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Spatial Constraints on Visual Statistical Learning of Multi-Element Scenes



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Spatial Constraints on Visual Statistical Learning of Multi Element Scenes Christopher M Conway cmconway indiana edu Department of Psychological Brain Sciences Indiana University Bloomington IN 47405 USA Robert L Goldstone rgoldsto indiana edu Department of Psychological Brain Sciences Indiana University Bloomington IN 47405 USA Morten H Christiansen mhc27 cornell edu Department of Psychology Cornell University Ithaca NY 14853 USA Abstract Visual statistical learning allows observers to extract high level structure from visual scenes Fiser Aslin 2001 Previous work has explored the types of statistical computations afforded but has not addressed to what extent learning results in unbound versus spatially bound representations of element cooccurrences We explored these two possibilities using an unsupervised learning task with adult participants who observed complex multi element scenes embedded with consistently paired elements If learning is mediated by unconstrained associative learning mechanisms then learning the element pairings may depend only on the co occurrence of the elements in the scenes without regard to their specific spatial arrangements If learning is perceptually constrained cooccurring elements ought to form perceptual units specific to their observed spatial arrangements Results showed that participants learned the statistical structure of element cooccurrences in a spatial specific manner showing that visual statistical learning is perceptually constrained by spatial grouping principles Keywords Visual Statistical Learning Associative Learning Perceptual Learning Spatial Constraints Introduction Structure abounds in the environment The sounds objects and events that we perceive are not random in nature but rather are coherent and regular Consider spoken language phonemes syllables and words adhere to a semi regular structure that can be defined in terms of statistical or probabilistic relationships The same holds true for almost all aspects of our interaction with the world whether it be speaking listening to music learning a tennis swing or perceiving complex scenes How the mind brain and body encode and use structure that exists in time and space remains one of the deep mysteries of cognitive science This issue has begun to be elucidated through the study of implicit or statistical learning1 Cleeremans Destrebecqz Boyer 1998 Conway Christiansen 2006 Reber 1993 Perruchet Pacton 2006 Saffran Aslin Newport 1996 Statistical learning SL involves relatively automatic learning mechanisms that are used to extract regularities and patterns 1 We consider implicit and statistical learning to refer to the same learning ability which we hereafter refer to simply as statistical learning distributed across a set of exemplars in time and or space typically without conscious awareness of what regularities are being learned SL has been demonstrated across a number of sense modalities and input domains including speech like stimuli Saffran et al 1996 visual scenes Fiser Aslin 2001 and tactile patterns Conway Christiansen 2005 Because SL appears to make contact with many aspects of perceptual and cognitive processing understanding the underlying cognitive mechanisms limitations and constraints affecting SL is an important research goal Initial work in SL emphasized its unconstrained associative nature e g see Frensch 1998 Olson Chun 2002 for discussion That is a common assumption has been that statistical relations can be learned between any two or more stimuli regardless of their perceptual characteristics or identity under this view there is no reason to believe that learning a pattern involving items A B and C should be any easier or harder than learning the relations among A D and E However recent research has shown that this kind of unconstrained unselective associative learning process may not be the best characterization of SL Bonatti Pe a Nespor Mehler 2005 Conway Christiansen 2005 Saffran 2002 TurkBrowne Junge Scholl 2005 Instead factors related to how the sensory and perceptual systems engage SL processes appear to provide important constraints on the learning of environmental structure In this paper we examine a largely unexplored constraint on visual statistical learning VSL the relative spatial arrangement of objects If VSL operates via unconstrained associative learning mechanisms we ought to expect that it is the co occurrence of two objects that is important not the relative spatial arrangement of those objects However another possibility is that VSL is akin to perceptual learning in which two frequently co occurring objects can form a new perceptual unit Goldstone 1998 Such unitization would be highly specific to not only the individual items but to their relative spatial arrangement as well Before describing the empirical study in full we first briefly review other work that points toward spatial constraints affecting visual processing The Role of Space in Visual Processing Intuitively each sensory modality seems biased to handle particular aspects of environmental input For instance vision and audition appear to be most adept at processing spatial and temporal input respectively Kubovy 1988 For instance whereas the auditory system must compute the location of sounds through differences in intensity and time of arrival at each ear the location of visual stimuli is directly mapped onto the retina and then projected topographically into cortical areas Bushara et al 1999 In general empirical work in perception and memory suggests that in visual cognition the dimensions of space weigh most heavily whereas for audition the temporal dimension is most prominent Friedes 1974 Kubovy 1988 Penney 1989 In the area of VSL the ways in which time and space constrain learning have only recently begun to be explored Although VSL can occur both with items displayed in a spatial layout Fiser Aslin 2001 2005 as well as with objects appearing in a temporal sequence Conway Christiansen 2006 Fiser Aslin 2002 Turke Brown et al 2005 some evidence suggests that it is the former that occurs most naturally and efficiently For instance Gomez 1997 suggested that visual learning of artificial grammars proceeds better when the stimulus elements are presented simultaneously that is spatially arrayed rather than sequentially presumably because a simultaneous format permits better chunking of the stimulus elements Likewise Saffran 2002 found that participants learned predictive relationships


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