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Associations between memory and verbal fluency tasks
Journal of Communication Disorders 83 (2020) 105968
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Associations between memory and verbal fluency tasks Gitit Kavéa,*, Smadar Sapir-Yogevb a b
T
Department of Education and Psychology, The Open University, Ra'anana, Israel The Municipal Psychoeducational Service, Jerusalem, Israel
ARTICLE INFO
ABSTRACT
Keywords: Memory assessment Category fluency Cognitive development Cognitive aging
Previous studies have provided inconsistent evidence concerning the association between delayed retrieval of story information from long-term memory and retrieval of words on verbal fluency tasks. The current study looks for shared retrieval mechanisms in these tasks from childhood to old age. Three-hundred and eighty participants (ages 5–91) performed tasks of story recall, digit span, phonemic fluency, and semantic fluency. Significant correlations between delayed story recall and both fluency tasks emerged in all age groups, and the contribution of delayed story recall to fluency performance remained significant when analyzing the combined effects of story recall and digit span. These findings suggest that retrieval of information through story recall and retrieval of words on fluency tasks may share similar cognitive components.
1. Introduction Verbal fluency tasks provide sensitive indication of cognitive and communication difficulties. However, the exact abilities underlying successful performance on these tasks are not fully understood (Kraan, Stolwyk, & Testa, 2013; Ruff, Light, Parker, & Levin, 1997; Stolwyk, Bannirchelvam, Kraan, & Simpson, 2015). The present study investigates the association between tasks of delayed story recall and working memory and tasks of phonemic and semantic verbal fluency. On standard fluency tasks, individuals are asked to generate as many different words as possible that follow a certain rule in a limited time frame, and the rule usually specifies an initial letter (phonemic task) or a conceptual category (semantic task). The neuropsychological literature assumes that fluency tasks rely on executive functions (Aita et al., 2018; Kavé, Kigel, & Kochva, 2008; Troyer, Moscovitch, & Winocur, 1997), as well as on word knowledge (Kavé & Yafé, 2014; Ruff et al., 1997), and lexical access (Gordon, Young, & Garcia, 2018; Shao, Janse, Visser, & Meyer, 2014). There is also indication that deficits in fluent lexical access coincide with impairment in story recall in various populations with communication disorders, such as autism (Barron-Linnankoski et al., 2015), specific language impairment (Conti-Ramsden, Ullman, & Lum, 2015), or primary progressive aphasia (Van Den Berg, Jiskoot, Grosveld, Van Swieten, & Papma, 2017). However, since verbal fluency tasks serve primarily in the assessment of executive functions and word retrieval (e.g., Aita et al., 2018; Unsworth, Spillers, & Brewer, 2011; Whiteside et al., 2016), their association with retrieval of information on memory tasks has received less attention. Psychologists working within the Cattell-Horn-Carroll (CHC) model of human intelligence (e.g., Avitia & Kaufman, 2014; McGrew, 2009) have suggested that fluency tasks may be part of the broad ability of Long Term Storage and Retrieval (called Glr in CHC terminology). This ability represents learning, storage, and fluent retrieval of stored information from long-term memory (Schneider & McGrew, 2012; Schneider & McGrew, 2013). Under this classification, tasks of verbal learning and recall and tasks of verbal fluency examine shared abilities (e.g., Floyd, Bergeron, Hamilton, & Parra, 2010). In an attempt to examine this model,
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Corresponding author at: Department of Education and Psychology, The Open University, 1 University Road, Ra'anana 4353701, Israel. E-mail address: [email protected] (G. Kavé).
https://doi.org/10.1016/j.jcomdis.2019.105968 Received 4 April 2019; Received in revised form 20 November 2019; Accepted 1 December 2019 Available online 04 December 2019 0021-9924/ © 2019 Published by Elsevier Inc.
Journal of Communication Disorders 83 (2020) 105968
G. Kavé and S. Sapir-Yogev
Jewsbury and Bowden (2017) analyzed five datasets of adult participants, using confirmatory factor analyses. Their analyses suggested that acquired knowledge (as measured for example by vocabulary tests) and processing speed were the most important abilities underlying performance on fluency tasks, although in some datasets fluency tasks also loaded on a factor that included tasks of long-term memory as well as working memory. Jewsbury and Bowden (2017) argued that it might be better to consider fluency tests as a broad ability in and of themselves, rather than to consider them as part of a broad ability that includes memory tasks. Indeed, in their revision of the CHC model, Schneider and McGrew (2018) split the Glr domain into two separate components, so that story recall no longer belongs in the same broad ability with verbal fluency tasks. This split requires further testing that will consider the entire lifespan. Relatively few studies have focused directly on the association between delayed story recall and verbal fluency, and the results are inconsistent. Ruff et al. (1997) demonstrated that scores on a 60-minute delayed recall test associated with phonemic fluency performance in a sample of 360 participants aged 16-70. Similarly, in a study of 300 young adults (aged 17–25), Ardila, Galeano, and Rosselli (1998) found a significant correlation between story recall and phonemic fluency. Looking at semantic fluency, Robertson, King-Kallimanis, and Kenny (2016) reported a significant association between a combined index of immediate and delayed memory and fluency scores in a sample of 5896 individuals over age 50. In a study of 80 university students, Weiss et al. (2006) found correlations between delayed story recall and semantic fluency scores. However, this correlation emerged only in female participants, and there was no corresponding correlation between delayed story recall and phonemic fluency. Note, though, that Ardila, Galeano, and Rosselli (1998) documented no correlation between story recall and semantic fluency in their sample of young adults. Hence, it is unclear whether the association between delayed story recall and verbal fluency is unique to only one fluency task, and whether it depends on participant age. A direct investigation of the association between delayed recall and the two fluency tasks in the same sample and across a wide age range could resolve some of these inconsistencies. If retrieval of information on memory tasks associates with retrieval of words on fluency tasks because both share similar components, this association should emerge in different age groups. In another conceptualization of verbal fluency tasks, Rosen and Engle (1997) specified four retrieval components: automatic spread of activation, self-monitoring of output to prevent repetition and error, suppression of previously retrieved responses, and generation of cues to access new words. The last component requires controlled strategic search that could also affect retrieval of information from long-term memory, which we will test through a delayed story recall task. The second and third components require working memory. Indeed, Ruff et al. (1997) documented a significant association between working memory, as measured by the digit span task, and phonemic fluency performance in 360 individuals aged 16-70. Moreover, Hedden, Lautenschlager, and Park (2005) reported that a combined measure of working memory predicted phonemic fluency scores in a sample of 345 participants between age 20 and age 92. Looking at semantic fluency, Rosen and Engle (1997) showed that participants with higher memory spans retrieved more words than did participants with lower spans. In addition, in a study of 156 university students, Unsworth et al. (2011) found that working memory predicted the total number of items generated on a combined measure of phonemic and semantic fluency more strongly than did measures of processing speed or inhibition. Other studies with adults have suggested that working memory capacity is important for performance on both phonemic and semantic fluency tasks (Ardila et al., 1998; Gordon et al., 2018; Shao et al., 2014). In the current study, our goal was to investigate the contribution of delayed story recall and working memory to both fluency tasks in the same participants, and to measure these contributions over a wider age range than previously examined. We expected that scores on a delayed story recall task, as well as scores on a task of working memory, would associate with performance on both the phonemic and the semantic fluency tasks. Because previous research on these associations is inconsistent, we could not derive specific hypotheses concerning possible differences between the two fluency tasks. We tested our hypotheses in a sample of neurologically healthy individuals from childhood to old age. This sample could also provide Israeli clinicians with a reference point against which to evaluate individuals with communication or memory disorders. 2. Method 2.1. Participants A convenience sample of 380 participants (52 % female) was approached through word of mouth in various middle-class communities. All participants were born in Israel, were native speakers of Hebrew, and Hebrew was their dominant language (even if they knew other languages). We recruited at least 30 participants in each of 12 age groups from age 5 to age 91 (see Table 1). In childhood, age groups consisted of three consecutive years, with about ten children in each year. In adulthood, age groups consisted of either half a decade (below 30) or a decade (30 and above). Children (< age 17, N = 128) had no developmental, neurological, or learning disorders, as reported by their parents. They were volunteers with parental consent, and since testing took place throughout the school year, ages rather than grades were used for recruitment and analyses. Adults ( > age 17, N = 252) were community-dwelling volunteers, who reported having no present or past psychiatric or neurological disease, as well as no learning disorders. All individuals over age 65 scored within the normal range (27–30) on the Mini-Mental State Examination (, 1975Folstein, Folstein, & McHugh, 1975). Adult education level (range 9–22 years) did not correlate significantly with age, r = .084, p = .183. The study received institutional ethics approval from the Open University of Israel.
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Journal of Communication Disorders 83 (2020) 105968
G. Kavé and S. Sapir-Yogev
Table 1 Sample characteristics by age groups. Age group
5–7 8–10 11–13 14–16 17–23 24–29 30–39 40–49 50–59 60–69 70–79 80+ Total
N
33 32 32 31 31 31 31 31 31 32 34 31 380
Females
Education
N
%
Mean
SD
16 18 16 15 15 16 15 16 15 16 20 20 198
48% 56% 50% 48% 48% 52 % 48% 52 % 48% 50% 59% 65% 52 %
12.35 14.65 16.42 16.48 16.42 15.31 14.35 14.71 15.06
0.95 1.94 2.20 2.76 2.16 1.97 2.62 2.83 2.59
Note: The total mean education average refers to adults alone.
2.2. Memory tasks 2.2.1. Story recall As there is currently no test of story recall designed for Hebrew speakers, we developed a new task that resembles equivalent tests in English, with five versions, one for each of the four age groups in childhood and one for adults. Each version included two different stories with age-appropriate topics and language, and the complexity of the stories, as well as their length in words, increased with age. In each story, we defined information units that involved actors, activities, objects, numbers, days, times, or places, and the total number of information units across the two stories increased with age (see Table 2 for story details). We then composed yes/no questions to examine recognition of information units. The correct answer to half the questions for each story was positive. For the task to be clinically useful, we also describe measures that did not enter the analyses (e.g., immediate recall, recognition, and percent saving). Immediate recall was assessed by reading aloud each story once, and asking the participant to repeat it verbatim after the experimenter finished reading. We report the number of information units recalled correctly across the two stories. Delayed recall was assessed by asking the participant to recall each story separately after a 25−35 minute delay, without telling participants in advance that delayed recall would be examined, and without reading the stories again. We report the number of information units recalled correctly across the two stories. Recognition was assessed by asking participants to answer a set of yes/no questions for each story following delayed recall of the two stories. Participants who were uncertain about the answer were encouraged to guess. We report the number of questions answered correctly across the two stories. Percent saving was calculated by dividing the total number of information units recalled after a delay by the total number of information units recalled immediately after presentation. The number could be greater than 100 if a participant recalled more information units after the delay than after the first presentation. Coding of the memory task. Recalled stories were written verbatim, and acceptable alternatives to the original words were counted according to a pre-determined coding manual. To examine reliability, two independent raters coded the stories provided by 10 % of the sample (21 children and 17 adults, with stories taken from different age groups). Raters were not involved in the development of the stories or the coding manual. There was no significant difference between the total number of information units across the two stories as coded by the two raters for the immediate recall task, t (74) = .188, p = .851, or for the delayed recall task, t (74) = .203, p = .840. The inter-rater correlation between the total number of information units across the two stories recalled immediately was high and significant, r = .989, p < .001, and the same was true for the delayed recall task, r = .984, p < .001. 2.2.2. Digit span We used the digit span subtest of the Wechsler Adult Intelligence Scale-III (Wechsler, 1997) to measure working memory. The examiner read series of digits of an increasing length and participants repeated them in the same order (digit forward) or in the reverse order (digit backward). Scores represent the number of series repeated correctly. 2.3. Fluency tasks Participants were asked to provide as many words as possible within 60 s on each of three letters (phonemic task) and on each of
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Journal of Communication Disorders 83 (2020) 105968
G. Kavé and S. Sapir-Yogev
three categories (semantic task). Responses were written verbatim, with errors or repetitions subsequently excluded from the total score according to a coding manual developed by Kavé (2005) and Kavé and Knafo-Noam (2015). When a questionable response was provided, clarifications were invited at the end of the one-minute interval. Phonemic fluency was assessed by obtaining the number of words generated in one minute for the letters bet (/b/), gimel (/g/), and shin (/š/). Instructions were as follows: "I want you to say as many Hebrew words as possible that begin with a certain letter [or a certain sound for the youngest children]. You may say any word except for names of people and places, such as Tomer or Tel Aviv. In addition, you should use different words rather than the same word with a different ending. For example, if you say tapuz ('orange'), don't also say tapuzim ('oranges'). If you say a verb, use the simplest form halax ('he went') rather than halaxti ('I went') or holex ('he goes'). Please don't say words that are attached to other words, such as mi-shamayim ('from the sky') or la-kise ('to the chair')". Semantic fluency was assessed by obtaining the number of words generated in one minute for each of the following three semantic categories: animals, fruits and vegetables, and vehicles. Instructions were to provide as many different words as possible in each category, regardless of initial letter. Fruits and vegetables were treated as one category in order to avoid the ambiguity between botanical definitions and common usage (as in 'avocado'). It was specified that for the category of vehicles only types of transportation should be provided, while brand names were unacceptable. Coding of the fluency tasks. When homophonic words were provided, the second mention was counted only if the participant pointed out the alternative meaning explicitly (i.e., gamal 'camel', 'repaid'). Words inflected in both masculine and feminine forms (e.g., gever-gveret 'mister-mistress'; sus-susa 'horse-mare') were counted as one, whereas an animal and its offspring were counted as separate words (e.g., para 'cow' and egel 'calf'). Synonyms were counted as two (matos and aviron 'airplane'). If names of numbers were produced on the phonemic task (e.g., shesh 'six'), only the first three were counted, and the same was true for compounds beginning with the same word (e.g., beit-sefer 'school', beit-kneset 'synagogue', beit-xolim 'hospital'). Names of subcategories on the semantic task (e.g., bird) were not given credit if specific items within that subcategory (e.g., dove, eagle) were also provided. Slang terms (e.g., shluk 'sip'), as well as foreign words (e.g., bandana, gangster), were generally acceptable. 2.4. Procedure Each participant was tested individually. Participants (or their parents) first provided demographic information, and individuals over age 65 completed the Mini-Mental State Examination (Folstein et al., 1975). Participants were then presented with each of the two stories for immediate recall. After immediate recall, there was a delay of 25−35 min, during which participants performed four tasks of numerical knowledge, as well as the digit span task. Following this delay, participants were asked to retell the two stories, and then to answer the recognition questions. The order of the two stories was the same for all participants. The two fluency tasks were administered after the completion of the story recall task. The order of the three letters, as well as the order of the three semantic categories, was constant across participants, and the semantic task followed the phonemic task. The entire testing session lasted between 40 min and 1 h. 2.5. Data analysis We report (1) mean raw scores on all tasks, (2) correlations between memory and fluency tasks, (3) analyses of story recall across the four childhood age groups, and (4) a series of regression analyses. We test our hypotheses with regressions that predict phonemic and semantic fluency scores, using age, scores on the delayed story recall task, and scores on the digit span task as predictors, separately for children and adults. We entered education as a predictor to the regression analyses of the adult sample only. To interpret these regressions, we also present correlations between predictors and outcome measures. As each of the four childhood age groups performed a different version of the story recall task, we conducted several auxiliary analyses to examine whether we can combine the childhood sample. We calculated the correlations between raw scores on the delayed story recall task and the fluency tasks in each age group to validate the correlations within the combined sample. We then used two one-way analyses of variance (ANOVA) to compare raw scores on the delayed story recall task and percentage scores across the four childhood age groups. 3. Results Due to various technical reasons (e.g., administration errors, loss of forms), data were missing for seven participants for the entire story recall task, for one participant on the delayed recall and recognition tasks, and for 12 other participants on the recognition task alone. One 5-year-old child could not complete the digit span task, and another 5-year-old child completed the forward task but lost patience on the backward task. Four 5-year-old children could not understand the phonemic fluency task, and one 10-year-old child did not perform this task as well. First, we calculated the mean raw scores for each age group on the story recall task (Table 2), as well as on the digit span and phonemic and semantic verbal fluency tasks (Table 3).
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Journal of Communication Disorders 83 (2020) 105968
G. Kavé and S. Sapir-Yogev
Table 2 Characteristics of the story recall task and mean number of responses, by age group. Age group
5–7 8–10 11–13 14–16 17–23 24–29 30–39 40–49 50–59 60–69 70–79 80+
Maximum Information units
Maximum Recognition questions
Immediate recall
Delayed recall
Recognition
Percent saving
N
N
Mean
SD
Mean
SD
Mean
SD
Mean
SD
27 32 38 45 50 50 50 50 50 50 50 50
16 20 24 28 32 32 32 32 32 32 32 32
18.45 19.72 24.48 25.74 30.00 27.61 29.52 29.32 29.19 27.19 24.12 24.00
3.31 4.45 5.01 4.65 5.29 6.53 5.16 5.60 5.49 4.59 6.65 6.15
16.82 18.59 23.24 24.19 27.77 25.48 26.87 24.61 26.71 24.66 20.91 20.52
4.60 4.48 5.07 5.34 5.98 6.29 5.46 5.28 5.25 4.97 6.73 5.86
14.81 16.31 21.45 23.23 26.55 25.35 26.27 26.13 26.94 26.39 23.39 24.84
1.65 2.35 2.13 2.50 2.90 3.03 3.27 3.29 2.76 3.05 3.58 3.44
90.49 94.45 95.20 94.55 92.30 92.60 90.68 84.54 91.75 90.71 86.65 86.14
18.36 15.13 11.17 14.64 9.44 11.08 8.14 12.19 9.53 10.39 11.15 13.58
Note: In each age group, numbers refer to both stories together. The percent saving score represents the number of information units recalled after a delay divided by the number of information units recalled immediately after presentation.
Table 3 Responses on the digit span and fluency tasks, by age group. Age group
5–7 8–10 11–13 14–16 17–23 24–29 30–39 40–49 50–59 60–69 70–79 80+
Digit forward
Digit backward
Digit span total
Phonemic fluency
Semantic fluency
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
6.72 8.38 8.84 9.39 11.10 10.87 10.35 11.52 10.42 10.03 9.68 8.77
1.73 1.77 1.46 2.16 2.75 2.33 2.37 1.79 1.86 2.63 1.70 1.73
2.61 4.75 5.38 7.03 8.03 7.23 7.58 7.94 7.19 6.41 6.15 5.94
1.38 1.90 1.62 2.24 2.29 2.59 2.03 2.25 2.09 2.03 1.74 1.36
9.35 13.13 14.22 16.42 19.13 18.10 17.94 19.45 17.61 16.44 15.82 14.71
2.50 2.92 2.54 3.56 4.43 4.37 3.80 3.67 3.35 4.29 2.82 2.83
10.48 23.00 29.78 38.74 44.45 40.29 42.97 42.77 41.35 45.88 38.53 34.48
5.73 6.26 8.46 9.35 10.05 10.57 9.59 9.11 11.04 13.39 11.96 11.91
31.18 38.59 50.75 55.00 58.52 56.68 61.35 59.00 64.68 58.56 52.18 47.35
9.14 8.31 9.82 11.56 8.80 11.87 9.23 9.60 9.34 12.71 7.66 11.29
Note: Scores on the digit span task represent the number of series repeated correctly. Scores on the fluency tasks represent the sum of words generated on all three letters together or on all three categories together.
Second, we analyzed the correlations between the raw scores on all four tasks, for all children together, for all adults together, and separately for each of the childhood age groups (Table 4). In the combined childhood sample as well as in the adult sample, a partial correlation between delayed story recall and digit span scores with age as a covariate was not significant. Similarly, the correlations between delayed story recall and digit span scores were not significant in any of the four childhood age groups. Table 4 shows that the two fluency tasks were significantly and positively correlated in all age groups, except for the youngest group. There were significant positive correlations between the raw scores on the delayed story recall and both fluency tasks in all age groups, with the exception of the 8–10 age group, in which the correlation between delayed story recall and phonemic fluency did not reach significance. The correlation between digit span and fluency scores was significant in both the combined childhood sample and in the adult sample for both fluency tasks, as seen in Table 4. Third, we conducted two one-way ANOVAs to examine whether it was possible to combine children data. As each test version presented a different maximum number of information units, raw scores increased with age. To test whether this increase was significant, we conducted an ANOVA with raw scores as the outcome measures. This analysis revealed the expected group difference, F (3, 118) = 16.661, p < .001, indicating that older children recalled more information units. As task construction attempted to equate the difficulty level of the various versions by increasing the number of words and information units to fit the increased age of the participants, we wanted to test whether this attempt was successful. Thus, for each participant we calculated percentage scores relative to the maximum number of information units in the relevant test version. We then conducted an ANOVA that compared percentage scores across age groups, finding no significant group differences, F (3, 118) = 2.264, p = .085. This difficulty equivalence allowed us to examine performance of the four childhood age groups together in the regression analyses. Fourth, to examine the contribution of delayed story recall and working memory together to the prediction of fluency scores, we 5
Journal of Communication Disorders 83 (2020) 105968
G. Kavé and S. Sapir-Yogev
Table 4 Correlations between memory tasks and verbal fluency tasks, by age group. Age group
Task
5–7
Phonemic fluency
Semantic fluency Delayed recall Digit span Semantic fluency Delayed recall Digit span Semantic fluency Delayed recall Digit span Semantic fluency Delayed recall Digit span Age Semantic fluency Delayed recall Digit span Age Education Semantic fluency Delayed recall Digit span
8–10 11–13 14–16 All children
Adults
Semantic fluency
N
r
N
29 29 28 31 28 31 32 29 32 31 31 31 123 123 117 122 252 252 252 250 252
.164 .420* .281 .400* .370 .176 .636** .463* −.109 .531** .370* .175 .793** .744** .613** .565** −.169** .228** .591** .264** .253**
r — .494** .157 — .503** .203 — .542** .077 — .366* −.014 .695** — .659** .478** −.274** .259** — .430** .216**
33 31 29 32 29 32 31 31 128 122 126 252 252 250 252
Note: The analysis of the delayed recall scores used the raw number of information units on the delayed story recall task. * p < .05; ** p < .001.
ran two regression analyses for children and adults, one for each fluency task (see Tables 5 and 6). The analyses of the combined childhood sample included age as an additional predictor, and the analyses of the adult sample included education level in addition to age. In light of the ANOVA results, we ran the regression analyses of the combined childhood data, using the percentage scores on the delayed story recall task as the predictor. Table 5 shows that in the combined childhood sample, the model explained 68.9 % of the variance in phonemic fluency scores. Age was the strongest predictor but delayed story recall explained a significant share of the variance as well. Digit span scores had no contribution to the prediction of phonemic fluency in the combined childhood sample. Very similar results emerged when using raw scores on the delayed story recall instead of percentage scores. Within the adult sample, the regression model explained 40.9 % of the variance in phonemic fluency. Education, story recall, and digit span scores significantly predicted the phonemic fluency score, whereas the contribution of age was not significant. Table 6 shows that in the combined childhood sample, the model explained 57.3 % of the variance in semantic fluency scores. Using raw scores on the delayed story recall task rather than percentage scores showed the same pattern of results. Within the adult sample, the regression model explained 25.8 % of the variance in semantic fluency. In both analyses, age and delayed story recall explained a significant share of the variance in semantic fluency scores, unlike digit span scores whose contribution was not significant.
Table 5 Linear regressions predicting phonemic fluency scores. Age group
Predictor
β
t
p
R2
F
df
p
Children
Age Delayed recall Digit span Age Education Delayed recall Digit span
.784 .234 .098 −.055 .192 .183 .179
11.178 4.334 1.410 −.826 3.182 2.859 2.813
< .001 < .001 .161 .409 .002 .005 .005
.689
82.804
3, 112
< .001
.145
10.379
4, 245
< .001
Adults
Note: Within the childhood sample, the regression analysis used the percentage of information units on the delayed story recall task relative to the maximum number of units on this task for the given age group. Within the adult sample, the regression analysis used the raw number of information units on the delayed story recall task.
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Journal of Communication Disorders 83 (2020) 105968
G. Kavé and S. Sapir-Yogev
Table 6 Linear regressions predicting semantic fluency scores, by age group. Age group
Predictor
β
t
p
R2
F
df
p
Children
Age Delayed recall Digit span Age Education Delayed recall Digit span
.727 .330 .048 −.151 .221 .335 .077
8.787 5.304 .581 −2.436 3.930 5.616 1.302
< .001 < .001 .562 .016 < .001 < .001 .194
.573
51.926
3, 116
< .001
.258
21.320
4, 245
< .001
Adults
Note: Within the childhood sample, the regression analysis used the percentage of information units on the delayed story recall task relative to the maximum number of units on this task for the given age group. Within the adult sample, the regression analysis used the raw number of information units on the delayed story recall task.
4. Discussion Our results show significant associations between performance on a delayed story recall task and performance on both phonemic and semantic fluency tasks across all age groups, whereas the associations between digit span scores and fluency scores were less robust. In childhood, digit span scores associated with both phonemic and semantic fluency scores in the combined sample, as reported in previous research on adults that found a connection between working memory and verbal fluency (e.g., Rosen & Engle, 1997; Ruff et al., 1997). However, in our childhood sample, this association was due to the increase in age, and it disappeared in the regression models that included age as a predictor, as well as in the separate correlation analyses of the four childhood age groups. It is possible that an underdeveloped working memory (Cowan, 2014) cannot support fluency performance in childhood, or that working memory is useful only after children reach adult level in other relevant skills, such as vocabulary. The finding that working memory did not significantly predict either fluency task in childhood suggests that other underlying processes might be at work. As Kavé and KnafoNoam (2015) have shown, the phonemic and semantic fluency tasks develop along a similar trajectory in childhood, most likely because they reflect an increase in shared cognitive abilities such as executive function, speed of processing, or vocabulary funds, which comes with the increase in age. In adults, once age, education, and delayed recall entered the analysis, digit span scores significantly predicted phonemic but not semantic fluency performance. These results fit well with Gordon et al.’s (2018) findings that the contribution of digit span scores to phonemic fluency remained significant once other cognitive abilities entered the regression, whereas their contribution to semantic fluency disappeared. Importantly, retrieval of words according to a phonemic criterion relies on controlled and ad-hoc assembly of words, whereas retrieval of words according to a semantic criterion reflects spread of activation along existing word associations (Carneiro, Albuquerque, & Fernandez, 2008; Hurks et al., 2010). Therefore, verbal short-term memory might be more important for the phonemic task, in which individuals must hold more information in memory in preparation for word generation. Alternatively, the association between working memory and phonemic fluency might reflect the fact that within the adult sample age did not predict performance on the phonemic task. This finding is in line with previous studies that demonstrated greater aging-related effect for semantic than for phonemic fluency (e.g., Gladsjo et al., 1999; Mathuranath et al., 2003). It is thus possible that the effect of age eliminated the contribution of working memory to the prediction of the semantic but not the phonemic fluency scores. Our results show that delayed story recall contributes to the prediction of both fluency tasks, in children as well as in adults. These findings are in line with the older CHC conceptualization that viewed story recall and fluency tasks as part of the same broad ability of Long-Term Storage and Retrieval (Schneider & McGrew, 2012; Schneider & McGrew, 2013), rather than with the newer model (Schneider & McGrew, 2018). Schneider and McGrew (2018) split the Glr domain into Learning Efficiency versus Retrieval Fluency, so that story recall no longer belongs in the same broad ability with verbal fluency tasks. Our findings of a robust association between delayed story recall and fluency performance in all age groups, and for both fluency tasks, calls this recent split into question. According to Rosen and Engle (1997), verbal fluency involves the generation of cues to access new words. This component requires controlled strategic search that could affect both the retrieval of words on verbal fluency tasks and the retrieval of information from long-term memory on the story-recall task. Had we included other tests of executive and language abilities in our analyses, we might have been able to account for some of the overlap between delayed story recall and verbal fluency tasks through other abilities. Indeed, studies that included such tests stressed that performance on fluency tasks depended less on verbal ability and on word production and more on the ability to store and update relevant information (e.g., Shao et al., 2014). However, as recent neuropsychological conceptualization of fluency performance relates fluency primarily to executive functions (e.g., Aita et al., 2018) rather than to memory, many such previous studies did not include delayed recall tests (see Table 2 in Kraan et al., 2013, for a summary). We acknowledge that the lack of other tests is a limitation of the current study. Future research of the components of fluency tasks should examine the contribution of delayed story recall tasks to fluency performance together with other tests of executive functions and language. Another limitation of our research is the fact that we had different versions of the memory task for each group of children. This is a weakness of our design, but we note that presenting the same story to children of all ages as well as to older adults might have been 7
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problematic in terms of the content and the complexity of the task. In addition, when we compared the percentage of correct responses on the delayed story recall task across the four childhood age groups, we found that the four versions were equivalent. Thus, although younger children recalled fewer items relative to older children, the test led to comparable percentage scores, demonstrating that the four versions were age appropriate. Furthermore, the correlation analyses within each group, and the regression analyses that used percentage and well as raw scores, revealed a similar pattern of associations between delayed story recall and fluency scores. We believe that showing lifespan task associations is a strength of our study, justifying the inclusion of the childhood data in the present paper. Such lifespan data could also serve Israeli practitioners as a reference point against which to base their clinical decisions. In summary, the current study demonstrates that delayed story recall is associated with both phonemic and semantic fluency tasks across a wide age range. It is possible that the association stems from reliance on a directed search component that allows controlled access to targeted information within memory. Thus, retrieval of newly acquired information and fluent retrieval of known words may not be the same type of retrieval, but they may share important components. As many studies of individuals with word retrieval deficits show that they also have impairment in story recall (e.g., Van Den Berg et al., 2017), it is especially important to examine story recall in these populations. Such examination could show that reduced verbal abilities limit performance on memory tasks, but it could also show that the deficit lies in the ability to develop complex search and organization strategies that allow lexical access, even in individuals with normal scores on tests of verbal intelligence (e.g., Barron-Linnankoski et al., 2015). Author statement Both authors contributed equally to research conceptualization and writing. Acknowledgements We thank Irit Barnea, Alon Tavor Ben Melech, Yaron Cohen, Rachel Epstein, Rita Gorokhod, Limor Levy, Alex Manevich, Ayelet Yerushalmi, and Ori Zamsh for their help in data collection and coding. This study was partly supported by the Open University of Israel (grant number 102186), with no involvement in publication. The authors declare no conflict of interests. References Aita, S. L., Beach, J. D., Taylor, S. E., Borgogna, N. C., Harrell, M. N., & Hill, B. D. (2018). Executive, language, or both? An examination of the construct validity of verbal fluency measures. Applied Neuropsychology: Adult. https://doi.org/10.1080/23279095.2018.1439830. Ardila, A., Galeano, L. M., & Rosselli, M. (1998). Toward a model of neuropsychological activity. Neuropsychology Review, 8, 171–190. https://doi.org/10.1023/ A:1021618218943. Avitia, M. J., & Kaufman, J. C. (2014). Beyond g and c: The relationship of rated creativity to long-term storage and retrieval (Glr). Psychology of Aesthetics, Creativity, and the Arts, 8, 293–302. https://doi.org/10.1037/a0036772. Barron-Linnankoski, S., Reinvall, O., Lahervuori, A., Voutilainen, A., Lahti-Nuuttila, P., & Korkman, M. (2015). Neurocognitive performance of children with higher functioning autism spectrum disorders on the NEPSY-II. Child Neuropsychology, 21(1), 55–77. https://doi.org/10.1080/09297049.2013.873781. Cowan, N. (2014). Working memory underpins cognitive development, learning, and education. Educational Psychology Review, 26(2), 197–223. https://doi.org/10.1007/s10648013-9246-y. Carneiro, C., Albuquerque, P., & Fernandez, A. (2008). Portuguese category norms for children. Behavior Research Methods, 40, 177–182. https://doi.org/10.3758/BRM.40.1.177. Conti-Ramsden, G., Ullman, M. T., & Lum, J. A. (2015). The relation between receptive grammar and procedural, declarative, and working memory in specific language impairment. Frontiers in Psychology, 6, 1090. https://doi.org/10.3389/fpsyg.2015.01090. Floyd, R. G., Bergeron, R., Hamilton, G., & Parra, G. R. (2010). How do executive functions fit with the Cattell-Horn-Carroll model? Some evidence from a joint factor analysis of the Delis-Kaplan executive function system and the Woodcock-Johnson III tests of cognitive abilities. Psychology in the Schools, 47(7), 721–738. https://doi.org/10.1002/pits. 20500. Folstein, M. F., Folstein, S. E., & McHugh, P. R. (1975). Mini-Mental State: A practical method for grading the cognitive state of patients for the clinician. Psychiatric Research, 12, 189–198. https://doi.org/10.1016/0022-3956(75)90026-6. Gladsjo, J. A., Schuman, C. C., Evans, J. D., Peavy, G. M., Miller, S. W., & Heaton, R. K. (1999). Norms for letter and category fluency: Demographic corrections for age, education, and ethnicity. Assessment, 6, 147–178. https://doi.org/10.1177/107319119900600204. Gordon, J. K., Young, M., & Garcia, C. (2018). Why do older adults have difficulty with semantic fluency? Aging, Neuropsychology, and Cognition, 25(6), 803–828. https://doi.org/ 10.1080/13825585.2017.1374328. Hedden, T., Lautenschlager, G., & Park, D. C. (2005). Contributions of processing ability and knowledge to verbal memory tasks across the adult life-span. The Quarterly Journal of Experimental Psychology Section A, 58, 169–190. https://doi.org/10.1080/02724980443000179. Hurks, P. P. M., Schrans, D., Meijs, C., Wassenberg, R., Feron, F. J. M., & Jolles, J. (2010). Developmental changes in semantic verbal fluency: Analyses of word productivity as a function of time, clustering, and switching. Child Neuropsychology, 16, 366–387. https://doi.org/10.1080/09297041003671184. Jewsbury, P. A., & Bowden, S. C. (2017). Construct validity of fluency and implications for the factorial structure of memory. Journal of Psychoeducational Assessment, 35, 460–481. https://doi.org/10.1177/0734282916648041. Kavé, G. (2005). Phonemic fluency, semantic fluency, and difference scores: Normative data for adult Hebrew speakers. Journal of Clinical and Experimental Neuropsychology, 27, 690–699. https://doi.org/10.1080/13803390490918499. Kavé, G., Kigel, S., & Kochva, R. (2008). Switching and clustering in verbal fluency tasks throughout childhood. Journal of Clinical and Experimental Neuropsychology, 30, 349–359. https://doi.org/10.1080/13803390701416197. Kavé, G., & Knafo-Noam, A. (2015). Lifespan development of phonemic and semantic fluency: Universal increase, differential decrease. Journal of Clinical and Experimental Neuropsychology, 37, 751–763. https://doi.org/10.1080/13803395.2015.1065958. Kavé, G., & Yafé, R. (2014). Performance of younger and older adults on tests of word knowledge and word retrieval: Independence or interdependence of skills? American Journal of Speech-Language Pathology, 23, 36–45. https://doi.org/10.1044/1058-0360(2013/12-0136). Kraan, C., Stolwyk, R. J., & Testa, R. (2013). The abilities associated with verbal fluency performance in a young, healthy population are multifactorial and differ across fluency variants. Applied Neuropsychology: Adult, 20, 159–168. https://doi.org/10.1080/09084282.2012.670157. Mathuranath, P. S., George, A., Cherian, P. J., Alexander, A., Sarma, S. G., & Sarma, P. S. (2003). Effects of age, education and gender on verbal fluency. Journal of Clinical and Experimental Neuropsychology, 25, 1057–1064. https://doi.org/10.1076/jcen.25.8.1057.16736. McGrew, K. S. (2009). CHC theory and the human cognitive abilities project: Standing on the shoulders of the giants of psychometric intelligence research. Intelligence, 37, 1–10. https://doi.org/10.1016/j.intell.2008.08.004. Robertson, D. A., King-Kallimanis, B. L., & Kenny, R. A. (2016). Negative perceptions of aging predict longitudinal decline in cognitive function. Psychology and Aging, 31, 71–81. https://doi.org/10.1037/pag0000061. Rosen, V. M., & Engle, R. W. (1997). The role of working memory capacity in retrieval. Journal of Experimental Psychology: General, 126(3), 211–227. https://doi.org/10.1037/
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0096-3445.126.3.211. Ruff, R. M., Light, R. H., Parker, S. B., & Levin, H. S. (1997). The psychological construct of word fluency. Brain and Language, 57, 394–405. https://doi.org/10.1006/brln.1997. 1755. Schneider, W. J., & McGrew, K. (2012). The Cattell-Horn-Carroll model of intelligence. In D. Flanagan, & P. Harrison (Eds.). Contemporary intellectual assessment: Theories, tests, and issues (pp. 99–144). (3rd ed.). New York, NY: Guilford Press. Schneider, W. J., & McGrew, K. S. (2013). Individual differences in the ability to process information. In B. Irby, G. Brown, R. Laro-Alecio, & S. Jackson (Eds.). Handbook of educational theories (pp. 767–782). Charlotte, NC: Information Age Publishing. Schneider, W. J., & McGrew, K. S. (2018). The Cattell-Horn-Carroll theory of cognitive abilities. In D. P. Flanagan, & E. M. McDonough (Eds.). Contemporary intellectual assessment: Theories, tests, and issues (pp. 73–163). (4th ed.). New York, NY: Guilford Press. Shao, Z., Janse, E., Visser, K., & Meyer, A. S. (2014). What do verbal fluency tasks measure? Predictors of verbal fluency performance in older adults. Frontiers in Psychology, 5, 772. https://doi.org/10.3389/fpsyg.2014.00772. Stolwyk, R., Bannirchelvam, B., Kraan, C., & Simpson, K. (2015). The cognitive abilities associated with verbal fluency task performance differ across fluency variants and age groups in healthy young and old adults. Journal of Clinical and Experimental Neuropsychology, 37, 70–83. https://doi.org/10.1080/13803395.2014.988125. Troyer, A. K., Moscovitch, M., & Winocur, G. (1997). Clustering and switching as two components of verbal fluency: Evidence from younger and older healthy adults. Neuropsychology, 11, 138–146. https://doi.org/10.1037/0894-4105.11.1.138. Unsworth, N., Spillers, G. J., & Brewer, G. A. (2011). Variation in verbal fluency: A latent variable analysis of clustering, switching, and overall performance. The Quarterly Journal of Experimental Psychology, 64, 447–466. https://doi.org/10.1080/17470218.2010.505292. Van Den Berg, E., Jiskoot, L. C., Grosveld, M. J., Van Swieten, J. C., & Papma, J. M. (2017). Qualitative assessment of verbal fluency performance in frontotemporal dementia. Dementia and Geriatric Cognitive Disorders, 44(1–2), 35–44. https://doi.org/10.1159/000477538. Wechsler, D. (1997). Manual for the Wechsler Memory Scale-Third Edition. San Antonio, TX: The Psychological Corporation. Weiss, E. M., Ragland, J. D., Brensinger, C. M., Bilker, W. B., Deisenhammer, E. A., & Delazer, M. (2006). Sex differences in clustering and switching in verbal fluency tasks. Journal of the International Neuropsychological Society, 12, 502–509. https://doi.org/10.1017/S1355617706060656. Whiteside, D. M., Kealey, T., Semla, M., Luu, H., Rice, L., Basso, M. R., et al. (2016). Verbal fluency: Language or executive function measure? Applied Neuropsychology: Adult, 23, 29–34. https://doi.org/10.1080/23279095.2015.1004574.
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Journal of Communication Disorders journal homepage: www.elsevier.com/locate/jcomdis
Associations between memory and verbal fluency tasks Gitit Kavéa,*, Smadar Sapir-Yogevb a b
T
Department of Education and Psychology, The Open University, Ra'anana, Israel The Municipal Psychoeducational Service, Jerusalem, Israel
ARTICLE INFO
ABSTRACT
Keywords: Memory assessment Category fluency Cognitive development Cognitive aging
Previous studies have provided inconsistent evidence concerning the association between delayed retrieval of story information from long-term memory and retrieval of words on verbal fluency tasks. The current study looks for shared retrieval mechanisms in these tasks from childhood to old age. Three-hundred and eighty participants (ages 5–91) performed tasks of story recall, digit span, phonemic fluency, and semantic fluency. Significant correlations between delayed story recall and both fluency tasks emerged in all age groups, and the contribution of delayed story recall to fluency performance remained significant when analyzing the combined effects of story recall and digit span. These findings suggest that retrieval of information through story recall and retrieval of words on fluency tasks may share similar cognitive components.
1. Introduction Verbal fluency tasks provide sensitive indication of cognitive and communication difficulties. However, the exact abilities underlying successful performance on these tasks are not fully understood (Kraan, Stolwyk, & Testa, 2013; Ruff, Light, Parker, & Levin, 1997; Stolwyk, Bannirchelvam, Kraan, & Simpson, 2015). The present study investigates the association between tasks of delayed story recall and working memory and tasks of phonemic and semantic verbal fluency. On standard fluency tasks, individuals are asked to generate as many different words as possible that follow a certain rule in a limited time frame, and the rule usually specifies an initial letter (phonemic task) or a conceptual category (semantic task). The neuropsychological literature assumes that fluency tasks rely on executive functions (Aita et al., 2018; Kavé, Kigel, & Kochva, 2008; Troyer, Moscovitch, & Winocur, 1997), as well as on word knowledge (Kavé & Yafé, 2014; Ruff et al., 1997), and lexical access (Gordon, Young, & Garcia, 2018; Shao, Janse, Visser, & Meyer, 2014). There is also indication that deficits in fluent lexical access coincide with impairment in story recall in various populations with communication disorders, such as autism (Barron-Linnankoski et al., 2015), specific language impairment (Conti-Ramsden, Ullman, & Lum, 2015), or primary progressive aphasia (Van Den Berg, Jiskoot, Grosveld, Van Swieten, & Papma, 2017). However, since verbal fluency tasks serve primarily in the assessment of executive functions and word retrieval (e.g., Aita et al., 2018; Unsworth, Spillers, & Brewer, 2011; Whiteside et al., 2016), their association with retrieval of information on memory tasks has received less attention. Psychologists working within the Cattell-Horn-Carroll (CHC) model of human intelligence (e.g., Avitia & Kaufman, 2014; McGrew, 2009) have suggested that fluency tasks may be part of the broad ability of Long Term Storage and Retrieval (called Glr in CHC terminology). This ability represents learning, storage, and fluent retrieval of stored information from long-term memory (Schneider & McGrew, 2012; Schneider & McGrew, 2013). Under this classification, tasks of verbal learning and recall and tasks of verbal fluency examine shared abilities (e.g., Floyd, Bergeron, Hamilton, & Parra, 2010). In an attempt to examine this model,
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Corresponding author at: Department of Education and Psychology, The Open University, 1 University Road, Ra'anana 4353701, Israel. E-mail address: [email protected] (G. Kavé).
https://doi.org/10.1016/j.jcomdis.2019.105968 Received 4 April 2019; Received in revised form 20 November 2019; Accepted 1 December 2019 Available online 04 December 2019 0021-9924/ © 2019 Published by Elsevier Inc.
Journal of Communication Disorders 83 (2020) 105968
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Jewsbury and Bowden (2017) analyzed five datasets of adult participants, using confirmatory factor analyses. Their analyses suggested that acquired knowledge (as measured for example by vocabulary tests) and processing speed were the most important abilities underlying performance on fluency tasks, although in some datasets fluency tasks also loaded on a factor that included tasks of long-term memory as well as working memory. Jewsbury and Bowden (2017) argued that it might be better to consider fluency tests as a broad ability in and of themselves, rather than to consider them as part of a broad ability that includes memory tasks. Indeed, in their revision of the CHC model, Schneider and McGrew (2018) split the Glr domain into two separate components, so that story recall no longer belongs in the same broad ability with verbal fluency tasks. This split requires further testing that will consider the entire lifespan. Relatively few studies have focused directly on the association between delayed story recall and verbal fluency, and the results are inconsistent. Ruff et al. (1997) demonstrated that scores on a 60-minute delayed recall test associated with phonemic fluency performance in a sample of 360 participants aged 16-70. Similarly, in a study of 300 young adults (aged 17–25), Ardila, Galeano, and Rosselli (1998) found a significant correlation between story recall and phonemic fluency. Looking at semantic fluency, Robertson, King-Kallimanis, and Kenny (2016) reported a significant association between a combined index of immediate and delayed memory and fluency scores in a sample of 5896 individuals over age 50. In a study of 80 university students, Weiss et al. (2006) found correlations between delayed story recall and semantic fluency scores. However, this correlation emerged only in female participants, and there was no corresponding correlation between delayed story recall and phonemic fluency. Note, though, that Ardila, Galeano, and Rosselli (1998) documented no correlation between story recall and semantic fluency in their sample of young adults. Hence, it is unclear whether the association between delayed story recall and verbal fluency is unique to only one fluency task, and whether it depends on participant age. A direct investigation of the association between delayed recall and the two fluency tasks in the same sample and across a wide age range could resolve some of these inconsistencies. If retrieval of information on memory tasks associates with retrieval of words on fluency tasks because both share similar components, this association should emerge in different age groups. In another conceptualization of verbal fluency tasks, Rosen and Engle (1997) specified four retrieval components: automatic spread of activation, self-monitoring of output to prevent repetition and error, suppression of previously retrieved responses, and generation of cues to access new words. The last component requires controlled strategic search that could also affect retrieval of information from long-term memory, which we will test through a delayed story recall task. The second and third components require working memory. Indeed, Ruff et al. (1997) documented a significant association between working memory, as measured by the digit span task, and phonemic fluency performance in 360 individuals aged 16-70. Moreover, Hedden, Lautenschlager, and Park (2005) reported that a combined measure of working memory predicted phonemic fluency scores in a sample of 345 participants between age 20 and age 92. Looking at semantic fluency, Rosen and Engle (1997) showed that participants with higher memory spans retrieved more words than did participants with lower spans. In addition, in a study of 156 university students, Unsworth et al. (2011) found that working memory predicted the total number of items generated on a combined measure of phonemic and semantic fluency more strongly than did measures of processing speed or inhibition. Other studies with adults have suggested that working memory capacity is important for performance on both phonemic and semantic fluency tasks (Ardila et al., 1998; Gordon et al., 2018; Shao et al., 2014). In the current study, our goal was to investigate the contribution of delayed story recall and working memory to both fluency tasks in the same participants, and to measure these contributions over a wider age range than previously examined. We expected that scores on a delayed story recall task, as well as scores on a task of working memory, would associate with performance on both the phonemic and the semantic fluency tasks. Because previous research on these associations is inconsistent, we could not derive specific hypotheses concerning possible differences between the two fluency tasks. We tested our hypotheses in a sample of neurologically healthy individuals from childhood to old age. This sample could also provide Israeli clinicians with a reference point against which to evaluate individuals with communication or memory disorders. 2. Method 2.1. Participants A convenience sample of 380 participants (52 % female) was approached through word of mouth in various middle-class communities. All participants were born in Israel, were native speakers of Hebrew, and Hebrew was their dominant language (even if they knew other languages). We recruited at least 30 participants in each of 12 age groups from age 5 to age 91 (see Table 1). In childhood, age groups consisted of three consecutive years, with about ten children in each year. In adulthood, age groups consisted of either half a decade (below 30) or a decade (30 and above). Children (< age 17, N = 128) had no developmental, neurological, or learning disorders, as reported by their parents. They were volunteers with parental consent, and since testing took place throughout the school year, ages rather than grades were used for recruitment and analyses. Adults ( > age 17, N = 252) were community-dwelling volunteers, who reported having no present or past psychiatric or neurological disease, as well as no learning disorders. All individuals over age 65 scored within the normal range (27–30) on the Mini-Mental State Examination (, 1975Folstein, Folstein, & McHugh, 1975). Adult education level (range 9–22 years) did not correlate significantly with age, r = .084, p = .183. The study received institutional ethics approval from the Open University of Israel.
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Table 1 Sample characteristics by age groups. Age group
5–7 8–10 11–13 14–16 17–23 24–29 30–39 40–49 50–59 60–69 70–79 80+ Total
N
33 32 32 31 31 31 31 31 31 32 34 31 380
Females
Education
N
%
Mean
SD
16 18 16 15 15 16 15 16 15 16 20 20 198
48% 56% 50% 48% 48% 52 % 48% 52 % 48% 50% 59% 65% 52 %
12.35 14.65 16.42 16.48 16.42 15.31 14.35 14.71 15.06
0.95 1.94 2.20 2.76 2.16 1.97 2.62 2.83 2.59
Note: The total mean education average refers to adults alone.
2.2. Memory tasks 2.2.1. Story recall As there is currently no test of story recall designed for Hebrew speakers, we developed a new task that resembles equivalent tests in English, with five versions, one for each of the four age groups in childhood and one for adults. Each version included two different stories with age-appropriate topics and language, and the complexity of the stories, as well as their length in words, increased with age. In each story, we defined information units that involved actors, activities, objects, numbers, days, times, or places, and the total number of information units across the two stories increased with age (see Table 2 for story details). We then composed yes/no questions to examine recognition of information units. The correct answer to half the questions for each story was positive. For the task to be clinically useful, we also describe measures that did not enter the analyses (e.g., immediate recall, recognition, and percent saving). Immediate recall was assessed by reading aloud each story once, and asking the participant to repeat it verbatim after the experimenter finished reading. We report the number of information units recalled correctly across the two stories. Delayed recall was assessed by asking the participant to recall each story separately after a 25−35 minute delay, without telling participants in advance that delayed recall would be examined, and without reading the stories again. We report the number of information units recalled correctly across the two stories. Recognition was assessed by asking participants to answer a set of yes/no questions for each story following delayed recall of the two stories. Participants who were uncertain about the answer were encouraged to guess. We report the number of questions answered correctly across the two stories. Percent saving was calculated by dividing the total number of information units recalled after a delay by the total number of information units recalled immediately after presentation. The number could be greater than 100 if a participant recalled more information units after the delay than after the first presentation. Coding of the memory task. Recalled stories were written verbatim, and acceptable alternatives to the original words were counted according to a pre-determined coding manual. To examine reliability, two independent raters coded the stories provided by 10 % of the sample (21 children and 17 adults, with stories taken from different age groups). Raters were not involved in the development of the stories or the coding manual. There was no significant difference between the total number of information units across the two stories as coded by the two raters for the immediate recall task, t (74) = .188, p = .851, or for the delayed recall task, t (74) = .203, p = .840. The inter-rater correlation between the total number of information units across the two stories recalled immediately was high and significant, r = .989, p < .001, and the same was true for the delayed recall task, r = .984, p < .001. 2.2.2. Digit span We used the digit span subtest of the Wechsler Adult Intelligence Scale-III (Wechsler, 1997) to measure working memory. The examiner read series of digits of an increasing length and participants repeated them in the same order (digit forward) or in the reverse order (digit backward). Scores represent the number of series repeated correctly. 2.3. Fluency tasks Participants were asked to provide as many words as possible within 60 s on each of three letters (phonemic task) and on each of
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three categories (semantic task). Responses were written verbatim, with errors or repetitions subsequently excluded from the total score according to a coding manual developed by Kavé (2005) and Kavé and Knafo-Noam (2015). When a questionable response was provided, clarifications were invited at the end of the one-minute interval. Phonemic fluency was assessed by obtaining the number of words generated in one minute for the letters bet (/b/), gimel (/g/), and shin (/š/). Instructions were as follows: "I want you to say as many Hebrew words as possible that begin with a certain letter [or a certain sound for the youngest children]. You may say any word except for names of people and places, such as Tomer or Tel Aviv. In addition, you should use different words rather than the same word with a different ending. For example, if you say tapuz ('orange'), don't also say tapuzim ('oranges'). If you say a verb, use the simplest form halax ('he went') rather than halaxti ('I went') or holex ('he goes'). Please don't say words that are attached to other words, such as mi-shamayim ('from the sky') or la-kise ('to the chair')". Semantic fluency was assessed by obtaining the number of words generated in one minute for each of the following three semantic categories: animals, fruits and vegetables, and vehicles. Instructions were to provide as many different words as possible in each category, regardless of initial letter. Fruits and vegetables were treated as one category in order to avoid the ambiguity between botanical definitions and common usage (as in 'avocado'). It was specified that for the category of vehicles only types of transportation should be provided, while brand names were unacceptable. Coding of the fluency tasks. When homophonic words were provided, the second mention was counted only if the participant pointed out the alternative meaning explicitly (i.e., gamal 'camel', 'repaid'). Words inflected in both masculine and feminine forms (e.g., gever-gveret 'mister-mistress'; sus-susa 'horse-mare') were counted as one, whereas an animal and its offspring were counted as separate words (e.g., para 'cow' and egel 'calf'). Synonyms were counted as two (matos and aviron 'airplane'). If names of numbers were produced on the phonemic task (e.g., shesh 'six'), only the first three were counted, and the same was true for compounds beginning with the same word (e.g., beit-sefer 'school', beit-kneset 'synagogue', beit-xolim 'hospital'). Names of subcategories on the semantic task (e.g., bird) were not given credit if specific items within that subcategory (e.g., dove, eagle) were also provided. Slang terms (e.g., shluk 'sip'), as well as foreign words (e.g., bandana, gangster), were generally acceptable. 2.4. Procedure Each participant was tested individually. Participants (or their parents) first provided demographic information, and individuals over age 65 completed the Mini-Mental State Examination (Folstein et al., 1975). Participants were then presented with each of the two stories for immediate recall. After immediate recall, there was a delay of 25−35 min, during which participants performed four tasks of numerical knowledge, as well as the digit span task. Following this delay, participants were asked to retell the two stories, and then to answer the recognition questions. The order of the two stories was the same for all participants. The two fluency tasks were administered after the completion of the story recall task. The order of the three letters, as well as the order of the three semantic categories, was constant across participants, and the semantic task followed the phonemic task. The entire testing session lasted between 40 min and 1 h. 2.5. Data analysis We report (1) mean raw scores on all tasks, (2) correlations between memory and fluency tasks, (3) analyses of story recall across the four childhood age groups, and (4) a series of regression analyses. We test our hypotheses with regressions that predict phonemic and semantic fluency scores, using age, scores on the delayed story recall task, and scores on the digit span task as predictors, separately for children and adults. We entered education as a predictor to the regression analyses of the adult sample only. To interpret these regressions, we also present correlations between predictors and outcome measures. As each of the four childhood age groups performed a different version of the story recall task, we conducted several auxiliary analyses to examine whether we can combine the childhood sample. We calculated the correlations between raw scores on the delayed story recall task and the fluency tasks in each age group to validate the correlations within the combined sample. We then used two one-way analyses of variance (ANOVA) to compare raw scores on the delayed story recall task and percentage scores across the four childhood age groups. 3. Results Due to various technical reasons (e.g., administration errors, loss of forms), data were missing for seven participants for the entire story recall task, for one participant on the delayed recall and recognition tasks, and for 12 other participants on the recognition task alone. One 5-year-old child could not complete the digit span task, and another 5-year-old child completed the forward task but lost patience on the backward task. Four 5-year-old children could not understand the phonemic fluency task, and one 10-year-old child did not perform this task as well. First, we calculated the mean raw scores for each age group on the story recall task (Table 2), as well as on the digit span and phonemic and semantic verbal fluency tasks (Table 3).
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Table 2 Characteristics of the story recall task and mean number of responses, by age group. Age group
5–7 8–10 11–13 14–16 17–23 24–29 30–39 40–49 50–59 60–69 70–79 80+
Maximum Information units
Maximum Recognition questions
Immediate recall
Delayed recall
Recognition
Percent saving
N
N
Mean
SD
Mean
SD
Mean
SD
Mean
SD
27 32 38 45 50 50 50 50 50 50 50 50
16 20 24 28 32 32 32 32 32 32 32 32
18.45 19.72 24.48 25.74 30.00 27.61 29.52 29.32 29.19 27.19 24.12 24.00
3.31 4.45 5.01 4.65 5.29 6.53 5.16 5.60 5.49 4.59 6.65 6.15
16.82 18.59 23.24 24.19 27.77 25.48 26.87 24.61 26.71 24.66 20.91 20.52
4.60 4.48 5.07 5.34 5.98 6.29 5.46 5.28 5.25 4.97 6.73 5.86
14.81 16.31 21.45 23.23 26.55 25.35 26.27 26.13 26.94 26.39 23.39 24.84
1.65 2.35 2.13 2.50 2.90 3.03 3.27 3.29 2.76 3.05 3.58 3.44
90.49 94.45 95.20 94.55 92.30 92.60 90.68 84.54 91.75 90.71 86.65 86.14
18.36 15.13 11.17 14.64 9.44 11.08 8.14 12.19 9.53 10.39 11.15 13.58
Note: In each age group, numbers refer to both stories together. The percent saving score represents the number of information units recalled after a delay divided by the number of information units recalled immediately after presentation.
Table 3 Responses on the digit span and fluency tasks, by age group. Age group
5–7 8–10 11–13 14–16 17–23 24–29 30–39 40–49 50–59 60–69 70–79 80+
Digit forward
Digit backward
Digit span total
Phonemic fluency
Semantic fluency
Mean
SD
Mean
SD
Mean
SD
Mean
SD
Mean
SD
6.72 8.38 8.84 9.39 11.10 10.87 10.35 11.52 10.42 10.03 9.68 8.77
1.73 1.77 1.46 2.16 2.75 2.33 2.37 1.79 1.86 2.63 1.70 1.73
2.61 4.75 5.38 7.03 8.03 7.23 7.58 7.94 7.19 6.41 6.15 5.94
1.38 1.90 1.62 2.24 2.29 2.59 2.03 2.25 2.09 2.03 1.74 1.36
9.35 13.13 14.22 16.42 19.13 18.10 17.94 19.45 17.61 16.44 15.82 14.71
2.50 2.92 2.54 3.56 4.43 4.37 3.80 3.67 3.35 4.29 2.82 2.83
10.48 23.00 29.78 38.74 44.45 40.29 42.97 42.77 41.35 45.88 38.53 34.48
5.73 6.26 8.46 9.35 10.05 10.57 9.59 9.11 11.04 13.39 11.96 11.91
31.18 38.59 50.75 55.00 58.52 56.68 61.35 59.00 64.68 58.56 52.18 47.35
9.14 8.31 9.82 11.56 8.80 11.87 9.23 9.60 9.34 12.71 7.66 11.29
Note: Scores on the digit span task represent the number of series repeated correctly. Scores on the fluency tasks represent the sum of words generated on all three letters together or on all three categories together.
Second, we analyzed the correlations between the raw scores on all four tasks, for all children together, for all adults together, and separately for each of the childhood age groups (Table 4). In the combined childhood sample as well as in the adult sample, a partial correlation between delayed story recall and digit span scores with age as a covariate was not significant. Similarly, the correlations between delayed story recall and digit span scores were not significant in any of the four childhood age groups. Table 4 shows that the two fluency tasks were significantly and positively correlated in all age groups, except for the youngest group. There were significant positive correlations between the raw scores on the delayed story recall and both fluency tasks in all age groups, with the exception of the 8–10 age group, in which the correlation between delayed story recall and phonemic fluency did not reach significance. The correlation between digit span and fluency scores was significant in both the combined childhood sample and in the adult sample for both fluency tasks, as seen in Table 4. Third, we conducted two one-way ANOVAs to examine whether it was possible to combine children data. As each test version presented a different maximum number of information units, raw scores increased with age. To test whether this increase was significant, we conducted an ANOVA with raw scores as the outcome measures. This analysis revealed the expected group difference, F (3, 118) = 16.661, p < .001, indicating that older children recalled more information units. As task construction attempted to equate the difficulty level of the various versions by increasing the number of words and information units to fit the increased age of the participants, we wanted to test whether this attempt was successful. Thus, for each participant we calculated percentage scores relative to the maximum number of information units in the relevant test version. We then conducted an ANOVA that compared percentage scores across age groups, finding no significant group differences, F (3, 118) = 2.264, p = .085. This difficulty equivalence allowed us to examine performance of the four childhood age groups together in the regression analyses. Fourth, to examine the contribution of delayed story recall and working memory together to the prediction of fluency scores, we 5
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Table 4 Correlations between memory tasks and verbal fluency tasks, by age group. Age group
Task
5–7
Phonemic fluency
Semantic fluency Delayed recall Digit span Semantic fluency Delayed recall Digit span Semantic fluency Delayed recall Digit span Semantic fluency Delayed recall Digit span Age Semantic fluency Delayed recall Digit span Age Education Semantic fluency Delayed recall Digit span
8–10 11–13 14–16 All children
Adults
Semantic fluency
N
r
N
29 29 28 31 28 31 32 29 32 31 31 31 123 123 117 122 252 252 252 250 252
.164 .420* .281 .400* .370 .176 .636** .463* −.109 .531** .370* .175 .793** .744** .613** .565** −.169** .228** .591** .264** .253**
r — .494** .157 — .503** .203 — .542** .077 — .366* −.014 .695** — .659** .478** −.274** .259** — .430** .216**
33 31 29 32 29 32 31 31 128 122 126 252 252 250 252
Note: The analysis of the delayed recall scores used the raw number of information units on the delayed story recall task. * p < .05; ** p < .001.
ran two regression analyses for children and adults, one for each fluency task (see Tables 5 and 6). The analyses of the combined childhood sample included age as an additional predictor, and the analyses of the adult sample included education level in addition to age. In light of the ANOVA results, we ran the regression analyses of the combined childhood data, using the percentage scores on the delayed story recall task as the predictor. Table 5 shows that in the combined childhood sample, the model explained 68.9 % of the variance in phonemic fluency scores. Age was the strongest predictor but delayed story recall explained a significant share of the variance as well. Digit span scores had no contribution to the prediction of phonemic fluency in the combined childhood sample. Very similar results emerged when using raw scores on the delayed story recall instead of percentage scores. Within the adult sample, the regression model explained 40.9 % of the variance in phonemic fluency. Education, story recall, and digit span scores significantly predicted the phonemic fluency score, whereas the contribution of age was not significant. Table 6 shows that in the combined childhood sample, the model explained 57.3 % of the variance in semantic fluency scores. Using raw scores on the delayed story recall task rather than percentage scores showed the same pattern of results. Within the adult sample, the regression model explained 25.8 % of the variance in semantic fluency. In both analyses, age and delayed story recall explained a significant share of the variance in semantic fluency scores, unlike digit span scores whose contribution was not significant.
Table 5 Linear regressions predicting phonemic fluency scores. Age group
Predictor
β
t
p
R2
F
df
p
Children
Age Delayed recall Digit span Age Education Delayed recall Digit span
.784 .234 .098 −.055 .192 .183 .179
11.178 4.334 1.410 −.826 3.182 2.859 2.813
< .001 < .001 .161 .409 .002 .005 .005
.689
82.804
3, 112
< .001
.145
10.379
4, 245
< .001
Adults
Note: Within the childhood sample, the regression analysis used the percentage of information units on the delayed story recall task relative to the maximum number of units on this task for the given age group. Within the adult sample, the regression analysis used the raw number of information units on the delayed story recall task.
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Journal of Communication Disorders 83 (2020) 105968
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Table 6 Linear regressions predicting semantic fluency scores, by age group. Age group
Predictor
β
t
p
R2
F
df
p
Children
Age Delayed recall Digit span Age Education Delayed recall Digit span
.727 .330 .048 −.151 .221 .335 .077
8.787 5.304 .581 −2.436 3.930 5.616 1.302
< .001 < .001 .562 .016 < .001 < .001 .194
.573
51.926
3, 116
< .001
.258
21.320
4, 245
< .001
Adults
Note: Within the childhood sample, the regression analysis used the percentage of information units on the delayed story recall task relative to the maximum number of units on this task for the given age group. Within the adult sample, the regression analysis used the raw number of information units on the delayed story recall task.
4. Discussion Our results show significant associations between performance on a delayed story recall task and performance on both phonemic and semantic fluency tasks across all age groups, whereas the associations between digit span scores and fluency scores were less robust. In childhood, digit span scores associated with both phonemic and semantic fluency scores in the combined sample, as reported in previous research on adults that found a connection between working memory and verbal fluency (e.g., Rosen & Engle, 1997; Ruff et al., 1997). However, in our childhood sample, this association was due to the increase in age, and it disappeared in the regression models that included age as a predictor, as well as in the separate correlation analyses of the four childhood age groups. It is possible that an underdeveloped working memory (Cowan, 2014) cannot support fluency performance in childhood, or that working memory is useful only after children reach adult level in other relevant skills, such as vocabulary. The finding that working memory did not significantly predict either fluency task in childhood suggests that other underlying processes might be at work. As Kavé and KnafoNoam (2015) have shown, the phonemic and semantic fluency tasks develop along a similar trajectory in childhood, most likely because they reflect an increase in shared cognitive abilities such as executive function, speed of processing, or vocabulary funds, which comes with the increase in age. In adults, once age, education, and delayed recall entered the analysis, digit span scores significantly predicted phonemic but not semantic fluency performance. These results fit well with Gordon et al.’s (2018) findings that the contribution of digit span scores to phonemic fluency remained significant once other cognitive abilities entered the regression, whereas their contribution to semantic fluency disappeared. Importantly, retrieval of words according to a phonemic criterion relies on controlled and ad-hoc assembly of words, whereas retrieval of words according to a semantic criterion reflects spread of activation along existing word associations (Carneiro, Albuquerque, & Fernandez, 2008; Hurks et al., 2010). Therefore, verbal short-term memory might be more important for the phonemic task, in which individuals must hold more information in memory in preparation for word generation. Alternatively, the association between working memory and phonemic fluency might reflect the fact that within the adult sample age did not predict performance on the phonemic task. This finding is in line with previous studies that demonstrated greater aging-related effect for semantic than for phonemic fluency (e.g., Gladsjo et al., 1999; Mathuranath et al., 2003). It is thus possible that the effect of age eliminated the contribution of working memory to the prediction of the semantic but not the phonemic fluency scores. Our results show that delayed story recall contributes to the prediction of both fluency tasks, in children as well as in adults. These findings are in line with the older CHC conceptualization that viewed story recall and fluency tasks as part of the same broad ability of Long-Term Storage and Retrieval (Schneider & McGrew, 2012; Schneider & McGrew, 2013), rather than with the newer model (Schneider & McGrew, 2018). Schneider and McGrew (2018) split the Glr domain into Learning Efficiency versus Retrieval Fluency, so that story recall no longer belongs in the same broad ability with verbal fluency tasks. Our findings of a robust association between delayed story recall and fluency performance in all age groups, and for both fluency tasks, calls this recent split into question. According to Rosen and Engle (1997), verbal fluency involves the generation of cues to access new words. This component requires controlled strategic search that could affect both the retrieval of words on verbal fluency tasks and the retrieval of information from long-term memory on the story-recall task. Had we included other tests of executive and language abilities in our analyses, we might have been able to account for some of the overlap between delayed story recall and verbal fluency tasks through other abilities. Indeed, studies that included such tests stressed that performance on fluency tasks depended less on verbal ability and on word production and more on the ability to store and update relevant information (e.g., Shao et al., 2014). However, as recent neuropsychological conceptualization of fluency performance relates fluency primarily to executive functions (e.g., Aita et al., 2018) rather than to memory, many such previous studies did not include delayed recall tests (see Table 2 in Kraan et al., 2013, for a summary). We acknowledge that the lack of other tests is a limitation of the current study. Future research of the components of fluency tasks should examine the contribution of delayed story recall tasks to fluency performance together with other tests of executive functions and language. Another limitation of our research is the fact that we had different versions of the memory task for each group of children. This is a weakness of our design, but we note that presenting the same story to children of all ages as well as to older adults might have been 7
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problematic in terms of the content and the complexity of the task. In addition, when we compared the percentage of correct responses on the delayed story recall task across the four childhood age groups, we found that the four versions were equivalent. Thus, although younger children recalled fewer items relative to older children, the test led to comparable percentage scores, demonstrating that the four versions were age appropriate. Furthermore, the correlation analyses within each group, and the regression analyses that used percentage and well as raw scores, revealed a similar pattern of associations between delayed story recall and fluency scores. We believe that showing lifespan task associations is a strength of our study, justifying the inclusion of the childhood data in the present paper. Such lifespan data could also serve Israeli practitioners as a reference point against which to base their clinical decisions. In summary, the current study demonstrates that delayed story recall is associated with both phonemic and semantic fluency tasks across a wide age range. It is possible that the association stems from reliance on a directed search component that allows controlled access to targeted information within memory. Thus, retrieval of newly acquired information and fluent retrieval of known words may not be the same type of retrieval, but they may share important components. As many studies of individuals with word retrieval deficits show that they also have impairment in story recall (e.g., Van Den Berg et al., 2017), it is especially important to examine story recall in these populations. Such examination could show that reduced verbal abilities limit performance on memory tasks, but it could also show that the deficit lies in the ability to develop complex search and organization strategies that allow lexical access, even in individuals with normal scores on tests of verbal intelligence (e.g., Barron-Linnankoski et al., 2015). Author statement Both authors contributed equally to research conceptualization and writing. Acknowledgements We thank Irit Barnea, Alon Tavor Ben Melech, Yaron Cohen, Rachel Epstein, Rita Gorokhod, Limor Levy, Alex Manevich, Ayelet Yerushalmi, and Ori Zamsh for their help in data collection and coding. This study was partly supported by the Open University of Israel (grant number 102186), with no involvement in publication. The authors declare no conflict of interests. References Aita, S. L., Beach, J. D., Taylor, S. E., Borgogna, N. C., Harrell, M. N., & Hill, B. D. (2018). Executive, language, or both? 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