Reassessing lateralization in calculation

Semenza, Carlo; Benavides-Varela, Silvia

doi:10.1098/rstb.2017.0044

Cited by 21 publications

(12 citation statements)

References 70 publications

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“…The association of different terms along the different axis defined domains of function that are not trivially associated. For instance, the axis 'symbolic communication' includes not only left lateralised maps related to the term /language/, but also left and right lateralized parietal maps related to /calculation/ in agreement with recent neuropsychology 42 . The axis perception/action includes left hemisphere component related to motor planning, consistent with the effects of left lesions on motor planning (apraxia) 43,44 , but also right hemisphere maps related to visuospatial attention and response inhibition.…”

Section: Discussionsupporting

confidence: 83%

The architecture of functional lateralisation and its relationship to callosal connectivity in the human brain

Karolis

Corbetta

Schotten

2018

Preprint

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Functional lateralisation is a fundamental principle of the human brain. However, a comprehensive taxonomy of functional lateralisation and its organisation in the brain is missing. We report the first complete map of functional hemispheric asymmetries in the human brain, reveal its low dimensional structure, and its relationship with structural interhemispheric connectivity. Our results suggest that the lateralisation of brain functions is distributed along four functional axes: symbolic communication, perception/action, emotion, and decision-making, and that cortical regions showing asymmetries in task-evoked activity have reduced connections with the opposite hemisphere.

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Section: Discussionsupporting

confidence: 83%

The architecture of functional lateralisation and its relationship to callosal connectivity in the human brain

Karolis

Corbetta

Schotten

2018

Preprint

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“…This hypothesis might explain the different time courses of the audio/visual correspondence effect on ERPs in the two experiments. We could speculate that in Experiment 1 visual features are ignored to facilitate numerical processing, consistent with recent theories regarding the cognitive basis of enumeration (Avancini et al, ; Semenza & Benavides‐Varela, ).…”

Section: Discussionsupporting

confidence: 85%

N2pc reflects two modes for coding the number of visual targets

et al. 2018

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Humans share with a variety of animal species the spontaneous ability to detect the numerical correspondence between limited quantities of visual objects and discrete auditory events. Here, we explored how such mental representation is generated in the visual modality by monitoring a parieto-occipital ERP component, N2pc, whose amplitude covaries with the number of visual targets in explicit enumeration. Participants listened to an auditory sequence of one to three tones followed by a visual search display containing one to three targets. In Experiment 1, participants were asked to respond based on the numerical correspondence between tones and visual targets. In Experiment 2, participants were asked to ignore the tones and detect a target presence in the search display. The results of Experiment 1 showed an N2pc amplitude increase determined by the number of visual targets followed by a centroparietal ERP component modulated by the numerical correspondence between tones and visual targets. The results of Experiment 2 did not show an N2pc amplitude increase as a function of the number of visual targets. However, the numerical correspondence between tones and visual targets influenced N2pc amplitude. By comparing a subset of amplitude/latency parameters between Experiment 1 and 2, the present results suggest N2pc reflects two modes for representing the number of visual targets. One mode, susceptible to subjective control, relies on visual target segregation for exact target individuation, whereas a different mode, likely enabling spontaneous cross-modal matching, relies on the extraction of rough information about number of targets from visual input.

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“…As grey matter volume and cortical thickness decrease with age [ 54 ], older adults relative to younger adults may show reduced associations between success in the acquisition of arithmetic competence and brain volume. This study together with very recent investigations using clinical, neuroimaging, and reversible inhibition methods [ 55 ] points to the contribution of the right hemisphere in calculation.…”

Section: Discussionsupporting

confidence: 54%

Arithmetic learning in advanced age

et al. 2018

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Acquisition of numerical knowledge and understanding of numerical information are crucial for coping with the changing demands of our digital society. In this study, we assessed arithmetic learning in older and younger individuals in a training experiment including brain imaging. In particular, we assessed age-related effects of training intensity, prior arithmetic competence, and neuropsychological variables on the acquisition of new arithmetic knowledge and on the transfer to new, unknown problems. Effects were assessed immediately after training and after 3 months. Behavioural results showed higher training effects for younger individuals than for older individuals and significantly better performance after 90 problem repetitions than after 30 repetitions in both age groups. A correlation analysis indicated that older adults with lower memory and executive functions at baseline could profit more from intensive training. Similarly, training effects in the younger group were higher for those individuals who had lower arithmetic competence and executive functions prior to intervention. In younger adults, successful transfer was associated with higher executive functions. Memory and set-shifting emerged as significant predictors of training effects in the older group. For the younger group, prior arithmetic competence was a significant predictor of training effects, while cognitive flexibility was a predictor of transfer effects. After training, a subgroup of participants underwent an MRI assessment. A voxel-based morphometry analysis showed a significant interaction between training effects and grey matter volume of the right middle temporal gyrus extending to the angular gyrus for the younger group relative to the older group. The reverse contrast (older group vs. younger group) did not yield any significant results. These results suggest that improvements in arithmetic competence are supported by temporo-parietal areas in the right hemisphere in younger participants, while learning in older people might be more widespread. Overall, our study indicates that arithmetic learning depends on the training intensity as well as on person-related factors including individual age, arithmetic competence before training, memory, and executive functions. In conclusion, we suggest that major progress can be also achieved by older participants, but that interventions have to take into account individual variables in order to provide maximal benefit.

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Reassessing lateralization in calculation

Cited by 21 publications

References 70 publications

The architecture of functional lateralisation and its relationship to callosal connectivity in the human brain

The architecture of functional lateralisation and its relationship to callosal connectivity in the human brain

N2pc reflects two modes for coding the number of visual targets

Arithmetic learning in advanced age

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