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Have there been any studies that found that an extremely high (or low) Stroop test score correlates with certain personal characteristics, abilities or aptitudes, or would all such conclusions be outside the test's scope?
N.B.: There are many varieties of "Stroop-like" tasks, which as a class are often called implicit association tests or IATs. Since those are likely to test attributes related to their specific construction, I have excluded them from this answer.
There have been a variety of papers studying correlations of Stroop performance with other characteristics.
Ward et al. (2001) examined the correlation between Stroop effects and executive control, finding that Stroop costs and other measures of executive control were not well correlated.
Brewer et al. (2008) found sizable correlations between neural activation in Stroop testing and treatment outcomes among cocaine-dependent patients; they interpret this to suggest executive control differences are determinative in treatment outcome, but this interpretation is contradicted by Ward et al. above.
Less recently, Golden (1975) found the Stroop test to correlate with measures of creativity; I have found no more recent discussion of this so cannot speak to its currency.
Ward, G., Roberts, M., & Phillips, L. (2001). Task-switching costs, Stroop-costs, and executive control: A correlational study. The Quarterly Journal of Experimental Psychology Section A: Human Experimental Psychology Volume 54, Issue 2, pages 491-511. DOI:10.1080/713755967
Brewer, J., et al. (2008). Pretreatment Brain Activation During Stroop Task Is Associated with Outcomes in Cocaine-Dependent Patients. Biological Psychiatry Volume 64, Issue 11, 1 December 2008, Pages 998-1004. doi:10.1016/j.biopsych.2008.05.024
Golden, C. (1975). The Measurement of Creativity by the Stroop Color and Word Test. Journal of Personality Assessment. Journal of Personality Assessment Volume 39, Issue 5, 1975 pages 502-506. DOI: 10.1207/s15327752jpa3905_9
The Stroop test also correlates with age and education level (Van der Elst et al. 2006).
When emotionally charged words are presented (Emo-Stroop), the Stroop task was found to correalte with high state anxiety.(Dresler et al 2009)
You can find a review in "Secondary Influences on Neuropsychological Test Performance", edited by Peter Arnett. (Its on google books)
Wim Van der Elst, Martin P. J. Van Boxtel, Gerard J. P. Van Breukelen, Jelle Jolles (2006) The Stroop color-word test: influence of age, sex, and education; and normative data for a large sample across the adult age range. Assessment. Vol 13, Issue 1, Mar 2006, Pages 62-79.
Thomas Dresler, Katja Mériau, Hauke R. Heekeren, Elke van der Meer (2009) Emotional Stroop task: effect of word arousal and subject anxiety on emotional interference Psychological Research Vol 73, Pages 364-371
Materials and methods
Outpatients with clinically defined ADHD, according to the DSM-IV, were recruited during visits at the ADHD outpatient clinic for adults of the University Hospital Leipzig, Germany. During these visits, experienced psychiatrists and psychologists examined patients’ mental status and obtained their clinical history. Structured clinical interviews and rating scales were applied to confirm the clinical diagnosis of ADHD.
Inclusion and exclusion criteria.
At screening, ADHD diagnosis was confirmed using both the standardized German version of the ADHD self-rating behavior questionnaire (ADHS-SB, ) and the Adult ADHD Self-Report Scale (ASRS-V1.1, ). To assess childhood symptoms, the short Wender Utah Rating Scale (WURS-k, ) was used. The Beck Depression Inventory (BDI, ) was applied to assess the occurrence and severity of comorbid depressive symptoms. Patients were included in the study if they had a confirmed diagnosis of ADHD, a value of 4 or higher in the ASRS and a total score of 30 or higher in the WURS-k. Patients could not be included if they showed signs of acute suicidality or current psychotic symptoms. Also, patients with diagnoses of bipolar disorder, borderline personality disorder, substance or alcohol abuse, schizophrenia and severe major depression were excluded form participation. Pregnancy or lactation was also an exclusion criterion for women.
During the screening procedures, the interviewing clinician also evaluated the intellectual capacities of patients. If, in the opinion of the investigator, patients showed indications of limitations, a short vocabulary test, the German Wortschatztest (WST, ), was applied to examine whether the intellectual performance would restrict the patient from participating in the study. Only if the WST was below average, a more extensive test for intelligence would have been done and the patient excluded from the study. This process relied on the clinical expertise of the senior physician.
69 patients (28 females, 40.6%) were included into the study. All of the patients identified as cisgender men and cisgender women, respectively. Therefore, we use the terms ‘gender’ and ‘sex’ interchangeably throughout the text. Written informed consent was obtained from all participants prior to any tests and the study was approved by the local ethics committee of the University of Leipzig (EC numbers 199-13-15072013 and 155/15-ff).
For a more thorough assessment of symptomology, the long version of the Conners’ Adult ADHD Rating Scale (CAARS, ) was applied in German. The CAARS was chosen as it is a well validated rating scale to assess symptom severity in adults in a German population sample [50, 51]. The 66-item questionnaire gives an in-depth overview of individual core symptom manifestation and severity, which is why it was chosen in this study to follow the shorter questionnaires used at screening, e.g. the ASRS. For the CAARS, standardized T-scores higher than 60 were interpreted as being indicative of ADHD in the CAARS sum and all of its subscales.
Secondly, in order to measure attentional deficits more objectively, the computerized Test of Attentional Performance (TAP, ) was used. The TAP is a commonly used validated tool for the evaluation of adults, and is available in Germany. Specifically, the alertness, working memory and Go/NoGo subtests were administered as they can quantify patients’ capacity in cognitive areas highly affected in ADHD patients, such as general wakefulness, information processing and behavioral control . The alertness task took place under two different conditions: uncued (intrinsic alertness) and cued (phasic alertness). The working memory test was used in its medium level of difficulty, as previous studies showed a high frequency of discontinuation when using the high difficulty level due to loss of patient motivation. Lastly, the Go/NoGo subtest was applied with the test form “1 of 2”. The T-scores were corrected for age, education and sex where possible. T-scores of below 40 were interpreted as being indicative of abnormal TAP results and therefore attentional difficulties.
The Pearson chi-square test (χ 2 ) was used to investigate gender differences (female vs. male) in educational degree as well as marital and smoker status. For other metrical variables, the independent sample t-Test was used to test for differences between groups based on gender. As the differences for age and other demographical variables between females and males (see Table 1) were not significant, we decided to execute the independent sample t-Test for metrical variables and the Pearson chi-square test (χ 2 ) in case of categorical variables to assess potential differences in cognition measured by the CAARS and TAP.
In order to investigate the relationship between self-report scales and performance-based measures, correlation analyses were performed. The Spearman rank correlation coefficient was applied to measure strengths in each group. In case of significant correlation, a partial correlation analysis with covariance of gender was conducted to avoid the confounding effect of gender. All statistical analyses were run in SPSS Statistics 24.0 (IBM corp., Armonk, New York). The significance level for all tests was set at p<0.05.
Impairments of Theory of Mind (ToM), or the inability to attribute intentions and mental states to others (Premack and Woodruff, 1978), is assumed to be an integral characteristic of schizophrenia (Frith, 1992), but the nature of ToM deficits and their connection to the manifestation of symptoms are still controversial. Schizophrenia is a heterogeneous concept, and the early research of Frith and colleagues (for example Corcoran et al., 1995 Pickup and Frith, 2001) showed that different symptoms of schizophrenia could be connected to different ToM deficits. Further results showed that patients with schizophrenia commit more mistakes when they attribute emotional states to others (affective ToM) compared to when they attribute thoughts or intentions to others (cognitive ToM) (Shamay-Tsoory et al., 2007). However, according to a later study conducted using the Movie for the Assessment of Social Cognition (MASC, Dziobek et al., 2006), both cognitive and affective ToM impairments are linked to schizophrenia (Montag et al., 2011). According to some neuropsychological findings ToM abilities are heterogeneous phenomena too. Results suggest that different neural structures of including the prefrontal cortex (PFC), anterior cingulate cortex, and striatum are involved in attributing cognitive, whereas networks of ventromedial and orbitofrontal cortices, the ventral anterior cingulate cortex, the amygdala and the ventral striatum in attributing affective states to others (for a review see Abu-Akel and Shamay-Tsoory, 2011).
Attributing overly simplistic mental states to others, or undermentalizing, has been connected to negative symptoms of schizophrenia (Montag et al., 2011). Contrarily, attributing overly complex mental states to others (Montag et al., 2011 Fretland et al., 2015), or overmentalizing, has been connected to positive symptoms of schizophrenia. Suspiciousness and delusions of persecution were found to be significant predictors of poor social functioning, and poor ToM performance (Hinting Test, Corcoran et al., 1995, Visual Cartoon Test, Corcoran et al., 1997) also plays a role in this relationship (Sullivan et al., 2013). However, other investigators using the Reading the Mind in the Eye Test (Baron-Cohen et al., 2001) found no connection between symptoms of schizophrenia and ToM (Kazemian et al., 2015). More research is needed to make the connection of ToM and schizophrenia symptoms clearer.
The question of the stability of ToM deficits within the schizophrenia spectrum has been debated at length. Some results suggest that ToM deficits are predominantly present in patients with acute disorganized and negative symptoms, and that remitted patients may perform just as well in false belief tasks (Pickup and Frith, 2001) and the Hinting Task (Corcoran et al., 1995) as healthy controls or patients with other psychiatric disorders. Some more recent research has revealed that ToM deficits are present in remitted patients (Wang et al., 2015), first episode patients (FEP) (Koelkebeck et al., 2010 Ho et al., 2015), ultra-high risk samples (Chung et al., 2008), and healthy siblings of patients with schizophrenia (Montag et al., 2012 Cella et al., 2015 Ho et al., 2015). Additionally, some longitudinal studies (Lysaker et al., 2011 Sullivan et al., 2014) showed that ToM deficits in FEP were still detectable 6- and 12-month follow-ups. It is not clear whether impaired ToM abilities can be also detected in healthy volunteers with high trait schizotypy, nor is it clear whether these are of similar nature to the deficits in schizophrenia and which aspect of schizotypy they are associated with. There is some evidence in support of ToM deficits in high trait schizotypy given in studies using a false-belief sequencing task (Langdon and Coltheart, 1999) and Happé’s (1994) Strange Stories Task (Pickup, 2006), but these studies do not differentiate between cognitive and affective ToM.
With regards to the types of mentalizing errors, first-grade relatives of patients with schizophrenia demonstrated higher levels of undermentalizing in cognitive ToM (Montag et al., 2012). In this respect, their performance was similar to patients with schizophrenia (Montag et al., 2011). However, no significant associations between the schizotypy dimensions and specific aspects of ToM were found in this study (Montag et al., 2012). On the one hand, ToM deficits have been associated with positive schizotypy – unusual perceptual experiences and magical thinking (Pickup, 2006 Barragan et al., 2011). On the other hand, they have also been associated with negative schizotypy – social withdrawal and anhedonia (Langdon and Coltheart, 1999). Moreover, through the use of the “Moving Shapes” (Abell et al., 2000) and Stories Task by Fletcher et al. (1995) a connection has been found between delusion-proneness and overmentalizing (Fyfe et al., 2008).
However, other studies have failed to find a connection between impaired ToM and high trait schizotypy, using TASIT (McDonald et al., 2003), a videotape-based measure (Jahshan and Sergi (2007). Similarly, Fernyhough et al. (2008) have failed to find an association between schizotypy and ToM performance using the Hinting Task and the Visual Cartoon Task. Gooding and Pflum (2011) revealed that the different results may be dependent on the variety of measures. Their participants with high positive schizotypy performed significantly worse in the Hinting Task than those with high negative or low schizotypy, whereas no between-group differences were identified using the Reading the Mind in the Eyes Test.
In order to measure social cognition, it is desirable to increase the ecological validity of the methods used and to make use of audiovisual stimuli (Dziobek, 2012). MASC is one of the few video-based measures showing complex and often ambiguous situations resembling real life scenarios (Montag et al., 2011). In some studies, MASC has been proven to be more sensitive than other non-video based measures including the Reading the Mind in the Eye Test or the Strange Stories Task, for example in differentiating individuals with Asperger syndrome from healthy controls (Dziobek et al., 2006) or detecting gender- and cortisol-dependent differences in ToM (Smeets et al., 2009). Moreover, using MASC has the advantage of presenting participants with the full complexity and dynamics of social situations. Unlike TASIT, MASC provides participants with the context of a full story, and the participants’ social interactions are interpreted within this framework (four people spending an evening together, having dinner).
The processes of attributing mental states to others and interpreting their actions require executive functions (Perner and Lang, 1999 Decety and Jackson, 2004). It is well-known that executive-function deficits are present in patients with schizophrenia (Heinrichs and Zakzanis, 1998), but it remains ambiguous as to whether these play a role in mentalizing deficits in patients. Neuropsychological findings support this proposition, regarding for example the involvement of the dorsolateral prefrontal cortex (DLPFC) in executive functions (Oldrati et al., 2016) and additionally, in the neural circuit partly responsible for cognitive ToM (Abu-Akel and Shamay-Tsoory, 2011).
Despite some contradicting results (Mazza et al., 2001 Schenkel et al., 2005 Pinkham and Penn, 2006), a large majority of studies did find a connection between poor ToM and deficits in executive functions – particularly inhibition and cognitive flexibility amongst individuals suffering from schizophrenia (for a review see Pickup, 2008). The crucial role of cognitive flexibility in the ToM performance of patient samples has been repeatedly demonstrated using several methods including a picture sequencing task (Abdel-Hamid et al., 2009) and an irony task (Champagne-Lavau et al., 2012).
Inhibition of one’s own perspective seems to be necessary to successful perspective-taking (Ruby and Decety, 2003), so it is logical to expect ToM deficits (especially of cognitive ToM) to be specifically connected to inhibition deficits in patients with schizophrenia. However, some results refer to at least a partial independence of cognitive inhibition. In a sample of schizophrenia patients, the Reading the Mind in the Eye Test found that impairments of cognitive inhibition had an effect on first-order ToM performance, but second-order ToM deficits were found to be independent of cognitive inhibition (Pentaraki et al., 2012). A case study of stroke patients with right prefrontal and temporal damage suggested that the inhibition of one’s own point of view may be a distinct neural process in inferring another person’s point of view when completing a false-beliefs task (Samson et al., 2005). These contradictions might be resolved by further results (Samson et al., 2010 Surtees et al., 2016) which suggest that level 1 perspective-taking (the ability to judge whether another person sees something) in more simple ToM tasks is possible without the involvement of cognitive functions such as inhibition, but that level 2 perspective-taking requires cognitive control.
According to the review of Giakoumaki (2012), the executive deficit caused by prefrontal dysfunction is also on the continuum similar to schizotypal traits. Some studies support the argument that high schizotypy and impaired inhibition are connected (Cimino and Haywood, 2008), particularly in cases of positive schizotypy (Louise et al., 2015). Impaired cognitive flexibility (as measured by the trail-making test or the Wisconsin Card Sorting Test) has been associated with the negative dimension of schizotypy (Louise et al., 2015 for a review see Giakoumaki, 2012). Results concerning the role of cognitive inhibition and/or cognitive flexibility as contributors to mentalizing deficits in individuals with high trait schizotypy are just as contradictory, although not as numerous as those conducted in samples with schizophrenia. It is not clear whether cognitive inhibition and flexibility have a significant effect on the differences between ToM performances in high- and low-schizotypy groups (Cella et al., 2015). It might rather be the case that some aspects of ToM deficits are mediated by general intellectual deficits (Pentaraki et al., 2012).
The knowledge of the self (Gallagher, 2000), the understanding of one’s own perspective and the ability to distinguish one’s own perspective from that of others are all prerequisites of successful mentalizing (Decety and Jackson, 2004 Bradford et al., 2015). Self-disturbances are well-known in schizophrenia (Mishara et al., 2014 Moe and Docherty, 2014), and deficits of self-perception and self-agency have been shown in the prodromal phase (Sass and Parnas, 2003) as well as in schizotypy (Platek and Gallup, 2002 Barnacz et al., 2004 Asai and Tanno, 2008). However, there is little evidence to support the connection of impaired self-agency and ToM deficits on the schizophrenia spectrum (Schimansky et al., 2010). It may be expected that agency deficits are connected to impaired ToM abilities in high trait schizotypy. According to our knowledge, this is the first study to investigate this possible association.
In light of previous inconsistent results, it seemed necessary to investigate cognitive and affective aspects of mentalizing, ToM error types, and their connections with the different dimensions of schizotypy in healthy individuals of the general population. According to our knowledge, this study is the first one ever to have done so, and the first to have analyzed the possible contribution of self-agency to ToM deficits of healthy people with high trait schizotypy. The main goal of the present study was to explore differences in ToM performance and specific ToM error types between groups with high and low trait schizotypy. The secondary aim was to find out whether participants with high and low schizotypy have different levels of cognitive flexibility, cognitive inhibition, and self-agency or if this is not the case, to see whether differences in ToM performance and the frequency of certain error types can be further explained by impaired cognitive flexibility, cognitive inhibition and self-agency. The tertiary aim was to see whether impaired ToM performance (especially impaired cognitive ToM) is connected to either dimensions of schizotypy – positive schizotypy in particular.
The Impact of Dyslexia on Stroop performance
The Stroop task allows interference between colour naming and reading to be studied. The interference found in Stroop task has been considered as an indicator of reading automaticity even though poor readers, who have been found to lack automaticity, display strong interference. This study aimed to test whether dyslexia would impact performance on the Stroop task. In order to test this, participants were required to participate in a series of tests including the Stroop test, reading, spelling, and working memory tasks. A 2 X 2 within-subjects factorial design was used to test 78 participants. The children recruited for this experiment were aged between 10-16 and the adolescent and young adults were between the ages of 16-24. The younger subjects were from a local Academy whereas the older students were from various Universities. The results suggested dyslexic individuals performed slower than non-dyslexics. However, the differences between the groups were not statistically significant. To conclude, although the differences between the groups were not significantly different, collecting more data may result in a more definitive answer.
The Stroop task is a psychological test which has been extensively investigated since its discovery in the 1930s (Stroop, 1935). It involves a decision about one dimension of a multidimensional stimulus in which other dimensions may disagree or agree with the judgement dimension (Logan, 1980). The Stroop effect refers to our tendency to experience difficulty naming a physical colour when it is used to spell the name of a different colour (the word blue written in red ink, usually referred to as the incongruent condition) but not when we simply read out colour words (the word red written in red ink, known as congruent condition), or when simply reading out words in black ink or colours of rectangle patches (neutral condition). The interference effect occurs in naming the print colour of a word when the word itself is the name of another colour (difference between incongruent and neutral condition), whereas interference within the congruent condition itself is named the incongruity effect (Wright & Wanley, 2003) and facilitation effect refers to the difference between congruent and neutral condition (Chen et al, 2001). Stroop task is considered as an assessment of interference and processing speed (MacLeod, 1991) and has been changed in a variety of different ways to the original list-based task. Thus, a large variety of studies have started to use pictorial versus words stimuli, card versus computer presentations, visual versus auditory stimuli, or list versus single stimuli, and tasks have been changed according to the vocal versus manual response format (MacLeod, 1991 Wright, 2017). These modified versions have been used in a wide area of psychological research, such as phobias, anxiety disorders and depression (Price & Karl-Hanson, 2007). The common finding is that a longer reaction time is observed in the incongruent condition compared to the congruent or neutral condition (Goldfarb & Henik, 2007 Verbruggen, Liefooghe & Vandierendonck, 2004 West & Alain, 2000) and greater Stroop interference has been regarded as an index of lower interference control (Ikeda et al., 2011).
3.1. Stroop task in children and adults
Stroop task, mainly the incongruent condition, is most often described as a measurement of ability to inhibit an overlearned response in favour of an unusual one (Armengol, 2002 Homack & Riccio, 2004 Strauss, Sherman & Spreen, 2006 Wecker et al., 2000). Previous studies have revealed that younger children are more affected by Stroop interference compared to older children (Carter, Mintun & Cohen, 1995 Vurpillot & Ball, 1979 Peru, Faccidi & Tassinari, 2006). For example, Bub, Masson and Lalonde (2006) conducted a study with 65 children aged 7-11 years using the congruent, incongruent, neutral conditions, as well as reading and spelling tests. The results demonstrated that the younger children (9 and under) showed larger Stroop interference effect compared to the older children (1267 ms vs 1042 ms respectively) as well as slowing of word reading in incongruent condition (943 ms vs 861 ms respectively).
In a typical Stroop task, interference control has an inverted U-shaped curve with age, increasing as 3-7-year-olds learn to read, then gradually decreasing (Leon-Carrion, Garcia-Orza & Perez-Santamaria, 2004). The most popular developmental study has been conducted by Comalli, Wapner and Werner (1962) from a sample of 235 subjects ranging from 7-80 years on tasks which included an incongruent and neutral condition. The results revealed that inference was greatest for 7 year olds, decreases with increasing age up to 17-19 year-old, remains constant during middle years (25 to 45) and then decreases again in the older group (65-80). Furthermore, the results demonstrated that response time is least for reading colour words, longer for naming actual colours and longest when there is interference.
In terms of facilitation, it is evident that facilitation effect receives little investigation in comparison to the interference effect, (Wright & Wanley, 2003) as many developmental Stroop studies do not contain a congruent condition alongside neutral and incongruent conditions. Under single mechanism accounts, both facilitation and interference were assumed to be caused by same semantic process aiding versus delaying performance, respectively (Botvinick et al., 2001 MacLeod, 1991 Melara & Algom, 2003). However, MacLeod and MacDonald (2000) propose that single mechanism accounts are not sufficient to explain the findings on difference between interference and facilitation. This conclusion was reached on the basis that some tasks affect facilitation but not interference, while others affect interference without affecting facilitation (Wright, 2017). However, MacLeod and MacDonald (2000) later argued that interference and facilitation are not caused by the same process and rather proposed a theory suggesting that interference is the result of semantic conflict between word and colour, whereas facilitation is due to inadvertent word-reading.
3.2. Reading, spelling and working memory
Dyslexia can be defined as having difficulties with reading, writing, and spelling characterised by issues in literacy acquisition including reading speed, reading comprehension, and phonological decoding, ranging from mild to severe (Rayner et al., 2012 Shaywitz & Shaywitz, 2003 Vellutino et al., 2004). As dyslexic individuals have deficiencies in the skill to segment the written word into its underlying phonologic elements, they experience problems in decoding and identifying printed words (Høien & Sundberg, 2000 Joanisse et al., 2000 Rey et al., 2002). The way in which their brain codes phonology is less effective than that of normally developing individuals irrespective of their strengths in other cognitive abilities such as semantic processing (Lyon, Shaywitz & Shaywitz, 2003 Snowling, 2001). When dyslexic individuals are required to read rare or pseudo-words their accuracy is lower and their performance is slower compared to individuals without dyslexia as it takes dyslexics longer to decode words correctly due to their poor letter-sound knowledge (Griffiths & Snowling, 2002 Leinonen et al., 2001). These difficulties can be seen in dyslexic children from a very young age into adulthood (Gallagher, Frith & Snowling, 2000).
If dyslexics have serious phonological problems, these difficulties should also show themselves in spelling as well as reading. Dyslexics’ spelling problems are often more severe and persistent than their reading problems (Bourassa & Treiman, 2003 Caravolas & Volin, 2001). Studies on spelling compare the ability to segment, manipulate and identify phonemes in spoken words by children with dyslexia and age matched controls (Bernstein, 2009). Studies demonstrate that children with dyslexia perform worse than controls in the identification of spoken consonants (Breier et al., 2002) and tend to mistake some vowels for similarly articulated items when spelling (Bertucci et al., 2003). For example, Cassar et al., (2005) compared spelling abilities of 25 dyslexics aged 11, with 25 normally developing children aged 7-8, matched on their ability to spell. The children with dyslexia had difficulties with the same phonological structures that cause problems for 7 year-olds. Both groups had difficulties with consonant clusters, letter name spellings and reduced vowels in unstressed syllables.
Dyslexics have additional difficulties that are not restricted to reading and spelling. Deficiencies in short-term memory (STM) and working memory (WM) have been described as one of the crucial characteristics of dyslexia (Smith-spark & Fisk, 2007). WM is defined as a processing resource of limited capacity, involved in the preservation of information while processing the same or other information (Unsworth & Engle, 2007 Miyake & Shah, 1999). Researchers have suggested that there is a domain-general processing deficit in children and adults with dyslexia, with both verbal and visual WM being affected as additional demands are placed on the central executive (Cohen-Mimran & Sapir, 2007 Swanson & Howell, 2001 Swanson, Zheng & Jerman, 2009). For example, Smith-Spark et al., (2003) conducted a study with two groups of University students using the digit span and word span memory test. The results revealed significant differences between dyslexics and non-dyslexics on both the tasks, with dyslexics performing worse than the control group, especially as the numbers or words presented increased from 8 items to 12 items thereby providing evidence for continuing dyslexic impairments of the WM into adulthood. The presence of deficits in the CE of WM in individuals with dyslexia is consistent with other deficits in the executive functions, such as selective and sustained attention, inhibition of routine responses, and inhibition of distracters (Brosnan et al., 2002).
3.3. Stroop task and dyslexia
The classic Stroop (1935) model is a typical conflictual situation, in which the physical appearance of alphabetic symbols is at odds with their meaning (incongruent condition). The notion of automaticity has been central for understanding and explaining the Stroop effect, since it is generally considered obligatory to read the word but not to name the colour (Protopapas et al., 2014). The concept of automatization refers to a gradual reduction in the need for conscious control as a new skill is learned. This leads to greater speed and efficiency and a decreased likelihood of breakdown of performance under stress, as well as the ability to perform a second task at little or no cost (Kapoula et al., 2010). It is suggested that reading is done for meaning and highly overlearned from 6 or 7 years of age (Braet et al., 2011 Seymour, Aro & Erskine, 2003). On this note, one would expect poor and less skilled readers to exhibit less interference than good readers as reading is less automatic, therefore they are more likely to skip the word and focus on the colour alone (Faccioli et al., 2008). Nevertheless, this prediction stands in contrast to empirically observed data. Children with dyslexia appear to be slower in reaching reading automation and full attentional-executive control (Facoetti et al., 2003). However, even if difficult and slow, reading is unavoidable also for these children. If they suffer from a reading automaticity problem, this should originate beyond the level where implicit reading determines Stroop interference (Everatt et al., 1997). The interference in turn would be larger than in normal readers due to a less effective control upon a reduced reading automaticity. This is in agreement with the model proposed by Coltheart et al., (1999) that predicts an increase in the size of Stroop effects whenever word processing is slowed.
A number of studies showed that poor readers produce more interference (Everatt et al., 1997 Helland & Asbjørnsen, 2000 Mano et al., 2016). Protopas, Archonti and Skaloumbakas (2007) revealed that reading ability is negatively related to Stroop interference. In their first study, they compared children with dyslexia (mean age 12.5) to age matched controls and reported greater interference for children with dyslexia. In a second study they examined the relationship between interference and reading skills in the general school population and found that poorer reading skills were associated with larger interference. Faccioli et al., (2008) confirms more interference in dyslexic children (7-11) relative to control of similar age. They demonstrated that children with dyslexia and normal readers performed very well on rectangles and congruent words (mean overall accuracy: 97.5% children with dyslexia, 99.6% normal readers), but they made several errors on incongruent words (mean accuracy: 84.9% children with dyslexia and 85.8% normal readers). With respect to speed of response, children with dyslexia were slower than normal readers (mean overall: 941.7 ms vs. 738.8 ms). In terms of facilitation, like normal readers, children with dyslexia were slower in responding to congruent names than rectangles, thus showing no facilitation.
The current study aimed to develop a better understanding of the impact of dyslexia on individuals’ performance on the Stroop task on a variety of age groups. Our first hypothesis (H 1 ) predicts that dyslexics will perform worse on all cognitive tasks (reading, spelling and working memory) than non-dyslexics. The second hypothesis (H 2 ) predicts that adults will have a faster reaction time on all five of the Stroop conditions in comparison to children. Thirdly, (H 3 ) anticipates a faster reaction time and better performance on Stroop task in non-dyslexics compared to dyslexic individuals. Finally, (H 4) predicts that facilitation and interference would be predicted by different variables for dyslexic and non-dyslexic participants.
4.1. Ethical Approval
This study received ethical approval from the Department of Life Sciences Research Ethics board at Brunel University on 12/12/2017 (Appendix A).
A total of 78 participants were recruited for this experiment. Of these, 68 (87.18%) were children between 10 and 16 years of age (M= 13.07, SD= 1.20) and 10 (12.82%) were adults (M= 20.55, SD= 2.48). None of the participants reported any problems related to anomalous colour perception, and had normal or corrected-to-normal visual acuity. The sample of participants recruited for this project were mixed some participants were students from a local school and the rest were volunteers from Universities.
The participants participated in a total of four tasks which included the Stroop task, reading, spelling, and working memory tests. The Stroop task consisted of 5 blocks with 1-dimensional blocks that did not present Stroop stimuli, and three mixed blocks each containing 48 words. Blocks one and five were both 1-dimensional conditions (colour and word have nothing to do with each other) however both had different randomised order. The words ‘car’, ‘plug’, ‘jigsaw’, ‘sheep’ and the colours ‘red’, ‘green’, ‘yellow’, and ‘blue’ were all written in black ink, or the colours were displayed in a rectangle (following Wright, 2017). The three other blocks were both a mixture of congruent (colour and word agree) and incongruent conditions (colour and word disagree). In these trials, participants were required to ignore the written word and read out the colour as fast as possible. For both the congruent and incongruent conditions, the same colours were used. In addition, the task consisted of one dimensional trials in order to measure the speed of colour naming in isolation, word reading in isolation, and word reading of colour in isolation. Word reading was either neutral words (one third), colour words (one third) or colour patches (one third).
For both reading and spelling, Wechsler Objective Reading Dimension tests were used. There was a total of (55) words for reading and (50) for spelling and these words ranged from easy words to more difficult words. The working memory test contained 14 trials of which two were practice trials. Of these 12 trials, four trials had three numbers to memorise, four trials had four numbers and four trials had five numbers.
Both the pre-tests and actual experiment were conducted using a Toshiba laptop. All the stimuli were presented on a low intensity white background to eliminate any biases and reduce fatigue. Colour patches were rectangular in shape, with on-screen dimensions of 2.2 cm high and 3.5 cm wide. The words were written in a font equivalent to Time New Roman with the participants sat approximately 60 cm away from the screen.
This study employed a 2 X 2 within-subjects factorial design. Participants performed the same computer task and participated in the same conditions. The first factor was the condition (i.e. three levels corresponding to the incongruent, neutral and congruent conditions) while the second factor was whether the participants had dyslexia or not. Measures of interference and facilitation were calculated from the condition times.
In advance to starting the actual experiment, consent was obtained from the school (Appendix B) and parents (Appendix C) for those participants in school. Participants in the school were separated on the basis of those in nurture class and average performing children. Upon arrival participants were presented with an information sheet (Appendix D) and a consent form (Appendix E). For most of the individuals in the nurture group, the information sheet had to be read verbally to them. Before starting the actual experiment, participants were required to take part in a spelling, reading and working memory test. This was to distinguish participants with dyslexia from those without. Reaction times were collected using a headset microphone. Error rates were noted in a hard copy format by sitting behind the participants during the experiment. All participants gave written informed consent before starting the experiment and were debriefed after the experiment (Appendix F).
The experiment took place in a cubicle located at the local school or the library to ensure a quiet and confidential environment. Participants were given verbal instructions regarding which responses to make and how to make them. Each participant was reminded that their verbal response was being recorded, therefore they had to speak in a loud and clear manner. After each verbal response, the next would appear, so participants were instructed to not make any noise other than their response to the stimuli.
The following tests were used to distinguish participants with dyslexia from participants without dyslexia. Wechsler Objective Reading Dimensions are individually administered tests designed for the assessment of children aged from 6 to 16 years.
4.5.1. Spelling test
The spelling test was used to determine the literacy ability of each participant. For this test, the participant was sat away from the laptop, therefore they could not see the screen. The researcher was placed in front of the laptop, facing the screen. The test required the participants to spell out the last word read out by the researcher. For example, the researcher would say ‘Cat. Anne’s cat had kittens. Cat’ and participants would have to spell out the word ‘cat’. The words ranged in difficulty, starting with easy words such as ‘cat’ or ‘no’ to moderate words such as ‘apparently’ and ‘assistants’ to more challenging words such as ‘pharmaceutical’ and ‘conscience’. Reaction times were recorded as soon as the researcher read out the last word. As soon as a correct response was given by the participant, the researcher pressed 6 on the keyboard however, if the participant gave an incorrect answer the researcher pressed 4 on the keyboard. If none was given, the researcher pressed 4 on the keyboard to record an incorrect response and to stop the timer.
4.5.2. Reading test
For this test, participants had to sit in front of the laptop, facing the screen, while the researcher was seated next to the participant with an external keyboard. In this test, participants had to read out the words on the screen. These words ranged from easy words such as ‘the’ and ‘up’ to more moderate words such as ‘accordion’ and ‘ridicule’ to more difficult words such as ‘euphemism’ and ‘hierarchical’. The researcher recorded reaction times by pressing 0 to bring up the word, followed by pressing 4 if the participant read the word incorrectly or 6 if they read it correctly. The researcher had to wait for the participant to completely say the word as they needed to factor in incorrect pronunciations.
4.5.3. Working memory test
The working memory test assessed numerical working memory. In this test, participants were informed that the experimenter would read out a group of numbers and they would have to remember the biggest, or smallest digit from that list. The decision of whether the participant recalled the biggest or the smallest number was pre-set on the laptop. In the first few trials, the task comprised of three numbers which increased to four, and later five and participants had to remember each of the numbers to figure out the smallest or largest from the group. Reaction times were recorded by the experimenter. As soon as the researcher said smallest or biggest, reaction times were recorded by pressing 0 on the keyboard. After the participants gave a response, incorrect responses were recorded by pressing 4, and correct responses were recorded by pressing 6. The reaction time records the time it takes between the researcher pressing 0 to pressing 4 or 6 depending on the response given by the participant.
To test the basic features of the data in this study, descriptive statistics were run. The descriptive statistics were run on the analyses of cognitive ability (reading, writing, and working memory) analyses of children versus adults’ performance on the Stroop task and finally analyses of dyslexia on Stroop task performance.
Participants were identified as dyslexic or non-dyslexic based on spelling and reading scores. Individuals who performed greater than 2sd below the average performance (for children) on both spelling and reading tests were identified as dyslexic. It is important to acknowledge that this is not a formal diagnoses of dyslexia, participants are identified as likely dyslexics due to performing extremely low on spelling and reading tasks. This gave a total of 12 participants identified as dyslexic. However, as three participants did not provide either complete reading or complete spelling scores they were eliminated automatically by SPSS for cases where they had relevant data that was blank, which left us with a total of as few as nine dyslexics, depending on the analysis that was run.
Note: Figures in parentheses are standard errors.
5.1. 2-Way ANOVA
As depicted in Table 1, the overall performance on reading, spelling and working memory was 37% better in non-dyslexics than dyslexics. In terms of spelling there was a massive difference on the performance of dyslexics compared to non-dyslexics, with non-dyslexics performing 41% better than dyslexics. Similarly, non-dyslexics performance on reading task was 39% better than dyslexics. The smallest difference was on working memory task, with non-dyslexics performing 30% better than dyslexics. Nevertheless, it is important to recognise the fact that reading was out of 55 and spelling was out of 50 whereas working memory was out of 12 therefore, it is better to compare them as percentages as these can be more meaningfully compared to each other in analyses than raw scores that have different maximums. See Appendix G for Table 2 redrawn with raw scores rather than percentages.
The tendency for the non-dyslexic group to do better than the dyslexic group on reading, spelling and working has been confirmed as statistically significant (F(1,73) = 57.398, p = 0.001, partial eta squared = 0.440, observed power = 1.000) . This confirms that dyslexic participants, as identified by the basis of standard deviations, did much worse than non-dyslexics. Overall, the difference between domain of cognition were statistically significant (F (2,146) = 363.199, p= 0.001, partial eta squared = 0.833, observed power =1.000). This demonstrates that performance on working memory task was highest, followed by reading task, and followed by spelling being lowest. Finally, a significant interaction was observed between dyslexia and domain of cognition (F(2,146) = 39.252, p=0.001, partial eta squared = 0.350, observed power = 1.000).
|Table 3: Summary of Stroop Performance According to Age Group.|
|Children (under 16)||Adults (over 16)||Both Groups|
|Congruent||843 (18)||789 (47)||816 (25)|
|Neutral||894 (17)||800 (45)||847 (24)|
|Incongruent||973 (23)||911 (62)||942 (33)|
|Overall Performance||903 (18)||833 (48)||868 (26)|
Note: Number of participants = 68 children and 10 adults. Figures in parentheses are standard errors.
The difference between children and adults for the congruent condition was 54ms, with the children responding slower than adults. For the neutral condition, the difference between children and adults was 94ms, with children responding slower than adults. Finally, the children responded 62ms slower than adults in incongruent condition. For both groups, the responses slowed down from neutral to congruent condition by 31ms, and from incongruent to neutral condition by 95ms. The conditions showed the typical Stroop profile, with the congruent condition faster than the neutral condition, and the incongruent condition slowest. The overall performance difference between children and adults was 70ms, with children performing slower than adults. For children the difference between congruent to neutral was 51ms, with responses being faster in the congruent condition whereas the difference for adults was 11ms thus, showing children had higher facilitation compared to adults. The difference between neutral to incongruent was 79ms for children and 111ms for adults thus, demonstrating that interference was higher for adults. Nevertheless, the differences between interference are more similar in both groups compared to facilitation.
Overall difference between children and adults on the Stroop task was not statistically significant (F(1,76) = 1.794, p= 0.184, partial eta squared = 0.023, observed power = 0.262. This confirms that age does not impact the performance on the Stroop task thus, we combined the Stroop data for adults and children and analysed all 78 participants in all further analysis. Overall, the difference between Stroop condition was statistically significant (F(2,152) = 27.002, p= 0.001, partial eta squared = 0.262, observed power =1.000). This demonstrates that performance on congruent condition was fastest, followed by the neutral condition, with the incongruent being slowest. The interaction between Stroop condition and age was not statistically significant (F(2,152) = 0.687, p= 0.505, partial eta squared = 0.009, observed power = 0.164).
|Table 4: Summary of Stroop Performance According to Dyslexia Status|
|Congruent||835 (18)||841 (43)||838 (23)|
|Neutral||873 (17)||930 (41)||901 (22)|
|Incongruent||955 (24)||1017 (56)||986 (30)|
|Overall Performance||888 (19)||929 (44)||908 (24)|
Note: Number of participants = 66 non-dyslexics and 9 dyslexics. Figures in parentheses are standard errors.
As seen in Table 4, dyslexics tended to respond slower than non-dyslexics on the Stroop task, by 41ms. For non-dyslexics, the difference between neutral and congruent condition was 38ms, however for dyslexic participants this was 89ms thus, demonstrating facilitation is higher in dyslexics compared to non-dyslexics. The difference between incongruent and neutral was 82ms for non-dyslexics and 87ms for dyslexics, thereby demonstrating that interference effect looks smaller compared to facilitation. The difference for congruent condition between dyslexic and non-dyslexics was 6ms, whereas it was 57ms for neutral condition and finally, 62ms for incongruent condition, with dyslexics responding slower than non-dyslexics in all three conditions.
Overall difference between dyslexics and non-dyslexics on Stroop performance was not statistically significant (F(1,76) = 0.731, p=0.395, partial eta squared = 0.010, observed power = 0.135. This demonstrates that even though dyslexics performed slower than non-dyslexics on all three of the condition, this difference was not effective enough to be significant. The overall difference on the Stroop task was statistically significant (F(2,152) = 40.874, p=0.001, partial eta squared = 0.350, observed power = 1.000. There was no significant interaction between dyslexia and the Stroop task (F(2,152) = 1.778, p = 0.172, partial eta squared = 0.023, observed power = 0.368. This reveals that although the two groups performed closely on the congruent condition and further apart for neutral and slightly further apart for incongruent condition, the overall tendency to be further apart as the condition got harder was not statistically significant.
|Table 5: Summary of regression for facilitation for non-dyslexics|
|Variable Name||Beta Unstandardized||Beta Standardised||Partial Correlation||P Level|
|Errors1 d av||-7.055||-0.045||-0.043||0.753|
|Errors2 d av||4.833||0.158||0.140||0.299|
|Wm all 12 score||-3.101||-0.084||-0.068||0.615|
|Item Removed at Step||2, Errors1 d av||3, Wm all 12 score||4, Ages||5, Errors2 d av|
5.2. Regression for facilitation excluding dyslexics
Our next hypothesis was assessed using regression analyses, two analyses were conducted for facilitation and another two analyses for interference. For facilitation, the first analysis was a linear regression run using the backward stepping method. This would allow the 7 predictor variables (Errors1 d av, Wm all 12 score, ages, Errors2 dAV, Spell50full, Read55score, Gender1f0m) to be reduced to only the few variables that work best together to predict Stroop RT performance. The first regression was based on only the participants who were not classified as dyslexics earlier in our first analysis (N = 66).
As presented in Table 5, the top of the table shows the initial model with all predictors entered simultaneously, with the middle of the table illustrating the variables that were excluded at each step. Finally, the bottom of the table displays the variables that survived to remain in the final model. In total, SPSS produced 6 models for this analysis.
The R value for the model was R = 0.377 and this model was statistically significant (F(3,59) = 3.265, p = 0.028). The R squared value for this model was R squared = 0.142, indicating that the model accounts for 14.2% of the variability of the facilitation RTs about the regression line.
Overall, there were 3 variables remaining in the final model. These variables were spelling score (Spell50full), word reading score (Read55score) and also gender (Gender1f0m).
The largest standardised beta was for spelling score however this coefficient was negative. This demonstrates that a lower spelling score predicted a higher amount of Stroop facilitation for non-dyslexic participants.
Read RT had the second largest coefficient but this was positive. This reveals that a higher reading score predicted a greater amount of Stroop facilitation. This further suggests that for normal readers, the better they are at reading, the bigger the difference between the congruent and incongruent condition (i.e., facilitation).
Finally, the last variable retained in the model was gender with a standardised beta coefficient that was positive. As males were coded 0 and females were coded 1, a positive beta illustrates that females tended to exhibit greater Stroop Facilitation.
Having done a regression for facilitation with only the non-dyslexic participants, it was intended to now do the same analysis for dyslexic participants. However, because there were at most nine dyslexic participants in the sample, and only seven of these had enough data to be included in the regressions, we could not run this analysis. An alternative to this was to combine dyslexic participants’ data with previous sample (non-dyslexics) and run a new regression. Although this would not allow us to assess the predictors of dyslexia very directly, it would allow us to look at these predictors indirectly. This is because adding the dyslexic participants to the non-dyslexic participants would allow us to see how this changed which predictors now were significant in a final model, as well as how many models there now were plus what the model R now became (e.g., is it higher or lower than before).
|Table 6: Summary of facilitation for both groups combined|
|Variable Name||Beta Unstandardized||Beta Standardised||Partial Correlation||P Level|
|Errors1 d av||-7.310||-0.044||-0.042||0.739|
|Errors2 d av||-4.110||-0.174||-0.150||0.236|
|Wm all 12 score||-7.616||-0.193||-0.165||0.193|
|Item Removed at Step||2, Errors1 d av||3, Ages||4, Wm all 12 score||5, Errors2 d av|
5.3. Regression for Stroop facilitation for both dyslexics and non-dyslexics
The second regression models used facilitation RT as the independent variable. This analysis was based on both the dyslexic and non-dyslexic participants (N=70). SPSS produced a total of 6 models for this analysis. As displayed in Table 6, there were 2 variables that remained in the final model these were gender (Gender1f0m) and spelling score (Spell50full).
The R value for this model was R = 0.402 and this model was statistically significant (F(2,67) = 6.444, p = 0.003). The R squared value for the model was R squared = 0.161, illustrating that the model accounts for 16.1% of the variability of facilitation RTs.
The Beta coefficient for this model are summarized in Table 6. The largest standardised beta was for gender and this was positive thus, implying that females tended to have higher facilitation than males.
The second variable retained in the model was spelling score, however this had a negative coefficient which was the same for non-dyslexic participants. This demonstrates that whether or not we assess non-dyslexics on their own, or assess dyslexic and non-dyslexic participants combined, spelling features an important predictor of the facilitation RT effect.
|Table 7: Summary of regression for interference for non-dyslexics|
|Variable Name||Beta Unstandardized||Beta Standardised||Partial Correlation||P Level|
|Errors1 d av||46.574||0.241||0.242||0.070|
|Errors2 d av||-19.213||-0.510||-0.444||0.001|
|Wm all 12 score||4.698||0.103||0.091||0.501|
|Item Removed at Step||2, Ages||3, Wm all 12 score||4, Gender1f0m||5, Spell50full|
|Errors1 d av||47.335||0.245||0.262||0.041|
|Errors2 d av||-21.024||-0.559||-0.508||0.000|
5.4. Regression for interference excluding dyslexics:
It is also important to assess interference in the same way as facilitation. The regression for interference for just the non-dyslexic participants on their own had a model R vale of R = 0.518 and this model was significant (F(3,59) = 7.217, p < 0.001). The R squared value was 0.268, indicating that the model accounted for 26.8% of variance of the interference data. This is approximately 10% higher than in either of the facilitation models above.
The Beta values for this model are presented in Table 7. Overall, SPSS produced 5 models, however only 3 variables remained in the final model. These were the number of errors made in total on the 1-dimnesional stimuli (Errors1 d av), the errors made on the 2-dimensional Stroop condition (Errors2 d av), and finally the word reading score (Read55score).
The largest standardised beta was for errors made on the 2-dimensional Stroop condition (Errors2 d av) however the coefficient was negative. This demonstrates that the less errors made on the 2-deminesiona condition predicts a higher interference for non-dyslexic participants.
Total errors made in 1-dimensional stimuli (Errors 1 d av) had the second largest positive coefficient. This implies that the more errors participants made, the more interference they had.
Finally, the last variable maintained in the model was word reading score, however the coefficient was negative. This reveals that lower scores on reading predicted higher interference.
|Table 8: Summary of Interference for both groups combined|
|Variable Name||Beta Unstandardized||Beta Standardised||Partial Correlation||P Level|
|Errors1 d av||45.855||0.211||0.192||0.128|
|Errors2 d av||-12.476||-0.405||-0.321||0.010|
|Wm all 12 score||3.418||0.067||0.055||0.665|
|Item Removed at Step||2, Ages||3, Gender1f0m||4, Spell50full||5, Wm all 12 score|
|Errors1 d av||48.715||0.225||0.214||0.080|
|Errors2 d av||-12.636||-0.410||-0.359||0.003|
5.5. Regression for interference for both dyslexics and non-dyslexics
In order to gain an understanding of how interference is for dyslexic participants, we combined dyslexic participants’ data to the non-dyslexic participants as we did for facilitation earlier. This final model is summarized in Table 8. The model R value of R = 0.394 and this model was significant (F(3,66) = 4.050, p = 0.011). The R squared value was 0.115, showing that the model only accounted for 15.5% of the variability of interference, which is much lower than the previous model of interference which did not contain the dyslexic participants.
As depicted in Table 8, for the analysis with all the data combined there were 5 models and only 3 remained in the final model. These variables were the same 3 variables in the previous analysis of non-dyslexic participants alone (i.e., Errors1 d av, Errors2 d av , and Read55score).
The largest standardised beta was for 2-dimensional Stroop condition, which had a negative coefficient. This means that less errors on the 2-dimensional Stroop condition predicts higher interference regardless of whether they are dyslexic or not dyslexic.
Errors made in 1-dimensional stimuli had the second largest with a positive coefficient. This means that for both dyslexic and non-dyslexic, more errors results in higher interference.
Finally, the last variable retained in the model was word reading score with a standardised beta coefficient that was negative. For all the participants, lower scores on reading meant a higher interference.
The aim of the present study was to investigate the impact of dyslexia on the performance on Stroop task in individuals aged between 10 and 23. Findings for this study have provided partial support for the proposed hypotheses. The first hypothesis (H 1 ) was fully supported by the findings, as the non-dyslexic group outperformed the dyslexic group on all cognitive tasks. With regards to (H 2 ), although children gave slower responses on all of the Stroop conditions, this was not statistically significant. For (H 3 ), although responses were fastest in the congruent condition, followed by neutral and slowest in the incongruent condition, and these responses were made faster by the non-dyslexic group, the overall difference on performance between the two groups were not statistically significant. For our final hypothesis (H 4 ), facilitation was predicted by spelling score, word reading score and gender for typically developing participants. On the other hand, for dyslexic participants, facilitation was predicted by spelling score and gender. In terms of interference, the predictors remained the same for both typically developing participants and those with dyslexia, these were errors made in total in 1-dimensional stimuli, errors made on 2-dimensional Stroop condition and word reading.
The results gathered from this study are in support of previous research conducted on the impact of dyslexia on reading, spelling and working memory. As discussed previously, dyslexics have issues with reading and spelling due to the ineffective coding of phonology compared to typically developing individuals (Snowling, 2001) as well as problems with working memory due to inability to retain information actively in mind (Swanson, Zheng & Jerman, 2009). Data from studies of children with dyslexia who have been followed prospectively support the concept that in adults, difficulties with reading fluency and spelling continue (Shaywitz & Shaywitz, 2005). For example, Lindgren and Laine’s (2011) study on University students, who have been identified as dyslexics from a young age demonstrated that dyslexic impairments were most visible in word and sentence segmentation, correctness in oral text reading and phoneme-to-grapheme awareness. These studies demonstrate that symptoms of dyslexia are persistent throughout the lifespan.
Performance on Stroop task has been tested extensively in the field of cognitive psychology. Nearly all existing models propose that interference results from competition between colour and word information and the need to suppress word information (West & Alain, 2000). A study by Wright and Wanley (2003) reveals that RT for interference looks similar in both adults and children whereas facilitation was greater in children than in adults. This implies that facilitation arises from a different system to interference. Following MacLeod and MacDonald’s (2000) inadvertent word-reading hypothesis, it can be predicted that children are more vulnerable to inadvertent word reading than adults (Ikeda et al., 2011 Imbrosciano & Berlach, 2005). One possibility that arises from these developmental differences is that younger children may be less able to suppress irrelevant stimulus dimensions and therefore may experience more difficulty than adolescents or adults, and that this skill may have a developmental trajectory during childhood (Bub, Masson & Lalonde, 2006). As stated previously, in a typical Stroop task, the performance has an inverted U-shaped pattern with age, the same can also be seen for inhibitory control which may explain the differences in performance according to age. Studies using other suppressions models, such as the stop-signal and negative priming provide evidence of significant improvements in the ability to inhibit a proponent course of action through childhood, but little change throughout adulthood (Bedard et al., 2002). For example, Williams et al., (1999) found that on average, older children (9-12 years) were 50ms faster in stopping their proponent responses than younger children (6-8 years), and younger adults (18-29 years) were 20ms faster than older adults (60-82 years).
Furthermore, children face difficulties guiding their actions by rules held in mind which conflict their learnings (Diamond, Kirkham & Amos, 2002). Consequently, the greater facilitation effect may be because children, especially younger ones, are less able to repeatedly apply the task set for colour naming, rather than that they are less able to inhibit incompatible word responses (Proulx & Elmasry, 2015).
Increased Stroop effect among dyslexic individuals may be due to poor cognitive control resulting in difficulty stopping the need to read the word instead of stating the colour. Many researchers attribute Stroop interference found in dyslexic groups to impaired executive functions (Altemeier, Abbott & Berninger, 2008 Varvara et al., 2014). Executive functions refer to a collection of cognitive abilities such as mentally playing with ideas, thinking before acting, resisting temptations (self-control), and interference control (selective attention and cognitive inhibition) (Diamond, 2013). Dyslexic and normal readers both have difficulty stopping word processing, however, automatic readers can control their proponent response much better than dyslexic individuals (Helland & Asbjørnsen, 2000). For example, Everatt et al., (1997) noted that dyslexics are incapable of stopping word processing prior to the point of interference.
There are several methodological issues which may limit the significance of our study results. First, the sample size of the groups compared was not equal. There was a high number of children (N = 68) compared to adults (N = 10) and higher amount of non-dyslexic participants (N = 66) compared to dyslexic participants (N = 9). Sample sizes are important as small samples can undermine the internal and external validity of the study and very large samples can transform small differences into statistically significant differences (Faber & Fonseca, 2014). Therefore, future research in this area should ensure an equal or similar sample size for each group.
Furthermore, the current study used a vocal response for Stroop task. The headset used in this experiment picked up any noise made around instantly and the Stroop task moved on. Therefore, with some participants, as there was noise in the background, the Stroop moved on before the participant was actually able to answer which was then recorded as an incorrect answer. In order to make sure that the Stroop task is only moving on because of the answers provided by participants, future research can use a Stroop task which relies on manual mode of response where the participant needs to signal the correct answer by pressing a predefined key. This will confirm that the differences recorded in errors are actually due to the answer provided by the participant and not to other, external factors.
The results from this study can be used in future research to advance our understanding of working memory capacity and attention as it demonstrates that such cognitive abilities can be assessed without such complex tasks. The Stroop task has several advantages as part of a larger test battery. Generally, the method employed to examine the nature of working memory, reading and spelling has involved assessing performance on a variety of complex skilled tasks, including language comprehension, complex learning, and reasoning (Long & Prat, 2002). In the case of children who are not really good at reading or spelling, when they are being tested using such complex tasks they may feel anxious and perform worse. The Stroop task gets around the potential anxiety children feel when asked to read as it does not necessarily require reading and does not test intelligence hence it will not impact how children perform. However, in order to get a better understanding of how reading changes with age or to see how individuals with learning disabilities cope with their disability by tracking students over time (Golden & Golden, 2002). The Stroop task can also be used to advance our understanding of changes in executive function, attention, concentration effectiveness and gender differences with age and education level (Penner et al., 2012). For example, this has already been done by Van der Elst et al., (2006) to assess the changes of executive functions by age and level of education and the results revealed that executive function, as measured by Stroop test, declines with age and that the decline is more pronounced in people with a low level of education.
Previous research has found that individuals with dyslexia perform worse on the Stoop task. This study aimed to enlighten the current research regarding the effects of dyslexia on Stroop performance. Contradictory to previous research, the results from this study showed that dyslexia does not seem to have an effect on an individual’s ability to perform well on a Stroop task. Although the results did not vary significantly, the results suggested that dyslexic participants performed slower on all Stoop conditions compared to non-dyslexic participants. This is in line with previous research which has suggested that the inability of dyslexic participants to perform as well as non-dyslexic participants is due to deficiencies in cognitive controls in dyslexics. However, what was significant was that individuals with dyslexia did perform worse on cognitive measures including reading, spelling, and working memory tasks. The findings from this study can be used in future research to test the effects of age on dyslexia by employing a longitudinal study. This can measure the same participants across different milestones and test if increased age impacts performance on the Stroop task.
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Keywords : personality traits, mindfulness, anxiety, executive functions, self-report measures
Citation: Jaiswal S, Tsai S-Y, Juan C-H, Liang W-K and Muggleton NG (2018) Better Cognitive Performance Is Associated With the Combination of High Trait Mindfulness and Low Trait Anxiety. Front. Psychol. 9:627. doi: 10.3389/fpsyg.2018.00627
Received: 21 November 2017 Accepted: 13 April 2018
Published: 03 May 2018.
Alain Morin, Mount Royal University, Canada
Lane Beckes, Bradley University, United States
Ankita Sharma, Indian Institute of Technology Jodhpur, India
Copyright © 2018 Jaiswal, Tsai, Juan, Liang and Muggleton. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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Questionnaires for emotional intelligence
Next, we examine the other popular means for assessment of EI, using questionnaires that rely on the person's self-reports of their emotional functioning. Our coverage here will be relatively brief, because we believe that questionnaire assessments are more appropriate for measuring personality than for assessing true abilities or intelligences.
Early studies: “Mixed-model” assessments
Questionnaires for EI started to appear soon after Mayer and Salovey's pioneering work, such as those by Bar-On (2000 ) and Schutte et al. (1998 ). Early measures were based on what Bar-On (2000 ) termed the “mixed model” of EI. That is, emotional competence was said to reside in both conventionally defined abilities and personality traits such as assertiveness that might facilitate the practical expression of abilities. On the face of it, using questionnaires to assess ability seems highly questionable. Research on conventional measures of intelligence shows that self-assessments of EI correlate approximately 0.3 with objective test scores ( Chamorro-Premuzic, Moutafi, & Furnham, 2005 ). Research in occupational contexts ( Dunning, Heath, & Suls, 2004 ) shows that people often have poor insight into their own social-emotional contexts, perhaps because of the lack of systematic standards for evaluating oneself. It is also paradoxical to expect individuals with low EI to have sufficient self-awareness to provide meaningful questionnaire responses ( Zeidner et al., 2009 ).
Nevertheless, criterion validity evidence has been obtained for a range of “mixed model” questionnaires (see Matthews et al., 2002 , for a review), particularly in relation to well-being and freedom from psychopathology. However, three problems are evident ( Matthews et al., 2002 Zeidner et al., 2009 ). First, the factor structure of “questionnaire EI” is unclear. Different researchers identify differing dimensions of EI, which sometimes failed to replicate, such as Saklofske, Austin, & Minski (2003 ). These psychometric difficulties may reflect lack of conceptual clarity. Second, questionnaires for EI have proven considerably more effective in predicting other questionnaire criteria than objective outcomes, such as work performance ( Van Rooy & Viswesvaran, 2004 ). Indeed, some EI questionnaires include items asking about the general mood of the respondent, which may partially account for their associations with well-being criteria.
Third, these questionnaires turn out to overlap highly with existing personality scales, such as those for the FFM. For example, Bar-On's (2000 ) EQ-i is largely a mixture of extraversion, agreeableness, conscientiousness, and low neuroticism. Thus, much of the validity of this questionnaire simply reflects overlap with traits of the FFM. Indeed, any personality scale that is substantially correlated with low neuroticism will inevitably predict greater well-being and lower stress vulnerability ( Matthews, Deary, & Whiteman, 2009 ). A few studies, such as that by Saklofske et al. (2003 ), have shown modest incremental validity for EI scales over the FFM, suggesting that the scales may have some novel content.
Questionnaires for “trait EI”
As a result of the problems just discussed, the idea that any true ability can be measured using a questionnaire has fallen into disfavor ( Murphy, 2006 O'Sullivan, 2007 ). However, a new perspective on questionnaire assessments is provided by the concept of “trait EI.” According to Petrides and Furnham (2003 ) we should see questionnaires for EI as a means for extending our understanding of personality. Specifying traits for emotional functioning elaborates existing personality models, such as the FFM. Petrides and Furnham (2003 ) developed a new questionnaire, the Trait Emotional Intelligence Questionnaire (TEIQue), which comprises 15 trait scales. It has been quite successful as a predictor of various well-being indices ( Martins et al., 2010 ) and other relevant criteria ( Petrides, Furnham, & Mavroveli, 2007 ). Some studies also show that the TEIQue is predictive of objective as well as subjective criteria, including cortisol response to stress ( Mikolajczak, Roy, Luminet, Fillée, & de Timary, 2007 ), and emotional memory ( Mikolajczak, Roy, Verstrynge, & Luminet, 2009 ).
In a recent appraisal of trait EI measures, we suggested that they share some of the weaknesses of their predecessors ( Matthews, Zeidner & Roberts, 2011 ). For example, Petrides et al. (2007 ) state that the FFM explains 50–80% of the variation in the TEIQue, suggesting that the problem of overlap with existing personality measures has not been solved. It is worth emphasizing that much individual variation in emotional functioning can be quite well explained by existing personality models ( Reisenzein & Weber, 2009 ), without any need to refer to EI. Indeed, the Big Five traits relate to distinct aspects of “EI” that may be better separated than lumped together ( De Raad, 2005 McCrae, 2000 ), including sociability (extraversion), resilience under stress (low neuroticism), self-control (conscientiousness), empathy and caring for others (agreeableness) and sensitivity to emotion (openness).
The issue then is whether recent trait EI studies add anything that is genuinely novel to existing personality models. Here, we may find some grounds for optimism. Although some dimensions of trait EI do no more than pin a new label on existing traits, others appear to be more original. For example, Vernon, Villani, Schermer, and Petrides (2008 ) identified four higher-level factors in the TEIQue, labeled as emotionality, self-control, sociability, and well-being. The well-being factor is an example of redundancy of measurement, and it appears to be largely a mixture of high extraversion and low neuroticism ( Matthews et al., 2011 ). However, the emotionality factor correlated at only approximately 0.20–0.30 with the traits of the FFM in a large sample of North American respondents ( Vernon et al., 2008 ), suggesting that it is distinct from existing personality constructs.
In fact, the term “emotionality” used by Vernon et al. (2008 ) is misleading. Usually, emotionality refers to the person's tendencies to experience emotions frequently and intensely typically, extraversion is linked to positive emotionality and neuroticism to negative emotionality ( Matthews et al., 2009 ). The TEIQue primary scales that define “emotionality” are in fact emotion perception (in self and others), emotion expression, relationship skills, and empathy. The common element here may be regulating emotion in social settings through effective communication of emotion, which in turn requires sensitivity to one's own emotional state.
More generally, we propose that what may be missing from the FFM is constructive emotion regulation. Some associations between the FFM and emotion regulation have been established, but they most commonly take the form of correlations between neuroticism and maladaptive regulative strategies such as rumination ( Matthews et al., 2009 ). Individual differences in intrapersonal emotional regulation are assessed using the Trait Meta-Mood Scale (TMMS Salovey, Mayer, Goldman, Turvey, & Palfai, 1995 ) which includes scales for Attention to one's emotions, Clarity of thinking about emotion, and Mood repair. The high scorer on the TMMS has awareness and insight into his or her emotions, and can use that understanding to regulate mood effectively. The TMMS appears to be acceptably distinct from the Big Five, and to have some validity as a predictor of objective stress responses ( Extremera & Fernandez-Berrocal, 2005 Salovey, Stroud, Woolery, & Epel, 2002 ). Like the Vernon et al. (2008 ) “emotionality” factor, the TMMS may assess aspects of emotion regulation that are not well represented in standard personality models although we should be hesitant to label these qualities as “abilities.”
A meta-analysis of the worst performance rule ☆
The worst performance rule (WPR) describes the phenomenon that individuals' slowest responses in a task are more predictive of their intelligence than their fastest or average responses. Because the WPR supposedly amplifies in heavily g-loaded tasks and in samples whose cognitive abilities factor structure is dominated by a strong g-factor, it has been suggested that whatever mechanism is giving rise to the positive manifold may not promote peak performance, but may rather limit performance in a wide range of cognitive tasks. The aim of the present meta-analysis was to provide a meta-analytically determined estimate of the strength, consistency, and generalizability of the WPR. Across 19 studies containing 23 datasets with a total of 3767 participants, there was robust evidence for the WPR. However, the increase in correlations across quantiles of the RT distribution did not follow a linear, but a logarithmic trend, suggesting that those cognitive processes contributing to fast responses in reaction time tasks are less strongly related to cognitive abilities (r = −0.18) than other cognitive processes contributing to average (r = −0.28) and slow responses (r = −0.33). There was no evidence that the strength of the worst performance rule increased with greater mean reaction times, in tests of general intelligence, or in samples with lower or average cognitive abilities. Instead, it was attenuated in less intelligent samples and greater when correlated with speed instead of intelligence or memory tests. Hence, the WPR may not be as characteristic for g and may play a smaller role for theoretical accounts of the positive manifold than previously thought.
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Ecological validity, in psychology, a measure of how test performance predicts behaviours in real-world settings. Although test designs and findings in studies characterized by low ecological validity cannot be generalized to real-life situations, those characterized by high ecological validity can be. The usefulness of ecological validity as a concept, however, has been much debated, with some questioning the importance of psychological realism (that is, how much processes appearing in the experiment mirror those in everyday life).
In the first place, descriptive analyses and comparisons between the primary and secondary school samples were carried out with a t test. In the second place, the scores on EF tests were standardized (z scores) for each grade level to enable comparison of performance by group/age. Furthermore, a Pearson correlational analysis was carried out between the EFs measures, the academic performance measures, and the social behavior measures. In the third place, to make the analyses simpler, an exploratory factor analysis was performed following the main component method, with Quartimax oblique rotation, using a scree plot as the factor extraction method, in order to determine the latent structure of the correlation matrix of the EFs, academic performance and social behavior measures. Finally, regression analyses were conducted in each level to test the predictive power of EF scores for school performance and social behavior. The analyses were performed with IBM SPSS Statistics v. 20.
The mean and standard deviations, the t test results for the EF task and the questionnaires for each group are included in Table 1. Secondary school students obtained higher scores than primary school students in all EFs measures. Differences in academic achievement were not significant. Differences between primary and secondary school peer tests of social behavior were not found. Lastly, teacher perceived prosocial behavior was higher in primary school students than in secondary school.
Table 1. Descriptive statistics and mean differences analysis between primary and secondary school
Note: VF: Verbal Fluency TMT-A: Trail Making Test (Part A) TMT-B: Trail Making Test (Part B) TP: Teacher-perceived PA: Peer-assessed.
Analysis of EF measures
Table 2 shows the Pearson correlations between the measures obtained from the EF-related tasks, the majority of which were significant, particularly between sub-parts of the same test. The factor analysis showed the existence of three latent variables that explained 69% of variance, and which corresponded to each of the different tasks: the three VF tests, the two parts of the TMT, and the two parts of the Stroop Test. VF tests explained 37% of variance, TMT 19%, and Stroop 13%.
Table 2. EF measure correlation matrix
Note: TMT-A: Trail Making Test (Part A) TMT-B: Trail Making Test (Part B) VF-A = Verbal Fluency, Animals VF-LP: Verbal Fluency, letter P VF-V: Verbal Fluency, Verbs.
Analysis of school performance measures
The Pearson correlation between the GPAs and the class tutor assessments of students’ academic skills (I-TRS) was high throughout the sample (r = 0.77, p = .001), and also when students were divided into primary (r = 0.81, p = .001) and secondary school (r = 0.74, p = .001) accordingly, a factor analysis was performed using data from all participants. The result indicated that a single factor accounted for most of the total variance (88.6%). There was also a strong correlation between this factor and the two achievement measures (r = .94 in both cases).
Analysis of social behavior measures
When the same method of analysis was applied to social behavior measures of the total sample, significant correlations were found between all values. However, when primary school students were differentiated from secondary school students, the correlation pattern changed. Pearson correlations remained moderate to high between all measures for primary students, while for secondary school students a high correlation (r = .61, p < .01) was found solely between peer-evaluated prosocial behavior and the social competence scores provided by class tutors (S-TRS). The correlation between scores for peer-evaluated social preference and social competence scores provided by class tutors (S-TRS) was not significant (r = .17). In the light of these results, separate exploratory factor analyses were performed for primary and secondary school students to include the three measures, and repeating the factor extraction procedure. In primary school students, a single factor for Social Behavior explained the variance (76,5 %) of the three measures, which were strongly correlated to this factor (peer-evaluated prosocial behavior = 0.90 peer social preference = .63 and S-TRS = .786). In secondary school students, however, two factors were obtained: the first (Prosocial Behavior) explained 61.4 % of variance and included peer-evaluated prosocial behavior and S-TRS scores the second (Social Preference), which explained 27.8 % of variance, was saturated with peer social preference scores.
Multiple Regression Analysis
Table 3 shows the correlation matrix of EFs measures and academic and social performance. The factors obtained from the previous factor analyses were used for regression analysis. EF-related variables were included as predictor variables: TMT (A and B), VF (animals, verbs, and the letter P), and the Stroop test (Stroop-C and Stroop-PC). Academic Performance and Social Behavior were included as dependent variables for primary school students School Performance and the two factors associated with social behavior at secondary school level, i.e. Prosocial Behavior and Social Preference, were included for secondary school students.
Table 3. Correlation matrix of EFs measures and academic and social performance
Note: GAP: Grade Average Point AC: Academic Capacities TP-P: Teacher-perceived prosocial PA-P: Peer-assessed prosocial SP: Social preference TMT-A: Trail Making Test (Part A) TMT-B: Trail Making Test (Part B) VF: Verbal Fluency
The first hierarchical regression analyses were performed to determine the predictive power of EF measures for academic performance. As shown in Table 4, in the primary student sample, all EF tests produced significant values, with TMT showing the greatest predictive power. The final model explained 41% of variance in School Performance. In the secondary school sample, a significant model was also obtained, although only the TMT showed significant predictive power. This model explained 13% of variance.
Table 4. Multiple regression analysis: EF predictors of School Performance and Social Behavior for each grade level
Note: TMT: Trail Making Test VF: Verbal Fluency Stroop: Stroop Test.
The second set of analyses was made to determine the predictive power of EFs for Social Behavior. All EF- and student age-related factors were included as predictor variables. In the primary school sample, the model was significant for the TMT and the Stroop task (see Table 3), and the TMT showed greater predictive power (explaining 29% of variance). Two regression models were used for secondary school level: the first, to predict the Prosocial Conduct factor, and the second, the Social Preference factor. Again, predictions of TMT and the Stroop Task for Prosocial Behavior were significant, the former being more predictive and that which explained 15% of variance. However, neither model predicted Social Preference Footnote 2 .