N = 65
The diagnosis of autism was made by a detailed evaluation using expert clinical judgment, the Autism Diagnostic Interview-Revised (ADI-R; LeCouteur et al. 1989 ; Lord et al. 1994 ), and the Autism Diagnostic Observation Schedule-Generic (ADOS; Lord et al. 1989 , 2000 ). All participants were required to have evidence of delayed and disordered language development, thus excluding individuals with Asperger’s Disorder as defined at that time in the DSM system (DSM-IV-R; American Psychiatric Association 2000 ). Participants with autism were excluded if they had associated neurologic, genetic, infectious, or metabolic disorders, such as tuberous sclerosis, fragile-X syndrome, or fetal cytomegalovirus infection.
The control participants were community volunteers recruited to match the autism participants on age, Verbal IQ, Full Scale IQ, gender, race, and years of education, and socioeconomic status of family of origin ( Hollingshead 1957 ). Potential control participants were recruited through advertisement and contacts with community organizations and were screened by questionnaire, telephone, personal interview, and observation during screening tests. Potential control participants were excluded if they had a history of birth or developmental abnormalities; brain injury; poor school attendance; current or past history of psychiatric or significant neurological disorder; family history of autism, developmental cognitive disorder, or learning disability; mood or anxiety disorder; or other neuropsychiatric disorder thought to have a genetic etiological component.
Tests were neuropsychological measures that were selected to target different aspects of conceptual reasoning or problem solving such as forming and changing hypotheses or plans, concept formation or deductive reasoning, concept identification or abstract reasoning based on rules or general knowledge, planning and organization to accomplish a goal, and formation of mental representations. The tests used in this analysis varied in modality of presentation, some involving language, others visual perceptual analysis, and others purposeful movements associated with problem solving. Tests included the: the Verbal Absurdities and Picture Absurdities subtests from the Stanford-Binet scales ( Thorndike et al. 1986 ), Tower of Hanoi (TOH) ( Simon 1975 ), the Wisconsin Card Sorting Test (WCST) ( Heaton et al. 1993 ), the Halstead Category Test (HCT) ( Halstead and Settlage 1943 ), the Hooper Visual Organization Test ( Hooper 1983 ), the Tactual Performance Test ( Reitan and Wolfson 1993 ), the 20 Questions Task ( Laine and Butters 1982 ), and the Trail Making Test, Part B ( Reitan and Wolfson 1993 ).
Behavioural Assessment of the Dysexecutive Syndrome (BADS; Wilson et al. 1996 ). The BADS is an assessment procedure that is individually administered in a laboratory setting. It provides a micro level of analysis of the skills needed for carrying out specific types of adaptive challenge by characterizing the ability to shift rules, develop a plan of action to solve a problem, develop a plan for a course of action, make temporal judgments, create a plan when structure is minimal as contrasted to use of an externally imposed strategy, and plan and organize multiple tasks. The BADS has been reported to have a higher ecological validity than similar tests of executive function and to be useful when evaluating skills for vocational planning ( Chamberlain 2003 ). Consistent with these prior characterizations of the usefulness of the BADS, for purposes of the present study, we used the instrument as a means of evaluating cognitive function or problem solving ability that underlies adaptive function.
The BADS contains six subtests. Rule Shift requires the subject to initially go through a deck of cards, saying ‘Yes’ for red or ‘No’ for black cards. Then, the rule is shifted by asking the subject to tell whether the card just turned over is the same as or different from the previous card. Scores are time and errors. In Action Sequences the subject attempts to remove a cork from a tube in a beaker filled with water using materials made available. The score is the number of problem solving stages completed independently. Key Search assesses the subject’s ability to plan an effective course of action to find a lost key. The score is the sum of 8 components of the search process, such as entering the field at the bottom. Temporal Judgment asks questions about the duration of events, an ability that contributes to organizing and planning. The Zoo Map Test evaluates planning when constrained by a set of rules. The task is for the subject to plan to visit a series of locations on a map of a zoo while obeying a set of rules (e.g., starting at the entrance and finishing at a designated area). An error score is used. The Modified Six Elements Test requires the subject to perform a dictation, arithmetic, and picture naming task. The test is scored for organizing ability, including the number of sub-tasks completed, rule-breaking on the tasks, and maximum amount of time spent on a subtask. The raw score for each BADS subtest was converted to a profile score ranging from 0 to 4. The profile scores were used in the analyses.
Vineland Adaptive Behavior Scales (VABS; Sparrow et al. 1984 ). As has been done in prior research examining the relationship between IQ and adaptive behavior, we used the VABS as a measure of functioning in the natural environment. The VABS Survey is a 261 item form that is administered to parents as a measure of how many age-appropriate, socially adaptive behaviors a child or adult exhibits in their natural environment. It is a well-recognized instrument, with demonstrable reliability and validity both for individuals who are typically developing and those with disabilities. It is also the preeminent measure for the assessment of adaptive functioning in children with autism ( Newsom and Hovanitz 1997 ). The VABS assesses three skill domains, each with three subdomains: Communication (receptive, expressive, and written language skills), Daily Living skills (personal self-care, domestic, and community living skills), and Socialization (interpersonal, play or leisure, and coping skills). The VABS provides standard scores ( m = 100, SD = 15) with higher scores indicating better functioning. Domain scores and the Adaptive Behavior composite score were used in the data analysis.
For purposes of data reduction, the conceptual reasoning tests were factor analyzed in order to assess the latent variables that underlie the series of tests that were used. The principal components method was used with Varimax rotation. Regression based factor scores were computed. Factor scores are composite variables for use in subsequent analyses following performance of a factor analysis. For this study, the factor scores were then correlated with the BADS and VABS scores. Because of narrow distributions of the factor scores in some cases, Spearman’s Rho was used as the correlation coefficient rather than Pearson’s r. Preliminary inspection of the data indicated that comparable results were obtained between the two coefficients. These correlations were computed separately for each group.
Differences between the autism and control groups and among the three age groups on the eleven conceptual reasoning tests were compared using a 3×2 factorial design analysis of variance for independent samples, with presence or absence of autism constituting one independent variable and age group the other. This form of analysis was also conducted for the BADS and VABS.
Comparisons were made between the autism and control groups on the BADS and VABS using t -tests. We also wanted to evaluate the differences in discrepancies on the various abilities measured by these two instruments. While individuals with autism may generally do more poorly than typically developing individuals at adaptive abilities, this discrepancy may not be of the same order of magnitude for all abilities. Specifically, it was hypothesized that adaptive functions requiring relatively high levels of conceptual ability will show a relatively greater level of discrepancy between individuals with autism and groups with typical development. Such differences can be evaluated through obtaining effect sizes and statistical power assessing the magnitude of the statistical significance of group differences. Effect size determination and power analyses were accomplished for all variables; the items were ranked by effect size from largest to smallest. Cohen’s d ( Cohen 1988 ) was the statistic used to obtain effect sizes; it is computed by taking the difference between the two obtained means and dividing by the pooled standard deviation. The effect size reflects the magnitude of a difference, whereas power reflects the capacity to reject the null hypothesis given a particular effect size. Thus, some differences may be so robust that acceptance of a false hypothesis is unlikely, whereas minimally significant findings with low power might raise the possibility of having made a Type I error or making false discoveries ( Benjamini and Hochberg 1995 ). Correspondingly, borderline non-significant findings raise the possibility of rejecting a true hypothesis or making a Type II error. The magnitude of the test performance difference between participants with autism and demographically matched normal control participants should provide an index of the extent to which the ability measured by the test characterizes the performance of the individuals with autism. Thus, those tests found to have larger effect sizes reflected by higher d’s and relatively greater statistical power to reject the null hypothesis of no difference between autism and normal control groups could be understood as reflecting specific aspects of dysfunction in autism, whereas those tests that do not discriminate measure abilities at which individuals with autism performed relatively similarly to individuals with typical development.
To estimate a more global association between conceptual reasoning and adaptive abilities, entry method and stepwise multiple regression analyses were performed. The three factor scores were the predictor variables and the summary scores (i.e., the Total Standard Score from the BADS and the Adaptive Behavior Composite Score from the VABS) were the dependent variables. The following method was used. Group was coded 1 for autism and 0 for control and multiplied by the factor scores. These new variables, often characterized as “dummy variables”, represent interaction between group and factor score. They were entered into the regression equations along with the unweighted diagnostic code itself (Autism or Control) and the factor scores were used as predictor variables with either the BADS or VABS summary score as the dependent variable. The analyses were performed using both the enter all variables and stepwise methods. In addition to the multiple regression coefficients (R), this analysis also provides Beta coefficients for the predictor variables. β represents the independent contributions of each independent variable to the prediction of the dependent variable. t tests were performed to determine the significance of the difference in β between groups for the predictor variables. Thus, for example, a significant difference for one of the factors would indicate that the groups differed with regard to their association with the dependent variable.
As a way of assessing the relationship between conceptual ability, problem solving, and adaptive function, we first performed a principal components factor analysis with Varimax rotation of the scores from the conceptual reasoning tests and then computed correlations between the obtained factor scores and the BADS and VABS. Using Kaiser’s Rule requiring stopping extraction of factors when an eigenvalue of below 1 is obtained, a three factor solution was obtained for the conceptual tests. The rotated component matrix is presented in Table 2 . The first factor received exceptionally high (>.5) loadings on the Verbal and Picture Absurdities test, the perseverative errors score from the WCST, and the number of constraint seeking questions from the 20 Questions task. These measures assess a high degree of flexibility of thought that underlies concept formation or the ability to spontaneously organize strategies for problem solving. We therefore named it the Flexible Thinking factor. The second factor received high loadings from the Tactual Performance test and the Hooper Visual Organization test, and a moderately high loading from the Picture Absurdities test. It would, therefore, appear to mainly describe reasoning based on perceptual characteristics. We named this the Perceptual Reasoning factor. The third factor received high loadings from the Category and Trail Making Tests and the Tower of Hanoi task. These procedures assess what we have described as concept identification or applying a previously established organizational strategy, and so we called it a Rule Application factor.
Rotated factor loadings for the conceptual reasoning tests
Factor 1: flexible thinking | Factor 2: perceptual reasoning | Factor 3: rule application | |
---|---|---|---|
Verbal absurdities | −.032 | −.116 | |
Picture absurdities | −.585 | −.009 | |
WCST perseverative errors | .044 | .444 | |
20Q constraint seeking | −.436 | −.193 | |
TPT-time | .138 | .353 | |
Hooper T score | −.328 | −.135 | |
Category test errors | −.076 | .135 | |
Trail making B–time | −.103 | −.054 | |
Tower of hanoi moves | −.385 | .189 | |
% Explained variance | 22.521 | 20.491 | 18.990 |
Spearman Rho correlations between the conceptual reasoning factor scores and the scores from the BADS and VABS are presented Table 3 . In general, there were few statistically significant correlations ( p <.05), with only four significant correlations in the autism group and three in the control group. Significant correlations in the autism group for the BADS were found between the Flexible Thinking factor and the BADS Key Search (strategy formation) and Rule Shift (changing an established pattern of responding) scores, and between the Perceptual Reasoning Factor and BADS Zoo Map (which involves topographical planning) score. Significant correlations in the autism group for the VABS were obtained between the Flexible Thinking factor and the VABS Adaptive Behavior composite score. In the control group, for the BADS, there were significant correlations between the Perceptual Reasoning factor and the Modified Six Elements (planning and performance monitoring). Significant correlations were found in the control group for the Flexible Thinking factor and the Socialization Domain and Adaptive Composite Behavior Scores on the VABS.
Rho correlations between factor scores, BADS, and VABS
Autism | Control | |||||
---|---|---|---|---|---|---|
Flexible thinking | Perceptual reasoning | Rule application | Flexible thinking | Perceptual reasoning | Rule application | |
BADS | ||||||
Rule shift | −.151 | −.053 | .190 | −.085 | −.063 | |
Action sequences | .041 | −.078 | −.112 | .186 | .005 | −.078 |
Key search | −.114 | −.209 | .030 | .073 | −.100 | |
Temporal judgment | .065 | −.037 | −.155 | −.033 | .075 | .158 |
Zoo map | .104 | −.134 | −.075 | −.026 | −.033 | |
Six elements | .243 | −.145 | .030 | .156 | −.053 | |
VABS | ||||||
Communication | .241 | −.111 | −.095 | .067 | −.008 | .118 |
Daily living skills | .004 | −.115 | −.034 | −.182 | .050 | −.037 |
Socialization | .119 | .047 | .158 | .201 | −.099 | |
Adaptive behavior | .018 | .013 | .201 | −.098 |
We ranked differences between autism and control groups on the measures from the BADS and VABS with regard to effect sizes and statistical power to evaluate what aspects of problem solving and adaptive behavior distinguish most strongly between the two groups (see Table 4 ). It was thought that the functions that made the greatest discrimination would have the largest effect size and greatest statistical power to reject the null hypothesis, with less discriminating abilities having lower effect sizes and power. Using Cohen’s (1988) conventions indicating that an effect size in the .2 range is small, one in the .5 range is medium, and one in the .8 range is large, then it is clear that there is a wide range of effect sizes. Only one of the BADS subtests, Action Sequences which involves practical problem solving, adequately discriminated between the autism and control groups. On the VABS, the Adaptive Behavior Composite score and Socialization Domain score had highly significant group differences and large effect sizes. The VABS Daily Living Skills and Communication Domains did not distinguish between individuals with autism and controls. Apparently adaptive function as measured by the VABS was more sensitive to differences between the autism and control groups than was the case for most of the tasks on the BADS, even though they are generally considered to have ecological validity (i.e., Chamberlain 2003 ).
Differences between autism and control groups on adaptive functioning ranked by effect size (d)
Test | Autism | Control | d | Power | ||||
---|---|---|---|---|---|---|---|---|
M | SD | M | SD | |||||
Behavioral assessment of the dysexecutive syndrome (BADS) | ||||||||
Action sequences (Practical problem solving) | 3.41 | .57 | 3.63 | .25 | .004 | .50 | .81 | |
Key search (Strategy formation) | 2.17 | .74 | 2.35 | .77 | −1.40 | .17 | .24 | .27 |
Modified six elements (Planning and performance monitoring) | 3.01 | .81 | 3.14 | .54 | −1.08 | .28 | .19 | .19 |
Rule shift (Ability to change an established pattern of responding) | 3.48 | .72 | 3.57 | .28 | −0.91 | .37 | .16 | .15 |
Temporal judgment (Ability to estimate how long various tasks take) | 1.13 | .46 | 1.10 | .28 | .50 | .62 | .08 | .07 |
Zoo map (Planning and following rules) | 2.56 | .78 | 2.54 | .63 | .14 | .89 | .03 | .04 |
Vineland adaptive behavior scales (VABS) | ||||||||
Adaptive behavior composite | 83.18 | 12.36 | 91.37 | 8.50 | <.001 | .77 | .99 | |
Socialization domain | 83.62 | 15.16 | 93.05 | 9.48 | <.001 | .75 | .99 | |
Daily living skills domain | 93.14 | 18.32 | 95.21 | 6.87 | −0.86 | .39 | .15 | .13 |
Communication domain | 99.52 | 16.00 | 98.23 | 7.84 | 0.59 | .56 | .10 | .08 |
Given previous reports of differences in the relationship between cognitive abilities and adaptive functioning at different ages for individuals with ASD (e.g., Kanne et al. 2011 ) and the possibility that the components of abstract reasoning, concept identification and concept formation, are influenced by developmental factors in autism ( Solomon et al. 2011 ), we conducted some analyses by age group. As described earlier, the data was separated into three age groupings for children, adolescents, and adults. ANOVA results for comparisons on the conceptual reasoning tests between the participants with and without autism and among the age groups are presented in Tables 5 and and6. 6 . Overall, the autism group performed significantly differently from the control group on all tests but the Halstead Category Test. These results suggest that the autism group as a whole was weaker in conceptual reasoning than the age and IQ-matched controls. As indicated in Table 6 , there were also several significant differences among the age groups. However, there were no significant interactions, leading to the conclusion that there are no significant differences in the age related changes in conceptual reasoning test performance between the autism and control groups.
Means and SDs of the three age groups in the autism and control groups on the cognitive tests
M + SD | Autism group | Control group | ||||||
---|---|---|---|---|---|---|---|---|
Age group | 8–12 | 13–20 | 21+ | Total | 8–12 | 13–20 | 21+ | Total |
Verbal absurdities | 8.16 ± 7.7 | 10.21 ± 4.3 | 10.85 ± 4.1 | 9.68 ± 5.7 | 11.11 ± 2.3 | 14.16 ± 1.0 | 13.45 ± 2.9 | 13.03 ± 2.5 |
Tower of hanoi | 161.31 ±44.5 | 178.16 ± 73.7 | 169.0 ± 60.4 | 169.06 ± 59.4 | 143.0 ± 51.9 | 110.12 ± 33.9 | 106.55 ±44.1 | 118.8 ±45.2 |
WCST PE | 16.93 ± 12.8 | 14.84 ± 10.9 | 16.39 ± 10.4 | 16.10 ± 11.3 | 11.84 ± 7.2 | 7.43 ± 4.9 | 7.98 ± 5.6 | 8.91 ± 6.1 |
Category errors | 41.66 ± 22.6 | 39.53 ± 21.2 | 40.64 ± 18.7 | 40.66 ± 20.6 | 41.61 ± 13.2 | 28.82 ± 14.6 | 41.83 ± 29.0 | 36.69 ± 20.6 |
HVOT | 54.14 ± 6.6 | 49.26 ± 5.2 | 51.5 ± 8.3 | 51.72 ± 7.0 | 52.58 ± 4.7 | 47.76 ± 2.9 | 46.6 ± 3.5 | 48.83 ± 4.4 |
Picture absurdities | 23.73 ± 2.7 | 27.42 ± 3.4 | 26.8 ± 3.2 | 25.89 ± 3.5 | 24.79 ± 2.2 | 28.72 ± 2.0 | 30.0 ± 2.3 | 27.94 ± 3.0 |
TPT | 945.78 ± 314.7 | 790.88 ± 366.2 | 885.31 ± 543.3 | 877.71 ± 415.3 | 916.41 ± 312.0 | 585.76 ± 177.6 | 723.34 ± 283.3 | 726.91 ± 287.7 |
20 Q | 14.14 ± 12 | 18.89 ± 8.9 | 16.74 ± 9.9 | 16.51 ± 10.4 | 19.47 ± 8.5 | 23.72 ± 7.3 | 22.35 ± 7.9 | 22.03 ± 7.3 |
Trails B | 43.68 ± 27.4 | 50.89 ± 28.0 | 77.10 ± 26.3 | 56.89 ± 30.5 | 38.37 ± 14.3 | 39.12 ± 17.6 | 55.7 ± 14.3 | 44.08 ± 17.3 |
F-ratios for main effects and interaction for conceptual reasoning tests
F | F | F | |
---|---|---|---|
Verbal absurdities | 17.05 | 4.83 | .28 |
Tower of Hanoi | 28.15 | .78 | 2.83 |
WCST perseverative | 18.36 | 1.40 | .36 |
Category errors | .75 | 1.78 | 1.08 |
HVOT | 7.29 | 9.65 | 1.29 |
Picture absurdities | 14.85 | 30.61 | 1.88 |
TPT | 4.55 | 5.20 | .73 |
20 Q | 10.6 | 2.67 | .02 |
Trails B | 10.44 | 15.45 | 1.36 |
The only significant group difference for the BADS was for Action Sequences which involves practical problem solving, with the autism group performing significantly poorer than the group with typical development. However, no significant age by diagnostic group interaction was obtained.
With regard to the adaptive functioning scale, only the age group main effect was significant for the VABS Daily Living Domain scale. In the autism group the 8–12 year olds group did more poorly than the older groups while in the control group there were very small mean differences among the age groups. Thus, the significant main effect was probably attributable to poor performance by the 8–12 year old autism group. There were two significant age group X diagnostic group interactions one for the VABS Socialization Domain Scale and the other for the VABS Adaptive Behavior Composite Score. Essentially the same patterns appeared in the Socialization Domain and Adaptive Behavior Scale. There were substantially higher mean scores obtained by the controls in the younger age groups, but essentially equal mean scores obtained by adult members of the autism and control groups. These findings would suggest that there were substantial differences in adaptive functioning in individuals with autism and typical development at younger ages, but that this difference was no longer evident in adulthood ( Tables 7 and and8 8 ).
Means and SDs for adaptive function variables of the three age groups in the autism and control groups
M + SD | Autism group | Control group | ||||||
---|---|---|---|---|---|---|---|---|
Age group | 8–12 | 13–20 | 21 + | Total | 8–12 | 13–20 | 21 + | Total |
BADS | ||||||||
Rule shift | 3.31 ± .76 | 3.59 ± .48 | 3.53 ± .91 | 3.47 ± .74 | 3.54 ± .2 | 3.53 ± .34 | 3.64 ± .27 | 3.57 ± .28 |
Action sequences | 3.57 ± .18 | 3.26 ± .71 | 3.33 ± .69 | 3.39 ± .58 | 3.58 ± .19 | 3.68 ± .26 | 3.62 ± .28 | 3.63 ± .26 |
Key search | 2.2 ± .28 | 1.89 ± .87 | 2.32 ± .96 | 2.15 ± .76 | 2.35 ± .24 | 2.2 ± .98 | 2.54 ± .8 | 2.35 ± .78 |
Temporal | 1.01 ± .33 | 1.22 ± .68 | 1.18 ± .36 | 1.13 ± .47 | 1.15 ± .21 | 1.07 ± .3 | 1.08 ± .33 | 1.1 ± .28 |
Zoo map | 2.34 ± .76 | 2.66 ± .66 | 2.69 ± .95 | 2.55 ± .8 | 2.41 ± .61 | 2.71 ± .64 | 2.46 ± .64 | 2.54 ± .63 |
6 Elements | 2.83 ± .64 | 3.09 ± .62 | 3.07 ± 1.15 | 2.99 ± .83 | 3.06 ± .03 | 3.04 ± .77 | 3.35 ± .44 | 3.14 ± .55 |
Total profile | 15.3 ± 2.05 | 15.84 ± 1.97 | 16.12 ± 3.65 | 15.74 ± 2.64 | 16.13 ± .52 | 16.25 ± 1.63 | 16.71 ± .95 | 16.36 ± 1.19 |
Total standard | 86.45 ± 9.6 | 89.0 ± 9.3 | 90.23 ± 17.58 | 88.48 ± 12.6 | 90.35 ± 2.61 | 91.06 ± 7.93 | 92.56 ± 4.1 | 91.32 ± 5.64 |
VABS communication | 93.64 ± 15.67 | 100.32 ± 10.85 | 99.50 ± 2.24 | 97.64 ± 11.49 | 97.63 ± 11.35 | 98.04 ± 8.14 | 99.0 ± .0 | 98.22 ± 7.90 |
VABS daily living | 86.55 ± 17.86 | 96.79 ± 17.41 | 94.21 ± 5.84 | 91.94 ± 15.18 | 94.02 ± 6.1 | 98.06 ± 9.15 | 92.90 ± .0 | 95.25 ± 6.92 |
VABS socialization | 80.04 ± 17.91 | 78.68 ± 16.63 | 88.77 ± 5.23 | 82.77 ± 14.92 | 97.01 ± 12.02 | 94.62 ± 9.51 | 87.60 ± .0 | 93.13 ±9.53 |
VABS adaptation | 80.18 ± 13.19 | 80.26 ± 15.42 | 88.5 ± 6.71 | 82.93 ± 12.72 | 93.32 ± 10.40 | 93.56 ± 9.31 | 87.0 ± .0 | 91.44 ± 8.55 |
F-ratios for main effects and interaction for BADS and VABS
F | F | F | |
---|---|---|---|
BADS rule shift | .88 | .94 | .72 |
BADS action sequences | 9.4 | .72 | 2.35 |
BADS key search | 2.69 | 2.70 | .13 |
BADS temporal | .30 | .28 | 1.71 |
BADS zoo map | .08 | 1.98 | .56 |
BADS 6 elements | 1.45 | 1.41 | .67 |
BADS total profile | 2.74 | 1.18 | .11 |
VABS communication | .05 | 1.85 | 1.14 |
VABS daily living | 1.88 | 3.56 | 1.51 |
VABS socialization | 23.03 | .40 | 7.03 |
VABS adaptation | 19.84 | .11 | 6.81 |
Results for the BADS Total Standard Score are presented in Table 9 . This score shows a high Multiple R (R = .459, p < .001). Using the stepwise method only the factor scores weighted by group membership were entered. Group membership alone and the three factor scores themselves were not entered. This finding would indicate that the multivariate association between the conceptual reasoning factors and the BADS measure interacts with group membership. If group membership is not considered, as when only the factor scores themselves are used, they are not entered.
Multiple regression analyses using conceptual reasoning factor scores as predictor variables and BADS total standard or VABS adaptive behavior composite summary scores as dependent measures
BADS total standard score | R | R | Adj R | F | P | B Group | t |
---|---|---|---|---|---|---|---|
Enter method model | .459 | .210 | .195 | 4.604 | .00 | 1.785 | 1.005 |
Stepwise method model | |||||||
AUT1 (Group 9 Factor 1 Score) | .319 | .101 | .094 | 14.340 | .000 | 4.040 | 4.379 |
AUT2 (Group 9 Factor 2 Score) | .395 | .156 | .142 | 11.621 | .000 | −2.950 | −3.152 |
AUT3 (Group 9 Factor 3 Score) | .444 | .197 | .178 | 10.254 | .000 | −2.278 | −2.55 |
VABS adaptive behavior composite score | |||||||
Enter method model | .428 | .183 | .136 | 3.878 | .001 | ||
Stepwise method | |||||||
AUT (Group) | .354 | .125 | .116 | 18.188 | .000 | −7.900 | −4.265 |
AUT1 (Group 9 Factor 1 Score) | .412 | .170 | .157 | 12.894 | .000 | 3.043 | 2.602 |
For the VABS Adaptive Behavior variable, the enter method also yields a significant multiple R of .428. However, the stepwise method entered group alone (autism vs. control) and Group weighted by Factor Score 1 (Flexible Thinking). It would appear that membership in the control group has little or no influence on the factor scores while membership in the autism group has a substantial influence. However, the analysis of the data presented in Table 3 indicates that the Rho correlation between the VABS Adaptive Behavior Scale and the Flexible Thinking factor is positive (.299) in the autism group while it is negative (−.263) in the control group. This discrepancy would not appear to justify the conclusion that adaptive behavior is negatively correlated with flexible thinking, particularly since the entire set of correlations considered are non-significant. However, this pattern of correlations might affirm the result of the regression analysis indicating that in typically developing individuals, level of adaptive functioning does not appear to be associated with intelligence.
In general, individuals with autism have relatively weaker conceptual reasoning abilities than individuals with typical development of similar age and overall cognitive ability. Despite this weakness, individuals with autism appear to be able to apply these conceptual reasoning abilities on most of the laboratory measures of adaptive flexibility, planning, and problem solving, resulting in a lack of differentiation from controls. The level of conceptual reasoning for most of these children and adults with autism allowed them to demonstrate problem solving abilities in a variety of structured or hypothetical situations as measured by the BADS. However, as indicated by the VABS data, individuals with autism may fail to apply these reasoning abilities to real life situations, resulting in dissociation between overall level of cognition and adaptive functioning. This result is consistent with reports of problems with adaptive functioning in children and adults with autism who have average or above IQs ( Kanne et al. 2011 ; Mazefsky et al. 2008 ). This dissociation between performance on structured tasks and observed daily performance may help explain the rather poor outcome in adult life of verbal individuals with autism despite their academic success in school programs ( Farley et al. 2009 ).
The underlying reason for the disconnect between the ability to apply reasoning in a controlled setting and the ability to demonstrate reasoning in real life situations is not clear, but some understanding may be gained by examining the obtained relationships between the measures of conceptual reasoning and the measures of problem solving and adaptive functioning. For the autism group, the Flexible Thinking factor was significantly correlated with the BADS subtests that assess strategy formation and rule shifting. This relationship suggests that individuals with autism who had more ability to think flexibly were able to form strategies and were more flexible in applying rules. It was not surprising to find that the Flexible Thinking factor was also associated with overall better adaptive functioning in autism. Taken together, these results suggest that the ability to flexibly form concepts is particularly important for better adaptive behavior in individuals with autism.
In a related area of research, it has been proposed that learning difficulties encountered in social situations by individuals with autism are not related to the implicit nature of the information but to a problem with flexibility of response to novel contexts ( Kourkoulou et al. 2012 ). In that study, intact implicit learning was found for contextual cuing tasks; however, deficits occurred in novel contexts, particularly when the paradigm biased learning to local stimuli, suggesting that flexibility of response to novel contexts was the underlying problem not implicit learning per se ( Kourkoulou et al. 2012 ).
The conclusion about the importance of flexible thinking to adaptive functioning in autism is generally supported by the results of the multiple regression analysis. These modest findings may suggest several potential explanations for this reversal of patterns of relationships. First, it may be due to the BADS being a laboratory-based assessment that provides a more micro-level analysis of the conceptual skills needed for carrying out a specific type of adaptive challenge, whereas the VABS scores reflect the integrative use and flexible application of these skills to solve real world problems. It is possible that individuals with autism can demonstrate problem solving and planning when there are reduced temporal demands and the problems are clearer and the solutions more limited. That is, they have adequate cognitive resources to meet these challenges and, therefore, can explain what should be done in a hypothetical situation. However, real world problems are seldom this structured and explicit, beginning with the necessity to identify what the problem to be solved is. Therefore, individuals with autism would have difficulty translating their knowledge into success in real life situations because the complexity of the processing task has increased exponentially. The impact of conceptual reasoning deficits in autism may not be as apparent in highly structured settings that provide rules like schools but is likely to become more evident under open field conditions such as jobs and independent living where there are few established rules that address a particular situation with constantly changing contexts that demand flexibility of thought. Individuals with autism who have a relatively stronger ability to manipulate and form new concepts, to think flexibly, would be at an advantage even as the environmental demands increase.
In addition to Flexible Thinking, another significant relationship was obtained between the Perceptual Reasoning factor and performance on the BADS Zoo Map subtest for the autism group. Abilities associated with the Perceptual Reasoning factor include ideational planning as measured by the TPT and visual imagery and integration assessed with the Hooper Visual Organization Test. Perceptual ability, involving the requirement of the tactual and visual processing demanded by these two measures, may be particularly important for individuals with autism for the aspect of adaptive functioning that involves imaging and planning. Therefore, perceptual reasoning is a type of process that might be capitalized on when helping individuals with autism develop skills to negotiate ever-changing social environments.
The results regarding age differences are of particular interest. It is understood that this was a cross-sectional study and inferences may not be made to the effect that differences noted would be observed in the development of individuals, as could be determined only by a longitudinal study. However, it has been noted for some time that the results of cross-sectional and longitudinal studies are typically the same ( Heaton and Drexler 1987 ). The cross-sectional results obtained here reflect differences among age groups that are not always the same for the autism and control groups, and may reflect differences in developmental course. The pattern for both the measures of conceptual reasoning and problem solving of improved performance from childhood until young adulthood is comparable in individuals with typical development and individuals with autism. Test scores were fairly consistently lower in the autism group, although linear trajectories were noted in both groups. A different pattern emerged for adaptive behavior as measured by the VABS with significant interactions between autism status and age group on the Socialization Doman and Adaptive Behavior Composite scores. We made the remarkable finding that, while the scores of the group with typical development far exceeded those of the autism group in the child and adolescent age groups, they were essentially equal in the adult groups, and, furthermore, were in the average range on these scales. In summary, age differences in cognitive abilities were found to be linear in both groups but at differing performance levels; however, some adaptive abilities do not have parallel trajectories in the autism and groups with typical development. Rather, the child and adolescent groups showed marked group differences between autism and control groups, while in the adult groups there was essentially no difference. Because this is not longitudinal data, we cannot infer the source for this difference to be developmental in nature. It is, however, important to note that this relatively high functioning group of adults with autism has been able to achieve strong adaptive skills even if they are continuing to be challenged in functioning in the social domain.
The results regarding age differences raise the obviously major question of whether or not individuals with autism undergo a course of development in which they possess certain normal adaptive abilities during adulthood they did not have during childhood, perhaps as a result of lifelong treatment or developmental changes associated with the course of the disorder. Longitudinal data, even retrospective information, might ultimately clarify this matter.
The findings from this study have important implications related to the provision of services to verbal individuals with autism who are relatively higher functioning. First, we provide further support for the argument that adults with autism should not be denied social support services because they have an IQ in the average range if they are demonstrating difficulties with real world functioning. Unlike individuals with typical development, the ability to perform well on formal measures such as the BADS may not necessarily reflect actual functioning for individuals with autism.
In particular, better adaptive functioning in autism appears to be related to the development of concept formation, flexible thinking and perceptual reasoning. Given that successful independent living is a goal for individuals with autism, cognitive remediation therapies explicitly targeting these skills seem warranted. However, the way in which this intervention is delivered would appear to be of particular importance for successful skill acquisition in individuals with autism.
Even when individuals with autism can explain what should be done in a hypothetical situation, they may not be able to translate this knowledge into success in real life situations. Based on the results of this study, we would predict that interventions that are limited to answering questions about hypothetical situations and artificial problem solving would have little to no impact on adaptive functioning in individuals with autism. Knowing how to solve a problem does not appear to be enough. Similarly, approaches that emphasize the acquisition of social skills through explicitly teaching social rules or engaging through role playing of social interactions (e.g. MacAfee 2002 ) may also result in a failure to translate this knowledge into a change in adaptive behavior unless these skills are practiced in the contexts in which they are to be applied.
Although time-consuming and resource intensive, practice of skills in the real world, appears to be essential for individuals with autism ( Rao et al. 2008 ). In fact, this recommendation is consistent with the conclusions of a recent review of research on behavioral interventions for adaptive skills in verbal young adults with autism with normal IQ scores ( Palmen et al. 2012 ). To further facilitate the transfer of reasoning abilities to everyday problem solving, the primary interaction partners of the individuals with autism should be trained to recognize opportunities for learning and to assist the individual with autism in the application of problem solving when faced with real world challenges.
Alternative intervention approaches such as those that incorporated virtual reality techniques may serve as cost efficient alternatives to training in the real world. Virtual reality has reportedly been used to successfully develop the social interaction and theory-of-mind skills in young adults who were on the autism spectrum ( Kandalaft et al. 2013 ). A similar approach could present individuals with autism with more realistic challenges, requiring them to develop solutions to common problems in a contextually-rich environment that might facilitate flexible thinking and generalization to real world settings.
This research was supported by Grant Sponsor: National Institute of Child Health and Human Development (NICHD), Grant Number: HD35469, a Collaborative Program of Excellence in Autism (CPEA); Grant Sponsor: National Institute on Deafness and other Communication Disorders (NIDCD), Grant Number K23DC006691 to Dr. Williams and by the VISN IV Mental Illness Research, Education and Clinical Center (MIRECC), VA Pittsburgh Healthcare System Pittsburgh PA.
The research described here has been approved by the appropriate IRB and full informed consent or assent has been obtained for all subjects.
Diane L. Williams, Department of Speech-Language Pathology, Duquesne University, Pittsburgh, PA, USA.
Carla A. Mazefsky, Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA, USA.
Jon D. Walker, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA 15240, USA.
Nancy J. Minshew, Departments of Psychiatry and Neurology, University of Pittsburgh, Pittsburgh, PA, USA.
Gerald Goldstein, VA Pittsburgh Healthcare System, University Drive, Pittsburgh, PA 15240, USA, ten.bn@dlogG .
Why vs. how: the role of abstract and concrete thinking in anxiety and depression, by jenna vieira.
June 29, 2020
Research has shown that anxious and depressed individuals tend to think about their problems abstractly. In a study conducted by Joachim Stöber and Thomas Borkovec (2002), individuals diagnosed with generalized anxiety disorder (GAD) were asked to describe the two main problems that they were currently worried about and their potential negative consequences. It was found that compared to people without the disorder, individuals with GAD described their problems in terms that were less specific and less distinct to the situation. A similar study carried out by Edward Watkins (2007) with depressed individuals found that compared to non-depressed people, they also described their problems less specifically and less distinctly.
Abstract thinking is vague, general, and non-specific to a situation. Thoughts are concerned with why something happened, what it means, and what it implies for the future. For example, an individual who is at a restaurant awaiting the arrival of a friend and notices that they are running late may wonder if something bad happened to their friend, if their friend does not like them anymore, or why this is happening to them. Rather than focusing on the details that make a situation unique (“I noticed there was an interruption in public transit when I was on my way to the restaurant, perhaps that’s why my friend is late”), one is instead thinking about the broad implications of the situation (“Maybe my friend does not respect me”).
Thinking about a problem generally rather than specifically makes it challenging to find alternative, more neutral explanations. It also makes it difficult to carry out specific actions to prepare for, prevent, or solve a problem. This encourages avoidance of the problem rather than head-on confrontation of it, meaning that it is never truly eliminated, and worry and negative thinking about it will continue.
It is clear that the tendency of anxious and depressed individuals to think about their problems abstractly is actually quite unhelpful. So how can one fix this? If thinking too generally is the issue, then one should see an improvement in their mood when they begin to think in a more specific, concrete way. To examine this idea, Edward Watkins, Celine Baeyens, and Rebecca Read (2009) developed an intervention called concreteness training (CNT), which aims to train individuals out of an abstract thinking style and into a more concrete one. In an initial study, an experimenter guided depressed individuals to practice thinking concretely about six provided scenarios (half positive and half negative) and three personal, specific scenarios from memory. This entailed focusing on the details of each scenario using all of one’s senses, noticing what made each scenario’s events unique, imagining the process of how the events unfolded, and coming up with a step-by-step plan of how to move forward. Individuals then received a recording of the training, which they were asked to practice with once per day for one week. It was found that after one week, depression symptoms for those who had received CNT (compared to those who received a comparison training that was not expected to do much) were significantly reduced.
Ultimately, concrete thinking may be beneficial because encouraging an individual to vividly imagine an event happening in the present moment urges them to confront it. This often allows them to realize that the situation is not actually that bad or threatening, leading them to overcome it. Further, this type of thinking encourages one to take action, meaning that they may be more likely to discover and use more effective strategies to cope with and solve the problems that worry or distress them. Given the success observed with training depressed individuals to adopt a more concrete thinking style, future research should seek to uncover whether this is an equally beneficial intervention for anxious individuals.
Stöber, J., & Borkovec, T. D. (2002). Reduced concreteness of worry in generalized anxiety disorder: Findings from a therapy study. Cognitive Therapy and Research , 26 (1), 89-96. doi:10.1023/A:1013845821848
Watkins, E. (2007). Reduced concreteness of rumination in depression: A pilot study. Personality and Individual Differences , 43 (6), 1386-1395. doi:10.1016/j.paid.2007.04.007
Watkins, E., Baeyens, C. B., & Read, R. (2009). Concreteness training reduces dysphoria: Proof-of-principle for repeated cognitive bias modification in depression. Journal of Abnormal Psychology , 118 (1), 55-64. doi:10.1037/a0013642
Abstract thinking skills are essential to succeed in the workplace. Yes, we all have abstract thinking skills, but they’re of…
Abstract thinking skills are essential to succeed in the workplace. Yes, we all have abstract thinking skills, but they’re of varying degrees. Certain jobs require a higher level of abstract thinking skills.
Abstract thinking helps us solve problems, make evaluations and select the right team. To give an example of abstract thinking , how does an HR Head reviewing two candidates with similar resumes make a selection? They probably observe the two candidates during their interviews and deduce their personality types, which involves using abstract thinking skills.
Concrete vs. abstract thinking, examples of abstract thinking, development of abstract thinking, how to improve abstract thinking, training with harappa.
So, let’s understand the meaning of abstract thinking.
Abstract thinking is a way of reasoning—a systematic approach to problem-solving that involves conceptualizing, making generalizations and arriving at conclusions. In abstract thinking, we process information received through our senses and try to connect it to the world.
Here’s another example of abstract thinking: two supervisors have been asked to inspect 100 boxes of mangoes to see if they’ve ripened. Supervisor A checks 10 mangoes per box. If he finds them ripe, he assumes the rest are too and finishes his task in 10 minutes. Supervisor B checks every mango in every box.
From the above abstract thinking example, it’s clear Supervisor A is able to identify a pattern, whereas Supervisor B painstakingly adopts the longer route.
The opposite of abstract thinking is concrete thinking, which is also sometimes called literal thinking. To better understand the meaning of abstract thinking , let’s look at the difference between the two.
Concrete thinking is reasoning based on what you can see, hear, feel and experience in the present moment. It’s sometimes called literal thinking because it focuses on the exact meaning of things. A concrete thinker will think of specific steps in a task and ‘how’ they’ll perform it, unconcerned about anything beyond the assigned task. An abstract thinker will want to know the ‘why’ behind the task.
Abstract thinking means possessing the ability to comprehend concepts that aren’t directly tethered to concrete, physical objects or experiences but are ‘invisible’, such as wisdom or strength. They can conceptualize without the need to see or touch. Abstract thinking is considered part of higher-order reasoning. People who think abstractly can analyze situations, understand concepts, innovate and formulate theories. They’re usually good at:
However, a combination of both abstract and concrete thinking skills is required in a work environment for creativity and productivity. Organizations can select the right people for a task, depending on the type of thinking they veer toward.
Abstract skills are valued not only in the workplace but also in educational institutions. The study of languages is an example of abstract thinking because it entails the expression of abstract concepts. So do science and math, which involve testing hypotheses and theories.
The meaning of abstract thinking can be best expressed through examples.
A wonderful example of abstract thinking is humor. A person sharing a joke is usually able to find connections between seemingly unrelated things.
Let’s have a look at some more examples to get a better idea of the meaning of abstract thinking . A storyteller who can visualize the whole narrative before they start writing it has strong abstract thinking skills. The ability to envision the whole picture without relying solely on existing knowledge is very useful in an organization. It enables one to think critically and find creative solutions to problems.
But, are people born with this ability or is it developed? Jean Piaget, a renowned Swiss psychologist, explained that the development of abstract thinking begins in childhood. According to Piaget, abstract thinking skills develop as children get older, interact with their environment and learn from new experiences.
Let’s now look at Piaget’s Theory of Cognitive Development to understand the development of abstract thinking.
According to Piaget, abstract thinking develops throughout the course of childhood—from birth through adolescence to early childhood. Piaget’s Theory of Cognitive Development identifies four distinct stages of intellectual development during childhood. They are:
The core idea of Piaget’s t heory is that intellectual development in children is not a quantitative process achieved by adding knowledge. As they grow older, there is a qualitative change in their thinking. According to Piaget, abstract thinking naturally develops as children begin to interact with the people and objects around them. The question then arises: Does the development of abstract thinking stop in adulthood?
Yes, it’s possible to improve abstract thinking skills , which in turn enhance your ability to solve problems, understand and share complex concepts and get more involved in creative activities. Once you know how to improve abstract thinking skills, you can make a conscious effort to practice them.
Activities that involve recognizing patterns, analyzing ideas, synthesizing information, solving problems and creating things help improve abstract thinking skills. Extempore theatre, playing with puzzles, optical illusions, creating models, writing poetry and all forms of art train the mind to think in multiple ways beyond the obvious. Abstract skills are also domain-specific. Research has shown those in the field of science can improve their abstract thinking abilities by pursuing art-related activities. Metaphors and analogies also stimulate abstract thinking as they connect the concrete to the abstract. So, abstract thinking is not really abstract but a matter of creating connections, a matter of training.
Harappa’s Thinking Critically course helps professionals, managers and team leaders think through situations before making decisions, engage with opposing views to evaluate all possible outcomes and articulate the reasons behind their decisions. Strengthen your problem-solving skills and grow as a leader with Harappa. Sign up today!
COMMENTS
Abstract thinking is a skill that is essential for the ability to think critically and solve problems. This type of thinking is also related to what is known as fluid intelligence, or the ability to reason and solve problems in unique ways. Fluid intelligence involves thinking abstractly about problems without relying solely on existing knowledge.
Research has shown that people in science, technology, engineering, and math professions enhance their abstract thinking abilities by doing arts and crafts projects. Explore optical illusions.
Critically, the distinction between abstract and concrete thinking remains a topic at the heart of several programs of research in the social (Trope & Liberman, 2010) and cognitive sciences (e.g., Barsalou et al., 2018; Bolognesi et al., 2020; Borghi, 2022).In social psychology, the influential (Adler & Sarstedt, 2021) Construal Level Theory (CLT) posits that psychological distance from events ...
Defining Abstract Thinking. Abstract thinking is a cognitive skill that allows us to understand complex ideas, make connections between seemingly unrelated concepts, and solve problems creatively. It is a way of thinking not tied to specific examples or situations. Instead, it involves thinking about the broader significance of ideas and ...
Both concrete and abstract thinking are necessary for human cognition. For instance, abstract thinkers may engage in the active practice of new ideas, while concrete thinkers might focus on classifying objects and dealing with the literal form of information. While abstract thought may be associated with higher-order cognitive processes, those ...
Abstract reasoning is a component of most intelligence tests. Skills such as mental object rotation, mathematics, higher-level language usage, and the application of concepts to particulars all ...
Abstract Thinking. Abstract thinking is a fundamental cognitive process that allows us to explore and understand concepts beyond the realm of concrete reality. It involves the ability to think conceptually, creatively, and symbolically, enabling us to grasp complex ideas, solve problems, and engage in higher-order thinking.
Abstract thinking involves thought processes that deviate from everyday rhythms, habits, and routines, providing a framework for simple to complex problem-solving scenarios. Delving into the concept of abstract thinking, it encompasses the ability to engage in unconventional thought processes, fostering creativity and strategic thinking.
Abstract Thinking. Abstract thinking refers to a cognitive concept involving higher‐order, or complex, thoughts. To be able to think in an abstract manner implies that one is able to draw conclusions or illustrate relationships among concepts in a manner beyond what is obvious. Often the terms "abstract thought" and "concept formation ...
Individuals with the ability to think abstractly come up with unique ideas and ways in which they can better engage in opportunities. This comes into play in terms of conversation and everyday tasks. 1. Abstract thinking is used for: Improved problem-solving skills. Improved creativity.
To give just a few examples—research on concept formation (e.g. Medin and Schaffer, 1978) focused on the process in which abstract categories are derived from concrete exemplars; research on relational thinking investigated how the extraction of abstract properties facilitates analogical thought (e.g. Gentner and Markman, 1997); and research ...
Topics for future research will be discussed, such as the role of medial RPFC in processing abstract thoughts in the social domain, the possibility of training abstract thinking in the domain of reasoning, and links to education. ... Touching on the relationship between abstract thinking about social vs. non-social information, an older study ...
1. Focus. Abstract thinking focuses on the intangible, theoretical, and complex aspects of a situation. It involves thinking beyond the immediate and considering possibilities and hypotheticals. On the other hand, concrete thinking focuses on the immediate, tangible, and factual aspects of a situation.
Abstract thinking is the ability to understand concepts that are real, but which are not directly tied to concrete physical objects and experiences. Abstract thinking must be complementary with ...
Abstract thinking is crucial for problem-solving, creativity, and critical thinking. Fortunately, there are many ways to improve these skills in your everyday life. 1. Incorporate puzzles into your life. Solving puzzles is a great way to practice abstract reasoning and exercise your brain.
Writing at a time in which speculative ways of thinking appear to be undergoing a reprise across the social sciences and humanities - whether through engagements with speculative cosmology (Stengers, 2006), speculative empiricism (Debaise, 2017), speculative fabulation (Haraway, 2011), speculative research (Wilkie et al., 2017), or speculative realism (Bryant et al., 2011) - in this ...
Abstract thinking and concrete thinking are two types of thought processes. Concrete thinking focuses on things that are real and tangible, while abstract thinking is a higher-level mode of thinking that involves processing theoretical concepts and allows us to make connections and see patterns. It's important to remember that you need both ...
Concrete Thinking Definition: Concrete vs. Abstract Thinking. Employing different forms of thinking can help you with problem-solving, inviting you to make sense of the world around you when grasping both abstract ideas and concrete thoughts. Learn more about concrete thinking.
This 'type' of abstract thinking can offer distinct advantages in certain contexts. One of the key strengths of 'type' thinking is its focus on the essence rather than the details, on the universal rather than the particular. This form of abstract thinking allows for a broader view, unencumbered by the constraints of specific imagery.
Abstract thinking is generally highly correlated with problem-solving ability which is predictive of better adaptive functioning. Measures of conceptual reasoning, an ecologically-valid laboratory measure of problem-solving, and a report measure of adaptive functioning in the natural environment, were administered to children and adults with and without autism.
Research has shown that anxious and depressed individuals tend to think about their problems abstractly. ... (CNT), which aims to train individuals out of an abstract thinking style and into a more concrete one. In an initial study, an experimenter guided depressed individuals to practice thinking concretely about six provided scenarios (half ...
Concrete Vs. Abstract Thinking ... Research has shown those in the field of science can improve their abstract thinking abilities by pursuing art-related activities. Metaphors and analogies also stimulate abstract thinking as they connect the concrete to the abstract. So, abstract thinking is not really abstract but a matter of creating ...
Journal of Personality and Social Psychology, 90, 578-596.], we predicted that thinking more abstractly would make one feel more powerful. Indeed, in four experiments, abstract thought led to a ...