Fast Mapping Integrates Information Into Existing Memory Networks

2014 ◽  
Author(s):  
Marc N. Coutanche ◽  
Sharon L. Thompson-Schill
Keyword(s):  
2015 ◽  
Vol 32 (1) ◽  
pp. 65-77 ◽  
Author(s):  
Danielle Van Horn ◽  
Pui Fong Kan

2014 ◽  
Vol 998-999 ◽  
pp. 1553-1556
Author(s):  
Hui Yuan

With the rapid economic development and the progress of science and technology, mapping technology has made great progress in our country. New mapping technologies are introduced and putted into use in more and more industry. In this paper, the fast mapping technologies are used to solve the sudden crisis situations, such as geological disasters and accidents.


2013 ◽  
Vol 38 (22) ◽  
pp. 4907 ◽  
Author(s):  
Woo June Choi ◽  
Seon Young Ryu ◽  
Jun Ki Kim ◽  
Jae Young Kim ◽  
Dong Uk Kim ◽  
...  

2007 ◽  
Vol 50 (6) ◽  
pp. 1546-1561 ◽  
Author(s):  
Andrea S. McDuffie ◽  
Heidi A. Sindberg ◽  
Linda J. Hesketh ◽  
Robin S. Chapman
Keyword(s):  

1997 ◽  
Vol 24 (3) ◽  
pp. 737-765 ◽  
Author(s):  
TASSOS STEVENS ◽  
ANNETTE KARMILOFF-SMITH

Williams syndrome (WS), a rare neurodevelopmental disorder, is of special interest to developmental psycholinguists because of its uneven linguistico-cognitive profile of abilities and deficits. One proficiency manifest in WS adolescents and adults is an unusually large vocabulary despite serious deficits in other domains. In this paper, rather than focus on vocabulary size, we explore the processes underlying vocabulary acquisition, i.e. how new words are learned. A WS group was compared to groups of normal MA-matched controls in the range 3–9 years in four different experiments testing for constraints on word learning. We show that in construing the meaning of new words, normal children at all ages display fast mapping and abide by the constraints tested: mutual exclusivity, whole object and taxonomic. By contrast, while the WS group showed fast mapping and the mutual exclusivity constraint, they did not abide by the whole object or taxonomic constraints. This suggests that measuring only the size of WS vocabulary can distort conclusions about the normalcy of WS language. Our study shows that despite equivalent behaviour (i.e. vocabulary test age), the processes underlying how vocabulary is acquired in WS follow a somewhat different path from that of normal children and that the atypically developing brain is not necessarily a window on normal development.


2015 ◽  
Vol 2015 ◽  
pp. 1-17 ◽  
Author(s):  
Tali Atir-Sharon ◽  
Asaf Gilboa ◽  
Hananel Hazan ◽  
Ester Koilis ◽  
Larry M. Manevitz

Neocortical structures typically only support slow acquisition of declarative memory; however, learning through fast mapping may facilitate rapid learning-induced cortical plasticity and hippocampal-independent integration of novel associations into existing semantic networks. During fast mapping the meaning of new words and concepts is inferred, and durable novel associations are incidentally formed, a process thought to support early childhood’s exuberant learning. The anterior temporal lobe, a cortical semantic memory hub, may critically support such learning. We investigated encoding of semantic associations through fast mapping using fMRI and multivoxel pattern analysis. Subsequent memory performance following fast mapping was more efficiently predicted using anterior temporal lobe than hippocampal voxels, while standard explicit encoding was best predicted by hippocampal activity. Searchlight algorithms revealed additional activity patterns that predicted successful fast mapping semantic learning located in lateral occipitotemporal and parietotemporal neocortex and ventrolateral prefrontal cortex. By contrast, successful explicit encoding could be classified by activity in medial and dorsolateral prefrontal and parahippocampal cortices. We propose that fast mapping promotes incidental rapid integration of new associations into existing neocortical semantic networks by activating related, nonoverlapping conceptual knowledge. In healthy adults, this is better captured by unique anterior and lateral temporal lobe activity patterns, while hippocampal involvement is less predictive of this kind of learning.


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