[MATHLINK] MCLS Symposium TOMORROW April 14: Neuroscience and mathematical cognition
MCLS Trainee
mclstrainee at gmail.com
Wed Apr 13 11:31:47 EDT 2022
Dear MCLS Community,
Please be sure to join us for our next symposium, tomorrow *Thursday, April
14 @ 9am EST // 2pm BST, *entitled, "Mathematical Cognition and the Brain".
This session features presentations by Ann Dowker (*University of Oxford,
UK*), Karin Kucian (*Center for MR-Research, University Children’s Hospital
Zurich, Zurich, Switzerland*), Daniel Ansari (*University of Western
Ontario, Canada*), and Roi Cohen Kadosh (*Oxford University, UK*).
*Click to join the meeting at anytime*: https://tinyurl.com/MCLS2021
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Hope to see you there!
The MCLS Conference Organizing Committee
------------------------------
*Past Events and News: *
- View previous recorded talks on our MCLS Trainee Youtube channel!
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- Access the full MCLS 2021 program (which include the talks scheduled
through May, 2022) by clicking here
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*Abstract:*
There is increasing interest in the neuroscience of mathematical cognition:
in how particular brain areas and functions may be involved in number
processing and arithmetic. This may be studied through neuropsychological
studies of how brain damage may impair or spare some or all aspects of
numerical cognition; studies of developmental disorders of numerical
cognition; neuroimaging studies of how patterns of brain activation in
different areas of the brain may reflect numerical cognition; and studies
of how brain stimulation may influence numerical learning. This symposium
presents aspects of all these, with a particular focus on individual
differences.
Esther Gosling, Nele Demeyere and Ann Dowker studied 9 chronic stroke
patients. They were compared to 11 healthy controls on tasks involving
nonsymbolic visual enumeration, symbolic addition, and symbolic
multiplication. Three patients showed no impairment compared with controls
on any of the mathematics tests. The others showed a variety of specific
deficits, not consistently related to specific areas of the brain. The
study focused on relationships between visual enumeration and other
arithmetical tasks, as it has often been suggested that nonsymbolic visual
enumeration is a foundation and prerequisite for arithmetic. In this group,
such relationships did not emerge. No overall correlations were found
between visual enumeration accuracy or reaction time and either addition or
multiplication; and double dissociations were found between visual
enumeration and arithmetic.
Karin Kucian and colleagues performed a longitudinal study of about 2,900
children from kindergarten to 5th grade. They found that children at risk
of developmental dyscalculia could be identified as early as kindergarten.
Early number magnitude competencies predicted future calculation skills.
Half of the children at risk received an intervention in kindergarten.
These children performed much better in the subsequent years than controls
who received no intervention; and were less likely to be diagnosed with
dyscalculia. This shows that it is possible and important to identify
children at risk for dyscalculia at an early stage, and that early
intervention may significantly reduce their risk.
Daniel Ansari and his colleagues carried out a series of neuroimaging
studies investigating the processing of Arabic numerals in the brain. The
studies indicated that Arabic numerals are represented in the left parietal
cortex and that this representation is invariant across handedness, differs
from letters and is amodal in nature. In this presentation, Daniel Ansari
discusses the differences between brain representation for symbolic and
non-symbolic number, and how the left-lateralization of symbolic number
might emerge over developmental time.
Roi Cohen Kadosh and colleagues developed a personalized neurostimulation
approach to reveal the involvement of qualitatively different neural
processes as a function of individual differences, and to use these to
successfully optimise arithmetic performance by searching, learning, and
recommending parameters for effective neurostimulation. An active machine
learning technique demonstrated the potential for this approach to be used
to improve mathematical learning and performance and translation to other
clinical and non-clinical domains.
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