Academia.eduAcademia.edu

Outline

Title
Abstract
Key Takeaways
Methods for Studying Strategy Selection
Methods
Subjects
Procedure
Overview
Results
Discussion
General Discussion
References
FAQs
All Topics
Psychology
Personality Psychology

Strategy Adaptivity and Individual Differences

Profile image of Christian SchunnChristian SchunnProfile image of Lynne RederLynne Reder

1998, Psychology of Learning and Motivation

https://doi.org/10.1016/S0079-7421(08)60185-1
visibility

…

description

34 pages

link

1 file

Abstract
sparkles

AI

The strategy adaptivity approach addresses individual differences in strategy selection, positing that individuals vary in their ability to adapt their strategies based on situational demands. This chapter aims to articulate the concept of strategy adaptivity, demonstrate the existence of individual differences in this regard, and explore the consistency and potential causes of these variations across different task domains, particularly arithmetic. Empirical evidence highlights the significant roles of strategy adaptivity in accuracy and response times within tasks, emphasizing the implications for understanding cognitive processes and educational practices.

Key takeaways
sparkles

AI

  1. The strategy adaptivity approach posits individuals vary in selecting appropriate strategies for tasks.
  2. Individual differences in strategy adaptivity significantly impact performance across various task domains.
  3. Most subjects exhibit a bimodal distribution of strategy adaptivity scores, indicating substantial variability.
  4. Extrinsic adaptivity correlates with cognitive abilities like inductive reasoning and working memory.
  5. Dynamic tasks show greater reliance on individual differences in strategy adaptivity compared to static tasks.

Related papers

Adaptability: Reflections
Jacob L. Mey

Ai & Society, 1992

The conclusion to be drawn from the preceding observations and theorizing should be that we must be very much aware of what has been called “technological functionalism” (Pieper, 1986:11). While functionalism as such is not bad, the moment it succumbs to mere structural technicality, the functions stop functioning: forced “adaptivity” takes the place of “adaptable” interaction. That this problem is not due to a primordial blame, to be attached to the computer, becomes clear when one compares the computerized environment to other surroundings, such as, for example, the psychiatric treatment. In the psychiatric interview, as Davis (1986, 1988) has shown, the interest of the therapist is often limited to establishing a “contract” for treatment: for the therapist to function properly, there must be a therapy-defined (or therapy-definable) problem for him/her to attack, using the skills and experiences of the profession of which he/she is a representative and for which he/she has been properly trained. This function, however, may not coincide with the patient's needs: it may well be the case that the problem which originally caused the patient to approach the therapist for treatment, in the end turns out not to be the problem that both agree on as the objective of the therapeutic treatment. “Re-formulating” the patients' problem in terms suitable to the available resources and techniques is thus typically a case of adapting the human to the system: again, we're faced with technological functionalism in the shape of what has been called “forced adaptivity” (see Mey, 1986). We need to think seriously about the way we handle our computerized structures and their functions, and in particular about the design of supportive systems such as large databases, computer conferencing, or even simple electronic mail services. While adaptivity always is a case of manipulation, by which humans are tooled to the needs and strictures of the computer, adaptability, by contrast, allows such manipulation only inasmuch as those needs and strictures reflect, and are imposed by, the users' needs. The blind, mechanical force that makes us adapt ourselves to the machine should be replaced by the enlightened, humanizing force of the adaptable computer.

View PDFchevron_right
Factors influencing analysis of complex cognitive tasks: A framework and example from industrial process control
Frank Marchak, Paul Feltovich

Human Factors: The …, 2000

View PDFchevron_right
Cognitive flexibility and adaptability to environmental changes in dynamic complex problem-solving tasks
Jose David Quesada

Ergonomics, 2003

People who show good performance in dynamic complex problem solving tasks can also make errors. Theories of human error fail to fully explain when and why good performers err.

View PDFchevron_right
Adaptability: Time to Start Thinking about Thinking
Cassandra Crosby

2015

The tendency to maintain familiar behaviors while evolving slowly and incrementally when faced with unfamiliar problems is the result of a gap in the US Army's understanding of adaptability and the conditions required to achieve it. Developing adaptive leaders is one of the Chief of Staff of the US Army's top priorities, yet no one seems to be talking about how to enable this critical capability. This monograph argues the US Army must foster "strength of the mind" at the individual level to enable the kind of adaptive behavior the Chief of Staff of the Army demands. Adaptability requires flexible, creative, unprejudiced, and reflective thinking; the thought patterns that enable cognitive agility. However, this kind of thinking is not something that merely happens in the mind. The interrelationship between mind, body and environment continuously and dynamically shapes the structure, functional organization, and connectivity of an individual' s brain rendering them either more or less likely to sustain cognitive agility in both short-term and long-term contexts. Previous efforts to improve the Army's adaptability focused on institutional development. However, the US Army needs to do more than ask how it can inculcate adaptability through its doctrine and training programs. Rather, the question that requires further research is if the patterns in the Army's current culture and climate support the kind of thinking that enables adaptability at the individual level, or if its tendencies stifle flexible, creative, unprejudiced, and reflective thinking. The answer to this question will provide the impetus for the US Army to take steps toward actionable and lasting change.

View PDFchevron_right
A theoretical note on the relationship between work domain analysis and task analysis
John Hajdukiewicz

Theoretical Issues in Ergonomics Science, 2004

The purpose of this note is to clarify the theoretical relationship between work domain analysis and task analysis, two classes of techniques that have been used by cognitive engineers to identify information requirements for systems design. The transformation from a work domain analysis to a task analysis (i.e., from a description of the object of control to a description of control itself) can be conceived as a discrete set of transformations. Work domain analysis identifies the set of all structural degrees of freedom that are available to any actor. Only a subset of these will be relevant for a particular context. At any particular point in time, actors will have to choose which of these relevant degrees of freedom to utilize. Finally, the utilized degrees of freedom will have a dynamic state that can usually be described quantitatively. Task analysis is the function that maps current states onto desired states via a set of human or automated control actions. By making these transformations explicit, the relevance of work domain analysis to worker (or automation) goals and actions becomes more clear.

View PDFchevron_right
Editors' Introduction to Tasks, Tools, and Techniques
Wayne Gray

Topics in Cognitive Science, 2021

Tasks, tools, and techniques that we perform, use, and acquire, define the elements of expertise which we value as the hallmarks of goal‐driven behavior. Somehow, the creation of tools enables us to define new tasks, or is it that the envisioning of new tasks drives us to invent new tools? Or maybe it is that new tools engender new techniques which then result in new tasks? This jumble of issues will be explored and discussed in this diverse collection of papers. Individually, few of the papers are related to each other by topic or by techniques of analysis. Collectively, all focus on tasks performed using tools and discuss the techniques of tool use which enable differences in performance and expertise across individuals, societies, and (even) species.

View PDFchevron_right
The Role of Strategy Knowledge for the Application of Strategies in Complex Problem Solving Tasks
Jarkko Hautamäki

Technology, Knowledge and Learning, 2014

Education in the twenty-first century must prepare students to meet the challenges of a dynamic and interconnected world. However, assessment of students' skills tends to focus primarily on static tasks. Therefore, it is not known whether knowledge about successful strategies displayed on static tasks can be transferred to interactive and dynamic environments. This study investigated whether students' knowledge of a certain strategy (i.e., vary-one-thing-at-a-time, VOTAT) that was assessed in a paper-and-pencilbased scientific reasoning task as well as their fluid intelligence and learning orientation would be sufficient to explain variance in the application of the VOTAT strategy in solving an interactive complex problem solving (CPS) task (i.e., CPS strategy). Furthermore, we analyzed whether CPS strategy mediated the relation between the predictors (i.e., scientific reasoning, learning orientation, fluid intelligence) and CPS performance. The sample consisted of N = 3,191 Finnish students attending the 6th and 9th grades. Results revealed that all predictors were significantly related to CPS strategy, but a substantial amount of variance in CPS strategy remained unexplained (DR 2 = .583). Furthermore, CPS strategy mediated the relation between the predictors and CPS performance. Three implications are discussed: Different demands on the problem solver, knowledge transfer from static versus interactive tasks, or metastrategic knowledge may explain the unexplained variance in CPS strategy. Additionally, the results of our mediation analyses emphasize the importance of measuring strategies via logfiles to gain a deeper understanding of determinants of students' CPS performance. Finally, fostering motivational factors such as students' learning orientation yields small improvements in CPS performance.

View PDFchevron_right
Task Characteristics and Expertise
Anna Mette Fuglseth
View PDFchevron_right
Compatibility and the use of information processing strategies
Marcus Selart (ORCID:0000-0001-7504-0873)

Journal of Behavioral …, 1998

When a prominent attribute looms larger in one response procedure than in another, a violation of procedure invariance occurs. A hypothesis based on compatibility between the structure of the input information and the required output was tested as an explanation of this phenomenon. It was also compared with other existing hypotheses in the field. The study had two aims: (1) to illustrate the prominence effect in a selection of preference tasks (choice, acceptance decisions, and preference ratings); (2) to demonstrate the processing differences in a matching procedure versus the selected preference tasks. Hence, verbal protocols were collected in both a matching task and in subsequent preference tasks. Silent control conditions were also employed. The structure compatibility hypothesis was confirmed in that a prominence effect obtained in the preference tasks was accompanied by a lower degree of attention to the attribute levels in these tasks. Furthermore, as predicted from the structure compatibility hypothesis, it was found that fewer comparisons between attribute levels were performed in the preference tasks than in the matching task. It was therefore concluded that both these processing differences may explain the occurrence of the prominence effects.

View PDFchevron_right
Cognitive task load design: model, methods and examples
Mark Neerincx

2003

View PDFchevron_right

References (75)

  1. Ackerman, P. L. (1988). Determinants of individual differences during skill acquisition: Cognitive abilities and information processing. Journal of Experimental Psychology: General, 117(3), 288-318.
  2. Ackerman, P. L. (1989). Individual differences and skill acquisition. In P. L. Ackerman, R. J. Sternberg, and R. Glaser (Eds.), Learning and individual differences (pp. 165-217). New York, NY: Freeman and Company.
  3. Ackerman, P. L., & Kanfer, R. (1994). CD-ROM Database, Data Collection Program, and Playback Program.
  4. Carver, C. S., Scheier, M. F. (1992). Perspectives on personality (2nd ed.). Needham Heights, MA: Simon & Shuster.
  5. Case, R. (1985). Intellectual development: A systematic reinterpretation. New York: Academic Press.
  6. Cavanaugh, J. C., & Perlmutter, M. (1982). Metamemory: A critical examination. Child Development, 53, 11-28.
  7. Chi, M. T. H., Bassok, M., Lewis, M., Reimann, P., & Glaser, R. (1989). Self-explanations: How students study and use examples in learning to solve problems. Cognitive Science, 13, 145-182.
  8. Chi, M. T. H., Feltovich, P. J., & Glaser, R. (1981). Categorization and representation of physics problems by experts and novices. Cognitive Science, 5, 121-152.
  9. Dunlosky, J., & Connor, L. T. (1997). Age differences in the allocation of study time account for age differences in memory performance. Memory & Cognition, 25(5), 691-700.
  10. Ericsson, K. A., & Polson, P. G. (1988). A cognitive analysis of exceptional memory for restaurant orders. In M. T. H. Chi, R. Glaser, & M. J. Farr (Eds.), The nature of expertise. Hillsdale, NJ: Erlbaum.
  11. Flavell, J. H. (1979). Metacognition and cognitive monitoring: A new area of cognitive- developmental inquiry. American Psychologist, 34, 906-911.
  12. Fry, A. F. & Hale, S. (1996). Processing speed, working memory, and fluid intelligence: Evidence for a developmental cascade. Psychological Science, 7(4), 237-241.
  13. Geary, D. C., & Brown, S. C. (1991). Cognitive addition: Strategy choice and speed-of-processing differences in gifted, normal, and mathematically disabled children. Developmental Psychology, 27(3), 398-406.
  14. Ginossar, Z., & Trope, Y. (1987). Problem solving in judgment under uncertainty. Journal of Personality and Social Psychology, 52, 464-473.
  15. Gopher, D. (1982). A selective attention test as a predictor of success in flight training. Human Factors, 24(2), 173-183.
  16. Gopher, D. (1996). Attention control: Explorations of the work of an executive controller. Cognitive Brain Research.
  17. Gopher, D., & Kahneman, D. (1973). Individual differences in attention and the prediction of flight criteria. Perceptual and Motor Skills, 33, 1335-1342.
  18. Greenfield, P. M., & Lave, J. (1982). cognitive aspects of informal education. In D. A. Wagner & H. W. Stevenson (Eds.), Cultural perspectives on child development. San Francisco, CA: Freeman.
  19. Hunt, E., Joslyn, S., & Sanquist, T. (1996). Evaluating individual differences in response to emergency situations. ONR Tech Report.
  20. Just, M. A. & Carpenter, P. A. (1992). A capacity theory of comprehension: Individual differences in working memory. Psychological Review, 99(1), 122-149.
  21. Kail, R. (1988). Development functions for speeds of cognitive processes. Journal of Experimental Child Psychology, 45, 339-364.
  22. Kanfer, R. & Ackerman, P. L. (1989). Motivation and cognitive abilities: An integrative/aptitude- treatment interaction approach to skill acquisition. Journal of Applied Psychology - Monograph, 74, 657-690.
  23. Koehler, J. J. (1996) The base rate fallacy reconsidered: Descriptive, normative, and methodological challenges. Behavioral and Brain Sciences, 19, 1-53.
  24. Kuhn, D. (1988). Cognitive development. In M. H. Bornstein & M. E. Lamb (Eds.), Developmental psychology: An advanced textbook (2nd ed.). Hillsdale, NJ: Erlbaum.
  25. Kyllonen, P. C. (1993). Aptitude testing inspired by information processing: A test of the four- sources model. The Journal of General Psychology, 120(3), 375-405.
  26. Kyllonen, P. C. (1994). Cognitive abilities testing: An agenda for the 1990s. In M. G. Rumsey, C. B. Walker, and J. H. Harris (Eds.), Personnel Selection and Classification. Hillsdale, NJ: Erlbaum.
  27. Kyllonen, P. C. (1995). CAM: A theoretical framework for cognitive abilities measurement. In D. Detterman (Ed.), Current topics in human intelligence: Volume IV, Theories of intelligence. Norwood, NJ: Ablex.
  28. Larkin, J. H., McDermott, J., Simon, D., & Simon, H. (1980). Expert and novice performance in solving physics problems. Science, 208, 140-156.
  29. LeFevre, J., Greenham, S., & Waheed, N. (1993). The development of procedural and conceptual knowledge in computational estimation. Cognition & Instruction, 11(2), 95-132.
  30. LeFevre, J., Sadesky, G. S., & Bisanz, J. (in press). Selection of procedures in mental addition: Reassessing the problem-size effect in adults. Journal of Experimental Psychology: Learning, Memory, and Cognition.
  31. Lemaire, P., & Siegler, R. S. (1995). Four aspects of strategic change: Contributions to children's learning of multiplication. Journal of Experimental Psychology: General, 124(1), 83-97.
  32. Logan, G. D. (1988). Toward an instance theory of automatization. Psychological Review, 95(4), 492-527.
  33. Lovett, M. C., & Anderson, J. R. (1996). History of success and current context in problem solving: Combined influences on operator selection. Cognitive Psychology, 31, 168-217.
  34. Lovett, M. C., & Schunn, C. D. (1997). Task representations, strategy variability and base-rate neglect. Paper submitted for publication.
  35. Lovett, M. C., Reder, L. M. & Lebiere, C. (1996) Modeling individual differences in a digit working memory task. Paper presented at the International Conference on Memory. Padova, Italy.
  36. Lovett, M. C., Reder, L. M. & Lebiere, C. (in press). Modeling working memory in a unified architecture: The ACT-R perspective. To appear in A. Miyake and P. Shah (Eds.), Models of Working Memory. Cambridge University Press.
  37. Luchins, A. S. (1942). Mechanization in problem solving. Psychological Monographs, 54.
  38. Lyon, D., & Slovic, P. (1976). Dominance of accuracy information and neglect of base rates in probability estimation. Acta Psychologica, 40, 287-298.
  39. Miller, P. H., & Seier, W. L. (1994). Strategy utilization deficiencies in children: When, where, and why. In H. W. Reese (Ed.), Advances in child development and behavior (Vol. 25). New York: Academic Press.
  40. Miyake, A., Carpenter, P. A., & Just, M. A. (1995). Reduced resources and specific impairments in normal and aphasic sentence comprehension. Cognitive Neuropsychology, 12, 651-679.
  41. Reder, L. M. (1979). The role of elaborations in memory for prose. Cognitive Psychology, 11, 221- 234.
  42. Reder, L. M. (1982). Plausibility judgments versus fact retrieval: Alternative strategies for sentence verification. Psychological Review, 89, 250-280.
  43. Reder, L. M. (1987). Strategy selection in question answering. Cognitive Psychology, 19, 90-137.
  44. Reder, L. M., & Schunn, C. D. (in press). Bringing together the psychometric and strategy worlds: Predicting adaptivity in a dynamic task. To appear in D. Gopher & A. Koriat (Eds.), Cognitive regulation of performance: Interaction of theory and application. Attention and Performance XVII.
  45. Reder, L. M., Wible, C., & Martin, J. (1986). Differential memory changes with age: Exact retrieval versus plausible inference. Journal of Experimental Psychology: Learning, Memory, & Cognition, 12(1), 72-81.
  46. Saccuzzo, D. P., Johnson, N. E., & Guertin, T. L. (1994). Information processing in gifted versus nongifted African American, Latino, Filipino, and White children: Speeded versus nonspeeded paradigms. Intelligence, 19(2), 219-243.
  47. Salthouse, T. A. (1994). Aging associations: Influence of speed on adult age differences in associative learning. Journal of Experimental Psychology: Learning, Memory, & Cognition, 20(6), 1486-1503.
  48. Schneider, W., & Pressley, M. (1989). Memory development between 2 and 20. New York: Springer-Verlag.
  49. Schooler, C., Neumann, E., Caplan, L. J., & Roberts, B. R. (1997). A time course analysis of Stroop interference and facilitation: Comparing normal individuals and individuals with schizophrenia. Journal of Experimental Psychology: General, 126(1), 19-36.
  50. Schunn, C. D., & Klahr, D. (1996). The problem of problem spaces: When and how to go beyond a 2-space model of scientific discovery. Symposium talk presented at The 18th Annual Conference of the Cognitive Science Society.
  51. Schunn, C. D., & Lovett, M. L. (1996). Representation and individual differences in base-rate neglect. Poster presented at 37th Annual Meeting of the Psychonomic Society, Chicago, IL (November, 1996).
  52. Schunn, C. D., & Reder, L. M. (1997). Another Source of Individual Differences: Strategy Adaptivity to Changing Rates of Success. Paper submitted for publication.
  53. Schunn, C. D., Reder, L. M., Nhouyvanisvong, A., Richards, D. R., & Stroffolino, P.J. (1997). To calculate or not calculate: A source activation confusion (SAC) model of problem- familiarity's role in strategy selection. Journal of Experimental Psychology: Learning, Memory, & Cognition, 23(1), 3-29.
  54. Shapira, N. & Kushnir, T. (1985). Aging and information seeking: Patterns in sampling of sucrose solutions. International Journal of Aging & Human Development, 20(2) 103-112.
  55. Shebilske, W., Goettl, B., Regian, J. W. Individual and group protocols for training complex skills in laboratory and applied settings. To appear in D. Gopher & A. Koriat (Eds.), Cognitive regulation of performance: Interaction of theory and application. Attention and Performance XVII.
  56. Shute, V. J. (1993). A macroadaptive approach to tutoring. Journal of Artificial Intelligence in Education, 4(1), 61-93.
  57. Siegler, R. S. (1983). Five generalizations about cognitive development. American Psychologist, 38, 263-277.
  58. Siegler, R. S. (1987). Strategy choices in subtraction. In J. Sloboda and D. Rogers (Eds.), Cognitive processes in mathematics, (pp. 81-106). Oxford: Oxford Books.
  59. Siegler, R. S. (1988). Strategy choice procedures and the development of multiplication skill. Journal of Experimental Psychology: General, 117, 258-275.
  60. Siegler, R. S. (1996). Emerging minds: The process of change in children's thinking. New York: Oxford University Press.
  61. Siegler, R. S., & Jenkins, E. (1988). How children discover new strategies. Hillsdale, NJ: Erlbaum.
  62. Siegler, R. S., & Lemaire, P. (in press). The choice/no-choice method: Applications to assessing the adaptiveness of older and younger adults strategy choices in multidigit multiplication. Journal of Experimental Psychology: General.
  63. Siegler, R. S., & Shipley, C. (1995). Variation, selection, and cognitive change. In G. Halford & T. Simon (Eds.), Developing cognitive competence: New approaches to process modeling, (pp. 31-76). New York: Academic Press.
  64. Siegler, R. S., & Shrager, J. (1984). Strategy choices in addition and subtraction: How do children know what to do. In C. Sophian (Ed.), Origins of cognitive skills, (pp. 229-293). Hillsdale, NJ: Erlbaum.
  65. Snow, R. E., Kyllonen, P. C., & Marshalek, B. (1984). The topography of ability and learning correlations. In R. Sternberg (Ed.), Handbook of human intelligence. New York: Cambridge University Press.
  66. Spearman, C. (1904). The proof and measurement of association between two things. American Journal of Psychology, 15, 72-101.
  67. Stanovich, K. E., & West, R. F. (in press). Who uses base rates and P(D/~H)? An analysis of individual differences. Memory & Cognition.
  68. Sternberg, R. J. (1977). Intelligence, information processing, and analogical reasoning. Hillsdale, NJ: Erlbaum.
  69. Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. New York: Cambridge University Press.
  70. Sternberg. R. J., & Grigorenko, E. L. (1997). Are cognitive styles still in style? American Psychologist, 52(7), 700-712.
  71. Swets, J. A. (1986a). Form of empirical ROCs in discrimination and diagnostic tasks: Implications for theory and measurement of performance. Psychological Bulletin, 99, 181-198.
  72. Swets, J. A. (1986b). Indices of discrimination or diagnostic accuracy: Their ROCs and implied models. Psychological Bulletin, 99, 100-117.
  73. Tversky, A., & Kahneman, D. (1982). Evidential impact of base rates. In D. Kahneman, P. Slovic, & A. Tversky, (Eds.), Judgment under uncertainty: Heuristics and biases. Cambridge University Press.
  74. Wagner, D. A. (1978). Memories of Morocco: The influence of age, schooling and environment on memory. Cognitive Psychology, 10, 1-28.
  75. Zbrodoff, N. J. (1995). Why is 9 plus 7 harder than 2 plus 3? Strength and interference as explanations of the problem-size effect. Memory & Cognition, 23(6), 689-700.

FAQs

sparkles

AI

What explains the different strategies used by age groups in problem solving?add

The research indicates older and younger children utilize both counting and retrieval strategies, with older children preferring retrieval, especially for difficult problems (source). This variation suggests developmental differences in cognitive processing styles affect strategy selection.

How does strategy adaptivity influence overall performance in complex tasks?add

Findings show that adaptivity correlates strongly with performance; for instance, the extent of adaptivity in strategy choices related to a r=0.69 with overall task scores (source). This indicates that flexibility in strategy selection is crucial for success in dynamic environments.

What are intrinsic and extrinsic adaptivity components in strategy selection?add

Intrinsic adaptivity refers to sensitivity to the familiarity of problems, while extrinsic adaptivity captures responsiveness to success rates across strategy types (source). Both components contribute to how effectively individuals adjust their strategy choices over time.

When do individuals show significant individual differences in strategy adaptivity?add

Substantial variations in strategy adaptivity are noted, with only 30% of subjects demonstrating full adaptive patterns; approximately 25% exhibit minimal adaptive behavior (source). This highlights distinct cognitive profiles influencing strategy responsiveness across individuals.

How did the study measure explicit awareness in relation to strategy adaptivity?add

Subject awareness of base-rate changes correlated with adaptivity; those aware of strategy shifts adapted more significantly (e.g., aware group had an adaptivity level of .32 versus .16 for unaware) (source). This suggests awareness is beneficial for strategic flexibility.

Related papers

Commentary on ?cognitive adaptation and its consequences: a test of cognitive continuum theory?
Ray W Cooksey

Journal of Behavioral Decision Making, 2000

The article by Dunwoody, Haarbauer, Mahan, Marion, and Tang (this issue) represents an important test of Cognitive Continuum Theory (CCT). Cognitive Continuum Theory has been around for a while (e.g. see discussions in but has receive little attention in terms of rigorous research evaluation. Hammond et al.'s (1987) ground-breaking study of highway engineers provided the ®rst real test of the theory. Hamm (1988) followed up this study with his investigation, based on the same data as Hammond et al., of moment-by-moment variations in cognitive activity of highway engineers. Since then, tests of the theoretical predictions of CCT have been few and far between as Dunwoody et al. indicate. The work by Dunwoody et al. is important for two reasons: (1) it attempts to concretely test speci®c predictions arising from CCT, and (2) it showcases the methodological issues one confronts when trying to test a dynamic decision theory.

View PDFchevron_right
Bringing Together the Psychometric and Strategy Worlds
Lynne Reder

Attention and Performance XVII, 1999

There are two traditional approaches to the study of individual differences in cognitive skill. One assumes that people differ in the strategies that they we. The other assumes that all people use the same strategies or processes but differ in one or more performance parametersaffecting how the~processes are executed (e.g.. speed memory capacity). This chapter explores another possibility. that people differ in how well they adaptively shift strategies in response to changing features of the task environment. To test this, we examined the performance of 57 participants in a variant of the Kanfer•Ackerman Air Traffic Control Task (Kanfer and Ackerman 1989), a dynamic task in which features of the environment frequently change. We found that. while most participants adap~ed their strategy selections in response to our manipulations of the task environment, not all participants were equally adaptive.-Furthermore, using the CM1 4 (Kyllonen 1993), a cognitive assessment battery, we were able to determine what cognitive subskills were associated with adaptiveness. In this context, we found that inductive reasoning slcill (and not working memory, declarative learning. procedural learning, or processing speed) was associated with adaptiveness to our specific manipulations, and to the general dynamic Character of the air tnffic control (ATC) task.

View PDFchevron_right
Adjustment to change in familiar and unfamiliar task constraints
Ross Sanders

Journal of Sports Sciences, 2009

Taylor & Francis makes every effort to ensure the accuracy of all the information (the "Content") contained in the publications on our platform. However, Taylor & Francis, our agents, and our licensors make no representations or warranties whatsoever as to the accuracy, completeness, or suitability for any purpose of the Content. Any opinions and views expressed in this publication are the opinions and views of the authors, and are not the views of or endorsed by Taylor & Francis. The accuracy of the Content should not be relied upon and should be independently verified with primary sources of information. Taylor and Francis shall not be liable for any losses, actions, claims, proceedings, demands, costs, expenses, damages, and other liabilities whatsoever or howsoever caused arising directly or indirectly in connection with, in relation to or arising out of the use of the Content.

View PDFchevron_right
Adaptors and Innovations: styles of creativity and problem solving
paul Iles

R & D Management, 1990

View PDFchevron_right
Models and paradigms....pdf
Gregory Boyle
View PDFchevron_right

Related topics

  • Individual Difference
    • 580 California St., Suite 400
    San Francisco, CA, 94104
    © 2025 Academia. All rights reserved