Neuronal Mechanisms of Perceptual Learning: Changes in Human Brain Activity with Training in Orientation Discrimination

Schiltz, Christine; Bodart, J M; Dubois, Sébastien; Dejardin, S; Michel, Christian; Roucoux, Alain; Crommelinck, Marc; Orban, Guy A.

doi:10.1006/nimg.1998.0394

Cited by 135 publications

(110 citation statements)

References 88 publications

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“…Although the current study and a PET study by Schiltz et al (1999) found reduced activations in visual cortex with learning, other fMRI studies have reported the opposite effect, i.e., enhanced activations (Schwartz et al, 2002;Furmanski et al, 2004;Sigman et al, 2005;Turk-Browne et al, 2007). It remains a puzzle, however, as to which conditions lead to reduced or enhanced activations as a result of perceptual learning.…”

Section: Changes In Visual Cortex Activation During Perceptual Learningcontrasting

confidence: 83%

“…The results, when superimposed on retinotopic maps of individual subjects, revealed that the reduced activations were seen as early as V1 but mainly in V3-V4 and beyond, all of which contain populations of neurons that are sensitive to stimulus contrast (Boynton et al, 1999;Reynolds et al, 2000;Gardner et al, 2005;Lu and Roe, 2007). Previous studies reported perceptual learning effects in early visual areas, including V1 and V4, when subjects were trained on fine discriminations of simple visual features, such as orientation and texture (Schiltz et al, 1999;Schoups et al, 2001;Schwartz et al, 2002;Furmanski et al, 2004;Yang and Maunsell, 2004;Sigman et al, 2005;Raiguel et al, 2006), and in motion-sensitive areas MT and MST, when subjects were trained on motion discrimination tasks (Zohary et al, 1994;Vaina et al, 1998). These results, together with ours, suggest that learning occurs in visual areas in which the task-relevant visual information is processed.…”

Section: Changes In Visual Cortex Activation During Perceptual Learningmentioning

confidence: 77%

“…In doing so, we were able to correlate activations during learning with the corresponding subjects' behavioral improvement rather than compare brain activations before and after training, as done previously with positron emission tomography (PET) and fMRI (Schiltz et al, 1999;Schwartz et al, 2002;Furmanski et al, 2004). We equated subjects for rates of learning instead of the amount of training, thereby enabling us to show tight coupling between improvement and brain activation changes.…”

Section: Methodsmentioning

confidence: 99%

“…Human imaging and monkey electrophysiological studies have accumulated more direct evidence for the involvement of early visual areas in perceptual learning. For example, several human imaging studies have found changes in activation in the primary visual cortex after training compared with before training on contrast discrimination (Schiltz et al, 1999), texture discrimination (Schwartz et al, 2002), contrast detection (Furmanski et al, 2004), and shape identification (Sigman et al, 2005) tasks. Single-unit recording studies of Yang and Maunsell (2004) and Raiguel et al (2006) examined the activity of neurons in visual cortical area V4 and found sharpened orientation tuning curves after training.…”

Section: Introductionmentioning

confidence: 99%

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Activations in Visual and Attention-Related Areas Predict and Correlate with the Degree of Perceptual Learning

Mukai¹,

Kim²,

Fukunaga³

et al. 2007

J. Neurosci.

151

131

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Repeated experience with a visual stimulus can result in improved perception of the stimulus, i.e., perceptual learning. To understand the underlying neural mechanisms of this process, we used functional magnetic resonance imaging to track brain activations during the course of training on a contrast discrimination task. Based on their ability to improve on the task within a single scan session, subjects were separated into two groups: "learners" and "nonlearners." As learning progressed, learners showed progressively reduced activation in both visual cortex, including Brodmann's areas 18 and 19 and the fusiform gyrus, and several cortical regions associated with the attentional network, namely, the intraparietal sulcus (IPS), frontal eye field (FEF), and supplementary eye field. Among learners, the decrease in brain activations in these regions was highly correlated with the magnitude of performance improvement. Unlike learners, nonlearners showed no changes in brain activations during training. Learners showed stronger activation than nonlearners during the initial period of training in all these brain regions, indicating that one could predict from the initial activation level who would learn and who would not. In addition, over the course of training, the functional connectivity between IPS and FEF in the right hemisphere with early visual areas was stronger for learners than nonlearners. We speculate that sharpened tuning of neuronal representations may cause reduced activation in visual cortex during perceptual learning and that attention may facilitate this process through an interaction of attention-related and visual cortical regions.

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Section: Changes In Visual Cortex Activation During Perceptual Learningcontrasting

confidence: 83%

Section: Changes In Visual Cortex Activation During Perceptual Learningmentioning

confidence: 77%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Activations in Visual and Attention-Related Areas Predict and Correlate with the Degree of Perceptual Learning

Mukai¹,

Kim²,

Fukunaga³

et al. 2007

J. Neurosci.

151

131

View full text Add to dashboard Cite

show abstract

“…On the one hand, some studies obtained decreased activation in the visual cortex compared to the pre-training state in visual orientation discrimination (e.g., Schiltz et al, 1999). On the other hand, there is also ample evidence showing expertise-related increase of fusiform activation in object recognition, including face (Rossion et al, 2001), Greeble (Gauthier et al, 1999b), car and bird (Gauthier et al, 2000a), and mirrored text (Poldrack and Gabrieli, 2001;Poldrack et al, 1998).…”

Section: Visual Form Training and Fusiform Activationmentioning

confidence: 99%

Language experience shapes fusiform activation when processing a logographic artificial language: An fMRI training study

et al. 2006

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The significant role of the left midfusiform cortex in reading found in recent neuroimaging studies has led to the visual word form area (VWFA) hypothesis. This hypothesis suggests that years of experience reading native language change the visual expertise of this region to be especially sensitive to the visual form of native language. The present study aimed at testing this hypothesis by exploring the role of language experience in shaping the fusiform activation. We designed a logographic artificial language (LAL) using the visual form and pronunciation of Korean Hangul characters (but their correspondence was shuffled) and assigning arbitrary meanings to these characters. Twelve native Chinese Mandarin speakers (6 male and 6 female, 18 to 21 years old) with no prior knowledge of Korean language were trained in the visual form of these characters for 2 weeks, followed by 2 weeks each of phonological and semantic training. Behavioral data indicated that training was effective in increasing the efficiency of visual form processing and establishing the connections among visual form, sounds, and meanings. Imaging data indicated that at the pre-training stage, subjects showed stronger activation in the fusiform regions for LAL than for Chinese across both one-back visual matching task and the passive viewing task. Visual form training significantly decreased the activation of bilateral fusiform cortex and the left inferior occipital cortex, whereas phonological training increased activation in these regions, and the right fusiform remained more active after semantic training. Increased activations after phonological and semantic training were also evident in other regions involved in language processing. These findings thus do not seem to be consistent with the visual-expertise-induced-sensitivity hypothesis about fusiform regions. Instead, our results suggest that visual familiarity, phonological processing, and semantic processing all make significant but different contributions to shaping the fusiform activation. D

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Resampling as a cluster validation technique in fMRI

Baumgartner¹,

Somorjai²,

Summers³

et al. 2000

J. Magn. Reson. Imaging

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Exploratory, data-driven analysis approaches such as cluster analysis, principal component analysis, independent component analysis, or neural network-based techniques are complementary to hypothesis-led methods. They may be considered as hypothesis generating methods. The representative time courses they produce may be viewed as alternative hypotheses to the null hypothesis, ie, "no activation." We present here a resampling technique to validate the results of exploratory fuzzy clustering analysis. In this case an alternative hypothesis is represented by a cluster centroid. For both simulated and in vivo functional magnetic resonance imaging data, we show that by permutation-based resampling, statistical significance may be computed for each voxel belonging to a cluster of interest without parametric distributional assumptions.

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Neuronal Mechanisms of Perceptual Learning: Changes in Human Brain Activity with Training in Orientation Discrimination

Cited by 135 publications

References 88 publications

Activations in Visual and Attention-Related Areas Predict and Correlate with the Degree of Perceptual Learning

Activations in Visual and Attention-Related Areas Predict and Correlate with the Degree of Perceptual Learning

Language experience shapes fusiform activation when processing a logographic artificial language: An fMRI training study

Resampling as a cluster validation technique in fMRI

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