Linking aberrant pauses during object naming to letter and word decoding speed in elderly with attention complaints

Abstract

Attention deficit and reading difficulty are often comorbid in neuropsychiatric disorders of childhood and adolescence. Although recent research has shown how these two domains may interact in children, knowledge about such interaction in elderly is lacking. The present study tested whether this association is also present in healthy elderly with undiagnosed attention problems. Thirty-two subjects (65+ years) with life-long complaints of attention and with a Mini Mental (MMSE) cutoff of 27 points were tested with MapCog Spectra (MCS), with a word recognition test (Word Chains test) and CANTAB subtests of attention. All tests were presented on a tablet, except for the Word Chains test. The participants mean MMSE score was 29 points and their mean age was 71.5 years. Strong correlations were seen between the Word Chains test and the MCS, suggesting that a high number of aberrantly long pauses during serial naming was associated with fewer identifications of letters, words and sentences. The number of aberrant pauses was also associated with slower Reaction Time and a lower score on the Attention Shifting task of the CANTAB. The results were not associated with either gender or general intelligence. This study shows that attention is linked to decoding speed irrespective of intelligence and gender. We therefore suggest that a clinical assessment of attention deficit should also include an assessment of decoding ability, and vice versa, as these cognitive functions are strongly interdependent.

Keywords:

• Attention difficulty
• CANTAB
• elderly
• MapCog Spectra
• Word Chains Test

Introduction

Cognitive functions are rarely discernable as discrete entities but are rather formed by the intrinsic cooperation by many subsystems, which overlap with other cognitive processes. Attention is one example of a ubiquitous function of the brain that plays a critical role in most cognitive abilities and everyday functioning. For example, numerous reports have shown that attention problems coexist with reading difficulties in as much as 40% of the cases (Czamara et al., 2013; Langer et al., 2019; Pliszka, 1998). As reading involves decoding of visual symbols into sounds and meaning and requires attention allocation, it follows that attention deficits could impact decoding ability and, hence, reading fluency. Whether a dysfunction in one of these cognitive functions automatically leads to a dysfunction of the other is however still a matter of conjecture (Willcutt et al., 2010). A commonly cited etiology model proposes that the comorbidity between attention deficit hyperactivity disorder (ADHD) and reading difficulty is a result of shared genetic/environmental factors (Davis et al., 2009; Kovas & Plomin, 2006; Pennington, 1999; Plomin & Haworth, 2009; Willcutt et al., 2010).

Although the underlying neural mechanisms need to be further investigated, a combination of shared and distinctive brain alterations have been reported, which supports the multiple deficit model in ADHD and reading difficulty (Langer et al., 2019; McGrath et al., 2011).

Recent studies have shown that visual-spatial attention, in addition to phonological skills, may be a predictor of language development in children, and that attentional difficulties may be an important predictor for reading difficulty (Franceschini et al., 2012; Gabrieli & Norton, 2012). According to the Simple View of Reading hypothesis, word decoding is the basic fundament for reading and requires that the individual understands and can handle the relation between sound and letter, which in turn is a prerequisite for language comprehension skills (Gough & Tunmer, 1986).

Rapid automatized naming (RAN)

The RAN test has been used for decades (Norton & Wolf, 2012) in the assessment of reading difficulties in children and adolescents and numerous studies have shown that RAN is a powerful predictor of reading difficulty (Denckla & Cutting, 1999; Li et al., 2009; Norton & Wolf, 2012; Pham et al., 2011). Results have consistently shown that subjects with reading difficulties have longer naming times compared with typical subjects. Of importance for the present study is the sluggishness and higher moment-to-moment variability in subjects with attention deficit, which is explained by longer pause times, while articulation time contributes less to the overall performance (Li et al., 2009; Neuhaus et al., 2001; Pham et al., 2011). We have assessed pause time duration using a modified RAN paradigm and confirmed that their duration is characterized by a high moment-to-moment variability in diverse clinical populations such as Alzheimer's disease, neuro-developmental disorders and stress-induced exhaustion disorder (Carlsson et al., 2015; Persson et al., 2015; Warkentin et al., 2015, 2008). We have recently shown that the occurrence of aberrantly long pauses is associated with attentional deficit (Bartfai et al., 2021). As attention is required for all types of cognitive functions, it is a reasonable assumption that also basic functions, such as decoding, are under the influence of the ubiquitous function as attention. The fact that the decoding is a prerequisite for learning to read, together with the possibility of being under attentional influence and that decoding may remain a problem also later in life, prompted us to investigate the degree to which attention plays a role in decoding in elderly with subjective complaints. The clinical importance of a possible association between attention and decoding is given by the fact that this possibility is still under-recognized in elderly, and although elderly have complained throughout their lives, they may not have been subjected to a thorough investigation for their difficulties (Fischer et al., 2012; Garcia et al., 2012). Therefore, the question remains unanswered whether elderly with subjective attention difficulties also have comorbid problems with decoding.

Aims

The aim of the present study was to investigate if attention is associated with word decoding in elderly, who report life-long subjective attention problems.

Material and methods

Participants

Forty-one native swedes (65 years and older) were recruited via an advertisement in a local newspaper and 32 subjects were finally included in the study. The Mini Mental State Examination (Folstein et al., 1975) was used to screen for symptoms of MCI, with a cut of ≥27 (of 30 points) used for inclusion in the study which is commonly used (Ledreux et al., 2019). Exclusion criteria were a MMSE-score below 27 points, abuse of alcohol or narcotic substances, a medical history of disorder that may affect the CNS such as stroke or head trauma, and current medication with a sedative effect or other drugs affecting cognitive functions. Nine subjects were excluded due to a psychiatric or medical disorder with cognitive impairment (n = 4) or an inability to find a suitable date for investigation or did not appear to agreed appointment (n = 5). There were no significant differences between included (n = 32) or excluded (n = 9) subjects regarding gender or age. One subject started self-assessment with WURS, but did not complete the task. No subjects dropped out of the study after the beginning of the investigation. Included were all subjects who reported life-long attention difficulties of various or unknown reasons. Twenty-two subjects (69%) reported difficulties with learning and concentration already during primary school. There was no gender difference in age or education. More women than men retrospectively estimated a presence of self-reported symptoms of ADHD during childhood (Wender Utah Rating Scale i.e., WURS-25) (Guldberg-Kjar & Johansson, 2015; Kouros et al., 2018; Ward et al., 1993), and almost an equal number of men and women reported reading difficulty at the interview, as shown in Table 1.

Table 1 Demographics.

	Male (n = 17)	Female (n = 15)	Significant difference
Age^a	72.8 (6.15)	70.2 (5.43)	n.s.
Years of education^a	11.9 (4.09)	13.5 (3.14)	n.s.
MMSE^a,b	29.1 (.827)	29.4 (.910)	n.s.
WURS^c,d	6 34.5 (20.82)	8 36.7 (16.37)	n.s.
Reported RD^d,e	6	7	n.s.

Note:

a t-test (two-tailed).

b Max 30, ≥27 for inclusion.

c Number of subjects who scored at cutoff 36 points or higher on the WURS-scale, retrospectively indicating ADHD during childhood.

d Chi².

e Reported difficulties in reading during lifetime.

n.s. denotes not statistically significant.

Mini Mental State Examination (MMSE), Wender Utah.

Rating Scale (WURS) and reading difficulty as reported (RD).

Instruments

CANTAB, questionnaires and semi-structured interviews

In this study we focused on the cognitive domain attention and therefore used recommended attention tests from the Cambridge Neuropsychological Test Automated Battery ("CANTAB Connect Research: Admin Application User Guide," 2020; "Cantab Research, Suite 6 Cambridge Cognition Ltd, Cambridge UK," 2020). The Motor Screening Test (MOT) was used as an initial task on the tablet screen, to check whether sensorimotor deficits or lack of comprehension will limit the collection of valid data, and as a specific test for sensorimotor skills. The following CANTAB tests were included; Attention Shifting Task (AST), which measures the ability to switch attention between two modes; Reaction Time (RTI), which examines motor and mental response speed; Rapid Visual Information Processing (RVP), which measures sustained attention, and Spatial Working Memory (SWM), which measures retention and manipulation of visuo-spatial information. In addition, the Swedish version of National Adult Reading Test (NART) was used to assess premorbid IQ (Rolstad et al., 2008). In this test, the test person reads and enunciates a list of common words including loan words, as Swedish pronunciation rules are fixed. The Wender-Utah Rating Scale (WURS) was used to screen retrospectively for symptoms of ADHD during childhood, were 36 points or more indicates the presence of ADHD (Guldberg-Kjar & Johansson, 2015). Data on quality of life by questionnaires and semi-structured interviews were also collected, but this information is not reported here.

The MapCog Spectra (MCS)

The MapCog Spectra is a tablet-based and validated test of attention and processing speed (Bartfai et al., 2021). The standardized measurement procedure of the MCS is performed as follows: First, a short training session is performed whereby the subject is asked to name four different color and shape combinations presented on the tablet screen (e.g., red heart, black ball, blue cube, and yellow star). As the methodology is based on sound recordings of the verbal responses, the subjects are instructed to avoid making extraneous sounds (e.g., coughing, laughing, etc.). After a successful training session, a grid of 40 (5 × 8) randomly ordered color-shape combinations appear on the screen (i.e., the same colors and shapes as in the training session, but re-randomized in different combinations). The subjects are asked to name the stimulus combinations beginning from top left to bottom right, of the screen, and is also instructed to name the color first and then the shape. After each of the 40 combinations is named, a second trial is performed. The time between the two trials is less than 30 seconds. The stimulus combinations are automatically re-randomized between the two trials to avoid learning effects. After completion of the second trial, the results automatically appeared on the screen in the form of a graph, as shown in Figure 1. The graph in Figure 1 shows 60 consecutive pause times (horizontal x-axis), and their duration transformed to z-scores (vertical y-axis). Note that all pause times of this individual (blue dots) lie below 2 SD’s of normo-typical performance. The performance is denoted below the curve as 0.000 Hz (i.e., the number of aberrant pauses above 2 SD is 0/60 = 0.000 Hz).

Figure 1 Example of visual presentation of MCS test performance.

Note: The graph shows 60 consecut ve pause times (horsontal X-axis) and their duration transformed to z-scores (vertical y-axis). The red bars denote ±2 standard deviations (SD) of z-scores. Note that all pause times of this individual (person a) blue dots lie below 2 SD: s of typical performance. The performance is denoted below the curve as 0.000 Hz (i.e., the number of aberrant pauses is 0/60 = 0.000 Hz).

Characteristic aberrant response patterns from an normal adult (person a) and an adult with attention deficit and reading disability (person b). Note that many z-scores exceed 2 SD: s in both trials.

Only the occurrence of pauses longer than 100 milliseconds is recorded due to an increasing inaccuracy of identifying the borders of sound onset and offset of pause time durations shorter than this time limit. The identification of the boundaries between speech offset and onset is automatically performed by Fast- Fourier analysis of the wave-files.

After 60 successive pauses had been recorded, the identification of additional pauses was automatically stopped.

After performing the two trials (and hitting the stop button), the duration of each individual pause time (in milliseconds) was automatically recalculated into z-values. This recalculation was based on the total number of typical reference values included in the database of the test (n = 347, ages 10–87 years). This database includes normo-typical school children (n = 164), university students (n = 53), and healthy elderly who had responded to local advertisements (n = 130). All subjects in the database are native Swedes. Subgroups of these reference values are included in several previous publications (Gunnarsson, Grahn, & Agerstrom, 2016; Warkentin et al., 2008; Waxegård et al., 2019).

The reason for recalculating pause times into z-values was to obtain a standardized metric against which each individual performance could be compared and graphically illustrated. Based on this recalculation, 60 z-values were plotted on the resulting graphical presentation of pauses obtained during a subject’s performance. In addition, the graphical presentation also includes an upper limit of 2 standard deviations of the normative z-value. An aberrant pause time means that the pause time duration lies above 2 standard deviations of the z-value. Each pause time is also depicted in a scroll list, which shows at which time the registered pause occurred, the corresponding z-value, and if the pause time was aberrant or not (data not shown).

The determination of a normal performance was made as follows: During a normal performance, an upper limit of six aberrant pauses was allowed (i.e., a pause time with a z-value above 2 SD). This was motivated with reference to Sonuga-Barke and Castellanos (2007) who hypothesized that the brains default mode network could interfere with test performance with a frequency of 0.01 < Hz > 0.1.

The Word Chains Test

The test Word Chains (Jacobson, 2014) was used to investigate decoding abilities. The test comprises of three subtests, Letters, Words, and Sentences. In the Letter subtest, the subject is presented with an A4 page of letter strings with 9–12 letters per string, and is asked to draw a line between two identical letters located next to each other, as quickly as possible in 2 minutes. This test assesses test persons eye-hand coordination, perceptual speed and the identification of targets among distractors, without reading. In the second subtest, Words, the subject must scan and decode or read three words written together in a chain without inter-word spaces. The subject is instructed to draw a line between each separate word. In the third subtest, Sentences, a chain of unrelated short sentences is presented and the subject is instructed to draw a line between the separate sentences. A person with normal score on the Letter-chains test but with subnormal results on the Word- and Sentence subtests is defined as having a specific reading disability (Jacobson, 2014). The Word Chains Test also provides The Word Recognition Index, which can be used as an index to identify subjects with dyslexia (Jacobson, 1995). In the present study we used the quotient of Word chain/Letter chain × 100 (qWL) to identify subjects with reading disability.

Procedure

Participants contacted us by telephone and a short interview was conducted with regard to inclusion and exclusion criteria. After inclusion in the study, an appointment was made to complete the interview and to explain the test procedure. The semi-structured interview and the test procedure was completed within the same session, which lasted about three hours, with one intermittent break (30 minutes). All the tests were administered by two of the principal investigators (RC and SW) and two trained psychology students. Participants were tested in a non-randomized consecutive order. Participants were payed with a gift card at the value of two cinema visits and travel costs if traveling from outside of the city. All investigations were performed at the Department of Psychology.

Ethics

Written and verbal informed consent was obtained from the participants. The study was approved by the Ethics Committee at Linköping University (2016/365-31), and was performed in accordance with the ethical standards laid down in the current version of the original 1964 declaration of Helsinki (World Medical Association, 2013).

Statistical methods

Analyses were carried out by the SPSS statistical package ("IBM SPSS Statistics for Windows, Version 26.0. IBM Corp.," 2019). Some of the variables were not normally distributed, thus Spearman correlation was used for examining the association between the MCS and other variables. A multiple regression analysis was conducted to analyze the predicative value of the MCS and decoding ability by the Word chains to explain the variance. Chi-square and Student’s t-test were used to test the differences between demographic variables. To avoid the risk of multicollinearity between the Word chains (Letter-Word-Sentence), only the test with the highest level of correlation with the MCS was included in the regression analysis. We used recommended outcome measures on CANTAB tests ("CANTAB Connect Research: Admin Application User Guide," 2020). MOT; Mean latency to correctly respond to stimulus, AST; The median latency of response in assessed block(s) in which the rule is switching, RTI; Simple median reaction time, RVP; Median response latency where the subject responded correctly, and, SWM; The number of times the subject incorrectly revisited a box in which a token had previously been found. An alpha-value of 0.05 was accepted in all analyses, to avoid spurious findings.

Results

The mean Hertz of aberrant pauses of the two MCS trials was 0.157 (0.095), which is similar to our previous findings in adults with diagnosed ADHD (Persson et al., 2015), and considerably higher than we have reported in normal subjects (Warkentin et al., 2015). This Hertz value means that they had an average of 9.42 aberrant pauses during the two trials, which is a performance level halfway between typical and atypical performance. There was no significant difference in age or education between the genders. Within the gender groups, 35.9% of the male subjects fulfilled the Swedish WURS-criteria for ADHD, while 53.3% of the female subject fulfilled the criteria. Thus, 43.8% of the total group of elderly reported symptoms qualifying them for a probable diagnosis of ADHD, while the remaining subjects (56.2%) reported symptoms of attention severity, which was less than that required for the diagnostic criteria of ADHD.

Table 3 shows strong positive associations between the all the Word Chains subtests Letters, Words and Sentences and also a strong positive correlation with MCS and the RTI (r = .515, p < .003). There was a strong positive association between the Sentences and NART (r = .582, p < .001). Twelve participants had a lower score than 1 Sd on qWL, indicating a higher speed in decoding simple letters, but a significantly lower score in decoding words. The remaining subtests of the CANTAB battery used in this study were not significantly correlated with the MCS results. Taken together, these associations suggest that a lower number of identified letters, and words of the Word Chain test were associated with a higher number of aberrant pauses on the MCS. The MCS results were not significantly associated with premorbid intellectual level (i.e., NART-SWE), which confirms our previous findings (Carlsson et al., 2015). Mean and SD are shown in Table 2 and correlations in Table 3.

Table 2 Test results, mean and SD.

MCS Hertz^a		Letter^b	Word^b	Sentence^b	qWL^c	NART^d	MOT^a	AST^a	RTI^a	RVP^e	SWM^f
M	0.143	36.3	36.5	37.2	103.1	119.0	1083.2	817.8	386.4	0.8	19.86
SD	0.08	10.32	10.81	11.79	29.04	8.97	257.79	103.22	6.72	0.06	7.49
N	32	32	32	32	32	32	31	30	31	28	29

Note:

a Milliseconds.

b Number of correct responses.

c Quotient between Words and Letters × 100 (qWL).

d IQ ±100 (SD = 15).

e Number of correct responses.

f Number of times of incorrect revisits. MapCog Spectra (MCS), National Adult Reading Test (NART), Motor Screening Test (MOT), Attention Shifting Task (AST), Reaction Time (RTI), Rapid Visual Information Processing (RVP) and Spatial Working Memory (SWM).

Table 3 Correlation matrix of the included psychometric tests.

	Letters	Words	Sentences	qWL	NART	MOT	AST	RTI	RVP	SWM
MCS	−.493**	−.525**	−.392	.058	−.013	.229	.493	.515**	−.321	−.040
Letters		.699**	.618**	−.344	.172	−.312	−.094	.468**	.355	−.153
Words			.727**	.313	.325	−.095	−.313	−.193	.441	.207
Sentences				.074	.582**	−.245	−.279	−.275	.266	.298
qWL					.229	.281	−.226	.477**	.045	.085
NART						−.266	−.043	−.117	.412	.277
MOT							.097	.236	.169	−.193
AST								.017	.014	.086
RTI									−.180	.225
RVP										−.270

Note: ** p < .001, MapCog Spectra (MCS), quotient between Words and Letters × 100 (qWL), National Adult Reading Test (NART), Motor Screening Test (MOT), Attention Shifting Task (AST), Reaction Time (RTI), Rapid Visual Information Processing (RVP) and Spatial Working Memory (SWM).

A multiple regression analysis was performed and all the included predictors Words, AST and RTI together explained 54.4% of the total variance in the MCS (F[3,29] = 10,319; p < .004), whereas the adjusted R-squared explained variance was 49.1%.

Discussion

The present study investigated if attention is associated with decoding fluency in elderly, who report life-long subjective attention problems. Our results suggest this to be the case, as a higher number of aberrantly long pause times during a serial naming task (i.e., MCS) was associated with lower decoding performance on all subtests of the Word Chains test. In other words, a higher number of aberrant pauses was associated with the identification of fewer letters, words and sentences.

Study limitations and strengths

First, the number of included subjects is relatively small, which limits the power of the study. However, individual results were compared to normative data in each test manual, respectively. Secondly, the clinical scales are not optional. For example, NART was not ideal to assess premorbid IQ and attention problems. Data for estimating premorbid intelligence level is rarely available and the NART test is widely used to assess one aspect of premorbid intelligence, vocabulary, and is considered to be a hold test. The WURS-questionnaire requires the subject to retrospectively estimate attentional problems before the age of 15. This questionnaire is sometimes considered a blunted measure, if only used alone. In the present study we conducted a semi-structured interview with the participants to obtain a self-report whether attentional problems were present or not during childhood. This procedure adopts that of previous studies combining the WURS with clinical interviews (Guldberg-Kjar & Johansson, 2015). For example, a recent study assessing the psychometric properties of the WURS-25 (Swedish version) by Kouros et al. (2018) reported that a combination of scores (cutoff at 36 points) and interview, similar to the one used in our study, achieved a sensitivity and specificity of 0.87 and 0.66, respectively, although a slightly higher cutoff of 39 points tended to increase discrimination further. In our study 17 participants had 34 points or less, while one participant had 36 points and 12 participants had 40 points or higher. In the light of this uncertainty of the optimal cutoff, further studies on elderly are needed, in order to verify cutoffs in aged groups. Using the WURS questionnaire as a self-assessment late in life to correctly recall childhood experiences related to inattention and hyperactivity is of course limited. There is a risk for over and under estimation of valid symptoms long ago. At the end of each test session, the investigator informed about the WURS result and 22 out of 32 participants could confirm that they have had inattention or hyperactivity or in combination, during their whole lives, which might confirm the accuracy of our WURS results. On the other hand, there is no alternative to assess childhood conditions, as neither parents nor teachers are available anymore. Several of our participants commented in the interview that some of their children and grandchildren had the same symptoms, but unfortunately, we missed to document this information. Here, we mainly focused on attention and possible difficulties related to reading disorder in elderly, but not from a clinical perspective. Therefore, we did not make any subgrouping of possible ADHD symptoms, nor provide a formal diagnosis.

Thirdly, we cannot preclude the risk of having included elderly subjects with early signs of dementia in our study. Recognizing this risk, we excluded subjects with an MMSE-score of below 27 points, which is a common cutoff in the literature. However, there is a lack of general consensus of a cutoff with the MMSE, which primarily depends on the fact that MMSE is age and education dependent.

Fourth, the performance on the MCS was self-paced in order to minimize any effects of impulsive responses. Despite this precaution, it is possible that performance strategies may have introduced a speed-accuracy tradeoff (Heitz, 2014). A closer analysis of this possibility is, however, not possible in the present study, as the number of errors were not recorded. The present findings signify that the ability to decode written information is associated with attention difficulties. In a broader sense, this association may suggest that attention mediates not only decoding speed, but that a slower decoding speed affects reading speed. If this association is substantiated in larger samples of elderly, we can conclude that attention not only affects reading in children and adolescents, but that this may be a trait characteristic with increasing age. The aberrant response pattern on the MCS is characterized by an increased number (compared to controls) of pause times longer than 2 standard deviations. The duration of such pause time varied between individual pauses throughout the performance, but was typically about 1 second or more. The number of aberrant pauses of a performance was defined as follows: During a normal performance, an upper limit of 6 aberrant pauses was allowed. This was motivated with reference to Sonuga-Barke and Castellanos (2007) who hypothesized that the brain’s default mode network could interfere with test performance with a frequency of 0.01 < Hz > 0.1. Therefore, a total of six aberrant pauses (of the total number of recorded pauses) were included in a normal performance.

Our participants were told in school they were lazy or unmotivated, which strongly affected their self-image, as being different from other people. Despite these difficulties, several of the participants found compensatory strategies and some even completed long and demanding academic study programs. They reported difficulties during life with vigilance when reading and a tendency to lose concentration if bored, but also to be able to be persistent in activities that interested. As adults, they have understood that the difficulties in school were not related to motivation, but to deficiencies in the ability to pay attention. Almost all participants reported that several of their children and grandchildren had the same problem and were diagnosed with ADHD.

Given these considerations, the present findings suggest that attentional problems and decoding speed are associated. This association may be explained by individual variations in the ability to decode visual information (Jacobson & Lundberg, 2000), but could also indicate generally lower processing speed, as indicated by a lower reaction time. In addition to these factors, Orthograhic decoding means that a reader uses previously learned memory pictures (i.e., internal representations) of word parts or even whole words to be able to identify and retrieve them as units. An automatized perception and identification of these visual units forms the prerequisite for the coupling between the orthographic pattern and the phonological representation. Thus, the association between the tests (i.e., MCS and Word Chains test) suggests that performance may involve similar mechanisms. One similarity between the tests is that both require a serial scanning of visual stimuli and that this scanning and the identification of each stimulus requires visuo-spatial attention (Taylor et al., 2019). On the other hand, one dissimilarity between the two tests is that the Word Chains test uses letters, words and sentences as stimuli, while the MCS does not. This difference in stimulus presentation between the tests and their strong association suggests that the same brain areas may be involved in the early decoding of symbols (i.e., shapes in general). Recent studies have indeed demonstrated that the identification of symbols involves the ventral occipital-temporal pathway (Wang et al., 2017) as a link between visual-spatial attention and reading abilities (Franceschini et al., 2012; Gabrieli & Norton, 2012). These findings clearly suggest that pause times during serial naming of geometric shapes includes attentional allocation, which is closely associated with, or even a prerequisite for the ability to decode visuo-spatial information and hence reading. The inter-correlations seen in our study not only confirm, but also ad new information to previous findings, suggesting that the number of aberrant pauses during RAN-performance is a sensitive measure in the mediation between reading fluency and attention (Pham & Riviere, 2015), also in elderly.

Conclusion

This study shows that attention problems interact with decoding ability in a complex way, and that this close interaction is seen in elderly who express a life-long subjective complaint of attention. Our findings highlight the need to examine attention problems in relation to decoding ability, in order to gain further insights into the subjective complaints of an under-recognized group of elderly.

Acknowledgement

We wish to thank all volunteers who participated in this study.

Disclosure statement

Warkentin is author of the MapCog Spectra. The copyright of the test is held by MapCog Science Ltd, Sweden, where he serves as CEO. The company provided the test free of charge and without any financial interest vested in this study. Carlsson, Svensson, and Jacobson declared no conflict of interest.

Additional information

Funding

The Family Kamprad Foundation, Växjö, Sweden (2013-2103) (Warkentin).