Implicit and Explicit Contributions to Object Recognition: Evidence from Rapid Perceptual Learning

Martens, Ulla; Wahl, Patricia W.; Hassler, Uwe; Friese, Uwe; Gruber, Thomas

doi:10.1371/journal.pone.0047009

Cited by 10 publications

(10 citation statements)

References 38 publications

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“…The source modeling results showed a distributed network of cortical regions underlying the perception of bodies and chairs including parietal, temporal and occipital areas. These localizations are in line with previous results using SSVEP in object representations (Kaspar et al 2010;Martens and Gruber 2012;Martens et al 2012b), as well as with imaging studies which showed the pivotal role of the lateral occipital complex in object recognition (Kourtzi and Kanwisher 2000;Grill-Spector et al 2001). According to Tanaka (2003), familiar stimuli consist of various "critical features" that are more complex than simple features such as orientation or color (which are also contained in unfamiliar images), but at the same time are not specific enough to represent the objects in their whole.…”

Section: Discussionsupporting

confidence: 92%

Steady-state visually evoked potential correlates of human body perception

et al. 2016

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In cognitive neuroscience, interest in the neuronal basis underlying the processing of human bodies is steadily increasing. Based on functional magnetic resonance imaging studies, it is assumed that the processing of pictures of human bodies is anchored in a network of specialized brain areas comprising the extrastriate and the fusiform body area (EBA, FBA). An alternative to examine the dynamics within these networks is electroencephalography, more specifically so-called steady-state visually evoked potentials (SSVEPs). In SSVEP tasks, a visual stimulus is presented repetitively at a predefined flickering rate and typically elicits a continuous oscillatory brain response at this frequency. This brain response is characterized by an excellent signal-to-noise ratio-a major advantage for source reconstructions. The main goal of present study was to demonstrate the feasibility of this method to study human body perception. To that end, we presented pictures of bodies and contrasted the resulting SSVEPs to two control conditions, i.e., non-objects and pictures of everyday objects (chairs). We found specific SSVEPs amplitude differences between bodies and both control conditions. Source reconstructions localized the SSVEP generators to a network of temporal, occipital and parietal areas. Interestingly, only body perception resulted in activity differences in middle temporal and lateral occipitotemporal areas, most likely reflecting the EBA/FBA.

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Section: Discussionsupporting

confidence: 92%

Steady-state visually evoked potential correlates of human body perception

et al. 2016

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“…Localization information has been drawn largely from fMRI based studies, whilst time-resolved EEG and MEG has provided temporal information about processing of disambiguated images. Recognition induced effects have been found at 120 ms ( Urakawa et al., 2015 ; Samaha et al., 2016 ), at the peak of N170 component ( Jemel et al., 2003 ), around 200 ms ( Goffaux et al., 2004 ), after 250 ms ( Minami et al., 2014 ), after 500 ms ( Flounders et al., 2019 ), after 750 ms ( Martens et al., 2012 ).…”

Section: Discussionmentioning

confidence: 99%

“…Various neuroimaging techniques have been used in these experiments: positron-emission tomography (PET) ( Dolan et al., 1997 ), functional magnetic resonance imaging (fMRI) ( Hegdé and Kersten, 2010 ; Ludmer et al., 2011 ; Gorlin et al., 2012 ; Van Loon et al., 2016 ; González-García et al., 2018 ), electroencephalography (EEG) ( Jemel et al., 2003 ; Goffaux et al., 2004 ; Martens et al., 2012 ; Minami et al., 2014 ; Samaha et al., 2016 ), MEG ( Urakawa et al., 2015 ; Flounders et al., 2019 ), repetitive transcranial magnetic stimulation (rTMS) ( Giovannelli et al., 2010 ) and even single neuron recording in a monkeys ( Tovee et al., 1996 ). In these studies, recognition effects were found in low-level visual areas ( Gorlin et al., 2012 ; Van Loon et al., 2016 ), ventral occipito-temporal regions ( Tovee et al., 1996 ; Dolan et al., 1997 ; Gorlin et al., 2012 ; Hegdé and Kersten, 2010 ; Martens et al., 2012 ; Urakawa et al., 2015 ; Van Loon et al., 2016 ), parietal cortex ( Dolan et al., 1997 ; Giovannelli et al., 2010 ; Minami et al., 2014 ; González-García et al., 2018 ), and frontal cortex ( Martens et al., 2012 ; González-García et al., 2018 ). Localization information has been drawn largely from fMRI based studies, whilst time-resolved EEG and MEG has provided temporal information about processing of disambiguated images.…”

Section: Discussionmentioning

confidence: 99%

“…The testing time of the disambiguation effect varies; either, immediately after a period of training ( Jemel et al., 2003 ; Minami et al., 2014 ; Chang et al., 2016 ), or later in time (e.g. after a week and/or after a wide set of intervening images was shown) ( Goffaux et al., 2004 ; Ludmer et al., 2011 ; Martens et al., 2012 ). This factor determines the different contributions of priming, repetition effects, and familiarity.…”

Section: Discussionmentioning

confidence: 99%

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Object recognition is enabled by an experience-dependent appraisal of visual features in the brain’s value system

et al. 2020

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This paper addresses perceptual synthesis by comparing responses evoked by visual stimuli before and after they are recognized, depending on prior exposure. Using magnetoencephalography, we analyzed distributed patterns of neuronal activity – evoked by Mooney figures – before and after they were recognized as meaningful objects. Recognition induced changes were first seen at 100–120 ms, for both faces and tools. These early effects – in right inferior and middle occipital regions – were characterized by an increase in power in the absence of any changes in spatial patterns of activity. Within a later 210–230 ms window, a quite different type of recognition effect appeared. Regions of the brain’s value system (insula, entorhinal cortex and cingulate of the right hemisphere for faces and right orbitofrontal cortex for tools) evinced a reorganization of their neuronal activity without an overall power increase in the region. Finally, we found that during the perception of disambiguated face stimuli, a face-specific response in the right fusiform gyrus emerged at 240–290 ms, with a much greater latency than the well-known N170m component, and, crucially, followed the recognition effect in the value system regions. These results can clarify one of the most intriguing issues of perceptual synthesis, namely, how a limited set of high-level predictions, which is required to reduce the uncertainty when resolving the ill-posed inverse problem of perception, can be available before category-specific processing in visual cortex. We suggest that a subset of local spatial features serves as partial cues for a fast re-activation of object-specific appraisal by the value system. The ensuing top-down feedback from value system to visual cortex, in particular, the fusiform gyrus enables high levels of processing to form category-specific predictions. This descending influence of the value system was more prominent for faces than for tools, the fact that reflects different dependence of these categories on value-related information.

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“…Kaspar et al [13] suggested that SSVEPs showed different amplitudes between familiar and unfamiliar objects, and the effect was dependent upon SSVEP frequencies [13]. Martens et al [14] suggested that SSVEPs are sensitive to implicit mechanisms involving object recognition [14]. Then, are SSVEPs modulated by the cognitive state, even though seeing the same images?…”

Section: Introductionmentioning

confidence: 99%

Steady-state visually evoked potential is modulated by the difference of recognition condition

2020

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Recent researches revealed that the EEG component caused by the flickering visual stimulus, which is called steady-state visually evoked potential (SSVEP), might be a potential index for object recognition. This study examined whether SSVEP reflects different states during object recognition. In one trial, a binary image (BI), which is difficult to recognize, was followed by a grayscale image (GI) of the same object as the answer. Both BI and GI were presented in a flickering manner at a frequency of 7.5 Hz. Participants were first asked to answer whether they could recognize BI. Then, after GI was shown, participants were requested to answer whether they recognized it. We analyzed the evoked and induced component of SSVEPs from the two recognition conditions. As a result, the SSVEPs to BI were significantly larger than that to GI. In addition, induced component to GI after the BI was unrecognized was smaller than after the BI was recognized. The present data provide evidence that SSVEPs reflect a transition of cognitive state to ambiguous figures is reflected.

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Implicit and Explicit Contributions to Object Recognition: Evidence from Rapid Perceptual Learning

Cited by 10 publications

References 38 publications

Steady-state visually evoked potential correlates of human body perception

Steady-state visually evoked potential correlates of human body perception

Object recognition is enabled by an experience-dependent appraisal of visual features in the brain’s value system

Steady-state visually evoked potential is modulated by the difference of recognition condition

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