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The Role of Maintenance and Disengagement in Predicting Reading Comprehension and Vocabulary Learning

American Psychological Association
Journal of Experimental Psychology: Learning, Memory, and Cognition
Authors:
Jessie Martin at Georgia Institute of Technology
Jessie Martin
Zach Shipstead at University of Illinois Urbana-Champaign
Zach Shipstead
Tyler L Harrison at University of North Georgia
Tyler L Harrison
Thomas S. Redick
Thomas S. Redick
Thomas S. Redick
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Abstract

This study uses a novel framework based on work by Shipstead, Harrison, and Engle (2016) that includes measures of both working memory capacity and fluid intelligence in an attempt to better understand the processes that influence successful reading comprehension at the latent level. Further, we extend this framework to a second educationally relevant ability: second-language vocabulary learning. A large sample of young adults received a battery of working memory, fluid intelligence, language comprehension, and memory updating tasks. The results indicate that individual differences in reading comprehension and vocabulary learning benefit from the ability to maintain active information, as well as to disengage from no longer relevant information. Subsequently, we provide an interpretation of our results based on the maintenance and disengagement framework proposed by Shipstead et al. (2016). (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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The Role of Maintenance and Disengagement in Predicting Reading
Comprehension and Vocabulary Learning
Jessie D. Martin
Georgia Institute of Technology
Zach Shipstead
Colby College
Tyler L. Harrison
University of North Georgia, Dahlonega, Georgia
Thomas S. Redick
Purdue University
Michael Bunting
University of Maryland, College Park, Maryland
Randall W. Engle
Georgia Institute of Technology
This study uses a novel framework based on work by Shipstead, Harrison, and Engle (2016) that includes
measures of both working memory capacity and fluid intelligence in an attempt to better understand the
processes that influence successful reading comprehension at the latent level. Further, we extend this
framework to a second educationally relevant ability: second-language vocabulary learning. A large
sample of young adults received a battery of working memory, fluid intelligence, language comprehen-
sion, and memory updating tasks. The results indicate that individual differences in reading comprehen-
sion and vocabulary learning benefit from the ability to maintain active information, as well as to
disengage from no longer relevant information. Subsequently, we provide an interpretation of our results
based on the maintenance and disengagement framework proposed by Shipstead et al. (2016).
Keywords: maintenance, disengagement, working memory capacity, fluid intelligence, reading
comprehension
Despite decades of research examining processes necessary for
successful reading comprehension, we still do not fully understand
or agree on the cognitive construct which underlie performance.
For example, we have established that working memory capacity
is important for predicting reading comprehension performance
(Daneman & Carpenter, 1980); however, a substantial amount
of-variance remains unaccounted for in studies exploring the re-
lationship between working memory capacity and reading com-
prehension (Borella, Carretti, & Pelegrina, 2010;Chiappe, Siegel,
& Hasher, 2000;Christopher et al., 2012;Engle, Cantor, & Car-
ullo, 1992;Was & Woltz, 2007). In an attempt to further explain
other factors that are essential to performance, many of these
studies have begun to take an increasingly fractionated approach to
filling in the pieces. This deconstructive approach has resulted in
seemingly conflicting conclusions regarding mechanisms of inter-
est, and has not left room for individual differences in mechanisms
used in retrieval as well as organized forgetting. In an effort to
return to a more parsimonious approach, we proposed a more
process-general latent variable framework for the understanding of
individual differences in reading comprehension.
Specifically, we contend that the current foundation for understand-
ing reading comprehension has overemphasized the use of tasks
which primarily reflect maintenance of information, while ignoring
the beneficial role of forgetting— or what we will refer to as disen-
gagement. We argue that individual differences in reading compre-
hension are not strictly related to maintenance processes captured by
complex span measures of working memory capacity, but also to
disengagement processes captured by measures of fluid intelligence.
This approach is based on work by Shipstead et al. (2016) that
defines the primary mechanisms of executive attention in terms of
general processes of maintenance and disengagement (reflected in
complex span and fluid intelligence measures, respectively), rather
than through increasingly specified mechanistic functions like
shifting, updating, and inhibition (Miyake et al., 2000; see Fried-
man & Miyake, 2017 for an updated perspective). We will identify
advantages to our process general approach as well as limitations
to current, more deconstructive, approaches including those that
emphasize the role of processing speed (Christopher et al., 2012)
This article was published Online First June 6, 2019.
Jessie D. Martin, Department of Psychology, Georgia Institute of Tech-
nology; Zach Shipstead, Department of Psychology, Colby College; Tyler
L. Harrison, Department of Psychology, University of North Georgia,
Dahlonega, Georgia; Thomas S. Redick, Department of Psychology, Pur-
due University; Michael Bunting, Center for the Advanced Study of
Language, University of Maryland, College Park, Maryland; Randall W.
Engle, Department of Psychology, Georgia Institute of Technology.
This work was supported by grants from the Office of Naval Research
(N00014-12-1-0406 and N00014-12-1-1011) to Randall W. Engle.
Correspondence concerning this article should be addressed to Jessie D.
Martin, Department of Psychology, Georgia Institute of Technology, North
Avenue NW, Atlanta, GA 30332. E-mail: jessie.martin@gatech.edu
This document is copyrighted by the American Psychological Association or one of its allied publishers.
This article is intended solely for the personal use of the individual user and is not to be disseminated broadly.
Journal of Experimental Psychology:
Learning, Memory, and Cognition
© 2019 American Psychological Association 2020, Vol. 46, No. 1, 140–154
0278-7393/20/$12.00 http://dx.doi.org/10.1037/xlm0000705
140
... For successful reading comprehension, WM is important for several reasons. A reader needs to direct and maintain attention to the text, allocate attentional resources to the mechanics of reading (oculomotor control, decoding, word recognition, etc.), as well as to higher-level processes such as retaining pertinent textual information active in memory to form mental representations of the content (Martin et al., 2020;Shin, 2020). These representations are then subject to constant updating from further incoming input, as the reader gathers new pieces of information from subsequent parts of the text and engages in an ongoing integration process (Burgoyne et al., 2022). ...
... With reference to reading, fluid intelligence may help the disengagement of activated, but contextually inappropriate meanings (e.g., the nontarget meaning(s) of a polysemous word), or with the disengagement of textual representations that are invalidated in later parts of the text. Although underexplored as an aspect of reading comprehension, recent findings showcase that fluid intelligence strongly correlates with reading comprehension, while WM may not have a direct influence beyond its association with fluid intelligence (Martin et al., 2020). That is, the correlation between WM and reading comprehension was found to be accounted for by variance that WM shared with fluid intelligence. ...
... For example, one would need to quickly disengage resources from the judgment task, in order to focus and perform well on the recall (memory) component. Nevertheless, as WM and fluid intelligence are still considered distinct mechanisms, if fluid intelligence exerts independent influence on the processing of irony, as it appears to be the case in reading comprehension (Martin et al., 2020), its effects should be visible irrespective of WM influences. ...
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View
... Considering the most important executive processes, previous research emphasizes executive/attentional control, working memory, maintenance, and disengagement, which are all thought to collaborate in completing a task goal (Shipstead et al. 2016;Mashburn et al. 2021;Engle 2018Engle , 2002Martin et al. 2020). During task completion, working memory is heavily involved in maintenance processes whereas disengagement is thought to heavily involve fluid aspects of intelligence (Shipstead et al. 2016). ...
... Therefore, the hierarchical structure as often suggested in the commonly used CHC model is not necessary when just as much of the variance can be explained without it . Thus, as a non-g model that is well supported by basic scientific evidence (Burgoyne et al. 2021(Burgoyne et al. , 2022Shipstead et al. 2016;Mashburn et al. 2021; see also Martin et al. 2020;Peng and Swanson 2022), process overlap theory is a more parsimonious approach to thinking about assessment. Further, considering the utility of examining subprocesses of g, as outlined above, non-g POT is also better for working toward equity and fairness goals. ...
... Although some findings are mixed about the extent to which training working memory and domain-general executive processes is beneficial for far transfer (see Redick et al. 2013;Shipstead et al. 2012), the recent evidence outlined above for tailored intervention for more diverse needs is promising. There is also empirical evidence suggesting the importance of examining domain-general vs. more domain-specific differences for how to better tailor forms of intervention (Martin et al. 2020;Peng and Swanson 2022). More sophisticated modeling with network and advanced statistical methods also suggests that applying non-g POT-based models would be useful when thinking about which cognitive processes are most important for how to redesign intelligence models in subsequent research ). ...
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View
... Considering the most important executive processes, previous research emphasizes executive/attentional control, working memory, maintenance, and disengagement, which are all thought to collaborate in completing a task goal (Shipstead et al., 2016;Mashburn et al., 2021;Engle, 2018;Martin et al., 2020; also see Engle, 2002). During task completion, working memory is heavily involved in maintenance processes whereas disengagement is thought to heavily involve fluid aspects of intelligence (Shipstead et al., 2016). ...
... Therefore, the hierarchical structure as often suggested in the commonly used CHC model is not necessary when just as much of the variance can be explained without it . Thus, as a non-g model that is well supported by basic scientific evidence (Burgoyne et al., 2021(Burgoyne et al., , 2022Shipstead et al., 2016;Mashburn et al., 2021; also see Martin et al., 2020;Peng & Swanson, 2022), Process Overlap Theory is a more parsimonious approach to thinking about assessment. Further, considering the utility of examining subprocesses of g, as outlined above, non-g POT is also better for working toward equity and fairness goals. ...
... Although some findings are mixed about the extent to which training working memory and domain general executive processes is beneficial for far transfer (see Redick et al., 2013;Shipstead et al., 2012), the recent evidence outlined above for tailored intervention for more diverse needs is promising. There is also empirical evidence suggesting the importance of examining domain general vs. more domain specific differences for how to better tailor forms of intervention (Martin et al., 2020;Peng & Swanson, 2022). More sophisticated modeling with network and advanced statistical methods also suggests that applying non-g POT-based models would be useful for thinking about which cognitive processes are most important for how to redesign intelligence models in subsequent research . ...
Modern Assessments of Intelligence Must Be Fair and Equitable
Preprint
Full-text available
  • Jun 2023
Historically, assessments of human intelligence have been virtually synonymous with practices that contributed to forms of inequality and injustice. As such, modern considerations for assessing human intelligence must focus on equity and fairness. First, we highlight the array of diversity, equity, and inclusion concerns in assessment practices and discuss strategies for addressing them. Next, we define a modern, non-g, emergent view of intelligence using Process Overlap Theory and argue for its use in improving equitable practices. We then review empirical evidence focusing on sub-measures of g to highlight the utility of non-g, emergent models in promoting equity and fairness. We conclude with suggestions for researchers and practitioners.
View
... It is also possible that neither decay nor interference can explain the current results. It might be that linguistic prediction is disengaged when it is less beneficial or more costly to maintain (see also Martin et al., 2020). For example, Wlotko and Federmeier (2015) found a lack of prediction-related effects on the N400 when the rate of incoming words during sentence reading was very high, suggesting that individuals did not engage the same mechanisms as when the rate was slower. ...
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... Many cognitive constructs (e.g., WM, ART, reasoning) are intercorrelated, complicating the attribution of outcomes to specific predictors (Dubois et al., 2018;James et al., 2018). High correlations across tasks can lead to inconsistent findings (e.g., Martin et al., 2020;Freed et al., 2017). A major challenge in this context is multicollinearity, where highly correlated predictors (e.g., WM and ART) complicate regression analysis (Cohen et al., 2003). ...
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... Several authors have emphasized the importance of working memory in reading, especially when keeping information active and to determine the coherence of a text [94][95][96][97]. Likewise, they consider that working memory can be considered a predictor of reading ability. ...
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The problem of reading comprehension at the university level has not been sufficiently explored in the field of education research. Understanding written texts is an essential prerequisite for academic success during university. Consequently, reading comprehension challenges can influence the dropout rate at the university level. On the other hand, research has been conducted from a cognitive perspective, without considering affective variables such as reading motivation and its relationship with linguistic and psychological variables. In this study, five questionnaires were administered to a sample of 65 teaching students from different disciplines. The questionnaires dealt with linguistic dimensions such as reading comprehension, at lexical, textual, and discursive levels; psychological aspects such as reading motivation; and cognitive aspects such as working memory. The results obtained through bivariate and mediation analyses show the mediating role of vocabulary in reading comprehension and other cognitive and affective variables. In this context, working memory has a positive indirect effect on more explicit processing tasks in reading comprehension of long texts associated with discourse comprehension. Finally, intrinsic motivation has a positive indirect effect on reading fluency and also on the comprehension of short texts. It also correlates positively with vocabulary tasks. These results are relevant in order to establish a reading profile of university students who have reading comprehension problems, as found in this study.
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... That said, researchers and test construction professionals more recently have also recognized the need to further refine and improve the measurement properties of complex span measures. One approach has been to give a battery of complex span measures to participants, conduct confirmatory factor analysis to derive a latent working memory variable from the manifest scores, and then use the latent variable in predictive analyses (e.g., Martin et al., 2020). Confirmatory factor analysis not only allows one to have greater confidence in measurement by appealing to the variance common across a set of imperfect measures but also corrects for measurement error. ...
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... Whereas maintenance refers to keeping track of goal-relevant information, disengagement refers to removing irrelevant (or no-longer-relevant) information from active processing and tagging it for nonretrieval. Both functions require attention control, although they can also be modeled as separate but correlated latent factors (see Martin, Shipstead, et al., 2020). ...
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Primary Mental Abilities
Chapter
  • Jan 1973
This publication is the opening number of a series which the Psychometric Society proposes to issue. It reports the first large experimental inquiry, carried out by the methods of factor analysis described by Thurstone in The Vectors of the Mind 1. The work was made possible by financial grants from the Social Science Research Committee of the University of Chicago, the American Council of Education, and the Carnegie Corporation of New York. The results are eminently worthy of the assistance so generously accorded. Thurstone’s previous theoretical account, lucid and comprehensive as it is, is intelligible only to those who have a knowledge of matrix algebra. Hence his methods have become known to British educationists chiefly from the monograph published by W. P. Alexander8. This enquiry has provoked a good deal of criticism, particularly from Professor Spearman’s school ; and differs, as a matter of fact, from Thurstone’s later expositions. Hence it is of the greatest value to have a full and simple illustration of his methods, based on a concrete inquiry, from Professor Thurstone himself.
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Control of information in working memory: Encoding and removal of distractors in the complex-span paradigm
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  • Nov 2016
The article reports four experiments with complex-span tasks in which encoding of memory items alternates with processing of distractors. The experiments test two assumptions of a computational model of complex span, SOB-CS: (1) distractor processing impairs memory because distractors are encoded into working memory, thereby interfering with memoranda; and (2) free time following distractors is used to remove them from working memory by unbinding their representations from list context. Experiment 1 shows that distractors are erroneously chosen for recall more often than not-presented stimuli, demonstrating that distractors are encoded into memory. Distractor intrusions declined with longer free time, as predicted by distractor removal. Experiment 2 shows these effects even when distractors precede the memory list, ruling out an account based on selective rehearsal of memoranda during free time. Experiments 3 and 4 test the notion that distractors decay over time. Both experiments show that, contrary to the notion of distractor decay, the chance of a distractor intruding at test does not decline with increasing time since encoding of that distractor. Experiment 4 provides additional evidence against the prediction from distractor decay that distractor intrusions decline over an unfilled retention interval. Taken together, the results support SOB-CS and rule out alternative explanations. Data and simulation code are available on Open Science Framework: osf.io/3ewh7.
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