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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.

Show MeSH
Preparation and movement times of young participants.Each dot represents a task. Horizontal: number of moves. Vertical: latency (averaged across participants). The trend lines and the equations are presented on the chart. The trend lines are determined separately for easy tasks (2–5 moves) and difficult tasks (5–8 moves).
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pone-0007263-g006: Preparation and movement times of young participants.Each dot represents a task. Horizontal: number of moves. Vertical: latency (averaged across participants). The trend lines and the equations are presented on the chart. The trend lines are determined separately for easy tasks (2–5 moves) and difficult tasks (5–8 moves).

Mentions: In this section, we present minimal results on the preparation and movement times of young participants. The only objective is to provide cues to interpret the foregoing results because it has already been mentioned that there is on-line planning during the movement phase. Figure 6 depicts preparation and movement time as a function of the number of moves and the corresponding trend lines computed separately for easy tasks (2 to 5 moves) and difficult tasks (5 to 8 moves). For easy tasks, preparation and movement time increase with the number of moves. For difficult tasks, the preparation time increases but the movement time decreases slightly.


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

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

Preparation and movement times of young participants.Each dot represents a task. Horizontal: number of moves. Vertical: latency (averaged across participants). The trend lines and the equations are presented on the chart. The trend lines are determined separately for easy tasks (2–5 moves) and difficult tasks (5–8 moves).
© Copyright Policy
Related In: Results  -  Collection

Show All Figures
getmorefigures.php?uid=PMC2748701&req=5

pone-0007263-g006: Preparation and movement times of young participants.Each dot represents a task. Horizontal: number of moves. Vertical: latency (averaged across participants). The trend lines and the equations are presented on the chart. The trend lines are determined separately for easy tasks (2–5 moves) and difficult tasks (5–8 moves).
Mentions: In this section, we present minimal results on the preparation and movement times of young participants. The only objective is to provide cues to interpret the foregoing results because it has already been mentioned that there is on-line planning during the movement phase. Figure 6 depicts preparation and movement time as a function of the number of moves and the corresponding trend lines computed separately for easy tasks (2 to 5 moves) and difficult tasks (5 to 8 moves). For easy tasks, preparation and movement time increase with the number of moves. For difficult tasks, the preparation time increases but the movement time decreases slightly.

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