Working memory as a moderator of training and transfer of analogical reasoning in children

Abstract

Working memory is related to children’s ability to solve analogies and other inductive reasoning tasks. The aim of this study was to examine whether working memory also plays a role in training and transfer effects of inductive reasoning in the context of a short training procedure within a pretest-training-posttest-transfer design. Participants were 64 children, aged 7–8 years (M = 7.6 years; SD = 4.7 months). All of the children were pre-tested on inductive reasoning and working memory tasks. The children were trained in figural analogy solving according to either the graduated prompts method or practice without feedback. The children were then post-tested on the trained task and three additional inductive reasoning measures. Regression models revealed that visuo-spatial working memory was related to initial performance on each of the inductive reasoning tasks (r ≈ .35). Children’s improvement from pretest to posttest in figural analogy solving, as measured with item response theory models, was somewhat related to visuo-spatial WM but not verbal WM scores or pretest scores. Furthermore, transfer of reasoning skills to an analogy construction task was related to initial ability, but not working memory; transfer to two inductive reasoning tasks with dissimilar content was not apparent. Performance change and ability to transfer trained skills to new tasks are not often used in psycho-educational assessment but may be separate constructs indicative of children’s learning and change.

Introduction

Many studies have demonstrated a strong relationship between working memory (WM) and inductive reasoning ability in adults (e.g., Buehner et al., 2005, Kyllonen and Christal, 1990) as well as children (e.g., Alloway et al., 2004, Tillman et al., 2008). Generally these studies focus on the role of working memory on inductive reasoning performance in a single testing session. However, working memory may influence how well a person profits from instruction in solving reasoning tasks. For example, working memory may become more efficient due to training and this automation of skills may affect training and transfer effects (e.g., Dahlin et al., 2008, Jaeggi et al., 2008, Klingberg et al., 2002). It is therefore plausible that working memory plays a role in children’s learning and change in inductive reasoning. In the present study we apply dynamic testing principles to train and assess children’s progression in inductive reasoning and examine whether working memory moderates training and transfer effects.

Dynamic testing diverges from traditional, static assessment methods in that feedback is provided by the examiner in order to facilitate learning and gain insight into learning efficiency (Elliott, Grigorenko, & Resing, 2010). In principle, dynamic testing formats do not differ from cognitive training formats, although cognitive training is often geared towards more extensive interventions. In dynamic testing, various indices are used to examine learning, such as performance improvement following feedback interventions (e.g., Hessels, 2009), the amount and type of instruction that best aides task solution (e.g., Bosma and Resing, 2012, Resing and Elliott, 2011), and the ability to transfer these newly developed skills to other problems (Campione & Brown, 1987). The current study uses a simple test-intervention-test format and aims to investigate children’s progression and transfer in the domain of inductive reasoning. The intervention principles we used come forth from dynamic testing research, more specifically the graduated prompts approach (e.g., Campione and Brown, 1987, Resing, 1993).

Inductive reasoning tasks are quite frequently used in cognitive testing and training studies (e.g., Ferrara et al., 1986, Resing et al., 2009), because they are considered central to intelligence (Carpenter et al., 1990, Carroll, 1993). Classical analogies (A:B::C:?) and figural matrices (see Fig. 1) are often included as measures of cognitive ability (Freund and Holling, 2011, Primi, 2001). Analogical reasoning, a form of inductive reasoning, is deemed essential to school learning and refers to the capacity to learn about a new situation by relating it to a structurally similar more familiar one (e.g., Goswami, 1992). The ability to reason by analogy is assumed to develop with great variability throughout childhood (e.g., Leech et al., 2008, Siegler and Svetina, 2002). Older children tend to perform better than younger children, which may be explained by improvements in efficiency of working memory capacity (Fry and Hale, 2000, Kail, 2007). Improvement in analogical reasoning can take place spontaneously with practice (e.g., Tunteler & Resing, 2002), with further learning effects provided by feedback (Cheshire, Ball, & Lewis, 2005), self-explanation (Siegler and Svetina, 2002, Stevenson et al., 2009) and other training formats (e.g., Alexander et al., 1987, Klauer and Phye, 2008). Training with graduated prompting techniques, a specific form of intervention used in dynamic tests, has been shown more effective than practice alone with regard to both learning and transfer (Bosma and Resing, 2006, Ferrara et al., 1986).

The ability to spontaneously generalize a problem-solving approach taught in one context to a different but applicable situation is referred to as transfer. This is considered an important aim of formal schooling (e.g., De Corte, 2003). Basically, each form of transfer requires noticing an analogy or similarity between two more or less similar problems (e.g., Holyoak, 1984). However, numerous studies have shown that transfer does not occur easily as learning is context-bound and children rarely recognize that their acquired problem solving skills can be applied in novel situations (e.g., Barnett and Ceci, 2002, Bransford and Schwartz, 1999, Detterman, 1993, Siegler, 2006). According to Holyoak (1984) the process of finding an analogy between the base (trained task) and the target problem (transfer task) will end unsuccessfully “if the problem solver fails to encode elements of the schema, in either the base or the target “ [problem], (Holyoak, 1984, p. 218). The use of base and target problems related to (the development of) analogical reasoning is mostly studied in the context of solving problem analogies (e.g., Gentner and Holyoak, 1997, Holyoak and Nisbett, 1988), but the processing steps for solving problem analogy versus classical analogy paradigms are equivalent: base and target problems (i.e. training versus transfer tasks) must have been mastered, features of both tasks have to be encoded, the potential relationship must be noticed, relationships of relevant task aspects must be mapped, and in this whole process inference and retrieval processes play an essential role (e.g. Gentner & Holyoak, 1997: Chen, 1995, Tunteler and Resing, 2004).

Jacobs and Vandeventer (1971) discerned near, far, and very far transfer, depending on the surface similarity of base and target task. Resing (1993) and Roth-van der Werf, Resing, and Slenders (2002) systematically assessed whether children trained in solving inductive reasoning tasks were able to generalize the taught problem solving skills to superficially similar and dissimilar problems measuring the same inductive reasoning skills. In their studies, trained children improved more on superficially similar tasks than those who only practiced with the same items. Progression on superficially dissimilar tasks, however, could be attributed to practice effects. Children may show greater transfer of knowledge when the targeted strategy has been mastered (Siegler, 2006). For example, Tunteler and Resing (2010) found that 8-year-olds who obtained high scores on a geometric analogy task improved more on a verbal analogies near-transfer task during the posttest than children with lower geometric analogy scores while using a microgenetic design with a training versus a repeated practice condition. Also in this study, progression in scores on the superficially dissimilar verbal analogy task was independent of having received training – practice alone appeared to elicit transfer in high ability children. Aside from practice effects, instructional conditions also appear to play a role in near-transfer. For example, Harpaz-Itay, Kaniel, and Ben-Amram (2006) found that 12-year-olds trained in verbal analogy solving also improved on geometric and numerical analogies, however, the transfer effects were greater in children trained in an analogy construction task as opposed to multiple-choice solution.

In this study we investigated the transfer of trained analogical reasoning to three related inductive reasoning tasks differing in superficial similarity, in content similarity, or in both content and superficial similarity. First, the geometric analogies task used by Tunteler and Resing (2010) was chosen as it differed in superficial structure from the figural analogies on which children were trained, but the deeper solving pattern (classical analogy) remains the same. Second, an analogy construction task (e.g., Harpaz-Itay et al., 2006) in a form for younger children where roles of examiner and child are reversed (Bosma & Resing, 2006) was administered, which differed in both surface structure and content, although the solving principle (classic analogy) remained the same. For this task, however, children had to actively demonstrate their understanding of the complete task solving process because they had to explain how to solve the tasks. Finally, a geometric and numerical seriation task (Durost, Gardner, & Madden, 1970), also included in Roth-van der Werf et al.’s study (2002), was used that differed in both surface and deep structure (i.e. series completion rather than analogical reasoning).

The structure of working memory, in which a central executive system is considered responsible for controlling attention and information processing and regulates the operation of two domain-specific systems: (1) phonological loop and (2) visuo-spatial sketchpad, appears present and assessable in young children (Gathercole et al., 2004, Swanson, 2008). Furthermore, working memory is related to young children’s ability to solve analogies (e.g., Cho et al., 2007, Morrison et al., 2001). For example, Richland, Morrison, and Holyoak (2006) found that children’s performance on scene analogy tasks was related to their working memory capacity.

The question then arises whether WM also influences the learning and transfer of inductive reasoning. Studies in which participants are trained on WM tasks appear to transfer to improvements in fluid reasoning. Jaeggi et al. (2008) found that training university students in an n-back task improved both working memory efficiency and fluid reasoning; the degree of improvement was dosage-dependent where more practice led to greater transfer effects. Similar transfer effects to reasoning tasks following extensive working memory training were found in children (Klingberg, 2010, Klingberg et al., 2002).

An aim of cognitive training studies is to assess cognitive change in learning and measure transfer of learning after an intervention. It is of particular interest to determine whether working memory moderates training and transfer effects in a dynamic testing context. For example, Tunteler and Resing’s (2010) microgenetic study of geometric analogy solving, children with a less efficient WM caught up with their peers with better WM performance following a graduated prompts training procedure. However, a moderating effect of WM was not present in a dynamic test utilizing a seriation task, where children with lower WM scores improved comparably to those with greater WM scores (Resing, Xenidou-Dervou, Steijn, & Elliott, 2011). An explanation for the differing role of WM may be found in task choice: visuo-spatial versus verbal. Perhaps visual WM is a better predictor of training and transfer effects in a cognitive training study utilizing visual inductive reasoning tasks. We therefore extend the work of previous studies by including measures of both verbal and visuo-spatial WM (e.g., Alloway et al., 2004) while assessing the children’s progression and transfer of inductive reasoning skills.

The main focus of this study was whether verbal or visuo-spatial working memory moderates children’s learning and transfer of inductive reasoning in a cognitive training context. First of all we expected children to progress in solving inductive reasoning problems as a consequence of training and that visual–spatial working memory would be related to these training effects (hypothesis 1; e.g., Tunteler & Resing, 2010). Secondly, we expected children to demonstrate transfer of learned skills to superficially similar inductive reasoning tasks (hypothesis 2a; Roth-van der Werf et al., 2002) and we investigated whether transfer effects were moderated by working memory (hypothesis 2b).