ERP topography and human perceptual learning in the peripheral visual field

Shoji, Hiroaki; Skrandies, Wolfgang

doi:10.1016/j.ijpsycho.2005.09.007

Cited by 22 publications

(16 citation statements)

References 32 publications

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“…Although we did not retest all subjects after longer intervals, cursory reports suggest that the learned strategy remains in long-term memory of the subjects. The rapid improvement in test performance is similar to our earlier observations on perceptual learning (see for example, Skrandies and Jedynak, 1999;Shoji and Skrandies, 2006;Skrandies, 2006) and on the learning of semantic meaning of previously unknown Japanese Kanji characters (Shinoda and Skrandies, 2013). Also with perceptual learning or the learning of semantic meaning a training period of less than 30 min yields a significant improvement in behavioral performance.…”

Section: Discussionsupporting

confidence: 88%

“…Perceptual learning is accompanied by changes of event-related brain activity (cf. Skrandies and Jedynak, 1999;Skrandies et al, 2001;Ludwig and Skrandies, 2002;Shoji and Skrandies, 2006 of electrophysiological brain responses (Shinoda and Skrandies, 2013). In general, learning is defined as "changes in behavior resulting from prior experience" (Thompson and Donegan, 1987) that in the present experiments are measured as performance on mathematical problems.…”

Section: Introductionmentioning

confidence: 96%

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Brain activity and learning of mathematical rules—Effects on the frequencies of EEG

Skrandies

Klein

2015

Brain Research

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Section: Discussionsupporting

confidence: 88%

Section: Introductionmentioning

confidence: 96%

Brain activity and learning of mathematical rules—Effects on the frequencies of EEG

Skrandies

Klein

2015

Brain Research

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“…Shoji and Skrandies (2006) showed that perceptual learning can take place in peripheral vision. Interestingly, Alexander, Packard, and Peterson (2002) showed that memory for the locations of objects (related to dorsal stream function) was better when the objects were presented in the peripheral visual field, as compared with central areas, suggesting a separation of peripheral memory processes and central memory processes.…”

Section: Discussionmentioning

confidence: 99%

The role of peripheral vision in implicit contextual cuing

Asselen

2009

Perception & Psychophysics

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76Our environment contains a large amount of visual information, such as different objects, buildings, and faces, making it impossible to process this complex information at once. In order to compensate for the limitations of the visual system, attention is allocated to the most relevant visual information. Attention can be guided by bottom-up and top-down processes. That is, a specific visual feature can attract attention (e.g., a fast-moving object or a bright color), resulting in an eye movement to the source of information (bottom-up process). In addition, knowledge about our environment can guide attention (top-down process).One of the top-down mechanisms that guide attention is a result of implicit contextual learning, as was initially shown by Chun and Jiang (1998). They suggested that visual information from our environment can be learned implicitly and can subsequently guide attention to a specific target location. That is, the association between a target and its surrounding visual context (such as spatial information) can be memorized, improving performance on a visual search task. The contextual-cuing paradigm is typically used to study implicit contextual learning of spatial information. It involves a visual search task, in which a rotated target stimulus (T) is presented among a number of rotated distractors (Ls). The participants have to locate the target as quickly as possible and indicate the direction of rotation. Half of the spatial configurations (i.e., positions of the stimuli) are repeated during the experiment. Interestingly, response times are shorter when the configurations are repeated than when they are new, indicating that contextual information was memorized (Chun, 2000;Chun & Jiang, 1998;Peterson & Kramer, 2001). Participants are not aware of the repetitions and perform at chance level on a recognition memory task, indicating that it is an implicit memory process. This effect is found after a few repetitions (Chun & Jiang, 1998) and remains for weeks after testing (Chun & Jiang, 2003;Jiang, Song, & Rigas, 2005), indicating that it is a robust mechanism.Recently, Brady and Chun (2007) proposed a model of implicit contextual learning, based on the idea that contextual learning results from the pairwise statistical association between the distractor locations and the target. They used this model to predict the outcome of different contextual-cuing tasks. An important and interesting aspect of the model is that it includes a spatial constraint, assuming that learning is restricted to the local area around the target. Thus, a very limited amount of contextual information is learned, which is spatially close to the target. They stated that "observers may be encoding just one snapshot of the local context surrounding the target when it is detected" (p. 813). Brady and Chun tested this model by comparing modeling results with behavioral results under different task conditions and found that the model was accurate in predicting the behavioral results of various experimental studies. The idea...

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“…However, little is known about how learning shapes interactions between these circuits and mediates perceptual improvements in the discrimination of global forms (Scott et al, 2006(Scott et al, , 2008Rossion et al, 2007). Previous EEG studies have focused on the temporal processes that mediate visual feature (e.g., orientation, motion) (Fahle and Skrandies, 1994;Skrandies et al, 2001;Ding et al, 2003;Shoji and Skrandies, 2006;Song et al, 2007;Pourtois et al, 2008;Bao et al, 2010) rather than global form learning. Here, we seek to identify the learning-dependent mechanisms that support distinct processes for visual form learning in clutter, ranging from the extraction of stimulus features from noise to the categorization of global forms.…”

Section: Introductionmentioning

confidence: 99%

Learning Acts on Distinct Processes for Visual Form Perception in the Human Brain

Mayhew

Li²,

Kourtzi³

2012

J. Neurosci.

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Learning is known to facilitate our ability to detect targets in clutter and optimize brain processes for successful visual recognition. Previous brain-imaging studies have focused on identifying spatial patterns (i.e., brain areas) that change with learning, implicating occipitotemporal and frontoparietal areas. However, little is known about the interactions within this network that mediate learningdependent improvement in complex perceptual tasks (i.e., discrimination of visual forms in clutter). Here we take advantage of the complementary high spatial and temporal resolution of simultaneous EEG-fMRI to identify the learning-dependent changes in spatiotemporal brain patterns that mediate enhanced behavioral sensitivity in the discrimination of global forms after training. We measured the observers' choices when discriminating between concentric and radial patterns presented in noise before and after training. Similarly, we measured the choices of a pattern classifier when predicting each stimulus from EEG-fMRI signals. By comparing the performance of human observers and classifiers, we demonstrated that learning alters sensitivity to visual forms and EEG-fMRI activation patterns related to distinct visual recognition processes. In particular, behavioral improvement after training was associated with changes in (1) early processes involved in the integration of global forms in higher occipitotemporal and parietal areas, and (2) later processes related to categorical judgments in frontal circuits. Thus, our findings provide evidence that learning acts on distinct visual recognition processes and shapes feedforward interactions across brain areas to support performance in complex perceptual tasks.

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ERP topography and human perceptual learning in the peripheral visual field

Cited by 22 publications

References 32 publications

Brain activity and learning of mathematical rules—Effects on the frequencies of EEG

Brain activity and learning of mathematical rules—Effects on the frequencies of EEG

The role of peripheral vision in implicit contextual cuing

Learning Acts on Distinct Processes for Visual Form Perception in the Human Brain

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