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Performance of humans vs. exploration algorithms on the Tower of London Test.

Fimbel E, Lauzon S, Rainville C - PLoS ONE (2009)

Bottom Line: However, for difficult tasks (5 to 8 moves) the execution time of young participants did not increase significantly, whereas for exploration algorithms, the execution time keeps on increasing exponentially.A pre-and post-test control task showed a 25% improvement of visuo-motor skills but this was insufficient to explain this result.The findings suggest that naive participants used systematic exploration to solve the problem but under the effect of practice, they developed markedly more efficient strategies using the information acquired during the test.

View Article: PubMed Central - PubMed

Affiliation: Biorobotics Department, Fatronik Foundation, San Sebastian, Spain. efimbel@fatronik.com

ABSTRACT
The Tower of London Test (TOL) used to assess executive functions was inspired in Artificial Intelligence tasks used to test problem-solving algorithms. In this study, we compare the performance of humans and of exploration algorithms. Instead of absolute execution times, we focus on how the execution time varies with the tasks and/or the number of moves. This approach used in Algorithmic Complexity provides a fair comparison between humans and computers, although humans are several orders of magnitude slower. On easy tasks (1 to 5 moves), healthy elderly persons performed like exploration algorithms using bounded memory resources, i.e., the execution time grew exponentially with the number of moves. This result was replicated with a group of healthy young participants. However, for difficult tasks (5 to 8 moves) the execution time of young participants did not increase significantly, whereas for exploration algorithms, the execution time keeps on increasing exponentially. A pre-and post-test control task showed a 25% improvement of visuo-motor skills but this was insufficient to explain this result. The findings suggest that naive participants used systematic exploration to solve the problem but under the effect of practice, they developed markedly more efficient strategies using the information acquired during the test.

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Young participants, execution time as a function of the task.Horizontal: tasks in the order of presentation. The scale indicates the number of moves. Vertical: latency (s). The indexes I(task), U(N) and B(N) are presented above. For clarity, the vertical scale is adjusted for tasks of 2 to 5 moves. Snapshot (upper right): indexes on the entire set of tasks.
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pone-0007263-g004: Young participants, execution time as a function of the task.Horizontal: tasks in the order of presentation. The scale indicates the number of moves. Vertical: latency (s). The indexes I(task), U(N) and B(N) are presented above. For clarity, the vertical scale is adjusted for tasks of 2 to 5 moves. Snapshot (upper right): indexes on the entire set of tasks.

Mentions: The execution time T as a function of the task is presented in Figure 4. A visual examination provides the following preliminary observations. 1) Like for elderly participants, for easy tasks (2 to 5 moves) the execution time increased but 2) it presented no clear trend for the difficult tasks (5 to 8 moves). 3) The execution times were markedly shorter for young than elderly participants and the steepness of the curve for easy tasks was markedly lower. 4) Like for elderly participants, there were marked differences of execution time among tasks with the same number of moves. 5) There was a visual resemblance between the curves I(task) and T: both presented peaks (long execution times) for the same tasks of 5 moves. The differences of slopes and execution times are illustrated on Figure 5. However, the foregoing observations are qualitative and require a quantitative validation before any generalization (see below).


Performance of humans vs. exploration algorithms on the Tower of London Test.

Fimbel E, Lauzon S, Rainville C - PLoS ONE (2009)

Young participants, execution time as a function of the task.Horizontal: tasks in the order of presentation. The scale indicates the number of moves. Vertical: latency (s). The indexes I(task), U(N) and B(N) are presented above. For clarity, the vertical scale is adjusted for tasks of 2 to 5 moves. Snapshot (upper right): indexes on the entire set of tasks.
© Copyright Policy
Related In: Results  -  Collection

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getmorefigures.php?uid=PMC2748701&req=5

pone-0007263-g004: Young participants, execution time as a function of the task.Horizontal: tasks in the order of presentation. The scale indicates the number of moves. Vertical: latency (s). The indexes I(task), U(N) and B(N) are presented above. For clarity, the vertical scale is adjusted for tasks of 2 to 5 moves. Snapshot (upper right): indexes on the entire set of tasks.
Mentions: The execution time T as a function of the task is presented in Figure 4. A visual examination provides the following preliminary observations. 1) Like for elderly participants, for easy tasks (2 to 5 moves) the execution time increased but 2) it presented no clear trend for the difficult tasks (5 to 8 moves). 3) The execution times were markedly shorter for young than elderly participants and the steepness of the curve for easy tasks was markedly lower. 4) Like for elderly participants, there were marked differences of execution time among tasks with the same number of moves. 5) There was a visual resemblance between the curves I(task) and T: both presented peaks (long execution times) for the same tasks of 5 moves. The differences of slopes and execution times are illustrated on Figure 5. However, the foregoing observations are qualitative and require a quantitative validation before any generalization (see below).

Bottom Line: However, for difficult tasks (5 to 8 moves) the execution time of young participants did not increase significantly, whereas for exploration algorithms, the execution time keeps on increasing exponentially.A pre-and post-test control task showed a 25% improvement of visuo-motor skills but this was insufficient to explain this result.The findings suggest that naive participants used systematic exploration to solve the problem but under the effect of practice, they developed markedly more efficient strategies using the information acquired during the test.

View Article: PubMed Central - PubMed

Affiliation: Biorobotics Department, Fatronik Foundation, San Sebastian, Spain. efimbel@fatronik.com

ABSTRACT
The Tower of London Test (TOL) used to assess executive functions was inspired in Artificial Intelligence tasks used to test problem-solving algorithms. In this study, we compare the performance of humans and of exploration algorithms. Instead of absolute execution times, we focus on how the execution time varies with the tasks and/or the number of moves. This approach used in Algorithmic Complexity provides a fair comparison between humans and computers, although humans are several orders of magnitude slower. On easy tasks (1 to 5 moves), healthy elderly persons performed like exploration algorithms using bounded memory resources, i.e., the execution time grew exponentially with the number of moves. This result was replicated with a group of healthy young participants. However, for difficult tasks (5 to 8 moves) the execution time of young participants did not increase significantly, whereas for exploration algorithms, the execution time keeps on increasing exponentially. A pre-and post-test control task showed a 25% improvement of visuo-motor skills but this was insufficient to explain this result. The findings suggest that naive participants used systematic exploration to solve the problem but under the effect of practice, they developed markedly more efficient strategies using the information acquired during the test.

Show MeSH