Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasia

Categorization is an important human skill that underlies our ability to recognize and instantly assign meaning to novel items. We focus on probabilistic categorization, a form of rule-based categorization based on the probabilistic association of cues and outcomes (Meeter et al., 2008). Probabilis...

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Main Authors: Sofia Vallila-Rohter, Idan Asher Blank, Evelina Fedorenko
Format: Article
Language:English
Published: Frontiers Media S.A. 2015-05-01
Series:Frontiers in Psychology
Subjects:
Online Access:http://journal.frontiersin.org/Journal/10.3389/conf.fpsyg.2015.65.00066/full
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spelling doaj-b3ad757dd4944eabaefddc655e7fca0a2020-11-24T23:27:31ZengFrontiers Media S.A.Frontiers in Psychology1664-10782015-05-01610.3389/conf.fpsyg.2015.65.00066150439Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasiaSofia Vallila-Rohter0Idan Asher Blank1Evelina Fedorenko2Boston UniversityMassachusetts Institute of TechnologyMassachusetts General HospitalCategorization is an important human skill that underlies our ability to recognize and instantly assign meaning to novel items. We focus on probabilistic categorization, a form of rule-based categorization based on the probabilistic association of cues and outcomes (Meeter et al., 2008). Probabilistic categories can be learned via implicit strategies supported by the caudate and putamen. Alternatively, learners can implement verbal and explicit rules, recruiting language regions of the brain, the hippocampus and lateral prefrontal cortex (Ashby et al., 1998). Research has demonstrated that patients with aphasia (PWA) show impaired learning of nonlinguistic probabilistic categories, and that they develop suboptimal strategies when approaching such tasks (Vallila-Rohter & Kiran, 2013, in press). In the current study, we use fMRI paradigms to examine the brain systems engaged as PWA and control individuals learn novel nonlinguistic categories. Methods Stimuli are two sets of fictional animals, organized into two categories based on the distribution of their features. One animal is selected as prototype A, and the animal that differs from that prototype by all features becomes prototype B. Animals must share a majority of their features with each prototype to be considered members of that category. Animals are presented one at a time on a screen and participants guess the animals’ category membership, receiving feedback following each trial. Participants complete 96 classification and 96 perceptual-motor baseline trials that require a button press whenever a unique set of animals appears. Participants also complete a language localizer task in which they read sentences and sequences of nonwords, a task designed to functionally locate language regions of the brain (Fedorenko et al., 2010). Functional and structural images are acquired in a block design using a 3T Siemens TimTrio scanner with a 32-channel head coil. We examine contrasts learning > baseline and baseline > learning and use Marsbar to conduct region of interest (ROI) analyses over regions associated with verbal strategies: MFG, IFG, hippocampus, and over regions associated with implicit strategies: caudate and putamen. Results We have collected data from 6 PWA and two controls and anticipate enrolling 10 PWA. Whole brain analyses reveal a set of regions activated for the learning>baseline contrast across all participants that includes bilateral middle frontal gyrus (MFG), right inferior frontal gyrus (IFG), right angular gyrus and bilateral middle temporal gyrus (MTG). ROI analyses over data for two PWA and two controls demonstrate that controls produce positive percent signal change differences in the caudate (implicit system) and negative percent signal change difference in the hippocampus (verbal, explicit system). In contrast, PWA show positive signal change in the hippocampus and negative percent signal change in the caudate. Conclusions PWA and controls engage a similar overall network of regions during probabilistic category learning. ROI analyses suggest, however, that PWA may exhibit a greater reliance on verbally mediated strategies, compared with a greater reliance on implicit strategies in controls. PWA with mild aphasia who have access to language, may be predisposed to utilize that language to learn, even if it is not a productive strategy.http://journal.frontiersin.org/Journal/10.3389/conf.fpsyg.2015.65.00066/fullVerbal LearningfMRIimplicit learningcategory learningExplicit learningaphasia rehabilitationprobabilistic categories
collection DOAJ
language English
format Article
sources DOAJ
author Sofia Vallila-Rohter
Idan Asher Blank
Evelina Fedorenko
spellingShingle Sofia Vallila-Rohter
Idan Asher Blank
Evelina Fedorenko
Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasia
Frontiers in Psychology
Verbal Learning
fMRI
implicit learning
category learning
Explicit learning
aphasia rehabilitation
probabilistic categories
author_facet Sofia Vallila-Rohter
Idan Asher Blank
Evelina Fedorenko
author_sort Sofia Vallila-Rohter
title Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasia
title_short Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasia
title_full Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasia
title_fullStr Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasia
title_full_unstemmed Does linguistic ability impact nonlinguistic learning? The neural bases of nonlinguistic learning in aphasia
title_sort does linguistic ability impact nonlinguistic learning? the neural bases of nonlinguistic learning in aphasia
publisher Frontiers Media S.A.
series Frontiers in Psychology
issn 1664-1078
publishDate 2015-05-01
description Categorization is an important human skill that underlies our ability to recognize and instantly assign meaning to novel items. We focus on probabilistic categorization, a form of rule-based categorization based on the probabilistic association of cues and outcomes (Meeter et al., 2008). Probabilistic categories can be learned via implicit strategies supported by the caudate and putamen. Alternatively, learners can implement verbal and explicit rules, recruiting language regions of the brain, the hippocampus and lateral prefrontal cortex (Ashby et al., 1998). Research has demonstrated that patients with aphasia (PWA) show impaired learning of nonlinguistic probabilistic categories, and that they develop suboptimal strategies when approaching such tasks (Vallila-Rohter & Kiran, 2013, in press). In the current study, we use fMRI paradigms to examine the brain systems engaged as PWA and control individuals learn novel nonlinguistic categories. Methods Stimuli are two sets of fictional animals, organized into two categories based on the distribution of their features. One animal is selected as prototype A, and the animal that differs from that prototype by all features becomes prototype B. Animals must share a majority of their features with each prototype to be considered members of that category. Animals are presented one at a time on a screen and participants guess the animals’ category membership, receiving feedback following each trial. Participants complete 96 classification and 96 perceptual-motor baseline trials that require a button press whenever a unique set of animals appears. Participants also complete a language localizer task in which they read sentences and sequences of nonwords, a task designed to functionally locate language regions of the brain (Fedorenko et al., 2010). Functional and structural images are acquired in a block design using a 3T Siemens TimTrio scanner with a 32-channel head coil. We examine contrasts learning > baseline and baseline > learning and use Marsbar to conduct region of interest (ROI) analyses over regions associated with verbal strategies: MFG, IFG, hippocampus, and over regions associated with implicit strategies: caudate and putamen. Results We have collected data from 6 PWA and two controls and anticipate enrolling 10 PWA. Whole brain analyses reveal a set of regions activated for the learning>baseline contrast across all participants that includes bilateral middle frontal gyrus (MFG), right inferior frontal gyrus (IFG), right angular gyrus and bilateral middle temporal gyrus (MTG). ROI analyses over data for two PWA and two controls demonstrate that controls produce positive percent signal change differences in the caudate (implicit system) and negative percent signal change difference in the hippocampus (verbal, explicit system). In contrast, PWA show positive signal change in the hippocampus and negative percent signal change in the caudate. Conclusions PWA and controls engage a similar overall network of regions during probabilistic category learning. ROI analyses suggest, however, that PWA may exhibit a greater reliance on verbally mediated strategies, compared with a greater reliance on implicit strategies in controls. PWA with mild aphasia who have access to language, may be predisposed to utilize that language to learn, even if it is not a productive strategy.
topic Verbal Learning
fMRI
implicit learning
category learning
Explicit learning
aphasia rehabilitation
probabilistic categories
url http://journal.frontiersin.org/Journal/10.3389/conf.fpsyg.2015.65.00066/full
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