Motor learning in physical interfaces for computational problem solving

Rohan McAdam

doi:10.1016/j.procs.2014.05.059

Motor learning in physical interfaces for computational problem solving

Rohan McAdam

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Abstract

Continuous Interactive Simulation (CIS) maps computational problems concerning the con-trol of dynamical systems to physical tasks in a 3D virtual environment for users to perform. However, deciding on the best mapping for a particular problem is not straightforward. This paper considers how a motor learning perspective can assist when designing such mappings. To examine this issue an experiment was performed to compare an arbitrary mapping with one designed by considering a range of motor learning factors. The particular problem studied was a nonlinear policy setting problem from economics. The results show that choices about how a problem is presented can indeed have a large eﬀect on the ability of users to solve the problem. As a result we recommend the development of guidelines for the application of CIS based on motor learning considerations.

Original language	English
Pages (from-to)	659-671
Number of pages	12
Journal	Procedia Computer Science
Volume	29
DOIs	https://doi.org/10.1016/j.procs.2014.05.059
Publication status	Published - 2014
Event	14th International Conference on Computational Science (ICCS 2014) - Cairns, Australia, Australia Duration: 10 Jun 2014 → 12 Jun 2014

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10.1016/j.procs.2014.05.059

23429063 Published articleFinal published version, 3.33 MBLicence: CC BY-NC-ND

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title = "Motor learning in physical interfaces for computational problem solving",

abstract = "Continuous Interactive Simulation (CIS) maps computational problems concerning the con-trol of dynamical systems to physical tasks in a 3D virtual environment for users to perform. However, deciding on the best mapping for a particular problem is not straightforward. This paper considers how a motor learning perspective can assist when designing such mappings. To examine this issue an experiment was performed to compare an arbitrary mapping with one designed by considering a range of motor learning factors. The particular problem studied was a nonlinear policy setting problem from economics. The results show that choices about how a problem is presented can indeed have a large eﬀect on the ability of users to solve the problem. As a result we recommend the development of guidelines for the application of CIS based on motor learning considerations.",

keywords = "Virtual reality, Simulation, Dynamical systems, Problem solving, Motor learning",

author = "Rohan McAdam",

note = "Includes bibliographical references.; 14th International Conference on Computational Science (ICCS 2014) ; Conference date: 10-06-2014 Through 12-06-2014",

year = "2014",

doi = "10.1016/j.procs.2014.05.059",

language = "English",

volume = "29",

pages = "659--671",

journal = "Procedia Computer Science",

issn = "1877-0509",

publisher = "Elsevier BV",

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T1 - Motor learning in physical interfaces for computational problem solving

AU - McAdam, Rohan

N1 - Includes bibliographical references.

PY - 2014

Y1 - 2014

N2 - Continuous Interactive Simulation (CIS) maps computational problems concerning the con-trol of dynamical systems to physical tasks in a 3D virtual environment for users to perform. However, deciding on the best mapping for a particular problem is not straightforward. This paper considers how a motor learning perspective can assist when designing such mappings. To examine this issue an experiment was performed to compare an arbitrary mapping with one designed by considering a range of motor learning factors. The particular problem studied was a nonlinear policy setting problem from economics. The results show that choices about how a problem is presented can indeed have a large eﬀect on the ability of users to solve the problem. As a result we recommend the development of guidelines for the application of CIS based on motor learning considerations.

AB - Continuous Interactive Simulation (CIS) maps computational problems concerning the con-trol of dynamical systems to physical tasks in a 3D virtual environment for users to perform. However, deciding on the best mapping for a particular problem is not straightforward. This paper considers how a motor learning perspective can assist when designing such mappings. To examine this issue an experiment was performed to compare an arbitrary mapping with one designed by considering a range of motor learning factors. The particular problem studied was a nonlinear policy setting problem from economics. The results show that choices about how a problem is presented can indeed have a large eﬀect on the ability of users to solve the problem. As a result we recommend the development of guidelines for the application of CIS based on motor learning considerations.

KW - Virtual reality

KW - Simulation

KW - Dynamical systems

KW - Problem solving

KW - Motor learning

U2 - 10.1016/j.procs.2014.05.059

DO - 10.1016/j.procs.2014.05.059

M3 - Article

SN - 1877-0509

VL - 29

SP - 659

EP - 671

JO - Procedia Computer Science

JF - Procedia Computer Science

T2 - 14th International Conference on Computational Science (ICCS 2014)

Y2 - 10 June 2014 through 12 June 2014

ER -

Motor learning in physical interfaces for computational problem solving

Abstract

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