Visual experiences during letter production contribute to the development of the neural systems supporting letter perception

Vinci‐Booher, Sophia; James, Karin H.

doi:10.1111/desc.12965

Cited by 15 publications

(19 citation statements)

References 82 publications

(147 reference statements)

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“…This observation also highlights a second loose end of this topic of research, which regards whether post-training gains in new (unknown) graphs by literate participants would generalize to those elicited in preliterate or illiterate participants (Naka and Naoi, 1995;Xu et al, 2020;Vinci-Booher and James, 2020;Vinci-Booher et al, 2021). This loose end is not specific to the stroke processing hypothesis, given that other accounts have also provided evidence with learners that had prior reading expertise in another script Vinci-Booher et al, 2021).…”

Section: Learners Who Are Already Experts In Reading and Handwritingmentioning

confidence: 89%

“…In other words, during HW, learners are exposed to variable (messy) visual stimuli more than in other types of training without a graphomotor activity ( James and Engelhardt, 2012 ; Li and James, 2016 ). Thus, HW training could broaden graph categories at the left vOT due to perceptual variability ( James and Engelhardt, 2012 ; Li and James, 2016 ): “experiencing visual variability would be more important for letter learning and subsequent visual recognition than experiencing the motor variability” ( Vinci-Booher and James, 2020 , p. 3).…”

Section: Why Is Handwriting So Special? Three Theoretical Proposals Looking For the Common Denominatorsmentioning

confidence: 99%

“…Most enthusiasm with sensorimotor accounts and the stroke processing hypothesis has come from fMRI evidence during visual presentation of written stimuli. Such neuroimaging studies have found functional activity at the PMd and functional connectivity between frontal and/or parietal regions (within the writing network; e.g., Roux et al, 2009 ) and the vOT (part of the visual ventral stream and a core region of the reading network; e.g., Dehaene et al, 2005 ) in two populations: (i) In fluent adult readers presented with graphs or words written in the script on which they are experts ( Longcamp et al, 2003 ; Nakamura et al, 2012 ; Vinci-Booher and James, 2020 ). (ii) In learners presented with visual graphs immediately after training (e.g., Longcamp et al, 2008 ; James and Atwood, 2009 ; James, 2010 ; James and Engelhardt, 2012 ; Vinci-Booher et al, 2016 ).…”

Section: Why Is Handwriting So Special? Three Theoretical Proposals Looking For the Common Denominatorsmentioning

confidence: 99%

“…Whether these regard strokes (units of movement, primitives of HW), visual features (image components, primitives of visual object recognition), or both must first be seriously discussed. Several authors have pointed out that visual analysis and dynamic movement could be involved (e.g., Courrieu and de Falco, 1989 ; Cao et al, 2013a ; Merritt et al, 2020 ; Vinci-Booher and James, 2020 ), but few studies tried to disentangle them.…”

Section: Our Proposal: Disentangling Strokes From Visual Featuresmentioning

confidence: 99%

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From Hand to Eye With the Devil In-Between: Which Cognitive Mechanisms Underpin the Benefit From Handwriting Training When Learning Visual Graphs?

Fernandes

Araújo

2021

Front. Psychol.

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Cognitive science has recently shown a renewed interest on the benefit from training in handwriting (HW) when learning visual graphs, given that this learning experience improves more subsequent visual graph recognition than other forms of training. However, the underlying cognitive mechanism of this HW benefit has been elusive. Building on the 50 years of research on this topic, the present work outlines a theoretical approach to study this mechanism, specifying testable hypotheses that will allow distinguishing between confronting perspectives, i.e., symbolic accounts that hold that perceptual learning and visual analysis underpin the benefit from HW training vs. embodied sensorimotor accounts that argue for motoric representations as inner part of orthographic representations acquired via HW training. From the evidence critically revisited, we concluded that symbolic accounts are parsimonious and could better explain the benefit from HW training when learning visual graphs. The future challenge will be to put at test the detailed predictions presented here, so that the devil has no longer room in this equation.

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Section: Learners Who Are Already Experts In Reading and Handwritingmentioning

confidence: 89%

Section: Why Is Handwriting So Special? Three Theoretical Proposals Looking For the Common Denominatorsmentioning

confidence: 99%

Section: Why Is Handwriting So Special? Three Theoretical Proposals Looking For the Common Denominatorsmentioning

confidence: 99%

Section: Our Proposal: Disentangling Strokes From Visual Featuresmentioning

confidence: 99%

See 2 more Smart Citations

From Hand to Eye With the Devil In-Between: Which Cognitive Mechanisms Underpin the Benefit From Handwriting Training When Learning Visual Graphs?

Fernandes

Araújo

2021

Front. Psychol.

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“…Much of our focus in studying category learning is on the capabilities of the learner. For example, the learner acquires some categories by producing them by hand ( Vinci-Booher & James, 2020;Vinci-Booher, Cheng, & James, 2019;James, 2017;James & Engelhardt, 2012) or by physically exploring the category (Slone, Smith, & Yu, 2019;James, Jones, Swain, Pereira, & Smith, 2014;James & Swain, 2011;James, 2010). Nonetheless, the learner is only one piece of the puzzle in this system.…”

Section: Introductionmentioning

confidence: 99%

The Effects of Frequency, Variability, and Co-occurrence on Category Formation in Neural Systems

Plebanek

James

2021

Journal of Cognitive Neuroscience

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Objects are grouped into categories through a complex combination of statistical and structural regularities. We sought to better understand the neural responses to the structural features of object categories that result from implicit learning. Adult participants were exposed to 32 object categories that contained three structural properties: frequency, variability, and co-occurrences, during an implicit learning task. After this exposure, participants completed a recognition task and were then presented with blocks of learned object categories during fMRI sessions. Analyses were performed by extracting data from ROIs placed throughout the fusiform gyri and lateral occipital cortex and comparing the effects of the different structural properties throughout the ROIs. Behaviorally, we found that symbol category recognition was supported by frequency, but not variability. Neurally, we found that sensitivity to object categories was greater in the right hemisphere and increased as ROIs were moved posteriorly. Frequency and variability altered the brain activation while processing object categories, although the presence of learned co-occurrences did not. Moreover, variability and co-occurrence interacted as a function of ROI, with the posterior fusiform gyrus being most sensitive to this relationship. This result suggests that variability may guide the learner to relevant co-occurrences and this is supported by the posterior ventral temporal cortex. Broadly, our results suggest that the internal features of the categories themselves are key factors in the category learning system.

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From Hand to Eye: a Meta-Analysis of the Benefit from Handwriting Training in Visual Graph Recognition

2022

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Handwriting (HW) training seems to boost recognition of visual graphs and learning to read more than other learning experiences. However, effects across studies appear to be variable and the underlying cognitive mechanism has been elusive. We thus conducted a meta-analysis on 50 independent experiments (with 1525 participants) to determine the magnitude of this HW benefit in visual graph recognition, while enlightening the underlying cognitive mechanism, by investigating four types of moderators: training program (type of control training, presence/absence of phonological training, and HW tasks adopted); set size and training regime (duration and frequency of session and total amount of training); granularity of visual discrimination and perceptual learning tasks; and age of participants.The benefit from HW training was moderate-to-large and significant (Hedge's g = 0.58, SE = .09) and was also modulated by type of control training (larger relative to motor, g = 0.78, than to visual control, g = 0.37), phonological training (larger when it was absent, g = 0.79, than present, g = 0.47), and granularity of visual discrimination (larger for fine-grained, g = 0.93, than coarse-grained, g = 0.19). These results are consistent with symbolic accounts that hold that the advantage from HW training in visual graph recognition is about perceptual learning rather than the motor act. Multiple meta-regressions also revealed that training regime moderated the HW benefit. We conclude that HW training is effective to improve visual graph recognition, and hence, is still relevant for literacy instruction in the present digital era.

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Visual experiences during letter production contribute to the development of the neural systems supporting letter perception

Cited by 15 publications

References 82 publications

From Hand to Eye With the Devil In-Between: Which Cognitive Mechanisms Underpin the Benefit From Handwriting Training When Learning Visual Graphs?

From Hand to Eye With the Devil In-Between: Which Cognitive Mechanisms Underpin the Benefit From Handwriting Training When Learning Visual Graphs?

The Effects of Frequency, Variability, and Co-occurrence on Category Formation in Neural Systems

From Hand to Eye: a Meta-Analysis of the Benefit from Handwriting Training in Visual Graph Recognition

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