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This is a contribution from Second Language Task Complexity. Researching the Cognition Hypothesis of language learning and performance. Edited by Peter Robinson. © 2011. John Benjamins Publishing Company

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chapter 1

Second language task complexity, the Cognition Hypothesis, language learning, and performance

Peter RobinsonAoyama Gakuin University

This chapter provides an overview of pedagogic and theoretical issues that have motivated recent research into second language task complexity. The first two sections describe how procedures for task analysis, and decisions about task complexity, can be accommodated in task-based language teaching program design. The third section describes a componential framework for analysing task demands. This framework distinguishes between cognitive factors contributing to Task Complexity, interactive factors involved in performing tasks under various Task Conditions, and the learner factors affecting perceptions of Task Difficulty. The fourth section describes five ancillary predictions the Cognition Hypothesis makes about how increasing task complexity leads to measurable increases in task-based learning and performance. The final sections describe how studies of these predictions have typically been measured, to date, and a rationale for investigating the effects on task-based learning of individual differences in learners’ cognitive abilities and affective dispositions. The chapter concludes by briefly summarising the organization of the chapters in this book and the empirical findings they report.

Introduction: Task analysis, task characteristics and task complexity

The chapters in this volume are all concerned with researching the effects of the com-plexity of second language (L2) tasks and with drawing conclusions about the extent to which varying the complexity of tasks affects language learning and performance in measurable ways. Each of the empirical studies of the effects of task complexity opera-tionalises one or more of the characteristics listed in a componential taxonomy, the Triadic Componential Framework (TCF, see Figure 1), which is described in detail in the following sections of this chapter. Each study also weighs evidence for and against some of the theoretical claims of the Cognition Hypothesis: for example, that complex tasks should promote more accurate and complex, though less fluent, language than

© 2011. John Benjamins Publishing CompanyAll rights reserved

Peter Robinson

simpler counterpart tasks, and that complex tasks promote more interaction, greater attention to, uptake and learning of information provided in the input to task perfor-mance than simpler tasks (Robinson, 2001a, 2001b, 2003b, 2005a). These claims, ratio-nales for them, and descriptions of some of the measures typically used to date in empirical research into task complexity are also described in more detail in later sec-tions of this chapter. In light of the findings they report, implications are drawn from the various empirical studies in this book concerning the basic pedagogic claim of the Cognition Hypothesis, that tasks should be sequenced for learners from simple to complex in order to promote success in performing complex tasks in the L2, as well as opportunities for further L2 learning and interlanguage development.

Task analysis and task-based program design

Two important aims of the research described in this book are to deepen our under-standing of how task characteristics can affect the second language acquisition (SLA) processes involved in learning while attempting to meet the challenges certain tasks set, as well as our understanding of how task demands can affect variation in the quality and quantity of L2 speech and writing produced during task performance. Such infor-mation will be important to accommodate in broad cognitive-interactionist theories of SLA (e.g., Ortega, 2007), and important too for test-designers concerned to elicit levels of L2 performance that most accurately characterize learners levels of proficien-cy (e.g., Iwashita, Elder, & McNamara, 2001). For example, researchers exploring the potential benefits for SLA processes of the opportunities for interaction that task-based language teaching provide (e.g., Mackey, 2007; Mackey & Gass, 2006), generally agree on the following:

– Tasks provide a context for negotiating and comprehending the meaning of lan-guage provided in task input, or used by a partner performing the same task.

– Tasks provide opportunities for uptake of (implicit or explicit) corrective feedback on a participant’s production, by a partner, or by a teacher.

– Tasks provide opportunities for incorporation of premodified input, containing ‘positive evidence’ of forms likely to be important to communicative success, and which may previously have been unknown or poorly controlled.

– Tasks provide opportunities for noticing the gap between a participant’s produc-tion and input provided, and for metalinguistic reflection on the form of output.

However, it is likely that the cognitive demands of pedagogic tasks that provide these opportunities for learning during interaction will also affect the extent to which learn-ers capitalize on them. The Cognition Hypothesis makes claims about how the cogni-tive complexity of tasks affects the extent of interaction, and the learning that accrues from it, when performing tasks individually, or in a sequence of progressively more cognitively complex tasks.


Chapter 1. Second language task complexity, the Cognition Hypothesis

Although they are not often performed in interaction with others, language testing tasks (e.g., those aimed at gauging the extent of a learner’s achievement in a language program, or the level of proficiency of an L2 user) also need to accommodate differences between the complexity of parallel versions of tasks designed to meet these purposes, so as to ensure comparability of findings across a population using multiple versions of these tasks. With regard to achievement, it is important that the level of complexity of the testing task be consistent across versions of it, and with regard to proficiency, it is important that the demands of testing tasks be incrementally increased in some princi-pled way, so as to distinguish between more and less proficient populations of L2 users.

The main pedagogic aim of the Cognition Hypothesis, however, is not to provide a metric to enable language testers to ensure the comparability of tasks used to mea-sure achievement, or to adjust differences in task demands so as to measure different levels of proficiency more distinctly – rather it aims to provide a rationale for how to sequence tasks in such a way as to lead to learning, and to different levels of L2 perfor-mance in language programs.

Within the context of Task-Based Language Teaching (TBLT) program delivery (e.g., Skehan, 2003; Van den Branden, 2006; Van den Branden, Bygate, & Norris, 2009) it is essential that we understand the nature of the tasks that learners will be perform-ing, in order to design instruction that will support learning, across sequences of tasks, and within the time programs allocate for performing them. Task analysis is probably the most important part of instructional design, for L2 learners, as it is for other learn-er populations with other learning targets, as has long been argued:

If I were faced with the problem of improving training, I should not look for much help from the well known learning principles like reinforcement, distribution of practice, response familiarity, and so on. I should look instead to the technique of task analysis, and the principles of component task achievement, intratask trans-fer, and the sequencing of subtask learning to find those ideas of greatest useful-ness in the design of effective learning. (Gagne, 1962, p. 90)

Partly prompted by Gagne’s thoughts on instructional design (e.g., Gagne, 1977) there are now a number of different approaches to analyzing tasks in order to identify charac-teristics that can be most useful in promoting learning (see Hoffman & Militello, 2009; Jonassen, Tessmer, & Hannum, 1999). These have been adopted by instructional design-ers in a wide variety of content domains, such as programs of instruction for trainee air traffic controllers (Wickens, Mavor & McGee, 1997) or management trainees (McGrath & Tschan, 2004), and these different approaches to task analysis are most often used at different stages of program development and delivery of task-based instruction.

In behavior descriptive approaches to analyzing and classifying tasks, categories of tasks are based on observation (both participant and non-participant) and descrip-tions (which may be elicited by structured, or unstructured interviews from job performers, supervisors, and so on) of what people actually do while performing a task – including the typical patterns of interaction participants engage in, the use they make of resources such as media tools, and the time tasks typically take to complete.


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Figure 1. The Triadic Componential Framework for pedagogic L2 Task Classification – Categories, Criteria, Analytic procedures, and Characteristics. Adapted by permission from Robinson, P. (2007). Criteria for classifying and sequencing pedagogic tasks. In M. Pilar Garcia-Mayo (Ed.), Investigating Tasks in Formal Language Learning, (Chapter 1, pp. 7–27). Multilingual Matters. Key: h = high, l = low. Note: References given are to em-pirical studies describing how task complexity, task condition, or task difficulty variables were operationalised in studies of their effects on task-based L2 learning and perfor-mance, or to helpful descriptions of these variables where empirical studies of their effects are not currently available

Examples of these approaches are behavioral typology analysis (Williams, 1977); clas-sification of common denominators of task performances required across industrial or other workplace jobs (McCormick, 1979); descriptions of jobs on the basis of worker functions or behaviors (Fine, 1974); and activity analyses of task performance in vari-ous contextual settings (Nardi, 1996). These approaches to analyzing tasks, their inter-actional demands, and the subtasks and steps needed to perform them (together with a sampling of target domain discourse), are important at the Needs Analysis stage and level of Figure 2, as input to L2 course design, and also to performance-referenced test-ing of abilities to accomplish tasks or consituent sub-tasks to some criterion measure of success (e.g., Gagne, Waver, Golas, & Keller, 2005; Long, 2005; Long & Crookes, 1992; Munby, 1978; Norris, 2009; Norris, Brown, Hudson, & Yoshioka, 1998; Van Avermaet & Gysen, 2006).

Information-theoretic approaches adopt a different level of description, analyzing and classifying task demands in terms of information processing stages, and the cogni-tive processes involved in mediating between input to the task performer and the out-put and interaction required for successful task completion. These approaches have often been used as a basis for task classification and sequencing in a number of educa-tional domains, in line with principles described in one or another instructional design theory (see Reigeluth & Carr-Chellman, 2009). Distinctions between the cognitive de-mands that certain tasks make have also been variously argued to influence task-based L2 learning, and to have implications for task sequencing and syllabus design (Bygate, Skehan, & Swain, 2001; Candlin, 1987; Duran & Ramaut, 2006; Johnson, 1996; Long, 1985; Prabhu, 1987; and see Ellis, 2003, and Samuda & Bygate, 2008, for review). Per-haps the most widely-cited and influential proposal for how the cognitive demands of tasks can be classified and manipulated during L2 instruction is that of Peter Skehan (e.g., 1998, 2009).

Skehan’s (1998) ‘Limited Capacity Hypothesis’ claims that more cognitively de-manding tasks “consume more attentional resources...with the result that less attention is available for focus on form” (p. 97), and therefore that sequencing tasks from less to more demanding optimizes opportunities for attention allocation to language form (thereby promoting opportunities for task-based language learning). In the framework for task-based instruction Skehan (1996, 1998) has described, task design is also seen


Peter Robinson

as a means to promote ‘balanced language development’ in the areas of accuracy, flu-ency and complexity of production. This can be done because certain task characteris-tics “predispose learners to channel their attention in predictable ways, such as clear macrostructure towards accuracy, the need to impose order on ideas towards com-plexity, and so on” (Skehan, 1998, p. 112). However, due to scarcity of attentional re-sources, tasks can lead either to increased complexity, or accuracy of production, but not to both. Skehan (1998) therefore recommends that tasks should be sequenced by choosing those with characteristics that lead to fluency, accuracy and complexity, at an appropriate level of task difficulty, as determined by three factors: (1) code complexity, which is described in ‘fairly traditional ways’, as in descriptions of structural syllabuses, or developmental sequences (p. 99); (2) cognitive complexity, which is the result of the familiarity of the task, topic or genre, and the processing requirements; information type, clarity and organization, and amount of computation required; and (3) commu-nicative stress, which involves six characteristics including time pressure, number of participants, and opportunities to control interaction. These characteristics, Skehan (2002) argues, can be manipulated during task design, and by teachers using tasks “to orient learners away from simply focusing on meaning, but also push them to extend-ing and at the same time achieving greater control over the language” (p. 293).

As Figure 2 illustrates, one major outcome of analyses of the cognitive demands of tasks could be a decision about how to design and sequence pedagogic L2 tasks so as to gradually approximate the complexity of targeted real world task performances identified by behavior descriptions at the needs-identification stage of course design. This is not an objective of Skehan’s proposal for task-based instruction – he is con-cerned with how tasks should be designed and sequenced to foster increases in ‘ge-neric’ L2 accuracy, complexity and also fluency, independently of any domain of use outside a language program that requires particular levels of these.

A third approach to task classification, the ability requirements approach, classi-fies tasks in terms of the human cognitive abilities required to perform them effec-tively (Carroll, 1993; Robinson, in press; Snow, 1994). This is important at the third stage of program delivery illustrated in Figure 2. Clearly, L2 learners differ in their strengths in abilities drawn on during information processing (such as working mem-ory capacity), and these differences, as well as differences in the information process-ing demands of pedagogic tasks themselves, will affect the outcomes of pedagogic task performance for individuals. Research into interactions between L2 learner’s cognitive processing abilities, their motivational and affective dispositions, and the demands of tasks has begun to be systematically approached in recent years (e.g., Baralt, 2010; Dörnyei, 2002; Mackey, Philp, Egi, Fujii, & Tatsumi, 2002; Robinson, 2002a, 2005b), and this focus will be important to identifying aptitudes for different kinds of task performance and L2 processing, and so to matching learners to, or supporting perfor-mance on, various pedagogic task types and practice sequences.



Stage

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Figure 2. Stage, domain, analyses and outcomes of task classification and sequencing procedures

Task complexity, the Cognition Hypothesis, and task sequencing

The basic pedagogic claim of the Cognition Hypothesis is that L2 pedagogic tasks should be sequenced for learners on the basis of increases in their cognitive complex-ity, therefore addressing stage 2 of the process of task-based program delivery illus-trated in Figure 2. At this stage, the target task demands identified previously at stage 1 are considered in terms of a framework identifying features contributing to their complexity, and these features are used to prepare a blueprint for sequencing peda-gogic task versions which is followed at stage 3 during the process of pedagogic task design. For example, where the desired outcome of L2 instruction for a group of learn-ers is the ability to reason about the intentional states of others that guide their actions and verbalize these when giving an explanation of other people’s behavior (as when a section chief is summoned urgently to report to a superior about what caused a distur-bance between co-workers in the workplace) then simple pedagogic task versions are performed prior to those requiring more complex intentional reasoning. This would involve first simply reporting the actions that occurred in the workplace (with the benefit of planning time) without reasoning about and explaining why the various parties undertook these actions, or responded to them in the way that they did. On


Peter Robinson

subsequent unplanned versions, intentional reasoning demands are progressively increased to target-task levels of complexity (see Figure 3). Ishikawa (this book) de-scribes the results of a study of the effects of increasing pedagogic task complexity in this way on L2 learner speech production.

The rationale for this proposal is that, on the one hand, it describes a parsimonious way to sequence L2 tasks in programs of instruction (since cognitive complexity is the sole basis of sequencing decisions) and that therefore, given a sufficiently detailed tax-onomy of task characteristics affecting their cognitive complexity, then this should also be a pedagogically feasible basis for decision-making by task and syllabus design-ers. On the other hand, the claim is also theoretically motivated (in ways I will de-scribe), since sequences of increasingly cognitively complex versions of pedagogic tasks should promote the development of the language needed to perform them under the demanding operating conditions that learners often face in using language to ac-complish task goals outside the L2 classroom. In addition, the claim that increasing the cognitive complexity of task demands leads to task-based language learning as well as to improvements in L2 performance is empirically researchable, as the papers describ-ing the effects of various task characteristics on language production and interaction in this book help to demonstrate.

The proposal that task complexity should be the only basis for task sequencing is consistent with some approaches to using tasks in language teaching programs, but not with others. It is consistent with the approach taken in the Bangalore Project, described by Prabhu (1987) in which tasks performed by learners were graded and sequenced in

Highly complex

Task involving complex reasoning about the intentions of others that led to con�icts of interests and a subsequent disturbance in the workplace, and reporting both what

happened and explaining why the disturbance occurred to a superior face to face,when called on to do so without warning.

Pegagogic Task 1

Reporting–Reasoning

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Reporting+Simple reasoning

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Target task

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Figure 3. Increasingly complex pedagogic task versions of a target task



accord with “a commonsense judgment of increasing complexity, the later tasks being either inclusive of the earlier ones or involving a larger amount of information, or an extension of the kind of reasoning done earlier” (p. 39). It is also consistent with the proposal of Long (1985, 1998; Long & Crookes, 1993; Long & Norris, 2000) that dur-ing task-based syllabus and materials design ‘pedagogic tasks’ (the units of classroom activity) are developed and sequenced to increasingly approximate the demands of real-world ‘target tasks’ needs, that is, those tasks which learners must be able to per-form using language in order to be successful in various domains of lifetime endeavor outside the language classroom, such as adequately answering queries about foreign imports and exports, visiting the doctor to obtain a needed prescription, or under-standing school or university-level content instruction well enough to pass an exam. The ‘task-based’ approach to sequencing the units of L2 instruction is distinct from what Samuda and Bygate (2008, p. 58) have called the ‘task-supported’ approach in which tasks can be used to promote fluency, accuracy and complexity in the use of language and/or to deliver units of language, such as grammatical structures (e.g., El-lis, 1993; Nunan, 1989) or lexical items and collocational patterns (e.g., Boers & Lind-stromberg, 2009; Willis, 1990). In the task-supported approach to L2 instruction the syllabus may therefore be determined, at least in part, on the basis of decisions made by syllabus and task designers about what language items need to be learned in order to complete tasks, and in what order they are to be sequenced and practiced on tasks. Such decisions about the forms that are the focus of task activity are based on various criteria for determining the sequence in which they are presented to learners, such as grammatical complexity, learnability, usefulness, frequency, and others (Robinson, 2009). In contrast to the task-supported approach, the Cognition Hypothesis proposes that tasks alone, not linguistic items needed to complete the task, are the units involved in decision-making about instructional sequences presented to learners.

The claim that holistic tasks should be sequenced in an order of increasing com-plexity for learners is also in line with other constructivist approaches to education, across a wide variety of content and skill domains, that propose instruction should scaffold and support learning processes in such a way as to lead to autonomous and successful complex task performance. Merrill (2006) puts this claim well, in a way which is compatible with the Cognition Hypothesis:

Learning to complete a single task leaves the learner with only one view of the task... A progression of tasks that are progressively more complex during training with the student performing more and more of the steps to task completion on their own enables them to tune their schema so that when confronted with yet a different or more complex task from the same family they are able to move for-ward to task completion. (p. 277)

Drawing on research in and about complex systems, and systems-based approaches to learning and instruction, Spector (2006) also describes a scenario for educational de-cision-making compatible with the Cognition Hypothesis:


Peter Robinson

A common theme in systems-based approaches is the notion that the full complex-ity of a problem situation should eventually be presented to the learner, and that helping the learner manage that complexity by gradually introducing additional problem factors can contribute to effective learning. Challenging problems typi-cally involve a complex system, and instruction should be aimed not only at a spe-cific facet of the problem but also at the larger system so as to help learners locate problems in their naturally larger contexts; this has been called a holistic approach (Spector, & Anderson, 2000) or a whole-task approach (van Merrienboer, 1997). (p. 19)

The Cognition Hypothesis is also compatible with Charles Reigeluth’s (1999) ‘Elabora-tion Theory’ of how to select and sequence content across a wide domain of cognitive and psychomotor learning – in particular it is consistent with his insistence on a ‘ho-listic approach’ to identifying and ‘sequencing’ the units of instruction:

The paradigm shift from teacher-centered and content-centered instruction to learner-centered instruction is creating new needs for ways to sequence instruc-tion. In the industrial-age paradigm the need was to break the content or task down into little pieces and teach those pieces one at a time. But most of the new approaches to instruction, including simulations, apprenticeships, goal-based scenarios, problem-based learning, and other kinds of situated learning, require a more holistic approach to sequencing, one that can simplify the content or task, not by breaking it into pieces, but by identifying simpler real-world versions of the task or content domain. (p. 427)

This emphasis on the value of holistic approaches to instruction is also one that is shared by a number of recent proposals for task-based pedagogy (Norris, 2009; Samu-da & Bygate, 2008).

The Triadic Componential Framework for task classification

Operationalizing the pedagogic claim of the Cognition Hypothesis requires a taxono-my of task characteristics that can be used by task and syllabus designers, across a wide variety of instructional settings, and with a wide variety of learner populations, to clas-sify and sequence a ‘progression’ of pedagogic tasks that increase in complexity across periods of instruction. The Triadic Componential Framework (TCF) (see Figure 1) is an attempt to provide such an operational taxonomy. The TCF describes characteris-tics contributing to the demands pedagogic tasks make on interaction, categorized under the heading Task Condition (such as whether information exchange is one-way, or reciprocal and two-way). In addition the TCF describes characteristics contributing to the intrinsic cognitive demands tasks make on learners, such as whether the task does not require reasoning about the mental states of others that cause them to per-form actions (–intentional reasoning) versus whether it does (+intentional reasoning).



These latter characteristics are categorized under the heading Task Complexity, and the Cognition Hypothesis proposes that increasing the cognitive demands of pedagogic tasks using these latter characteristics should be the sole operational basis of task-based syllabus design. In contrast, characteristics of Task Condition are held constant and replicated each time more cognitively complex pedagogic versions are attempted so as to help ensure development and successful transfer of the monologic or interac-tive schemata, and related scripts for performance, which the condition requires (see Robinson, 2007a; Schank, 1999; Schank, Berman, & MacPherson, 1999). Increas-ing the complexity of the cognitive demands of pedagogic tasks through one or an-other of these Task Complexity features (for example – versus + intentional reasoning) should help to explain within-learner variation in the language they use to perform them. An ancillary theoretical claim of the Cognition Hypothesis, described in more detail below, is that more cognitively complex tasks will prompt the use of more complex and accurate language to meet the increased demands they make, compared to simple counterpart tasks.

A third category in the TCF is Task Difficulty. This concerns not task factors, but learner factors which can be expected to affect learning and performance on tasks. These are divided into ability and affective factors both of which affect perceptions of tasks, and which are used to investigate Task Difficulty. These factors can be expected to contribute to between-learner variation in successfully performing any one L2 task, and their influence is likely to be particularly apparent in differentiating learner per-formance on complex versions, in the same way that individual differences between learners in their aptitude for mathematics is unlikely to predict variation in success in performing simple addition problems, but much more likely to predict success in per-forming cognitively complex calculus problems (see Ackerman & Ciancolo, 2002; Snow, Kyllonen, & Marshalek, 1984). What the factors are that contribute most to perceptions of the difficulty of – and the likelihood of success on – tasks having one or another of the characteristics listed under the categories of Task Condition or Task Complexity is, as yet, an unknown issue, but one which is extremely important to fu-ture research into the effects of task characteristics on L2 learning and performance. The chapters in the final part of this book all describe findings from studies of the in-fluence of learner factors, such as the extent of learners’ anxiety or working memory capacity, on learning and performance on tasks having one or another task complexity characteristic. The prospects that further, cumulative research in this area hold for deepening our understanding of the effects of task design on learning, performance and for learner-centered task-based pedagogy are clear. For example, if it could be shown that certain cognitive abilities enabled learners to successfully attribute inten-tions to others, and use these to reason about why others performed certain actions, then learners high in these cognitive abilities might be expected to perform differently – be pushed to a different level of linguistic performance – on a complex intentional reasoning task in the L2 than those lower in those cognitive abilities. For research purposes then, one would want to compare the performance of learners at similar


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levels of these intentional reasoning abilities in order to identify the extent to which simple and complex versions of intentional reasoning demand tasks (the task charac-teristic being manipulated) affect language production. For pedagogic purposes, one would want to know what these abilities for performing certain kinds of complex task were, either in order to support L2 performance on them (where the abilities the task draws on are low), or else to match learners to complex tasks they are well suited to performing, so as to optimize their opportunities for learning and performance. In a later section of this chapter I return to this issue of research into task-ability matching and its implications for instruction and task-based learning research.

Task characteristics that direct attention to language

In the Triadic Componential Framework an important theoretical distinction is made between resource-directing and resource-dispersing task characteristics (Robinson 2003a). The first subcategory distinguishes task characteristics on the basis of the con-cepts that the task requires, in order to be expressed and understood (e.g., relative time, spatial location, causal relationships, and intentionality). Increasing the complexity of resource-directing characteristics has the potential to connect cognitive resources, such as attention and memory, with effort at conceptualization and the L2 means to express it, thereby promoting second language development. Clearly effort at concep-tualization is more, or less demanding of cognitive resources as evidenced, for example, by the growth of working memory capacity and the complexity of mental modelling throughout childhood (Halford, 1993). In first language acquisition many have argued that cognitive development, and the increasing sophistication of conceptualization it makes possible, are jointly responsible for the development of the complex language needed to express those conceptualizations (e.g., Bartsch & Wellman, 1995; E.Clark, 2004; Lindholm, 1988; Lohman & Tomasello, 2003; Mandler, 2004; Slobin, 1973; Tomasello, 2003). For example H. Clark (1973) argued that “the child knows much about space and time before he learns the English terms for space and time, and his acquisition of these terms is built onto his prior cognitive development” (p. 28).

The strong form of the Cognition Hypothesis for child language acquisition, as argued for by Macnamara (1972) and Cromer (1974), is rooted in Piaget’s belief that the emergence of language is contingent on the development of other, more general representational abilities (Piaget, 1955). In this view, the acquisition of certain con-cepts, such as notions of displacement in time and space (the There-and-Then), ushers in the need to express these linguistically, and so one sees in child language develop-ment the emergence of past tense morphology at around the age of two years when concepts associated with the past become available to the child. A similar proposal underpins the Cognition Hypothesis’ claim that increasing task complexity along re-source-directing dimensions promotes adult L2 learning. Ontogenetically motivated, incremental changes in the complexity of the conceptual demands of tasks, following



the ‘natural order’ in which these concepts become available for linguistic expression in childhood, “also provide optimum contexts for the development of needed (task relevant) function-form mappings in the L2” (Robinson, 2005a, p. 6). However, as Slobin (1993) notes, the adult second language learner’s task is clearly different from the child’s, since adults are cognitively developed, and have access to a full range of concepts when they begin to learn a language:

For the child the construction of the grammar and the construction of semantic/pragmatic concepts go hand-in-hand. For the adult construction of the grammar often requires a ‘revision’ of semantic/pragmatic concepts, along with what may well be the more difficult task of perceptual identification of the relevant morpho-logical elements. (p. 243)

What adults need to learn is how the L2 encodes concepts, lexically, morphologically and syntactically, and how this differs from the way their native language encodes them. For this reason, expending the mental effort (Schmidt, 1983) needed to make more demanding cognitive/conceptual distinctions in language should prime L2 learners – and direct their attentional and memory resources – to lexical, morpho-logical and syntactic aspects of the L2 system required to accurately understand and convey them, thereby facilitating selective attention to, ‘noticing’ and perceptual iden-tification of these (Robinson 1995b, 1996, 2003a; Schmidt 1990, 2001), while also pro-moting the ‘rethinking-for-speaking’ or ‘revision’ of semantic/pragmatic concepts that Slobin (1993, 2003) refers to, when mapping conceptualisation to linguistic expression in the L2.

Increasing the complexity of resource-directing task characteristics therefore has the potential to direct learners’ attentional and memory resources to the way the L2 structures and codes concepts (e.g., Talmy, 2000, 2008), often in ways that differ from how they are structured and coded in the L1 (Han & Cadierno, 2010; Negueruela & Lantolf, 2006; Odlin, 2008; Pavlenko, 2011; Robinson & Ellis, 2008a, 2008b; von Stutterheim & Nuese, 2004). In terms of Levelt’s (1989) model of speech production, increasing the conceptual demands of tasks (naturally) leads to greater effort at con-ceptualization, and ‘macroplanning’ at the stage of message preparation. This creates the conditions for development and re-mapping of conceptual and linguistic catego-ries during subsequent ‘microplanning’ and during the lexico-grammatical encoding stage that macroplanning feeds into. In Levelt’s (1989) model, the conceptualization stage generates a ‘preverbal message’: “the message should contain the features that are necessary and sufficient for the next stage of processing – in particular for grammati-cal encoding” (p. 70). Therefore greater effort at conceptualization during message preparation, induced by conceptually demanding tasks, should lead to what Dipper, Black and Bryan (2005, p. 422) called “paring down” of conceptual information into a “linguistically relevant representation” for subsequent encoding at the microplanning stage, with positive consequences for accurate and complex performance (see Kormos, this book). Specific measures of the language used to refer to concepts of space, and


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time, as well as mind and motion, are needed to most effectively capture these pro-posed effects of task demands and effort at conceptualisation on speech production. Below I illustrate some specific aspects of language that can be used to express concep-tualisation along resource-directing dimensions of pedagogic tasks.

Intentional reasoning

For example, in L2 English, tasks which require complex reasoning about the inten-tional states that motivate others to perform actions can be expected to draw heavily on the use of cognitive state terms for reference to other minds – she suspected, won-ders, and so on – and in so doing orient learner attention to the complement construc-tions accompanying them – suspected that, wonders whether – so promoting awareness of, and effort at, complex L2 English syntax (e.g., Deissel, 2004; Ishikawa, this volume; Lee & Rescorla, 2002; Lohman & Tomasello, 2003; Nixon, 2005; Robinson, 2007c).

Spatial reasoning

Similarly, in L2 English, tasks which require complex spatial reasoning and articula-tion of this in describing how to move, and in what manner, from point A to point E, by way of intermediary landmark points B, C and D, can be expected to draw heavily on the use of constructions for describing motion events. Such tasks have the potential to promote awareness of lexicalization patterns in L2 English for describing these mo-tion events, in which motion and manner are typically conflated on verbs (rushed, staggered), and paths are concatenated in a series of satellites (out, through, along) – themselves expressing a variety of locational meanings, and which may differ from the way in which prepositional path satellites semantically divide up paths of motion and spatial location in the learner’s L1 (e.g., Berman & Slobin, 1994; Bylund, 2011; Cadierno & Robinson, 2009; Filopovic, 2007; Tyler & Evans, 2003).

The Here-and-Now and the There-and-Then

In yet a different conceptual domain, tasks requiring reference to events happening now, in a shared context (Here-and-Now) orient learner attention to morphology for conveying tense and aspect in the present, compared to events requiring much more cognitively demanding reference to events happening elsewhere in time and space (There-and-Then). The ability to talk about what happened in the past, elsewhere, is later emerged in childhood (Donaldson, 1992; Sachs, 1983) than the ability to talk about what is happening here and now, and requires greater effort at conceptualization (since events are not visually available in a shared context) and makes greater demands on memory (Robinson, 1995a). Cognitively less demanding Here-and-Now tasks ori-ent learners to the prototypical meanings which progressive aspect (prototypically



used to refer to activities e.g., she is running) and past tense morphology (prototypically used to refer to achievements e.g., she arrived at the station) accompany (Andersen & Shirai, 1996; Shirai, 1999). In contrast, on cognitively and conceptually more demand-ing There-and-Then tasks learners are increasingly prompted to use tense and aspect morphology to mark non-prototypical meanings (e.g., states for past tense, and ac-complishments for progressive) (Robinson, Cadierno, & Shirai, 2009; and see Kim & Tracy-Ventura, this volume).

Task characteristics that disperse attention over non-linguistic task demands

In contrast to resource-directing variables, resource-dispersing task characteristics make performative and procedural demands on cognition. However increasing the complexity of the cognitive demands these characteristics of tasks make does not direct learner attention and effort at conceptualization to any particular aspects of language code. For example, making a task more performatively complex by removing planning time for it simply disperses learner attention over many linguistic and non-linguistic aspects of the task that need to be handled simultaneously (see Ellis, 2005; Skehan, 1998). Similarly, making a task complex by adding a secondary, or third task demand (answering the phone, while monitoring a TV screen, and also listening for a child cry-ing in a separate room) also disperses attention over many non-linguistic aspects of the task. Performing increasingly complex versions of tasks on these dimensions promotes not noticing of language code, and interlanguage development of new linguistic, con-ceptual form-function mappings, but rather consolidation and fast real-time access to existing interlanguage resources. In Bialystok’s terms (1994), increasing complexity along resource-directing dimensions promotes greater analysis, and representational redescription of L2 conceptual-linguistic knowledge, and form-function mappings, while increasing complexity along resource-dispersing dimensions promotes greater control over, and faster access to, existing interlanguage systems of knowledge.

Combining task characteristics and researching sequencing effects

Real world, target-tasks for L2 learners, identified at stage 1 of Figure 2, are very often complex along a number of the resource-directing and dispersing dimensions of task demand described above. A theoretically motivated, operationally feasible metric for increasing task demands along these dimensions to target-task levels of real world performance is needed for syllabus and task designers to follow. One proposal (Robinson, 2009, 2010) for such a metric is illustrated in Figure 3. Initially pedagogic tasks are made complex by increasing demands made along resource-dispersing di-mensions (by removing planning time), following the rationale that this promotes control over current interlanguage resources. Subsequently, pedagogic versions of the


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target-task are increased in complexity along resource-directing dimensions (by in-creasing reasoning demands) following the rationale that this promotes analysis and understanding of how the L2 can be used to describe concepts, such as intentions and mental states, and to explain behavior as a consequence of these.

In this proposal, then, the sole basis for sequencing pedagogic tasks is increases in their complexity so as to first promote control over what is already known of the L2, and then analysis of the form-meaning connections that resource-directing dimen-sions of tasks require to be made in the L2. Whether sequencing tasks in this way leads to more effective L2 learning and performance than some other proposal, such as in-creasing resource-directing demands first followed by respource-dispersing demands, is an issue that research is only just beginning to address (Baralt, 2011; Levkina & Gilabert, 2011; Romanko & Nakatsugawa, 2010). It is possible too that sequencing tasks in a linear order of increasing complexity is less effective in promoting L2 learning and performance than some non-linear sequence, such as complex, simple, less complex. Comparisons of the short and longer-term effects of different sequencing options on L2 acquisition and performance are clearly needed as an evidential basis for decisions about syllabus design, just as workflow-based systems for sequencing computer deliv-ered instruction, in a variety of content domains, have been compared to other systems for sequencing instructional content (Marjanovic, 2007). A necessary starting point for studying the cumulative influence of sequences of tasks on learning, however, is to study their individual impact on learning (at different levels of complexity), in order to analyze how these effects (if they are found) are subsequently multiplied (or not) by sequences in which different tasks, or versions of the same task, are performed.

The Cognition Hypothesis, task-based language learning, and performance

All of the empirical studies in this book manipulate one or another of the characteris-tics listed in the TCF under the category Task Complexity with the aim of identifying the extent to which increasing L2 task complexity causes variation in performance, and specifically with respect to whether or not this variation in simple and complex task performance by any individual, or by groups, is consistent with five ancillary the-oretical claims the Cognition Hypothesis makes about the likely effects of task com-plexity on language learning and production.

1. Output

The first of these is that that increasing the cognitive demands of tasks contributing to their relative complexity along resource-directing dimensions described in the TCF should push learners to greater accuracy and complexity of L2 production in order to meet the consequently greater functional/communicative demands they place on the learner, while negatively affecting fluency, compared to simple task performance. That is, for reasons described previously, greater effort at conceptualization may in many



cases lead learners to develop the L2 linguistic resources they have for expressing such conceptualizations. This should most clearly be evident during monologic task perfor-mance. For dialogic/interactive tasks the greater amount of interaction they promote (see below, and Michel, this volume) will likely mitigate attempts at complex syntax for participants, although greater accuracy, but less fluency, on complex interactive tasks should still be evidenced.

2. Uptake and Interaction

The second claim is that cognitively complex tasks should lead to more interaction and negotiation of meaning to resolve the communicative challenge they pose, relative to simpler counterpart tasks. They should also promote heightened attention to, noticing of (Schmidt, 2001) and memory for input (since learners will look to the input for more and more help to complete the task as its conceptual and communicative demands increase), therefore increasing learning from the input, and incorporation of forms made salient in the input. So, for example, there should be more uptake of oral recasts on complex, compared to simpler tasks, or more use of written input provided to help learners perform tasks (see Nuevo, Adams, & Ross-Feldman, this volume; Révész, Sachs, & Mackey, this volume).

3. Memory and Retention

Related to this is the third claim that the heightened attention to input that complex tasks promote will lead to greater depth of processing (see Craik & Tulving, 1972; Hulstijn, 2001) and so should lead to longer-term retention of input provided (e.g., written prompts or oral feedback) than on simpler tasks (as Nembhard, 2000, and Schneider, Healy, & Bourne, 2002, have found for tasks aimed at promoting textile as-sembly skills, and vocabulary learning respectively).

4. Automaticity

Fourthly, the inherent repetition and sheltered elaboration involved in performing simple to complex sequences should also lead to greater automaticity and efficient scheduling of the components of complex L2 task performance (see Bygate 2001; Reigeluth, 1999; Segalowitz, 2010), compared to when tasks are performed in some other sequence, such as complex to simple, simple to complex to simple, and so on.

5. Individual Differences

Fifthly, and importantly, individual differences in affective and cognitive abilities con-tributing to perceptions of task difficulty will increasingly differentiate learning and performance as tasks increase in complexity (see Albert, this book; Ishikawa, this book; Kormos & Trebits, this volume). So there should be less variation between learn-ers in performing simpler tasks than there is when performing more complex versions (see Snow, 1994).


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Of course, in order to identify these posited effects, it is clearly necessary to op-erationalize a host of behavioral (and potentially other) indicators in research. Some measures which have been adopted, to date, in research addressing one or another of these ancillary theoretical claims of the Cognition Hypothesis are described in the fol-lowing section of this chapter.

Issues in researching and measuring the effects of task complexity on learning and performance

Researching and measuring language production

Most studies of the effects of task demands on speech production have employed gen-eral measures of accuracy and complexity, such as percentage of error free, or clauses per AS-unit, C-unit or T-unit (see Ellis, 2003; and Ellis & Barkhuizen, 2005 for re-view), as do many of the studies in this book. Following arguments by Givon (1985) that “greater structural complexity tends to accompany greater functional complexity in syntax” (p. 1021), and that demanding, formal communicative tasks and contexts elicit a syntactic mode of production (characterized by greater use of morphology, greater syntactic subordination, and a higher noun to verb ratio) in contrast to a sim-pler pragmatic mode (p. 1018), the Cognition Hypothesis predicts greater L2 accuracy (more target-like suppliance of morphology) and complexity (greater amounts of sub-ordination and clausal embedding) using such general measures of production, on complex versus simpler tasks having resource-directing characteristics. This latter claim about the effects of resource-directing dimensions of tasks on the complexity of language production, and measures that can be used to capture these effects, has been expanded on by Norris and Ortega (2009). They have argued that linguistic subordina-tion (as measured, for example, using indices of clauses per C-unit, or S nodes per T-unit) is a relatively simple mechanism for syntactic complexification (associated with development in the earlier but not later stages of L2 proficiency), and that it has been demonstrated that subordination does not increase linearly in L2 development. They argue that syntactic complexity must be measured multidimensionally, and also that general measures of ‘phrasal elaboration’ are more suitable than measures of sub-ordination for capturing the means “by which syntactic complexity is achieved at the most advanced levels of language development and maturity” (p. 563).

Such general measures of subordination or phrasal elaboration, or both, however, will also need to be supplemented by specific measures of the accuracy and complexity of production, as these are relevant to particular resource-directing characteristics. For example, as mentioned above, tasks requiring complex spatial reasoning, event construal, and reference to motion, can be expected to lead learners to attempt to use developmentally later acquired lexicalization patterns for describing motion events (Berman & Slobin 1994; Cadierno 2008; Cadierno, & Robinson, 2009; Schmiedtova,



von Sutterheim, & Carroll, 2011). Similarly, tasks requiring increasingly complex rea-soning about, and reference to the intentional states of others causing them to perform actions can be expected to involve greater use of psychological and cognitive state terms such as ‘think’, ‘expect’, ‘wonder’, and the developmentally later acquired verb-complement constructions that they typically occur in (Astington & Baird, 2005; Lohman & Tomasello, 2003; Nixon, 2005; and see also Wulff & Gries, this volume).

In contrast to these predictions, along resource-dispersing dimensions of tasks which divide but do not direct attention to features of linguistic code, such as taking away planning time, or making dual or multiple simultaneous task demands, then ac-curacy and complexity of production can be expected to decrease on complex tasks. Skehan’s (1998) Limited Capacity Hypothesis makes the same predictions for the ef-fects of planning time, and other resource-dispersing dimensions, such as removing supporting task structure. One area where the Cognition Hypothesis differs from the Limited Capacity Hypothesis in theoretical motivation is over the claims described above for the beneficial effects on accuracy and complexity of increasing the resource-directing dimensions of tasks. The resource-directing/dispersing distinction is one that Skehan (1998, 2009; Skehan & Foster, 2001) does not make, leading him to claim complex task performance, along any dimensions, degrades accuracy, fluency and complexity simultaneously.

The Cognition Hypothesis, importantly, also claims that there are likely to be syn-ergetic effects on speech production when tasks are made complex along both resource-directing and resource-dispersing dimensions simultaneously (as is often the case in real world task performance, such as impromptu reasoning about and explanations of the causes of a multi-party social conflict without the benefit of planning time). In such cases the beneficial effects on speech production of increasing the complexity of a resource-directing characteristic, may possibly be weakened or negated by increas-ing the complexity of the resource-dispersing characteristic (for example, by removing planning time) when compared to the same task made simpler along that resource-dispersing dimension, where planning time is available (see Gilabert, 2005, 2007, and Gilabert, Barön, & Levkina, this volume). This points to an issue which the now exten-sive research on the effects of planning time on speech production (e.g., the studies reported in Ellis, 2005) has so far ignored, but which could explain hitherto unac-counted-for variation in the effects of length of planning time on measures taken of L2 speech produced, that is, that planning time is unlikely to have its effects on learners’ attempts to produce the L2 independently of other dimensions of the cognitive de-mands of tasks which they are engaged in planning to perform.

Researching and measuring interaction and learning opportunities

The Cognition Hypothesis also connects input and interaction to the cognitive and con-ceptual demands of tasks that lead to differential amounts of interaction, and also uptake


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of forms made salient in the input to tasks, as these have been empirically researched in studies of the effects of conversational interaction on SLA (see Mackey, 2007). The at-tempt to meet more conceptually and communicatively complex task demands across sequences of tasks should progressively cognitively enhance interaction, in an acquisi-tionally supportive way, and so promote learning from interaction in ways that have been traditionally rationalised, investigated and measured in SLA research (e.g., Gass, 1997; Long, 1983; Pica, Young, & Doughty, 1987). Along resource-directing dimensions, and in general too along resource-dispersing dimensions, more complex dialogic tasks should result in greater amounts of interaction, and negotiation for meaning.

Initial evidence supporting this claim has been provided using measures of the numbers of turns, clarification requests and confirmation checks that complex interac-tive tasks result in as well as measures of higher ratios of confirmation checks per turn (Robinson, 2001a, 2007c). The Cognition Hypothesis also claims, following Long (1996), that such negotiation provides a context for attending to problematic forms in the input and output, and additionally that on complex versions of tasks there will be greater attention to, and uptake of, forms made salient during provision of reactive Focus on Form techniques such as recasts (see Doughty 2001; Long 2007). This should be particularly so when the recast is targeted at a linguistic feature which the concep-tual demands of a task create the need to use and comprehend. Some initial evidence for this claim comes from Révész (2009) study showing greater uptake of recasts tar-geted at use of regular past tense morphology on complex There-and-Then tasks (which involve reference to prior occurring events), than on simple Here-and-Now tasks, which do not. Related to this issue, Kim (2009a, 2009b) and Gilabert, Llanes, and Barön (2009) showed higher incidences of Language Related Episodes (LREs) on complex compared to simpler task versions. Alternatively, where proactive Focus on Form is provided, for example in the form of premodified input to the task, then there should be greater use of this on complex, versus simpler task versions. Robinson (2007c) provided some related evidence, showing higher ratios of ‘partial uptake’ per turn (in which partial use of one or more words of the premodified input was incorpo-rated into a learner’s speech) on complex compared to simple tasks, although ‘exact uptake’ of the proactively provided premodified input was equivalent across task ver-sions. Each of the studies just mentioned, however, have examined the incidence of uptake of recasts or premodified input on tasks immediately following presentation of the input. It is quite possible that such input may have delayed effects and additionally be acted on across longer periods of time, and so research into the extent to which input provided on one task promotes uptake and use of it while performing subsequent tasks is needed to complement the findings from these studies. Here again, one might expect to see task complexity effects, with more evidence of delayed uptake and use of input presented during a prior complex task than uptake and use of input provided during a prior simpler task.



Researching the effects of learner factors on task-based learning and performance

Finally, the Cognition Hypothesis acknowledges that learner factors (contributing to perceived difficulty) interact with task factors (contributing to their complexity) in determining the extent of the above predicted effects of task demands on speech pro-duction, interaction and uptake, and retention of task-input. When the ability and af-fective factors drawn on in meeting complex task demands are high in any group of learners, then the effects will be found most clearly, in contrast to learners low in the ability and affective variables implicated in successful complex task performance. An example of this interaction of task difficulty and task complexity with language pro-duction was found by Robinson (2007c) where only those learners low in output anxi-ety (measured using MacIntyre & Gardner’s, 1994, input-processing-output anxiety questionnaire) responded to complex reasoning task demands by producing the predicted increasingly complex speech (measured using a general measure of clauses per C-unit, as well as specific measures such as WH-clauses, infinitival phrases and conjunctively conjoined clauses). Learners high in output anxiety were not induced by complex task demands to ‘push’ or ‘stretch’ production in this way. Yet, it is in perform-ing complex versions of pedagogic tasks that progress in educational programs hinges, and so it is particularly important to research and understand what these complex task demand – learner factor interactions are. Some of these are nominated as worthy of further research below.

Cognitive abilities and task difficulty

Working memory capacity is one ability factor that has been increasingly researched in the SLA literature (see Kormos & Trebits, this volume), showing for example gener-ally positive correlations with incidental learning (Niwa, 2000; Robinson, 2002b, 2007b; Williams, 1999) and in successful uptake of implicit negative feedback deliv-ered by recasts (Mackey, Egi, Philp, Fuji, & Tatsumi, 2002), both of which task-based learning environments encourage and provide opportunities for. Working memory capacity (see Cowan, 2005) is likely implicated specifically in successful performance on There-and-Then tasks, which require learners to hold in memory a description of some event, while verbalizing it concurrently, and also in performance on tasks requir-ing dual, simultaneous performance of subtasks (such as answering a phone call while monitoring a TV screen).

Related to this latter dimension of task complexity, task-switching (see Monsell, 2003) involves the executive control ability to switch back and forth quickly and effi-ciently from the demands of one component of a task, to another component (e.g., from monitoring a fuel gauge to talking to a co-driver) and is also likely positively related to


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the abilities drawn on in performing dual-tasks, as opposed to single tasks, as these characteristics are described in Figure 1. Similarly, there are many measures of abilities drawn on in successfully reasoning about causal relations (see Lohman, 2000; Stanovitch, 1999), and these are likely related positively to successful performance on tasks making causal reasoning demands listed in Figure 1 (such as explaining why a bridge fell down in a thunderstorm, or why a marketing campaign will result in great-er sales revenue). More recently research has begun measuring the cognitive abilities affecting the extent to which people are able to successfully attribute intentions and mental states to others, and to reason from this to a conclusion about why others per-formed certain actions (Goldman, 2006; Langdon, Coltheart, Ward, & Catts, 2002; Malle, 2004). These measures of intentional reasoning ability are clearly likely to be related to success on complex L2 tasks high in the intentional reasoning demand char-acteristic nominated in Figure 1.

In general then, it might be expected that individual differences in the cognitive abilities nominated in Figure 1 as contributing to perceptions of Task Difficulty would interact with characteristics of tasks contributing to their Task Complexity – inhibit-ing or promoting successful adaptation to, and so successful learning and performance on tasks having these characteristics. Some potential interactions worthy of future research have been described above. A broad summary of the potential for learner ability, task complexity interactions is as follows, and is presented in schematic form in Figure 4. Along resource-directing dimensions of Task Complexity, task performance requires the cognitive abilities drawn on when engaging in complex thought (i.e., THINKING) in different conceptual domains. I would argue that these cognitive abilities will be closely related to measures of crystallized intelligence (G c) opera-tionalised in intelligence tests (see Albert, this book), such as the ability to reason about a domain. In contrast, along resource-dispersing dimensions of Task Complex-ity, tasks also require that learners act on their thoughts in complex performative envi-ronments (i.e. REACTING) and I would argue that the cognitive abilities contributing to this are likely to be related to measures of fluid intelligence (G f) operationalised in intelligence tests, such as the ability to switch attention rapidly between concurrent task demands – an ability involving measurable differences in processing speed, and the divisibility of attention.

Affective factors and task difficulty

While the ability factors nominated in Figure 1 are likely most influential when per-forming tasks differing in complexity, affective (personality) and conative (motiva-tional) factors nominated there are likely more influential in performing tasks under different Task Conditions, and the demands they impose. Figure 4 also illustrates this broad relationship between affective/conative (AFCON) factors and characteristics of Task Conditions. During pedagogic task performance in complex interactional and



Task complexity Task condition

a. Resource-directing, developmental dimensions a. Participation variables

T AH ± here and now F ± open solutionI ± few elements C ± one way �owN ± spatial reasoning O ± convergent solutionK ± causal reasoning N ± few participantsI ± intentional reasoning ± few contributions neededN ± perspective taking 1 ± negotiation not neededG (Gc)(e.g., IDs in reasoning) (Ge, Gm) (e.g., IDs in tolerance of ambiguity)

b. Resource-dispersing, performative dimensions b. Participant variables

R AE ± planning time F ± same pro�ciencyA ± prior knowledge C ± same genderC ± single task O ± familiarT ± task structure N ± shared content knowledgeI ± few steps ± equal status and roleN ± independency of steps 2 ± shared cultural knowledgeG (Gf) (e.g., IDs in �exible attention, task switching) (Ge, Gm) (e.g., motivational intensity, control, anxiety, self-e�cacy)

Figure 4. Mapping the interactions of task difficulty, task complexity and task conditions Key: Ge = emotional intelligence, Gm = motivational intelligence, Gf = fluid intelligence, Gc = crytallized intelligence, ID = individual differences

interpersonal environments, adaptation to the various participation and participant characteristics is proposed to be facilitated by greater self regulation and motivational intensity (G m factors), and emotional control (a G e factor) (e.g., Collis & Messick, 2001; Dörnyei, Csizer, & Nemeth, 2006; Forgas, 2001; MacIntyre, 2002; Matthews, & Deary, 1998; Mayer, Salovey, & Caruso, 2000; Snow & Farr, 1987). For example, when the solution to a task learners are performing is indeterminate and not fixed (+open) as opposed to determinant and fixed (+closed) then individual differences in measures of emotional control, such as openness to experience, and tolerance of ambiguity (Costa & Macrae, 1985; Furnham & Ribchester, 1995) may predict more, or less, suc-cessful engagement in task participation to meet these goals. Those learners who are more open to experience, and more tolerant of ambiguity may adapt better to partici-pation in open tasks than those who are less open and less tolerant of ambiguity. Sim-ilarly, when some participants in a task are at lower levels of proficiency than their partners then individual differences between them in anxiety and motivation may af-fect the extent to which they participate in tasks. Those lower proficiency learners with greater motivational intensity and control (see Dörnyei, 2005), lower output anxiety (MacIntyre & Gardner, 1994) and greater self-efficacy (Bandura, 1997) may make greater efforts to participate, and so benefit more from the L2 interaction, than those


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lower in motivational intensity, self-efficacy and higher in output anxiety (see for re-lated recent work in these areas Dörnyei, 2002; Kim & Tracy-Ventura, this volume; Sheen, 2008).

The chapters in this book

This book is in four parts. The chapters in Part 1 each address theoretical and method-ological issues in research into the effects of task complexity on learning and perfor-mance in general, and the claims of the Cognition Hypothesis in particular. Following the present chapter, the chapter by Kormos draws on Levelt’s (1989) model of L1 speech production to propose a bilingual model of speech production (see Kormos, 2006). She argues, in line with the Cognition Hypothesis, that tasks which are complex along resource-directing dimensions call learners’ attention to the differences between the existing L1 conceptual system and L2 concepts, and drive the expansion of learn-ers’ repertoires of memorized units for expressing form-meaning relations in the L2, as well as their morphological and syntactic development. In their chapter, Wulff and Gries consider how increases in the accuracy with which L2 learners formally encode meaning during task performance can be captured by a measure of the extent to which the frequencies with which learners use words in larger constructions matches their frequencies of co-occurrence in corpora of native-speaker speech and writing. They argue that exposure to authentic target L2 samples, accompanied by interaction to promote understanding these, as well as speech production in response to the specific nature of task demands, all lead conjunctively to opportunities for fine-tuning proba-bilistic knowledge of the contingencies between co-occurring units in the L2, and so to more native-like and natural L2 use for task-based language learners.

The chapters in Part 2 are both concerned with the extent to which increasing task complexity on tasks of different types, or in different modes, has similar effects on ac-curacy, complexity and fluency in language production. In their chapter, Kuiken and Vedder describe a study of the effects of increasing task complexity in terms of the number of elements the task requires to be described and related to a decision – given in the form of written or oral advice to a friend – about which holiday destination to choose. The participants were Dutch L1 speaking learners of L2 Italian. One group performed the simple and complex versions in the oral mode, and the other in the written mode, with the result that there were similar beneficial effects of increased task complexity on accuracy in both modes (using measures of errors per T-unit in the written mode, and errors per AS-unit in the oral mode) but no effects of task complex-ity on syntactic complexity or lexical variation. In their chapter, Gilabert, BarÓn, and Levkina describe the results of two studies which increased resource-directing dimen-sions of complexity on three different types of task. One of these manipulated the complexity along the Here-and-Now, There-and-Then dimension of complexity, a second along the dimension of causal reasoning demands, and a third manipulated



the number of elements that had to be referred to and distinguished. One group per-formed these tasks in the monologic mode, and another group performed them in a dialogic mode. They found variable results for the effects of task complexity on accu-racy, fluency and complexity of production, when performing these different task types. They also found that mode of performing a task type led to differences in per-formance, and that the effects of proficiency on task performance differed in mono-logic and dialogic modes.

The chapters in Part 3 all focus on the effects of increasing task complexity on the amount of interaction on tasks, and the extent to which task complexity promotes modified output, and uptake of feedback provided during task-based interaction. The chapter by Michel shows that increasing the complexity of tasks requiring reference to few versus many elements only leads to greater lexical diversity on the complex many elements task. Comparing tasks performed in the monologic versus dialogic mode, as did Gilabert et al., Michel finds that interactive tasks promote greater accuracy and fluency, compared to their monologic counterpart tasks. In the next chapter, Nuevo, Adams, and Ross-Feldman examine whether increasing the reasoning demands of tasks promotes greater modified output and uptake of information about past tense and locative preposition use. The focus here, importantly, is on whether task complex-ity leads to development in specific domains of the L2 (assessed using pre and post tests of L2 knowledge) that are important to meeting the conceptual demands that tasks make. They find that greater task complexity led to more modified output, as measured by instances of self-repair, and to learning of locative prepositions, whereas lower task complexity promoted learning of past tense morphology. The chapter by Révész, Sachs, and Mackey also focuses on the extent to which task complexity promotes more inter-action, and L2 development, as a consequence of the uptake of recasts that interaction provides opportunities for. They find that, as the Cognition Hypothesis claims, there is more uptake of recasts targeted at progressive past tense forms on complex tasks, al-though uptake only positively predicted development of these forms (again measured using pre and posttests of L2 knowledge of them) on less complex tasks.

The chapters in Part 4 all address one or another of the issues relating to the effects of task complexity on interaction, learning and speech production that are the focus of the studies reported in Part 2 and Part 3. However, the chapters in Part 4 all opera-tionalise measures of individual differences in cognitive abilities (creativity in the case of Albert’s chapter, and working memory capacity in the case of Kormos and Trebits’ chapter), or affective factors (anxiety in the case of Kim and Tracy-Ventura’s chapter, and a variety of motivational and other factors affecting perceptions of task difficulty in the case of Ishikawa’s chapter). The chapters by Albert, and by Kormos and Trebits, both operationalise task complexity in terms of the amount of structure provided to support monologic narrative performance. On this resource-dispersing dimension of complexity, the Cognition Hypothesis predicts lower accuracy, complexity and fluency of performance, for narratives performed with less support available, compared to those where support is provided. Albert finds that creativity is more influential on


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complex than on simple task performance, while Kormos and Trebits find that learners with higher working memory capacity may be more able (than their lower capacity counterparts) to produce clausally complex language on narrative tasks. Kim and Tracy-Ventura find that anxiety for speaking, assessed using a questionnaire they de-veloped, is not related to performance on tasks at different levels of reasoning com-plexity, although (and in contrast to the findings reported by Révész et al.) they find complex tasks are more facilitative of past tense development than simple tasks. Like Kim and Tracy-Ventura, Ishikawa manipulated the complexity of the reasoning de-mands tasks impose. Responses to questionnaires administered after each of the three tasks (simple, more complex, most complex) showed that increasing task complexity did affect learners perceptions of their difficulty, but also promoted greater interest in the tasks. Additionally, the number of significant correlations between individual dif-ferences in perceptions of the difficulty of tasks and measures of learner production on them increased as tasks became more complex, as the Cognition Hypothesis predicts.

While it is still too soon to expect definitive answers to the various questions about the effects of task complexity on second language learning and performance raised in each of the empirical studies in this book, these studies do provide models for how future research into them can proceed. Together with the findings from other recent studies addressing the same issues concerning the effects of L2 task complexity (e.g., Garcia-Mayo, 2007; Révész, 2009; Robinson & Gilabert, 2007), the chapters in this book provide, at the least, a basis for further cumulative research into them, with the prospect in view of basing instructional decisions about how to manipulate task complexity, and task-based syllabus design, on established empirical findings.

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