Box 1. Simulations linking neuromodulation with behavioral data
Identifieur interne : 001866 ( Istex/Corpus ); précédent : 001865; suivant : 001867Box 1. Simulations linking neuromodulation with behavioral data
Auteurs : Shu-Chen Li ; Ulman Lindenberger ; Sverker SikströmSource :
- Trends in Cognitive Sciences [ 1364-6613 ] ; 2001.
English descriptors
- KwdEn :
- Teeft :
- Academic press, Activation function, Asymptotic performance, Attentional, Attentional mechanisms, Behavioral, Behavioral level, Behavioral manifestations, Bilateral activation, Cerebral cortex, Cognition, Cognitive, Cognitive deficits, Cognitive neuroscience, Cognitive performance, Cognitive processes, Cognitive sciences, Cognitive sciences november, Computational, Computational theories, Cortical, Cortical representations, Deficient, Deficient dopaminergic modulation, Deficient neuromodulation, Different levels, Different stimuli, Digit span, Dopamine, Dopamine receptors, Dopaminergic, Dopaminergic modulation, Dopaminergic system, Fluid intelligence, Frontal cortex, Functional relations, Functional relationships, Glucose metabolism, Human brain, Human subjects, Hypothesis generation, Information processing, Internal activation patterns, Internal representations, Intraindividual performance variability, Logistic activation function, Long list, Long lists, Memory capacity, Memory deficits, Memory performance, Modulation, More trials, Negative adult, Neural, Neural information processing, Neural information processing fidelity, Neural mechanisms, Neural noise, Neurobiological, Neurobiological level, Neurobiological levels, Neuromodulation, Neurosci, November, Older adults, Olds olds, Opinion trends, Paradigm shift, Performance level, Performance variability, Planck institute, Plenum press, Prefrontal, Prefrontal cortex, Process information, Processing resources, Processing speed, Processing steps, Processing units, Psychol, Reaction time, Receptor, Receptor mechanisms, Representational distinctiveness, Short lists, Simulation, Summary data, Theoretical link, Trends cognit, Unpublished data, Variability, Various facets, Various levels, Word pairs, Young networks.
Abstract
Basic cognitive functions, such as the abilities to activate, represent, maintain, focus and process information, decline with age. A paradigm shift towards cross-level conceptions is needed in order to obtain an integrative understanding of cognitive aging phenomena that cuts across neural, information-processing, and behavioral levels. We review empirical data at these different levels, and computational theories proposed to enable their integration. A theoretical link is highlighted, relating deficient neuromodulation with noisy information processing, which might result in less distinctive cortical representations. These less distinctive representations might be implicated in working memory and attentional functions that underlie the behavioral manifestations of cognitive aging deficits.
Url:
DOI: 10.1016/S1364-6613(00)01769-1
Links to Exploration step
ISTEX:ECAADE673B68979390F838B7AAA8C5B4C6026260Le document en format XML
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Morrison</name></json:item></editor></serie><host><title>Trends in Cognitive Sciences</title><language><json:string>unknown</json:string></language><publicationDate>2001</publicationDate><issn><json:string>1364-6613</json:string></issn><pii><json:string>S1364-6613(00)X0046-0</json:string></pii><volume>5</volume><issue>11</issue><pages><first>479</first><last>486</last></pages><genre><json:string>journal</json:string></genre></host><categories><wos><json:string>social science</json:string><json:string>psychology, experimental</json:string><json:string>science</json:string><json:string>neurosciences</json:string><json:string>behavioral sciences</json:string></wos><scienceMetrix><json:string>health sciences</json:string><json:string>psychology & cognitive sciences</json:string><json:string>experimental psychology</json:string></scienceMetrix><inist><json:string>sciences humaines et sociales</json:string></inist></categories><publicationDate>2001</publicationDate><copyrightDate>2001</copyrightDate><doi><json:string>10.1016/S1364-6613(00)01769-1</json:string></doi><id>ECAADE673B68979390F838B7AAA8C5B4C6026260</id><score>1</score><fulltext><json:item><extension>pdf</extension><original>true</original><mimetype>application/pdf</mimetype><uri>https://api.istex.fr/document/ECAADE673B68979390F838B7AAA8C5B4C6026260/fulltext/pdf</uri></json:item><json:item><extension>zip</extension><original>false</original><mimetype>application/zip</mimetype><uri>https://api.istex.fr/document/ECAADE673B68979390F838B7AAA8C5B4C6026260/fulltext/zip</uri></json:item><istex:fulltextTEI uri="https://api.istex.fr/document/ECAADE673B68979390F838B7AAA8C5B4C6026260/fulltext/tei"><teiHeader><fileDesc><titleStmt><title level="a" type="main" xml:lang="en">Box 1. Simulations linking neuromodulation with behavioral data</title><title level="a" type="main" xml:lang="en">Aging cognition: from neuromodulation to representation</title></titleStmt><publicationStmt><authority>ISTEX</authority><publisher>ELSEVIER</publisher><availability><p>©2001 Elsevier Science Ltd</p></availability><date>2001</date></publicationStmt><notesStmt><note type="content">Section title: Opinion</note><note type="content">Fig. 1: A summary of cognitive aging issues addressed by researchers of different specializations working at various levels of analysis.</note><note type="content">Fig. 2: Age-related changes in information processing and neurotransmitter density. (a) Negative adult age differences in working memory measured by three types of span test (computational, reading and backward digit span), scaled in Z score metric. (b) Negative adult age differences in processing speed measured by three perceptual speed tests (digit symbol substitution, pattern and letter comparison), scaled in a Z-score metric. (a and b adapted with permission from Ref. 27.) (c) Aging-related declines in dopamine D2-like receptor availability in the frontal cortex. (Adapted with permission from Ref. 19.)</note><note type="content">Fig. 3: Simulations from computational theories of cognitive aging: effects of deficient neuromodulation. (a) The S-shaped logistic activation function at different values of the gain parameter, G. Physiological evidence suggests that the logistic function with a negative bias captures the function relating the strength of an input signal to a neuron's firing rate, with its steepest slope around the baseline firing rate. Reducing mean G flattens the activation function such that a unit becomes less responsive. Aging-related decline of dopaminergic modulation can be simulated by sampling values of G from a distribution with a lower mean. (b) G and the variability of activation across processing steps. Reducing mean G (0.8 and 0.3 for the ‘young’ and ‘old’ networks, respectively) increases the temporal variability of a unit's response to an identical input signal (set to 4.0) across 1000 trials. (c) Internal activation patterns across five hidden units of one ‘young’ and one ‘old’ network in response to four different stimuli (S1 to S4). The internal representations of the four stimuli are much less differentiable in the ‘old’ than in the ‘young’ network. (Adapted from Ref. 13 with permission.)</note><note type="content">Fig. I: Comparing simulations with human behavioral data. (a) Aging deficits in paired-associate learning in human subjects and simulations. There is good agreement between the simulations and human data: like the 50-yr olds the ‘old’ networks (NW) required more trials to reach harder recall criteria. (b) Aging impairments at asymptotic performance in human subjects and simulations. The human performance is reasonably well simulated by reducing the average gain (G) of the network's processing units. (c) Increases in susceptibility to interference in dual-list paired-associate learning are seen both in human subjects and in young and old network simulations. (d) The effect of mean G reduction on intra-network variability in performance level across different study lists in four conditions. The old networks (lower mean G) show a greater intra-network variability. (e) The G parameter and inter-network variability. Across different list lengths, reducing mean G not only reduces mean recall performance, but also increases inter-network variability. (f) G and covariation of performances. Reducing mean G increases the correlation between performances with short and long lists. The correlation is stronger for the ‘old’ (r = 0.66) than for the ‘young’ (r = 0.43) networks (difference between correlations is statistically significant, z = 2.4). (Adapted with permission from Ref. 13.)</note></notesStmt><sourceDesc><biblStruct type="inbook"><analytic><title level="a" type="main" xml:lang="en">Box 1. Simulations linking neuromodulation with behavioral data</title><title level="a" type="main" xml:lang="en">Aging cognition: from neuromodulation to representation</title><author xml:id="author-0000"><persName><forename type="first">Shu-Chen</forename><surname>Li</surname></persName><email>shuchen@mpib-berlin.mpg.de</email><affiliation>Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, D-14195 Berlin, Germany</affiliation></author><author xml:id="author-0001"><persName><forename type="first">Ulman</forename><surname>Lindenberger</surname></persName><affiliation>School of Psychology, Saarland University, Saarbrücken, Germany</affiliation></author><author xml:id="author-0002"><persName><forename type="first">Sverker</forename><surname>Sikström</surname></persName><affiliation>Dept of Psychology, Stockholm University, Stockholm, Sweden</affiliation></author><idno type="istex">ECAADE673B68979390F838B7AAA8C5B4C6026260</idno><idno type="DOI">10.1016/S1364-6613(00)01769-1</idno><idno type="PII">S1364-6613(00)01769-1</idno></analytic><monogr><title level="j">Trends in Cognitive Sciences</title><title level="j" type="abbrev">TICS</title><idno type="pISSN">1364-6613</idno><idno type="PII">S1364-6613(00)X0046-0</idno><imprint><publisher>ELSEVIER</publisher><date type="published" when="2001"></date><biblScope unit="volume">5</biblScope><biblScope unit="issue">11</biblScope><biblScope unit="page" from="479">479</biblScope><biblScope unit="page" to="486">486</biblScope></imprint></monogr></biblStruct></sourceDesc></fileDesc><profileDesc><creation><date>2001</date></creation><langUsage><language ident="en">en</language></langUsage><abstract xml:lang="en"><p>Basic cognitive functions, such as the abilities to activate, represent, maintain, focus and process information, decline with age. A paradigm shift towards cross-level conceptions is needed in order to obtain an integrative understanding of cognitive aging phenomena that cuts across neural, information-processing, and behavioral levels. We review empirical data at these different levels, and computational theories proposed to enable their integration. A theoretical link is highlighted, relating deficient neuromodulation with noisy information processing, which might result in less distinctive cortical representations. These less distinctive representations might be implicated in working memory and attentional functions that underlie the behavioral manifestations of cognitive aging deficits.</p></abstract><textClass><keywords scheme="keyword"><list><head>article-category</head><item><term>Opinion</term></item></list></keywords></textClass><textClass xml:lang="en"><keywords scheme="keyword"><list><head>Keywords</head><item><term>Aging</term></item><item><term>Dopaminergic modulation</term></item><item><term>Working memory</term></item><item><term>Cortical representation</term></item><item><term>Neural networks</term></item><item><term>Gain parameter</term></item></list></keywords></textClass></profileDesc><revisionDesc><change when="2001">Published</change></revisionDesc></teiHeader></istex:fulltextTEI><json:item><extension>txt</extension><original>false</original><mimetype>text/plain</mimetype><uri>https://api.istex.fr/document/ECAADE673B68979390F838B7AAA8C5B4C6026260/fulltext/txt</uri></json:item></fulltext><metadata><istex:metadataXml wicri:clean="Elsevier, elements deleted: ce:floats; body; tail"><istex:xmlDeclaration>version="1.0" encoding="utf-8"</istex:xmlDeclaration><istex:docType PUBLIC="-//ES//DTD journal article DTD version 4.5.2//EN//XML" URI="art452.dtd" name="istex:docType"><istex:entity SYSTEM="gr1" NDATA="IMAGE" name="gr1"></istex:entity><istex:entity SYSTEM="gr2" NDATA="IMAGE" name="gr2"></istex:entity><istex:entity SYSTEM="gr3" NDATA="IMAGE" name="gr3"></istex:entity><istex:entity SYSTEM="gr4" NDATA="IMAGE" name="gr4"></istex:entity></istex:docType><istex:document><converted-article version="4.5.2" docsubtype="rev" xml:lang="en"><item-info><jid>TICS</jid><aid>00001769</aid><ce:pii>S1364-6613(00)01769-1</ce:pii><ce:doi>10.1016/S1364-6613(00)01769-1</ce:doi><ce:copyright type="full-transfer" year="2001">Elsevier Science Ltd</ce:copyright><ce:doctopics><ce:doctopic><ce:text>Opinion</ce:text></ce:doctopic></ce:doctopics></item-info><head><ce:dochead><ce:textfn>Opinion</ce:textfn></ce:dochead><ce:title>Aging cognition: from neuromodulation to representation</ce:title><ce:author-group><ce:author><ce:given-name>Shu-Chen</ce:given-name><ce:surname>Li</ce:surname><ce:cross-ref refid="AFF1"><ce:sup>a</ce:sup></ce:cross-ref><ce:e-address>shuchen@mpib-berlin.mpg.de</ce:e-address></ce:author><ce:author><ce:given-name>Ulman</ce:given-name><ce:surname>Lindenberger</ce:surname><ce:cross-ref refid="AFF2"><ce:sup>b</ce:sup></ce:cross-ref></ce:author><ce:author><ce:given-name>Sverker</ce:given-name><ce:surname>Sikström</ce:surname><ce:cross-ref refid="AFF3"><ce:sup>c</ce:sup></ce:cross-ref></ce:author><ce:affiliation id="AFF1"><ce:label>a</ce:label><ce:textfn>Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, D-14195 Berlin, Germany</ce:textfn></ce:affiliation><ce:affiliation id="AFF2"><ce:label>b</ce:label><ce:textfn>School of Psychology, Saarland University, Saarbrücken, Germany</ce:textfn></ce:affiliation><ce:affiliation id="AFF3"><ce:label>c</ce:label><ce:textfn>Dept of Psychology, Stockholm University, Stockholm, Sweden</ce:textfn></ce:affiliation></ce:author-group><ce:abstract><ce:section-title>Abstract</ce:section-title><ce:abstract-sec><ce:simple-para>Basic cognitive functions, such as the abilities to activate, represent, maintain, focus and process information, decline with age. A paradigm shift towards cross-level conceptions is needed in order to obtain an integrative understanding of cognitive aging phenomena that cuts across neural, information-processing, and behavioral levels. We review empirical data at these different levels, and computational theories proposed to enable their integration. A theoretical link is highlighted, relating deficient neuromodulation with noisy information processing, which might result in less distinctive cortical representations. These less distinctive representations might be implicated in working memory and attentional functions that underlie the behavioral manifestations of cognitive aging deficits.</ce:simple-para></ce:abstract-sec></ce:abstract><ce:keywords class="keyword"><ce:section-title>Keywords</ce:section-title><ce:keyword><ce:text>Aging</ce:text></ce:keyword><ce:keyword><ce:text>Dopaminergic modulation</ce:text></ce:keyword><ce:keyword><ce:text>Working memory</ce:text></ce:keyword><ce:keyword><ce:text>Cortical representation</ce:text></ce:keyword><ce:keyword><ce:text>Neural networks</ce:text></ce:keyword><ce:keyword><ce:text>Gain parameter</ce:text></ce:keyword></ce:keywords></head></converted-article></istex:document></istex:metadataXml><mods version="3.6"><titleInfo lang="en"><title>Box 1. Simulations linking neuromodulation with behavioral data</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>Box 1. Simulations linking neuromodulation with behavioral data</title></titleInfo><titleInfo lang="en"><title>Aging cognition: from neuromodulation to representation</title></titleInfo><titleInfo type="alternative" lang="en" contentType="CDATA"><title>Aging cognition: from neuromodulation to representation</title></titleInfo><name type="personal"><namePart type="given">Shu-Chen</namePart><namePart type="family">Li</namePart><affiliation>E-mail: shuchen@mpib-berlin.mpg.de</affiliation><affiliation>Center for Lifespan Psychology, Max Planck Institute for Human Development, Lentzeallee 94, D-14195 Berlin, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Ulman</namePart><namePart type="family">Lindenberger</namePart><affiliation>School of Psychology, Saarland University, Saarbrücken, Germany</affiliation><role><roleTerm type="text">author</roleTerm></role></name><name type="personal"><namePart type="given">Sverker</namePart><namePart type="family">Sikström</namePart><affiliation>Dept of Psychology, Stockholm University, Stockholm, Sweden</affiliation><role><roleTerm type="text">author</roleTerm></role></name><typeOfResource>text</typeOfResource><genre type="review-article" displayLabel="Review article"></genre><originInfo><publisher>ELSEVIER</publisher><dateIssued encoding="w3cdtf">2001</dateIssued><copyrightDate encoding="w3cdtf">2001</copyrightDate></originInfo><language><languageTerm type="code" authority="iso639-2b">eng</languageTerm><languageTerm type="code" authority="rfc3066">en</languageTerm></language><physicalDescription><internetMediaType>text/html</internetMediaType></physicalDescription><abstract lang="en">Basic cognitive functions, such as the abilities to activate, represent, maintain, focus and process information, decline with age. A paradigm shift towards cross-level conceptions is needed in order to obtain an integrative understanding of cognitive aging phenomena that cuts across neural, information-processing, and behavioral levels. We review empirical data at these different levels, and computational theories proposed to enable their integration. A theoretical link is highlighted, relating deficient neuromodulation with noisy information processing, which might result in less distinctive cortical representations. These less distinctive representations might be implicated in working memory and attentional functions that underlie the behavioral manifestations of cognitive aging deficits.</abstract><note type="content">Section title: Opinion</note><note type="content">Fig. 1: A summary of cognitive aging issues addressed by researchers of different specializations working at various levels of analysis.</note><note type="content">Fig. 2: Age-related changes in information processing and neurotransmitter density. (a) Negative adult age differences in working memory measured by three types of span test (computational, reading and backward digit span), scaled in Z score metric. (b) Negative adult age differences in processing speed measured by three perceptual speed tests (digit symbol substitution, pattern and letter comparison), scaled in a Z-score metric. (a and b adapted with permission from Ref. 27.) (c) Aging-related declines in dopamine D2-like receptor availability in the frontal cortex. (Adapted with permission from Ref. 19.)</note><note type="content">Fig. 3: Simulations from computational theories of cognitive aging: effects of deficient neuromodulation. (a) The S-shaped logistic activation function at different values of the gain parameter, G. Physiological evidence suggests that the logistic function with a negative bias captures the function relating the strength of an input signal to a neuron's firing rate, with its steepest slope around the baseline firing rate. Reducing mean G flattens the activation function such that a unit becomes less responsive. Aging-related decline of dopaminergic modulation can be simulated by sampling values of G from a distribution with a lower mean. (b) G and the variability of activation across processing steps. Reducing mean G (0.8 and 0.3 for the ‘young’ and ‘old’ networks, respectively) increases the temporal variability of a unit's response to an identical input signal (set to 4.0) across 1000 trials. (c) Internal activation patterns across five hidden units of one ‘young’ and one ‘old’ network in response to four different stimuli (S1 to S4). The internal representations of the four stimuli are much less differentiable in the ‘old’ than in the ‘young’ network. (Adapted from Ref. 13 with permission.)</note><note type="content">Fig. I: Comparing simulations with human behavioral data. (a) Aging deficits in paired-associate learning in human subjects and simulations. There is good agreement between the simulations and human data: like the 50-yr olds the ‘old’ networks (NW) required more trials to reach harder recall criteria. (b) Aging impairments at asymptotic performance in human subjects and simulations. The human performance is reasonably well simulated by reducing the average gain (G) of the network's processing units. (c) Increases in susceptibility to interference in dual-list paired-associate learning are seen both in human subjects and in young and old network simulations. (d) The effect of mean G reduction on intra-network variability in performance level across different study lists in four conditions. The old networks (lower mean G) show a greater intra-network variability. (e) The G parameter and inter-network variability. Across different list lengths, reducing mean G not only reduces mean recall performance, but also increases inter-network variability. (f) G and covariation of performances. Reducing mean G increases the correlation between performances with short and long lists. The correlation is stronger for the ‘old’ (r = 0.66) than for the ‘young’ (r = 0.43) networks (difference between correlations is statistically significant, z = 2.4). (Adapted with permission from Ref. 13.)</note><subject><genre>article-category</genre><topic>Opinion</topic></subject><subject lang="en"><genre>Keywords</genre><topic>Aging</topic><topic>Dopaminergic modulation</topic><topic>Working memory</topic><topic>Cortical representation</topic><topic>Neural networks</topic><topic>Gain parameter</topic></subject><relatedItem type="host"><titleInfo><title>Trends in Cognitive Sciences</title></titleInfo><titleInfo type="abbreviated"><title>TICS</title></titleInfo><genre type="journal">journal</genre><originInfo><dateIssued encoding="w3cdtf">20011101</dateIssued></originInfo><identifier type="ISSN">1364-6613</identifier><identifier type="PII">S1364-6613(00)X0046-0</identifier><part><date>20011101</date><detail type="volume"><number>5</number><caption>vol.</caption></detail><detail type="issue"><number>11</number><caption>no.</caption></detail><extent unit="issue pages"><start>457</start><end>501</end></extent><extent unit="pages"><start>479</start><end>486</end></extent></part></relatedItem><identifier type="istex">ECAADE673B68979390F838B7AAA8C5B4C6026260</identifier><identifier type="DOI">10.1016/S1364-6613(00)01769-1</identifier><identifier type="PII">S1364-6613(00)01769-1</identifier><accessCondition type="use and reproduction" contentType="copyright">©2001 Elsevier Science Ltd</accessCondition><recordInfo><recordContentSource>ELSEVIER</recordContentSource><recordOrigin>Elsevier Science Ltd, ©2001</recordOrigin></recordInfo></mods></metadata></istex></record>Pour manipuler ce document sous Unix (Dilib)
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