Supramodal processing optimizes visual perceptual learning and plasticity.

Question

1. What are the contributions mentioned in the paper "Supramodal processing optimizes visual perceptual learning and plasticity" ?

2. What was the inverse operator used to calculate the dSPM?

3. What was used for the luminance calibration of the random dot kinematograms?

4. What is the main prediction in this study?

Accepted Answer

In this paper, three different groups of twelve individuals were recorded with magnetoencephalography ( MEG ) while they performed a visual discrimination task and the main task consisted in determining which of a red or green intermixed population of RDKs was most coherent in the visual display.

Accepted Answer

The forward solution, noise and source covariance matrices were used to calculate the depth-weighted (parameter γ = 0.8) and noise-normalized dynamic statisticalparametric mapping (dSPM) (Dale et al., 2000) inverse operator.

Accepted Answer

Heterochromatic Flicker Photometry (HFP) was used for luminance calibration of the red and green Random Dots Kinematograms (RDKs) on a per individual basis.

Accepted Answer

One major prediction in this study was the that perceptual improvements in coherence discrimination thresholds would be commensurate with post-training activity in hMT+ which is known to be responsive to global (Watson et al., 1993; Zeki et al., 1993; Tootell et al., 1995) and translational (Morrone et al., 2000) motion processing.

Accepted Answer

Once cortical models were complete, deformable procedures could be performed including surface inflation (Fischl et al., 1999a) and registration to a spherical atlas (Fischl et al., 1999b).

Accepted Answer

The co-registration of MEG data with an individual’s aMRI was carried out by realigning the digitized fiducial points with the multimodal markers visible in MRI slices.

Accepted Answer

The modulation of hMT+ by mSTS could either enhance the salience of visual coherent motion during training (e.g. Lewis and Noppeney, 2010) or facilitate the extraction of task-relevant features for visual processing (Sasaki et al., 2010).

Accepted Answer

For instance early in development, auditory neurons can respond to light patches when rewired to receive visual information (Roe et al., 1990; Mao et al., 2011) and cooling specific parts of auditory cortex in deafened cats selectively perturbs the detection of visual motion and localization (Lomber et al., 2010).

Accepted Answer

The three categories were ‘hard’, ‘easy’ and ‘learned’: the ‘hard’ category consisted of all stimuli that remained below an individual’s perceptual threshold after training - i.e. RDK coherence levels that never benefitted from training and did not become perceptually discriminable for a given participant.

Accepted Answer

The coherence discrimination threshold was set to 75% of correct performance and quantified by fitting a Weibull function (Wichmann and Hill, 2001) to each individual’s data using: 2 3, 4, 5 1 6 0.5 *8 9:; < = > .

Accepted Answer

Prior to the study, participants were randomly split into three training groups, namely: an only visual training group (V, n=12, 4 females), an audiovisual training group using acoustic textures (AV group, n=12, 6 females) or using acoustic noise (AVn which is also a control group, n=12, 6 females).

Accepted Answer

The dissociation of subjective awareness observed here – i.e. improved performance without improved confidence rating - could be tentatively explained by the nature of what has been learned: the response pattern observed in ITC suggests that color-motion binding has improved in all participants irrespective of their training whereas visual coherence discrimination was solely observed in the AV group.

Accepted Answer

During pre- and post-training, participants were asked to rate their confidence level following their coherence discrimination response.

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From each fit, the neurometric threshold of an individual could thus be defined as the stimulus coherence level corresponding to half the amplitude of the sigmoid curve (see Supp.

Accepted Answer

Their results suggest that supramodal processing allowed the fine-tuning of downstream selectivity in visual cortices in agreement with the reverse hierarchy hypothesis (Ahissar and Hochstein, 2004; Proulx et al., 2012); if this hypothesis is correct, multisensory training can open new empirical venues for the understanding of top-down plasticity in implicit perceptual learning and greatly speed up the use of sensory-substitution devices in sensory-impaired population.

Supramodal processing optimizes visual perceptual learning and plasticity.

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Most frequently asked questions

1. What are the contributions mentioned in the paper "Supramodal processing optimizes visual perceptual learning and plasticity" ?

2. What was the inverse operator used to calculate the dSPM?

3. What was used for the luminance calibration of the random dot kinematograms?

4. What is the main prediction in this study?

5. What were the procedures for morphing the cortical models?

6. How was the co-registration of MEG data with an individual’s aMRI?

7. What is the role of mSTS in the visual processing?

8. What is the role of the auditory cortex in the development of blind humans?

9. What were the three categories of stimuli that did not become discriminable after training?

10. How was the coherence discrimination threshold set?

11. What was the only RDK that was randomized across the three training groups?

12. What is the reason for the dissociation of subjective awareness observed in ITC?

13. What was the main effect of training on the confidence rating of participants?

14. What is the neurometric threshold of an individual?

15. What is the hypothesis that a multisensory training can improve the use of sensory substitution devices?

Figures

Citations

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Human V5 demonstrated by magnetoencephalography using random dot kinematograms of different coherence levels

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The Impact and Status of Carol Fowler's Supramodal Theory of Multisensory Speech Perception

References

Smoothing and Differentiation of Data by Simplified Least Squares Procedures.

The VideoToolbox software for visual psychophysics: transforming numbers into movies.

Cortical surface-based analysis. I. Segmentation and surface reconstruction

FieldTrip: open source software for advanced analysis of MEG, EEG, and invasive electrophysiological data

Nonparametric statistical testing of EEG- and MEG-data

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