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Transfer (of learning) research can be loosely framed as the study of the dependency of human conduct on prior experience. The notion was originally introduced as transfer of practice by Edward Thorndike and Robert S. Woodworth (1901)[1]. They explored how individuals would transfer learning in one context to another context that shared similar characteristics. Their theory implied that transfer of learning depends on the proportion to which the learning task and the transfer task are identical, also known as 'identical elements'. Transfer research has since attracted much attention in numerous domains, producing a wealth of empirical findings and theoretical interpretations. However, there remains considerable controversy about how transfer of learning should be conceptualized and explained, what its relation is to learning, or whether it exists at all (e.g., Detterman, 1993; Helfenstein, 2005 [2]).

Transfer of learning should not be confused with knowledge transfer, as the former concerns intra-individual, constructivist perspective, and the latter, an inter-individual or inter-organizational perspective.

Most discussions of transfer to date can be developed from a common operational definition, describing it as the process and the effective extent to which past experiences (also referred to as transfer source) affect learning and performance in a present novel situation (transfer target) (Ellis, 1965; Woodworth, 1938). This, however, is usually also where the general consensus between various research approaches ends.
Indeed, there is a wide variety of viewpoints taken on transfer; and therefore research should be reviewed from many different perspectives. These perspectives relate to:

  • a taxonomical approach to transfer research that usually intends to categorize transfer into different types;
  • an application domain-driven approach by focusing on developments and contributions of different disciplines that have traditionally been interested in transfer;
  • the examinination of the psychological scope of transfer models with respect to the psychological functions or faculties that are being regarded; and
  • a concept-driven evaluation, which reveals underlying relationships and differences between theoretical and empirical traditions.

Transfer taxonomies

Of the various attempts to delineate transfer, typological and taxonomic approaches belong to the more common ones (see, e.g., Barnett & Ceci, 2002; Butterfield, 1988; Detterman, 1993; Gagné, 1977; Reeves & Weisberg, 1994; Salomon & Perkins, 1989; Singley & Anderson, 1989). Taxonomies are concerned with distinguishing different types of transfer, and are as such less involved with labeling the actual vehicle of transfer, i.e., what is the explanatory mental unit of transfer that is carried over. Hence, a key problem with many transfer taxonomy is that they offer an excessive number of labels for different types of transfer without really engaging in a discussion of the underlying concepts that would justify their distinction, i.e., similarity and the nature of transferred information. This makes it very difficult to appreciate the internal validity of the models.

Here is a table, presenting different types of transfer, as adapted from Schunk (2004, p. 220).

Type Characteristics
Near Overlap between situations, original and transfer contexts are similar
Far Little overlap between situations, original and transfer settings are dissimilar
Positive What is learned in one context enhances learning in a different setting (+)
Negative What is learned in one context hinders or delays learning in a different setting (+)
Vertical Knowledge of a previous topic is essential to acquire new knowledge (++)
Horizontal Knowledge of a previous topic is not essential but helpful to learn a new topic (++)
Literal Intact knowledge transfers to new task
Figural Use some aspect of general knowledge to think or learn about a problem
Low Road Transfer of well-established skills in almost automatic fashion
High Road Transfer involves abstraction so conscious formulations of connections between contexts
High Road /Forward Reaching Abstracting situations from a learning context to a potential transfer context
High Road / Backward Reaching Abstracting in the transfer context features of a previous situation where new skills and knowledge were learned

(+) from Cree and Macaulay, (2000). (++) from Ormrod (2004).

Apart from the effect-based distinction between negative and positive transfer, taxonomies have largely been constructed along two, mostly tacit, dimensions. One concerns the predicted relationship between the primary and secondary learning situation in terms of categorical overlap of features and knowledge specificity constraints. The other concerns some general assumptions about how transfer relationships are established, in terms of mental effort and cognitive process.

The effect-perspective: positive vs. negative transfer

Starting by looking at the effect side of transfer, i.e., in terms of the common performance criteria, speed and accuracy, transfer theories distinguish between two broad classes of transfer that underlie all other classifications: negative and positive transfer. Negative transfer refers to the impairment of current learning and performance due to the application of non-adaptive or inappropriate information or behaviour. Negative transfer is therefore a type of interference effect of prior experience causing a slow-down in learning, completion or solving of a new task when compared to the performance of a hypothetical control group with no respective prior experience. Positive transfer, in contrast, emphasizes the beneficial effects of prior experience on current thinking and action. It is important to understand the positive and negative effects of transfer are not mutually exclusive, and therefore real life transfer effects are probably mostly a mixture of both.

The situation-perspective: specific vs. general, near vs. far transfer

The situation-driven perspective on transfer taxonomies is concerned with describing the relation between transfer source (i.e., the prior experience) and transfer target (i.e., the novel situation). In other words, the notion of novelty of the target situation per se is worthless without specifying the degree of novelty in relation to something that existed before. Butterfield and Nelson (1991), for example, distinguish between within-task, across-task, and inventive transfer. A similar classification approach reappears in many situation-driven transfer taxonomies (e.g., similar vs. different situations; example-to-principle and vice versa; simple-to-complex and vice versa) and can be noted as distinctions made along the specific-versus-general dimension. Mayer and Wittrock (1996, pp. 49ff.), for their part, discuss transfer under the labels of general "transfer of general skill" (e.g., "Formal Discipline", e.g., Binet, 1899), "specific transfer of specific skill" (e.g., Thorndike’s, 1924a, b, "identical elements" theory), "specific transfer of general skill" (e.g., Gestaltists’ transfer theory, see origins with Judd, 1908), and "meta-cognitive control of general and specific skills" as a sort of combination of the previous three views (see, e.g., Brown, 1989).

Haskell’s (2001) taxonomy proposes a more gradual scheme of similarity between tasks and situations. It distinguishes between non-specific transfer (i.e., the constructivist idea that all learning builds on present knowledge), application transfer (i.e., the retrieval and use of knowledge on a previously learned task), context transfer (actually meaning context-free transfer between similar tasks), near versus far transfer, and finally displacement or creative transfer (i.e., an inventive or analytic type of transfer that refers to the creation of a new solution during problem solving as a result of a synthesis of past and current learning experiences). Both, near and far transfer, are are widely used terms in the literature. The former refers to transfer of learning when task and/or context change slightly but remain largely similar, the latter to the application of learning experiences to related but largely dissimilar problems.

The process-perspective

In fact, the specific-versus-general dimension applies not just to the focus on the relation between source and target (i.e., from where to where is transferred), but also to the question about the transfer process itself (i.e., what is transferred and how). Reproductive versus productive transfer (see Robertson, 2001) are good examples of this type of distinction. Whereas reproductive transfer refers to the simple application of knowledge to a novel task, productive transfer implies adaptation, i.e. mutation and enhancement, of retained information.

A similar dichotomous distinction is the one between knowledge transfer and problem-solving transfer (Mayer & Wittrock, 1996). Knowledge transfer takes place when knowing something after learning task ‘A’ facilitates or interferes with the learning process or performance in task ‘B’. Knowledge used is referred to by many different terms such as declarative or procedural types (Anderson, 1976), but it means for our purposes that there are representational elements that suit ‘A’ and ‘B’. Problem solving transfer, on the other hand, is described as somewhat more "fluid knowledge" transfer, so that experience in solving a problem ‘A’ helps finding a solution to problem ‘B’. This can mean that the two problems share little in terms of specific declarative knowledge entities or procedures, but call for a similar approach, or solution search strategies (e.g., heuristics and problem solving methods).

The issues discussed in problem-solving transfer literature are also closely related to the concepts of strategic and theoretic transfer (Haskell, 2001, p. 31), and cognitive research on analogical reasoning, rule-based thinking and meta-cognition. Indeed, far transfer can be considered as the prototypical type of transfer, and it is closely related to the study of analogical reasoning (see also Barnett & Ceci, 2002, for a taxonomy of far transfer). Within the problem-solving literature the distinction between specific and general methods is made mostly with reference to Newell and Simon’s (1972) strong versus weak problem solving methods (Chi, Glaser & Farr, 1988; Ericsson & Smith, 1991; Singley & Anderson, 1989; Sternberg & Frensch, 1991).

Another concern that is frequently addressed in transfer taxonomies is the question of conscious effort. High-road vs. low-road transfer (Mayer & Wittrock, 1996; Salomon & Perkins, 1989) expresses a distinction between such instances of transfer where active retrieval, mapping and inference processes take place, as opposed to those instances that occur rather spontaneously or automatically. Hence, low-road transfer concerns frequently employed mental representations and automated, proceduralized knowledge, and occurs preferably in near transfer settings. In contrast, high-road transfer is more conception-driven and requires cognitive and meta-cognitive effort.

Traditional fields of transfer research

Obviously, there are a nearly unlimited number of research fields that share some applied interest into the study of transfer, as it pertains to learning in general. Three fields that contributed in most substantial ways to the progress of transfer research, both from a conception and empirical point of view, are the fields of education science, linguistics, and human-computer interaction (HCI). In fact, most transfer research has been conducted in reference to one of these applied settings, rather than in basic cognitive psychological laboratory conditions.

Education science: "teaching for transfer"

Due to their core concern with learning, educational science and practice are the classic fields of interest regarding transfer research, and probably the prime target for the application of theories. In fact, transfer of learning represents much of the very basis of the educational purpose itself. What is learned inside one classroom about a certain subject should aid in the attainment of related goals in other classroom settings, and beyond that it should be applicable to the student’s developmental tasks outside the school. Indeed, the need for transfer becomes more accentuated. This is because the world educators teach in today is different from the world they themselves experienced as students, and differs equally from the one their students will have to cope with in future.

By nature of their applied interest, educationalists’ main concern has been less with the question of how transfer takes place, and much more with under what conditions, or, that it happens at all. Obviously, the basic conviction that student’s learning and achievement levels depend primarily on learning and achievement prerequisites, has constituted a central part in educational learning theories for quite some time (Gage & Berliner, 1983; Glaser, 1984). The major focus in educational transfer studies has therefore been on what kind of initial learning enables subsequent transfer: teaching for transfer.

From Formal Discipline to meta-cognition

Educational transfer paradigms have been changing quite radically over the last one hundred years. According to the doctrinaire beliefs of the Formal Discipline (Binet, 1899) transfer was initially viewed as a kind of global spread of capabilities accomplished by training basic mental faculties (e.g., logic, attention, memory) in the exercise of suitable subjects like Latin or Geometry. With the turn of the 20th century, learning, and therefore also transfer of learning, was increasingly captured in behavioral and empiricist terms, as in the Connectionist and Associationist theories of Thorndike (e.g., 1932), Guthrie (e.g., 1935), Hull (e.g., 1943), and Skinner (e.g., 1938). Thorndike (1923, 1924a and b) attacked the Formal Discipline empirically and theoretically and introduced the theory of “identical elements”, which is probably still today the most influential conception about transfer (Thorndike, 1906; Thorndike & Woodworth, 1901a, b and c). Thorndike’s belief that transfer of learning occurs when learning source and learning target share common stimulus-response elements, prompted calls for a hierarchical curricular structure in education. “Lower” and specific skills should be learned before more complex skills, which were presumed to consist largely of configuration of basic skills. This small-to-large learning also referred to as part-to-whole or vertical transfer has been popular with theories of learning hierarchies (Gagné, 1968).

It has later been challenged from conceptualistic point of views, which argue that learning is not just an accumulation of pieces of knowledge (i.e., rote memorization), but rather a process and product of active construction of cognitive knowledge structures (Bruner, 1986; Bruner, Goodnow & Austin, 1956). Knowledge, from a constructivist perspective, was no more believed to be a simple transfer by generalization to all kinds of situations and tasks that contain similar components (i.e., stimulus-response patterns; see also Logan, 1988; Meyers & Fisk, 1987; Osgood, 1949; Pavlov, 1927).

The critical issue, subsequently, was the identification of similarities in general principles and concepts behind the facades of two dissimilar problems, i.e., transfer by insight. This idea became popular in the Gestaltists’ view on transfer (e.g., Katona, 1940), and, in combination with growing interest in learners as self activated problem-solvers (Bruner, 1986), encouraged the search for abstract problem-solving methods and mental schemata, which serve as analogy enhancing transfer-bridges between different task situations. Emerging from these developments a new theme started to dominate educationalists’ research in transfer: meta-cognition (Brown, 1978; Brown & Campione, 1981; Campione & Brown, 1987; Flavell, 1976). In contrast to classical knowledge forms like declarative and procedural knowledge, different types of meta-knowledge and meta-cognitive skills such as strategic knowledge, heuristics, self-monitoring skills and self-regulation became quickly the royal road to learning and transfer. Characterized as self-conscious management and organization of acquired knowledge (Brown, 1987) it is evident that meta-cognitive awareness of task features, problem structures, and solution methods makes relations between different situations cognitively salient: Only an individual who learns from learning, learns for future learning. Soini (1999) developed on the same core ideas an examination of the preconditions for active transfer. Her emphasis is on the active and self-reflected management of knowledge to increase its accessibility.To some researchers, meta-cognition and transfer have become so entangled that the argument was generated that only the measurement of positive transfer effects truly supports inferences that meta-cognitive learning has taken place (e.g. MacLeod, Butler & Syer, 1996).

The generality predicament: return to the specificity view

Ever since the introduction of the meta-knowledge theme in education science, transfer discussions have been oscillating between the position taken by those representing the meta-cognitive view, and those who stress that generic knowledge forms alone do not allow an effective transfer of learning: When knowledge stays "on the tip of the tongue", just knowing that one knows a solution to a problem, without being able to transfer specific declarative knowledge (i.e., know-what) or automated procedural knowledge (i.e., know-how), does not suffice. Specific teaching of the cognitive and behavioural requisites for transfer marked in principle a return to the identical element view, and can be summarized with Dettermann’s (1993) conclusion that transfer does not substantially go beyond the restricted boundaries of what has been specifically taught and learned. It, thus, appears that the basic transfer paradigms in educational psychology keep replicating themselves. And fundamental promotion of transfer itself is seen to be achievable through sensibilization of students by creating a general culture and "a spirit of transfer" inside the classroom on the one hand, and by allowing concrete learning from transfer models on the other (Haskell, 2001).

Inter-language transfer

Another traditional field of applied research is inter-language transfer. Here the central questions were (a) how does learning one language L1 (or more generally: language[m]) facilitate or interfere (Weinreich, 1953) with the acquisition of and proficiency in a second language L2 (language[m]), and (b) how does the training and use of L2, in turn, affect L1. Several variations of this conception of inter-language transfer can be found in the literature, also referred to as mother tongue influence or cross language interference (Corder, 1983, 1994; Faerch & Kasper, 1987; Jiang & Kuehn, 2001; Odlin, 1989; O’Malley and Chamot, 1990). What makes inter-language transfer a complex but at the same time very valuable research matter is the fact that language knowledge skills continuously develop. This is so for L1 as well as for L2, when only bilingualism is considered, while alternately at least one of them is also continuously in use. This has led to the development of very different models of how languages are mentally represented and managed, with L1 and L2 seen (a) as two independent or autonomous mental systems (e.g. Genesee, 1989; Grosjean, 1989), (b) as being represented in a single unified system (e.g. Redlinger & Park, 1980; Swain, 1977), and (c) as rooting in a common underlying, multi-lingual conceptual base (CUCB; see Kecskes & Papp, 2000).

Human-Computer Interaction: "designing for transfer"

A third research area that has produced a variety of transfer models and empirical results can be located within the field of Human-Computer Interaction (HCI). Indeed, with the start of the user age in the 1980s, HCI and all kinds of virtual environments have in many ways become something like psychological micro-worlds for cognitive research. This is naturally also reflected in the study of transfer. Developments in favour of cognitive approaches to transfer research were especially accelerated by rapid changes in modern lifestyles, resulting in a virtual upsurge of cognitive demands in interaction with technology. Thus the call was on clearly domain-focused cognitive models to study the way users learn and perform when interacting with information technological systems (Card, Moran & Newell, 1980a and b, 1983; Olson & Olson, 1990; Payne & Green, 1986; Polson, 1987, 1988).

Transfer based on the user complexity theory

Thorough investigations of cognitive skills involved in HCI tasks have their origins with the research on text editing (e.g., Kieras & Polson, 1982, 1985; Singley & Anderson, 1985). The offsprings of this type of research were computational cognitive models and architectures of various degrees of sophistication, suitable for all kinds of man-machine interaction studies, as well as studies outside of the HCI domain (see the section of cognitive transfer). The original examples for these have become Kieras and Polson’s (1985) user complexity theory (later rephrased as cognitive complexity theory) and the GOMS family (i.e., Goals, Operators, Methods, Selection rules) based on the Model Human Processor framework (Card et al., 1980a and b, 1983; John & Kieras, 1996a and b). All of these models have their roots in the basic principles of production systems and can be comprehended with the help of ends-means-selections and IF-THEN-rules, combined with the necessary declarative and procedural knowledge (Anderson, 1995; Newell & Simon, 1972).

The crucial perspective for transfer became that of technology design. By applying cognitive models scientists and practitioners aimed at minimizing the amount and complexity of (new) knowledge necessary to understand and perform tasks on a device, without trading off too much utility value (Polson & Lewis, 1990). A key responsibility was hereby given to skill and knowledge transfer. And because the cognitive complexity theory is in fact a psychological theory of transfer applied to HCI (Bovair, Kieras, & Polson, 1990; Polson & Kieras, 1985), the central question was, how these models, united under the GOMS-umbrella, can be used to explain and predict transfer of learning. The basic transfer-relevant assumptions of the emerging models were that production rules are cognitive units, that they are all equally difficult to learn, and that learned rules can be transferred to a new task without any cost. Because learning time for any task is seen as a function of the number of new rules that the user must learn, total learning time is directly reduced by inclusion of productions the user is already familiar with. Hence, the basic message of the cognitive complexity theory is to conceptualize and induce transfer from one system to another by function of shared production rules is, which is a new interpretation of Thorndike’s (1923, 1924a and b) identical element premise and eventually echoed in Singley and Anderson’s (1989) theory of transfer (Bovair et al., 1990; Kieras & Bovair, 1986; Polson & Kieras, 1985; Polson, Muncher & Engelbeck, 1986).

A practical implication of the procedural communality principle has been formulated by Lewis and Rieman (1993), who suggest something like "transfer of design" on the side of the industry: "You should find existing interfaces that work for users and then build ideas from those interfaces into your systems as much as practically and legally possible."

Emergence of holistic views of use

Discouraged by the confined character of the GOMS-related transfer models many research groups began to import and advance new concepts such as schemata principles and general methods; a general development encouraged by the emerging cognitive approach to transfer that was also witnessed by other applied fields. Bhavnani and John (2000) analyzed different computer applications and strived to identify such user strategies (i.e., general methods to perform a certain task) which generalize across three distinct computer domains (word processor, spreadsheet, and CAD). Their conclusive argument is that "strategy-conducive systems could facilitate the transfer of knowledge" (p. 338). Other research groups' authors that assessed the questions about how people learn in interaction with information systems, evaluated the usefulness of metaphors and how these should be taken into consideration when designing for exploratory environments (e.g. Baecker, Grudin, Buxton, & Greenberg, 1995; Carroll & Mack, 1985, Condon, 1999).

As researchers became increasingly interested in the quality of a user’s knowledge representation (e.g., Gott, Hall, Pokorny, Dibble, & Glaser, 1993), mental models and adaptive expertise, as knowledge and skills which generalizes across different contexts of complex problem- solving tasks, became of paramount concern (Gentner & Stevens, 1983; Gott, 1989; Kieras & Bovair, 1984). In contrast to the knowledge of strategies (Bhavnani & John, 2000), the accentuation shifted hereby towards strategic knowledge (Gott et al., 1993). Gott et al. demonstrated that surface similarities between different technical domains alone did not essentially facilitate transfer of learning because they limited the user’s flexibility in the adaptation process. In accord with the ideas of schema-based and meta-cognitive transfer, the authors further formulated that "robust performance is one in which procedural steps are not just naked, rule-based actions, but instead are supported by explanations that perform like theories to enable adaptiveness" (p. 260).

Gott et al. (1993) finally note that mental models might be powerful instruments to analyze similarities between tasks as represented within a formulized cognitive architecture. However, they do not explain what particular similarities and dissimilarities are sufficiently salient from the individual’s mental point of view to affect transfer of learning; nor can they predict motivational or emotional conditions of transfer that are essential requisites for every learning process.

Psychological scope of transfer research

As transfer pertains to the dependency of an individual’s experience and behaviour on prior experience and behaviour, its research must involve all aspects of psychological functioning, ranging from physical activities, cognitive processes (e.g., thinking), emotion and connation, to its social and environmental dimensions. Although the cognitive connotation of skill has largely emerged as the dominant conception, is not truly possible to appreciate the real meaning of skill without linking it to its motor or behavioural origins (Adams, 1987; Pear, 1927, 1948), and without extending its scope to include socio-emotional dimensions.

Cognitive transfer

The greatest bulk of theoretical and empirical research published in recent decades has been done with reference to transfer of cognitive skills and knowledge, for example with regard to problem-solving and analogical reasoning (Gentner & Gentner, 1983; Gick & Holyoak, 1980, 1983; Holland, Holyoak, Nisbett, & Thagard, 1986; Robertson, 2001). The cognitive shift in psychology showed a great impact on the evolvement of new and refined concepts, methods, theories, and empirical data in transfer research, and it put the investigation of the phenomenon back on the general research agenda after a clear decline in relevant scientific publications between 1960 and the 80ies (Cormier & Hagman, 1987; Haskell, 2001).

Cognition-oriented theories reinforced a series of key research frameworks to the study of transfer, including production systems, analogical reasoning (Gentner & Gentner, 1983; Gick & Holyoak, 1980; Holland et al., 1986), mental models, schema, heuristics, and meta-cognition (Brown, 1978; Flavell, 1976; Gentner & Stevens, 1983; Gott, 1989; Kieras & Bovair, 1984). Specifically, research on transfer has profited from three main drivers within the study of human cognition: these are analogy, the computational metaphor, and the intensified interests with the nature and quality of mental representations.

Metaphor and analogy

Metaphor refers to the use of a word or phrase to denote an object or concept not in a literary sense, but rather by suggesting an enhancement or replacement of the understanding and interpretation of the targeted object with the metaphor. The object we are indicating by a metaphor is holistically mapped onto the metaphor – and essentials of the metaphor’s content are therefore transferred to the representation of the denoted object. Indeed, the term metaphor comes from the Greek word "metapherein", meaning “to transfer” (see Ortony, 1991, for a overview).

In contrast to metaphor, the concepts of similarity and analogy are actually less inherently linked to the mental nature of transfer because they refer only to the circumstance of the relation between two representations. Here, object P is "seen" to be like Q (according to the Latin word "similis", meaning "like") in certain aspects; and by inferring that there might be other similar states between P and Q to be found, P can be used as an analog for Q. Transfer by analogy is not understood in the holistic way as is the case with metaphorical substitution of meaning, but rather in a channeled fashion due to aspectual (perceived or inferred) resemblance between P and Q.

Nevertheless, research on analogy, in all its nuances, proved to be a most influential to the conceptualization of cognitive transfer. Indeed, many cognitive scientists, as well as road leading philosophers, consider analogy to be one if not the core principle of human thinking and thought (e.g., Forbus, 2001; Hesse, 1966; Hofstadter, 2001). According to these views transfer has to be placed within the framework of analogy, rather than the other way around. Although research into analogy frequently penetrates traditional cognitive boundaries, for instance by involving emotionality and social cognition (see Thagard et al., 2002), it is usually associated with analogical reasoning and problem solving; both of which are closely related to the issue of transfer (Robertson, 2001).

Computational models

The nearly unifying cognitive metaphor is known as the information-processing approach (Eysenck, 2000; Kuhn, 1970; Lachman, Lachman & Butterfield, 1979), and with the understanding of the learning individual inspired by the General Problem Solver (GPS; Newell, Shaw & Simon, 1958 and 1960; Newell & Simon, 1963, 1972). Cognitive research brought forth a variety of computational models and methods to study and simulate knowledge acquisition, retention, and use (e.g. Anderson, 1983, 1985, 1993; Anzai & Simon, 1979; Atwood & Polson, 1976; Hayes & Simon, 1974 and 1977; Simon & Hayes, 1976). This also provided a new framework for transfer theory development, particularly Singley and Anderson’s (1985, 1989) cognitive account of Thorndike’s identical element theory. Hereby, emphasis is put on the classic knowledge form distinction between declarative and procedural knowledge (Anderson, 1995) as well as between weak problem solving methods (i.e., generalized, domain-independent knowledge and skills) and strong problem solving methods (i.e., domain specific knowledge and skills) (Anderson, 1987, Klahr, 1985; Larkin, 1985; Newell, 1980; Newell & Simon, 1972; Simon & Simon, 1978).

Anderson (1995) criticized preceding research on analogical transfer for its dominant focus on traits of the source and target in terms of declarative knowledge, instead of performance orientated processing aspects. He points out for skill acquisition that declarative memory plays only initially a significant role and is in the course of practice quickly replaced by procedural memory; encoded and strengthened in the form use specific production rules (also called the effect of Einstellung; Luchins, 1942). The performance benefits from already compiled production rules are believed to be automatic, errorless, independent of each other, and largely independent of contextual variations of tasks within the same knowledge domain. Hence, the transfer distance between the performances in two tasks, or the solutions to two problems, is assumed to decrease proportionally to the number of share specific procedures. This procedural "proportionality-relationship" (Allport, 1937) is in effect the most straightforward interpretation of the Greek term of analogy, meaning proportion, and has in ideal cases of procedure-to-procedure transfer settings, been shown to make relatively good predictions (see also Moran, 1983; Polson & Kieras, 1985; Singley & Anderson, 1985, 1989). Anderson's assessment echoed the fact that research on human learning and problem-solving started to put increasing emphasis on issues like cognitive skills and mental operators, which found implementations in a variety of cognitive architectures such as Soar (i.e., State, Operator, And Result; Laird, Newell & Rosenbloom, 1987; Laird, Rosenbloom & Newell, 1984; Newell, 1990; Rieman et al., 1994), CE+ (Polson, Lewis, Rieman, & Wharton, 1992; Wharton, Rieman, Lewis & Polson, 1994), and the development of several versions of Anderson’s ACT theory (Adaptive Control of Thought; e.g., ACT-R, see Anderson, 1982, 1983, 1993, 1996; Anderson & Lebiere, 1998).

In recent decades, cognitive scientists have developed numerous computational models of analogy such as the Structure Mapping Engine (SME) and the "model of similarity-based retrieval" (MAC/FAC; Forbus, Ferguson, & Gentner, 1994; Gentner & Forbus, 1991), Analogical Coherence Models (Holyoak & Thagard, 1989, 1995) Learning and Inference with Schemas and Analogies (LISA; Holyoak & Hummel, 2001) to name just a few (see Gentner, Holyoak & Kokinov, 2001, for an overview). Within LISA’s cognitive architecture, for instance, analogical mapping and retrieval functions are based on the premise that structural units in long-term memory (i.e., propositions, sub-propositions, objects and predicates) of source and target are represented by a collection of shared activated semantic units (Holyoak & Hummel, 2001; Hummel & Holyoak, 1997).

Motor transfer

Senso-motor skills are an essential ingredient in learning and performance in most tasks and can be categorized into continuous (e.g., tracking), discrete, or procedural movements (see Magill, 2004; Schmidt & Wrisberg, 2004, for recent basic overviews). Proceduralized motor skills have recently become the most referred to because they are consistent with the models of cognitive architectures and because they are seen as relevant to nearly all physical interactions with the environment; as is the case in transfer situations as well.

Open-loop and closed-loop processes

Before the birth of the proceduralization concept, theories of motor learning have been influenced by the open-loop versus closed loop system distinction (Adams, 1971; Schmidt, 1975). The original formulation of the closed-loop view on motor performance and learning build on the momentum of internal feedback from executed movements, which allow for error detection and adjustment of actions through the process of contrasting perceptual traces against memory representations (Adams, 1971). Motor learning was accordingly seen as dependent on repetition, accuracy, refinement, and synchronization of a series of called-up movement units (i.e., open-loop structures) that are regulated by closed-loop structures.

In response to this view a different open-loop perspective emerged, namely the one of motor programs (Schmidt, 1975). The learning of motor skills was hereby seen in terms of the build-up, modification, and strengthening of schematic relations among movement parameters and outcomes. This learning results in the construction “generalized motor programs” (i.e., a sequence or class of automated actions) that are triggered by associative stimuli, habit strengths, and re-enforcers, and can be executed without delay (Anderson, 1995; Schmidt, 1975, 1988).

Both theories have their origin with Thorndike’s "Law of Effect", because the formation of motor behaviour is essentially dependent on knowledge of the outcome of the action taken. This is regardless of whether the essence of motor skills is seen with specific movements or parameters in a schematic motor program (Adams, 1971; Bartlett, 1947a and b, Schmidt, 1988). Another, classic theme that was revived in the literature on transfer of motor skill is the part-to-whole transfer of training (Adams, 1987, p. 51ff.; Thorndike, 1924a and b). It emerged, because it is nearly unconceivable to learn a highly complex motor task as a complete entity. Much like in curriculum research, positive generalization of skill units into coherent task situations has been very limited. Particularly it was found that initial whole-task performances after part-task training remains seriously impaired due to difficulties in the time-sharing of the activities. In consequence whole task training remains generally superior to the part-task-whole-task transfer approach of learning (Adams, 1987; Adams & Hufford, 1962; Briggs & Brodgen, 1954).

Finally, motor research provided some evidence for context- and task-independent savings in learning effort on a new task that seems to be explainable by heightened plasticity and functional reorganisation in the senso-motor neural network system. This is naturally in line with the formal discipline argument.

Socio-emotional dimensions of transfer

Motor and cognitive transfer are in many respects inseparable from issues of emotion and motivation, just as cognitive research in general must embrace affective dimensions of experience and behaviour (Barnes & Thagard, 1996; Thagard & Shelley, 2001). This basic awareness has a long tradition in psychology and, of course, in the philosophical works of Aristoteles, Descartes, and Hume, but has to date not been sufficiently regarded in cognitive research (Damasio, 1994; Leventhal & Scherer, 1987; Mandler, 1975; Oatley & Johnson-Laird, 1987; Rapaport, 1950; Scherer, 1995).

Naturally, emotions and especially motivation have always been closely linked to learning in educational psychology, but their role was generally conceptualized as more of an assistant or moderating nature, i.e., in facilitating versus hindering cognition (Bruner, 1960; Gudjons, 1999; Pea, 1987, 1988; Pintrich, Marx, & Boyle, 1993; Salomon & Perkins, 1989; Thorndike, 1932). Approaches that focus on the same kind of relation between affect and transfer belong to the group that study main effects of affective beliefs on cognition in general, and in particular on transfer-relevant moderation and mediation effects of "will" on "skill" (see also Bong, 2002; Gist, Stevens, & Bavetta, 1991; Mathieu, Martineau, & Tannenbaum, 1993; Saks, 1995). In short: “Knowing how to solve problems and believing that you know how to solve problems are often dissonant” (Jonassen, 2000, p. 14).

Transfer of emotions

Emotional transfer must, however, be regarded as a distinct aspect or type of transfer itself, i.e., one where the experiential relation between two situations is of affective nature (e.g., affective connotations and skills). It occurs wherever previously experienced feelings and attitudes toward a situation, object, or task are re-evoked in a current confrontation with related "symbols" (see Hobson & Patrick, 1995). The preferred emotional transfer model to date has been the one of analogical inference, e.g., if you like product X, and product Y is similar to X, then you will probably like Y. Thagard and Shelley (2001) criticized the simplicity of analogical inference based on mere comparison of objects and properties and proposed a more complex model that accounts for structures of analogies, e.g., by including relations and causality structures. Their emotional coherence theory implemented this idea in the form of the HOTCO model (standing for “hot coherence”) by drawing on assumptions made in preceding models, including explanatory coherence (ECHO), conceptual coherence (IMP), analogical coherence (ACME), and deliberative coherence (DECO) (see Thagard, 2000).

Conceptual foundation of transfer research

The cognitive shift in psychology encouraged the research of mental forms and processes engaged in learning and transfer rather than the simple modification of overt reproductional behaviour; a change in viewpoint that the early Gestalt psychologists and constructivists such as Köhler, Wertheimer, or Piaget had already propagated for a couple of decades. The investigation of cognitive dimensions in transfer became quickly the major driver of research across applied domains and cognitive transfer emerged in many ways as the quintessential view of transfer in general.

Mental representations and transfer: Common element-based vs. schema-based approaches

The majority of mental processes studied in research on human cognition have one thing in common: They all pertain in one way or another to the construction of mental representations. This is true, for instance, for perceiving, learning, problem-solving, reasoning and thinking, and recalling, as much as it is true for the phenomenon of transfer.

Although research on mental representation has been utterly manifold, two main traditions can be discerned. Some researchers have regarded mental representations in terms of abstract schemata, frames, patterns or mental models (Bartlett, 1932; Chase & Simon, 1973; Gentner & Stevens, 1984; Johnson-Laird, 1983; Johnson-Laird & Byrne, 1990; Minsky, 1975), while others have paid attention to semantic information and propositional nature of mental representations (Anderson, 1976, 1983, 1994; Collins & Quillian, 1968; Medin & Ribs, 2005; Medin & Smith, 1984; Minsky, 1968; Rosch, 1978). These differntial conceptuatlizations have in general been driven by distinct psychological paradigms adopted, such as Associationism and Connectionism, Behaviorism, Gestaltism, and Cognitivism.

GOMS and ACT-based procedural transfer theses are a good example of modern explanations fitting the atomistic and mechanistic nature of the Connectionist paradigm, i.e., by seeing transfer as an effect of commonality in semantic conditions-action-goal structures, mainly instantiated as IF-THEN production rule associations overlap. This view on transfer clearly replaced Behaviorist explanatory concepts of stimuli and response with more sophisticated mental concepts that serve as units of transfer. The cognitive architecture background also added important processing capabilities and some degree of flexibility concerning the identicality constraint (e.g., declarative-to-procedural, and declarative-to-declarative transfer); it did however not essentially defy the common underlying common element-based thought model of transfer.

Both the original habitual response-based idea of common element transfer as well as the modern production rule compilation and knowledge encapsulation account are in their core assumptions refuted by Gestaltists’ theories. Koffka’s (1925) scrutiny of Thorndike’s (1911, 1913) and Köhler’s (1917) arguments and findings revealed that explanations of learning and transfer based on the notions of association and automation fall short of explicating the nature of mental activity even for simple problem solving tasks. Novel explanatory concepts were needed to account for “learning by understanding” (Katona, 1940) and problem solving transfer (Mayer & Wittrock, 1996). These were found with reference to the organization and structure of knowledge (Clement & Gentner, 1991; Gentner & Gentner, 1983; Gentner & Toupin, 1986), abstraction and general principle inferences (Bourne, Ekstrand, & Dominowski, 1971, p. 104ff.; Judd, 1908, 1939; Simon & Hayes, 1976), the goal- and meaning-directedness of thinking and its holistic nature (Bühler, 1907, 1908a; Holyoak, 1985; Humphrey, 1924; Selz, 1913, 1922), and functional relations (Duncker, 1935; Köhler, 1917). Because this tradition of investigating transfer is based on Gestaltist ideas, we could summarize them under the header of schema-based theories of transfer.

In accord with the traditions regarding research on mental representation, we can conclude on two mainstream explanatory models for transfer to date: One is the model of common element-based transfer, rooting in Thorndikean ideas, which explains transfer as confined to elementary correspondences between a primary and a secondary learning situation, e.g., procedures, and their automated effect (e.g., Allport, 1937; Singley & Anderson, 1985, 1989; Thorndike, 1924a, b). The other model emerging from the Gestalt tradition can be labeled schema-based or analogical transfer, emphasizing elementary loosened structural or principle/rule-based coherence between transfer source and target (e.g., Duncker, 1935; Gentner, 1983; Gentner & Gentner, 1983; Gick & Holyoak, 1980, 1983; Köhler, 1917/1957; Reed, 1993). They continued Judd’s (1908) line of work resulting in further accentuation of "insightful" transfer, using terms like knowledge structures and schemata, solution principles, and functionality (Katona, 1940; Wertheimer, 1945/1959).

The problem is that, as far as transfer of learning in both traditions refers to one and the same phenomenon, there can not be a situation with two incompatible theoretical frameworks standing side-by-side. Conceptual resolution in some form is clearly imperative. Several efforts have been made in recent years to review and revive transfer research, and to resolve controversies (cf. the content- and apperception-based approach: Helfenstein, 2005[3]), but empirical justification is still in early stages.

The similarity predicament

The notion of similarity has been particularly problematic for transfer research for a number of reasons. The main problem is that similarity implies dissimilarity, i.e., although two instances may in parts be identical, they are after all also different.

First, similarity has been the cause for debate about how to distinguish transfer of learning from learning or problem solving. The distinction of transfer (of learning) from learning is usually done with reference to a cut-off point on the similarity dimensions, by which the relation between a current and a past situation is estimated. The more similar two situations are rated, the more probable it becomes that any witnessed improvement in performance is due to learning rather than to transfer. The same logic is true in the other direction of the transfer-learning dimension. The discussion on the dissimilarity-similarity distinction has the ambivalent character of being conducted in reference to a dimensional or polar conception and dichotomous model interchangeably. Hence, learning is usually implicitly awarded its own place at the periphery of transfer taxonomies that are based on near-far distinctions. And this raises the question whether it would not be sounder to concentrate more intensively on the common cognitive bases of learning and transfer, than on some conceptual distinction between them.
For instance, while Butterfield and Nelson's (1991) categorization is intuitively appealing, it also conveys some typical problems and challenges. For instance, if transfer is to a task or situation, which is so similar to a previously experienced one that it actually can be considered as the same task (i.e., within-task transfer), then how do we distinguish transfer from learning in general? The corresponding deliberation is that learning refers to mental processes involved in the course of a repeated confrontation with a certain type of task or situation, of which the single accounts can never be identical. Butterfield and Nelson have themselves not been blind to this argument, but they still refrain from equating learning and transfer as proposed by Salomon and Perkins (1989, p. 115). Across-task transfer, according to Butterfield and Nelson’s (1991) model refers to the application of a learned principle in a new task situation which is superficially different, yet functionally equivalent to the prior one. Inventive transfer, finally, is used to describe incidences where learners can not make use of the same solution principles previously learned, but have to develop a new solution on the grounds of similarities and critical differences of source and target task. Understandably, Butterfield and Nelson pose the question to whether this should be rather characterized as problem-solving than transfer.

Second, transfer theories are build on the premise of identical constituents between transfer source and target, while differences, are usually seen as cause of transfer failure. In spite of the manifold attempts to dissociate from one-to-one similarity concepts, the identicality constraint continued to produce most of the headaches to cognitive scientists, especially in the area of analogy research. Considering the diversity of transfer conditions, application domains, and contextual dependency of analogical thought, it is not surprising that few psychologists have conclusively put their fingers on what see as the essence of analogical relations. While the talk of "sameness" and "transpositional similarity" appeals to common sense, much about what similarity means precisely , how it is established mentally, and, therefore, what justifies analogical reasoning, remains unclear.
Overall, similarity constraint factors have been identified with respect to predicates, objects and propositions, relational and structural isomorphism, procedural matches, in relation to purpose or goals of tasks or episodes under analogical consideration (see e.g., Robertson, 2001), as well as in relation to the level or type of mental engagement (see results from research on Transfer-appropriate processing (TAP); e.g., Cermak & Craik, 1979; Francis, Jameson, Augustini, & Chavez, 2000; Jacoby, 1983; Roediger & Blaxton, 1987; Schacter, Cooper, Delaney, Peterson & Tharan, 1991; Vriezen, Moscovitch, & Bellos, 1995). As noted, analogical transfer and analogical memory recall has been demonstrated with respect to similarity in superficial traits rather than in respect to relational analogy or structural correspondence (e.g., Kaiser, Jonides, & Alexander, 1986), and has been best attained in within-domain and near transfer settings; in spite of the claim that similarity between analogs fundamentally refers to the qualitative “alikeness” in the relations that hold within one common structure of mental objects, and not simply to the quantitative surface similarity of properties or features from which analogy is then inferred (Forbus, 2001; Gentner, 1982, 1983).
Nevertheless, if transfer by analogy is not to stumble over the boundaries of identical matches - be these superficial attributes between target and retrieved source, elements of declarative knowledge, procedural memory content, relational aspects, or otherwise - then the question what similarity means in the context of dissimilarity should be resolved. The focus should be on explicating the sameness in mental representations and assessing their impact on transfer; and not so much on the question "how similar is similar enough to be considered as an analog?"

See also


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