Skip to main content
NCBI home page
International Journal of Developmental Disabilities logoLink to International Journal of Developmental Disabilities
. 2023 Feb 28;70(8):1433–1440. doi: 10.1080/20473869.2023.2182897

An experimental study on the categorization ability of children with moderate intellectual disabilities in China

Wangqian Fu 1,✉, Huixing Chen 2, Yawen Xiao 3, Cui Yin 3
PMCID: PMC11660301  PMID: 39713514

Abstract

Background

Little is known about the categorization ability of children with intellectual disabilities (ID) in China, which is critical in guiding teaching practice and learning support strategies for those students. The study has aimed to explore the characteristics of categorization ability of children with ID.

Method

This study used an experimental method to compare the classification ability of children with moderate ID (n = 40, average age of 10.6, average IQ = 47.03), typically developing children of the same age (n = 40, average age of 10.5), and kindergarten middle-class children (n = 20, average age of 6.2) to find differences in categorization abilities, including selecting items in the same category, excluding items from a different category, and direct classification.

Findings

We found that the classification capacity of 10-year-old children with moderate ID was significantly lower than that of typically developing children of the same age. There was no significant difference in the total classification score between 10-year-old children with moderate ID and 6-year-old typically developing children, while there were differences in the task of direct classification. Besides, there was a significant positive correlation between IQ and classification capability in children with moderate ID.

Conclusion

Educators should develop corresponding educational programs based on the cognitive level and categorization ability characteristics of children with moderate ID.

Keywords: children with intellectual disabilities, classification capability, pattern of classification

Categorization is a vital cognitive skill that allows individuals to structure the world by classifying things according to their common attributes and characteristics, and adapting to the changing environment (Vanpaemel and Bayer 2021). It is a basic cognitive process that is used to organize and understand the world around us, and it is therefore, fundamental (Igier and Pennequin 2022). As an important cognitive activity for effectively sensing the world, categorization promotes internal and external communication and coordination by comparing and simplifying the processes of understanding, processing, and extracting things (Hahn and Ramscar 2001). Conducting research on children’s categorization ability can help to understand their cognitive development and provide a scientific basis for education and training.

Intellectual disability (ID) is one of the main neurodevelopmental disorders, characterized by both a significant impairment in general cognitive functioning and adaptive behavior deficits (American Psychiatric Association 2013). In terms of the categorization ability of children with ID, some limited but important studies have found that there are both similarities and differences between children with intellectual disabilities and typical children (Shulman et al. 1995, Yin 2016). The most significant similarity between these two groups of children is that, whether a child has ID or not, both follow a similar developmental sequence of cognitive ability, such as the development of categorization skills (Facon et al. 2012). And when compared with typically developing children, the categorization ability of children with ID is lower than that of them (Rattat and Collié 2020, Zhang and Fang 2009). Due to insufficient experimental research, the characteristics of ID children’s categorization ability are still obscure. This study used a comparative approach to examine behaviors corresponding to the categorization of children with moderate ID, and to compare categorization among children with moderate ID, typically developing children of the same age, and kindergarten middle-class children in one kindergarten.

The different categorization strategies of children

Completing categorization tasks demands that participants find similarities and divide all targets into different types according to some criteria. There are different categorization strategies for children, and similarity-based categorization is an important one. Children mainly categorize stimuli based on the similarity between things. Prototype and exemplar strategies are two of the most prominent strategies for similarity-based categorization (Mo and Chen 2003).

According to prototype theory, prototype is the sum of a set of typical attributes or representations (Eysenck and Keane 2015). Some members are closer to the center of the category, which means that they have either more main features or more similar appearances to the prototype. This makes categorizing typical category members faster than atypical category members (Murphy and Medin 1985), and typical category members are also categorized with higher accuracy than non-typical members (Xue 2018). The exemplar-based view believes that there are multiple samples stored in our minds. When making a category judgment, people do not need to abstract core features (that is, the prototype) from all instances of a category but only compare the new samples with the specific instances or samples of the relevant category stored in their mind (Medin and Schaffer 1978). Even though both prototype and exemplar strategies belong to the similarity-based categorization, they are different, since the former is classified based on the core features while the latter one is based on the samples (Zhang 2015). According to Thierry and Alison’s study, young children tend to use simple clues, including color or shape, to categorize things, such as putting apples and balloons together, but two-year-old children can also use complex clues, such as the names of objects (Thierry and Alison 2000). Young children can learn categories by using examples to represent the overall similarity of categories (Krascum and Andrews 1993). Six-year-old children start to notice highly typical features in categories (Ford 2003), while they are still more likely to apply the exemplar strategy than the prototype strategy (Liu et al. 2012).

Apart from prototype and exemplar strategies, some researchers argue that children can categorize targets based on concepts they have acquired through experience. Thematic relations, scenario-based relations, and taxonomic relations are the main ways that people deal with conceptual knowledge (Hashimoto et al. 2007, Lin and Murphy 2001, Lucariello and Nelson 1985). Thematic relations organize knowledge according to the co-occurrence of the event schema, the external, related, or complementary relationships between things, and the continuous relationships between things in time or space. Scenario-based relationships such as slot-filler relationships rely on a fixed scenario. For example, some researchers vividly describe ‘breakfast’ as a ‘slot’ and bread, milk, and soy milk as ‘filler’. The category is called the script-based slot-filler relationship (Nelson 1988). Taxonomic relations refer to the abstract generalization of things, according to the similarity between things that is used to form different levels of abstract category relations. A word may be used by children in early childhood, but this does not indicate they have already mastered the category concepts of it. They may still categorize based on direct perceptual characteristics (Wang et al. 1964). It is believed that during their development, children transition from using perceptual categorization criteria to using conceptual characteristics (Zhang 2015).

The categorization ability of children with intellectual disabilities

Categorization seems to be deficient in children with intellectual disability (Facon et al. 2012). Children with intellectual disabilities have significantly lower intelligence than average development along with defective behaviors and, hence, have a significant gap in their cognitive abilities such as sensory perception, language, and thinking compared to typically developing children of the same age (Witt and Vinter 2013), which is closely related to their intelligence quotient (IQ) (Zhang et al. 2007). Similar to the developmental stage of typically developing children, individuals with intellectual disabilities gradually shift from categorizing by perceptual characteristics to concepts (Yin 2016). Research has shown that the ability of semantic abstract generalization in children aged 3–6 years old is not yet high, and the abstraction of the level of generalization increases with age, with the continuous transition from concrete to abstraction, and that the ability of theoretical categorization improves (Bovet et al. 2005, Lv et al. 1987, Zhang 2015). Students in lower grades mainly categorize objects by their perceptual characteristics, followed by situations; students in middle and senior grades mainly categorize by concepts, followed by functions. This is in line with the study by Zhang et al. (2007).

When studying the basic color naming and categorization ability of ID children, it has been found that in children with intellectual disabilities, the correct rate of naming increases with age, and they can categorize colors according to lightness. Fang et al. (2011) results are slightly different. The study found that ID children in junior grades basically do not have categorization standards or effective modes for the organization of concepts; ID children in middle grades begin to categorize according to the situation, with slot-filler connections between concepts; and ID children in senior grades form a set of taxonomic relations mainly based on the concept of similarity categorization (Fang et al. 2011). This difference may be related to categorical materials and children’s cognitive level; the higher the structural similarity and association with children’s life experience, the better the ID children perform in categorization tasks (Costanzo et al. 2013, Rattat and Collié 2020). Zhang and Fang (2009) also found that the conceptual level of ID children increases with age, and that the type of conceptual relations changes from slot-filler relations to taxonomic relations. Their concept development has the same trend as typically developing children but lags greatly behind typically developing children in speed and level.

According to Mo and Chen (2003) study about the choice of categorization strategies, categorization strategy selection is based on situational changes. When potential features in the learning context are difficult to master, individuals find it hard to abstract the characteristic dimension in the prototype strategy and will tend to adopt exemplar strategy. When there are multiple rules to choose from, people tend to choose those that are more abstract and general. This argument has not been validated in ID children, but it has been found that ID children generally adapt to learning situations less efficiently than mentally age-matched typically developing children (Witt and Vinter 2013). ID children will encounter more difficulties in choosing categorization strategies based on learning or living contexts. Few studies have compared the development of the categorization abilities of children with different types of ID (Fang et al. 2013). A review study appeared to confirm that autistic individuals have more difficulties performing prototype-distortion tasks than non-autistic individuals (Vanpaemel and Bayer 2021).

The current study

The current categorization research have primarily focused on typically developing youngsters and have made significant strides in the areas of techniques, standards, and the degree of their categorization abilities. Such study has steadily been conducted in the area of special education in recent years, but the focus is quite limited, focusing on the degree of categorizing ability of kids with intellectual disabilities. To inform teaching practices and provide support for kids with intellectual disabilities, it is important to fully comprehend the aspects of categorizing ability in children. This study used an experimental approach to investigate how well middle-class children, children with usual development at the same age, and children with mild intellectual disability could be categorized. We chose to compare the categorization between middle-class children in one kindergarten and ID children aged 10 according to the previous study (Yin 2016, Zhang 2015, Zhang and Fang 2009). It has been concluded that the level of categorization ability of children with IDs aged 9–10 is close to that of children aged 6 years old (Yin 2016, Zhang 2015, Zhang and Fang 2009).

The ability of ID children to categorize was thoroughly investigated in terms of techniques, resources, and comparisons with other groups of kids. The research questions in the study were:

  1. What are the level and characteristics of the categorization ability of ID children?

  2. Is there a relationship between the intelligence quotient (IQ) of ID children and their categorization ability?

Method

Participants

The experimental group in the study included 40 moderate ID children from four special schools for children with intellectual disabilities. There were 28 boys and 12 girls identified with an average age of 10.6, an IQ range of 40–55 (the Wechsler intelligence test IQ of the participants was provided by the schools, which were reported by the official hospitals), and the average IQ was 47.03. The second group consisted of 40 typical students from one primary school in Shanghai. The mean age was 10.5 years, and the male to female ratio was 1:1. A total of 40 typically developing children of this age group were selected as the subjects because they were matched with the experimental group in age. The third group consisted of 20 children from a kindergarten middle class in Shanghai. The mean age was 6.2 years, and the gender ratio was 1:1.

Experimental design and materials

This experiment was a 3*3 mixed experimental design. The independent variables of this experiment were child type (ID children, typical children at the same age and children in a kindergarten middle class) and categorization methods (choose the same kind, exclude categorization and direct categorization), and the dependent variable was the accuracy of the categorization.

These experimental materials included three parts. The materials were pictures of 200 * 150 pixels, and the background was white. All were presented on a laptop using the E-prime platform.

Part one: Choose the same kind

There were eight questions in part one. Four images in two rows were presented at once in each trial. One image was on the first row and the other three pictures were in the second row. Participants were required to choose one image out of the three in the second row which was in the same category with the image in the first row. Each of the three images in the bottom either had the same color, a similar function, or belonged to the same category (e.g. the image of the first row was a yellow dog, and the images in the second row were a giraffe, a bone, and a yellow bike in one trail) (see Figure 1). Participants could get 1 point for selecting the picture with the same color, get 2 points for selecting the picture with connection of the function, and get 3 points for selecting the picture with the same concept. There were a total of eight trails in part one and children scored one point if they answered correctly in one trail. The instruction given to the participants was:

Figure 1.

Figure 1.

Open in a new tab

An example in part one.

Hello. I’m going to invite you to take a small task. Each time you will see four pictures in the screen, one in the top row and three in the bottom row. I want you to look at these pictures carefully and pick one of the three images below that you think is in the same category as the one above. And click on the picture you have selected. If you understand the requirements, please click on the “Start” button to start.

Part two: Excluding categorization

There were sixteen questions in part two and participants scored one point if they answered correctly in one trail. Four images in two rows were presented at once in each trial, and two images were in one row. Three pictures belonged to the same category and one image did not. Participants were required to pick out the image that was not in the same category (e.g. the four pictures were a flower, a basket, a kind of vegetable, and an apple in one trail) (see Figure 2). The instruction given to the participants was:

Figure 2.

Figure 2.

Open in a new tab

An example in part two.

Hello, we are going to move onto the second part of the task. In this part, you will also see four pictures at a time, but this time, you will have to choose one of the four pictures that you think is not in the same category as the other three pictures are. And click on the picture you have selected. If you understand the requirements, please click on the “Start” button to start.

Part three: Direct categorization

There were sixteen trails in part three, with eleven pictures presented at one time in each trail. Children were required to divide them into three categories, including three images in one category, and the other two pictures as interference options. Participants could get 1 point for selecting one category, 2 points for two groups, and 3 points for three groups. All of the images in part three belonged to four categories, including animals, plants, furniture, and transportation (e.g. the pictures in one trail were office chair, strawberry, gopher, bed, yak, lotus, conference table, sparrow, eggplant) (see Figure 3). The instruction given to the participants was:

Figure 3.

Figure 3.

Open in a new tab

An example in part three.

Hello, let’s move on to the final part of the task, in which you’ll see 11 pictures at a time. Nine of the 11 images can be selected and divided into three categories, and each category is of three images. Please drag three pictures that belong to the same category in the same row. Let’s see who can finish them quickly and accurately. If you understand the requirements, please click on the “Start” button to start.

Procedure

Pilot study

Five children of each three groups were chosen to participate the experiment. We considered the participant to have understood the instructions and been able to reply appropriately if they clicked ‘Start’ and then the photographs they selected. The experimenter had to make sure the kids with moderate ID stayed focused on the activity at hand. If a person found the task boring, they may take a break and pick it back up later. Additionally, the experimenter had prepared prizes for the subjects. The experiment’s timing was suitably changed based on the participants’ responses.

Formal experiment

This experiment was a one-to-one individual test. Participants had to reply in accordance with the needs of each phase of the experiment while the investigator ran the computer. We did not control the duration of each trial. The participant was asked to reply (‘Please respond now’) by the experimenter if they had not done so after the stimulus had been displayed on the screen for more than one minute. The entire experiment lasted about 30 min. The kids had their pick of snacks, games, or pencils as a prize for finishing the experiment. Throughout the experiment, the experimenter kept track of the results.

Data processing

Since the students participated in the experiment with the experimenter one-by-one, all of them finished the test and the valid data were 100%. The means of the classification levels and different sorting tasks were calculated and the differences in the classification levels among participants in different sorting tasks were tested with ANOVAs in SPSS software version 20.0.

Results

Comparison of categorization ability among the three groups

The descriptive data of the classification scores of the three groups of children in the experiment are shown in Table 1. The total classification score of typically developing children was higher than the other groups, and the degree of dispersion of typically developing children’s classification score was lower than in the other two groups (SD = 7.73 < 10.39 < 12.62).

Table 1.

Comparison of categorization ability among the three groups.

  Typically developing children
(M±SD)		 ID children
(M±SD)		 kindergarten children
(M±SD)		 F
(df = 97)
Choose the same kind 23.25 ± 1.10 16.93 ± 4.33 17.05 ± 4.38 40.18***
Excluding categorization 12.58 ± 1.77 10.13 ± 4.61 9.45 ± 2.82 7.84***
Direct categorization 39.88 ± 5.98 32.22 ± 6.47 27.45 ± 8.88 25.26***
Total score 75.70 ± 7.73 59.28 ± 10.39 53.95 ± 12.62 42.23***
Open in a new tab

The ANOVA analysis was applied to compare the classification level of three groups of children, indicating that there were significant differences among the three groups of ‘Choose the same kind’ (F = 40.18, p < 0.001), ‘Excluding categorization’ (F = 7.84, p < 0.001) and ‘Direct categorization’ (F = 40.18, p < 0.001).

To further compare the categorization ability of moderate ID children and middle class children in the kindergarten, the Least Significance Difference of post hoc test was applied. The results showed there was no significant difference between ‘Choose the same kind’ (LSD-t = 0.13, p = 0.90 > 0.05) and ‘Excluding categorization’ (LSD-t = 0.74, p = 0.46 > 0.05). There was no significant difference in the total score (LSD-t = 1.96, p = 0.05 > 0.05) between ID children and 6 year-old typically developing children. In the ‘Direct categorization’ part, significant differences were observed (LSD-t = 2.55, p = 0.01 < 0.05).

In terms of the stability of classification, for the categorization criteria in part one (Choose the same kind), 37.5% moderate ID children used three criteria (color, function, and concept) to classify in one task, and 15% kindergarten children used three criteria in one task when choosing similar items. When kindergarteners were asked to arrange three items into one category, they frequently labeled two closely related items at first and failed to think about the third item at the same time. When given this job, for instance, kindergarteners prefer to group ‘police personnel’ with ‘police cars’, then ‘police cars’ and ‘fighter aircraft’. The terms ‘police personnel’, ‘police cars’, and ‘fighter planes’ are consequently placed under the same heading.

The relationship between the categorization ability and IQ of moderate ID children

Pearson correlation analysis was conducted to explore the relationship between the children’s IQ (the report of Wechsler intelligence test) (M = 47.03, SD = 2.33) and their categorization ability. There was significant positive correlation (rdf=39 = 0.66, p = 0.00 < 0.05) between ID children’s IQ and the total score of ‘Choose the same kind’. There was a significant positive correlation (rdf=39 = 0.35, p = 0.04 < 0.05) between IQ and ‘Direct categorization’. And the correlation between IQ and exclusion classification was 0.24 (p = 0.08 > 0.05, df = 39), which was not significant at the 0.05 level. The mean score on the exclusion classification of ID children was 10.13 with 4.61 SD. The data showed there was no ceiling effect or floor effect.

Discussion

Characteristics of the categorization ability of children with moderate ID

We found that the level of categorization ability development of children with moderate ID significantly lags behind that of peers at the same age. This result is consistent with previous studies (Rattat and Collié 2020, Zhang and Fang 2009). ID children have delayed executive development, so they performed poorly on the categorization task (Rattat and Collié 2020), and they have not yet formed a clear conceptual organization and semantic space (Fang et al. 2011, Zhang and Fang 2009). Further explanations of this phenomenon may be insufficient development of the ability to extract concepts. Rattat and Collié (2020) argued that children with mild ID have greater difficulty extracting the conceptual invariants needed to mobilize the categories. Because they have an increased degree of disability, children with moderate ID will obviously encounter more difficulties when trying to extract concepts.

We also found that there was no significant difference between the total score of children with moderate ID and 6-year-old children, while there were differences in the task of direct classification. And there were different degrees of mixed categorization standards in children with moderate ID and in the kindergarten, and 10-year-old typical children formed clear categorization strategies and could skillfully use concepts for categorization. Markman and Wachtel (1988) and Duarte and Baer (1994) found that children acquire categorization ability at early ages, and children aged three and under can be categorized according to certain criterion, but the conclusions on when children developed stable categorization criteria were inconsistent. Yang’s study on the color categorization ability of typical children found that most 3-year-old children cannot adhere to the same standard, and 4- to 5-year-old children are skilled in categorizing material by color (Yang 2015). With increasing age, the number of children who cannot adhere to the same standard when categorizing materials gradually decreases (Li and Xiang 2008).

In terms of the stability of classification, the categorization criteria of typically developing children aged 10 years were more stable than those of typically developing children in the kindergarten since the criteria they used in the first part of the test were more consistent. And the coexistence of the three criteria in moderate ID and kindergarten children in part one since they tended to use colors and functions as the basis for categorization. While with the lack of the sufficient accumulation of concepts, they failed to choose unified criteria and give the right answers (Zhang 2015). The confusion of criteria means that testing children with ID have not yet formed a sufficient conceptual basis. The higher score of ID children in direct classification may be resulting from richer life experience of ID children with older age. The accumulation of life experience partly compensates for the negative effects of insufficient concept extraction ability and unified criteria (Yin 2016). Studies have found that the sensitivity of both typical and ID children to prototype concepts has improved after the researchers have shown them a large number of learning materials (Hayes and Conway 2000). In daily life and the natural environment, the things themselves are presented in combination with functional relationships. Although the cognitive development of ID children lags behind that of their typical peers, school education and daily life help ID children master certain experiences, which becomes the basis for future improvement of their ability to master concepts. Even though moderate ID children have lower categorization ability and relatively confused selection of categorization criteria, when compared with typically developing children in the kindergarten, they have accumulated more reliable strategies to consciously perform categorization tasks rather than randomly dividing materials. Therefore, when simply comparing the level of categorization ability of ID children with their typically developing peers, children with ID cannot be comprehensively described by having lagging development and lower levels of categorization ability.

Influencing factors of children’s categorization ability

This study found that the total scores of children with moderate ID showed significant positive associations with IQ. The fact that the Wechsler Intelligence test includes several components, including categorization tests, may be one reason for this outcome. A child’s level of ID impacts whether they are able to successfully learn, remember, generalize, and employ different concepts in categorization exercises (Hayes and Conway 2000). For ID children, it is difficult to summarize which characteristics similar things have in common, clearly distinguish objects with the same characteristics, summarize the relationships between the characteristics of things, and combine the characteristics and characteristics of things to form the concept of things (Fang et al. 2013). Therefore, ID children often have difficulties mastering various rules and concepts.

Limitations and further studies

There are some limitations in the study. First, the sample of participants was limited, especially the number of kindergarten children was half of the other two groups. A larger sample size should be included in future research. Second, typically developing students and kindergarten-aged children were selected from one primary school and one kindergarten. Their classification ability may be related to their knowledge taught in schools. Children from various schools may be included in related studies. Third, the study has focused on moderate ID children at an average age of 10 years old. We can reveal the development of ID children’s category learning ability more methodically and thoroughly if we compare the development of category representation, attention, and learning strategies in ID children’s category ability from different age levels. The classification ability of ID kids with different age levels should be studied further.

Practical implications

It has been worthwhile to analyze the level and characteristics of ID children’s classification, which can provide information for educational training, and educators should develop corresponding educational programs based on the cognitive level and categorization ability characteristics of children with ID (Carteau-Martin et al. 2014, Maintenant et al. 2021). We found that they scored higher in the task of direct categorization than the kindergarten children in the selected kindergarten, which may due to students with moderate ID having longer living experience (Yin 2016). That is living experience can help ID children’s category ability. Therefore, when teaching classification in classes for ID students, teachers should connect new objects with ones that are familiar to students. ID children are generally better at implicit learning due to limited language ability, and implicit processes could help them build cognitive representations that are isomorphic to the structure of a learning situation and help develop their feeling of familiarity with the material to be learned, thereby making its processing more fluent (Witt and Vinter 2013).

Conclusion

The classification capacity of 10-year-old children with moderate ID was significantly lower than that of typically developing children of the same age. There was no significant difference in the total classification score between 10-year-old children with moderate ID and 6-year-old typically developing children, while there were differences in the task of direct classification. Besides, there was a significant positive correlation between IQ and classification capability in children with moderate ID.

Funding Statement

This work was supported by the Fundamental Research Funds for the Central Universities (2021NTSS34).

Ethics statement

The studies involving human participants were reviewed and approved by East China Normal University. The participants and their parents provided written informed consent to participate in this study.

Disclosure statement

No potential conflict of interest was reported by the authors.

References

  1. American Psychiatric Association . 2013. Diagnostic and statistical manual of mental disorders. 5th ed. Washington, DC: APA. [Google Scholar]
  2. Bovet, D., Vauclair, J. and Blaye, A.. 2005. Categorization and abstraction abilities in 3-year-old children: A comparison with monkey data. Animal Cognition, 8, 53–59. [DOI] [PubMed] [Google Scholar]
  3. Carteau-Martin, I.Amado, I.Thillay, A.Houy-Durand, E.Barthelemy, C. and Bonnet-Brilhault, F.. 2014. [Theoretical and practical aspects of cognitive remediation in intellectual disabilities: relevance of the cognitive remediation therapy program (crt).]. l encéphale. [DOI] [PubMed]
  4. Costanzo, F., Varuzza, C., Menghini, D., Addona, F., Gianesini, T. and Vicari, S.. 2013. Executive functions in intellectual disabilities: A comparison between Williams syndrome and down syndrome. Research in Developmental Disabilities, 34, 1770–1780. [DOI] [PubMed] [Google Scholar]
  5. Duarte, A. M. M. and Baer, D. M.. 1994. The effects of self-instruction on preschool children’s sorting of generalized in-common tasks. Journal of Experimental Child Psychology, 57, 1–25. [DOI] [PubMed] [Google Scholar]
  6. Eysenck, M. W. and Keane, M. T.. 2015. Cognitive psychology. A student’s handbook. 7th ed. East Sussex: Psychology Press. [Google Scholar]
  7. Facon, B., Magis, D. and Courbois, Y.. 2012. On the difficulty of relational concepts among participants with Down syndrome. Research in Developmental Disabilities, 33, 60–68. [DOI] [PubMed] [Google Scholar]
  8. Fang, Y., Zhang, J. and Yin, G.. 2013. An experimental study of semantic deficits in moderately retarded children. Chinese Journal of Special Education, 20, 19–25. [Google Scholar]
  9. Fang, Y., Zhang, J., Ma, Z. and Yin, G.. 2011. Free classification of common foods in mentally retarded children. Chinese Journal of Special Education, 18, 19–24. [Google Scholar]
  10. Ford, R. M. 2003. Task variations and attention shifts in young children’s category learning. International Journal of Behavioral Development, 27, 495–504. [Google Scholar]
  11. Hahn, U. and Ramscar, M.. 2001. Similarity and categorization. Oxford: Oxford University Press. [Google Scholar]
  12. Hashimoto, N., McGregor, K. K. and Graham, A.. 2007. Conceptual organization at 6 and 8 years of age: Evidence from the semantic priming of object decisions. Journal of Speech, Language, and Hearing Research : JSLHR, 50, 161–176. [DOI] [PMC free article] [PubMed] [Google Scholar]
  13. Hayes, B. K. and Conway, R. N.. 2000. Concept acquisition in children with mild intellectual disability: Factors affecting the abstraction of prototypical information. Journal of Intellectual & Developmental Disability, 25, 217–234. [Google Scholar]
  14. Igier, S. and Pennequin, V.. 2022. Roles of the metacognition and emotional systems in a categorization task for adults with moderate and severe learning disabilities. International Journal of Developmental Disabilities, 68, 281–289. [DOI] [PMC free article] [PubMed] [Google Scholar]
  15. Krascum, R. M. and Andrews, S.. 1993. Feature-based versus exemplar-based strategies in preschoolers’ category learning. Journal of Experimental Child Psychology, 56, 1–48. [DOI] [PubMed] [Google Scholar]
  16. Li, H. and Xiang, H.. 2008. Talking about the characteristics and cultivation of children’s classification ability. Time Education, 10, 289. [Google Scholar]
  17. Lin, E. L. and Murphy, G. L.. 2001. Thematic relations in adults’ concepts. Journal of Experimental Psychology: General, 130, 3–28. [DOI] [PubMed] [Google Scholar]
  18. Liu, Z., Song, X. and Carol, A. S.. 2012. Categorical learning ability of six-year-old children: Category representation, attention and classification strategies. Acta Psychologica Sinica, 57, 634–646. [Google Scholar]
  19. Lucariello, J. and Nelson, K.. 1985. Slot-filler categories as memory organizers for young children. Developmental Psychology, 21, 272–282. [Google Scholar]
  20. Lv, J., Pang, H. and Wang, W.. 1987. Study on the development of generalizing ability in classification between the ages of 3 and 6. Acta Psychologica Sinica, 32, 27–31. [Google Scholar]
  21. Maintenant, C., Nanty, I. and Pivry, S.. 2021. Categorical flexibility in children with intellectual disability: Effects of a metacognitive training. Pratiques Psychologiques, 27, 93–106. [Google Scholar]
  22. Markman, E. M. and Wachtel, G. F.. 1988. Children’s sensitivity to constraints on word meaning: Taxonomic vs. thematic relations. Cognitive Psychology, 20, 121–157. [DOI] [PubMed] [Google Scholar]
  23. Medin, D. L. and Schaffer, M. M.. 1978. Context theory of classification learning. Psychological Review, 85, 207–238. [Google Scholar]
  24. Mo, L. and Chen, Z.. 2003. Rule-based categorization strategy and example-based categorization strategy in categorization. Acta Psychologica Sinica, 35, 29–36. [Google Scholar]
  25. Murphy, G. L. and Medin, D. L.. 1985. The role of theories in conceptual coherence. Psychological Review, 92, 289–316. [PubMed] [Google Scholar]
  26. Nelson, K. 1988. Where do taxonomic categories come from? Human Development, 31, 3–10. [Google Scholar]
  27. Rattat, A. C. and Collié, I.. 2020. Duration judgments in children and adolescents with and without mild intellectual disability. Heliyon, 6, e05514. [DOI] [PMC free article] [PubMed] [Google Scholar]
  28. Shulman, C., Yirmiya, N. and Greenbaum, C. W.. 1995. From categorization to classification: A comparison among individuals with autism, mental retardation, and normal development. Journal of Abnormal Psychology, 104, 601–609. [DOI] [PubMed] [Google Scholar]
  29. Thierry, N. and Alison, G.. 2000. A shift in children’s use of perceptual and causal cues to categorization. Development Science, 2, 389–396. [Google Scholar]
  30. Vanpaemel, W. and Bayer, J.. 2021. Prototype-based category learning in autism: A review. Neuroscience and Biobehavioral Reviews, 127, 607–618. [DOI] [PubMed] [Google Scholar]
  31. Wang, X., Liu, J. and Fan, C.. 1964. An investigation into the development of concepts in children of 4–9 years II. Generalization development as reflected in children’s ability to classify. Acta Psychologica Sinica, 9, 131–144. [Google Scholar]
  32. Witt, A. and Vinter, A.. 2013. Children with intellectual disabilities may be impaired in encoding and recollecting incidental information. Research in Developmental Disabilities, 34, 864–871. [DOI] [PubMed] [Google Scholar]
  33. Xue, M. 2018. A study on the development characteristics of classification ability in 3–6 years old children. Master Dissertation. Shenyang Normal University. [Google Scholar]
  34. Yang, L. J. 2015. Experimental study on the classification characteristics of autistic children. Master Dissertation. East China Normal University. [Google Scholar]
  35. Yin, M. 2016. Research on the classification activity for children with intellectual disabilities. Master Dissertation. Liao Ning Normal University. [Google Scholar]
  36. Zhang, J. and Fang, Y.. 2009. A study on the conceptual structure of common foods in children with intellectual disabilities. Chinese Journal of Special Education, 16, 54–62. [Google Scholar]
  37. Zhang, J., Zhang, Y., Dang, Y., Wang, Z. and Liang, M.. 2007. A study on the development of color naming and color classifying of children with intellectual disabilities. Chinese Journal of Special Education, 6, 20–27. [Google Scholar]
  38. Zhang, L. 2015. Study on influencing factors and educational countermeasures of children aged 3–6. Master Dissertation. Shannxi Normal University. [Google Scholar]

Articles from International Journal of Developmental Disabilities are provided here courtesy of The British Society of Developmental Disabilities

RESOURCES