The role of attention in challenging the constraints of spatial statistical learning

Context and relevance. Statistical learning is ability to extract and learn regularities from the sensory environment. If these regularities concern with the spatial arrangement of objects in relation to each other we say about visual spatial statistical learning. When we have some hierarchical structure as a pattern, information about the whole set or about some of its subsets can be learned. For example, only pairwise connections between elements can be learned. In a row of experiments on statistical learning it has been demonstrated that when global information about a set of elements is learned, information about subsets is blocked, and vice versa - learning of embedded structure is accompanied by blocking of information about the whole set. Objective. We were interested in the situation where attention is directed to some elements of a complex stimuli more often than to others. The experiment examined whether, in this case, global information about the set of elements would be retained, or only about the subset to which attention was more frequently directed. Methods and materials. Subjects (N=104) performed a search task for the target element of complex stimuli. All the stimuli were composed by the same rule. The frequency of directing attention to one or another element of the stimuli was varied by special instructions. Just after the learning phase subjects completed a series of two-alternative forced choice tests with new correct and incorrect complete and incomplete stimuli. Results. We found that correct complete stimuli and correct subsets of more attended elements were appeared more familiar than stimuli with disrupted patterns. Conclusions. We demonstrate in our experiment the possibility of learning spatial information about both the global pattern and its substructure. The results are discussed in terms of two types of statistical learning, attention-dependent (explicit) and attention-independent (implicit).

1. Деева, Т.М., Козлов, Д.Д. (2022). Знак или форма? Имплицитное усвоение пространственной закономерности при сравнении величин чисел и фигур [Электронный ресурс]. Психологические исследования, 15(82), 4. https://doi.org/10.54359/ps.v15i82.1089
   Deeva, T.M., Kozlov, D.D. (2022). A sign or a shape? Implicit learning of spatial regularity in size comparison tasks using numbers or geometrical figures. Psychological Studies, 15(82), 4. (In Russ.). https://doi.org/10.54359/ps.v15i82.1089
2. Batterink, L.J., Reber, P.J., Neville, H.J., Paller, K.A. (2015). Implicit and explicit contributions to statistical learning. Journal of memory and language, 83, 62—78. https://doi.org/10.1016/j.jml.2015.04.004
3. Bays, B.C., Turk-Browne, N.B., Seitz, A.R. (2015). Dissociable behavioral outcomes of visual statistical learning. Visual Cognition, 23(9—10), 1072—1097. https://doi.org/10.1080/13506285.2016.1139647
4. Brady, T.F., Chun, M.M. (2007). Spatial constraints on learning in visual search: modeling contextual cuing. Journal of experimental psychology. Human perception and performance, 33(4), 798—815. https://doi.org/10.1037/0096-1523.33.4.798
5. Chun, M.M., Jiang, Y. (1998). Contextual cueing: implicit learning and memory of visual context guides spatial attention. Cognitive psychology, 36(1), 28—71. https://doi.org/10.1006/cogp.1998.0681
6. Conway, C.M. (2020). How does the brain learn environmental structure? Ten core principles for under-standing the neurocognitive mechanisms of statistical learning. Neuroscience and Biobehavioral Reviews, 112, 279—299. https://doi.org/10.1016/j.neubiorev.2020.01.032
7. Conway, C.M., Christiansen, M.H. (2005). Modality-constrained statistical learning of tactile, visual, and auditory sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 31, 24—39. https://doi.org/10.1037/0278-7393.31.1.24
8. Cox, J.A., Aimola Davies, A.M. (2022). Age differences in visual statistical learning: Investigating the effects of selective attention and stimulus category. Psychology and Aging, 37(6), 698—714. https://doi.org/pag0000697
9. Dale, R., Duran, N., Morehead R. (2012). Prediction during statistical learning, and implications for the implicit/explicit divide. Advances in cognitive psychology, 8(2), 196—209. https://doi.org/10.5709/acp-0115-z
10. de Diego-Balaguer, R., Martinez-Alvarez, A., Pons, F. (2016). Temporal attention as a scaffold for language development. Frontiers in psychology, 7, 44. https://doi.org/10.3389/fpsyg.2016.00044
11. Deroost, N., Soetens, E. (2006). Spatial processing and perceptual sequence learning in SRT tasks. Experimental Psychology, 53(1), 16—30. https://doi.org/10.1027/1618-3169.53.1.16
12. Endress, A.D., Mehler, J. (2009). The surprising power of statistical learning: when fragment knowledge leads to false memories of unheard words. Journal of Memory and Language, 60(3), 351—367. https://doi.org/10.1016/j.jml.2008.10.003
13. Fiser, J., Aslin, R.N. (2005). Encoding multielement scenes: statistical learning of visual feature hierarchies. Journal of Experimental Psychology: General, 134(4), 521. https://doi.org/10.1037/0096-3445.134.4.521
14. Fiser, J., Aslin, R.N. (2002). Statistical learning of higher-order temporal structure from visual shape sequences. Journal of Experimental Psychology: Learning, Memory, and Cognition, 28(3), 458—467. https://doi.org/10.1037/0278-7393.28.3.458
15. Fiser J., Aslin R.N. (2001). Unsupervised statistical learning of higher-order spatial structures from visual scenes. Psychological Science, 12, 499—504. https://doi.org/10.1111/1467-9280.00392
16. Franco, A., Destrebecqz, A. (2012). Chunking or not chunking? How do we find words in artificial language learning? Advances in Cognitive Psychology, 8(2), 144—154. https://doi.org/10.5709/acp-0111-3
17. Gao, Y., Theeuwes, J. (2020). Independent effects of statistical learning and top-down attention. Attention, perception & psychophysics, 82(8), 3895—3906. https://doi.org/10.3758/s13414-020-02115-x
18. Geyer, T., Zehetleitner, M., Müller, H.J. (2010). Contextual cueing of pop-out visual search: When context guides the deployment of attention. Journal of Vision, 10(5), 20. https://doi.org/10.1167/10.5.20
19. Goujon, A., Didierjean, A., Thorpe, S. (2015). Investigating implicit statistical learning mechanisms through contextual cueing. Trends in Cognitive Sciences, 19(9), 524—533. https://doi.org/10.1016/j.tics.2015.07.009
20. Hendricks, M.A., Conway, C.M., Kellogg, R.T. (2013). Using dual-task methodology to dissociate automatic from nonautomatic processes involved in artificial grammar learning. Journal of Experimental Psychology. Learning, Memory, Cognition, 39(5), 1491—1500. https://doi.org/10.1037/a0032974
21. Herff, S.A., Zhen, S., Yu, R., Agres, K.R. (2020). Age-dependent statistical learning trajectories reveal differences in information weighting. Psychology and Aging, 35(8), 1090. https://doi.org/10.1037/pag0000567
22. Himberger, K.D., Finn, A.S., Honey, C.J. (2022). On the automaticity of visual statistical learning. bioRxiv, 07. https://doi.org/10.1101/2022.07.04.498716
23. Jiang, Y., Leung, A.W. (2005). Implicit learning of ignored visual context. Psychonomic Bulletin and Review, 12, 100—106. https://doi.org/10.3758/BF03196353
24. Keele, S.W., Ivry, R., Mayr, U., Hazeltine, E., Heuer, H. (2003). The cognitive and neural architecture of sequence representation. Psychological review, 110(2), 316—339. https://doi.org/10.1037/0033-295X.110.2.316
25. Kidd, E., Arciuli, J., Christiansen, M.H., Smithson, M. (2023). The sources and consequences of individual differences in statistical learning for language development. Cognitive Development, 66, 101335. https://doi.org/10.1016/j.cogdev.2023.101335
26. Lengyel, G., Nagy, M., Fiser, J. (2021). Statistically defined visual chunks engage object-based attention. Nature communications, 12(1), 1—12. https://doi.org/10.1038/s41467-020-20589-z
27. Lleras, A., Von Mühlenen, A. (2004). Spatial context and top-down strategies in visual search. Spatial vision, 17(4—5), 465—482. https://doi.org/10.1163/1568568041920113
28. Orbán, G., Fiser, J., Aslin, R.N., Lengyel, M. (2008). Bayesian learning of visual chunks by human observers. Proceedings of the National Academy of Sciences, 105(7), 2745—2750. https://doi.org/10.1073/pnas.0708424105
29. Perruchet, P., Pacton, S. (2006). Implicit learning and statistical learning: One phenomenon, two approaches. Trends in Cognitive Sciences, 10, 233—238. https://doi.org/10.1016/j.tics.2006.03.006
30. Remillard, G. (2008). Implicit learning of second-, third-, and fourth-order adjacent and nonadjacent sequential dependencies. Quarterly journal of experimental psychology, 61(3), 400—424. https://doi.org/10.1080/17470210701210999
31. Rutar, D., de Wolff, E., Kwisthout, J., Hunnius, S. (2022). Statistical learning mechanisms are flexible and can adapt to structural input properties. Available at SSRN 4027230. https://doi.org/10.21203/rs.3.rs-2402303/v1
32. Saffran, J.R., Aslin, R.N., Newport, E.L. (1996). Statistical learning by 8-month-old infants. Science, 274, 1926—1928. https://doi.org/10.1126/science.274.5294.1926
33. Saffran, J.R., Johnson, E.K., Aslin, R.N., Newport, E.L. (1999). Statistical learning of tone sequences by human infants and adults. Cognition, 70(1), 27—52. https://doi.org/10.1016/S0010-0277(98)00075-4
34. Servan-Schreiber, E., Anderson, J.R. (1990). Learning artificial grammars with competitive chunking. Journal of Experimental Psychology: Learning, Memory, and Cognition, 16(4), 592. https://doi.org/10.1037/0278-7393.16.4.592
35. Siegelman, N., Bogaerts, L., Frost, R. (2017). Measuring individual differences in statistical learning: Current pitfalls and possible solutions. Behavior research methods, 49, 418—432. https://doi.org/10.3758/s13428-016-0719-z
36. Siegelman, N., Bogaerts, L., Kronenfeld, O., Frost, R. (2018). Redefining “Learning” in Statistical Learning: What Does an Online Measure Reveal About the Assimilation of Visual Regularities? Cognitive Science, 42, 692—727. https://doi.org/10.1111/cogs.12556
37. Stoet, G. (2010). PsyToolkit - A software package for programming psychological experiments using Linux. Behavior Research Methods, 42(4), 1096—1104.
38. Stoet, G. (2017). PsyToolkit: A novel web-based method for running online questionnaires and reaction-time experiments. Teaching of Psychology, 44(1), 24—31.
39. Theeuwes, J. (2019). Goal-driven, stimulus-driven, and history-driven selection. Current opinion in psychology, 29, 97—101. https://doi.org/10.1016/j.copsyc.2018.12.024
40. Theeuwes, J., Bogaerts, L., van Moorselaar, D. (2022). What to expect where and when: how statistical learn-ing drives visual selection. Trends in Cognitive Sciences, 26(10), 860—872. https://doi.org/10.1016/j.tics.2022.06.001
41. Turk-Browne, N.B. (2012). Statistical learning and its consequences. The influence of attention, learning, and motivation on visual search. Springer, New York, NY, 117—146. https://doi.org/10.1007/978-1-4614-4794-8_6
42. Turk-Browne, N.B., Jungé, J.A., Scholl, B.J. (2005). The automaticity of visual statistical learning. Journal of experimental psychology. General, 134(4), 552—564. https://doi.org/10.1037/0096-3445.134.4.552
43. Turk-Browne, N.B., Scholl, B.J., Chun, M.M., Johnson, M.K. (2009). Neural evidence of statistical learning: Efficient detection of visual regularities without awareness. Journal of cognitive neuroscience, 21(10), 1934—1945. https://doi.org/10.1162/jocn.2009.21131
44. Turk-Browne, N.B., Scholl, B.J., Johnson, M.K., Chun, M.M. (2010). Implicit perceptual anticipation triggered by statistical learning. Journal of neuroscience, 30(33), 11177—11187. https://doi.org/10.1523/JNEUROSCI.0858-10.2010
45. Vickery, T.J., Park, S.H., Gupta, J., Berryhill, M.E. (2018). Tasks determine what is learned in visual statistical learning. Psychonomic Bulletin and Review, 25(5), 1847—1854. https://doi.org/10.3758/s13423-017-1405-6
46. Zang, X., Assumpção, L., Wu, J., Xie, X., Zinchenko, A. (2021). Task-Irrelevant Context Learned Under Rapid Display Presentation: Selective Attention in Associative Blocking. Frontiers in Psychology, 12, 675848. https://doi.org/10.3389/fpsyg.2021.675848
47. Zhao, J., Ngo, N., McKendrick, R., Turk-Browne, N.B. (2011). Mutual interference between statistical summary perception and statistical learning. Psychological Science, 22(9), 1212—1219. https://doi.org/10.1177/0956797611419304