The main aim of this thesis was to examine whether learning processes occur in fluid intelligence (Gf) tasks, whether it is essential for them to occur for induction to take place and whether they contribute to individual differences in performance. In mainstream differential research, Gf is conceptualised as a factor important in induction tasks that are considered novel and context-free (Cattell, 1963, 1987). Thus, performance has typically been assumed to be uninfluenced by previous acquisitions of knowledge structures. Sources of individual differences in Gf task performance have been attributed to working memory capacity (WMC), particularly individual differences in the ability to combat proactive interference. In contrast, the cognitive reasoning literature associates induction with the use of prior conceptual knowledge. A middle-ground position is that Gf tasks may require learning to occur across the task, which would draw upon WMC. That is, individual differences in Gf task performance may be due to knowledge learnt across the task, rather than knowledge brought to the task. Gf items have traditionally been presented in easy-to-hard order but easier items may unintentionally provide learning opportunity for harder items. This would contradict both classic and modern test theories which make the assumption that items within a task are independent of each other.
The learning hypothesis was explored in the current work along with the issue of whether it is possible to reliably solve complex Gf items without some relevant, prior knowledge. Also, the distinction between within-item induction and across-item learning was investigated, along with the relationship between across-item learning and proactive interference. An experimental-differential approach was used to manipulate learning opportunity within Gf tasks in four experiments.
The first experiment examined whether learning takes place in Raven’s Advanced Progressive Matrices (Raven, 1962) and if so, to what extent this learning is a source of individual differences. Specifically, whether rule learning within the task is necessary for abstraction to take place and whether those of higher Gf ability learn faster than those of lower Gf ability.
The next three experiments examined the distinction between knowledge that may be brought to the task, learning that occurs across multiple items in the task and induction within a single item that may be independent of any prior knowledge including knowledge learnt across the task. The effect of proactive interference as a consequence of learning and knowledge was also investigated. The experiments examined which of these are relevant to general performance (i.e., common to everyone) and which contribute to individual differences. Learning-opportunity was manipulated in a task from the cognitive reasoning literature – the Modified Sweller and Gee (MSG) Task. Traditional Series Completion tasks were used as Gf markers and data analyses employed Hierarchical Linear Modelling (HLM).
The advantage of the MSG Task is that it has qualities typical of Gf tasks but unlike conventional Gf tasks, it is able to assess within-item induction in isolation from any potential influences from across-item learning. This is because it involves multiple attempts within each item with feedback, allowing single items to be administered reliably. When across-item learning opportunity is absent, the MSG Task is able to provide an estimate of participants’ within-item induction success through the number of attempts they need within a single item. The amount participants learn across items can be approximated by comparing performance on items preceded by learning opportunity (i.e., easier items with similar rule-types), with items not preceded by learning opportunity. Lastly, the effects of proactive interference can be evaluated by comparing performance on items preceded by interference (i.e., items with different rule-types) with those that are not preceded by interference.
Overall, it was found that with no learning opportunity leading up to novel items (to provide relevant prior knowledge), solution was nearly impossible for all participants. When learning opportunity was provided, all participants were able to greatly improve their performance but those of higher Gf improved more. It was concluded that while Gf tasks appear visually novel, they must contain a combination of familiar elements in earlier items (which make use of knowledge that participants bring to the task) and novel elements in later items (which require the use of knowledge that must be learnt from earlier items); and those of higher Gf perform better on Gf tasks, at least partly because they are able to benefit more from the learning opportunity provided by earlier items. It was found that proactive interference affects all participants when they learn from prior items. However, insufficient evidence was found to suggest that the ability to combat proactive interference contributes to individual differences in performance.