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Sáringer S, Fehér Á, Sáry G, Kaposvári P. Gamma oscillations in visual statistical learning correlate with individual behavioral differences. Front Behav Neurosci 2023; 17:1285773. [PMID: 38025386 PMCID: PMC10663268 DOI: 10.3389/fnbeh.2023.1285773] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2023] [Accepted: 10/23/2023] [Indexed: 12/01/2023] Open
Abstract
Statistical learning is assumed to be a fundamentally general sensory process across modalities, age, other cognitive functions, and even species. Despite this general role, behavioral testing on regularity acquisition shows great variance among individuals. The current study aimed to find neural correlates of visual statistical learning showing a correlation with behavioral results. Based on a pilot study, we conducted an EEG study where participants were exposed to associated stimulus pairs; the acquisition was tested through a familiarity test. We identified an oscillation in the gamma range (40-70 Hz, 0.5-0.75 s post-stimulus), which showed a positive correlation with the behavioral results. This change in activity was located in a left frontoparietal cluster. Based on its latency and location, this difference was identified as a late gamma activity, a correlate of model-based learning. Such learning is a summary of several top-down mechanisms that modulate the recollection of statistical relationships such as the capacity of working memory or attention. These results suggest that, during acquisition, individual behavioral variance is influenced by dominant learning processes which affect the recall of previously gained information.
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Affiliation(s)
| | | | | | - Péter Kaposvári
- Department of Physiology, Albert Szent-Gyögyi Medical School, University of Szeged, Szeged, Hungary
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2
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Yan C, Ehinger BV, Pérez-Bellido A, Peelen MV, de Lange FP. Humans predict the forest, not the trees: statistical learning of spatiotemporal structure in visual scenes. Cereb Cortex 2023; 33:8300-8311. [PMID: 37005064 PMCID: PMC7614728 DOI: 10.1093/cercor/bhad115] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/16/2022] [Revised: 03/11/2023] [Accepted: 03/13/2023] [Indexed: 04/04/2023] Open
Abstract
The human brain is capable of using statistical regularities to predict future inputs. In the real world, such inputs typically comprise a collection of objects (e.g. a forest constitutes numerous trees). The present study aimed to investigate whether perceptual anticipation relies on lower-level or higher-level information. Specifically, we examined whether the human brain anticipates each object in a scene individually or anticipates the scene as a whole. To explore this issue, we first trained participants to associate co-occurring objects within fixed spatial arrangements. Meanwhile, participants implicitly learned temporal regularities between these displays. We then tested how spatial and temporal violations of the structure modulated behavior and neural activity in the visual system using fMRI. We found that participants only showed a behavioral advantage of temporal regularities when the displays conformed to their previously learned spatial structure, demonstrating that humans form configuration-specific temporal expectations instead of predicting individual objects. Similarly, we found suppression of neural responses for temporally expected compared with temporally unexpected objects in lateral occipital cortex only when the objects were embedded within expected configurations. Overall, our findings indicate that humans form expectations about object configurations, demonstrating the prioritization of higher-level over lower-level information in temporal expectation.
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Affiliation(s)
- Chuyao Yan
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, Nijmegen 6525 EN, The Netherlands
- School of Psychology, Nanjing Normal University, Nanjing 210098, China
| | - Benedikt V Ehinger
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, Nijmegen 6525 EN, The Netherlands
- Stuttgart Center for Simulation Science, University of Stuttgart, Stuttgart 70049, Germany
| | - Alexis Pérez-Bellido
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, Nijmegen 6525 EN, The Netherlands
- Department of Cognition, Development and Educational Psychology, University of Barcelona, Barcelona 17108035, Spain
- Institute of Neurosciences, University of Barcelona, Barcelona 17108035, Spain
| | - Marius V Peelen
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, Nijmegen 6525 EN, The Netherlands
| | - Floris P de Lange
- Donders Institute for Brain, Cognition and Behaviour, Radboud University, Kapittelweg 29, Nijmegen 6525 EN, The Netherlands
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Graves KN, Sherman BE, Huberdeau D, Damisah E, Quraishi IH, Turk-Browne NB. Remembering the pattern: A longitudinal case study on statistical learning in spatial navigation and memory consolidation. Neuropsychologia 2022; 174:108341. [PMID: 35961387 PMCID: PMC9578695 DOI: 10.1016/j.neuropsychologia.2022.108341] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2021] [Revised: 07/10/2022] [Accepted: 07/24/2022] [Indexed: 10/15/2022]
Abstract
Distinct brain systems are thought to support statistical learning over different timescales. Regularities encountered during online perceptual experience can be acquired rapidly by the hippocampus. Further processing during offline consolidation can establish these regularities gradually in cortical regions, including the medial prefrontal cortex (mPFC). These mechanisms of statistical learning may be critical during spatial navigation, for which knowledge of the structure of an environment can facilitate future behavior. Rapid acquisition and prolonged retention of regularities have been investigated in isolation, but how they interact in the context of spatial navigation is unknown. We had the rare opportunity to study the brain systems underlying both rapid and gradual timescales of statistical learning using intracranial electroencephalography (iEEG) longitudinally in the same patient over a period of three weeks. As hypothesized, spatial patterns were represented in the hippocampus but not mPFC for up to one week after statistical learning and then represented in the mPFC but not hippocampus two and three weeks after statistical learning. Taken together, these findings suggest that the hippocampus may contribute to the initial extraction of regularities prior to cortical consolidation.
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Affiliation(s)
- Kathryn N Graves
- Department of Psychology, Yale University, 2 Hillhouse Ave., New Haven, CT, 06520, USA.
| | - Brynn E Sherman
- Department of Psychology, Yale University, 2 Hillhouse Ave., New Haven, CT, 06520, USA
| | - David Huberdeau
- Department of Psychology, Yale University, 2 Hillhouse Ave., New Haven, CT, 06520, USA
| | - Eyiyemisi Damisah
- Department of Neurosurgery, Yale University, 333 Cedar St., New Haven, CT, 06510, USA
| | - Imran H Quraishi
- Department of Neurology, Yale University, 800 Howard Ave., New Haven, CT, 06519, USA
| | - Nicholas B Turk-Browne
- Department of Psychology, Yale University, 2 Hillhouse Ave., New Haven, CT, 06520, USA; Wu Tsai Institute, Yale University, 100 College St, New Haven, CT, 06510, USA
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Abstract
Vision and learning have long been considered to be two areas of research linked only distantly. However, recent developments in vision research have changed the conceptual definition of vision from a signal-evaluating process to a goal-oriented interpreting process, and this shift binds learning, together with the resulting internal representations, intimately to vision. In this review, we consider various types of learning (perceptual, statistical, and rule/abstract) associated with vision in the past decades and argue that they represent differently specialized versions of the fundamental learning process, which must be captured in its entirety when applied to complex visual processes. We show why the generalized version of statistical learning can provide the appropriate setup for such a unified treatment of learning in vision, what computational framework best accommodates this kind of statistical learning, and what plausible neural scheme could feasibly implement this framework. Finally, we list the challenges that the field of statistical learning faces in fulfilling the promise of being the right vehicle for advancing our understanding of vision in its entirety. Expected final online publication date for the Annual Review of Vision Science, Volume 8 is September 2022. Please see http://www.annualreviews.org/page/journal/pubdates for revised estimates.
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Affiliation(s)
- József Fiser
- Department of Cognitive Science, Center for Cognitive Computation, Central European University, Vienna 1100, Austria;
| | - Gábor Lengyel
- Department of Brain and Cognitive Sciences, University of Rochester, Rochester, New York 14627, USA
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Bogaerts L, Siegelman N, Christiansen MH, Frost R. Is there such a thing as a 'good statistical learner'? Trends Cogn Sci 2021; 26:25-37. [PMID: 34810076 DOI: 10.1016/j.tics.2021.10.012] [Citation(s) in RCA: 12] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/18/2021] [Revised: 10/23/2021] [Accepted: 10/25/2021] [Indexed: 12/31/2022]
Abstract
A growing body of research investigates individual differences in the learning of statistical structure, tying them to variability in cognitive (dis)abilities. This approach views statistical learning (SL) as a general individual ability that underlies performance across a range of cognitive domains. But is there a general SL capacity that can sort individuals from 'bad' to 'good' statistical learners? Explicating the suppositions underlying this approach, we suggest that current evidence supporting it is meager. We outline an alternative perspective that considers the variability of statistical environments within different cognitive domains. Once we focus on learning that is tuned to the statistics of real-world sensory inputs, an alternative view of SL computations emerges with a radically different outlook for SL research.
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Affiliation(s)
- Louisa Bogaerts
- Vrije Universiteit Amsterdam, 1081 HV Amsterdam, The Netherlands.
| | | | - Morten H Christiansen
- Haskins Laboratories, New Haven, CT 06511, USA; Cornell University, Ithaca, NY 14850, USA; Aarhus University, 8000 Aarhus, Denmark
| | - Ram Frost
- Haskins Laboratories, New Haven, CT 06511, USA; The Hebrew University of Jerusalem, 91905 Jerusalem, Israel; Basque Center for Cognition, Brain, and Language, 20009 Donostia, Spain
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Statistically defined visual chunks engage object-based attention. Nat Commun 2021; 12:272. [PMID: 33431837 PMCID: PMC7801661 DOI: 10.1038/s41467-020-20589-z] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2020] [Accepted: 12/07/2020] [Indexed: 11/09/2022] Open
Abstract
Although objects are the fundamental units of our representation interpreting the environment around us, it is still not clear how we handle and organize the incoming sensory information to form object representations. By utilizing previously well-documented advantages of within-object over across-object information processing, here we test whether learning involuntarily consistent visual statistical properties of stimuli that are free of any traditional segmentation cues might be sufficient to create object-like behavioral effects. Using a visual statistical learning paradigm and measuring efficiency of 3-AFC search and object-based attention, we find that statistically defined and implicitly learned visual chunks bias observers' behavior in subsequent search tasks the same way as objects defined by visual boundaries do. These results suggest that learning consistent statistical contingencies based on the sensory input contributes to the emergence of object representations.
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Sherman BE, Graves KN, Turk-Browne NB. The prevalence and importance of statistical learning in human cognition and behavior. Curr Opin Behav Sci 2020; 32:15-20. [PMID: 32258249 DOI: 10.1016/j.cobeha.2020.01.015] [Citation(s) in RCA: 36] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/24/2022]
Abstract
Statistical learning, the ability to extract regularities from the environment over time, has become a topic of burgeoning interest. Its influence on behavior, spanning infancy to adulthood, has been demonstrated across a range of tasks, both those labeled as tests of statistical learning and those from other learning domains that predated statistical learning research or that are not typically considered in the context of that literature. Given this pervasive role in human cognition, statistical learning has the potential to reconcile seemingly distinct learning phenomena and may be an under-appreciated but important contributor to a wide range of human behaviors that are studied as unrelated processes, such as episodic memory and spatial navigation.
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Affiliation(s)
- Brynn E Sherman
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06520, USA
| | - Kathryn N Graves
- Department of Psychology, Yale University, 2 Hillhouse Avenue, New Haven, CT 06520, USA
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