Cognitive segmentation and fluid reasoning in childhood

Abstract

The ability to solve novel complex problems predicts success in a wide range of areas. Recent research suggests that the ability to cognitively segment complex problems into smaller parts constrains nonverbal reasoning in adults. This study aimed to test whether cognitively segmenting problems improves nonverbal reasoning performance for children as it does for adults. A total of 115 children aged 6-10 years completed two versions of a modified traditional matrix reasoning task in which demands on working memory, integration, and processing speed were minimised, such that the only significant requirement was to break each problem into its constituent parts. In one version of the task, participants were presented with a traditional 2×2 Matrix and asked to draw the missing matrix item into a response box below. In a second version, the problem was broken down into its component features across three separate cells, reducing the need for participants to segment the problem. As with adults, performance was better in the condition in which the problems were separated into component parts. Children with lower fluid intelligence did not benefit more in the separated condition than children with higher fluid intelligence, and there was no evidence that segmenting problems was more beneficial for younger than older children. This study demonstrates that cognitive segmentation is a critical component of complex problem-solving for children, as it is for adults. By forcing children to focus their attention on separate parts of a complex visual problem, their performance can be dramatically improved.

Keywords: Cognitive segmentation; analogical reasoning; fluid intelligence; matrix reasoning; problem-solving.

Figure 1.

(a) Example of a traditional matrix reasoning task item. (b) Cognitive segmentation combined item. (c) Cognitive segmentation separated item. Participants see cognitive segmentation task items in black and white. Features are shown in colour here for illustration purposes.

Figure 2.

Association between performance in the combined and separated conditions and fluid reasoning (Cattell). R² values come from simple correlations per condition (combined: R² = .16; separated: R² = .14).

Figure 3.

Association between performance in the combined and separated conditions and age in months. R² values come from simple correlations per condition (combined: R² = .14; separated: R² = .03).

Figure 4.

Mean number of errors of each type, within the Combined and Separated conditions. Error bars are 95% between-subject confidence intervals.