Learning and interactivity in solving a transformation problem

Abstract

Outside the psychologist's laboratory, thinking proceeds on the basis of a great deal of interaction with artefacts that are recruited to augment problem-solving skills. The role of interactivity in problem solving was investigated using a river-crossing problem. In Experiment 1A, participants completed the same problem twice, once in a low interactivity condition, and once in a high interactivity condition (with order counterbalanced across participants). Learning, as gauged in terms of latency to completion, was much more pronounced when the high interactivity condition was experienced second. When participants first completed the task in the high interactivity condition, transfer to the low interactivity condition during the second attempt was limited; Experiment 1B replicated this pattern of results. Participants thus showed greater facility to transfer their experience of completing the problem from a low to a high interactivity condition. Experiment 2 was designed to determine the amount of learning in a low and high interactivity condition; in this experiment participants completed the problem twice, but level of interactivity was manipulated between subjects. Learning was evident in both the low and high interactivity groups, but latency per move was significantly faster in the high interactivity group, in both presentations. So-called problem isomorphs instantiated in different task ecologies draw upon different skills and abilities; a distributed cognition analysis may provide a fruitful perspective on learning and transfer.

What the Jeweller’s Hand Tells the Jeweller’s Brain: Tool Use, Creativity and Embodied Cognition

“…For example, in their study of Tetris playing, Kirsh and Maglio (1994) distinguish between the pragmatic acts involved in moving the pieces to form patterns, and the epistemic actions involved in moving the pieces in an exploratory manner to see whether patterns are available. In a study of the 'wolves and chicken' problem, Guthrie et al (2015) show that the ability to interact with physical objects led to a reduction in 'illegal' moves and a reduction in decision latency (suggesting that participants were more efficient when they could manipulate physical objects than when they verbalised the approach to the problem). Steffensen et al (2015), in a fine-grained analysis of strategy in solving the 17 Animals problem, demonstrate how rearranging the physical artefacts in the task led to serendipitous recognition of the path to a solution.…”

Cognitive events in a problem-solving task: a qualitative method for investigating interactivity in the 17 Animals problem

“…This interplay between action and perception in the dynamics between cognitive agents and their environment have in recent years been studied under the rubric of interactivity (Cowley & Nash, 2013;Cowley & Vallée-Tourangeau, 2013;Guthrie, Vallée-Tourangeau, Vallée-Tourangeau, & Howard, 2015;Kirsh, 2015;Steffensen, 2013;Vallée-Tourangeau, 2012Vallée-Tourangeau, Euden, & Hearn, 2011;F. Vallee-Tourangeau & Payton, 2008;G.…”

A systematic information problem-solving process

“…Hence, who publishing the IDEAL problem-solving process: (1) Identifying problems; (2) Defining and representing them with precise; (3) Exploring possible strategies; (4) Acting these strategies; (5) looking at the effects. [4] proposed 6 steps: (1) Defining problem; (2) Shaping the nature of problem; (3) Proposing problem-solving project; (4) Practicing project; (5) Evaluating and assessing the plan; (6) Evaluating project. [17] summarized different point of views to construct software development project solution -common model, its implementation steps were: (1) Describing problem (Stating and defining problem); (2) Planning solution project (Confirming and selecting solution problem, and dividing into sub-problem); (3) Designing solution project (Establishing the logics and connection between sub-problems); (4) Translating solution project (Coding project into program script); (5) Testing solution (Examining the correctness of program); (6) Presenting results (Documenting solution project and publishing results).…”

“…The space including (1) Initial state (Explaining current situation and related factors of problem); (2) Goal state (Illustrating expected goal after solving problem); (3) Intermediate state (Presenting all path and movement sets to solving problem). In the intermediate state, they had to propose the rules and constraints to solving problem, then showed the application of a series of operators/transformation functions, even reached goal state [4]. [5] taked "The Tower of Hanoi puzzle" to explain the information processing.…”