Running head: READING COMPREHENSION AND WORKING …

Running head: READING COMPREHENSION AND WORKING MEMORY

i

Working memory and reading comprehension: The relationship between central executive

functioning and discourse-level reading ability

Jaimée Catherine Taylor

33071318

Thesis Supervised by Dr Bethanie Gouldthorp.

Word Count (From title to end of discussion): 9712

This thesis is presented in partial fulfilment of the requirements for the Bachelor of Arts

(Psychology) Honours degree, Murdoch University, 2017.

Address for correspondence: Dr Bethanie Gouldthorp

School of Psychology and Exercise Science

Murdoch University

South Street

Murdoch, WA, 6015

Email: [email protected]

Telephone: +61 8 9360 2348

Fax: +61 8 9360 6492

Jaimee Taylor

School of Psychology and Exercise Science

Murdoch University

South Street

Murdoch, WA, 6015

Email: [email protected]

Telephone: +61 4 0729 7785

mailto:[email protected]

mailto:[email protected]

READING COMPREHENSION AND WORKING MEMORY

ii

Declaration

I declare that this thesis is my own account of my research and contains as its main

content work that has not previously been submitted for a degree at any tertiary education

institution.

Jaimée Catherine Taylor


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Table of Contents

Title Page i

Declaration ii

Table of contents iii

List of Figures v

List of Tables vi

Acknowledgement vii

Abstract 1

Introduction 2

Reading comprehension and working memory 4

Individual differences in reading comprehension 5

Aims and Hypotheses 9

Method 10

Participants 10

Materials 10

Reading Integration Test 10

NIH Toolbox Cognition Battery 12

Nelson-Denny Reading Test 13

Apparatus 14

Procedure 15

Reading Integration Test 15

NIH Toolbox Cognition Battery 16

Nelson-Denny Reading Test 18

Results 18

Reaction time 18

Accuracy 20

Speed-accuracy trade-off effect 20

Discourse-level reading comprehension and cognitive processes 21

Propositional-level reading comprehension and cognitive

processes

22

Propositional-level versus discourse-level reading

comprehension

24

Discussion 24


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Facilitation effect 25

Relationship between cognitive processes and discourse-level

comprehension

27

Relationship between cognitive processes and propositional-level

comprehension

31

Discourse-level versus propositional-level reading

comprehension

33

Limitations 35

Implications 36

Future Research 37

Conclusion 39

References 41

Appendices 50

Appendix A Ethics Approval Letter 50

Appendix B Information Letter 51

Appendix C Consent Form 54

Appendix D Detailed Procedure 55

Appendix E Debrief Sheet 57

Appendix F Instructions for NIH Toolbox Cognition Battery

Example

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Appendix G Example stimuli from Reading Integration Task 61

Appendix H Statistical Output 62

Appendix I Statistical Calculations 72

Appendix J Project summary 73


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List of Figures

Figure 1 Baddeley’s 2000 Working Memory Model 5

Figure 2 Example stimuli of the RIT depicting the different levels

of context for target words

16

Figure 3 Partial Correlations for Predictors in a Multiple Regression

Model predicting Discourse-Level and Propositional-

Level Reading Comprehension Ability

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List of Tables

Table 1 Example of Target Type (Word or Non-Word) as a

Function of Level of Context

12

Table 2 Mean RT (ms) and Mean Accuracy (%) for Target (Words)

as a Function of Sentence Context

19

Table 3 Unstandarised (B) and standardised errors (SE B) as well

as standardised (β) Regression Coefficients and Part

Correlations (sr²) for Predictors in a Multiple Regression

Model Predicting Discourse-level Reading

Comprehension.

22

Table 4 Unstandarised (B) and standardised errors (SE B) as well

as standardised (β) Regression Coefficients and Part

Correlations (sr²) for Predictors in a Multiple Regression

Model Predicting Propositional-level-Level Reading

Comprehension.

23


vii

Acknowledgements

During the time of writing I received support from many people. I am indebted to my

supervisor, Dr Bethanie Gouldthorp, who was very generous with her time and

knowledge and provided continuous support, motivation and guidance. I would like to

thank my whole family for all their love, support, kind words and encouragement

throughout the year. Specifically, I would like to say a massive thank-you to my mother,

Lynda, who read an earlier draft of this thesis and to my sister, Robyn, who was always

there to help. I would also like to extend my sincerest gratitude to my dearest friends,

Rachael, Kushca and Damon, for their never-ending support, encouragement and

understanding during this intense time. Lastly, I would like to thank my peers and all

those who have assisted with this study. I could not have asked for a better group of people

to have worked with and I am very appreciative of the time and effort that has gone into

making this study possible.


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Abstract

Research has repeatedly demonstrated that a relationship exists between reading

comprehension and working memory (WM). However, there are inconsistencies in the

literature regarding which executive functions (EFs) of WM contribute to overall reading

comprehension ability. This study attempted to address why these inconsistencies exist

by investigating to what extent the ability to manipulate, integrate, and update information

quickly and efficiently was related to adults’ overall reading comprehension. Fifty-one

participants, aged between 18 and 56 years without any cognitive impairments, completed

a standardised reading comprehension assessment and a novel computer-based task which

assessed propositional-level and discourse-level reading comprehension, respectively.

Additionally, participants completed a cognition battery which assessed WM, inhibitory

and attentional control, attention, cognitive flexibility and episodic memory. The findings

indicated that context (i.e., low, medium and high) had a significant effect on discourse-

level reading comprehension ability. This was accompanied by a significant relationship

between WM, its central EFs and adults’ propositional-level reading comprehension.

Unexpectedly, WM and the EFs did not significantly account for individual differences

in discourse-level ability comprehension performance. Moreover, inhibition and attention

negatively predicted participants’ discourse-level reading comprehension ability. Sound

understanding of the relationship between WM and its central EFs has important practical

implications for developing training programs aiming to improve children and adult’s

executive skills and academic performance.

Key words: working memory (WM), executive functions (EFs), situation model

construction, adults, propositional-level reading comprehension, discourse-level reading

comprehension.


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Working memory and reading comprehension: The relationship between central executive

functioning and discourse-level reading ability

Understanding underlying factors which influence individual differences in

reading comprehension has important implications for society as it is associated with a

broad range of psychosocial, cognitive, economic and health benefits (Schwanenflugel &

Knapp, 2016). A well established predictor of individual differences in reading

comprehension is working memory (WM; Cain, Oakhill, & Bryant, 2004; Kiss, Pisio,

Francois, & Schopflocher, 1998; Nouwens, Groen, & Verhoeven, 2017; Oakhill, Cain, &

Bryant, 2003). Comprehension skills rely on the processing and storage of words and

their meanings, which demand various cognitive resources (e.g., WM; Cain et al., 2004;

Oakhill et al., 2003). Yet, the executive functions (EFs) central to WM, which underlie

individual differences in discourse-level reading comprehension, is less clearly

understood.

Current theories of discourse-level comprehension assert that comprehension

skills rely on the construction of a multi-level representation of a text (Fincher-Kiefer,

1993). These representations consist of two lower levels (i.e., surface and propositional)

and a higher discourse-level (Fincher-Kiefer, 1993; Zwaan & Radvansky, 1998; Zwaan,

Stanfield & Yaxley, 2002). Consider the following sentences: (i) The ranger saw the

eagle in the sky; and (ii) The ranger saw the eagle in the nest (Zwaan et al., 2002). At

surface level, individual words are decoded based on the grammatical information while,

at propositonal level, the two sentences are nearly identical with the eagle’s locality being

the only difference (i.e., sky or nest; Zwaan et al., 2002). This propositional processing

draws on the textual information to construct a fairly basic, amodal meaning (Zwaan et

al., 2002). The discourse-level builds on these two lower levels, with the first sentence

activating a visual experience of an eagle with stretched-out wings and the second


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activating a visual experience in which the eagle has its wings folded-in (Zwaan et al.,

2002).

This discourse-level processing refers to higher-level multi-modal

representations, or ‘situation models’, of a situation described in a text (i.e., characters,

places and actions; Davoudi & Moghadam, 2015; Zwaan et al., 2002). Cook and Myers

(2004) suggest that discourse-level processing may influence reading comprehension by

facilitating the integration of the target concept with the preceding text. This often relies

on knowledge-based inferences (i.e., the activation of additional information from

background knowledge during the reading process; Ababneh & Ramadan, 2013; Davoudi

& Moghadam, 2015; Yeari & van den Broek, 2015; Zwaan et al., 2002). Knowledge-

based inferences provide an explanation why readers can create the visual experience of

an eagle with out-stretched or folded wings, although this information is not described in

the text (Yeari & van den Broek, 2015; Zwaan et al., 2002).

There is a lack of consensus in the literature regarding how to measure situation

models. While different components utilised in the construction of a situation model (e.g.,

inference generation, metaphor comprehension and integration) have been explored by

various authors (e.g., Beeman et al., 1994; Cook & Myers, 2004; Federmeier, 2007;

Zwaan & Radvansky, 1998; Zwaan et al., 2002), not many have attempted to directly

measure overall situation model construction. Thus, experimental tasks theoretically

employing discourse-level comprehension processing, such as the integration of

information across sentences, are relied upon to measure individual differences in

situation model construction (Gouldthorp & Coney, 2011). For example, Gouldthorp and

Coney (2011) suggest that facilitation of target words in their Reading Integration Task

(RIT) is greater when readers are provided with three context sentences (i.e., “I am

sweet”, “I am spread on toast” and “I am made by insects”) than only one or two context


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sentences. Thus, this measure can be used to examine individual differences in readers’

abilities to construct a coherent situation model by integrating information from world-

knowledge (i.e., reader’s prior knowledge) across context sentences (Gouldthorp &

Coney, 2011).

Individual differences in the ability to construct meaning across sentences (termed

sequencing ability) has been examined in studies such as Gouldthorp, Katsipis and

Mueller (2017). Specifically, Gouldthorp et al found that sequencing ability facilitated

children’s discourse-level comprehension skills. This supports Zwaan et al’s (2002) claim

that discourse-level reading ability is fundamental for gaining a comprehensive

understanding of a text. Moreover, the construction of situation models is associated with

WM’s central executive component, relying on the ability to hold incoming information

in memory while retrieving existing knowledge from long-term memory (LTM; Davoudi

& Moghadam, 2015; García-Madruga, Vila, Gόmez-Veiga, Duque & Elosúa, 2014).

Reading comprehension and working memory

WM is widely associated with comprehension performance as it determines an

individual’s ability to hold incoming information and knowledge from LTM (García-

Madruga et al., 2014) and to integrate these into a coherent situation model (Cantin,

Gnaedinger, Gallaway, Hesson-McInnis & Hund, 2016; Davoudi & Moghadam, 2015;

Gouldthorp et al., 2017). WM is defined as a multi-component system which briefly

retains information while simultaneously carrying out other mental processing operations,

after which information is encoded into LTM or is forgotten (Baddeley, 2012; Davoudi

& Moghadam, 2015; Engel de Abreu et al., 2014; García-Madruga et al., 2014; Nouwens,

Groen, & Verhoeven, 2016). Different WM models are depicted in literature (e.g.,

Baddeley’s Multiple Component Model, the Object-Oriented Episodic Record and the

Embedded-Process Model; Chein & Fiez, 2010). Although these (and other) models


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conceptualise WM differently, they all posit a central EF or a similar attentional control

mechanism (Chein & Fiez, 2010; McCabe, Roediger, McDaniel, Balota, & Hambrick,

2010), of which Baddeley’s 2000 WM model is most clearly articulated (Baddeley, 2012;

Chein & Fiez, 2010; Figure 1).

The executive component is described as a flexible, limited storage capacity,

attentional control system (Iglesias-Sarmiento, Carriedo-López, & Rodríguez-Rodríguez,

2015). It is particularly important in explaining individual differences in complex

cognitive tasks (Iglesias-Sarmiento et al., 2015). These often involve planning and control

(e.g., comprehension, decision-making and problem solving; Iglesias-Sarmiento et al.,

2015).

Individual differences in reading comprehension

Literature suggests that WM capacity (i.e., the ability to maintain task-relevant

information in a highly active state; Meinz & Hambrick, 2010) explains individual

performance in reading comprehension (Daneman & Carpenter, 1980; Dutke & von

Hecker, 2011; Turner & Engle, 1989). This is evident as individuals with greater WM

capacity (i.e., information storage) performed better on comprehension tasks than those

with lower WM capacity (Dutke & von Hecker, 2011; Turner & Engle, 1989).

Figure 1. Baddeley's 2000 Working Memory Model (Baddeley, 2012, p. 16).


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Consequently, Daneman and Carpenter (1980) suggests that skilled readers rely on fewer

cognitive processes in comparison to less skilled readers when completing the same task.

For example, skilled comprehenders could skip intermediate steps in some or all stages

of decoding, inferencing and integrating incoming information (Daneman and Carpenter,

1980).

Emerging research suggests that EFs are crucial in becoming skilled in reading,

problem solving and controlling goal-directed behaviour, particularly during early

childhood (Nouwens et al., 2016). EFs refer to the ability to plan, organise and monitor

deliberate, goal-orientated behaviours (National Institutes of Health & Northwestern

University (NIH & NU), 2016a). This enable readers to manipulate thoughts and

behaviours, monitor novel information, inhibit behavioural responses and maintain focus

on completing challenging tasks (Cantin et al., 2016, Diamond, 2013; Engle de Abreu et

al., 2014). Types of EFs include inhibition, cognitive flexibility, and attention.

Inhibition potentially influences reading comprehension by allowing readers to

suppress automatic reactions and ignore irrelevant information (Cain, 2006; Cantin et al.,

2016; Diamond, 2013; Engel de Abreu et al., 2014; Nouwens et al., 2016). Cognitive

flexibility allows readers to adapt WM representations of events according to the situation

presented in the text (Diamond, 2013; Engel de Abreu et al., 2014). Moreover, it enables

readers to coordinate reading strategies and processes to achieve success in reading

(Gnaedinger, Hund & Hesson-McInnis, 2016). Lastly, attention aids readers in directing

attentional resources towards task-relevant information to achieve goal-orientated tasks,

thereby facilitating reading ability (García-Madruga et al., 2014; Yogev‐Seligmann,

Hausdorff, & Giladi, 2008).

An alternative cognitive process which differs from WM and EFs is episodic

memory, which has been considered necessary for successful reading comprehension


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performance (Mumper, 2013). Episodic memory relates to a reader’s ability to reactivate

information stored in LTM (Mumper, 2013). Information stored in LTM includes

contextual information that is either essential for preserving coherence or related to the

information currently active in memory (Mumper, 2013). Essentially, these assist in

developing knowledge-based inferences described by Ababneh and Ramadan (2013),

Yeari and van den Broek (2015) and Zwaan et al (2002).

While the literature strongly supports that WM accounts for individual differences

in reading comprehension ability, most of this research has focused on surface and

propositional-level comprehension processes (Zwaan et al., 2002). Moreover, other

researchers claim that WM capacity does not distinguish between good and poor reading

comprehenders (Daneman & Carpenter, 1980). This may be due to the digit span and

word span measures (to assess WM capacity) not effectively tapping into the WM

processing component (Daneman & Carpenter, 1980). This is further demonstrated by

inconsistency in the research findings concerning EFs of WM and discourse-level reading

ability (Follmer, 2017).

Explicitly, some research has demonstrated that EFs are associated with

differences in discourse-level reading comprehension ability (Follmer, 2017), while

others (e.g., Georgiou & Das, 2016) indicate that WM is not heavily involved in situation

model construction. This difference is possibly explained by the opposing perspectives

of various authors (Follmer, 2017). For example, some authors concentrate on modality-

specific WM stores (i.e., the phonological loop), whilst others focus the central executive,

or attentional control system (Follmer, 2017). It has been suggested that the central

executive component is of more importance than the modality-specific WM stores as it is

more directly involved in the processing and manipulation of incoming textual

information (Gouldthorp et al., 2017).


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Inconsistency in the literature exists in terms of determining which EFs have been

directly implicated with individual differences in reading comprehension (Follmer,

2017). That is, cognitive switching (and to a lesser extent, attention) has been shown to

successfully predict reading comprehension performance (Arrington, Kulesz, Francis,

Fletcher, & Barnes, 2014; Cantin et al., 2016; Engel de Abreu et al., 2014; García-

Madruga, Gómez-Veiga, & Vila, 2016; Gerst, Cirino, Fletcher, & Yoshida, 2017;

Nouwens et al., 2016). However, Nouwens and colleagues (2016) found that planning did

not predict performance on a comprehension task. This discrepancy in findings may be

due to sampling differences as some studies assessed older children (Georgiou & Das,

2016) and adults (Chrysochoou, Bablekou & Tsigilis, 2011), while others assessed

younger children (Nouwens et al., 2017; García-Madruga et al., 2016). Likewise, some

measures of EFs may not be scientifically valid (i.e., fail to measure what they purport to

measure), as conveyed by a lack of psychometric information of some EF tasks

(Arrington et al., 2014; Christopher et al., 2012).

Additionally, these discrepancies may be attributed to either (or both) the

underlying model of WM and the vastly different WM measures used by different authors

(Follmer, 2017). For example, studies conducted by Arrington et al (2014) and Cantin et

al (2016) suggest that participants with high reading comprehension performance achieve

higher scores on different WM measures. This is inconsistent with Georgiou and Das

(2016). They claimed that WM does not affect reading ability as different measures of

WM may not accurately assess WM in general (i.e., the listening span and digit span

backward tasks). In addition, they suggest that the nature of the reading task may itself

influence the outcome of the study (Georgiou & Das, 2016).

Nevertheless, WM and its central EFs are considered crucial in underpinning a

wide range of higher cognitive abilities, including reasoning, learning and comprehension


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(Arrington et al., 2014; Cantin et al., 2016; Chein & Fiez, 2010; Collette & Van der

Linden, 2002; Engel de Abreu et al., 2014; García-Madruga et al., 2014; 2016; Gerst et

al., 2017; Kiss et al., 1998; Nouwens et al., 2016; Oakhill et al., 2003). Unfortunately, the

existing literature has not systematically evaluated the different EF processes to establish

their contributions to discourse-level reading comprehension ability. Thus, this thesis

addresses the underlying theory of EFs and considers how these contribute to reading

comprehension ability at propositional and discourse levels of processing.

Aims and Hypotheses

The purpose of this study was to elucidate why there are inconsistencies in the

literature regarding the role of WM in reading comprehension ability. Specifically, the

aim of the study was to examine how central EFs in WM (i.e., the ability to manipulate,

integrate and update information quickly and efficiently regardless of modality) may

account for individual variation in the ability to produce higher-level (discourse) meaning

during reading. Thus, the following research question was posed:

Are individual differences in reading comprehension and situation model

construction partly explained by differences in functioning of specific EFs central

to WM?

To investigate this question, a within-group study was conducted. Adult

participants completed three tasks: the Nelson-Denny Comprehension Test (a

standardised measure of surface and propositional-level reading comprehension ability;

Brown, Fishco, & Hanna, 1993); the NIH Cognitive battery, consisting of a series of

cognitive tasks (Gershon et al., 2013); and an adapted Reading Integration Task (RIT)

which assesses discourse-level reading comprehension (as indicated by the ability to

integrate information from world-knowledge across multiple sentences to produce

implicit meaning; Gouldthorp & Coney, 2011).


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Three hypotheses guided this study. First, it was hypothesised that the

presentation of three complete sentences (i.e., high-context condition) would produce

greater facilitation for target words which will indicate stronger, discourse-level

comprehension skills. Second, it was hypothesised that performance on the EF subtests

would account for a significant proportion of variance on the outcome variable of

discourse-level reading comprehension ability (i.e., high-context facilitation effect from

the RIT). Third, it was hypothesised that the EF cognitive flexibility would contribute a

significant proportion of variance on both propositional and discourse levels of reading

comprehension ability in comparison to inhibition and attention, WM capacity and

episodic memory.

Method

Participants

Fifty-one participants (13 males and 38 females) aged between 18 and 56 years

(M = 26.94, SD = 9.77), who have been (or are currently) enrolled in an English-speaking

tertiary education institution, were recruited for this study. All participants self-reported

to predominantly speak English, have normal or corrected-normal vision and hearing and

lacked cognitive impairment. This study was approved by Murdoch University’s Human

Research Ethics Committee (approval number 2017/047; Appendix A).

Materials

RIT (adapted from Gouldthorp & Coney, 2011). This measure assessed

individual differences in relation to the ability to integrate provided information across

sentences with world-knowledge to produce discourse-level meaning (Gouldthorp &

Coney, 2011). The task required participants to make lexical decisions regarding

centrally-presented targets, primed by centrally-presented sentences (consisting of three

to eight words) which establish context (Gouldthorp & Coney, 2011). Previous work


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using these stimuli established that the presentation of three complete sentences (i.e.,

high-context condition) generated greater facilitation for targets, which reflected

discourse-level comprehension (Gouldthorp & Coney, 2011). Target words were short

(3 to 5 letters), highly concrete and highly imageable nouns as determined by the MRC

Psycholinguistic database (see Gouldthorp & Coney, 2011). Moreover, the stimuli have

been evaluated to discount alternative explanations of these facilitation effects (e.g.,

summation priming from processing individual words rather than the whole sentence;

Gouldthorp & Coney, 2011).

The RIT employed a 4x2 repeated-measures design, with the independent

variables (IV) of level of context (i.e., neutral = neutral sentence only, low = one context

sentence, medium = two context sentences and high = three context sentences) and target

type (word or non-word; Gouldthorp & Coney, 2011; refer to Table 1). The dependent

variables (DV) were: reaction time (RT) to stimuli (i.e., whether target was a word or a

non-word); and accuracy of responses (Gouldthorp & Coney, 2011). To facilitate a

concise measure of discourse-level comprehension ability for this study, the RIT was

adapted from Gouldthorp and Coney (2011) to 80 trials (with 20 trials per condition).

These trials were without the repetition of stimuli and with an equal distribution of target

words and non-words for each condition. Additional example stimuli are presented in

Appendix G. Administration of this task took approximately 20 minutes, with a rest-break

opportunity after every tenth trial.


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Table 1

Example of Target Type (Word or Non-Word) as a Function of Level of Context.

NIH Toolbox Cognition Battery (Gershon et al., 2013). This measure was

designed for use in epidemiological studies and clinical trials for ages 3 to 85 years and

include the following domains: cognition, emotion, motor and sensation (Gershon et al.,

2013). Heaton and colleagues (2014) assessed the NIH Cognition Battery’s reliability and

validity in an adult sample. The cognition battery was considered reliable and valid, with

high test-retest reliability (r = .86 - .92) and strong convergent (r = .78 - .90) and

discriminant (r = .19 - .39) validities (Heaton et al., 2014; Weintraub et al., 2014). The

measure consisted of four instruments which assessed WM capacity, episodic memory

and three central EFs (i.e., inhibition, attention and cognitive flexibility; Gershon et al.,

Target Context First Sentence Second Sentence Third Sentence

Word

HONEY Neutral “This is a neutral

sentence.”

Low “I taste sweet.”

Medium “I can be put on

toast.”

High

“I am made by an

insect.”

Non-word

KIVES Neutral “This is a neutral

sentence.”

Low “I am placed on

tables.”

Medium “I am used to eat.”

High “I can cut.”


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2013). Each subtask had automated instructions which were presented on an iPad screen.

This measure required approximately 30 minutes to complete, with each subtask taking

between 90 seconds to 7 minutes to complete (Gershon et al., 2013).

The Flanker Inhibitory Control and Attention Test measured inhibitory and

attentional control (Gershon et al., 2013). Participants were asked to complete four

practice trials, accompanied by 20 test-trials (Gershon et al., 2013). The List Sorting WM

Test assessed WM capacity and consisted of two conditions: 1-List condition which

contained a series of object-pictures (i.e., food OR animal) which were to be size-ordered

from smallest to biggest; and 2-List condition which contained a series of object-pictures

(i.e., food AND animal) which were to be categorically and size-ordered (i.e., food first

then animal), from smallest to biggest (Gershon et al., 2013). A maximum of seven

picture-objects (i.e., food and/or animal objects) was presented to participants (Gershon

et al., 2013).

The Dimensional Change Card Sort Test measured cognitive flexibility using a

mixed block of 30 items (Gershon et al., 2013). Two target pictures which varied along

two dimensions (shape and colour) were presented to participants, from which they were

required to made selections according to the primed dimension (Gershon et al., 2013).

The Picture Sequence Memory Test (Form A) assessed episodic memory, specifically the

acquisition, storage and retrieval of new information (Gershon et al., 2013). There was

one practice sequence containing four items which had to be ordered according to the

sequence presented, followed by two test sequences; the first contained 15 items while

the second had 18 items (Gershon et al., 2013).

Nelson-Denny Reading Test (Form G; Brown et al., 1993). The purpose of this

standardised measure was to provide an assessment of participants’ reading ability in two

areas of academic achievement: reading rate and reading comprehension (at propositional


63

level). It consisted of seven passages, with a total of 38 questions (Brown et al., 1993).

This measure required approximately 20 minutes to compete, in which the first minute of

the test measured reading rate (i.e., words read per minute). Regarding the Reading Rate

subtest, the Nelson-Denny Test manual reports a moderate reliability coefficient (r) of

.69. Lewandowski, Codding, Kleinmann and Tucker (2003) report an adequate reliability

coefficient (r) of .81 for alternative forms (i.e., Forms G and H). The Nelson-Denny

Reading Test was used in this study because it was considered a common measure of

adults’ reading performance (Lewandowski et al., 2003).

Apparatus

The RIT (adapted from Gouldthorp & Coney, 2011) was programmed for stimulus

presentation and data collection using DirectRT (Empirisoft Corporation, 2011), which

used Windows 7 operating system. A DirectIN High Speed Button-Box v2012 was used

to record participants’ responses. The 9-button response box was at a fixed position

(50cm) from the computer monitor. Laminated rectangular labels were taped under the

left and right-most buttons which specified which button corresponded to ‘word’ or

‘nonword’ (which was counter-balanced across participants).

The Nelson-Denny Reading Test required: the Part II Comprehension Test

booklet, which contained the seven text passages and series of multiple choice questions

(Brown et al., 1993); a pen (black or blue ink) supplied by the investigator; and a timer

(i.e., iPhone 7) which was set to 20 minutes.

Administration of the NIH Cognition Battery required: a baseline measure

instrument which assessed participants’ RTs during the Flanker Inhibitory Control and

Attention Test and the Dimensional Change Card Sort Test; an examiner scoring sheet

which guided the scoring of participants’ responses during the List Sorting WM Task; a

wireless key-pad which the investigator used to record participants’ verbal responses


64

during the List Sorting WM Task; and an iPad with the NIH Toolbox App pre-installed

(Gershon et al., 2013).

Procedure

This thesis project was approved by the Murdoch University’s Human Research

Ethics Committee. Participants attended one data collection session in a venue at a

university in Perth (Western Australia), which lasted approximately 120 minutes. The

session was divided into two data collection periods (approximately 50 minutes each),

separated by a 20-minute break. Participants read the information sheet (Appendix B) and

voluntarily signed the consent form (Appendix C) at the beginning of the session.

Participants then completed the three tasks, with the order of the tasks rotated across

participants to minimise possible fatigue and order effects (Shaughnessy, Zechmeister, &

Zechmeister, 2006). Refer to Appendix D for a more comprehensive account of the

procedure. Upon completion of all three measures, participants were provided with a

summary of the purpose of the study (Appendix E).

RIT (adapted from Gouldthorp & Coney, 2011). Participants were required to

read each presented sentence at their own pace and progress to the next sentence or the

target word by pressing any key. One, two or three sentences were centrally presented on

the computer screen, followed by a centrally-presented fixation cross and then a target

word or non-word (Figure 2).


65

NIH Toolbox Cognition Battery (Gershon et al., 2013). To become proficient

in the administration of the NIH Toolbox Cognition Battery, the investigator underwent

extensive training. This included accessing online-training resources, thoroughly working

through the NIH Toolbox manual (NIH & NU, 2016a; Slotkin et al., 2012) and practising

each instrument several times to ensure competency of administration. During the

administration process, the investigator read the instructions presented on the iPad screen

to the participants (refer to Appendix F for example of one test item). Administration of

each instrument remained consistent across participants.

During the Flanker Inhibitory Control and Attention Task, a row of five arrows

arrayed in different directions were displayed on the iPad screen accompanied by two

response options; an arrow pointing left or an arrow pointing towards the right.

Participants were required to select the arrow that matched the direction the centre arrow

Figure 2. Example stimuli of the RIT depicting the different levels of context

for target words (adapted from Gouldthorp & Coney, 2011).


66

was pointing (Gershon et al., 2013). A baseline measure (refer to Apparatus) was used,

thus participants were instructed to respond as quickly and as accurately as possible.

Participants then completed the List Sorting WM Test, where they were exposed

to a series of verbal and visual stimuli (i.e., animal or food; Gershon et al., 2013).

Participants were asked to verbally recall the objects in size order from smallest to biggest

once the iPad screen went blank. For example, when presented animal objects (e.g., horse

and dog), the correct verbal response was ‘dog, horse’ (NIH & NU, 2016a). Consecutive

trials increased in difficulty; the first trial consisted of two stimuli, while the last possible

trial consisted of seven. The exposure time to stimuli varied as trials increased in

complexity. The task concluded once participants responded incorrectly during two

consecutive test-trials. The second section of this activity employed the same process,

however, participants were instructed to recall food objects first, from smallest to biggest,

then animal objects, again from smallest to biggest (e.g., popcorn then dog). The examiner

scoring sheet and the wireless keyboard (refer to Apparatus) was used to record

participants’ responses (i.e., the investigator pressed ‘1’ for correct response and ‘0’ for

incorrect response on the wireless keyboard; NIH & NU, 2016a).

During the Dimensional Card Change Sorting Test, participants were instructed

to match the target stimuli according to two cognitive dimensions (i.e., colour or shape)

as quickly as possible (Gershon et al., 2013). A series of ‘switch’ trials were presented

where participants were required to match a series of bivalent test pictures (i.e., yellow

ball and blue truck) to the target picture according to either colour (e.g., yellow or blue)

or shape (e.g., truck or ball; NIH & NU, 2016a). The baseline measure was used to assess

participants’ RTs (refer to Apparatus). Lastly, the Picture Sequence Memory Test was

administered where participants were presented with a sequence of images associated

with playing in a park, accompanied by a narration of the sequence (Gershon et al., 2013).


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They were then required to order the images in the same sequence originally presented

(NIH & NU, 2016a).

Nelson-Denny Reading Test (Form G; Brown et al., 1993). This test was

administered in accordance with the guidelines specified in the Comprehension Test

manual (Brown et al., 1993). Participants were instructed to read the seven passages at

their own pace and then answer the corresponding multiple-choice questions provided at

the end of each passage. Reading rate was recorded 60 seconds into the test, where

participants were required to circle the number next to the respective sentence they were

reading at that time, upon the investigator’s instruction. According to test procedure,

participants were required to complete the test within the 20-minute time limit.

Results

Reaction time

The RIT data was initially screened using an outlier deletion criterion of ±2.00

standard deviations from participants’ mean response times for each condition. This

resulted in 5.80% of the total observations being excluded. A one-way repeated-measures

analysis of variance (ANOVA) was initially performed on the IV of context (e.g., neutral,

low, medium and high; summarised in Table 2). The results indicated the assumption of

sphericity was violated for the main effect of context. The Huynh-Feldt correction was

used in this instance. The ANOVA revealed a significant effect of context, F(2.53,

126.24) = 10.30, MSe = 19868.27, p < .001. Participants’ RTs decreased as context

increased from neutral (i.e., slowest RTs) to low to medium to high (i.e., quickest RTs).


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Table 2

Mean RT (ms) and Mean Accuracy (%) for Target (Words) as a Function of Sentence Context.

Level of context Mean RT (SD) Mean Accuracy (SD)

Neutral 818.44 (323.18) 91.57 (7.58)

Low 810.23 (311.82) 91.57 (6.74)

Medium 714.48 (264.93) 93.14 (7.07)

High 713.24 (248.85) 92.35 (6.51)

Note. N = 51.

Additionally, a one-way repeated measures ANOVA was conducted on the IV of

context facilitation, which was computed by subtracting RTs of the low, medium and

high-context conditions from the neutral condition. These results also indicated the

assumption of sphericity was violated from the main effect of context and the Huynh-

Feldt correction was thus applied. The main effect of context was significant, F(1.77,

88.53) = 12.69, MSe = 14052.24, p < .001, reflecting that increasing context from low (M

= 8.22, SD = 233.80 ms) to medium (M = 103.96, SD = 188.45 ms) to high (M = 105.20,

SD = 188.31 ms) resulted in a large increase in facilitation.

A paired samples t-test with an α of .05 was used to compare mean facilitation

effects of the medium and high-context conditions. On average, there was little difference

in facilitation effects between the high and medium-context conditions (D = 1.23 ms, 95%

CI [-33.29, 35.76]), which was not statistically significant, t(50) = .072, p > .05, and a

small, d = .01. Furthermore, post-hoc pairwise comparisons (using Bonferroni correction)

for mean RTs of each target word context condition (i.e., neutral, low, medium and high)

revealed that the difference in mean RTs between low and medium-context conditions

was statistically significant, D = 95.75 ms, 95% CI [31.82, 159.68], p = .001.


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Accuracy

A one-way repeated-measures ANOVA was conducted on the IV accuracy for

each context level for word-target trials (summarised in Table 2). Mauchly’s test indicated

that assumption of sphericity was not violated for the main effect of context. A main

effect of context, F(3, 150) = .578, MSe = 49.76, p > .05 was not statistically significant,

indicating that there was no significant difference in accuracy (%) as context increased

from neutral (M = 91.57; SD = 7.58) to low (M = 91.57; SD = 6.74) to medium (M =

93.14; SD = 7.07) to high (M = 92.35; SD = 6.51).

Speed-accuracy trade-off effect

To assess whether there were consistent strategies of sacrificing speed for

accuracy, or vice versa, the relationship between RT and accuracy for target words was

examined. Specifically, to investigate the size and direction of the linear relationship

between mean accuracy (%) and overall mean RT (ms) for target words, Spearman’s rho

correlation coefficient (𝑟𝑟𝑠𝑠) was calculated, as the assumptions of normality was violated

for the Pearson’s product-moment correlation. The results indicated an absence of a

significant correlation between mean accuracy (M = 92.16, SD = 3.39) and overall mean

RT (M = 764.10, SD = 266.28), 𝑟𝑟𝑠𝑠 = -.03, p > .05, two-tailed, N = 51, evidencing that

participants were not strategically trading off accuracy for speed, or vice versa.

In addition, Spearman’s rho correlation coefficients (𝑟𝑟𝑠𝑠) were performed between

mean RT and accuracy within each condition (i.e., within each context level; neutral, low,

medium and high) to ensure no strategic responding was occurring within a single context

condition. No significant correlations were identified in any of the four context conditions

(lowest p = .457). Thus, no evidence of a speed-accuracy trade-off effect was identified.


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Discourse-level reading comprehension and cognitive processes

Majority of the cognitive functions scores fell broadly within the expected range

(i.e., age-corrected standard scores, for which the normative mean is 100 (SD = 15; NIH

& NU, 2016b). The cognitive flexibility measure (M = 111.86, SD = 17.26) was an

exception, in which the mean scores were over one standard deviation above the national

average when compared to like-aged participants (NIH & NU, 2016b). Participants

scored highest for the EF cognitive flexibility, followed by episodic memory (M = 106.12,

SD = 17.58), WM capacity (M = 99.33, SD = 14.94) and lastly, inhibition and attention

(M = 97.82, SD = 13.62).

A standard multiple regression analysis (MRA) was conducted to test whether

components of WM and EFs would account for a significant proportion of variance in

adults’ discourse-level comprehension ability, as indicated by the extent of the facilitation

effect produced in the RIT. Examination of the standardised residuals identified one

possible outlier (1.96% of cases). Cook’s and Mahalanobis distances, average leverage,

and standardised DfBeta values were calculated to determine whether the outlier exerted

any undue influence in the regression model, determining it did not (Field, 2013; refer to

Appendix I for statistical calculations). The covariance ratio calculations identified four

values exceeding the upper limit of an acceptable covariance ratio. However, given

Cook’s distance for each of these participants fell within the acceptable range, the

covariance ratios were not considered to be of concern. Visual inspection of the

scatterplot of the normal probability of standardised residuals and standardised residuals

in relation to standardised predicted values indicated the assumptions of normality,

linearity and homoscedasticity of residuals were met. High tolerances for predictors

evidenced that multicollinearity did not interfere with the ability to interpret the outcome

of the MRA.


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WM and EFs accounted for a nonsignificant 10% of the variance in discourse-

level comprehension ability, R² = .099, adjusted R² = -.021, F(4, 46) = 1.27, p = .30.

Unstandarised (B) and standardised errors (SE B) as well as standardised (β) regression

coefficients and squared semi-partial (‘part’) correlations (sr²) for each cognitive process

in the multiple regression model are presented in Table 3, identifying inhibition and

attention as significant predictors of adults’ discourse-level reading comprehension

ability.

Table 3

Unstandarised (B) and standardised errors (SE B) as well as standardised (β) Regression Coefficients and

Part Correlations (sr²) for Predictors in a Multiple Regression Model Predicting Discourse-level Reading

Comprehension.

Cognitive Process B [95% CI] SE B β sr² p

Inhibition and Attention -7.67 [-15.01, -.33]* 3.65 -.55 .09 .041

Cognitive flexibility 4.93 [-.94, 10.81] 2.92 .45 .06 .098

WM capacity -3.26 [-7.91, 1.40] 2.31 -.26 .04 .17

Episodic memory 1.60 [-2.30, 5.51] 1.94 .15 .01 .41

Note. N = 51. CI = confidence interval. * p < .05. ** p < .01

Propositional-level reading comprehension and cognitive processes

A standard MRA was conducted to determine whether WM and its EFs would

account for a significant proportion of variance in adults’ propositional-level reading

comprehension ability, as indicated by performance on the Nelson Denny Reading

Comprehension Test. Participants scored an average of 75.49% (SD = 16.95) on the

reading comprehension measure, which was above the standardised scores (M = 59.07%,

SD = 12.29) specified in the Nelson-Denny Reading Test Administration and Scoring

Manual. Inspection of the scatterplot of the normal probability of standardised residuals

and standardised residuals in relation to standardised predicted values indicated the


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assumptions of normality, linearity and homoscedasticity of residuals were met.

Furthermore, Mahalanobis distance did not exceed the critical 𝜒𝜒² for df = 4 (at α = .001)

of 18.47 for the data set, indicating multivariate outliers were not of concern.

Multicollinearity did not require further investigation, as tolerances for predictors in the

regression model were moderately high.

WM and the EFs accounted for a significant 25% of the variance in propositional-

level comprehension ability, R² = .25, adjusted R² = .19, F(4, 46) = 3.90, p = .008.

Unstandarised (B) and standardised errors (SE B) as well as standardised (β) regression

coefficients and squared semi-partial (‘part’) correlations (sr²) for each cognitive process

in the regression model are reported in Table 4. Significant predictors of participants’

propositional-level reading comprehension ability included inhibition and attention, WM

capacity and cognitive flexibility.

Table 4

Unstandarised (B) and standardised errors (SE B) as well as standardised (β) Regression Coefficients and

Part Correlations (sr²) for Predictors in a Multiple Regression Model Predicting Propositional-Level

Reading Comprehension.

Cognitive Process B [95% CI] SE B β sr² P

Inhibition and Attention .66 [.06, 1.26] * .30 .53 .08 .033

Cognitive flexibility -.58 [-1.06, -.10] * .24 -.59 .09 .020

WM capacity .56 [.18, .94] ** .19 .49 .14 .005

Episodic memory .02 [-.28, .36] .16 .04 .00 .82

Note. N = 51. CI = confidence interval. * p < .05. ** p < .01


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Propositional-level versus discourse-level reading comprehension

The partial correlation coefficients for each cognitive variable in relation to

discourse-level and propositional-level reading comprehension ability are depicted in

Figure 3. Refer to Appendix H for relevant statistical data output.

Discussion

The current study investigated the relationship between WM, its central EFs and

adults’ reading comprehension performance, particularly the contributions to discourse-

level comprehension ability. The study aimed to investigate whether specific functions of

WM, in addition to episodic memory, attributed to individual differences in reading

comprehension and situation model construction. Participants completed a series of

cognitive tasks, a standardised reading comprehension task and a measure of discourse-

level reading comprehension. Three hypotheses were postulated for this study, of which

only the first was supported.

First, the high-context condition of the RIT task (i.e., the presentation of the three

context sentences; adapted from Gouldthorp & Coney, 2011) was proposed to produce

greater facilitation for target words than the low and medium-context conditions. The

Figure 3. Partial Correlations for Predictors in a Multiple Regression Model predicting Discourse-

Level and Propositional-Level Reading Comprehension Ability. Note * p < .05; ** p < .01.


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findings supported this hypothesis; it demonstrated that facilitation for target words

increased as more contextual information became available to participants. Second, it was

hypothesised that performance on cognitive tasks from the NIH measure would account

for a significant proportion of variance in discourse-level reading comprehension ability.

This premise was not supported as the results indicated that WM, its central EFs and

episodic memory did not significantly predict discourse-level comprehension

performance. Third, the hypothesis that the EF cognitive flexibility will predict

performance on both propositional-level and discourse-level of processing was not

supported. The findings indicated that inhibition and attention had a greater influence on

propositional-level and discourse-level comprehension in contrast to the other cognitive

processes assessed.

Facilitation effect

To test the first hypothesis, participants were presented centrally-presented targets

primed by sentential contexts and neutral sentences (Gouldthorp & Coney, 2011). The

results supported this hypothesis; target word RTs decreased as the level of context

increased from neutral to high. This demonstrated that, by integrating target concepts (i.e.,

words) with preceding text, context did influence reading comprehension performance

(i.e., faster RTs as context increased), which was consistent with Gouldthorp and Coney

(2011). Thus, stronger discourse-level comprehension skills were involved in the

construction of mental representations described in text (Davoudi & Moghadam, 2015;

Zwaan et al., 2002). This suggested that, although each sentence individually provided

minimal contextual information, cumulatively they contributed to greater context

(Gouldthorp & Coney, 2011). The aforementioned finding substantiates that individual

differences in integrating information across sentences with world-knowledge influenced

the production of discourse-level meaning.


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Previous research identified that the presentation of three sentences specifically

facilitated greater recognition of target words than situations where one or two sentences

preceded a target word (Gouldthorp & Coney, 2011). In contrast to the findings of

Gouldthorp and Coney (2011), the present study found no significant difference in

facilitation effects between the high-context condition (i.e., three context sentences) and

the medium-context condition (i.e., two context sentences). This inconsistency in findings

possibly indicated that participants utilised contextual information from two related

sentences to accurately predict the forthcoming word; however, they did not substantially

benefit from the presentation of a third context sentence. This may have been influenced

by the cognitive strategies utilised by participants to complete the task. For example,

while completing the RIT, participants may have relied on the first two context sentences

to construct mental representations of the situation, suggesting they ignored information

provided from a third context sentence. This allowed a quicker response to the target

word, supporting the argument that the second context sentence primed the target word

as opposed to the third context sentence.

The results indicated that there was no significant difference in accuracy as

context increased from neutral to high. This was inconsistent with the findings presented

in Gouldthorp and Coney (2011); their results mirrored the pattern found for their

facilitation data (i.e., there was an increase in accuracy with more contextual support). In

the present study, the RIT (adapted from Gouldthorp & Coney, 2011) demonstrated high

levels of accuracy across all conditions, which potentially highlights the presence of

ceiling effects for this study (Ho & Yu, 2015). As a result, no meaningful variation in

participants’ performance was evidenced, thus, individual differences in reading

comprehension ability could not be identified within this study.


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Relationship between cognitive processes and discourse-level reading ability

The results did not support the second hypothesis which proposed that

performance on EF tasks would account for a significant proportion of variance in

discourse-level reading comprehension ability. However, the results did indicate a

significant negative relationship between inhibition and attention and discourse-level

comprehension ability. This suggested the ability to construct mental representations of a

situation described in a text was negatively influenced by participants’ ability to ignore

irrelevant information and direct attentional resources to task-relevant information. This

was contrary to what was expected as past findings claimed that inhibition and attention

increased reading comprehension performance (Cain, 2006; Cantin et al., 2016; Diamond,

2013; Engel de Abreu et al., 2014; Nouwens et al., 2016).

This could be explained by the RIT being predicated on the situation model and

participants’ ability to create mental representations of a situation described in text

(Davoudi & Moghadam, 2015; Zwaan et al., 2002). Inhibition in this case should have

represented a participant’s ability to ‘de-activate’ or rule out irrelevant mental

representations as context from the sentences increased from neutral to high (i.e., lexical

inhibition; Davis & Lupker, 2006). However, the Flanker task employed in this study was

more appropriate for measuring response inhibition (e.g., the suppression of automatic

response tendencies which may interfere with goal achievement; NIH & NU, 2016a).

Hence, the measure may not have accurately portrayed lexical inhibition in this study. As

previously discussed in relation to context and facilitation effects, participants may have

utilised the second context sentence to conceptualise the target word and ignored the third

context sentence. Consequently, participants possibly engaged in inhibition by not

attending to the third context sentence.


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Although cognitive flexibility did not significantly predict performance in

discourse-level reading comprehension ability, the results depicted a moderate positive

relationship between cognitive flexibility and discourse-level reading comprehension

performance. As such, it is possible that the study design did not have sufficient power

(based on a priori power analysis) to detect the significance of this relationship, which is

attributed to a smaller effect size obtained from the limited sample than was expected. A

larger sample size may have potentially detected this dependence.

The positive correlation between cognitive flexibility and discourse-level reading

comprehension can be explained by participants’ ability to adapt WM representations of

a situation presented in the text (Diamond, 2013; Engel de Abreu et al., 2014). As context

increased, participants were able to make amendments to the meanings they associated

with the words in the context sentence, thereby constructing a more accurate situation

model. This would have facilitated discourse-level reading comprehension ability by

allowing participants to respond more rapidly to the congruent target words. A crucial

part of this process was the activation and manipulation of different language codes (e.g.,

phonological, orthoraphic and semantic), which enabled readers to adapt mental

representations from textual information (Colé, Duncan & Blaye, 2014).

Individual differences in language processing can explain the relationship

between inhibition, attention and cognitive flexibility and discourse-level reading

comprehension. Individual differences, such as the cognitive strategies employed by

participants, may have influenced participants’ ability to construct accurate mental

representations of the textual information described in the RIT task. Moreover, it may

have masked the true effect of inhibition and cognitive flexibility, in discourse-level

reading comprehension performance. For example, the meanings participants associated

with words presented in text cannot be controlled with this experimental design. These


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meanings are often multimodal (e.g., lexical or visual) and rely on the preference of the

reader (Davoudi & Moghadam, 2015; Zwaan et al., 2002). In addition, these meanings

can become irrelevant to the situation and thus were inhibited or forgotten as more context

became available to participants (Baddeley, 2012; Davoudi & Moghadam, 2015; Engel

de Abreu et al., 2014; García-Madruga et al., 2014; Nouwens et al., 2016).

Conversely, participants may have focused on only one meaning for each sentence

rather than multiple meanings. This single meaning was either replaced or affirmed as

context increased. This strategy relied on attentional control where participants allocated

attentional resources to a specific meaning (García-Madruga et al., 2014) and disregarded

alternative meanings which were relevant to the target word. This may have hindered

reading comprehension ability as the meanings associated with the context sentences

would be inconsistent with the target words, thereby delaying facilitation effects. The

delay in facilitation would not be strongly influenced by this strategy if the participants

were able to attribute meaning to the sentences that were closely related to the target word

(e.g., JAM and HONEY vs. KITE and MOON; Faust, Bar-Lev, & Chiarello, 2003).

Furthermore, participants may have utilised cognitive flexibility by switching between

the abovementioned strategies to determine which was most effective for obtaining

accurate responses on the adapted RIT measure (Nouwens et al., 2016). For example,

participants may have utilised one strategy by focussing on one meaning for each sentence

at the start and, as trials progressed, they may have changed their processing strategy to

include multiple meanings.

The results indicated a weak negative correlation between WM capacity and

discourse-level reading ability, however this was not significant. Consequently, WM

capacity did not differentiate between good and poor comprehenders, which concurred

with Daneman and Carpenter’s (1980) and Georgiou and Das’s (2016) findings. The


79

results from this study suggested that integration of information did not rely on

participants having to recall the spatial and/or temporal order in which context sentences

were provided to respond to target words. Moreover, the greater amount of information

participants stored in WM for cognitive processing may potentially obstruct their ability

to integrate information into coherent situation models. Accordingly, the ability to store

and manipulate information in WM was not a crucial component for discourse-level

processing (Dutke & von Hecker, 2011; Meinz & Hambrick, 2010; Turner & Engle,

1989).

There was a weak positive correlation between episodic memory and discourse-

reading comprehension, however not as significant as postulated by Mumper (2013). This

supported the claim that retrieving contextual information previously stored in LTM

slightly facilitated reading comprehension ability. Thus, participants could generate

inferences by activating additional information from long-term memory during the

reading process (Ababneh & Ramadan, 2013; Davoudi & Moghadam, 2015; Yeari & van

den Broek, 2015; Zwaan et al., 2002). This enabled participants to create visual

experiences of the textual information, thereby aiding in the construction of situation

models (Yeari & van den Broek, 2015; Zwaan et al., 2002).

Individual differences in discourse-level reading comprehension may not have

been apparent in terms of the underlying cognitive processes (i.e., inhibition and attention,

cognitive flexibility, WM capacity and episodic memory) due to the nature of discourse-

level reading comprehension measure (Georgiou and Das, 2016). This was illustrated by

the inability of the RIT to differentiate between good and poor reading comprehenders,

which potentially masked the true interactions between WM, its central EFs, episodic

memory and discourse-level comprehension. Due to the multi-dimensional nature of

discourse-level comprehension (Ababneh & Ramadan, 2013; Davoudi & Moghadam,


80

2015; Yeari & van den Broek, 2015; Zwaan et al., 2002), there was no apparent consensus

in the literature for situation model construction. Moreover, there is no direct measure of

overall discourse-level reading comprehension. The RIT measure employed in this study

is thus a preliminary measure which assessed the integration of world-knowledge across

sentences, which is one component of situation model construction. Consequently, it has

not effectively tapped into overall discourse-level reading comprehension processes.

Relationship between cognitive processes and propositional-level reading ability

The results indicated that WM, its central EFs and episodic memory significantly

accounted for individual differences in propositional-level reading comprehension

ability, which was consistent with several past studies (e.g., Cain, 2006; Cantin et al.,

2016; Diamond, 2013; Engel de Abreu et al., 2014; Nouwens et al., 2016). This study

revealed a significant positive relationship between inhibition and attention and

propositional-level comprehension performance. This corresponded with Arrington et al

(2014) and Gerst et al (2017), who suggested inhibition enabled readers to focus on words

and sentences relevant to the main topic while simultaneously ignoring irrelevant

information as it was presented. In addition, response inhibition facilitated propositional-

level reading ability as it enabled readers to suppress automatic and dominant responses

to the text (García-Madruga et al., 2016). Similar to García-Madruga et al (2014) and

Yogev‐Seligmann et al (2008), a ttention facilitated propositional-level comprehension

performance by enabling participants to successfully direct their attentional resources

towards task-relevant information to complete the reading comprehension task.

Contrary to previous research (e.g., Cantin et al., 2016; Diamond, 2013; Engel de

Abreu et al., 2014; Gerst et al., 2017; Nouwens et al., 2016), cognitive flexibility

negatively contributed to propositional-level reading comprehension ability. The findings

indicated that, as cognitive flexibility increased, comprehension performance on the


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Nelson-Denny Comprehension Test decreased. This may be attributed to participants

relying on multiple reading strategies whilst completing the standardised comprehension

measure. By relying on multiple strategies, participants were potentially unable to

successfully adjust their cognitive strategies to meet the reading objective (i.e., selecting

the appropriate response to the corresponding reading passage; Colé et al., 2014;

Nouwens et al., 2016), thus impeding their propositional-level comprehension

performance.

There was a moderate positive correlation between WM capacity and

propositional-level reading comprehension, which significantly accounted for individual

differences in participants’ reading comprehension performance. This was consistent with

previous research which claimed that participants have a limited WM capacity (Cowan,

2010) and that the ability to store and manipulate task-relevant information temporarily

in WM accounted for individual differences in propositional-level reading

comprehension performance (Daneman & Carpenter, 1980; Dutke & von Hecker, 2011;

Meinz & Hambrick, 2010; Turner & Engle, 1989). Furthermore, this was indicative of

participants with higher scores on the comprehension measure having greater WM

capacity than those who obtained lower scores (Dutke & von Hecker, 2011; Turner &

Engle, 1989). This suggested that, in addition to the ability to store and manipulate

information in WM, skilled comprehenders could rely on fewer cognitive processes in

comparison to less skilled comprehenders (Daneman and Carpenter, 1980; Dutke & von

Hecker, 2011; Turner & Engle, 1989).

Lastly, there was a very weak positive correlation between episodic memory and

propositional-level reading comprehension ability, which was not significant. The

correlation indicated that participants did not rely on background knowledge stored in

LTM to complete the Nelson-Denny Comprehension Test. Consequently, the results


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demonstrated that individual differences in adults’ propositional-level reading

comprehension ability was attributed to WM and its central EFs inhibition, attention and

cognitive flexibility rather than episodic memory.

Propositional-level versus discourse-level reading ability

The third hypothesis postulated that the EF cognitive flexibility would predict

performance on both propositional-level and discourse-level reading comprehension

ability more strongly in comparison to inhibition, WM capacity, attention and episodic

memory. The results contested this hypothesis; inhibition and attention appeared to more

strongly predict performance on both reading comprehension measures. Specifically,

inhibition and attention positively correlated with propositional-level reading

comprehension and negatively correlated with discourse-level reading comprehension.

The findings suggested the ability to inhibit dominant or automatic responses while

attending to task-relevant information aided propositional-level reading comprehension

ability, however hindered discourse-level reading comprehension ability. An explanation

for this difference in relationship can be attributed to discourse-level requiring a deeper

level of cognitive processing (i.e., construction of situation models; Fincher-Kiefer, 1993;

Zwaan & Radvansky, 1998; Zwaan et al., 2002). Furthermore, the impact of inhibition

on discourse-level reading performance may have been masked by participants utilising

various cognitive strategies whilst completing the RIT task, as previously discussed.

Cognitive flexibility significantly predicted performance on propositional-level

reading comprehension ability, however, it was not a significant predictor of discourse-

level reading comprehension performance. Cognitive flexibility positively correlated

with discourse-level processing, though the current study obtained a smaller effect size

than anticipated with the limited sample size (based on the priori power analysis).

Additionally, cognitive flexibility negatively correlated with propositional-level reading


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comprehension. This difference between propositional-level and discourse-level

processing can be attributed to participants’ use of cognitive flexibility. Specifically, the

ability to adapt mental representations to the contextual information provided during the

RIT facilitated reading comprehension (Diamond, 2013; Engel de Abreu et al., 2014)

whereas switching between multiple reading strategies appeared to prevent participants

from achieving desired reading goals (Colé et al., 2014; Nouwens et al., 2016).

WM capacity did significantly contribute to propositional-level but not to

discourse-level reading comprehension ability, implying that the ability to hold

information in WM was not necessary for gaining a deeper meaning of a text. Moreover,

WM capacity positively correlated with propositional-level reading comprehension and

negatively correlated with discourse-level reading comprehension performance. Thus,

greater WM capacity appeared to assist participants to perform better on the

propositional-level comprehension measure than those with less WM capacity.

Alternatively, greater WM capacity could have hindered participants’ ability to construct

coherent situation models of textual information in relation to the discourse-level reading

comprehension.

Lastly, episodic memory was not significantly correlated with either

propositional-level or discourse-level reading comprehension performance, suggesting

that retrieving knowledge from LTM was not required to construct basic or deeper

meanings of information depicted in a text. Neither reading processes specifically relied

on the retrieval of temporal and spatial information from LTM (as assessed by the Picture

Sequence Memory Test; NIH &NU, 2016a). However, episodic memory was slightly

more positively correlated with discourse-level reading comprehension. Thus, gaining a

deeper meaning of textual information potentially relied on drawing information from

background information and generating inferences from world knowledge (Ababneh &


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Ramadan, 2013; Davoudi & Moghadam, 2015; Yeari & van den Broek, 2015; Zwaan et

al., 2002).

Limitations

First, the nature of the discourse-level reading comprehension measure may have

affected the results of the study (Georgiou and Das, 2016). The RIT is a preliminary

measure of discourse-level reading comprehension and only assessed one aspect of

situation model construction – the participants’ ability to integrate information with

world-knowledge (Gouldthorp & Coney, 2011). Hence, this measure did not measure

overall situation model construction. However, an alternative measure which assesses the

multiple components of discourse-level reading comprehension (e.g., inference

generation, metaphor comprehension and integration of information) is yet to be

developed (Gouldthorp & Coney, 2011). The difficulty in constructing such a measure

can be attributed to the multi-dimensional nature of discourse-level processing (Davoudi

& Moghadam, 2015; Zwaan et al., 2002).

Second, the design of the study may have unduly influenced the results obtained.

For example, the limited sample size of the study reduced the effect size and power to

detect the relationship between cognitive flexibility and discourse-level reading

comprehension. Furthermore, the sample was restricted to participants with post-

secondary qualifications. Restricting the sample to individuals who are more

academically inclined may have skewed the results and produced higher performance on

the RIT task. Consequently, the results indicated less performance variation in the sample

thereby reducing the ability to detect individual differences in participants’ discourse-

level reading comprehension.

Third, the inability to control or monitor participants’ strategies when completing

the RIT task and the nature of the inhibition and attention measure (i.e., Flanker task) may


85

have affected the facilitation effect attained on the RIT. The study was unable to

determine whether participants utilised the desired WM and cognitive processes (e.g.,

inhibition and attention). Furthermore, the Flanker task may not have been appropriate in

measuring inhibition in relation to situation model construction. A measure of lexical

inhibition is possibly more relevant in terms of measuring participants’ ability to rule out

irrelevant mental representations constructed by textual information (Davis & Lupker,

2006). Nevertheless, this study provided an insight of how general cognitive abilities,

such as inhibition and attention, are drawn upon for language-based tasks (e.g., reading).

Implications

The current study’s findings are of theoretical importance as they provide an

insight into the central mechanisms of Baddeley’s (2000) WM model commonly depicted

in the reading comprehension literature. Moreover, it is a preliminary investigation into

how these central cognitive processes influence discourse-level reading comprehension.

The results of this study attempted to elucidate why inconsistencies exist in relation to

WM and discourse-level reading comprehension ability. These inconsistencies were

attributed to the validity and reliability of the reading comprehension and EF measures

used in conjunction with the relative sample and effect size of the study’s design. This

can be related to previous studies, which demonstrated similar inconsistencies in findings

(Arrington et al., 2014, Cantin et al., 2016; Christopher et al., 2012, Engel de Abreu et

al., 2014; Gerst et al., 2017; Gouldthorp et al., 2016). Furthermore, this study has

important implications in interpreting existing literature, as previous research has mainly

focussed on propositional-level comprehension. Hence, inconsistencies in the literature

can also be explained by the type of comprehension what was investigated (i.e.,

propositional-level versus discourse-level), as demonstrated in this study.


86

Regarding the practical implications, these results partially demonstrate that

individual differences in discourse-level and propositional-level reading comprehension

ability depend on multiple EFs. Consequently, the ability to identify, conceptualise and

evaluate these EFs is advantageous for allied health professionals as well as educators

and researchers. Comprehensively understanding the relationship between WM and

discourse-level reading comprehension can lead to the development of theoretical

models, with the focus on the importance of EFs underpinning discourse-level reading

comprehension (Sesma et al., 2009). The benefit of the abovementioned model is its

potential to facilitate the development and implementation of training and intervention

programs (e.g., García-Madruga et al., 2016), with aims of improving executive skills and

academic performance across various age ranges. In addition, the variation in the findings

between proposition and discourse-level comprehension has important implications for

developing assessment and intervention programs which can accommodate reading

impairment at both levels of comprehension.

Future research

Future studies could examine the link between WM, EFs and discourse-level

reading comprehension. Specifically, studies should develop discourse-level reading

comprehension measures and evaluate their psychometric properties to establish reliable

and valid assessment measures. Additional studies should also be conducted to gain

clearer understanding of the relationship between EFs and the different levels of reading

comprehension to clarify the unique contributions of EFs to comprehension abilities. The

research would include modelling analyses to investigate the effect of cognitive

processes, such as updating, planning and temporal awareness, in mediating the

relationship between WM, its central EFs and discourse-level reading comprehension


87

(García-Madruga et al., 2014; Gerst et al., 2017; Nouwens et al., 2016; Sesma et al.,

2009).

It is also recommended that this study be replicated with a larger sample size.

Participants should be recruited with a variety of education backgrounds to explicate

whether education had a significant influence on discourse-level reading comprehension

performance. That is, a sample which includes individuals with poorer reading

comprehension ability and/or lower EF and WM would enable researchers to detect more

distinct relationships between reading comprehension and WM. Alternative measures of

WM and EFs should be explored, such as the Colour-word inference test for inhibition

and the Trail-Making test for cognitive flexibility, to establish concurrent validity of the

measures (Cicchetti, 1994). Alternative measures of discourse-level reading

comprehension include those employed in studies by Fincher-Kiefer (1993; 2001) on

inferencing and Zwaan and colleagues (1998; 2002) on situation model construction.

These may assist in evaluating reading comprehension at discourse-level, however their

ability to identify individual differences in reading comprehension is yet to be assessed.

Moreover, a minor improvement can be made to the RIT (adapted from

Gouldthorp & Coney, 2011). Target words which are incongruent with the centrally-

presented sentences (e.g., replacing the target word HONEY with CAKE) should be

included. This will allow investigators to assess whether an inhibition effect for

incongruent conditions would occur (Faust et al., 2003). Thus, it may provide further

evidence of a facilitation effect where congruent words are strengthened by the context

sentences. Additionally, it would demonstrate that participants are integrating

information across sentences to develop a deeper, discourse-level understanding of the

text.


88

Although the relationship between WM and children’s propositional-level reading

comprehension performance has been studied extensively, this relationship with

discourse-level reading comprehension has been less empirically explored.

Approximately 10% of school-aged children demonstrate age-appropriate word decoding

skills, yet have difficulty understanding the meaning of what they read (Cain & Oakhill,

2006). Thus, it is of interest to replicate the study with children (e.g., between the ages of

7 and 14 years). Moreover, assessing reading comprehension is vital for monitoring

students’ progress, detecting comprehension difficulties and testing theories of

comprehension (Cain & Oakhill, 2006; Carlson, Seipel, & McMaster, 2011). Hence, it is

recommended that a study be conducted to examine the underlying cognitive processes

and abilities that allow primary-school aged children to produce meaning (i.e.,

comprehension) during reading. Future research in this area may identify individual

differences in children’s level of reading comprehension as well as how WM and its

central EFs account for these differences.

Conclusion

This study aimed to investigate whether individual differences in reading

comprehension and situation model construction were partly explained by differences in

functioning of specific EFs central to WM. The study’s results only substantiated the first

hypothesis (i.e., facilitation of target words increased as context increased from neutral to

high). The remaining two hypotheses were not substantiated; WM and its EF did not

significantly influence participants’ discourse-level comprehension ability; and cognitive

flexibility did not predict reading comprehension more strongly than inhibition and

attention, WM capacity and episodic memory.

Understanding the relationship between WM and its central EFs is crucial for

developing training programs aimed at improving individuals’ executive skills and


89

academic performance. Considering the limitations and recommendations for future

studies, this study had important theoretical and practical implications. It provided an

insight into the underlying mechanisms of Baddeley’s (2000) WM model as well as the

potential for the development of a theoretical, reading comprehension model based on the

central EFs of WM. This study also has important applications for future research,

particularly in identifying individual differences in adults’ levels of reading

comprehension as a function of these cognitive processes. Furthermore, it is

recommended future studies examine how these cognitive processes in children affect

their ability to produce meaning.


90

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Appendix A

Ethics Approval Letter


100

Appendix B

Information Letter

(continued)


101

Appendix B Continued

(continued)


102

Appendix B Continued


103

Appendix C

Consent Form


104

Appendix D

Detailed Procedure

The Relationship between Working Memory and Higher-Level Reading Comprehension

Ability in Adults

Procedure

Sessions will be conducted in small groups, with a maximum of three participants at a time. Test sessions will last approximately 120 minutes* (which includes rest breaks), each participant will be asked to:

1. Complete the Nelson-Denny Comprehension Test (Form G) which will take approximately 20 minutes

a. Read the instructions on the test and ask the researcher any questions if needed

b. Read first passage. The first minute will be used to assess your reading rate. Upon instruction from the investigator, participants will circle the number corresponding to the respective sentence they were reading at that time. Participants are then to continue reading the remainder of the passage.

c. Answer the questions about the passage d. Continue until participants have read all seven passages and completed all

questions, or until the researcher asks them to stop.

2. Complete the NIH Toolbox Cognition Battery This task will be completed using an iPad and, for some of the subtests, a wireless keyboard. It takes approximately 25 minutes in total and involves the following tasks

a. Picture Sequence Memory Test, assessing processes involved in the acquisition, storage and retrieval of new information. 7 minutes.

b. Flanker Task, assessing inhibitory control and attention. 3 minutes. c. Listing Sorting Working Memory Test, assessing the ability to store

information until the amount of information exceeds one’s capacity to hold that information. 7 minutes.

d. Dimensional Change Card Sort Test, assessing the capacity to plan, organise and monitor the execution of behaviours that are strategically directed in a goal-orientated manner. 4 minutes.

In each of these component tests, participants are presented with automated instructions on the iPad, followed by practice items before completing the task itself. Copies of instructions and examples of items are attached here.

(continued)


105

Appendix D Continued

3. Complete the Reading Integration Task

This task will be completed on a computer. One, two or three sentences will be presented to vary the degree of constraint on the possibilities for the upcoming target word. The sentences will be presented in the centre of the computer screen and participants will read them at their own pace, progressing to the next sentence or target word by pressing a key. A central fixation cross will then be presented, followed by a target word or nonword. The participant will then press one of two buttons to indicate whether the target is a word or nonword. Reaction time and accuracy will be recorded. There will be 10 trials per condition, resulting in 80 trials in total. There will be 20 trials for each of: 3 sentences (high constraint), 2 sentences (medium constraint), 1 sentence (low constraint) and a neutral sentence (no constraint). Half of the trials in each of these conditions will involve a word-target and half will involve a nonword-target. Rest breaks will be offered after every 10 trials, to produce 8 blocks in total. The order of conditions presented within each block will be randomised across participants. Example stimuli are attached here.

*In both sessions, the order of the tasks listed above will be rotated across participants. Additionally, the order of the sessions will be rotated across participants.


106

Appendix E

Debriefing sheet

(continued)


107

Appendix E Continued


108

Appendix F

Instructions for NIH Toolbox Cognition Battery Example

C.2.2.4 NIH Toolbox Flanker Inhibitory Control and Attention Test Instructions Ages

12+

This table outlines the item content read by as well as the actions for the examiner.

(continued)


109

Appendix F Continued


110

Appendix G

Example stimuli from Reading Integration Task In the examples below, the capatalised work is the target word. The first three lines show the three sentences that could be presented for that target word: only the first sentence will be presented for the low contraint condition; the first two sentences will be presented for the medium constraint condition; the first three sentences will be presented for the high constraint condition. The fourth sentence woll be presented individually for the neutral condition. HORSE I am fast. People bet on me. I can be ridden. This is a neutral sentence. WEED I am considered unattractive. People pull me out. I am a plant. This is a neutral sentence. GRAPE I have a skin. I come in bunches. I am used to make wine. This is a neutral sentence. KITE I can fly. I need wind. I have a string attached to me. This is a neutral sentence. PLANE I am folded. I am made of paper. I can fly. This is a neutral sentence.


111

Appendix H

Statistical Output

Reaction Time: Repeated-Measures Analysis of Variance (ANOVA)


112

Facilitation Effect: ANOVA


113


114

Accuracy: ANOVA


115

Speed-accuracy trade-off effect


116


117

Discourse reading comprehension ability and cognitive processes


118


119

Propositional reading comprehension and cognitive processes


120


1

Appendix I

Statistical Calculations Standardised Residuals

1 case with standardised residuals with absolute value > 3

= 151

× 100

= 1.96% of cases*

2 cases with standardised residuals > 2

= 251

× 100

= 3.92% of cases* Note. * % below that specified by Field (2013).

Cook’s and Mahalanobis Distance

Max Cook's distance = .62*; max Mahal. distance was 10.96**

Note. * value below 1.00; ** value below 18.47 at df = 4

Average Leverage

𝐴𝐴𝐴𝐴𝐴𝐴𝑟𝑟𝐴𝐴𝐴𝐴𝐴𝐴 𝐿𝐿𝐴𝐴𝐴𝐴𝐴𝐴𝑟𝑟𝐴𝐴𝐴𝐴𝐴𝐴 = 3 �𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝑛𝐴𝐴𝑟𝑟 𝑜𝑜𝑜𝑜 𝑝𝑝𝑟𝑟𝐴𝐴𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑝𝑜𝑜𝑟𝑟𝑝𝑝 + 1

𝑝𝑝𝐴𝐴𝑛𝑛𝑝𝑝𝑠𝑠𝐴𝐴 𝑝𝑝𝑝𝑝𝑖𝑖𝐴𝐴� = 3 �

4 + 151

�

𝐴𝐴𝐴𝐴𝐴𝐴𝑟𝑟𝐴𝐴𝐴𝐴𝐴𝐴 𝐿𝐿𝐴𝐴𝐴𝐴𝐴𝐴𝑟𝑟𝐴𝐴𝐴𝐴𝐴𝐴 = .098*

0 cases > 3 times average leverage (i.e., .29) Note. * Max centered levearage < .29

Standardised DFBeta

0 case > standardised DFBeta value of 1.00

Covariance Ratio

Upper limit: 1 + (3 × average leverage) = 1.29

Lower Limit: 1- (3 × average leverage) = -1.29 4 cases > 1.29

Note. Average leverage is .098; * Max covariance value = 1.44


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Appendix J

Project Summary

Research has repeatedly demonstrated that the ability to temporary hold incoming

information in the mind whilst carrying out other mental processes contribute to

individual differences in adults’ reading comprehension. However, less is known how

these related to an individuals’ ability to build visual imagery of information in a text.

The cognitive processes investigated within this study included individuals’ ability to:

ignore information that is not relevant to complete the task (i.e., inhibition); switch

between various mental states (i.e., cognitive flexibility); pay attention to task-relevant

information (i.e., attention); and retrieve information stored in long-term memory (i.e.,

episodic memory).

Existing literature has different stances regarding the effect of cognitive processes

on reading comprehension ability. To explain these inconsistencies, this study

investigated to what extent the ability to manipulate, integrate, and update information

quickly and efficiently was related to the depth of meaning adults gain when reading

information. Fifty-one participants, aged 18 to 56 years, completed three tasks: two

measured reading comprehension ability at lower and higher levels, and one which

measured cognitive processes.

Three hypothesises were tested in this study. First, it was proposed that presenting

three sentences (e.g., “I am sweet”, “I am spread on toast” and “I am made by insects”)

will produce greater predictability of word targets, in this case, HONEY. Second, it was

hypothesised that performance on the cognitive task will strongly predict performance on

the higher-level comprehension measure. Third, it was theorised that cognitive flexibility

will predict lower and higher-level comprehension ability more strongly in comparison

to inhibition, WM capacity, attention, and episodic memory.


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The results only supported the first hypothesis; presenting three sentences enabled

participants to more accurately predict the target word at a faster rate than when the target

word was presented after a neutral sentence, one sentence or two sentences. The

remaining two hypotheses were not supported; WM and its cognitive processes did not

significantly influence participants’ high-level comprehension ability; and cognitive

flexibility did not predict reading comprehension more strongly than inhibition and

attention, WM capacity and episodic memory.

Clearly understanding the relationship between WM and its cognitive processes

is crucial for the development of training and educational programs which try to improve

children’s and adults’ decision-making skills and academic performance. Although this

study did have some limitations (e.g., small sample size and inability to directly measure

overall higher-level reading comprehension), it did have important implications. It

provided an insight into the cognitive processes underpinning reading comprehension.

Moreover, this study demonstrated that the different research stances can be can be

explained by the depth of comprehension ability that was investigated (i.e., lower versus

higher-level reading comprehension ability). This study also has important applications

for future research, particularly in identifying individual differences in cognitive

processes and how these relate to the depth of adults’ reading comprehension.

Furthermore, it is also recommended future studies examine these how these cognitive

processes in children affect their ability to produce meaning when reading.

Documents

Running head: READING COMPREHENSION AND WORKING …