual experience? Trends in Cognitive Sciences, 20(5), 324–335. https://doi.org/10.1016/j.tics.2016.03.006.CrossRefGoogle ScholarPubMed
Corbett, J. E., & Melcher, D. (2014). Stable statistical representations facilitate visual search. Journal of Experimental Psychology: Human Perception and Performance, 40(5), 1915–1925. https://doi.org/10.1037/a0037375.Google ScholarPubMed
Corbett, J. E., Utochkin, I., & Hochstein, S. (2023). The Pervasiveness of Ensemble Perception: Not Just Your Average Review. Cambridge University Press. https://doi.org/10.1017/9781009222716.CrossRefGoogle Scholar
Corpuz, R. L., & Oriet, C. (2022). Within-person variability contributes to more durable learning of faces. Canadian Journal of Experimental Psychology / Revue Canadienne de Psychologie Expérimentale, 76(4), 270–282. https://doi.org/10.1037/cep0000282.CrossRefGoogle ScholarPubMed
Dakin, S. C. (2001). Information limit on the spatial integration of local orientation signals. Journal of the Optical Society of America A, 18(5), 1016–1026. https://doi.org/10.1364/JOSAA.18.001016.CrossRefGoogle ScholarPubMed
Dakin, S. C., & Watt, R. J. J. (1997). The computation of orientation statistics from visual texture. Vision Research, 37(22), 3181–3192. https://doi.org/10.1016/S0042-6989(97)00133-8.CrossRefGoogle ScholarPubMed
Danckert, J., & Goodale, M. A. (2000). Blindsight: A conscious route to unconscious vision. Current Biology, 10(2), R64–R67. https://doi.org/10.1016/S0960-9822(00)00284-0.CrossRefGoogle ScholarPubMed
Daniels, D. P., Neale, M. A., & Greer, L. L. (2017). Spillover bias in diversity judgment. Organizational Behavior and Human Decision Processes, 139, 92–105. https://doi.org/10.1016/j.obhdp.2016.12.005.CrossRefGoogle Scholar
Desimone, R., & Duncan, J. (1995). Neural mechanisms of selective visual attention. Annual Review of Neuroscience, 18, 193–222. https://doi.org/10.1146/annurev.ne.18.030195.001205.CrossRefGoogle ScholarPubMed
Driver, J., McLeod, P., & Dienes, Z. (1992). Motion coherence and conjunction search: Implications for guided search theory. Perception & Psychophysics, 51(1), 79–85. https://doi.org/10.3758/BF03205076.CrossRefGoogle ScholarPubMed
Duncan, J., & Humphreys, G. W. (1989). Visual search and stimulus similarity. Psychological Review, 96(3), 433–458. https://doi.org/10.1037/0033-295x.96.3.433.CrossRefGoogle ScholarPubMed
Fahle, M. (2005). Perceptual learning: Specificity versus generalization. Current Opinion in Neurobiology, 15(2), 154–160. https://doi.org/10.1016/j.conb.2005.03.010.CrossRefGoogle ScholarPubMed
Failing, M., & Theeuwes, J. (2018). Selection history: How reward modulates selectivity of visual attention. Psychonomic Bulletin & Review, 25(2), 514–538. https://doi.org/10.3758/s13423-017-1380-y.CrossRefGoogle ScholarPubMed
Fernandez-Duque, D., & Thornton, I. M. (2000). Change detection without awareness: Do explicit reports underestimate the representation of change in the visual system? Visual Cognition, 7(1–3), 323–344. https://doi.org/10.1080/135062800394838.CrossRefGoogle Scholar
Fernandez-Duque, D., & Thornton, I. M. (2003). Explicit mechanisms do not account for implicit localization and identification of change: An empirical reply to Mitroff et al. (2002). Journal of Experimental Psychology: Human Perception and Performance, 29(5), 846–858. https://doi.org/10.1037/0096-1523.29.5.846.Google Scholar
Fischer, J., & Whitney, D. (2014). Serial dependence in visual perception. Nature Neuroscience, 17(5), 738–743. https://doi.org/10.1038/nn.3689.CrossRefGoogle ScholarPubMed
Fiser, J., Berkes, P., Orbán, G., & Lengyel, M. (2010). Statistically optimal perception and learning: From behavior to neural representations. Trends in Cognitive Sciences, 14(3), 119–130. https://doi.org/10.1016/j.tics.2010.01.003.CrossRefGoogle ScholarPubMed
Freeman, J., & Simoncelli, E. P. (2011). Metamers of the ventral stream. Nature Neuroscience, 14(9), 1195–1201. https://doi.org/10.1038/nn.2889.CrossRefGoogle ScholarPubMed
Friston, K. J. (2010). The free-energy principle: A unified brain theory? Nature Reviews Neuroscience, 11(2), 127–138. https://doi.org/10.1038/nrn2787.CrossRefGoogle ScholarPubMed
Gao, Y., Xue, K., Odegaard, B., & Rahnev, D. (2023). Common computations in automatic cue combination and metacognitive confidence reports (p. 2023.06.07.544029). bioRxiv. https://doi.org/10.1101/2023.06.07.544029.CrossRefGoogle Scholar
Geng, J. J., DiQuattro, N. E., & Helm, J. (2017). Distractor probability changes the shape of the attentional template. Journal of Experimental Psychology: Human Perception and Performance, 43(12), 1993–2007. https://doi.org/10.1037/xhp0000430.Google ScholarPubMed
Gibson, J. J. (1950). The Perception of the Visual World (pp. xii, 242). Houghton Mifflin.Google Scholar
Gibson, J. J. (1962). Observations on active touch. Psychological Review, 69(6), 477–491. https://doi.org/10.1037/h0046962.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Gold, J. I., & Watanabe, T. (2010). Perceptual learning. Current Biology, 20(2), R46–R48. https://doi.org/10.1016/j.cub.2009.10.066.CrossRefGoogle ScholarPubMed
Goodale, M. A., & Milner, A. D. (1992). Separate visual pathways for perception and action. Trends in Neurosciences, 15(1), 20–25. https://doi.org/10.1016/0166-2236(92)90344-8.CrossRefGoogle ScholarPubMed
Haberman, J., Brady, T. F., & Alvarez, G. A. (2015a). Individual differences in ensemble perception reveal multiple, independent levels of ensemble representation. Journal of Experimental Psychology: General, 144(2), 432–446. https://doi.org/10.1037/xge0000053.CrossRefGoogle ScholarPubMed
Haberman, J., Lee, P., & Whitney, D. (2015b). Mixed emotions: Sensitivity to facial variance in a crowd of faces. Journal of Vision, 15(4), 16. https://doi.org/10.1167/15.4.16.CrossRefGoogle Scholar
Haberman, J., & Whitney, D. (2011). Efficient summary statistical representation when change localization fails. Psychonomic Bulletin & Review, 18(5), 855–859. https://doi.org/10.3758/s13423-011-0125-6.CrossRefGoogle ScholarPubMed
Haberman, J., & Whitney, D. (2012). Ensemble perception: Summarizing the scene and broadening the limits of visual processing. In Wolfe, J. M. & Robertson, L. (Eds.), From Perception to Consciousness: Searching with Anne Treisman (pp. 339–349). Oxford University Press.CrossRefGoogle Scholar
Hansmann-Roth, S., Chetverikov, A., & Kristjánsson, Á. (2019). Representing color and orientation ensembles: Can observers learn multiple feature distributions? Journal of Vision, 19(9), 1–17. https://doi.org/10.1167/19.9.2.CrossRefGoogle ScholarPubMed
Hansmann-Roth, S., Chetverikov, A., & Kristjánsson, Á. (2023). Extracting statistical information about shapes in the visual environment. Vision Research, 206, 108190. https://doi.org/10.1016/j.visres.2023.108190.CrossRefGoogle ScholarPubMed
Hansmann-Roth, S., Kristjánsson, Á., Whitney, D., & Chetverikov, A. (2021). Dissociating implicit and explicit ensemble representations reveals the limits of visual perception and the richness of behavior. Scientific Reports, 11, 3899. https://doi.org/10.1038/s41598-021-83358-y.CrossRefGoogle ScholarPubMed
Hansmann-Roth, S., Þorsteinsdóttir, S., Geng, J. J., & Kristjánsson, Á. (2022). Temporal integration of feature probability distributions. Psychological Research, 86, 2030–2044. https://doi.org/10.1007/s00426-021-01621-3.CrossRefGoogle ScholarPubMed
Harrison, W. J., McMaster, J. M. V., & Bays, P. M. (2021). Limited memory for ensemble statistics in visual change detection. Cognition, 214, 104763. https://doi.org/10.1016/j.cognition.2021.104763.CrossRefGoogle ScholarPubMed
Haun, A. M., Tononi, G., Koch, C., & Tsuchiya, N. (2017). Are we underestimating the richness of visual experience? Neuroscience of Consciousness, 2017(1), niw023. https://doi.org/10.1093/nc/niw023.CrossRefGoogle ScholarPubMed
Hawkins, B., Evans, D., Preston, A. et al. (2022). Color diversity judgments in peripheral vision: Evidence against ‘cost-free’ representations. PLOS ONE, 17(12), e0279686. https://doi.org/10.1371/journal.pone.0279686.CrossRefGoogle ScholarPubMed
Hebart, M. N., Donner, T. H., & Haynes, J. D. (2012). Human visual and parietal cortex encode visual choices independent of motor plans. NeuroImage, 63(3), 1393–1403. https://doi.org/10.1016/j.neuroimage.2012.08.027.CrossRefGoogle ScholarPubMed
Hecht, S., Shlaer, S., & Pirenne, M. H. (1942). Energy, quanta, and vision. Journal of General Physiology, 25(6), 819–840. https://doi.org/10.1085/jgp.25.6.819.CrossRefGoogle ScholarPubMed
Herce Castañón, S., Moran, R., Ding, J. et al. (2019). Human noise blindness drives suboptimal cognitive inference. Nature Communications, 10(1), 1719. https://doi.org/10.1038/s41467-019-09330-7.CrossRefGoogle ScholarPubMed
Higuchi, Y., Ueda, Y., Shibata, K., & Saiki, J. (2020). Spatial variability induces generalization in contextual cueing. Journal of Experimental Psychology: Learning, Memory, and Cognition, 46(12), 2295–2313. https://doi.org/10.1037/xlm0000796.Google ScholarPubMed
Hochstein, S., Pavlovskaya, M., Bonneh, Y. S., & Soroker, N. (2018). Comparing set summary statistics and outlier pop out in vision. Journal of Vision, 18(13), 12. https://doi.org/10.1167/18.13.12.CrossRefGoogle ScholarPubMed
Hoffman, D. D., & Richards, W. A. (1984). Parts of recognition. Cognition, 18(1), 65–96. https://doi.org/10.1016/0010-0277(84)90022-2.CrossRefGoogle ScholarPubMed
Huang, L., Holcombe, A. O., & Pashler, H. (2004). Repetition priming in visual search: Episodic retrieval, not feature priming. Memory & Cognition, 32(1), 12–20.CrossRefGoogle Scholar
Hussain, Z., Bennett, P. J., & Sekuler, A. B. (2012). Versatile perceptual learning of textures after variable exposures. Vision Research, 61, 89–94. https://doi.org/10.1016/j.visres.2012.01.005.CrossRefGoogle ScholarPubMed
Iakovlev, A. U., & Utochkin, I. S. (2021). Roles of saliency and set size in ensemble averaging. Attention, Perception, & Psychophysics, 83(3), 1251–1262. https://doi.org/10.3758/s13414-020-02089-w.CrossRefGoogle ScholarPubMed
Iakovlev, A. U., & Utochkin, I. S. (2023). Ensemble averaging: What can we learn from skewed feature distributions? Journal of Vision, 23(1), 5. https://doi.org/10.1167/jov.23.1.5.CrossRefGoogle ScholarPubMed
Im, H. Y., & Halberda, J. (2013). The effects of sampling and internal noise on the representation of ensemble average size. Attention, Perception, & Psychophysics, 75(2), 278–286. https://doi.org/10.3758/s13414-012-0399-4.CrossRefGoogle ScholarPubMed
Im, H. Y., Tiurina, N. A., & Utochkin, I. S. (2020). An explicit investigation of the roles that feature distributions play in rapid visual categorization. Attention, Perception, and Psychophysics, 83, 1050–1069. https://doi.org/10.3758/s13414-020-02046-7.CrossRefGoogle Scholar
Jackson-Nielsen, M., Cohen, M. A., & Pitts, M. A. (2017). Perception of ensemble statistics requires attention. Consciousness and Cognition, 48, 149–160. https://doi.org/10.1016/j.concog.2016.11.007.CrossRefGoogle ScholarPubMed
Jeong, J., & Chong, S. C. (2020). Adaptation to mean and variance: Interrelationships between mean and variance representations in orientation perception. Vision Research, 167, 46–53. https://doi.org/10.1016/j.visres.2020.01.002.CrossRefGoogle ScholarPubMed
Jeong, J., & Chong, S. C. (2021). Perceived variability reflects the reliability of individual items. Vision Research, 183, 91–105. https://doi.org/10.1016/j.visres.2021.02.008.CrossRefGoogle ScholarPubMed
Kanaya, S., Hayashi, M. J., & Whitney, D. (2018). Exaggerated groups: Amplification in ensemble coding of temporal and spatial features. Proceedings of the Royal Society B: Biological Sciences, 285(1879), 20172770. https://doi.org/10.1098/rspb.2017.2770.CrossRefGoogle ScholarPubMed
Karni, A., & Sagi, D. (1991). Where practice makes perfect in texture discrimination: Evidence for primary visual cortex plasticity. Proceedings of the National Academy of Sciences, 88(11), 4966–4970. https://doi.org/10.1073/pnas.88.11.4966.CrossRefGoogle ScholarPubMed
Kaunitz, L. N., Rowe, E. G., & Tsuchiya, N. (2016). Large capacity of conscious access for incidental memories in natural scenes. Psychological Science, 27(9), 1266–1277. https://doi.org/10.1177/0956797616658869.CrossRefGoogle ScholarPubMed
Kay, K., Bonnen, K., Denison, R. N., Arcaro, M. J., & Barack, D. L. (2023). Tasks and their role in visual neuroscience. Neuron, 111(11), 1697–1713. https://doi.org/10.1016/j.neuron.2023.03.022.CrossRefGoogle ScholarPubMed
Khayat, N., & Hochstein, S. (2018). Perceiving set mean and range: Automaticity and precision. Journal of Vision, 18(9), 23. https://doi.org/10.1167/18.9.23.CrossRefGoogle ScholarPubMed
Khayat, N., & Hochstein, S. (2019). Relating categorization to set summary statistics perception. Attention, Perception, & Psychophysics, 81(8), 2850–2872. https://doi.org/10.3758/s13414-019-01792-7.CrossRefGoogle ScholarPubMed
Khvostov, V. A., & Utochkin, I. S. (2019). Independent and parallel visual processing of ensemble statistics: Evidence from dual tasks. Journal of Vision, 19(9), 1–18. https://doi.org/10.1167/19.9.3.CrossRefGoogle ScholarPubMed
Kim, M., & Chong, S. C. (2020). The visual system does not compute a single mean but summarizes a distribution. Journal of Experimental Psychology: Human Perception and Performance, 46(9), 1013–1028. https://doi.org/10.1037/xhp0000804.Google ScholarPubMed
Knill, D. C., & Pouget, A. (2004). The Bayesian brain: The role of uncertainty in neural coding and computation. Trends in Neurosciences, 27(12), 712–719. https://doi.org/10.1016/j.tins.2004.10.007.CrossRefGoogle ScholarPubMed
Koblinger, Á., Fiser, J., & Lengyel, M. (2021). Representations of uncertainty: Where art thou? Current Opinion in Behavioral Sciences, 38, 150–162. https://doi.org/10.1016/j.cobeha.2021.03.009.CrossRefGoogle ScholarPubMed
Körding, K. P., Beierholm, U., Ma, W. J. et al. (2007). Causal inference in multisensory perception. PLoS ONE, 2(9). e943. https://doi.org/10.1371/journal.pone.0000943.CrossRefGoogle ScholarPubMed
Kristjánsson, Á. (2022). Priming of probabilistic attentional templates. Psychonomic Bulletin & Review, 30, 22–39. https://doi.org/10.3758/s13423-022-02125-w.CrossRefGoogle ScholarPubMed
Kristjánsson, Á., & Ásgeirsson, Á. G. (2019). Attentional priming: Recent insights and current controversies. Current Opinion in Psychology, 29, 71–75. https://doi.org/10.1016/j.copsyc.2018.11.013.CrossRefGoogle ScholarPubMed
Kristjánsson, Á., & Draschkow, D. (2021). Keeping it real: Looking beyond capacity limits in visual cognition. Attention, Perception, & Psychophysics, 83(4), 1375–1390. https://doi.org/10.3758/s13414-021-02256-7.CrossRefGoogle ScholarPubMed
Kristjánsson, Á., & Driver, J. (2008). Priming in visual search: Separating the effects of target repetition, distractor repetition and role-reversal. Vision Research, 48(10), 1217–1232. https://doi.org/10.1016/j.visres.2008.02.007.CrossRefGoogle ScholarPubMed
Kristjánsson, Á., Vuilleumier, P., Malhotra, P., Husain, M., & Driver, J. (2005). Priming of color and position during visual search in unilateral spatial neglect. Journal of Cognitive Neuroscience, 17(6), 859–873. https://doi.org/10.1162/0898929054021148.CrossRefGoogle ScholarPubMed
Kristjánsson, Á., Wang, D., & Nakayama, K. (2002). The role of priming in conjunctive visual search. Cognition, 85(1), 37–52.CrossRefGoogle ScholarPubMed
Lamme, V. A. F. (2010). How neuroscience will change our view on consciousness. Cognitive Neuroscience, 1(3), 204–220. https://doi.org/10.1080/17588921003731586.CrossRefGoogle ScholarPubMed
Lamy, D. F., Antebi, C., Aviani, N., & Carmel, T. (2008). Priming of pop-out provides reliable measures of target activation and distractor inhibition in selective attention. Vision Research, 48(1), 30–41. https://doi.org/10.1016/j.visres.2007.10.009.CrossRefGoogle ScholarPubMed
Lange, R. D., Shivkumar, S., Chattoraj, A., & Haefner, R. M. (2023). Bayesian encoding and decoding as distinct perspectives on neural coding. Nature Neuroscience, 26(12), Article 12. https://doi.org/10.1038/s41593-023-01458-6.CrossRefGoogle ScholarPubMed
Lathrop, R. G. (1967). Perceived variability. Journal of Experimental Psychology, 73, 498–502. https://doi.org/10.1037/h0024344.CrossRefGoogle ScholarPubMed
Lau, H., & Rosenthal, D. (2011). Empirical support for higher-order theories of conscious awareness. Trends in Cognitive Sciences, 15(8), 365–373. https://doi.org/10.1016/j.tics.2011.05.009.CrossRefGoogle ScholarPubMed
Lau, J. S., & Brady, T. F. (2018). Ensemble statistics accessed through proxies: Range heuristic and dependence on low-level properties in variability discrimination. Journal of Vision, 18(9), 3. https://doi.org/10.1167/18.9.3.CrossRefGoogle ScholarPubMed
Lau, J. S.-H., Pashler, H., & Brady, T. F. (2021). Target templates in low target-distractor discriminability visual search have higher resolution, but the advantage they provide is short-lived. Attention, Perception, & Psychophysics, 83(4), 1435–1454. https://doi.org/10.3758/s13414-020-02213-w.CrossRefGoogle ScholarPubMed
Li, A. Y., Liang, J. C., Lee, A. C. H., & Barense, M. D. (2020). The validated circular shape space: Quantifying the visual similarity of shape. Journal of Experimental Psychology: General, 149(5), 949. https://doi.org/10.1037/xge0000693.CrossRefGoogle ScholarPubMed
Li, V., Herce Castañón, S., Solomon, J. A., Vandormael, H., & Summerfield, C. (2017). Robust averaging protects decisions from noise in neural computations. PLoS Computational Biology, 13(8), e1005723. https://doi.org/10.1371/journal.pcbi.1005723.CrossRefGoogle ScholarPubMed
Li, V., Michael, E., Balaguer, J., Herce Castañón, S., & Summerfield, C. (2018). Gain control explains the effect of distraction in human perceptual, cognitive, and economic decision making. Proceedings of the National Academy of Sciences, 115(38), E8825–E8834. https://doi.org/10.1073/pnas.1805224115.Google ScholarPubMed
Ma, W. J. (2012). Organizing probabilistic models of perception. Trends in Cognitive Sciences, 16(10), 511–518. https://doi.org/10.1016/j.tics.2012.08.010.CrossRefGoogle ScholarPubMed
Ma, W. J. (2019). Bayesian decision models: A primer. Neuron, 104(1), 164–175. https://doi.org/10.1016/j.neuron.2019.09.037.CrossRefGoogle ScholarPubMed
Ma, W. J., Kording, K. P., & Goldreich, D. (2023). Bayesian Models of Perception and Action: An Introduction. The MIT Press.Google Scholar
Mack, A., & Rock, I. (1998). Inattentional Blindness. Cambridge, MA: The MIT Press.CrossRefGoogle Scholar
Mack, A., Tang, B., Tuma, R., Kahn, S., & Rock, I. (1992). Perceptual organization and attention. Cognitive Psychology, 24, 475–501. https://doi.org/10.1016/0010-0285(92)90016-U.CrossRefGoogle ScholarPubMed
Maljkovic, V., & Nakayama, K. (1994). Priming of pop-out: I. Role of features. Memory & Cognition, 22(6), 657–672.CrossRefGoogle ScholarPubMed
Manassi, M., Liberman, A., Chaney, W., & Whitney, D. (2017). The perceived stability of scenes: Serial dependence in ensemble representations. Scientific Reports, 7(1), Article 1. https://doi.org/10.1038/s41598-017-02201-5.CrossRefGoogle ScholarPubMed
Manassi, M., Lonchampt, S., Clarke, A., & Herzog, M. H. (2016). What crowding can tell us about object representations. Journal of Vision, 16(3), 35. https://doi.org/10.1167/16.3.35.CrossRefGoogle ScholarPubMed
Manenti, G. L., Dizaji, A. S., & Schwiedrzik, C. M. (2023). Variability in training unlocks generalization in visual perceptual learning through invariant representations. Current Biology, 33(5), 817–826.e3. https://doi.org/10.1016/j.cub.2023.01.011.CrossRefGoogle ScholarPubMed
Marr, D., Nishihara, H. K., & Brenner, S. (1978). Representation and recognition of the spatial organization of three-dimensional shapes. Proceedings of the Royal Society of London. Series B. Biological Sciences, 200(1140), 269–294. https://doi.org/10.1098/rspb.1978.0020.Google ScholarPubMed
Maule, J., & Franklin, A. (2015). Effects of ensemble complexity and perceptual similarity on rapid averaging of hue. Journal of Vision, 15(4), 6. https://doi.org/10.1167/15.4.6.CrossRefGoogle ScholarPubMed
Maule, J., & Franklin, A. (2020). Adaptation to variance generalizes across visual domains. Journal of Experimental Psychology. General, 149(4), 662–675. https://doi.org/10.1037/xge0000678.CrossRefGoogle ScholarPubMed
Mazyar, H., Berg, R. V. D., & Seilheimer, R. L. (2013). Independence is elusive: Set size effects on encoding precision in visual search. Journal of Vision, 13(2013), 1–14. https://doi.org/10.1167/13.5.8.CrossRefGoogle ScholarPubMed
McKeefry, D. J., Watson, J. D. G., Frackowiak, R. S. J., Fong, K., & Zeki, S. (1997). The activity in human areas V1/V2, V3, and V5 during the perception of coherent and incoherent motion. NeuroImage, 5(1), 1–12. https://doi.org/10.1006/nimg.1996.0246.CrossRefGoogle ScholarPubMed
Michael, E., de Gardelle, V., & Summerfield, C. (2014). Priming by the variability of visual information. Proceedings of the National Academy of Sciences, 111(21), 7873–7878. https://doi.org/10.1073/pnas.1308674111.CrossRefGoogle ScholarPubMed
Mihali, A., & Ma, W. J. (2020). The psychophysics of visual search with heterogeneous distractors (p. 2020.08.10.244707). bioRxiv. https://doi.org/10.1101/2020.08.10.244707.CrossRefGoogle Scholar
Mijalli, Y., Price, P. C., & Navarro, S. P. (2023). Spillover bias in social and nonsocial judgments of diversity and variability. Psychonomic Bulletin & Review, 30, 1829–1839. https://doi.org/10.3758/s13423-023-02259-5.CrossRefGoogle ScholarPubMed
Mishkin, M., & Ungerleider, L. G. (1982). Contribution of striate inputs to the visuospatial functions of parieto-preoccipital cortex in monkeys. Behavioural Brain Research, 6(1), 57–77. https://doi.org/10.1016/0166-4328(82)90081-X.CrossRefGoogle Scholar
Mitroff, S. R., & Simons, D. J. (2002). Changes are not localized before they are explicitly detected. Visual Cognition, 9(8), 937–968. https://doi.org/10.1080/13506280143000476.CrossRefGoogle Scholar
Moore, C. M., & Egeth, H. (1997). Perception without attention: Evidence of grouping under conditions of inattention. Journal of Experimental Psychology: Human Perception and Performance, 23, 339–352. https://doi.org/10.1037/0096-1523.23.2.339.Google ScholarPubMed
Morgan, M. J., Chubb, C., & Solomon, J. A. (2008). A ‘dipper’ function for texture discrimination based on orientation variance. Journal of Vision, 8(11), 9–9. https://doi.org/10.1167/8.11.9.CrossRefGoogle ScholarPubMed
Most, S. B., Simons, D. J., Scholl, B. J. et al. (2001). How not to be seen: The contribution of similarity and selective ignoring to sustained inattentional blindness. Psychological Science, 12(1), 9–17. https://doi.org/10.1111/1467-9280.00303.CrossRefGoogle ScholarPubMed
Nagy, A. L., Neriani, K. E., & Young, T. L. (2005). Effects of target and distractor heterogeneity on search for a color target. Vision Research, 45, 1885–1899. https://doi.org/10.1016/j.visres.2005.01.007.CrossRefGoogle ScholarPubMed
Nakayama, K., Maljkovic, V., & Kristjánsson, Á. (2004). Short-term memory for the rapid deployment of visual attention. In Gazzaniga, Michael S. (Ed.), The Cognitive Neurosciences (pp. 397–408). Cambridge, MA: MIT Press.Google Scholar
Navalpakkam, V., & Itti, L. (2007). Search goal tunes visual features optimally. Neuron, 53(4), 605–617. https://doi.org/10.1016/j.neuron.2007.01.018.CrossRefGoogle ScholarPubMed
Noe, A. (2002). Is the visual world a grand illusion? Journal of Consciousness Studies, 9(5–6), 1–12.Google Scholar
Noe, A., Pessoa, L., & Thompson, E. (2000). Beyond the grand illusion: What change blindness really teaches us about vision. Visual Cognition, 7(1–3), 93–106. https://doi.org/10.1080/135062800394702.CrossRefGoogle Scholar
Norman, L. J., Heywood, C. A., & Kentridge, R. W. (2015). Direct encoding of orientation variance in the visual system. Journal of Vision, 15, 1–14. https://doi.org/10.1167/15.4.3.CrossRefGoogle ScholarPubMed
O’Regan, J. K. (1992). Solving the ‘real’ mysteries of visual perception: The world as an outside memory. Canadian Journal of Psychology, 46(3), 461–488.CrossRefGoogle ScholarPubMed
O’Regan, J. K., & Noë, A. (2001). A sensorimotor account of vision and visual consciousness. Behavioral and Brain Sciences, 24(5), 939–973. https://doi.org/10.1017/S0140525X01000115.CrossRefGoogle ScholarPubMed
O’Regan, J. K., Rensink, R. A., & Clark, J. J. (1999). Change-blindness as a result of ‘mudsplashes’. Nature, 398(6722), Article 6722. https://doi.org/10.1038/17953.Google ScholarPubMed
Oriet, C., & Brand, J. (2013). Size averaging of irrelevant stimuli cannot be prevented. Vision Research, 79, 8–16. https://doi.org/10.1016/j.visres.2012.12.004.CrossRefGoogle ScholarPubMed
Oriet, C., & Hozempa, K. (2016). Incidental statistical summary representation over time. Journal of Vision, 16(3), 1–14. https://doi.org/10.1167/16.3.3.CrossRefGoogle ScholarPubMed
Pascucci, D., Ceylan, G., & Kristjánsson, Á. (2022). Feature distribution learning by passive exposure. Cognition, 227, 105211. https://doi.org/10.1016/j.cognition.2022.105211.CrossRefGoogle ScholarPubMed
Pascucci, D., Tanrikulu, Ö. D., Ozkirli, A. et al. (2023). Serial dependence in visual perception: A review. Journal of Vision, 23(1), 9. https://doi.org/10.1167/jov.23.1.9.CrossRefGoogle ScholarPubMed
Payzan-LeNestour, E., Balleine, B. W., Berrada, T., & Pearson, J. (2016). Variance after-effects distort risk perception in humans. Current Biology, 26(11), 1500–1504. https://doi.org/10.1016/j.cub.2016.04.023.CrossRefGoogle ScholarPubMed
Pleskac, T. J., & Busemeyer, J. R. (2010). Two-stage dynamic signal detection: A theory of choice, decision time, and confidence. Psychological Review, 117(3), 864–901. https://doi.org/10.1037/a0019737.CrossRefGoogle ScholarPubMed
Põder, E. (2012). On the rules of integration of crowded orientation signals. I-Perception, 3, 440–454. https://doi.org/10.1068/i0412.CrossRefGoogle ScholarPubMed
Portilla, J., & Simoncelli, E. P. (2000). Parametric texture model based on joint statistics of complex wavelet coefficients. International Journal of Computer Vision, 40(1), 49–71. https://doi.org/10.1023/A:1026553619983.CrossRefGoogle Scholar
Pouget, A., Beck, J. M., Ma, W. J., & Latham, P. E. (2013). Probabilistic brains: Knowns and unknowns. Nature Neuroscience, 16(9), 1170–1178. https://doi.org/10.1038/nn.3495.CrossRefGoogle Scholar
Pouget, A., Dayan, P., & Zemel, R. S. (2000). Information processing with population codes. Nature Reviews Neuroscience, 1(2), 125–132. https://doi.org/10.1038/35039062.CrossRefGoogle ScholarPubMed
Rafiei, M., Chetverikov, A., Hansmann-Roth, S., & Kristjánsson, Á. (2021a). You see what you look for: Targets and distractors in visual search can cause opposing serial dependencies. Journal of Vision, 21(10), 3. https://doi.org/10.1167/jov.21.10.3.CrossRefGoogle ScholarPubMed
Rafiei, M., Hansmann-Roth, S., Whitney, D., Kristjánsson, Á., & Chetverikov, A. (2021b). Optimizing perception: Attended and ignored stimuli create opposing perceptual biases. Attention, Perception, & Psychophysics, 83(3), 1230–1239. https://doi.org/10.3758/s13414-020-02030-1.CrossRefGoogle ScholarPubMed
Rahnev, D. (2017). The case against full probability distributions in perceptual decision making. bioRxiv. https://doi.org/10.1101/108944.CrossRefGoogle Scholar
Rahnev, D., Block, N., Denison, R. N., & Jehee, J. (2021). Is perception probabilistic? Clarifying the definitions. PsyArXiv. https://doi.org/10.31234/osf.io/f8v5r.CrossRefGoogle Scholar
Rahnev, D., & Denison, R. N. (2018). Suboptimality in perceptual decision making. Behavioral and Brain Sciences, 41, e223. https://doi.org/10.1017/S0140525X18000936.CrossRefGoogle ScholarPubMed
Ramgir, A., & Lamy, D. (2022). Does feature intertrial priming guide attention? The jury is still out. Psychonomic Bulletin & Review, 29(2), 369–393. https://doi.org/10.3758/s13423-021-01997-8.CrossRefGoogle ScholarPubMed
Rao, R. P. N., & Ballard, D. H. (1999). Predictive coding in the visual cortex: A functional interpretation of some extra-classical receptive-field effects. Nature Neuroscience, 2(1), Article 1. https://doi.org/10.1038/4580.CrossRefGoogle ScholarPubMed
Rao, R. P., Olshausen, B. A., & Lewicki, M. S. (2002). Probabilistic Models of the Brain: Perception and Neural Function. Cambridge, MA: MIT Press.CrossRefGoogle Scholar
Raviv, L., Lupyan, G., & Green, S. C. (2022). How variability shapes learning and generalization. Trends in Cognitive Sciences, 26(6), 462–483. https://doi.org/10.1016/j.tics.2022.03.007.CrossRefGoogle ScholarPubMed
Raviv, O., Ahissar, M., & Loewenstein, Y. (2012). How recent history affects perception: The normative approach and its heuristic approximation. PLOS Computational Biology, 8(10), e1002731. https://doi.org/10.1371/journal.pcbi.1002731.CrossRefGoogle ScholarPubMed
Rees, G., Friston, K. J., & Koch, C. (2000). A direct quantitative relationship between the functional properties of human and macaque V5. Nature Neuroscience, 3(7), 716–723. https://doi.org/10.1038/76673.CrossRefGoogle ScholarPubMed
Rensink, R. A. (2000). Visual search for change: A probe into the nature of attentional processing. Visual Cognition, 7(1–3), 345–376. https://doi.org/10.1080/135062800394847.CrossRefGoogle Scholar
Rensink, R. A., O’Regan, J. K., & Clark, J. J. (1997). To see or not to see: The need for attention to perceive changes in scenes. Psychological Science, 8(5), 368–373. https://doi.org/10.1111/j.1467-9280.1997.tb00427.x.CrossRefGoogle Scholar
Rosenholtz, R. (2001). Visual search for orientation among heterogeneous distractors: Experimental results and implications for signal-detection theory models of search. Journal of Experimental Psychology: Human Perception and Performance, 27(4), 985–999. https://doi.org/10.1037//0096-1523.27.4.985.Google ScholarPubMed
Rosenholtz, R. (2016). Capabilities and limitations of peripheral vision. Annual Review of Vision Science, 2(1), 437–457. https://doi.org/10.1146/annurev-vision-082114-035733.CrossRefGoogle ScholarPubMed
Rosenholtz, R. (2020). Demystifying visual awareness: Peripheral encoding plus limited decision complexity resolve the paradox of rich visual experience and curious perceptual failures. Attention, Perception, & Psychophysics, 82(3), 901–925. https://doi.org/10.3758/s13414-019-01968-1.CrossRefGoogle ScholarPubMed
Rubin, N., Nakayama, K., & Shapley, R. (1997). Abrupt learning and retinal size specificity in illusory-contour perception. Current Biology, 7, 461–467. https://doi.org/10.1016/S0960-9822(06)00217-X.CrossRefGoogle ScholarPubMed
Saevarsson, S., Jóelsdóttir, S., Hjaltason, H., & Kristjánsson, Á. (2008). Repetition of distractor sets improves visual search performance in hemispatial neglect. Neuropsychologia, 46(4), 1161–1169. https://doi.org/10.1016/j.neuropsychologia.2007.10.020.CrossRefGoogle ScholarPubMed
Sama, M. A., Srikanthan, D., Nestor, A., & Cant, J. S. (2021). Global and local interference effects in ensemble encoding are best explained by interactions between summary representations of the mean and the range. Attention, Perception, & Psychophysics, 83(3), 1106–1128. https://doi.org/10.3758/s13414-020-02224-7.CrossRefGoogle ScholarPubMed
Schwartz, S., Maquet, P., & Frith, C. (2002). Neural correlates of perceptual learning: A functional MRI study of visual texture discrimination. Proceedings of the National Academy of Sciences, 99(26), 17137–17142. https://doi.org/10.1073/pnas.242414599.CrossRefGoogle ScholarPubMed
Scolari, M., & Serences, J. T. (2009). Adaptive allocation of attentional gain. The Journal of Neuroscience, 29(38), 11933–11942. https://doi.org/10.1523/JNEUROSCI.5642-08.2009.CrossRefGoogle ScholarPubMed
Seitz, A. R., & Watanabe, T. (2005). A unified model for perceptual learning. Trends in Cognitive Sciences, 9(7), 329–334. https://doi.org/10.1016/j.tics.2005.05.010.CrossRefGoogle ScholarPubMed
Semizer, Y., & Boduroglu, A. (2021). Variability leads to overestimation of mean summaries. Attention, Perception, & Psychophysics, 83(3), 1129–1140. https://doi.org/10.3758/s13414-021-02269-2.CrossRefGoogle ScholarPubMed
Seriès, P., & Seitz, A. R. (2013). Learning what to expect (in visual perception). Frontiers in Human Neuroscience, 7(October), 668. https://doi.org/10.3389/fnhum.2013.00668.CrossRefGoogle ScholarPubMed
Simons, D. J. (2000). Current approaches to change blindness. Visual Cognition, 7(1–3), 1–15. https://doi.org/10.1080/135062800394658.CrossRefGoogle Scholar
Simons, D. J., & Chabris, C. F. (1999). Gorillas in our midst: Sustained inattentional blindness for dynamic events. Perception, 28(9), 1059–1074.CrossRefGoogle ScholarPubMed
Solomon, J. A. (2010). Visual discrimination of orientation statistics in crowded and uncrowded arrays. Journal of Vision, 10(14), 19. https://doi.org/10.1167/10.14.19.CrossRefGoogle ScholarPubMed
Son, S., Lee, J., Kwon, O.-S., & Kim, Y.-J. (2021). Effect of spatiotemporally changing environment on serial dependence in ensemble representations (p. 2021.11.30.470662). bioRxiv. https://doi.org/10.1101/2021.11.30.470662.CrossRefGoogle Scholar
Spence, M. L., Dux, P. E., & Arnold, D. H. (2016). Computations underlying confidence in visual perception. Journal of Experimental Psychology: Human Perception and Performance, 42(5), 671–682. https://doi.org/10.1037/xhp0000179.Google ScholarPubMed
Spence, M. L., Mattingley, J. B., & Dux, P. E. (2018). Uncertainty information that is irrelevant for report impacts confidence judgments. Journal of Experimental Psychology: Human Perception and Performance, 44(12), 1981–1994. https://doi.org/10.1037/xhp0000584.Google ScholarPubMed
Sperling, G. (1960). The information available in brief visual presentations. Psychological Monographs: General and Applied, 74, 1–29. https://doi.org/10.1037/h0093759.CrossRefGoogle Scholar
Suárez-Pinilla, M., Seth, A. K., & Roseboom, W. (2018). Serial dependence in the perception of visual variance. Journal of Vision, 18(7), 1–24. https://doi.org/10.1167/18.7.4.CrossRefGoogle ScholarPubMed
Sun, P., Chubb, C., Wright, C. E., & Sperling, G. (2018). High-capacity preconscious processing in concurrent groupings of colored dots. Proceedings of the National Academy of Sciences, 115(52), E12153–E12162. https://doi.org/10.1073/pnas.1814657115.CrossRefGoogle ScholarPubMed
Tanrıkulu, Ö. D., Chetverikov, A., Hansmann-Roth, S., & Kristjánsson, Á. (2021a). What kind of empirical evidence is needed for probabilistic mental representations? An example from visual perception. Cognition, 217, 104903. https://doi.org/10.1016/j.cognition.2021.104903.CrossRefGoogle ScholarPubMed
Tanrıkulu, Ö. D., Chetverikov, A., & Kristjánsson, Á. (2020). Encoding perceptual ensembles during visual search in peripheral vision. Journal of Vision, 20(8), 20. https://doi.org/10.1167/jov.20.8.20.CrossRefGoogle ScholarPubMed
Tanrıkulu, Ö. D., Chetverikov, A., & Kristjánsson, Á. (2021b). Testing temporal integration of feature probability distributions using role-reversal effects in visual search. Vision Research, 188(July), 211–226. https://doi.org/10.1016/j.visres.2021.07.012.CrossRefGoogle ScholarPubMed
Thornton, I. M., & Fernandez-Duque, D. (2000). An implicit measure of undetected change. Spatial Vision, 14(1), 21–44.CrossRefGoogle ScholarPubMed
Tinsley, J. N., Molodtsov, M. I., Prevedel, R. et al. (2016). Direct detection of a single photon by humans. Nature Communications, 7(1), Article 1. https://doi.org/10.1038/ncomms12172.CrossRefGoogle ScholarPubMed
Tiurina, N. A., Markov, Y. A., Choung, O.-H., Herzog, M. H., & Pascucci, D. (2022). Unlocking crowding by ensemble statistics. Current Biology, 32(22), P4975-4981.e3. https://doi.org/10.1016/j.cub.2022.10.003.CrossRefGoogle ScholarPubMed
Tokita, M., Ueda, S., & Ishiguchi, A. (2016). Evidence for a global sampling process in extraction of summary statistics of item sizes in a set. Frontiers in Psychology, 7(MAY). https://doi.org/10.3389/fpsyg.2016.00711.CrossRefGoogle ScholarPubMed
Tokita, M., Yang, Y., & Ishiguchi, A. (2020). Can we match the variance across different visual features? CogSci2020. https://cognitivesciencesociety.org/cogsci20/papers/0835/0835.pdf.Google Scholar
Tong, K., Ji, L., Chen, W., & Fu, X. (2015). Unstable mean context causes sensitivity loss and biased estimation of variability. Journal of Vision, 15(4), 15. https://doi.org/10.1167/15.4.15.CrossRefGoogle Scholar
Tran, R., Vul, E., & Pashler, H. (2017). How effective is incidental learning of the shape of probability distributions? Royal Society Open Science, 4(8), 170270. https://doi.org/10.1098/rsos.170270.CrossRefGoogle ScholarPubMed
Tsuchiya, N., Wilke, M., Frässle, S., & Lamme, V. A. F. (2015). No-report paradigms: Extracting the true neural correlates of consciousness. Trends in Cognitive Sciences, 19(12), 757–770. https://doi.org/10.1016/j.tics.2015.10.002.CrossRefGoogle ScholarPubMed
Tyler, C. W. (2015). Peripheral color demo. I-Perception, 6(6), 2041669515613671. https://doi.org/10.1177/2041669515613671.CrossRefGoogle ScholarPubMed
Ueda, S., Yakushijin, R., & Ishiguchi, A. (2023). Variance aftereffect within and between sensory modalities for visual and auditory domains. Attention, Perception, & Psychophysics. https://doi.org/10.3758/s13414-023-02705-5.CrossRefGoogle Scholar
Usher, M., Bronfman, Z. Z., Talmor, S., Jacobson, H., & Eitam, B. (2018). Consciousness without report: Insights from summary statistics and inattention ‘blindness’. Philosophical Transactions of the Royal Society B: Biological Sciences, 373(1755), 20170354. https://doi.org/10.1098/rstb.2017.0354.CrossRefGoogle ScholarPubMed
Utochkin, I. S. (2013). Visual search with negative slopes: The statistical power of numerosity guides attention. Journal of Vision, 13(3), 1–14. https://doi.org/10.1167/13.3.18.CrossRefGoogle ScholarPubMed
Utochkin, I. S., & Brady, T. F. (2020). Individual representations in visual working memory inherit ensemble properties. Journal of Experimental Psychology: Human Perception and Performance, 46, 458–473. https://doi.org/10.1037/xhp0000727.Google ScholarPubMed
Utochkin, I. S., Choi, J., & Chong, S. C. (2023). A population response model of ensemble perception. Psychological Review, No Pagination Specified-No Pagination Specified. https://doi.org/10.1037/rev0000426.CrossRefGoogle Scholar
Utochkin, I. S., & Tiurina, N. A. (2014). Parallel averaging of size is possible but range-limited: A reply to Marchant, Simons, and De Fockert. Acta Psychologica, 146, 7–18. https://doi.org/10.1016/j.actpsy.2013.11.012.CrossRefGoogle ScholarPubMed
Utochkin, I. S., & Yurevich, M. A. (2016). Similarity and heterogeneity effects in visual search are mediated by ‘segmentability’. Journal of Experimental Psychology: Human Perception and Performance, 42(7), 995–1007. https://doi.org/10.1037/xhp0000203.Google ScholarPubMed
van Bergen, R. S., & Jehee, J. F. M. (2019). Probabilistic representation in human visual cortex reflects uncertainty in serial decisions. Journal of Neuroscience, 39(41), 8164–8176. https://doi.org/10.1523/JNEUROSCI.3212-18.2019.CrossRefGoogle ScholarPubMed
van Bergen, R. S., Ma, W. J., Pratte, M. S., & Jehee, J. F. M. (2015). Sensory uncertainty decoded from visual cortex predicts behavior. Nature Neuroscience, 18(12), 1728–1730. https://doi.org/10.1038/nn.4150.CrossRefGoogle ScholarPubMed
Van de Cruys, S., Lemmens, L., Sapey‐Triomphe, L. et al. (2021). Structural and contextual priors affect visual search in children with and without autism. Autism Research, 14(7), 1484–1495. https://doi.org/10.1002/aur.2511.CrossRefGoogle ScholarPubMed
van den Berg, R., & Ma, W. J. (2012). Robust averaging during perceptual judgment is not optimal. Proceedings of the National Academy of Sciences, 109(13), E736–E736. https://doi.org/10.1073/pnas.1119078109.CrossRefGoogle Scholar
Vilares, I., Howard, J. D., Fernandes, H. L., Gottfried, J. A., & Kording, K. P. (2012). Differential representations of prior and likelihood uncertainty in the human brain. Current Biology, 22(18), 1641–1648. https://doi.org/10.1016/j.cub.2012.07.010.CrossRefGoogle ScholarPubMed
Vincent, B. T., Baddeley, R. J., Troscianko, T., & Gilchrist, I. D. (2009). Optimal feature integration in visual search. Journal of Vision, 9(5), 1–11. https://doi.org/10.1167/9.5.15.CrossRefGoogle ScholarPubMed
Vul, E., Goodman, N., Griffiths, T. L., & Tenenbaum, J. B. (2014). One and done? Optimal decisions from very few samples. Cognitive Science, 38(4), 599–637. https://doi.org/10.1111/cogs.12101.CrossRefGoogle ScholarPubMed
Walker, E. Y., Cotton, R. J., Ma, W. J., & Tolias, A. S. (2020). A neural basis of probabilistic computation in visual cortex. Nature Neuroscience, 23(1), 122–129. https://doi.org/10.1038/s41593-019-0554-5.CrossRefGoogle ScholarPubMed
Walker, E. Y., Pohl, S., Denison, R. N., Barack, D. L., Lee, J., Block, N., Ma, W. J., & Meyniel, F. (2023). Studying the neural representations of uncertainty. Nature Neuroscience, 26(11), Article 11. https://doi.org/10.1038/s41593-023-01444-y.CrossRefGoogle ScholarPubMed
Wallis, T. S. A., Bethge, M., & Wichmann, F. A. (2016). Testing models of peripheral encoding using metamerism in an oddity paradigm. Journal of Vision, 16(2), 1–30. https://doi.org/10.1167/16.2.4.CrossRefGoogle Scholar
Wang, D., Kristjánsson, Á., & Nakayama, K. (2005). Efficient visual search without top-down or bottom-up guidance. Perception & Psychophysics, 67(2), 239–253. https://doi.org/10.3758/BF03206488.CrossRefGoogle ScholarPubMed
Webster, M. A., Halen, K., Meyers, A. J., Winkler, P., & Werner, J. S. (2010). Colour appearance and compensation in the near periphery. Proceedings of the Royal Society B: Biological Sciences, 277(1689), 1817–1825. https://doi.org/10.1098/rspb.2009.1832.CrossRefGoogle ScholarPubMed
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.CrossRefGoogle ScholarPubMed
Whitney, D., & Levi, D. M. (2011). Visual crowding: A fundamental limit on conscious perception and object recognition. Trends in Cognitive Sciences, 15(4), 160–168. https://doi.org/10.1016/j.tics.2011.02.005.CrossRefGoogle ScholarPubMed
Whitney, D., & Yamanashi Leib, A. (2018). Ensemble perception. Annual Review of Psychology, 69(1), 105–129. https://doi.org/10.1146/annurev-psych-010416-044232.CrossRefGoogle ScholarPubMed
Witkowski, P. P., & Geng, J. J. (2019). Learned feature variance is encoded in the target template and drives visual search. Visual Cognition, 27(5–8), 487–501. https://doi.org/10.1080/13506285.2019.1645779.CrossRefGoogle ScholarPubMed
Witkowski, P. P., & Geng, J. J. (2022). Attentional priority is determined by predicted feature distributions. Journal of Experimental Psychology: Human Perception and Performance, 48(11), 1201–1212. https://doi.org/10.1037/xhp0001041.Google ScholarPubMed
Witkowski, P. P., & Geng, J. J. (2023). Prefrontal cortex codes representations of target identity and feature uncertainty. Journal of Neuroscience, 43(50), 8769-8776. https://doi.org/10.1523/JNEUROSCI.1117-23.2023.CrossRefGoogle ScholarPubMed
Witzel, C., & Gegenfurtner, K. R. (2013). Categorical sensitivity to color differences. Journal of Vision, 13(2013), 1–33. https://doi.org/10.1167/13.7.1.CrossRefGoogle ScholarPubMed
Witzel, C., & Gegenfurtner, K. R. (2015). Categorical facilitation with equally discriminable colors. Journal of Vision, 15(8), 22. https://doi.org/10.1167/15.8.22.CrossRefGoogle ScholarPubMed
Won, B.-Y., & Geng, J. J. (2018). Learned suppression for multiple distractors in visual search. Journal of Experimental Psychology: Human Perception and Performance, 44(7), 1128–1141. https://doi.org/10.1037/xhp0000521.Google ScholarPubMed
Woodman, G. F., Carlisle, N. B., & Reinhart, R. M. G. (2013). Where do we store the memory representations that guide attention? Journal of Vision, 13(3), 1–1. https://doi.org/10.1167/13.3.1.CrossRefGoogle ScholarPubMed
Yoo, A. H., Acerbi, L., & Ma, W. J. (2021). Uncertainty is maintained and used in working memory. Journal of Vision, 21(8), 13. https://doi.org/10.1167/jov.21.8.13.CrossRefGoogle ScholarPubMed
Yu, X., & Geng, J. J. (2019). The attentional template is shifted and asymmetrically sharpened by distractor context. Journal of Experimental Psychology: Human Perception and Performance, 45(3), 336–353. https://doi.org/10.1037/xhp0000609.Google ScholarPubMed
Zemel, R. S., Dayan, P., & Pouget, A. (1998). Probabilistic interpretation of population codes. Neural Computation, 10(2), 403–430. https://doi.org/10.1162/089976698300017818.CrossRefGoogle ScholarPubMed
Zylberberg, A., Roelfsema, P. R., & Sigman, M. (2014). Variance misperception explains illusions of confidence in simple perceptual decisions. Consciousness and Cognition, 27(1), 246–253. https://doi.org/10.1016/j.concog.2014.05.012.CrossRefGoogle ScholarPubMed
Accessibility standard: Unknown
Why this information is here
This section outlines the accessibility features of this content - including support for screen readers, full keyboard navigation and high-contrast display options. This may not be relevant for you.Accessibility Information
Accessibility compliance for the HTML of this element is currently unknown
and may be updated in the future.
- 9
- Cited by