Which of the following instances call for training with an emphasis on far transfer?

4.3.2 Information Processing Model

Historically, the concept of transfer of learning lay squarely within the applied instructional arena of education. Accordingly, until recent decades, cognitive psychology developed no theory of transfer per se. Currently, transfer as a function figures prominently in many areas of cognitive research. With few exceptions, this research has not been concerned primarily with instructional processes but rather with laying the foundation for an information processing framework that explains the internal representation, processing, and retention of information. One central area in contemporary cognitive psychology directly relating to transfer is schema research. As Phye notes, the information processing model tends to emphasize learner performance, not learner acquisition of knowledge (i.e., learning).

Transfer of learning means the use of previously acquired knowledge and skills in new learning or problem-solving situations. Thereby similarities and analogies between previous and actual learning content and processes may play a crucial role. The transfer phenomenon is presented within a general perspective of learning. Transfer of skills and analogical transfer are illustrated and discussed along with two examples: playing the violin and identifying an unknown object. Basic considerations are presented on the problem of how to enhance transfer effects: Newly acquired knowledge structures need consolidation in order to be transferred, and noncognitive aspects of learning (social, emotional, and motivational) need particular consideration in the original learning process.

2.1 Transfer and Retention

Transfer of learning is highly related to retention of learning. In both cases, performance is assessed both during learning itself and during testing after learning is completed, with the test often occurring after a delay. In the case of pure retention, the same tasks are examined during training and testing, whereas in the case of transfer different tasks are examined during training and testing. Naturally, previous studies that have included assessments of both pure retention and transfer have typically shown better performance at test when pure retention is assessed (i.e., when the training task matches the testing task) than when transfer is assessed (i.e., when the training and testing tasks differ) (see, e.g., Schneider, Healy, Barshi, & Kole, 2011).

For assessments of transfer, when the two tasks are closely related “near transfer” is evaluated, whereas when they are more distantly related “far transfer” is evaluated. Thus, depending on the similarity between the tasks used during training and testing, there can be a continuum of evaluations ranging from pure retention to far transfer. Most research on transfer has focused on tasks that are highly similar to the training tasks, but many current studies (see Morrison & Chein, 2011, and Tang & Posner, 2009, for reviews) have examined transfer for tasks that are quite different from the training tasks, in an effort to see whether general skills or abilities, such as those involving working memory or attention, can be trained.

Desirable training outcomes: near- and far-transfer effects

Transfer of learning is the most important goal of computerized cognitive training. In practice, it reflects the ability to transfer what is learned in one context or situation to another. Sometimes it occurs at a subconscious level and requires minimal conscious effort if the learning to be transferred has achieved automaticity and is sufficiently similar to the learning in the new situation. This type of learning is referred to as a near-transfer effect and reflects the extent to which cognitive training improves performance on untrained tasks measuring the identical EF-related processes targeted during training. Several computerized cognitive training programs reviewed in the following section, for example, use simple span tasks (e.g., digits) to train children’s short-term verbal memory. If a child recalls 3 digits pretraining and 6 digits posttraining, and a comparable degree of improvement is demonstrated on a simple span word list task, the near-transfer effect would be quite considerable (i.e., 100%). Near-transfer training effects are typically estimated in the literature by calculating effect sizes (ESs) based on pre–posttraining score differences.

The true efficacy of computerized EF training programs lies in their ability to improve important, ecologically valid outcomes such as academic performance, ADHD symptomatology, and behavior, which depend on trained abilities, as well as cognitive functions that are different from those used during training but rely on overlapping brain regions (i.e., far-transfer effects). Far-transfer presents a significantly greater challenge for children because the similarity and pragmatic relevance between what is trained initially and the abilities required by the transfer task are greatly reduced (i.e., the transfer tasks usually require additional abilities, processes, and knowledge over and above those included during the training). Training children’s WM and demonstrating that training improves math achievement following the intervention represents an example of a far-transfer effect. The expected level of far-transfer can be estimated a priori by determining the shared variance (R2) between the training and transfer tasks in the literature. For example, if measures of WM and applied mathematical problem are correlated 50% based on extant literature, a 100% improvement in WM following training can theoretically translate into a 25% (0.50×0.50) performance improvement in a child’s math problem-solving ability.

The most critical far-transfer effects in need of improvement in children with ADHD involve core areas of foundation learning such as reading and math, as reviewed previously. Achieving far-transfer effects in these areas are particularly difficult because foundational learning involves the simultaneous use of multiple WM upper (CE) and lower level (STM) processes in conjunction with core-specific processes such as automatized decoding abilities and basic math knowledge among others.

1.29.10.1 Categorization of Visual Stimuli

Positive transfer of learning is a distinctive characteristic of categorization performance. Categorization refers to the classification of perceptual input into defined functional groups (Harnard, 1987). It can be defined as the ability to group distinguishable objects or events on the basis of a common feature or set of features, and therefore to respond similarly to them (Troje et al., 1999; Delius et al., 2000; Zentall et al., 2002; See Chapter 1.08). Categorization deals, therefore, with the extraction of these defining features from objects of the subject’s environment. A typical categorization experiment trains an animal to extract the basic attributes of a category and then tests it with novel stimuli that were never encountered before and that may or may not present the attributes of the category learned. If the animal chooses the novel stimuli based on these attributes, it classifies them as belonging to the category and therefore exhibits positive transfer of learning.

Using this basic design in which procedural modifications can be introduced, several studies have recently shown the ability of visual categorization in free-flying honeybees trained to discriminate different patterns and shapes. For instance, van Hateren et al. (1990) trained bees to discriminate two given gratings presented vertically and differently oriented (e.g., 45° vs. 135°) by rewarding one of these gratings with sucrose solution and not rewarding the other. Each bee was trained with a changing succession of pairs of different gratings, one of which was always rewarded, while the other was not. Despite the difference in pattern quality, all the rewarded patterns had the same edge orientation, and all the nonrewarded patterns also had a common orientation, perpendicular to the rewarded one. Under these circumstances, the bees had to extract and learn the orientation that was common to all rewarded patterns to solve the task. This was the only cue predicting reward delivery. In the tests, bees were presented with novel patterns, to which they had never been exposed, and that were all nonrewarded, but that exhibited the same stripe orientations as the rewarding and nonrewarding patterns employed during the training. In such transfer tests, bees chose the appropriate orientation despite the novelty of the structural details of the stimuli. Thus, bees could categorize visual stimuli on the basis of their global orientation.

They can also categorize visual patterns based on their bilateral symmetry. When trained with a succession of changing patterns to discriminate bilateral symmetry from asymmetry, bees learn to extract this information from very different figures and transfer it to novel symmetrical and asymmetrical patterns (Giurfa et al., 1996). Similar conclusions apply to other visual features such as radial symmetry, concentric pattern organization and pattern disruption (see Benard et al., 2006 for review), and even photographs belonging to a given class (e.g., radial flower, landscape, plant stem) (Zhang et al., 2004).

How could bees appropriately classify different photographs of radial flowers if these vary in color, size, dissection, and so on? An explanation is provided by Stach et al. (2004), who expanded the demonstration that bees can categorize visual stimuli based on their global orientation to show that different coexisting orientations can be considered at a time and integrated into a global stimulus representation that is the basis for the category (Stach et al., 2004). Thus, a radial flower would be, in fact, the conjunction of five or more radiating edges. Besides focusing on a single orientation, honeybees were shown to assemble different features to build a generic pattern representation, which could be used to respond appropriately to novel stimuli sharing this basic layout. Honeybees trained with a series of complex patterns sharing a common layout comprising four edge orientations remembered these orientations simultaneously in their appropriate positions and transferred their response to novel stimuli that preserved the trained layout (Figure 5). Honeybees also transferred their response to patterns with fewer correct orientations, depending on their match with the trained layout. These results show that honeybees extract regularities in their visual environment and establish correspondences among correlated features such that they generate a large set of object descriptions from a finite set of elements.

Thus, honeybees show positive transfer of learning from a trained to a novel set of stimuli, and their performance is consistent with the definition of categorization. Visual stimulus categorization is not, therefore, a prerogative of certain vertebrates. However, this result might not be surprising because it admits an elemental learning interpretation. To explain this interpretation, the possible neural mechanisms underlying categorization should be considered. If we admit that visual stimuli are categorized on the basis of specific features such as orientation, the neural implementation of category recognition could be relatively simple. The feature(s) allowing stimulus classification would activate specific neuronal detectors in the optic lobes, the visual areas of the bee brain. Examples of such feature detectors are the orientation detectors whose orientation and tuning have been already characterized by means of electrophysiological recordings in the honeybee optic lobes (Yang and Maddess, 1997). Thus, responding to different gratings with a common orientation of, say, 60° is simple because all these gratings will elicit the same neural activation in the same set of orientation detectors despite their different structural quality. In the case of category learning, the activation of an additional neural element is needed. This type of element would be a reinforcement neuron equivalent to VUMmx1 (Hammer, 1993; see above) but contacting the visual circuits at its relevant processing stages. Other VUM neurons whose function is still unknown are present in the bee brain (Schroter et al., 2006). It could be conceived that one of them (or more than one) acts as the neural basis of reinforcement in associative visual learning. Category learning could thus be reduced to the progressive reinforcement of an associative neural circuit relating visual-coding and reinforcement-coding neurons, similar to that underlying simple associative (e.g., Pavlovian) conditioning. From this perspective, even if categorization is viewed as a nonelemental learning form because it involves positive transfer of learning, it may simply rely on elemental links between CS and US.

D Part–Whole Transfer of Learning

A number of issues fall under the general heading of transfer of learning. The potential effect of negative transfer on systematic movement bias was discussed above. Another transfer issue that is particularly relevant for coordination tasks is that of “part–whole” training. This refers to the techinque of decomposing a skill into logical components or “parts” that are initially practiced individually. Once a reasonable degree of success has been reached in performing the components, they are reassembled into the “whole” skill. Theoretically, this technique may facilitate the acquisition of some multicomponent skills by simplifying their performance.

Although we have not performed a systematic study of part-whole transfer using this task, a general indication of its efficacy can be gained by comparing studies with different protocols. Most of our experiments have required subjects either to initially practice with the same movement in both arms to gain a feel for the correct duration, or to perform the goal, disparate bimanual task from the outset. Two studies, however, have required subjects to perform each arm movement individually for a series of trials before practicing the action bimanually (Swinnen et al., 1990; Swinnen, Young, Walter, & Serrien, 1991c). It is admittedly unwise to place too much faith in a direct comparison of data among studies. But it is interesting to note that the average interlimb correlations at the beginning of bimanual practice for the latter studies (.5 to .6) are slightly lower than those for studies that have not provided initial practice with each arm individually, which have ranged from .5 to .9 (most falling between .6 and .8; Swinnen & Walter, 1991; Swinnen et al., in press; Walter & Swinnen, 1990a, 1992). Practicing each limb individually may thus improve performance, to a modest degree, as compared to no practice at all. Subjects generally fail to approach the levels attained by subjects with a similar amount of bimanual practice, however, if augmented feedback is provided.

Ending Phase of Therapy

The last phase of therapy is devoted to further generalization and transfer of learning, self-attribution for gains made, and relapse prevention. Termination in cognitive therapy begins in the first session. Since the goal of CT is not cure, but more effective coping, the therapy is seen as time-limited. When formal assessments such as the BID, the patient’s reported symptoms, observations of significant others, and the therapist’s observation confirm decreased depression, greater activity, higher levels of adaptive functioning, and increased skills, the therapy can move toward termination.

Termination is accomplished in a planned, graded manner, with sessions tapered off from weekly to every other week, monthly, and then as follow-up sessions for outcome assessment or as part of a relapse prevention strategy. Contact between client and therapist between sessions may be scheduled or simply allowed as needed. Clients may call to get reinforcement of a particular behavior, to report success, to get information, and so on. The collaborative consulting role of the cognitive therapist allows this as appropriate and important.

It appears that even those persons who relapse within the current episode of depression continue for some time following termination to attempt to apply the skills and methods learned in the therapy. Thus, even those that eventually relapse take longer to do so than for other therapies, or for pharmacotherapy. Correlates of relapse include a history of prior depressive episodes, greater severity of symptoms at intake, slower response to therapy, unmarried status, and higher BID and DAs scores at termination (Beach & O’Leary, 1992; Clarke, Hops, Lewinsohn, Andrews & Williams, 1992; Evans, Hollon, DeRubeis, Piasecki, Grove, Garvery & Tuason, 1992).

Relapse prevention strategies address a number of key factors. Goals of therapy and initial symptoms are reviewed. Progress is measured both against initial symptoms and against goals. It is important for the person to identify how far they have come, and to develop a scale against which to measure current concerns and moods.

The person is asked to account for changes made: what changed? How did that come about? What did the person do to effect this change? The purpose is to self-attribute the successes experienced. Further, the person is asked to identify new learning, attitudes and skills, and to contrast these with old ones. We often have clients list the things that they will take away from the therapy, and plan where to keep this list for easy referral and reminder.

The person is asked to anticipate stressors. The therapist ensures that the list developed includes events similar to those that brought the person into therapy, events similar to those assumed to be the origin of the underlying depressogenic schema, and anticipated life events, such as developmental transitions the person or their family will encounter. The person is asked to imagine as vividly as possible that these events is occurring or has occurred, and to identify the skills, reattributions, and new patterns of behavior or cognition that can be called to bear on the stressor. This behavioral and imaginal rehearsal is conducted in as specific detail as possible, with an emphasis on coping thoughts and behaviors, self-attribution of efforts and success, and referral to the use of new resources. Both resourcefulness and utilizing therapy as a resource are promoted.

Finally, the meaning of the therapy to the person and to their life, and the meaning of the relationship with the therapist, is addressed. The goal is to integrate the experience of therapy into the personal narrative of the person so that it is seen as part of, rather than apart from, their life.

The transfer of learning

For over a century, researchers have tried to grasp with the complex issue of the transfer of learning. Royer, Mestre and Dufresne (2005, p. viii) argue that ‘how transfer works and how transfer can be facilitated, is a vitally important educational issue’. Although many other writers agree with this statement, there is little agreement about how to define the concept of the transfer of learning, whether transfer is likely, what types of transfer exist and how transfer can be encouraged in schools. The views of Detterman are regularly cited in the literature and Detterman (1993, p. 21) concludes that ‘significant transfer is probably rare and accounts for very little human behaviour… We generally do what we have learned to do and no more’. Detterman’s views are not shared by later researchers, particularly those who adopt, as Royer, Mestre and Dufresne (2005) suggest, a more sociocultural approach to transfer, which takes into account the learner’s environment and social aspects of learning such as interaction with others.

There are many definitions of transfer, some of which argue that transfer is mainly possible where the original learning situation and the new learning situation are the same, or very similar; this is termed ‘near transfer’. Other definitions allow for the possibility of transfer across different learning situations and this is termed ‘far transfer’, although authors such as Haskell (2001) and Detterman (1993) cast doubt on how often far transfer is likely to occur. A constructivist view of transfer is taken by Joanne Lobato (2003), who defines transfer as ‘The personal construction of similarities across activities, i.e. seeing situations as the same’.

What facilitates the transfer of learning and what hinders or prevents transfer are also the subject of wide debate. Cognitive views of transfer have been developed (Haskell 2001). An increasing number of researchers, however, now view the transfer of learning as something that is partly based on an individual’s mental processes (for example, how existing knowledge can be used to understand new knowledge in a new situation) and also influenced by the individual learner’s social environment. For example, Greeno et al (1993, p. 102) take the view that transfer can be based on affordances, in that activities can involve personal or group aims and depend on aspects of the situation as well as on personal or group characteristics. Also, Volet’s (1999) sociocultural view of transfer argues that the motivation and emotional state of a learner, as well as that learner’s expectations of the learning environment, must be taken into consideration when considering transfer. Thus the sociocultural view of transfer implies that the transfer of learning is not a simple process of transferring knowledge gained from one situation to another, but that transfer depends on the individual constructing and reconstructing knowledge in situations which are influenced by the social environment in which learning takes place.

In the context of information literacy in schools, it is clear that students transfer some aspects of learning, but this transfer is often viewed as limited and is classified as near transfer. What is not clear, however, from information literacy research, both in schools and in university or workplace contexts, is whether students transfer more complex skills or conceptual abilities across contexts. The study reported in this chapter sought the views of Year 12 students on the issue of transfer in relation to their views on information literacy skills.

A What Changes with Learning?

A central hypothesis of the present framework is that the entire layout of the coordination dynamics changes with learning, not simply the particular coordination pattern being learned. This chapter presents preliminary findings about transfer of learning (Zanone & Kelso, 1992a) that strongly support this idea. In expanding the topic of transfer to that of generalization of learning, one is led to ask the question of what is learned when a given behavioral pattern is acquired, and in particular, how independent such learning is from that which was actually involved in the original task.

3.1 Posterior Parietal Cortex

Posterior parietal cortex (PPC) receives information from somatosensory, auditory and visual processing areas (Reep et al., 1994; Reep and Corwin, 2009) and has been implicated in crossmodal transfer of learning in monkeys (Wilson and Wilson, 1962; Wilson, 1964), auditory-place and visual-place object conditional learning in rats (Tees, 1999) and tactile–visual crossmodal DNMS in monkeys (Zhou and Fuster 1997, 2000). Given its established role in multisensory integration, Winters and Reid (2010) explored the involvement of the PPC in tactile–visual CMOR using rats. Bilateral neurotoxic lesions of the PPC impaired performance on the tactile–visual CMOR task, as well as the unimodal tactile SOR task with a 1-h retention delay; the unimodal visual task was not impaired (Winters and Reid, 2010). Presample and prechoice transient PPC inactivation with the sodium channel blocker lidocaine also impaired tactile–visual CMOR and unimodal tactile task performance (Jacklin et al., 2016), strongly suggesting that PPC-mediated processing of tactile object information is crucial to both the encoding and retrieval stages of the CMOR task when rats have not been preexposed to the objects.