6
Transfer of Learning and Flexibility in Childhood
Jérôme CLERC and Laureen JOSSERON
LPNC, Grenoble Alpes University, France
6.1. Introduction
Transfer of learning involves applying what has been learned to new tasks or situations in a flexible manner (Helsdingen et al. 2011, p. 383). In psychology, research on transfer emerged in the early 20th century (Thorndike 1903) and remains a vibrant field of research (Chang et al. 2019; Sydney and Thompson 2019), attesting to the importance of the phenomenon to researchers.
Studies have long focused on the transfer capacities of adults, based on paradigms that were initially associationist (Thorndike and Woodworth 1901), then behaviorist (Osgood 1949) and cognitivist (Gick and Holyoak 1980). The associationist models focused mainly on perceptual learning (estimation of geometric areas) and did not consider the need for a form of flexibility in order to successfully transfer this learning.
Behaviorist models were based on precise correspondences between stimuli and learned responses; although the concept of generalizing the response to other stimuli can be related to the transfer of learning, flexibility was not a central concern to researchers. As we shall see, it was not until the cognitivist movement that the need to be flexible in order to transfer learning became apparent. But before we get to that point, the first question is whether children can also transfer their learning.
6.1.1. The child who transfers: a little history
As early as the beginning of the 20th century, Woodrow (1917) showed that children as young as 10 years old were capable of transferring the learning of rules for sorting geometric shapes. At the turn of the 1980s, in the midst of the golden age of cognitivism, researchers intensified their work on the transfer of learning in children, in particular thanks to Ann Brown’s pioneering work on transfer by analogy (Brown et al. 1986; Brown and Kane 1988; Brown 1990).
This work has shown that transfer plays a central role in cognitive development, insofar as a child actively builds on what they already know to learn what they do not yet know (Cox 1997). It is therefore crucial from infancy onwards, as it enables the child to deal with new tasks by reusing previously learned knowledge, a principle – fundamentally Piagetian – upon which analogies are based. Thus, when transferring learning, the child does not need to relearn everything but rather to adapt the knowledge to the new task encountered (Schwartz et al. 2012).
Indeed, transfer requires that the knowledge to be transferred be adapted to account for differences between the first task (main task) and the second task (transfer task). Considered a foundation of cognitive functioning during childhood, transfer is an issue of interest to both developmental (Schroth 1992; Klahr and Chen 2011) and educational psychology researchers (Goldstone and Day 2012).
It may also be of interest to differentialists who may see it as an opportunity to study the child’s processes of adaptation to the new task: the intra-individual variability specific to transfer, since the same child performs several tasks, as well as the study of individual profiles of children who are more or less good “transferers”, which amounts to studying the inter-individual variability of intra-individual variations.
6.1.2. Surface, structure, context
Transfer of learning is enabled by similarity between tasks (Thorndike and Woodworth 1901; Taatgen 2013). This can be broken down into similarity between surface elements and similarity between structural elements (Gick and Holyoak 1980; Gentner 1989). The surface elements make up the outward appearance of a task and specifically include perceptual elements. The structure of a task is defined by its constituent elements, which cannot be modified without changing the logic of its resolution (Gentner and Toupin 1986; Sander and Richard 2005; Chi and VanLehn 2012).
Moreover, using structural similarity between two tasks in order to effect a transfer from one to the other implies a strong command of the structure of the main task, so that it can be adapted to the transfer task by taking surface differences into account (Nokes 2009; Chi and VanLehn 2012).
Recently, Klahr and Chen (2011) proposed a general model of transfer that takes the classical dimension of surface and structural similarities as well as incorporating the elapsed time between tasks and the similarity of presentation contexts as additional dimensions. This is the first model in which the role of contextual similarity in transfer is taken into account.
Referring to the work on the effects of context on learning and the hypothesis of encoding specificity (Tulving 1983), the role of contextual similarities is to be understood in this model in the same way as that of similarities between tasks. Thus, the more similar the presentation context of the main task and the transfer task, the better the transfer.
To date, Klahr and Chen’s (2011) model is the only one to jointly take account of task similarities and context similarities. From this point of view, it represents a major advance in research on transfer of learning. Furthermore, it can be hypothesized that the higher the degree of similarity between tasks and contexts, the less cognitive flexibility is required to mentally switch between tasks and contexts.
Klahr and Chen’s (2011) model thus provides a simple articulation of transfer and flexibility, placing the amount of flexibility required to transfer effectively at the heart of successful transfer. The stronger the similarities, the less flexibility is required for transfer, and vice versa. It also allows for transfer to be envisaged regardless of the type of knowledge envisaged, and allows the transfer of declarative versus procedural knowledge.
To test transfer, researchers often use isomorphic tasks, defined as being identical in structure but different on the surface (Gick and Holyoak 1980; see Chapter 5). Numerous studies using isomorphic tasks have shown that children have difficulty transferring learning, with transfer frequently accompanied by a decline in performance (Brown et al. 1983; Holyoak et al. 1984; Pacton et al. 2001; Detable and Vinter 2006; Clerc and Miller 2013; Clerc et al. 2017). The difficulty in transferring learning from a main task to a transfer task is related to surface differences between the tasks (Gentner et al. 1993). Without explicit assistance, children have difficulty overcoming surface differences to identify the common structure between two tasks and to transfer learning from one to the other (Holyoak et al. 1984; Brown et al. 1986; Brown and Kane 1988; Gamo et al. 2010; see Brown (1990) for a contrary result in children aged 17–36 months).
Two explanations are typically offered to explain children’s difficulties in transferring : 1) the structure of the main task is not understood well enough to allow transfer (Chi and VanLehn 2012); 2) metacognitive help is needed from the adult (Ringel and Springer 1980). In this chapter, we discuss cognitive flexibility as a third avenue that may help explain the transfer difficulties observed in children.
For many authors, flexibility is integral to transfer, the latter involving flexible application of the knowledge acquired in the main task to a transfer task (Helsdingen et al. 2011; Stott and Hobden 2016). This idea can be found in works that address reasoning by analogy (Campione et al. 1985; Chen and Siegler 2013) or problem-solving performance in Tower of Hanoitype tasks (Schiff and Vakil 2015).
The idea that flexibility is required to adapt prior knowledge from a first task, in order to successfully transfer it to a new task, is therefore recognized. Surprisingly, however, the literature rarely explicitly mentions the processes of cognitive flexibility involved.
For example, Ann Brown’s work on analogical transfer in young children emphasized that transfer by analogy is enabled by the existence of a mental model (a schematic representation) of the main task that is sufficiently abstract and flexible to allow its reuse in a transfer task (Brown et al. 1986; Brown and Kane 1988). Thus, the child is said to be able to reuse knowledge in a flexible manner, with transfer even being confused with flexibility, as evidenced by the recurring expression flexible transfer.
At the time this work was conducted, cognitive flexibility had not yet benefited from the recent research interest in executive functions, which are control functions (Diamond 2013), and the notion of representational flexibility (Eichenbaum 1997; Hayne 2006). As a result, it was not defined as precisely as it is today. Recent theoretical considerations of the nature of cognitive flexibility and the processes that comprise it (Clément 2006; Cragg and Chevalier 2012) now allow us to go further.
In the remainder of this chapter (section 6.2), we will begin by providing an overview of the developmental evolution of the transfer of learning. We will then take a closer look at the links between flexibility and transfer, focusing on analogical and strategic transfer, and discuss recent research that suggests a protective role of cognitive flexibility on performance in transfer tasks. The chapter concludes with suggested avenues of research to be explored to better understand the links between the transfer of learning and flexibility (see section 6.4).
6.2. Transfer of learning: a developmental overview
It is accepted that babies begin to learn from birth, in particular by identifying regularities and invariants in their environment. At the same time, their neuromotor and neurocognitive development allows them to adjust quite early to variations in their environment, by adapting their actions to new situations encountered.
One of the first forms of transfer observed in infants is intermodal transfer, whereby an object previously identified in one sensory modality can be recognized in another sensory modality. Three-day-old infants are thus able to visually identify a small object that they are seeing for the first time, but which they had previously held in their hand (Streri and Gentaz 2004). This shows that transfer is a skill that is present from birth. Nevertheless, this is a transfer without prior learning, since it occurs before the baby has had the opportunity to learn that it is the same object by repeated associations of the object touched and seen. Nevertheless, from birth, human beings are endowed with the ability to transfer information from one situation to another.
In the following sections (6.2.1–6.2.4) we present work showing that transfer of learning can be observed as early as a few months of age, and successively takes the form of perceptual, imitative, analogical and strategic transfer.
6.2.1. The transfer of perceptual properties
Transfer of learning is very early in ontogenetic development. One of the first manifestations is the transfer of visual properties of objects, visible during the first year. Several studies have used a habituation–dishabituation paradigm, in which a stimulus is presented visually several times, leading to a progressive decrease in the child’s visual fixation time. A new stimulus is then presented, causing an increase in visual fixation time, which is interpreted as the child having recognized a new and different stimulus from the previous one (reaction to novelty).
Five-month-old children, who were repeatedly presented with a photo of an object during the habituation phase, then stared for less time at the real object presented in front of them (when the photo was removed and replaced by the object itself) than at another object that was also presented and whose photo they had not seen. The shorter visual fixation time for the previously photographed object than for the new object indicates that the children have transferred the perceptual properties of the photographed object (2D) to the real object (3D) (DeLoache et al. 1979).
Using the same paradigm, Rose (1977) showed that 6-month-old children transfer visual properties from 2D to 3D, but also from 3D to 2D. By 9 months, children are able to transfer visual properties from 2D to 3D, not just from pictures but also from drawings of objects (Jowkar-Baniani and Schmuckler 2009).
6.2.2. Transfer by imitation
A second manifestation of transfer can be seen in older children in action-imitation tasks. Action-imitation is considered transfer of learning in reference to the view that transfer occurs between two situations whenever the task and/or context changes (Barnett and Ceci 2002; Klahr and Chen 2011).
In these works, it is mainly the context that changes, insofar as the action to be imitated is shown on a paper medium (book) or digital medium (television, digital tablet) while the child will have to perform this action physically. The task also varies, but to a lesser extent since the action to be imitated does not change, even if perceptual differences can be detected between the action shown by the model and the action performed by the child (texture, colors).
From the second year, children are able to reproduce by imitation actions that have been previously demonstrated in front of them by an adult or seen in a book (2D) or on a video (3D). Children of 14 months of age correctly imitate an action seen on television after a delay of 24 h (Meltzoff 1988). This transfer by imitation is still observed after a 2-week delay following the initial presentation in 18-month-old children, and a 4-week delay in 24-month-old children (Brito et al. 2012).
Transfer is less successful when there are perceptual differences (color or image precision) between the objects used in previously seen actions and in that which the child must produce (Barr and Hayne 1999; Hayne et al. 2003; Ganea et al. 2008).
Demonstration of the action by an adult is the condition that leads to the highest transfer performance (correct imitation of the action), due to a significant overlap between the two actions actually performed, one by the experimenter and the other by the child (Barr and Hayne 1999; Anderson and Pempek 2005; Simcock and Deloache 2006).
6.2.3. Solution transfer by analogy
A third manifestation of transfer, which appears in the third year, is the transfer of solution by analogy, between two or more problems. Analogy consists of relating a known situation to a new, less well-known situation in order to facilitate the resolution or understanding of the latter (Ripoll and Coulon 2001).
Reasoning by analogy consists of extracting a relational structure common to both situations (Gick and Holyoak 1980; Gentner 1983). To do this, the child must map the elements of the source problem (the first problem, encountered previously) onto those of the target problem (the second problem, to be solved), as well as the relationships that exist between these elements (mapping), and then transfer the knowledge from the source problem to the target problem by adapting it to the new constraints specific to the latter (Holyoak et al. 1984; Cauzinille-Marmèche et al. 1985).
Analogical transfer is a complex form of transfer, which makes it less likely to be observed in young children. However, many studies have shown that young children are capable of analogical transfer to varying degrees. The transfer of solutions from a first problem to a second analogous problem is facilitated by the use of tasks adapted to the young age of the children (Brown et al. 1986; Brown and Kane 1988).
As early as age 3, children are capable of analogical transfer when they receive adult assistance in making explicit the relationships between the source and target tasks (Brown and Kane 1988) or when there is strong perceptual (Holyoak et al. 1984) or relational (Thibaut and Witt 2015) similarity between the tasks.
From the age of 4, many children are able to transfer their knowledge spontaneously (Brown and Kane 1988; Tunteler and Resing 2002). Moreover, among the explanatory hypotheses for the development of analogical reasoning in children, one concerns the maturation of executive functions (Diamond 2013), among which we find cognitive flexibility (Thibaut and Witt 2015; Thibaut and French 2016).
6.2.4. The transfer of cognitive strategies
Cognitive strategy transfer, or strategic transfer, is a particular case of cognitive transfer (Chen and Klahr 1999; Clerc and Leconte 2003) that can be observed from the age of 3: it consists of the reuse, in a new task, of a strategy previously acquired in a different task (Detterman and Sternberg 1993).
Cognitive strategies are procedures or sets of procedures implemented to achieve higher level goals (Lemaire and Reder 1999). Research on strategy transfer in children has focused on memory strategies in particular (Pressley and Hilden 2006; Bjorklund et al. 2009).
Numerous research studies have shown at least partially successful transfer of memory strategies in kindergarteners (Borkowski et al. 1976; Lange and Pierce 1992; Clerc and Miller 2013) or elementary school-aged children (Borkowski et al. 1978; Pressley and Dennis-Rounds 1980; Black and Rollins 1982; O’Sullivan and Pressley 1984; Carr et al. 1989; Schwenck et al. 2007).
Transfer can also be applied to problem-solving strategies, whether they are relatively general strategies, such as matching strategies (Blöte et al. 1999; Clerc et al. 2017), control of variable strategies (Chen and Klahr 1999) or positive capture (Fay and Klahr 1996; Klahr and Chen 2003), or more specific strategies such as arithmetic strategies (Gamo et al. 2010; Riggs et al. 2017) or tool manipulation (Brown 1990; Elsner and Schellhas 2012).
Nevertheless, there are recurrent difficulties for children in transferring memory or problem-solving strategies. Thus, children often benefit from help from an adult or from the implementation of an experimental structure to transfer their cognitive strategies (Ringel and Springer 1980; Lange and Pierce 1992; Crowley and Siegler 1999; Klahr and Chen 2003; Rittle-Johnson 2006; Cook et al. 2013).
The studies reported so far illustrate the precociousness of transfer of learning during development, thus testifying to the formidable capacity of human beings to adapt to their environment from the beginning of their existence. Transferring knowledge learned in a previous task, while adapting it to the characteristics of the new task, means the child does not have to relearn everything with each new task or situation encountered.
Taking advantage of the continuous increase in their cognitive capacities as a result of their cerebral and cortical development in particular (Fuster 2002), the child can progressively transfer more and more complex knowledge: perceptive properties, actions performed by others, relational properties, cognitive strategies.
Consistent with the neuro-constructivist theoretical framework (Westermann et al. 2010), the child also appears sufficiently malleable that environmental characteristics can influence their ability to transfer. Work has thus shown better transfer when the transfer task is presented in the same context as the main task rather than in an equally novel context (for a review, see Robins (1996)), a result consistent with Klahr and Chen’s (2011) model.
This dual movement of the child toward their environment and vice versa contributes to the child interacting with their environment in a proactive and efficient way: transfer of learning is a manifestation of this. To the extent that transferring learned knowledge requires adaptation to the new task, the cognitive functions required for adaptation to a new task should logically contribute to transfer.
Among these functions, identified as the executive functions, there is the question of the specific contribution of cognitive flexibility to the transfer of learning.
6.3. Transfer and flexibility
As we have noted, much research in developmental and educational psychology has shown that transfer of learning in children is possible but difficult. Several explanations for this difficulty have been put forward, the two main ones being the need to understand the main task in depth and the high metacognitive level required. A third source of difficulty appears to be the need for flexibility in transferring.
This third pathway is, however, recent in the literature, and the precise nature of the relationship between flexibility processes and transfer of learning is not always addressed. This is particularly the case in a number of works that defend the idea that flexibility is necessary for reasoning by analogy, without specifying the nature of the processes at work (Brown et al. 1986; Brown and Kane 1988; Schiff and Vakil 2015; Thibaut and French 2016). We propose to consider flexibility from two different perspectives, one conceptual and the other attentional.
Such a dichotomy is (necessarily) somewhat artificial because of the partial overlap of these two angles of attack, but it seems to be conducive to progress in the identification of flexibility processes favorable to transfer.
6.3.1. Transfer and conceptual flexibility
A first way of dealing with the role of flexibility in transfer of learning is to consider it from a conceptual or semantic perspective. It is then defined as representing the same task from different perspectives.
This approach is used in problem solving, in particular, a domain in which flexibility is linked to creativity, since it is seen as producing new procedures or strategies when the existing ones do not allow the problem to be solved (Clément 2006). A problem is a situation involving both the existence of a goal and the absence of an immediately available solution to achieve it (see Chapter 5). It is characterized by the elements that compose it, the relationships between these elements and the constraints that must be respected to reach the goal (Clément 2006). The discovery of a solution to the problem is enabled by the person’s ability to consider different ways of achieving the goal or, in other words, the ability to be flexible.
Indeed, when an individual experiences difficulty in finding the solution to a problem, this difficulty is interpreted as the fact that they have coded (understood) the properties of the situation in an erroneous way. They then find themselves in an impasse, from which they must disengage by articulating different perspectives on the problem in order to draw out its salient aspects.
This integrated consideration of different points of view, which will enable the individual to construct a new representation of the problem and break the deadlock, is a form of representational flexibility (Clément 2001, 2008). Thanks to this flexibility, the individual generates an alternative mental representation, more faithful to the characteristics of the situation encountered: this is the semantic recoding hypothesis (Richard and Zamani 2003; Gamo et al. 2010).
In the theoretical framework of semantic recoding, procedures that are implemented from an erroneous representation of the situation lead to dead ends, and the individual must revise their representation of the problem and generate a new one that will enable them to solve it (Clément and Richard 1997).
To do this, they must disengage from the action undertaken and proceed to the semantic recoding of the situation, that is, reinterpret it from a new angle. The latter is conceptual, since it is the meaning attributed to the situation and its characteristics that pushes the individual to use it to disengage from an initial erroneous representation and form a new representation that is more adequate and more effective for solving the problem. Flexibility would thus allow the individual to develop different perspectives on the problem in order to choose the most suitable one, that is, the one giving access to efficient procedures of resolution.
A study by Gamo et al. (2010) showed that training in semantic recoding facilitates the transfer of an arithmetical strategy between two isomorphic problems in children aged 8–12. Moreover, it seems that semantic recoding of a problem situation requires a high level of abstraction.
Indeed, representing the problem at a low level of abstraction would not allow detachment from the surface characteristics and consideration of the source task and the target task in terms of their common structure (Sander and Richard 2005). On the other hand, a high level of abstraction would allow flexible consideration of several tasks in terms of their common structure.
The concept of semantic recoding, used in the field of problem solving, is close to that of representational redescription in developmental psychology, proposed by Karmiloff-Smith (1992). Both are based on the idea that a mental representation, once formed, is likely to be modified under the effect of interactions between the individual and their environment.
These two conceptions are based on the common idea that a representation is by nature flexible since it is subject to change. This is also the case with the thesis defended by Eichenbaum (1997) in the context of the theory of representational flexibility. This is a theory of the functioning of declarative memory, based on the hypothesis of encoding specificity (Tulving 1983).
This theory predicts that the greater the difference between the encoding and retrieval contexts, the more flexibility is necessary to retrieve the information. Thus, during infancy, memory performance would be largely determined by the presence, during the recall phase, of cues already present during the encoding phase. Even minor differences between the cues present during these two phases would cause performance in the recall phase to drop. Nevertheless, as the child encounters new tasks in new contexts, they would become aware of more and more cues, which they would be able to use in different recall situations involving this or that cue already encountered. They would thus become progressively more tolerant of differences between encoding and recall situations, therefore becoming more efficient at retrieving information stored in memory. This would translate directly into increasing flexibility in the ability to retrieve in memory information that which was initially encoded in a different context.
Resituated in the context of transfer of learning, representational flexibility would allow for the decontextualization of information, and thereby facilitate the transfer of information to a new task and/or context. Note that this view is consistent with Klahr and Chen’s (2011) model of transfer, which predicts that success in transfer is proportional to the degree of contextual similarity between tasks.
Consistent with representational flexibility theory, imitative transfer in very young children (Hayne et al. 2003; Ganea et al. 2008) is less successful as the degree of overlap between the presentation context of the action and the context of its production through imitation decreases.
Moreover, Eichenbaum (1997) considers that only knowledge stored in declarative memory can be transferred because of the flexibility that characterizes it. Procedural knowledge, which is not accessible to consciousness, is supposed to be inflexible and therefore non-transferable. This distinction is based on the fact that the theoretical position supported by Eichenbaum is rooted in neurobiology and that the cognitive possibility of transfer directly reflects the capacities of cerebral plasticity (Eichenbaum et al. 1990; Bunsey and Eichenbaum 1996).
This position, according to which only declarative knowledge is transferable because of its inherent flexibility, nevertheless seems to be contradicted by the numerous studies showing transfers of cognitive strategies. The latter, procedural in nature, are indeed transferable, which suggests that the child displays a form of flexibility in order to be able to adapt the strategy of the main task to a transfer task. If the flexibility in question is not representational, what form might it take?
6.3.2. Transfer and attentional flexibility
In this section, we show that the transfer of learning may also benefit from a form of attentional flexibility. In the theoretical framework of executive function, flexibility is defined as shifting one’s attentional focus from one element to another in order to selectively process each element and perform the task optimally (Diamond 2013; Carroll et al. 2016).
The dominant paradigm used to study this is that of switching tasks, comprising at least two phases between which the child must switch their attentional focus. The tasks in question involve providing a response according to a first criterion, then switching to a second criterion in competition with the first.
The criterion can be a physical dimension, shape versus color (Espy et al. 2006; Zelazo 2006; Willoughby et al. 2010), or a symbolic dimension, numbers versus letters (Williams et al. 1995; Monette et al. 2015). What these switching tasks have in common is that the shift in attentional focus has the aim of producing the best possible response to the task, among several competing solutions.
Another manifestation of flexibility can be seen in the shift in attentional focus within working memory. This allows us to switch between different strategies for retrieving information from memory (Baddeley and Hitch 1994). The aim is no longer to choose the best response to the task according to a changing sorting criterion but to mentally alternate between several strategies in a dynamic way, in order to choose the most suitable. It should be noted that we are approaching spontaneous flexibility here, whereas switching tasks instead call for a form of reactive flexibility (Eslinger and Grattan 1993).
The contribution of attentional flexibility to the transfer of learning, and in particular to the transfer of cognitive strategies, can be seen in the extension of this work. Indeed, transferring a strategy to a new task requires the child to choose the one that seems most relevant from their strategic repertoire (Pressley and Hilden 2006). This relevance is based both on the appropriateness of the strategy to the goal of the task and on the extent of the modifications to be made in order to adapt the chosen strategy to the new task.
The choice of the strategy to be transferred thus stems from consideration of the cost/benefit ratio, and this is where cognitive flexibility can come into play in order to estimate the costs incurred and the expected benefits. At present this is only a hypothesis because, to our knowledge, no study has directly linked cognitive flexibility capacities and the transfer of cognitive strategies. In order to advance this hypothesis, let us look more closely at how cognitive flexibility might benefit strategic transfer.
6.3.2.1. Strategic transfer and flexibility
Let us recall that cognitive strategies are procedural knowledge (Lemaire and Reder 1999). Their development is early as their first traces are noted from the age of 3, including pointing strategies (Fletcher and Bray 1996; Fletcher 1997), then problem-solving strategies at age 4 (Blöte et al. 1999; Clerc et al. 2017) and progressively more specific strategies, such as arithmetic strategies from age 5 onwards (Siegler 1995).
Memory strategies are particularly interesting to study, as intentional memorization of information is often required at school and their implementation is cognitively costly (Schlagmüller and Schneider 2002). Among them, the strategies of selective attention (Miller 1990), rehearsal (Schneider et al. 2009) and sorting/clustering (Bjorklund et al. 1992) are often called upon.
The procedural part of cognitive strategies allows an individual to be sensitive to context effects, since the retrieval of procedural knowledge is partly determined by the conditions of its use (George 1988), i.e. by the context. The use of a cognitive strategy by a child can thus be influenced by different contextual elements (Barnett and Ceci 2002; Verschaffel et al. 2009) such as the physical (home or school), human (parent or teacher) or functional (school task or leisure activity) context.
If we refer to Klahr and Chen’s (2011) model, the joint presence of similarities between tasks and similarities between their respective contexts of presentation is likely to maximize the transfer of learning. Indeed, since cognitive strategies are procedural, they refer to specific contexts of use: since a strategy is linked to certain specific contexts, encountering one of these contexts in a subsequent task will facilitate the retrieval of the strategy and its transfer to a new task presented in this context.
In other words, relying on both task similarities and contextual similarities, a child should be able to transfer cognitive strategies with relative ease. What about the role of cognitive flexibility?
Transferring a strategy requires the child to mentally alternate between the source task and the transfer target task, in order to analyze the similarities between tasks and between contexts and to adapt the strategy to the transfer task. This adaptation also requires an evaluation of the degree of transformation applied to the strategy, in order to verify that this transformation has not been excessive.
In Clerc and Miller’s (2013) study, 4- to 5-year-olds were asked to perform three isomorphic memory tasks, each involving the same selective attention strategy to be used in the primary task and transferred to the other two tasks.
In such a strategic transfer situation, children must focus their attention alternately on the different tasks in order to be able to decide that they all feature the same resolution structure (the strategy) despite the surface differences they present (the outward appearance). The whole process is therefore only possible if children show a sufficient level of efficiency in their flexibility processes (Clerc et al. 2014).
The recent study by Stad et al. (2017) provided an explicit connection between strategy transfer and cognitive flexibility. The authors asked 7-year-old children to perform a main task of series completion, followed by a transfer task consisting of the construction of a new series, the completion of which was this time the responsibility of the experimenter (reversal procedure). The series to be completed consisted of several plastic figures whose body parts (head, arms, legs, trunk) varied according to predefined geometric patterns and colors.
Each series consisted of three figures and had to be completed with three more, with the three figures already present responding to a succession structure that had to be understood by the child and reapplied in the placement of the other three figures. Flexibility skills were measured using a modified version of the Wisconsin Card Sorting Task (M-WCST) (Schretlen 2010).
The results showed that the flexible use of information, measured via the M-WCST, supports a task-solving strategy that is still fragile, as it has been recently taught and not yet automated. Moreover, the contribution of cognitive flexibility to the success of the transfer task was mainly observed when the difficulty level of the task was high, a high difficulty level being interpreted as an obstacle to the transfer of the recently learned strategy.
This explanation is consistent with the idea that a strategy that is not yet automated generates a high cognitive cost, which will spill over during the transfer of this strategy by lowering transfer performance (Clerc et al. 2014). A high level of cognitive flexibility thus seems to be able to compensate for a lack of automatization of the newly acquired strategy. The following section discusses this point in the specific case of strategy utilization deficiencies.
6.3.2.2. A special case: transfer utilization deficiencies
As we have noted, strategic transfer is difficult for children (Pressley and Dennis-Rounds 1980; Carr et al. 1989; Elsner and Schellhas 2012; Clerc and Miller 2013; Marchandise et al. 2014). For a long time, this difficulty specific to strategic transfer has been assessed by deduction, that is, only on the basis of performance expected to benefit from the transferred strategy, and not by measuring strategic quality per se. Thus, in the case of memory strategies, only recall performance in the transfer task was taken into consideration, without direct measurement of the quality of the strategy produced.
However, such direct measures of strategy quality are regularly used in the literature, for example, the ARC1 score in sorting/clustering strategies (Schlagmüller and Schneider 2002), or the number of rehearsed words in the case of rehearsal (Kron-Sperl et al. 2008).
Several recent studies suggest that quality of strategic transfer would benefit from being assessed using several indices: these include studies of the transfer-utilization deficiency (t-UD) phenomenon.
t-UD is a special case of utilization deficiency. Utilization deficiency consists of the correct production of a given cognitive strategy, which is accompanied by a lower performance on the task than would be expected from the use of the strategy. In other words, it is a loss of performance of the cognitive strategy implemented by the child. Utilization deficiencies have been studied since Miller (1990).
In the case of a memory strategy, it is the concomitance of a high strategy score and a lower than expected recall score that provides evidence of a utilization deficiency (for a review in French, see Clerc (2013)).
In the particular case of cognitive strategy transfer, t-UD is characterized by the maintenance of strategic performance at the same level between the main task and the transfer task and a concomitant decrease in the recall score in the transfer task compared to the main task.
A phenomenon recently described in the literature (Clerc and Miller 2013), t-UD reflects the difficulty in transferring a cognitive strategy, a difficulty encountered by many children. These authors have thus shown that the introduction of a transfer task may give rise to a previously neglected form of utilization deficiency. Using the original task used by Miller and Weiss (1981) in which the child must memorize the location of six drawings of animals or objects, each drawing being placed in one of the 12 compartments of a wooden box, the authors proposed this task to children aged 4–5 years, as well as two other isomorphic tasks. These tasks differed from the first in their outward appearance but required the use of the same selective attention strategy.
With the order of presentation of the three tasks counterbalanced, the results showed that participants maintained the same level of strategic performance across the three tasks – that is, the same average number of strategic trials – but the first task performed (main task) resulted in significantly higher recall than that observed in the two transfer tasks: this pattern is specific to a t-UD.
The explanation put forward to account for t-UD adopts the classical interpretation of utilization deficiencies in terms of cognitive overload (Miller and Seier 1994). Nevertheless, t-UD differs from other utilization deficiencies in that it is the introduction of a transfer task that specifically causes its occurrence, likely due to the fact that the strategy must be adapted to the transfer task (Schwartz et al. 2012). It is this adaptation that would cause the cognitive overload responsible for the t-UD.
Moreover, a strategic transfer situation that is suitable for the emergence of a t-UD necessarily calls for flexibility, since the child must alternately consider the main task and the transfer task, in order to be able to decide to transfer the appropriate cognitive strategy by adapting it to the transfer task (Clerc et al. 2014).
Recent research has shown that cognitive flexibility, and in particular attentional switching, is related to the occurrence of t-UD (Clerc et al. 2021). In this research, the authors show, through three studies conducted with children aged 3–7 years, that t-UD is observed for four cognitive strategies tested in the main task and in the transfer task: selective attention, rehearsal, categorical grouping during encoding (sorting) and categorical grouping during recall (clustering).
For each of these cognitive strategies, the strategy score is maintained, and the recall score decreases significantly from the main task to the transfer task, which is the specific pattern of a t-UD. Furthermore, regression analyses have shown that attentional switching, as measured by the Children’s Color Trail (CTC) (Williams et al. 1995), is predictive of recall score in the transfer task. The most flexible children, that is, those with the best performance on the CTC, also had the best recall scores in the transfer task. The decline in recall in the transfer task, characteristic of a t-UD, thus seems to be limited by good cognitive flexibility abilities.
In the specific case of t-UD, the high cognitive load would come from the efforts to adapt the strategy to the transfer task. Children with low flexibility would be forced to allocate a lot of cognitive resources to adapting the strategy to the transfer task, to compensate for their low level of flexibility. This large amount of resources would leave too few resources to devote to the memory processes themselves, thus lowering the recall score in the transfer task.
In contrast, children with a high level of cognitive flexibility would adapt the strategy more easily to the transfer task, using only moderate effort to do so. Their preserved cognitive resources would allow them to be efficient in the memorization itself, and to maintain their recall score in the transfer task at the same level as in the main task.
The study by Clerc et al. (2021) thus opens up a promising avenue in the study of the links between cognitive flexibility and transfer of learning in the context of t-UD, by suggesting a beneficial role of flexibility on transfer. Being correlational in nature, the conclusions it allows nevertheless need to be confirmed by studies investigating the causal link between flexibility and transfer.
6.4. Conclusion
As we have seen in this chapter, transfer of learning begins very early at the ontogenetic level. The equally early development of cognitive flexibility suggests links between the two, very early in development. Nevertheless, it is only recently that researchers have begun to investigate the precise relationship between transfer and flexibility. We believe that three avenues might be explored in order to make progress in this area.
The first avenue is that of t-UDs. While transfer of learning is a long-standing concern in psychology that regularly gives rise to theoretical (Cox 1997; Goldstone and Day 2012) and methodological (Green et al. 2014) updates, many studies still rely on simple or univariate measures of transfer, such as a success score (Ganea et al. 2008; Cook et al. 2013; Huber et al. 2016).
Yet, the need to use multiple measures to assess the success of transfer of learning is advocated and implemented by a growing number of researchers (Nokes 2009; Resing et al. 2016; Riggs et al. 2017). The concept of t-UD can, from this perspective, help us better understand the processes underlying transfer. This is due to the fact that a t-UD can only be attested to by measuring both the strategic score and the task score, constituting a multiple measure of transfer.
Moreover, the study of t-UDs may allow us to learn more about the role of cognitive flexibility, and in particular attentional flexibility, in transfer of learning. Thus a first avenue to explore seems to be that of t-UDs and their links with cognitive flexibility.
Furthermore, the links between transfer and flexibility will need to be investigated by incorporating metacognition. Metacognition and flexibility seem to share common processes, especially in terms of monitoring (Roebers 2017; see Chapter 3). On the other hand, a child’s metacognitive activity is related to the effectiveness of the cognitive strategies they implement (Ringel and Springer 1980), with strategy utilization deficiencies that may be partly explained by a weakness in certain metacognitive processes (Miller and Seier 1994; DeMarie and Ferron 2006).
Because researchers’ concerns about the precise nature of the links between transfer of learning and cognitive flexibility are recent, there are not yet any studies that address transfer, flexibility and metacognition at the same time. Considering these three concepts together, however, seems to be able to help us better understand how a child adapts to transfer situations on a daily basis (Clerc and Clément 2016).
A third avenue concerns the role of digital as a particular learning context that might have a specific impact on transfer of learning and the role of cognitive flexibility in such digital transfer. Several recent studies vary the contexts in which tasks are presented, or vary the tasks themselves, with the latter being presented in a digital context (video, digital tablet) or in a tangible context (physical objects) (Schiff and Vakil 2015; Aladé et al. 2016; Huber et al. 2016).
In one study (Huber et al. 2016), results with 6-year-olds indicate a positive transfer from the digital to the tangible. Indeed, participants improved their resolution scores on the Tower of Hanoi task over four consecutive trials, the first three of which were performed on a version presented on a digital tablet, and the last one was performed on a tangible wooden version. These results, confirmed by Tarasuik et al. (2017), extend those reported above on the ability of very young children to transfer from video media (for a review, see Barr (2010)), and suggest that transferring learning from a multimedia universe (video, tablet) to tangible reality is an early developmental trend.
These results seem to contradict the literature indicating a drop in performance at transfer. It is possible that the transition from digital to tangible results in a positive transfer because only the context changes (digital versus tangible) and the task remains strictly the same, which is consistent with the idea of transfer appropriate processing (Morris et al. 1977).
The flexibility efforts required would then be moderate as they would only concern the change of context and not the task itself, which would remain identical. Insofar as flexibility seems to be necessary for transfer, a moderate flexibility effort – because it only has to be applied to the change of context – would thus be favorable to transfer for many children, including those with limited capacities for flexibility.
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