Symbolic categorization of novel multisensory stimuli in the human brain

Viganò, Simone; Borghesani, Valentina; Piazza, Manuela

doi:10.1016/j.neuroimage.2021.118016

Cited by 14 publications

(9 citation statements)

References 77 publications

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“…Thus, the inhibition of interhemispheric transfer by way of the hippocampal commissure could support the behavioral observations of mirror-image inhibition, reported or suggested by many authors (Duñabeitia et al, 2011;Borst et al, 2015;Ahr et al, 2016;Brault-Foisy et al, 2017;Soares et al, 2019). This suggestion fits well with the description of the hippocampus as a "suppressor of inappropriate associations" (McNaughton and Wickens, 2003), less well albeit not in contradiction with its capacity to abstract and generalize 1 from a format to the other (Viganò et al, 2021).…”

Section: Model 2 Of Interhemispheric Reversalsupporting

confidence: 85%

A Test of Three Models of Character Reversal in Typically Developing Children’s Writing

Fischer

Luxembourger

2021

Front. Commun.

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Multisensory learning to read involves, to a large extent, learning to write. A major problem in the initial teaching of handwriting is preventing children from producing reversed letters, especially when the reversed letters are identical to other letters. Torres et al. (2021) offer an efficient method for remediating this problem. Here, we analyze the reversals in their writing data, obtained on Brazilian first-graders (Mage = 6.0 years). Surprisingly, this analysis led to the observation that the first graders almost systematically reverse both the letters b and d in the particular copying conditions (the students look at one letter at a time for 3 s, then immediately after they had to write it while blindfolded). We first describe succinctly and discuss three models susceptible to account for reversal writing, with the aim to question their capacity of account for the curious observation just mentioned. The three models respectively attribute a major role to 1) initial (perceptive) mirror equivalence, 2) intra-hemispheric transfer, 3) orientation of the letters. Because none of the three models examined accounts convincingly for the observation, we accommodated and specified Model 2, adding also a major idea of Model 3. The resulting model assumes that the mirror-letter reversed image representation (b for d and vice-versa) is strongly activated in the right cerebral hemisphere, and that the top-down processes originating from this hemisphere were exacerbated by the eyes closed condition. Of course, this post-hoc and speculative model should be tested in other conditions and with other children.

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Section: Model 2 Of Interhemispheric Reversalsupporting

confidence: 85%

A Test of Three Models of Character Reversal in Typically Developing Children’s Writing

Fischer

Luxembourger

2021

Front. Commun.

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“…To lessen the impact of visual residuals on MI and to better match the scenario to the actual application, the paradigm mandates that subjects perform the MI task after watching the visual guidance and that they do not perform visual guidance while performing the MI task. In the research on brain science and semeiology, specific brain regions such as the hippocampus change while watching symbols and stimuli with different levels of abstraction [42]. In psychological research, different moving stimuli have different abstract memory representations [43].…”

Section: Methodsmentioning

confidence: 99%

“…brain science and semeiology, specific brain regions such as the hippocampus change while watching symbols and stimuli with different levels of abstraction [42]. In psychological research, different moving stimuli have different abstract memory representations [43].…”

Section: Methodsmentioning

confidence: 99%

Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces

et al. 2022

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Motor imagery brain–computer interface (MI-BCI) systems hold the possibility of restoring motor function and also offer the possibility of sustainable autonomous living for individuals with various motor and sensory impairments. When utilizing the MI-BCI, the user’s performance impacts the system’s overall accuracy, and concentrating on the user’s mental load enables a better evaluation of the system’s overall performance. The impacts of various levels of abstraction on visual guidance of mental training in motor imagery (MI) may be comprehended. We proposed hypotheses about the effects of visually guided abstraction on brain activity, mental load, and MI-BCI performance, then used the event-related desynchronization (ERD) value to measure the user’s brain activity, extracted the brain power spectral density (PSD) to measure the brain load, and finally classified the left- and right-handed MI through a support vector machine (SVM) classifier. The results showed that visual guidance with a low level of abstraction could help users to achieve the highest brain activity and the lowest mental load, and the highest accuracy rate of MI classification was 97.14%. The findings imply that to improve brain–computer interaction and enable those less capable to regain their mobility, visual guidance with a low level of abstraction should be employed when training brain–computer interface users. We anticipate that the results of this study will have considerable implications for human-computer interaction research in BCI.

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“…Thus, place and grid cells may afford a general computational mechanism whose function extends to selectively organize knowledge (about space, but also experiences, concepts, etc) into internal predictive maps [32,33]. This encapsulates the relationships between taskrelevant variables [26,29,34,35] , even at the level of very abstract structures such as words in language [36] and social dimensions [37]. In sum, the predictive cognitive map could arise as the consequence of memory integration, to support flexible generalization.…”

Section: Memory Integrationmentioning

confidence: 99%

How do we generalize?

Taylor

Cortese

Barron

et al. 2021

Neurons, Behavior, Data Analysis, and Theory

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Humans and animals are able to generalize or transfer information from previous experience so that they can behave appropriately in novel situations. What mechanisms–computations, representations, and neural systems–give rise to this remarkable ability? The members of this Generative Adversarial Collaboration (GAC) come from a range of academic backgrounds but are all interested in uncovering the mechanisms of generalization. We started out this GAC with the aim of arbitrating between two alternative conceptual accounts: (1) generalization stems from integration of multiple experiences into summary representations that reflect generalized knowledge, and (2) generalization is computed on-the-fly using separately stored individual memories. Across the course of this collaboration, we found that–despite using different terminology and techniques, and although some of our specific papers may provide evidence one way or the other–we in fact largely agree that both of these broad accounts (as well as several others) are likely valid. We believe that future research and theoretical synthesis across multiple lines of research is necessary to help determine the degree to which different candidate generalization mechanisms may operate simultaneously, operate on different scales, or be employed under distinct conditions. Here, as the first step, we introduce some of these candidate mechanisms and we discuss the issues currently hindering better synthesis of generalization research. Finally, we introduce some of our own research questions that have arisen over the course of this GAC, that we believe would benefit from future collaborative efforts.

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Symbolic categorization of novel multisensory stimuli in the human brain

Cited by 14 publications

References 77 publications

A Test of Three Models of Character Reversal in Typically Developing Children’s Writing

A Test of Three Models of Character Reversal in Typically Developing Children’s Writing

Exploring the Visual Guidance of Motor Imagery in Sustainable Brain–Computer Interfaces

How do we generalize?

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