The Experts below are selected from a list of 33732 Experts worldwide ranked by ideXlab platform
Nash Unsworth - One of the best experts on this subject based on the ideXlab platform.
-
working Memory and fluid intelligence capacity attention control and Secondary Memory retrieval
Cognitive Psychology, 2014Co-Authors: Nash Unsworth, Keisuke Fukuda, Edward K VogelAbstract:Several theories have been put forth to explain the relation between working Memory (WM) and gF. Unfortunately, no single factor has been shown to fully account for the relation between these two important constructs. In the current study we tested whether multiple factors (capacity, attention control, and Secondary Memory) would collectively account for the relation. A large number of participants performed multiple measures of each construct and latent variable analyses were used to examine the data. The results demonstrated that capacity, attention control, and Secondary Memory were uniquely related to WM storage, WM processing, and gF. Importantly, the three factors completely accounted for the relation between WM (both processing and storage) and gF. Thus, although storage and processing make independent contributions to gF, both of these contributions are accounted for by variation in capacity, attention control and Secondary Memory. These results are consistent with the multifaceted view of WM, suggesting that individual differences in capacity, attention control, and Secondary Memory jointly account for individual differences in WM and its relation with gF.
-
working Memory capacity attention control Secondary Memory or both a direct test of the dual component model
Journal of Memory and Language, 2010Co-Authors: Nash Unsworth, Gregory J. SpillersAbstract:The current study examined the extent to which attention control abilities, Secondary Memory abilities, or both accounted for variation in working Memory capacity (WMC) and its relation to fluid intelligence. Participants performed various attention control, Secondary Memory, WMC, and fluid intelligence measures. Confirmatory factor analyses suggested that attention control, Secondary Memory, and WMC were best represented as three separate, yet correlated factors, each of which was correlated with fluid intelligence. Structural equation modeling suggested that both attention control and Secondary Memory accounted for unique variance in WMC. Furthermore, structural equation modeling and variance partitioning analyses suggested that a substantial part of the shared variance between WMC and fluid intelligence was due to both attention control and Secondary Memory abilities. Working Memory capacity also accounted for variance in fluid intelligence independently of what was accounted for by the other two factors. The results are interpreted within a dual-component model of WMC which suggests that both attention control and Secondary Memory abilities (as well as other abilities) are important components of WMC.
-
The contributions of primary and Secondary Memory to working Memory capacity: an individual differences analysis of immediate free recall.
Journal of experimental psychology. Learning memory and cognition, 2010Co-Authors: Nash Unsworth, Gregory J. Spillers, Gene A. BrewerAbstract:The present study tested the dual-component model of working Memory capacity (WMC) by examining estimates of primary Memory and Secondary Memory from an immediate free recall task. Participants completed multiple measures of WMC and general intellectual ability as well as multiple trials of an immediate free recall task. It was demonstrated that there are 2 sources of variance (primary Memory and Secondary Memory) in immediate free recall and that, further, these 2 sources of variance accounted for independent variation in WMC. Together, these results are consistent with a dual-component model of WMC reflecting individual differences in maintenance in primary Memory and in retrieval from Secondary Memory. Theoretical implications for working Memory and dual-component models of free recall are discussed. Working Memory is usually referred to as a general purpose system that is responsible for the active maintenance of task- or goal-relevant information while simultaneously processing or act- ing on other information (Baddeley, 2007). Given the need of such a general purpose system for a wide variety of activities— including problem solving, reading, coordination and planning, and basic intellectual functioning more broadly—recent work has been devoted to measuring the capacity of working Memory and investigating individual differences in working Memory capacity (WMC). Beginning with Daneman and Carpenter (1980), most researchers have utilized complex working Memory span tasks in which to-be-remembered (TBR) items are interspersed with some processing activity. For instance, in the reading span task partici- pants attempt to remember words or letters while reading and comprehending sentences (Daneman & Carpenter, 1980). These tasks can be contrasted with simple Memory span tasks in which TBR items are presented without any additional processing activ- ities. The complex span tasks nicely capture the idea that the dynamics of processing and storage are needed to fully understand the essence of working Memory and tap its capacity. Furthermore, these tasks can be used to estimate an individual's WMC and examine the correlation between this capacity and other important cognitive abilities. Due to the popularity of complex span tasks and the fact that they provide good estimates of WMC, a number of theories have been proposed to account for performance on these tasks and to explain working Memory more broadly. For instance, many orig- inal accounts of complex span tasks emphasized the notion that resources have to be shared between processing and storage ac- tivities and thus the capacity of working Memory is the amount of total resources that individuals have at their disposal (e.g., Dane- man & Carpenter, 1980). Individuals with more resources can effectively deal with the processing task while continuing to main- tain activation of the TBR items, which leads to better performance than in the case of individuals with fewer resources. Alternatively, it is possible that the complex span tasks do not index overall resource-sharing abilities but rather that the processing task dis- places items from working Memory, and thus a rapid switching mechanism is needed to refresh items before they are lost due to time-based forgetting processes such as decay (Towse, Hitch, &
-
There’s more to the working Memory capacity—fluid intelligence relationship than just Secondary Memory
Psychonomic Bulletin & Review, 2009Co-Authors: Nash Unsworth, Gene A. Brewer, Gregory J. SpillersAbstract:The present study examined the claim that Secondary Memory processes account for the correlation between working Memory capacity and fluid intelligence via a latent variable analysis. In the present study, participants performed multiple measures of Secondary Memory, working Memory capacity, and fluid intelligence. Structural equation modeling suggested that both Secondary Memory and working Memory capacity account for unique variance in fluid intelligence. These results are inconsistent with recent claims that working Memory capacity does not account for variance in fluid intelligence over and above what is accounted for by Secondary Memory. Rather, the results are consistent with models of working Memory capacity that suggest that both maintenance and retrieval processes are needed to account for the substantial relation between working Memory capacity and fluid intelligence.
-
there s more to the working Memory capacity fluid intelligence relationship than just Secondary Memory
Psychonomic Bulletin & Review, 2009Co-Authors: Nash Unsworth, Gene A Ewe, Gregory J. SpillersAbstract:The present study examined the claim that Secondary Memory processes account for the correlation between working Memory capacity and fluid intelligence via a latent variable analysis. In the present study, participants performed multiple measures of Secondary Memory, working Memory capacity, and fluid intelligence. Structural equation modeling suggested that both Secondary Memory and working Memory capacity account for unique variance in fluid intelligence. These results are inconsistent with recent claims that working Memory capacity does not account for variance in fluid intelligence over and above what is accounted for by Secondary Memory. Rather, the results are consistent with models of working Memory capacity that suggest that both maintenance and retrieval processes are needed to account for the substantial relation between working Memory capacity and fluid intelligence.
Gregory J. Spillers - One of the best experts on this subject based on the ideXlab platform.
-
working Memory capacity attention control Secondary Memory or both a direct test of the dual component model
Journal of Memory and Language, 2010Co-Authors: Nash Unsworth, Gregory J. SpillersAbstract:The current study examined the extent to which attention control abilities, Secondary Memory abilities, or both accounted for variation in working Memory capacity (WMC) and its relation to fluid intelligence. Participants performed various attention control, Secondary Memory, WMC, and fluid intelligence measures. Confirmatory factor analyses suggested that attention control, Secondary Memory, and WMC were best represented as three separate, yet correlated factors, each of which was correlated with fluid intelligence. Structural equation modeling suggested that both attention control and Secondary Memory accounted for unique variance in WMC. Furthermore, structural equation modeling and variance partitioning analyses suggested that a substantial part of the shared variance between WMC and fluid intelligence was due to both attention control and Secondary Memory abilities. Working Memory capacity also accounted for variance in fluid intelligence independently of what was accounted for by the other two factors. The results are interpreted within a dual-component model of WMC which suggests that both attention control and Secondary Memory abilities (as well as other abilities) are important components of WMC.
-
The contributions of primary and Secondary Memory to working Memory capacity: an individual differences analysis of immediate free recall.
Journal of experimental psychology. Learning memory and cognition, 2010Co-Authors: Nash Unsworth, Gregory J. Spillers, Gene A. BrewerAbstract:The present study tested the dual-component model of working Memory capacity (WMC) by examining estimates of primary Memory and Secondary Memory from an immediate free recall task. Participants completed multiple measures of WMC and general intellectual ability as well as multiple trials of an immediate free recall task. It was demonstrated that there are 2 sources of variance (primary Memory and Secondary Memory) in immediate free recall and that, further, these 2 sources of variance accounted for independent variation in WMC. Together, these results are consistent with a dual-component model of WMC reflecting individual differences in maintenance in primary Memory and in retrieval from Secondary Memory. Theoretical implications for working Memory and dual-component models of free recall are discussed. Working Memory is usually referred to as a general purpose system that is responsible for the active maintenance of task- or goal-relevant information while simultaneously processing or act- ing on other information (Baddeley, 2007). Given the need of such a general purpose system for a wide variety of activities— including problem solving, reading, coordination and planning, and basic intellectual functioning more broadly—recent work has been devoted to measuring the capacity of working Memory and investigating individual differences in working Memory capacity (WMC). Beginning with Daneman and Carpenter (1980), most researchers have utilized complex working Memory span tasks in which to-be-remembered (TBR) items are interspersed with some processing activity. For instance, in the reading span task partici- pants attempt to remember words or letters while reading and comprehending sentences (Daneman & Carpenter, 1980). These tasks can be contrasted with simple Memory span tasks in which TBR items are presented without any additional processing activ- ities. The complex span tasks nicely capture the idea that the dynamics of processing and storage are needed to fully understand the essence of working Memory and tap its capacity. Furthermore, these tasks can be used to estimate an individual's WMC and examine the correlation between this capacity and other important cognitive abilities. Due to the popularity of complex span tasks and the fact that they provide good estimates of WMC, a number of theories have been proposed to account for performance on these tasks and to explain working Memory more broadly. For instance, many orig- inal accounts of complex span tasks emphasized the notion that resources have to be shared between processing and storage ac- tivities and thus the capacity of working Memory is the amount of total resources that individuals have at their disposal (e.g., Dane- man & Carpenter, 1980). Individuals with more resources can effectively deal with the processing task while continuing to main- tain activation of the TBR items, which leads to better performance than in the case of individuals with fewer resources. Alternatively, it is possible that the complex span tasks do not index overall resource-sharing abilities but rather that the processing task dis- places items from working Memory, and thus a rapid switching mechanism is needed to refresh items before they are lost due to time-based forgetting processes such as decay (Towse, Hitch, &
-
There’s more to the working Memory capacity—fluid intelligence relationship than just Secondary Memory
Psychonomic Bulletin & Review, 2009Co-Authors: Nash Unsworth, Gene A. Brewer, Gregory J. SpillersAbstract:The present study examined the claim that Secondary Memory processes account for the correlation between working Memory capacity and fluid intelligence via a latent variable analysis. In the present study, participants performed multiple measures of Secondary Memory, working Memory capacity, and fluid intelligence. Structural equation modeling suggested that both Secondary Memory and working Memory capacity account for unique variance in fluid intelligence. These results are inconsistent with recent claims that working Memory capacity does not account for variance in fluid intelligence over and above what is accounted for by Secondary Memory. Rather, the results are consistent with models of working Memory capacity that suggest that both maintenance and retrieval processes are needed to account for the substantial relation between working Memory capacity and fluid intelligence.
-
there s more to the working Memory capacity fluid intelligence relationship than just Secondary Memory
Psychonomic Bulletin & Review, 2009Co-Authors: Nash Unsworth, Gene A Ewe, Gregory J. SpillersAbstract:The present study examined the claim that Secondary Memory processes account for the correlation between working Memory capacity and fluid intelligence via a latent variable analysis. In the present study, participants performed multiple measures of Secondary Memory, working Memory capacity, and fluid intelligence. Structural equation modeling suggested that both Secondary Memory and working Memory capacity account for unique variance in fluid intelligence. These results are inconsistent with recent claims that working Memory capacity does not account for variance in fluid intelligence over and above what is accounted for by Secondary Memory. Rather, the results are consistent with models of working Memory capacity that suggest that both maintenance and retrieval processes are needed to account for the substantial relation between working Memory capacity and fluid intelligence.
Kurt Mehlhorn - One of the best experts on this subject based on the ideXlab platform.
-
leda sm extending leda to Secondary Memory
Lecture Notes in Computer Science, 1999Co-Authors: Andreas Crauser, Kurt MehlhornAbstract:During the last years, many software libraries for in-core computation have been developed. Most internal Memory algorithms perform very badly when used in an external Memory setting. We introduce LEDA-SM that extends the LEDA-library [22] towards Secondary Memory computation. LEDA-SM uses I/O-efficient algorithms and data structures that do not suffer from the so called I/O bottleneck. LEDA is used for in-core computation. We explain the design of LEDA-SM and report on performance results.
-
Algorithm Engineering - LEDA-SM Extending LEDA to Secondary Memory
Lecture Notes in Computer Science, 1999Co-Authors: Andreas Crauser, Kurt MehlhornAbstract:During the last years, many software libraries for in-core computation have been developed. Most internal Memory algorithms perform very badly when used in an external Memory setting. We introduce LEDA-SM that extends the LEDA-library [22] towards Secondary Memory computation. LEDA-SM uses I/O-efficient algorithms and data structures that do not suffer from the so called I/O bottleneck. LEDA is used for in-core computation. We explain the design of LEDA-SM and report on performance results.
Jason M. Grayson - One of the best experts on this subject based on the ideXlab platform.
-
gammaherpesvirus latency differentially impacts the generation of primary versus Secondary Memory cd8 t cells during subsequent infection
Journal of Virology, 2014Co-Authors: Erik S. Barton, Sujana Rajkarnikar, P. K. Langston, Madeline J. Price, Jason M. GraysonAbstract:Unlike laboratory animals, humans are infected with multiple pathogens, including the highly prevalent herpesviruses. The purpose of these studies was to determine the effect of gammaherpesvirus latency on T cell number and differentiation during subsequent heterologous viral infections. Mice were first infected with murine gammaherpesvirus 68 (MHV68), a model of Epstein-Barr virus (EBV) infection, and then after latency was established, they were challenged with the Armstrong strain of lymphocytic choriomeningitis virus (LCMV). The initial replication of LCMV was lower in latently infected mice, and the maturation of dendritic cells was abated. Although the number of LCMV-specific effector CD8+ T cells was not altered, they were skewed to a Memory phenotype. In contrast, LCMV-specific effector CD4+ T cells were increased in latently infected mice compared to those in mice infected solely with LCMV. When the Memory phase was reached, latently infected mice had an LCMV-specific Memory T cell pool that was increased relative to that found in singly infected mice. Importantly, LCMV-specific Memory CD8+ T cells had decreased CD27 and increased killer cell lectin-like receptor G1 (KLRG1) expression. Upon Secondary challenge, LCMV-specific Secondary effector CD8+ T cells expanded and cleared the infection. However, the LCMV-specific Secondary Memory CD8+ T cell pool was decreased in latently infected animals, abrogating the boosting effect normally observed following rechallenge. Taken together, these results demonstrate that ongoing gammaherpesvirus latency affects the number and phenotype of primary versus Secondary Memory CD8+ T cells during acute infection. IMPORTANCE CD8+ T cells are critical for the clearance of intracellular pathogens, including viruses, certain bacteria, and tumors. However, current models for Memory CD8+ T cell differentiation are derived from pathogen-free laboratory mice challenged with a single pathogen or vaccine vector. Unlike laboratory animals, all humans are infected with multiple acute and chronic pathogens, including the highly prevalent herpesviruses Epstein-Barr virus (EBV), cytomegalovirus (CMV), herpes simplex viruses (HSV), and varicella-zoster virus (VZV). The purpose of these studies was to determine the effect of gammaherpesvirus latency on T cell number and differentiation during subsequent heterologous viral infections. We observed that ongoing gammaherpesvirus latency affects the number and phenotype of primary versus Secondary Memory CD8+ T cells during acute infection. These results suggest that unlike pathogen-free laboratory mice, infection or immunization of latently infected humans may result in the generation of T cells with limited potential for long-term protection.
-
Gammaherpesvirus Latency Differentially Impacts The Generation Of Primary Versus Secondary Memory CD8+T Cells During Subsequent Infection
Journal of virology, 2014Co-Authors: Erik S. Barton, Sujana Rajkarnikar, P. K. Langston, Madeline J. Price, Jason M. GraysonAbstract:Unlike laboratory animals, humans are infected with multiple pathogens, including the highly prevalent herpesviruses. The purpose of these studies was to determine the effect of gammaherpesvirus latency on T cell number and differentiation during subsequent heterologous viral infections. Mice were first infected with murine gammaherpesvirus 68 (MHV68), a model of Epstein-Barr virus (EBV) infection, and then after latency was established, they were challenged with the Armstrong strain of lymphocytic choriomeningitis virus (LCMV). The initial replication of LCMV was lower in latently infected mice, and the maturation of dendritic cells was abated. Although the number of LCMV-specific effector CD8+ T cells was not altered, they were skewed to a Memory phenotype. In contrast, LCMV-specific effector CD4+ T cells were increased in latently infected mice compared to those in mice infected solely with LCMV. When the Memory phase was reached, latently infected mice had an LCMV-specific Memory T cell pool that was increased relative to that found in singly infected mice. Importantly, LCMV-specific Memory CD8+ T cells had decreased CD27 and increased killer cell lectin-like receptor G1 (KLRG1) expression. Upon Secondary challenge, LCMV-specific Secondary effector CD8+ T cells expanded and cleared the infection. However, the LCMV-specific Secondary Memory CD8+ T cell pool was decreased in latently infected animals, abrogating the boosting effect normally observed following rechallenge. Taken together, these results demonstrate that ongoing gammaherpesvirus latency affects the number and phenotype of primary versus Secondary Memory CD8+ T cells during acute infection. IMPORTANCE CD8+ T cells are critical for the clearance of intracellular pathogens, including viruses, certain bacteria, and tumors. However, current models for Memory CD8+ T cell differentiation are derived from pathogen-free laboratory mice challenged with a single pathogen or vaccine vector. Unlike laboratory animals, all humans are infected with multiple acute and chronic pathogens, including the highly prevalent herpesviruses Epstein-Barr virus (EBV), cytomegalovirus (CMV), herpes simplex viruses (HSV), and varicella-zoster virus (VZV). The purpose of these studies was to determine the effect of gammaherpesvirus latency on T cell number and differentiation during subsequent heterologous viral infections. We observed that ongoing gammaherpesvirus latency affects the number and phenotype of primary versus Secondary Memory CD8+ T cells during acute infection. These results suggest that unlike pathogen-free laboratory mice, infection or immunization of latently infected humans may result in the generation of T cells with limited potential for long-term protection.
-
Peroxiredoxin II regulates effector and Secondary Memory CD8+ T cell responses
Journal of virology, 2012Co-Authors: Ryan D. Michalek, Katie E. Crump, Ashley E. Weant, Elizabeth M. Hiltbold, Daniel G. Juneau, Eun-yi Moon, Leslie B. Poole, Jason M. GraysonAbstract:Reactive oxygen intermediates (ROI) generated in response to receptor stimulation play an important role in cellular responses. However, the effect of increased H2O2 on an antigen-specific CD8+ T cell response was unknown. Following T cell receptor (TCR) stimulation, the expression and oxidation of peroxiredoxin II (PrdxII), a critical antioxidant enzyme, increased in CD8+ T cells. Deletion of PrdxII increased ROI, S phase entry, division, and death during in vitro division. During primary acute viral and bacterial infection, the number of effector CD8+ T cells in PrdxII-deficient mice was increased, while the number of Memory cells were similar to those of the wild-type cells. Adoptive transfer of P14 TCR transgenic cells demonstrated that the increased expansion of effector cells was T cell autonomous. After rechallenge, effector CD8+ T cells in mutant animals were more skewed to Memory phenotype than cells from wild-type mice, resulting in a larger Secondary Memory CD8+ T cell pool. During chronic viral infection, increased antigen-specific CD8+ T cells accumulated in the spleens of PrdxII mutant mice, causing mortality. These results demonstrate that PrdxII controls effector CD8+ T cell expansion, Secondary Memory generation, and immunopathology.
Gene A. Brewer - One of the best experts on this subject based on the ideXlab platform.
-
The contributions of primary and Secondary Memory to working Memory capacity: an individual differences analysis of immediate free recall.
Journal of experimental psychology. Learning memory and cognition, 2010Co-Authors: Nash Unsworth, Gregory J. Spillers, Gene A. BrewerAbstract:The present study tested the dual-component model of working Memory capacity (WMC) by examining estimates of primary Memory and Secondary Memory from an immediate free recall task. Participants completed multiple measures of WMC and general intellectual ability as well as multiple trials of an immediate free recall task. It was demonstrated that there are 2 sources of variance (primary Memory and Secondary Memory) in immediate free recall and that, further, these 2 sources of variance accounted for independent variation in WMC. Together, these results are consistent with a dual-component model of WMC reflecting individual differences in maintenance in primary Memory and in retrieval from Secondary Memory. Theoretical implications for working Memory and dual-component models of free recall are discussed. Working Memory is usually referred to as a general purpose system that is responsible for the active maintenance of task- or goal-relevant information while simultaneously processing or act- ing on other information (Baddeley, 2007). Given the need of such a general purpose system for a wide variety of activities— including problem solving, reading, coordination and planning, and basic intellectual functioning more broadly—recent work has been devoted to measuring the capacity of working Memory and investigating individual differences in working Memory capacity (WMC). Beginning with Daneman and Carpenter (1980), most researchers have utilized complex working Memory span tasks in which to-be-remembered (TBR) items are interspersed with some processing activity. For instance, in the reading span task partici- pants attempt to remember words or letters while reading and comprehending sentences (Daneman & Carpenter, 1980). These tasks can be contrasted with simple Memory span tasks in which TBR items are presented without any additional processing activ- ities. The complex span tasks nicely capture the idea that the dynamics of processing and storage are needed to fully understand the essence of working Memory and tap its capacity. Furthermore, these tasks can be used to estimate an individual's WMC and examine the correlation between this capacity and other important cognitive abilities. Due to the popularity of complex span tasks and the fact that they provide good estimates of WMC, a number of theories have been proposed to account for performance on these tasks and to explain working Memory more broadly. For instance, many orig- inal accounts of complex span tasks emphasized the notion that resources have to be shared between processing and storage ac- tivities and thus the capacity of working Memory is the amount of total resources that individuals have at their disposal (e.g., Dane- man & Carpenter, 1980). Individuals with more resources can effectively deal with the processing task while continuing to main- tain activation of the TBR items, which leads to better performance than in the case of individuals with fewer resources. Alternatively, it is possible that the complex span tasks do not index overall resource-sharing abilities but rather that the processing task dis- places items from working Memory, and thus a rapid switching mechanism is needed to refresh items before they are lost due to time-based forgetting processes such as decay (Towse, Hitch, &
-
There’s more to the working Memory capacity—fluid intelligence relationship than just Secondary Memory
Psychonomic Bulletin & Review, 2009Co-Authors: Nash Unsworth, Gene A. Brewer, Gregory J. SpillersAbstract:The present study examined the claim that Secondary Memory processes account for the correlation between working Memory capacity and fluid intelligence via a latent variable analysis. In the present study, participants performed multiple measures of Secondary Memory, working Memory capacity, and fluid intelligence. Structural equation modeling suggested that both Secondary Memory and working Memory capacity account for unique variance in fluid intelligence. These results are inconsistent with recent claims that working Memory capacity does not account for variance in fluid intelligence over and above what is accounted for by Secondary Memory. Rather, the results are consistent with models of working Memory capacity that suggest that both maintenance and retrieval processes are needed to account for the substantial relation between working Memory capacity and fluid intelligence.
