Edited by:
Reviewed by:
*Correspondence:
This article was submitted to Cognition, a section of the journal Frontiers in Psychology
This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
We administered a semantic verbal fluency (SVF) task to two groups of children (age range from 5 to 8): 47 diagnosed with Autism Spectrum Disorder (ASD Group) and 53 with typical development (Comparison Group), matched on gender, chronological age, and non-verbal IQ. Four specific indexes were calculated from the SVF task, reflecting the different underlying cognitive strategies used: clustering (component of generativity and lexical-semantic access), and switching (executive component, cognitive flexibility). First, we compared the performance of the two groups on the different SVF task indicators, with the ASD group scoring lower than the Comparison Group, although the difference was greater on switching than on clustering. Second, we analyzed the relationships between the different SVF measures and chronological age, verbal IQ and non-verbal IQ. While in the Comparison Group chronological age was the main predictor of performance on the SVF task, in the ASD Group verbal IQ was the best predictor. In the children with ASD, therefore, greater linguistic competence would be associated with better performance on the SVF task, which should be taken into account in speech therapies designed to achieve improvements in linguistic generativity and cognitive flexibility.
The DSM-5 (
In addition, one of the theoretical frameworks proposed to explain the disorder is the theory of executive dysfunction (
Specifically, the semantic verbal fluency tasks (SVF) consist of asking the subject to produce the greatest possible number of words pertaining to a certain semantic category (e.g., animals, fruits, jobs, kitchen utensils…) within a certain time limit, usually 60 s. Although performance of these types of tasks has usually been evaluated based on the total number of correct words produced within the established time period, some studies have also used specific indicators of the cognitive strategies used to complete these fluency tasks successfully (
Most studies using fluency tests have been carried out in adults, showing a strong relationship between performance on these tasks and educational (
In the case of children with ASD, most studies have only used the overall indicator of VF, that is, the total number of correct words produced, finding worse performance than that of children with typical development in some cases (
Objectives and hypotheses of the present study aimed to:
Compare the performance of a group of children with ASD (ASD Group) to that of a group of children with typical development (Comparison Group) -matched on gender, chronological age and non-verbal IQ with the ASD Group- on an SVF task, both for the total number of correct words produced and for the specific indicators of clustering and switching. The performance on the different SVF task indicators evaluated would be expected to be lower in the ASD Group than in the Comparison Group, given the limited linguistic competence and the supposed EF impairment in the ASD group. However, we expect these differences to be greater on switching than on clustering, in agreement with the prediction made by
Analyze, in each group of children separately, the relationship between the SVF measures and chronological age, verbal IQ and non-verbal IQ. A significant relationship would be expected between the performance on the different indicators obtained on the SVF task and chronological age and verbal IQ, given that VF is a skill that includes executive and linguistic components. We think these components would improve with the increase in the child’s linguistic competence that takes place due to the maturational processes associated with age and learning processes. We expect the correlations with age to be greater in the case of switching than in clustering, as in the results obtained by
In the present study, participants consisted of a total of 100 children, with ages ranging from 5 to 8 years old, and non-verbal IQ ranging from 75 to 135 on the Raven test (
Initially, the ASD Group was composed of a total of 67 children from the 18 schools that voluntarily agreed to participate, but 20 children were excluded from the study for different reasons, such as not being able to understand the Raven test, not having oral language, or not receiving their parents’ informed consent. The Comparison Group was initially made up of 350 children who attended 11 of the 18 schools where the children with ASD were enrolled. The two groups of children were matched one-to-one on non-verbal IQ, chronological age and gender, so that of the initial 350 children without ASD, 53 were selected.
No statistically significant differences were found between the two groups of children on gender (χ2 = 0.279,
This study is part of a broader investigation that was approved and funded by the University of Valencia and had the official and written authorization of the General Direction and School Management (Valencia Education, Training and Employment Department). All of the Valencian state schools with TEACCH integrated classrooms were invited, via an informative meeting, to participate in the research. From the schools that voluntarily agreed to participate, some classrooms of 5- to 8-year-old children were selected. The parents of the children gave written informed consent to participate in the research.
Each child’s non-verbal IQ, verbal IQ and SVF task performance were individually evaluated by the school psychologist in a noise- and distraction-free office. In all cases, the tasks were administered on different days and in the same order (first: non-verbal IQ task, second: verbal IQ task, and third: SVF task). Information about autism symptoms was obtained from the GARS-2, by means of an interview with the parents of the ASD Group.
This is a non-verbal test administered to children between 4 and 9 years old. It is a measure of reasoning ability that provides an estimation of the deductive capacity and the “
This instrument is widely used to assess receptive vocabulary. It consists of 192 items: the examiner names a word (noun, verb, adjective, etc.), and the child has to point out an image from four images presented. This implies a decision making process, in which EF is also implicated. We chose this instrument in order to estimate the linguistic competence level in the children of our sample, given that it does not require an oral or written response, and it is a test that can be rapidly administered and utilized to evaluate people with language problems. Thus, according to
This is a screening scale that provides a norm-referenced measure that helps to identify autism and estimate its severity. It can be filled out by professionals or parents of people between 3 and 22 years old. Based on the DSM-IV-TR diagnostic criteria (
The task on this ITPA subscale consists of eliciting as many words from a specific semantic category as possible within a time limit of 60 s. It has four different categories: words, body parts, animals, and fruits. For this study, only the semantic category of animals was used, as it is one of the most widely used semantic categories in SVF studies (
The definition of the different types of semantic subcategories and the calculation of the indicators was carried out according to the criteria proposed by
Next we present, as an example, a word list generated by one of the participants: cat, cow, chicken, owl, elephant, tiger, lion, leopard, ant, spider, fly, whale, and shark. In this case, the total number of correct words was 13, the number of switches was 4, the number of clusters was 4, and the average size of the clusters was 2. All the calculations were made independently by two of the authors of the manuscript, who were blind to group membership. Inter-rater reliability was calculated for each indicator using Pearson correlation coefficients, with all the correlations above 0.9 and significant at the 0.01 level.
Analyses were performed with the SPSS statistical package, version 19 for Windows. First, multivariate analyses of variance (MANOVA) were carried out to compare the SVF measures for the ASD Group and the Comparison Group. Second, for each group separately, Pearson correlation analyses were conducted of the SVF measures and chronological age, non-verbal IQ and verbal IQ. The results of these analyses suggest that the key variables were chronological age and verbal IQ; therefore, we performed Pearson correlation analyses to study the relationship between them in each group. Additionally, in order to investigate whether there was any association between the severity of the autism symptomatology and the SVF performance, Pearson correlation analyses were conducted of autism severity (the global index or AI score, obtained from the Gars-2) and the SVF measures in the ASD Group. Finally, to investigate whether these factors contributed significantly to the explained variance of the SVF measures, in each group separately (ASD Group and Comparison Group), we performed several hierarchical regression analyses, one for each SVF measure. In the case of the ASD Group, the SVF measure correlations are only statistically significant with verbal IQ, but not with the other variables. For this reason, non-verbal IQ, chronological age and gender were entered as covariates in the first step, and then verbal IQ was entered as a predictor variable in the second step, in order to find out the percentage of variance of each of the SVF measures explained by verbal IQ. In the case of the Comparison Group, the SVF measure correlations were only statistically significant with chronological age, but not with the other variables. Therefore, non-verbal IQ, verbal IQ, and gender were entered as covariates in the first step, and then chronological age was entered as a predictor variable in the second step, in order to find out the percentage of variance of each of the SVF measures explained by chronological age.
The MANOVA performed with the scores obtained on the SVF measures revealed statistically significant differences between the ASD Group and the Comparison Group [Wilk’s Lambda (λ) = 0.82;
Means, standard deviations, and
ASD |
Comparison |
||||||
---|---|---|---|---|---|---|---|
Total correct words | 5.98 | 4.28 | 9.17 | 3.25 | 17.83** | 0.000 | 0.154 |
Number of clusters | 1.55 | 1.29 | 2.47 | 1.18 | 13.66** | 0.000 | 0.122 |
Average cluster size (or clustering) | 1.63 | 1.74 | 2.23 | 1.13 | 4.23* | 0.042 | 0.041 |
Number of switches (or switching) | 2.57 | 2.38 | 4.00 | 1.74 | 11.83** | 0.001 | 0.108 |
Pearson correlations were carried out to examine the relationship between the SVF measures and chronological age in the two groups separately. Regarding the ASD group, there were no statistically significant correlations. Regarding the Comparison Group, all the SVF measures -with the exception of the average cluster size, or clustering- showed a statistically significant correlation with chronological age (
Correlations between the semantic verbal fluency measures (total correct words, number of clusters, average cluster size, and number of switches) and chronological age (CA), non-verbal IQ (NVIQ), and verbal IQ (VIQ), in ASD Group and Comparison Group.
ASD |
Comparison |
||||||
---|---|---|---|---|---|---|---|
CA | NVIQ | VIQ | CA | NVIQ | VIQ | ||
Total correct words | -0.081 | 0.083 | 0.519** | 0.329* | 0.148 | 0.162 | |
0.589 | 0.578 | 0.000 | 0.016 | 0.289 | 0.246 | ||
Number of clusters | -0.276 | 0.158 | 0.407** | 0.502** | 0.009 | 0.053 | |
0.060 | 0.290 | 0.005 | 0.000 | 0.951 | 0.706 | ||
Average cluster size (or clustering) | -0.106 | 0.224 | 0.357* | -0.179 | 0.157 | 0.199 | |
0.479 | 0.131 | 0.014 | 0.199 | 0.260 | 0.153 | ||
Number of switches (or switching) | 0.068 | -0.046 | 0.408** | 0.285* | 0.081 | -0.007 | |
0.651 | 0.757 | 0.004 | 0.038 | 0.564 | 0.958 |
Hierarchical regression analyses for gender, CA, NVIQ, and VIQ predicting the semantic verbal fluency measures in the Comparison Group.
Variables | Adjusted |
β | |||
---|---|---|---|---|---|
Step 1 | |||||
Gender, VIQ, NVIQ | 0.03 | 0.03 | 0.61 | ||
Step 2 | |||||
Gender, VIQ, NVIQ | 0.03 | 0.03 | 0.61 | ||
CA | 0.26 | 0.22 | 0.13 | 0.55** | 0.00** |
Step 1 | |||||
Gender, VIQ, NVIQ | 0.00 | 0.00 | 0.98 | ||
Step 2 | |||||
Gender, VIQ, NVIQ | 0.00 | 0.00 | 0.98 | ||
CA | 0.36 | 0.35 | 0.06 | 0.69** | 0.00** |
Step 1 | |||||
Gender, VIQ, NVIQ | 0.04 | 0.04 | 0.51 | ||
Step 2 | |||||
Gender, VIQ, NVIQ | 0.04 | 0.04 | 0.51 | ||
CA | 0.05 | 0.01 | -0.01 | -0.11 | 0.48 |
Step 1 | |||||
Gender, VIQ, NVIQ | 0.06 | 0.06 | 0.34 | ||
Step 2 | |||||
Gender, VIQ, NVIQ | 0.06 | 0.06 | 0.34 | ||
CA | 0.18 | 0.11 | 0.05 | 0.39* | 0.01* |
Coefficients of the variables in the regression models from
Step 1 |
Step 2 | |||||||
---|---|---|---|---|---|---|---|---|
Variables | β | β | ||||||
Constant | 4.18 | 3.79 | 1.10 | -12.30 | 5.47 | -2.24* | ||
Gender | 0.44 | 1.18 | 0.05 | 0.38 | -0.03 | 1.05 | -.00 | -0.03 |
VIQ | 0.01 | 0.031 | 0.08 | 0.51 | 0.06 | 0.03 | 0.33 | 2.12* |
NVIQ | 0.02 | 0.034 | 0.12 | 0.78 | 0.04 | 0.03 | 0.18 | 1.31 |
CA | 0.13 | 0.03 | 0.55 | 3.81** | ||||
Constant | 2.17 | 1.40 | 1.55 | -5.39 | 1.85 | -2.90** | ||
Gender | -0.02 | 0.43 | -0.01 | -0.06 | -0.24 | 0.35 | -.08 | -0.69 |
VIQ | -0.00 | 0.01 | -0.02 | -0.11 | 0.02 | 0.01 | 0.30 | 2.05* |
NVIQ | 0.00 | 0.01 | 0.06 | 0.38 | 0.01 | 0.01 | 0.13 | 1.04 |
CA | 0.06 | 0.01 | 0.69 | 5.17** | ||||
Constant | 0.57 | 1.32 | 0.43 | 1.77 | 2.16 | 0.82 | ||
Gender | -0.05 | 0.41 | -0.02 | -0.14 | -0.02 | 0.41 | -0.00 | -0.05 |
VIQ | 0.00 | 0.01 | 0.09 | 0.55 | 0.00 | 0.01 | 0.03 | 0.20 |
NVIQ | 0.01 | 0.01 | 0.16 | 1.01 | 0.01 | 0.01 | 0.14 | 0.91 |
CA | -0.01 | 0.01 | -0.11 | -0.70 | ||||
Constant | 1.90 | 2.00 | 0.95 | -4.49 | 3.07 | -1.46 | ||
Gender | 1.06 | 0.62 | 0.24 | 1.71 | 0.88 | 0.59 | 0.20 | 1.49 |
VIQ | 0.00 | 0.01 | 0.05 | 0.31 | 0.02 | 0.01 | 0.23 | 1.41 |
NVIQ | -0.00 | 0.01 | -0.03 | -0.20 | 0.00 | 0.01 | 0.01 | 0.08 |
CA | 0.05 | 0.02 | 0.39 | 2.63* |
Pearson correlations were carried out to examine the relationship between the SVF measures and verbal IQ in the two groups separately. Regarding the ASD group, all the SVF measures showed a statistically significant correlation with verbal IQ. Regarding the Comparison Group, there were no statistically significant correlations (
Hierarchical regression analyses for gender, CA, NVIQ, and VIQ predicting the semantic verbal fluency measures in the ASD Group.
Variables | Δ |
β | |||
---|---|---|---|---|---|
Step 1 | |||||
Gender, CA, NVIQ | 0.01 | 0.01 | 0.93 | ||
Step 2 | |||||
Gender, CA, NVIQ | 0.01 | 0.01 | 0.93 | ||
VIQ | 0.31 | 0.30 | 0.14 | 0.65** | 0.00** |
Step 1 | |||||
Gender, CA, NVIQ | 0.09 | 0.09 | 0.24 | ||
Step 2 | |||||
Gender, CA, NVIQ | 0.09 | 0.09 | 0.24 | ||
VIQ | 0.21 | 0.12 | 0.02 | 0.41* | 0.01* |
Step 1 | |||||
Gender, CA, NVIQ | 0.06 | 0.06 | 0.44 | ||
Step 2 | |||||
Gender, CA, NVIQ | 0.06 | 0.06 | 0.44 | ||
VIQ | 0.14 | 0.08 | 0.03 | 0.34* | 0.04* |
Step 1 | |||||
Gender, CA, NVIQ | 0.00 | 0.00 | 0.94 | ||
Step 2 | |||||
Gender, CA, NVIQ | 0.00 | 0.00 | 0.94 | ||
VIQ | 0.26 | 0.26 | 0.07 | 0.60** | 0.00** |
Coefficients of the variables in the regression models from
Step 1 |
Step 2 | |||||||
---|---|---|---|---|---|---|---|---|
Variables | β | β | ||||||
Constant | 6.43 | 6.95 | 0.92 | 0.96 | 5.97 | 0.16 | ||
Gender | -0.01 | 0.05 | -0.04 | -0.28 | -0.00 | 0.04 | -0.00 | -0.02 |
CA | -0.32 | 1.85 | -0.02 | -0.17 | 0.37 | 1.56 | 0.03 | 0.24 |
NVIQ | 0.01 | 0.03 | 0.06 | 0.38 | -0.05 | 0.03 | -0.26 | -1.62 |
VIQ | 0.14 | 0.03 | 0.65 | 4.35** | ||||
Constant | 3.62 | 2.02 | 1.79 | 2.57 | 1.94 | 1.32 | ||
Gender | -0.02 | 0.01 | -0.23 | -1.39 | -0.02 | 0.02 | -0.20 | -1.28 |
CA | -0.41 | 0.54 | -0.11 | -0.76 | -0.28 | 0.51 | -0.07 | -0.54 |
NVIQ | 0.01 | 0.01 | 0.07 | 0.43 | -0.01 | 0.01 | -0.13 | -0.78 |
VIQ | 0.03 | 0.01 | 0.41 | 2.55* | ||||
Constant | -0.66 | 2.758 | -0.23 | -1.84 | 2.72 | -0.67 | ||
Gender | -0.00 | 0.02 | -0.03 | -0.22 | -0.00 | 0.02 | -0.01 | -0.08 |
CA | 0.49 | 0.73 | 0.10 | 0.67 | 0.64 | 0.71 | 0.13 | 0.90 |
NVIQ | 0.02 | 0.01 | 0.20 | 1.22 | 0.00 | 0.02 | 0.03 | 0.15 |
VIQ | 0.03 | 0.01 | 0.35 | 2.06* | ||||
Constant | 2.42 | 3.87 | 0.62 | -0.38 | 3.445 | -0.11 | ||
Gender | 0.01 | 0.03 | 0.07 | 0.42 | 0.02 | 0.02 | 0.11 | 0.77 |
CA | -0.40 | 1.03 | -0.06 | -0.39 | -0.04 | 0.90 | -0.00 | -0.05 |
NVIQ | -0.00 | 0.02 | -0.01 | -0.06 | -0.03 | 0.02 | -0.31 | -1.86 |
VIQ | 0.07 | 0.02 | 0.60 | 3.87** |
Pearson correlations were carried out to examine the relationship between the SVF measures and non-verbal IQ in the two groups separately. There were no statistically significant correlations in either of the two groups (
Pearson correlations were carried out to examine the relationship between chronological age and verbal IQ in the two groups separately. Regarding the Comparison Group, there was a statistically significant correlation (
Pearson correlations were carried out to examine the relationship between the SVF measures and autism severity in the ASD Group. We did not find associations between autism severity and any of the measures from the SVF task (
Verbal fluency tasks are linguistic production tasks that are considered a good indicator of EF (
The first objective of our study was to compare the performance of the two groups of children on the different specific indicators of the SVF task. As expected, on all the SVF measures, the ASD Group performed worse than the Comparison Group, showing more limited skills on linguistic generativity and cognitive flexibility. The children in the ASD Group produced a lower total number of correct words than the children in the Comparison Group, and they also made fewer jumps, changes or switches from one semantic subcategory to another (switching) and, therefore, obtained fewer clusters. Regarding the average cluster size (or clustering), the difference between the two groups, although reaching statistical significance, was much smaller than in the case of the other measures. Therefore, although there were differences between the two groups in the relatively automatic process of recovering information stored in the semantic long-term memory and producing words, the differences were much greater in the processes of strategic search, cognitive flexibility and set shifting, which, in a more controlled and conscious way, are necessary when performing a SVF task. In summary, switching -as the executive component involved in the task- would be especially affected, in agreement with the theory of executive dysfunction in ASD (
On some of the SVF task indicators, the results we obtained would agree with the
The second objective of our study was to analyze, in each group separately, the relationships of the different specific SVF indicators with chronological age, verbal IQ and non-verbal IQ. In the case of the Comparison Group, we obtained significant correlations between age and the measures of the total number of correct words produced, the number of clusters, and the number of switches (or switching), but not with the average cluster size (or clustering). These results would support the idea that, at the ages considered, there would be an increase in the total production, the use of a greater number of clusters, and more jumps (or switches) made, given that there would be an increase in the capacity to change or jump from one subcategory to another (switching), while the clustering component (the number of words in the same subcategory) would remain stable (
Moreover, in the Comparison Group, we did not obtain a relationship between any of the specific SVF indicators and verbal IQ or non-verbal IQ. In the age ranges considered, IQ does not seem to be associated with performance on the SFV task in children with typical development, as revealed in the results of the multiple regression analyses. Chronological age was the only variable that explained a statistically significant percentage of variance in the specific measures of SVF (with the exception of the average cluster size, or clustering). Therefore, in the children with typical development, age was an important variable in predicting the total number of correct words produced, as well as the number of clusters and switches (or switching), but not the size of the clusters (or clustering). However, verbal and non-verbal IQ were not relevant variables in predicting the performance on any of the indicators obtained from the SVF task. With regard to age, the results obtained in this study reinforce those obtained by previous studies in children with typical development, where a direct relationship was found between the performance on VF tasks and age (
Additionally, the results of the analysis of the relationship between chronological age and verbal IQ in the Comparison Group indicated an inverse relationship between the two variables, which would support the idea that the improvement in the performance on the SVF task that is produced with greater chronological age would not be associated with higher verbal IQ. Given that, in addition, the switching component is related to age, but the cluster component is not, we hypothesized that the improvement in the executive component – and not the linguistic one- would be the main factor associated with the SVF task improvement that occurs with age in this group.
In the case of the ASD Group, neither chronological age nor non-verbal IQ correlated with any of the measures obtained on the SVF task. In this group, verbal IQ correlated significantly with all the measures obtained from the SVF task: total number of correct words produced, number of switches (or switching), number of clusters, and average cluster size (or clustering). In the age range considered, neither age nor non-verbal IQ seems to be associated with performance on the SFV task in children with ASD, as the results of the multiple regression analyses revealed. Verbal IQ was the only variable that explained statistically significant percentages of variance in each of the specific measures from the SVF. Therefore, in the children with ASD, only linguistic skills were shown to be an important variable in predicting performance on the SVF task.
In addition, the results of the analysis of the relationship between chronological age and verbal IQ in the ASD Group indicates an inverse relationship- almost marginally significant- between the two variables, which would support the idea that the improvement in the performance on the SVF task that occurs with a higher verbal IQ would not be associated with chronological age. Furthermore, the absence of a relationship between the performance on the SVF task and the severity of the autism symptomatology also supports the idea that the level of linguistic competence or ability would be the main factor associated with the performance on the SVF task in the ASD Group, given that other factors such as age and autism severity were not found to be associated with the task performance in this group.
In summary, while in the group of children with typical development, age was the main predictor of performance on the SVF task, in the case of the children with ASD, age alone was not a significant predictor of performance on the SVF task, but verbal IQ was. Although, we believe it would be advisable to carry out future studies along the same lines in order to verify the reach of these conclusions, the idea seems important that, in children with ASD, greater linguistic competence would be associated with better performance on the SVF task. This leads us to hypothesize that to achieve improvements in children with ASD on linguistic generativity, semantic-lexical access, cognitive flexibility and search strategies- the most relevant skills involved in SVF task performance-, learning processes and the development of linguistic skills through adequate intervention would be more important than maturation due to age. Therefore, speech therapy and verbal language stimulation therapies for children with ASD should be directed not only toward pragmatic aspects (communicative and functional aspects of language), but also toward other aspects of language that are necessary to reach good linguistic competences (e.g., morphological and syntactic aspects). These intervention techniques could contribute to strengthening the necessary cognitive skills to improve the performance of children with ASD on SFV tasks.
In any case, it would be interesting to investigate the mutual influence between executive and language impairments. In this sense, some hypotheses have been proposed, such as the language mediation hypothesis of executive dysfunctions in autism (
Our study presents some limitations. First, not all of the autism spectrum disorder was represented because children with serious behavioral problems or very low cognitive functioning were not part of the sample. One of our objectives was to study the relationship between the different indicators of the SVF task and IQ, but it is important to note that there were no participants in this study with a non-verbal IQ under 75. Second, there is no information about whether the children had received or were receiving speech therapy or any other treatments at the time of the evaluation. Third, this research used cross-sectional data, so that it did not study the variables over time. Finally, this research did not include a comparison group with a different psychological disorder -e.g., ADHD-, and so we cannot definitively conclude that the group differences were unique to autism.
Conceived and designed the work: M-IF-A, GP-C, and MF-A. Acquired data: M-IF-A, GP-C, and FG-S. Coded data: MF-A and GP-C. Corrected data: MF-A and FG-S. Analyzed data: M-IF-A and GP-C. Interpreted data: M-IF-A, GP-C, MF-A, and FG-S. Wrote the paper: M-IF-A, GP-C, and MF-A. Drafted the article and revised it critically: GP-C and FG-S.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The reviewer ZS and handling Editor declared their shared affiliation, and the handling Editor states that the process nevertheless met the standards of a fair and objective review.
The authors thank the families, the children, and the schools for their participation in this research, and the Generalitat Valenciana Government (Spain) for providing the required financial resources [grant number GV/2014/066].