Pub Date : 2021-05-31DOI: 10.1017/9781108635462.029
Robert Vargas, M. Just
concepts elicited greater activation than concrete concepts in such verbal processing areas. By contrast, concrete concepts elicited greater activation than abstract concepts in visuospatial processing (precuneus, posterior cingulate, and fusiform gyrus). This meta-analysis was limited to univariate comparisons of categories of concepts and did not have access to the activation patterns evoked by individual concepts. This limitation potentially overlooks nuanced distinctions in the representational structure. Univariate contrasts potentially overlook critical relationships across neural states and neural regions (Mur et al., 2009). Through the use of MVPA techniques, more recent studies have begun to examine the underlying semantic structure of sets of abstract concepts. The next section focuses on various imaging studies examining the neural activation patterns associated with abstract concepts and explores the possible semantic structures that are specific to abstract concepts. Neurosemantic Dimensions of Abstract Meaning As in the case of concrete concepts, the semantic dimensions underlying abstract concept categories can be identified from their activation patterns. One of the first attempts to decode the semantic content of abstract semantic information was conducted by Anderson, Kiela, Clark, and Poesio (2017). A set of individual concepts that belonged to various taxonomic categories (tools, locations, social roles, events, communications, and attributes) were decoded from their activation patterns. Whether a concept belonged to one of two abstract semantic categories (i.e., Law orMusic) was also decoded from the activation patterns of individual concepts. Although these abstract semantic categories could be decoded based on their activation patterns, the localization of this dissociation is unclear. Neurally-based semantic dimensions underlying abstract concepts differ from the dimensions underlying concrete concepts. Vargas and Just (2019) investigated the fMRI activation patterns of 28 abstract concepts (e.g., ethics, truth, spirituality) focusing on individual concept representation and the relationship between the activation profiles of these concept representations. Factor analyses of the activation patterns evoked by the stimulus set revealed three underlying semantic dimensions. These dimensions corresponded to 1) the degree to which a concept was Verbally Represented, 2) whether a concept was External (or Internal) to the individual, and 3) whether the concept contained Social Content. The Verbal Representation dimension was present across all participants and was the most salient of the semantic dimensions. Concepts with large positive factor scores for this factor included compliment, faith, and ethics, while concepts with large negative scores for this factor included gravity, force, and acceleration. The former three concepts seem far less perceptual than the latter three. For the Externality factor, a concept that is externa
这种解释符合这样一种直觉，即抽象性不是二元结构，而是将概念渐变地翻译为更口头的编码。这个结论有些令人惊讶，因为这28个概念在性质上都是抽象的，因为它们没有直接的感知参照。一个抽象概念所引起的LIFG的激活量与其言语表征因子得分相对应。这些结果提出了一个有趣的理论和心理学问题，即神经语言系统，特别是LIFG，在抽象概念的口头表征中的作用。也就是说，从神经学和心理学的角度来看，一个抽象概念被口头表达意味着什么?抽象概念作为言语表征概念作为言语表征抽象概念在涉及言语处理的区域中被表征并在LIFG中激活意味着什么?当通过反复使用经颅磁刺激(TMS)人工损伤LIFG时，健康参与者在理解抽象概念(如机会)时的反应时间会慢150毫秒(Hoffman, Jefferies， & Lambon Ralph, 2010)。同样的基于tms的损伤程序对对具体概念(如苹果)做出反应所需的时间没有影响。然而，当抽象概念在特定环境中呈现时(例如，具体和抽象概念的神经表征457 https://www.cambridge.org/core/terms)，经颅磁刺激的影响差异就被抵消了。https://doi.org/10.1017/9781108635462.029从https://www.cambridge.org/core下载。卡内基梅隆大学，2021年9月15日00:03:10，受剑桥核心使用条款的约束，可在“你没有机会”处获得)。这些结果表明，一个概念的抽象性取决于它是否需要跨多个上下文整合意义(Crutch & Warrington 2005;2010;霍夫曼2016;Hayes & Kraemer, 2017)。此外，LIFG似乎参与了上下文依赖的意义整合。考虑到LIFG似乎参与了抽象概念意义的语境化(Hoffman et al.， 2010)，并且LIFG的激活程度与其口头表示的程度成正比(Vargas & Just, 2019)，综合这些结果表明，LIFG的激活反映了语境化词汇概念意义所需的心理活动的大小。与单词级概念相比，LIFG已被证明能引发句子级表征的更大激活(Xu, Kemeny, Park, Frattali， & Braun, 2005)。可能的情况是，LIFG神经激活背后的中心认知机制代表了跨多个表征的意义整合，以形成一个新的表征，该表征是其组成部分的产物。也就是说，与机会概念相比，苹果意义的组成部分生成复合表示所需的计算(在LIFG中)更少。此外，提供偶然的上下文，如“你没有机会”，通过提供其含义的更明确版本来减少认知负荷。类似的机制可以解释LIFG中句子比单个单词更大的激活，因为构建句子级表示需要以相互上下文约束的方式组合单个概念表示的含义。如前所述，另一个参与概念意义整合的区域是前颞叶(ATL)。ATL涉及到语义特征的整合，以形成对象概念的复合表示(Coutanche & Thompson-Schill, 2015)。然而，与LIFG不同的是，ATL似乎没有区分基于口头表示程度的抽象概念(如Vargas和Just(2019)中的因子得分所定义)。总之，抽象概念表示与句子中其他概念的整合似乎需要额外的计算。然而，目前尚不清楚这些整合计算是处理一些情景上下文(如Hoffman等人2010年的结果所示)，还是处理一些特定的概念表征，或者使用一些更一般的模态表征格式。混合概念:既不是完全具体的也不是完全抽象的混合概念是可以直接体验的概念，但需要在五种基本感知能力之外进行额外的处理，即458 r. vargas和m.a. just https://www.cambridge.org/core/terms。https://doi.org/10.1017/9781108635462.029从https://www.cambridge.org/core下载。卡内基梅隆大学，在2021年9月15日00:03:10，受剑桥核心使用条款的约束，可在诱发。这些概念并不完全符合具体与抽象的二分法。
{"title":"The Neural Representation of Concrete and Abstract Concepts","authors":"Robert Vargas, M. Just","doi":"10.1017/9781108635462.029","DOIUrl":"https://doi.org/10.1017/9781108635462.029","url":null,"abstract":"concepts elicited greater activation than concrete concepts in such verbal processing areas. By contrast, concrete concepts elicited greater activation than abstract concepts in visuospatial processing (precuneus, posterior cingulate, and fusiform gyrus). This meta-analysis was limited to univariate comparisons of categories of concepts and did not have access to the activation patterns evoked by individual concepts. This limitation potentially overlooks nuanced distinctions in the representational structure. Univariate contrasts potentially overlook critical relationships across neural states and neural regions (Mur et al., 2009). Through the use of MVPA techniques, more recent studies have begun to examine the underlying semantic structure of sets of abstract concepts. The next section focuses on various imaging studies examining the neural activation patterns associated with abstract concepts and explores the possible semantic structures that are specific to abstract concepts. Neurosemantic Dimensions of Abstract Meaning As in the case of concrete concepts, the semantic dimensions underlying abstract concept categories can be identified from their activation patterns. One of the first attempts to decode the semantic content of abstract semantic information was conducted by Anderson, Kiela, Clark, and Poesio (2017). A set of individual concepts that belonged to various taxonomic categories (tools, locations, social roles, events, communications, and attributes) were decoded from their activation patterns. Whether a concept belonged to one of two abstract semantic categories (i.e., Law orMusic) was also decoded from the activation patterns of individual concepts. Although these abstract semantic categories could be decoded based on their activation patterns, the localization of this dissociation is unclear. Neurally-based semantic dimensions underlying abstract concepts differ from the dimensions underlying concrete concepts. Vargas and Just (2019) investigated the fMRI activation patterns of 28 abstract concepts (e.g., ethics, truth, spirituality) focusing on individual concept representation and the relationship between the activation profiles of these concept representations. Factor analyses of the activation patterns evoked by the stimulus set revealed three underlying semantic dimensions. These dimensions corresponded to 1) the degree to which a concept was Verbally Represented, 2) whether a concept was External (or Internal) to the individual, and 3) whether the concept contained Social Content. The Verbal Representation dimension was present across all participants and was the most salient of the semantic dimensions. Concepts with large positive factor scores for this factor included compliment, faith, and ethics, while concepts with large negative scores for this factor included gravity, force, and acceleration. The former three concepts seem far less perceptual than the latter three. For the Externality factor, a concept that is externa","PeriodicalId":206489,"journal":{"name":"The Cambridge Handbook of Intelligence and Cognitive Neuroscience","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115306078","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-05-31DOI: 10.1017/9781108635462.009
A. Barbey
Flexibility is central to human intelligence and is made possible by the brain’s remarkable capacity to reconfigure itself – to continually update prior knowledge on the basis of new information and to actively generate internal predictions that guide adaptive behavior and decision making. Rather than lying dormant until stimulated, contemporary research conceives of the brain as a dynamic and active inference generator that anticipates incoming sensory inputs, forming hypotheses about that world that can be tested against sensory signals that arrive in the brain (Clark, 2013; Friston, 2010). Plasticity is therefore critical for the emergence of human intelligence, providing a powerful mechanism for updating prior beliefs, generating dynamic predictions about the world, and adapting in response to ongoing changes in the environment (Barbey, 2018). This perspective provides a catalyst for contemporary research on human intelligence, breaking away from the classic view that general intelligence (g) originates from individual differences in a fixed set of cortical regions or a singular brain network (for reviews, see Haier, 2017; Posner & Barbey, 2020). Early studies investigating the neurobiology of g focused on the lateral prefrontal cortex (Barbey, Colom, & Grafman, 2013b; Duncan et al., 2000), motivating an influential theory based on the role of this region in cognitive control functions for intelligent behavior (Duncan & Owen, 2000). The later emergence of network-based theories reflected an effort to examine the neurobiology of intelligence through a wider lens, accounting for individual differences in g on the basis of broadly distributed networks. For example, the Parietal-Frontal Integration Theory (P-FIT) was the first to propose that “a discrete parieto-frontal network underlies intelligence” (Jung & Haier, 2007) and that g reflects the capacity of this network to evaluate and test hypotheses for problem-solving (see also Barbey et al., 2012). A central feature
{"title":"Human Intelligence and Network Neuroscience","authors":"A. Barbey","doi":"10.1017/9781108635462.009","DOIUrl":"https://doi.org/10.1017/9781108635462.009","url":null,"abstract":"Flexibility is central to human intelligence and is made possible by the brain’s remarkable capacity to reconfigure itself – to continually update prior knowledge on the basis of new information and to actively generate internal predictions that guide adaptive behavior and decision making. Rather than lying dormant until stimulated, contemporary research conceives of the brain as a dynamic and active inference generator that anticipates incoming sensory inputs, forming hypotheses about that world that can be tested against sensory signals that arrive in the brain (Clark, 2013; Friston, 2010). Plasticity is therefore critical for the emergence of human intelligence, providing a powerful mechanism for updating prior beliefs, generating dynamic predictions about the world, and adapting in response to ongoing changes in the environment (Barbey, 2018). This perspective provides a catalyst for contemporary research on human intelligence, breaking away from the classic view that general intelligence (g) originates from individual differences in a fixed set of cortical regions or a singular brain network (for reviews, see Haier, 2017; Posner & Barbey, 2020). Early studies investigating the neurobiology of g focused on the lateral prefrontal cortex (Barbey, Colom, & Grafman, 2013b; Duncan et al., 2000), motivating an influential theory based on the role of this region in cognitive control functions for intelligent behavior (Duncan & Owen, 2000). The later emergence of network-based theories reflected an effort to examine the neurobiology of intelligence through a wider lens, accounting for individual differences in g on the basis of broadly distributed networks. For example, the Parietal-Frontal Integration Theory (P-FIT) was the first to propose that “a discrete parieto-frontal network underlies intelligence” (Jung & Haier, 2007) and that g reflects the capacity of this network to evaluate and test hypotheses for problem-solving (see also Barbey et al., 2012). A central feature","PeriodicalId":206489,"journal":{"name":"The Cambridge Handbook of Intelligence and Cognitive Neuroscience","volume":"267 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-05-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130486229","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1017/9781108635462.030
{"title":"Index","authors":"","doi":"10.1017/9781108635462.030","DOIUrl":"https://doi.org/10.1017/9781108635462.030","url":null,"abstract":"","PeriodicalId":206489,"journal":{"name":"The Cambridge Handbook of Intelligence and Cognitive Neuroscience","volume":"48 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127061886","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1017/9781108635462.013
{"title":"Neuroimaging Methods and Findings","authors":"","doi":"10.1017/9781108635462.013","DOIUrl":"https://doi.org/10.1017/9781108635462.013","url":null,"abstract":"","PeriodicalId":206489,"journal":{"name":"The Cambridge Handbook of Intelligence and Cognitive Neuroscience","volume":"54 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122701504","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1017/9781108635462.023
{"title":"Translating Research on the Neuroscience of Intelligence into Action","authors":"","doi":"10.1017/9781108635462.023","DOIUrl":"https://doi.org/10.1017/9781108635462.023","url":null,"abstract":"","PeriodicalId":206489,"journal":{"name":"The Cambridge Handbook of Intelligence and Cognitive Neuroscience","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127671512","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1017/9781108635462.024
M. Posner, M. Rothbart
{"title":"Enhancing Cognition","authors":"M. Posner, M. Rothbart","doi":"10.1017/9781108635462.024","DOIUrl":"https://doi.org/10.1017/9781108635462.024","url":null,"abstract":"","PeriodicalId":206489,"journal":{"name":"The Cambridge Handbook of Intelligence and Cognitive Neuroscience","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129020664","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1017/9781108635462.010
R. Kievit, I. Simpson-Kent
{"title":"It’s about Time","authors":"R. Kievit, I. Simpson-Kent","doi":"10.1017/9781108635462.010","DOIUrl":"https://doi.org/10.1017/9781108635462.010","url":null,"abstract":"","PeriodicalId":206489,"journal":{"name":"The Cambridge Handbook of Intelligence and Cognitive Neuroscience","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125152619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 1900-01-01DOI: 10.1017/9781108635462.007
{"title":"Theories, Models, and Hypotheses","authors":"","doi":"10.1017/9781108635462.007","DOIUrl":"https://doi.org/10.1017/9781108635462.007","url":null,"abstract":"","PeriodicalId":206489,"journal":{"name":"The Cambridge Handbook of Intelligence and Cognitive Neuroscience","volume":"121 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121834122","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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