Cognitive predictors of rapid picture naming

Learning and Individual Differences 25 (2013) 141–149

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Cognitive predictors of rapid picture naming Scott L. Decker ⁎, Alycia M. Roberts, Julia A. Englund Department of Psychology, University of South Carolina, 1512 Pendleton St., Columbia, SC 29208, USA

a r t i c l e

i n f o

Article history: Received 1 May 2012 Received in revised form 19 February 2013 Accepted 26 March 2013 Keywords: Rapid automatized naming Dyslexia Reading

a b s t r a c t Deficits in rapid automatized naming (RAN) have been found to be a sensitive cognitive marker for children with dyslexia. However, there is a lack of consensus regarding the construct validity and theoretical neurocognitive processes involved in RAN. Additionally, most studies investigating RAN include a narrow range of cognitive measures. The current study examined the cognitive correlates of RAN with a comprehensive battery of cognitive measures representing the entire Cattell–Horn–Carroll model of cognitive ability. Cognitive correlates of RAN were investigated for 1307 children across a range of developmental ages (5–12 years). Cognitive predictors of RAN differed by developmental age, which may partially explain inconsistencies in previous research studies. Despite developmental variation, lexical access tasks were related to RAN performance across all developmental ages. Results from this study suggest RAN performance likely consists of multiple cognitive processes, both those associated with lexical access and others that depend on developmental age. © 2013 Elsevier Inc. All rights reserved.

1. Introduction Deficits in rapid automatized naming (RAN) have long been considered to be core symptoms of dyslexia and are important in distinguishing children with dyslexia from children without dyslexia (Denckla & Rudel, 1976; Rudel, Denckla, & Broman, 1978; Wolf & Bowers, 2000). Measures of RAN are typically timed procedures that involve identification of pictures, numbers, letters, or colors. The relationship between behavioral measures of RAN deficits and reading difficulties has been found to be invariant across languages (Araujo, Pacheco, Faisca, Petersson, & Reis, 2010; Brizzolara, Chilosi, & Cipriani, 2006), and deficits in RAN may persist into adulthood (Korhonen, 1995). Indeed, RAN performance is considered one of the most powerful predictors of reading in children (Boets et al., 2010; Brizzolara et al., 2006; Wolf et al., 2002). Currently, the reason RAN tasks are predictive of reading problems remains unknown. One hypothesis suggests that RAN is linked to reading through the more general cognitive process of processing speed (Kail, Hall, & Caskey, 1999). Alternatively, Catts (1989a, 1989b) has proposed articulatory rate as an explanation for deficits in RAN. Since phonological deficits also reliably co-occur with RAN deficits, some researchers have proposed RAN deficits as part of a larger deficit in phonological processing (Katz, 1986; Morris et al., 1998; Vaessen, Gerretsen, & Blomert, 2009); although several studies suggest RAN deficits are independent from phonological awareness (Bowers & Swanson, 1991; Wolf et al., 2002). Orthographic processes have also been ruled out (Moll, Fussenegger, Willburger, & Landerl, 2009), but low-level ⁎ Corresponding author. Tel.: +1 803 777 4137; fax: +1 803 777 6508. E-mail addresses: [email protected] (S.L. Decker), [email protected] (A.M. Roberts), [email protected] (J.A. Englund). 1041-6080/$ – see front matter © 2013 Elsevier Inc. All rights reserved. http://dx.doi.org/10.1016/j.lindif.2013.03.009

perceptual deficits have not (Stainthorp, Stuart, Powell, Quinlan, & Garwood, 2010), as suggested in magnocellular deficit theories of dyslexia (Stein & Walsh, 1997; but see Skoyles & Skottun, 2009). Still others have suggested that RAN is predictive of later reading problems due to a deficit in retrieval automaticity (Meyer, Wood, Hart, & Felton, 1998). Finally, several researchers postulate RAN deficits in individuals with dyslexia originate from disruption in the timing of information processes involved in reading, or in auditory temporal sampling and processing more generally (Goswami, 2011; Nicolson & Fawcett, 2008; Wolff, 2002; Wolf & Bowers, 2000). However, hypotheses linking temporal processing deficits to dyslexia have received significant criticism in the literature (e.g., Seidenberg, 2011; Skottun & Skoyles, 2010). Similarly, there is a lack of consensus in how RAN is defined as a construct in many test batteries. For example, the NEPSY II (Korkman, Kirk, & Kemp, 2007) includes a measure of Speeded Naming described as measuring the processes of naming ability, expressive language, working memory, and processing speed. The Comprehensive Test of Phonological Processing (Wagner, Torgesen, & Rashotte, 1999) includes RAN tasks of objects, colors, digits, and letters, which are described as measures of fluid access to verbal names. The Rapid Picture Naming test of the Woodcock–Johnson Tests of Cognitive Abilities, Third Edition (WJ III COG) is categorized under the broad processing speed (Gs) factor and the naming facility narrow ability (Woodcock, McGrew, & Mather, 2001). The PAL-II (Process Assessment of the Learner) has several automatic naming tasks, which are described as measures of the phonological loop of working memory (Berninger, 2007). Each of these widely used standardized batteries attempts to measure the construct of RAN, but there are inconsistencies in the cognitive processes used to describe RAN tasks. The lack of consensus in both the theoretical nature and measurement of RAN has been noted by researchers calling for further investigation of

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the underlying component cognitive processes involved in RAN (Savage, Pillay, & Melidona, 2007). As a follow-up to Wolf and Bowers (1999) double-deficit hypothesis, for instance, Denckla and Cutting (1999) reviewed the history of the RAN construct and studies investigating the relationship of RAN to other cognitive predictors of reading (i.e., phonological awareness, memory span, orthographic awareness, processing speed, and articulation). They found that while processing speed could account for a great deal of the effects of RAN on word reading, none of these variables could fully explain RAN or its relationship to reading, supporting a multi-componential model. Närhi et al. (2005) also supported a multi-componential model of RAN. This study found that RAN was independently associated with a variety of cognitive processes (i.e., processing speed, verbal, and motor skills) in addition to pseudoword decoding abilities in children with learning disabilities. Finally, Savage et al. (2007) found that RAN was related to both generic naming speed and phonological decoding tasks in typical readers. They also found that RAN was related to cerebellar function, though to a lesser extent. Notably, however, Savage et al. (2007) also found an Alphanumeric RAN speed factor independent of generic speed and phonological decoding factors, which uniquely predicted 2% of the variance in literacy. As the authors pointed out, while the unique contribution of RAN was modest, the finding is consistent with other studies that have shown that RAN can be statistically distinguished from phonological decoding (e.g., Wolf & Bowers, 1999). However, there are several limitations with previous studies investigating the cognitive correlates of RAN. Many studies use a limited number of cognitive variables, primarily representing cognition as a verbal/non-verbal dichotomy. For example, Savage et al. (2007) include only two cognitive measures beyond those for naming speed and phonological decoding: a picture vocabulary test (verbal reasoning) and Raven's Progressive Matrices (non-verbal reasoning). Närhi et al. (2005) included neuropsychological measures of verbal skills, processing speed, and motor skills, but did not investigate more specific cognitive abilities such as visual–spatial processing, auditory processing, or short-term memory. Additionally, many studies include measures of specific cognitive abilities of interest for the particular study but not a comprehensive measure of multiple cognitive abilities. For example, studies investigating the relationship between RAN as a measure of processing speed typically include only measures of processing speed— as opposed to a comprehensive cognitive battery including multiple specific cognitive abilities. Although studies using specific cognitive measures may provide evidence for the role of RAN in specific tasks, these results do not provide enough evidence to establish that RAN is related only to processing speed—or whichever specific cognitive ability is being tested. Without measures of additional specific cognitive abilities, alternative hypotheses about the relationship between RAN and other specific cognitive abilities cannot be ruled out. 1.1. Neuro-cognitive components of RAN To help clarify the cognitive components in RAN and how they may differ across development, we developed a simple hypothetical model of the processing demands in a rapid picture naming task. A rapid picture task was selected because of its ubiquitous use in other research studies, particularly for pre-readers. Although it is not ideally suited for fluent readers, it is the only RAN task performed in the Woodcock Johnson, Tests of Cognitive Abilities, Third Edition

(WJ III COG), which has demonstrated high reliability and validity. Our model was guided by neuropsychological research and theory. Although there is not a consensus on the neuro-cognitive processes involved in RAN, the underlying cognitive processes can be divided into the major stages of Perceptual Analysis/Encoding, Recognition, Semantic Labeling, and Verbal Response. The first stage involves simple visual encoding of information. It requires a rapid analysis of the perceptual components of the object. In addition, this stage involves detailed visual analysis that must converge into a global coherent whole that includes the relationships of the distinct components of the object in a picture. Object recognition involves a rapid matching of the visual details of the object with a stored representation of the object derived from past experiences. The third stage, semantic labeling, involves retrieving a semantic association with the higher-level visual representation of the object. The retrieval of the semantic representation includes an association of the phonological language representation (i.e., the name). The semantic association requires activation to enable such a label to be verbally reported. The final step involves oral production of the phonological representation (i.e., speaking the name of the object). Aside from understanding the basic auditory directions, neurologically, the first stage of processing for RAN would involve the low level visual processing components of the eye, optic tracts, and occipital cortex. Because RAN tasks require object identification, rather than object localization, ventral stream activation would be expected (Borst, Thompson, & Kosslyn, 2011; Mishkin & Ungerleider, 1982). Clinically, damage to these areas results in object agnosia, presenting as a double-dissociation between object identification and object localization (Farah, 1997; Kosslyn, Ganis, & Thompson, 2001). Deficits in the perceptual components of object recognition, which are clinically called apperceptive agnosia, are associated with the right posterior hemisphere; whereas, deficits in retrieving the correct semantic label, clinically described as associative agnosia, involves more left posterior brain areas associated with language (Warrington, 1985). Interestingly, the visual word form area, an area of the brain with special involvement in visual word analysis, is also part of the ventral processing areas of the temporal lobe (Cohen, Jobert, Le Bihan, & Dehaene, 2004; Dehaene & Cohen, 2011; Gaillard et al., 2006; Henry et al., 2005; Vinckier et al., 2006). Because of these common neuro-cognitive pathways, RAN performance may depend on the integrity of object-recognition and visual word-recognition circuits in the left hemisphere of the brain (Lervag & Hulme, 2009). Table 1 provides a synthesis of the neuro-cognitive correspondence for the stages of cognitive processing and the brain areas involved with performance. Based on our processing analysis of the components of rapid picture naming tasks, individual differences in RAN at early ages may involve more visual, attentional, and perceptual components. As children develop, however, these skills may become a less important source of individual differences. In fact, development of visual perceptual and attentional capacities slows as children reach school age, with only slight improvements between preschool ages through age 12 (Macchi et al., 2003). It is therefore possible that memory retrieval and language may emerge as primary contributors to predicting RAN performance at older ages. One important element of a developmental processing model of RAN is that cognitive processes may contribute differentially to RAN performance at different developmental periods. Although common objects are used in picture naming tasks, few would suggest RAN tasks

Table 1 Processing model of rapid picture naming. Processing stage

Process

Brain area

Developmental sequence

Cognitive prediction

Input (Stage 1) Processing (Stages 2 and 3) Output (Stage 4)

Visual–perceptual analysis Lexical retrieval object name Oral-production

Occipital, right hemisphere Left posterior temporal Wernicke's area Frontal-lobe, Broca's area

Attention, vision Language, memory Language expression

Attention, visual–spatial Language, memory retrieval Oral motor fluency

S.L. Decker et al. / Learning and Individual Differences 25 (2013) 141–149 Table 2 Demographics by age group. Age (n) 5

6

7

8

9

10

11

12

5–12

Variable Sex Male Female Race Caucasian African American Indian Asian/Pacific Islander

61 59

81 62

83 75

88 86

72 95

101 95

94 87

98 70

678 629

99 16 2 3

108 29 1 5

115 23 10 10

131 28 6 9

118 32 6 11

148 31 3 14

134 38 5 4

130 18 6 14

983 215 39 70

Note. N = 1307.

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Normative Update for the standardization sample of the Woodcock– Johnson Tests of Cognitive Abilities, Third Edition (WJ III) (Woodcock et al., 2001). See Tables 2 and 3 for sample demographics and subtest scores by age group. Additional information regarding sampling and the demographic characteristics of the standardization sample can be found in the Technical Manual (McGrew, Woodcock, & Schrank, 2007). As the purpose of this study was to examine the cognitive correlates of RAN, only subjects with rapid picture naming data were selected for the study. Additionally, only subjects with a complete profile consistent with the extended battery of tests (i.e., tests 1–7, 11–17) were selected.

2.2. Measures

are a measure of cultural knowledge. However, individual differences in RAN tasks may be impacted by cultural knowledge at very early ages, such that children with less familiarity in naming stimuli do worse on the task. This includes both children who are unfamiliar with the particular stimuli, as well as those who lacked direct instruction in their formative years (i.e., did not attend preschool or daycare, and/or lacked exposure to culturally salient stimuli and practices, such as the naming of objects). The impact of cultural knowledge may be even greater for RAN tasks using numbers and letters, because these stimuli are learned after object naming. This is a challenge in testing different theories of RAN in that seemingly competing theories could both be correct, depending on the developmental period of the child. 1.2. The current study The purpose of this study is to test neuro-cognitive correlates of RAN performance across an early developmental age range using a comprehensive measure of cognitive abilities. Additionally, we provided a processing model to test specific hypotheses related to the cognitive correlates of RAN for specific developmental periods. This step helped in making a priori hypotheses to test specific theories of the cognitive processes involved in RAN. 2. Method 2.1. Participants Participants for this study were 1307 individuals (629 females, 678 males, Mage = 8.74 (2.21), age range = 5–12 years) from the

The WJ III was ideally suited to test the questions proposed in this study as it 1) contains a measure of RAN, and 2) includes a comprehensive battery of tests covering a range of cognitive abilities. The cognitive measures were modeled on the Cattell–Horn–Carroll (CHC) theory of cognitive abilities. Because this study was exploring specific cognitive hypotheses, individual subtests were used rather than factors or composites of subtests. As the construct of interest was RAN, the rapid picture naming subtest was used as the dependent variable. This task required participants to view a series of images that depict common objects, and to name them as quickly as possible. The task has been shown to have very high reliability across the developmental range of interest, with reliability coefficients ranging from r11 = .96 to .98 for children ages 5–12. According to CHC theory, there are seven principle broad abilities— fluid intelligence/reasoning, crystallized intelligence/knowledge, visual– spatial abilities, short-term/working memory, long-term storage and retrieval, auditory processing, and cognitive processing speed—which are universally accepted (McGrew, 2003). Within each of these broad abilities, there are also several narrow abilities, which are represented by the individual subtests. For instance, fluid intelligence is measured by two subtests—concept formation and analysis–synthesis— each intended to tap into an individual's reasoning skills. Concept formation (r11 = .94–.96), assesses inductive reasoning, while analysis synthesis (r11 = .88–.94) measures sequential processing and/or deductive reasoning. As CHC theory requires a minimum of two subtests to adequately assess a given ability, data from the extended battery of tests was selected. The WJ III COG has been demonstrated to be a highly reliable and valid instrument across age and different cultures (Edwards & Oakland, 2006; Taub & McGrew, 2004). The reliability

Table 3 Descriptive statistics by age group. Age Variable

Rapid picture naming Verbal comprehension Visual–auditory learning Spatial relations Sound blending Concept formation Visual matching Numbers reversed General information Retrieval fluency Picture recognition Auditory attention Analysis–synthesis Decision speed Memory for words

5

6

7

8

9

10

11

12

5–12

N = 120

N = 143

N = 158

N = 174

N = 167

N = 196

N = 181

N = 168

N = 1307

M (SD)

M (SD)

M (SD)

M (SD)

M (SD)

M (SD)

M (SD)

M (SD)

M (SD)

102.87 (12.26) 100.88 (13.97) 100.22 (16.24) 100.34 (12.97) 100.29 (13.42) 100.09 (18.09) 99.61 (12.17) 99.58 (15.23) 100.37 (14.54) 102.58 (13.56) 100.73 (14.76) 100.58 (13.16) 100.23 (14.40) 99.24 (14.84) 98.18 (15.79) 100.49 (15.59) 101.86 (14.44) 100.36 (14.69) 105.34 (11.35) 102.99 (12.09) 103.57 (14.03) 100.39 (13.60) 100.86 (15.56) 99.07 (15.58) 98.71 (13.76) 100.35 (14.94) 101.14 (14.20) 105.16 102.28 103.08 103.49 103.46 102.51 102.53 104.01 100.07 99.77 101.44 102.79

(14.89) (13.29) (14.87) (13.24) (15.06) (14.48) (13.59) (14.69) (16.57) (16.25) (13.70) (14.05)

101.82 (13.49) 101.75 (14.54) 100.95 (12.86) 98.79 (13.16) 99.77 (14.34) 102.43 (13.24) 100.67 (14.53) 101.79 (14.15) 93.92 (15.57) 98.87 (14.00) 99.30 (15.30) 100.98 (15.83)

99.04 104.44 101.96 99.16 100.36 101.82 101.99 100.83 96.91 101.79 100.95 101.74

(14.28) (15.13) (16.60) (14.03) (14.32) (14.58) (13.78) (15.79) (13.83) (15.34) (15.11) (14.60)

101.95 104.01 101.53 100.33 100.29 101.53 100.81 101.34 95.10 100.04 101.75 102.62

(13.72) (15.50) (16.42) (14.56) (16.04) (14.60) (15.31) (15.24) (14.82) (17.50) (14.86) (16.26)

100.23 101.95 98.87 98.96 98.22 100.39 103.11 100.96 95.34 98.62 98.04 101.14

(15.18) (14.60) (15.37) (15.62) (15.17) (14.83) (14.85) (15.10) (13.77) (15.56) (15.53) (13.29)

100.25 101.00 100.69 99.99 99.89 99.22 101.21 101.72 95.57 99.09 100.76 99.47

(15.76) (14.94) (16.56) (14.85) (17.38) (14.57) (14.39) (13.87) (15.34) (16.58) (15.10) (16.85)

98.39 101.16 100.02 99.42 99.01 102.04 102.31 98.79 97.47 99.83 100.39 101.52

(13.83) (13.25) (15.61) (13.08) (16.30) (15.06) (12.79) (14.05) (15.13) (15.32) (14.94) (13.81)

100.49 101.06 101.77 99.50 99.32 102.12 100.62 100.40 98.25 99.42 101.15 102.01

(15.40) (14.98) (16.13) (15.09) (16.31) (13.50) (14.87) (14.89) (14.03) (15.27) (15.11) (15.36)

100.72 102.17 101.02 99.85 99.91 101.41 101.63 101.11 96.48 99.66 100.47 101.46

(14.69) (14.61) (15.69) (14.31) (15.76) (14.40) (14.29) (14.74) (14.91) (15.78) (15.01) (15.11)

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Table 4 Subtest reliabilities for ages 5–12. Subtest

Broad CHC factor

Narrow CHC ability

Reliability coefficient range

Verbal comprehension General information Concept formation Analysis synthesis Visual–auditory learning Retrieval fluency Spatial relations Picture recognition Sound blending Auditory attention Visual matching Decision speed Numbers reversed Memory for words

Crystallized intelligence/knowledge Crystallized intelligence/knowledge Fluid intelligence/reasoning Fluid intelligence/reasoning Long-term storage and retrieval Long-term storage and retrieval Visual–spatial abilities Visual–spatial abilities Auditory processing Auditory processing Cognitive processing speed Cognitive processing speed Short-term/working memory Short-term/working memory

Lexical knowledge, language development General (verbal) knowledge Inductive reasoning Deductive reasoning Associative memory Ideational fluency Visualization, spatial relations Visual memory Phonetic coding: synthesis Speech–sound discrimination, attention Perceptual speed, visual scanning Semantic processing speed Working memory Memory span

r11 r11 r11 r11 r11 r11 r11 r11 r11 r11 r11 r11 r11 r11

coefficients, as well as the relevant CHC factors, and specific abilities for each of the subtests used in this study are included in Table 4. 2.3. Procedures During initial data collection, participants were administered the complete WJ III battery to compile a normative database for future use. For this study, however, the data selected from the standardization sample (i.e., participants ages 5–12 with a complete profile for the extended battery) were re-analyzed using the following statistical procedures. To explore the cognitive correlates of RAN across an early developmental period, linear regression analyses were used across each age block (i.e., age 5, age 6, …age 12). Additionally, the analyses were replicated for the entire sample (i.e., ages 5–12). In each analysis, rapid picture naming (RPN) was specified as the dependent variable and the 14 subtests of the WJ III COG extended battery were entered simultaneously as the independent variables, in order to determine which cognitive factor(s) were likely contributing to performance on the RAN task. We also accounted for possible demographic effects of sex and race by including those variables in regression models for each age group. Only significant predictors are included in the tables; however, the full analyses by age are included as Appendix A. 3. Results Across the entire sample, rapid picture naming (RPN) was correlated with every subtest, with correlations ranging from .09 (spatial relations) to .44 (retrieval fluency). While the large number of significant correlations was likely influenced by the large sample size, it also verified that a relationship exists between our measure of RAN and the cognitive processes being investigated. Additionally, for ages 5–12, the following subtests—verbal comprehension, concept formation, visual matching, and retrieval fluency—and sex were found to be significant predictors of RPN, F (16, 1290) = 27.25, p ≤ .001 (see Table 5). Overall, these results suggest language and processing speed are important components of RAN. Additionally, females outperformed males, which is consistent with previous literature suggesting a female advantage on language tasks. Although the phonological measure (sound blending) was significantly correlated with RPN, it was not statistically significant within the regression analysis. This supports previous research of the general independence of phonological awareness and RAN tasks. In addition to examining the sample as a whole, results were also examined for specific ages to better understand the developmental trajectory of RAN performance. At age 5, significant effects were found for verbal comprehension, retrieval fluency, and auditory attention, F (16, 103) = 3.21, p ≤ .001. At age 6, verbal comprehension was no

= = = = = = = = = = = = = =

.88–.90 .82–.89 .94–.96 .88–.94 .84–.88 .79–.83 .75–.90 .61–.78 .81–.90 .86–.93 .82–.91 .86–.89 .84–.92 .72–.82

longer a significant predictor, but retrieval fluency and auditory attention remained significant, F (16, 126) = 4.28, p ≤ .001, in addition to sex and race. However, at age 7, sex and retrieval fluency were the only two significant predictors of RPN performance with the addition

Table 5 Significant predictors of rapid picture naming by age group. Variable Ages 5–12 Intercept Sex Verbal comprehension Concept formation Visual matching Retrieval fluency Age 5 Verbal comprehension Retrieval fluency Auditory attention Age 6 Intercept Sex Race Retrieval fluency Auditory attention Age 7 Intercept Sex Visual matching Retrieval fluency Age 8 Intercept Retrieval fluency Age 9 Intercept Race Verbal comprehension Sound blending Retrieval fluency Age 10 Concept formation Retrieval fluency Age 11 Intercept Concept formation Visual matching Retrieval fluency Age 12 Intercept Retrieval fluency Note. ⁎ p ≤ .05. ⁎⁎ p ≤ .01.

B

SE B

35.38⁎⁎ 1.57⁎ .11⁎⁎ −.12⁎⁎ .16⁎⁎ .35⁎⁎ .23⁎ .19⁎ .17⁎

4.57 .73 .04 .03 .03 .03 .11 .09 .07

95% CI [26.43, 44.34] [.14, 3.00] [.03, .19] [−.18, −.06] [.09, .22] [.29, .40] [.01, .45] [.02, .35] [.03, .30]

44.48⁎⁎ 4.57⁎ −4.50⁎⁎ .36⁎⁎ .16⁎

16.99 2.24 1.73 .08 .08

[10.85, 78.10] [.14, 9.01] [−7.93, −1.08] [.20, .53] [.002, .31]

31.58⁎ 4.48⁎ .40⁎⁎ .44⁎⁎

14.53 2.23 .10 .10

[2.85, 60.32] [.08, 8.88] [.20, .61] [.26, .63]

54.04⁎⁎ .21⁎⁎

11.84 .07

[30.65, 77.43] [.07, .36]

46.51⁎⁎ 2.76⁎⁎ .28⁎⁎ .16⁎ .36⁎⁎

11.76 1.14 .10 .08 .07

[23.28, 69.74] [.51, 5.02] [.08, .47] [.01, .31] [.22, .49]

−.19⁎ .54⁎⁎

.09 .10

55.19⁎⁎ −.20⁎ .22⁎ .19⁎

12.23 .08 .09 .08

[31.04, 79.35] [−.36, −.05] [.04, .39] [.04, .34]

33.69⁎⁎ .45⁎⁎

12.86 .09

[8.29, 59.09] [.28, .62]

[−.37, −.006] [.35, .73]


of visual matching, F (16, 141) = 6.15, p ≤ .001. For nine-year-olds, race, verbal comprehension, sound blending, and retrieval fluency were found to be significant predictors of RPN, F (16, 150) = 5.68, p ≤ .001. At ages 10 and 11, concept formation and retrieval fluency were found to be statistically significant predictors of RPN, F (16, 179) = 8.11, p ≤ .001 and F (16, 164) = 2.71, p ≤ .001, respectively. And finally, at ages 8 and 12, only retrieval fluency was statistically significant, F (16, 157) = 2.65, p ≤ .001 and F (16, 151) = 5.14, p ≤ .001, respectively. The results suggest retrieval fluency is a significant predictor of RAN for all ages, though retrieval fluency does not account for all of the variance in RAN performance, suggesting the influence of other processes. According to these results, there is no one or two specific processes, in concurrence with retrieval fluency, that predict performance on RAN tasks. However, some patterns are evident. For instance, for the youngest participants (i.e., ages 5 and 6), auditory attention was an important predictor. This task is similar to RPN in that an individual is asked to match orally presented words with pictures. With young children, this is a technique often employed in learning; however, as children grow and develop more complex skills, this process becomes less important. On the older end of the spectrum (i.e., ages 10 and 11), concept formation was a significant predictor, suggesting that as children age, inductive reasoning skills, such as the ability to categorize, become more important. The regression coefficient for concept formation was negative at all ages, which was interesting. However, while not statistically significant, the majority of other variables with a negative regression coefficient typically engaged more right-hemispheric processes. 4. Discussion This study examined the cognitive correlates of a rapid picture naming task across an early developmental period. The study included a hypothetical model specifying the cognitive processes involved in RAN and predictions of cognitive correlates by developmental age. General results from this study suggest the importance of retrieval fluency as a predictor of RAN performance. As rapid naming abilities are key predictors of reading performance (Boets et al., 2010; Brizzolara et al., 2006; Wolf et al., 2002), these results demonstrate the importance of considering the inclusion of lexical retrieval and/ or rapid naming measures when completing assessments, particularly for specific learning disabilities in reading. Additionally, the results further demonstrate the independence of rapid naming and phonology, as phonological awareness was not a significant predictor of RAN. The results of this study also suggest RAN measures are predicted by multiple cognitive skills that differ across developmental ages. For instance, the authors proposed four neuro-cognitive components of RAN (i.e., visual–perceptual analysis, lexical retrieval, semantic naming, and constructing a verbal response), for which the contributing cognitive processes change over the course of development. We hypothesized both that the specific cognitive processes involved in RAN, as well as their importance in its prediction, would shift as children develop. The results of this study supported this conception by demonstrating that the significant predictors of RAN performance differed across the developmental age range. Specific patterns are discussed below. Additionally, sex may have some influence on RAN performance. Results tentatively supported our hypotheses about which cognitive processes may best predict individual differences in RAN performance; however, our specification of the developmental sequence in which these cognitive skills would be predictive of RAN performance was generally refuted. Consistent with our hypotheses, we found that tasks assessing language (e.g., verbal comprehension), visual/perceptual reasoning (e.g., concept formations), attention (e.g., auditory attention), and memory retrieval (e.g., retrieval fluency) significantly predicted RAN performance across an early developmental range. In fact, in examining

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ages 5 to 12 together, three of the five significant predictors were language, visual/perceptual reasoning, and memory retrieval. Most notably, memory retrieval was the only cognitive process that significantly predicted RAN at all ages. In addition, we found that depending on age, race, sex, and various cognitive processes (e.g., processing speed and auditory processing) also significantly predicted RAN performance. These results support both previous research and our hypothesized multi-componential model of RAN. For example, Närhi et al. (2005) found that phonological skills, processing speed, motor skills and verbal fluency predicted rapid serial naming performance. Similarly, we found auditory decoding skills, processing speed, and verbal retrieval fluency were predictive of rapid picture naming performance. These results are similar, in spite of the fact that this and other previous studies (i.e., Savage et al., 2007) did not include broad cognitive measures of multiple abilities, but rather specific measures of the particular cognitive abilities being investigated (i.e., Peabody Picture Vocabulary Test, Boston Naming Test, Raven's Colored Progressive Matrices, Comprehensive Test of Phonological Processing, Beery Developmental Test of Visual–Motor Integration). The inclusion of a comprehensive cognitive measure including multiple specific abilities is, therefore, a strength of the current study. However, we had also hypothesized that at earlier ages, visual, attention, and perceptual abilities would be more predictive of RAN performance, whereas at older ages, language and memory retrieval would primarily contribute to RAN performance. Although, attention was an important factor in predicting RAN performance at ages 5 and 6, the results of this study generally refuted our hypothesized developmental progression. For instance, verbal comprehension, a language task, was a significant predictor at ages 5 and 9, which is inconsistent with our hypothesis that the importance of language for RAN performance would not emerge until later in the developmental trajectory. Additionally, at ages 8 and 12, memory retrieval was the only significant cognitive predictor, further suggesting that the impact of language on RAN performance is not confined to the older end of the developmental age range. Furthermore, visual/perceptual reasoning skills were hypothesized to be important factors in RAN performance at younger ages, but the results demonstrated that visual scanning skills, and visually presented reasoning tasks were significant predictors of RAN at ages 7 and 11, and 10 and 11, respectively, suggesting that these skills are more important later in development. This study contributes to the existing literature by suggesting RAN may involve multiple cognitive components and these components may change across the developmental age range. This finding is important in that in may explain the lack of consensus in previous research studies. That is, differences in the cognitive correlates of RAN may depend on the specific age of the child. Additionally, the relationship between RAN and reading may depend on the specific cognitive processes involved in the reading task as well as the age range of the children in particular studies. There are numerous reasons for why this line of research is important. As previously mentioned, these findings have important relevance for understanding the core deficits involved in dyslexia, which in turn has many practical applications. For example, understanding these core deficits can impact the identification and remediation of children with reading problems, who constitute the largest percentage of children in special education (USDOE, Office of Special Education Programs, Data Analysis System, 2008). Additionally, changes in the regulations for identifying children with dyslexia provide an opportunity for assessment methods informed by brain sciences and neuropsychology to have practical implications in schools (Decker, 2008a; Decker, Hale, & Flanagan, 2013). Although dyslexia is unlikely to be a result of a single cognitive deficit, the deficits in these children are select and specific, and directly relate to problems in reading (Ackerman, Dykman, & Gardner, 1990; Araujo et al., 2010; Castles & Coltheart, 1993; Coltheart, Rastle, Perry, Langdon, & Ziegler, 2001).

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For instance, deficits in phonological processing represent a clear cognitive deficit that has been linked to reading problems, constituting a major cause and predictor of early reading failure (Fletcher, Morris, & Lyon, 2003; Fletcher et al., 2011; Shaywitz et al., 2004; Torgesen, Wagner, Rashotte, Herron, & Lindamood, 2010; Wagner et al., 1999; Wolff, 2009). As a result, the most common reading interventions used in schools focus on improving phonological processing. RAN performance, like phonological processing, has also been shown to predict reading ability—albeit without demonstration of a clear causal relationship (Boets et al., 2010; Brizzolara et al., 2006; Wolf et al., 2002). Results from the current study suggest, however, that the underlying cognitive deficits contributing to RAN performance are fluid, changing across an early developmental period. As such, a developmental perspective may be the best approach for both predicting and remediating reading failure, rather than a “one-size-fits-all” strategy targeting phonological processing, RAN, or both skills at all ages. Compared to standardized approaches that do not take developmental level into account, interventions including considerations of the differential pattern of cognitive deficits found to contribute to RAN performance, as well as phonological contributions to reading, at each age level may provide better support to those children at risk for dyslexia (Decker, 2008b, 2012). Such interventions would incorporate a more nuanced understanding of the complex relationships among phonological processing, RAN, specific cognitive abilities, and reading across development than pure phonological strategies do when applied universally.

Appendix A

Table 1 Predictors of rapid picture naming—ages 5–12. Variable

B

SE B

95% CI

Intercept Sex Race Verbal comprehension Visual–auditory learning Spatial relations Sound blending Concept formation Visual matching Numbers reversed General information Retrieval fluency Picture recognition Auditory attention Analysis–synthesis Decision speed Memory for words Adjusted R2 F

35.38⁎⁎ 1.57⁎

4.57 .73 .47 .04 .03 .03 .03 .03 .03 .03 .04 .03 .03 .03 .03 .03 .03

[26.43, 44.34] [.14, 3.00] [−.91, .93] [.03, .19] [−.004, .12] [−.05, .05] [−.02, .09] [−.18, −.06] [.09, .22] [−.07, .04] [−.04, .11] [.29, .40] [−.05, .04] [−.03, .08] [−.09, .02] [−.03, .09] [.02, .08]

.01 .11⁎ .06 −.003 .03 −.12⁎⁎ .16⁎⁎ −.01 .03 .35⁎⁎ −.005 .03 −.03 .03 .03 .25 27.25⁎⁎ (16, 1290)

Note. N = 1307. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.

4.1. Limitations and suggestions for future research There are several limitations to this study. The first is the study incorporated a cohort design rather than a longitudinal design. As a result, the research is limited to the standard problems associated with cohort designs in contrast to longitudinal designs. Additionally, the sample was a normative population. Future studies could benefit from examining the cognitive components of RAN with a population of students with reading disabilities. Another limitation of this study is that the neurological components of the provided model were based on previous research. Future studies may benefit by incorporating measures of brain functioning and activation along with the specific measures used in the study. Furthermore, the measure of RAN used in the study was a rapid object-naming task. Future studies could benefit from replicating this study with other measures of RAN (i.e., color naming). Additionally, the neuro-cognitive processing model specified oral motor components of performance in RAN; however, few of the cognitive tests used in this study adequately measured this component. Future research may benefit by incorporating a specific measure of oral fluency to determine its contribution to RAN across a developmental age range. Finally, our analyses showed significant effects of demographic variables (race at ages 6 and 9 and sex at ages 6 and 7) on RAN. These correlations may be spurious (e.g., the result of varying cell sizes for males and females and for each race across age groups, or the result Type I error from multiple comparisons), or they may reflect a viable demographic contribution to RAN. Although our hypotheses did not specify a role for sex and race in predicting RAN, future investigations may wish to include demographic variables to further explore possible demographic differences in RAN and other cognitive and reading outcomes. Acknowledgments The authors would like to thank the Woodcock–Muñoz Foundation for granting us permission to use the standardization data from the Normative Update of the Woodcock–Johnson Tests of Cognitive Abilities, Third Edition.

Table 2 Predictors of rapid picture naming—age 5. Variable

B

SE B

95% CI


28.49 .02 1.18 .23⁎ −.01 .10 −.06 .06 .06 .02 −.03 .19⁎ .10 .17⁎

14.57 2.11 1.70 .11 .13 .08 .09 .10 .12 .08 .10 .09 .08 .07 .08 .09 .08

[−.41, 57.39] [−4.16, 4.20] [−2.20, 4.55] [.01, .45] [−.26, .24] [−.04, .25] [−.24, .12] [−.13, .26] [−.16, .28] [−.14, .18] [−.23, .17] [.02, .35] [−.05, .25] [.03, .30] [−.16, .17] [−.20, .17] [−.28, .05]

.01 −.02 −.11 .23 3.21⁎⁎ (16, 103)

Note: N = 120. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.


B

SE B

95% CI

Intercept Sex Race Verbal comprehension Visual–auditory learning Spatial relations Sound blending Concept formation Visual matching Numbers reversed

44.48⁎ 4.57⁎ −4.50⁎

16.99 2.24 1.73 .10 .10 .08 .09 .11 .09 .08

[10.85, 78.10] [.14, 9.01] [−7.93, −1.08] [−.13, .25] [−.18, .25] [−.03, .28] [−.10, .24] [−.37, .06] [−.24, .12] [−.31, .02]

.06 .04 .13 .07 −.15 −.06 −.14

S.L. Decker et al. / Learning and Individual Differences 25 (2013) 141–149 (continued) Table 3 (continued) Variable General information Retrieval fluency Picture recognition Auditory attention Analysis–synthesis Decision speed Memory for words Adjusted R2 F

B .05 .36⁎⁎ −.06 .16⁎ .12 .05 −.003 .27 4.28⁎⁎ (16, 126)

SE B .11 .08 .08 .08 .09 .09 .07

95% CI [−.16, .26] [.20, .53] [−.22, .10] [.002, .31] [−.05, .29] [−.12, .21] [−.15, .14]

Note: N = 143. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.


B

SE B

95% CI


31.58⁎ 4.48⁎

14.53 2.23 1.42 .13 .10 .08 .08 .09 .10 .09 .11 .10 .07 .09 .09 .08 .08

[2.85, 60.32] [.08, 8.88] [−2.28, 3.32] [−.02, .47] [−.24, .17] [−.27, .06] [−.14, .19] [−.34, .02] [.20, .61] [−.05, .31] [−.32, .12] [.26, .63] [−.26, .03] [−.16, .20] [−.23, .12] [−.28, .04] [−.06, .27]

.52 .23 −.04 −.11 .03 −.16 .40⁎⁎ .13 −.10 .44⁎⁎ −.12 .02 −.06 −.12 .11 .34 6.15⁎⁎ (16, 141)

Note: N = 158. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.


B

SE B

95% CI


54.04⁎⁎ 2.54 −.52 .01 .01 .02 .10 −.07 .12 .08 .10 .21⁎ −.03 −.07 −.01 −.05 .04 .13 1.25⁎⁎ (16, 157)

11.484 1.93 1.31 .11 .09 .08 .08 .07 .09 .07 .10 .07 .07 .07 .07 .08 .07

[30.65, 77.43] [−1.26, 6.35] [−3.11, 2.08] [−.19, .22] [−.17, .18] [−.14, .17] [−.05, .25] [−.21, .08] [.06, .29] [−.07, .22] [−.09, .29] [.07, .36] [−.17, .10] [−.21, .07] [−.15, .12] [−.20, .11] [.09, .17]

Note: N = 174. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.

147


B

SE B

95% CI


46.51⁎⁎ 3.32 2.76⁎ .28⁎ .11 −.10 .16⁎

11.76 1.94 1.14 .10 .07 .07 .08 .08 .08 .06 .10 .07 .06 .08 .07 .07 .08

[23.28, 69.74] [−.51, 7.15] [.51, 5.02] [.08, .47] [−.03, .25] [−.23, .32] [.01, .31] [−.26, .05] [−.12, .17] [−.17, .09] [−.34, .04] [.22, .49] [−.18, .07] [−.12, .20] [−.25, .02] [−.14, .14] [−.09, .21]

−.11 .03 −.04 −.15 .36⁎⁎ −.06 .04 −.11 .00009 .06 .31 5.68⁎⁎ (16, 150)

Note: N = 167. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.


B

SE B

95% CI


3.69 .84 1.72 .00 .003 −.04 .07 −.19⁎

11.97 2.27 1.31 .13 .09 .08 .09 .09 .09 .08 .12 .10 .08 .08 .09 .09 .08

[−19.93, 27.31] [−3.63, 5.31] [−.85, 4.30] [−.25, .25] [−.17, .17] [−.19, .11] [−.11, .24] [−.37, −.006] [−.02, .35] [−.27, .02] [−.02, .46] [.35, .73] [−.03, .31] [−.28, .04] [−.22, .12] [−.01, .35] [−.004, .30]

.17 −.13 .22 .54⁎⁎ .14 −.12 .05 .17 .15 .37 8.11⁎⁎ (16, 179)

Note: N = 196. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.


B

SE B

95% CI

Intercept Sex Race Verbal comprehension Visual–auditory learning Spatial relations Sound blending Concept formation Visual matching Numbers reversed

55.19⁎⁎ −.25 −1.85 .06 .04 −.07 .12 −.20⁎ .22⁎

12.32 1.97 1.45 .11 .08 .07 .08 .08 .09 .07

[31.04, 79.35] [−4.14, 3.65] [−4.71, 1.01] [−.16, .27] [−.12, .19] [−.20, .07] [−.03, .27] [−.36, −.05] [.04, .39] [−.10, .15]

.03

(continued on next page)

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(continued) Table 8 (continued) Variable General information Retrieval fluency Picture recognition Auditory attention Analysis–synthesis Decision speed Memory for words Adjusted R2 F

B .008 .19⁎ .03 .01 .08 .15 −.05 .13 2.71⁎⁎ (16, 164)

SE B .10 .08 .07 .07 .07 .08 .07

95% CI [−.18, .20] [.04, .34] [−.10, .16] [−.12, .14] [−.07, .22] [−.14, .17] [−.20, .10]

Note: N = 181. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.


B

SE B

95% CI


33.69⁎ .1.17 −.33 −.07 .14 .11 −.03 −.04 .17 .01 .13 .45⁎⁎ −.04 .06 −.18 .08 −.11 .28 5.14⁎⁎ (16, 151)

12.86 2.26 1.21 .15 .09 .08 .08 .10 .09 .07 .14 .09 .07 .09 .10 .09 .08

[8.29, 59.09] [−3.30, 5.64] [−2.72, 2.05] [−.37, .22] [−.03, .31] [−.05, .26] [−.20, −.13] [−.23, −.15] [−.01, .34] [−.13, .16] [−.15, .41] [.28, .62] [−.19, .10] [−.11, .23] [−.37, .006] [−.11, .27] [−.27, .06]

Note: N = 168. ⁎ p ≤ .05. ⁎⁎ p ≤ .001.

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