Limits...
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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Search space of the TOL.A: configurations using nomenclature of [3]. B: search space. The nodes represent the configurations. The edges represent licit moves that transform a configuration into another (bi-directional).
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pone-0007263-g001: Search space of the TOL.A: configurations using nomenclature of [3]. B: search space. The nodes represent the configurations. The edges represent licit moves that transform a configuration into another (bi-directional).

Mentions: The search space of the TOL (Figure 1) contains 36 configurations and 108 licit moves (i.e., 36 nodes and 54 bi-directional edges). The number of licit moves from a given configuration (branching factor) is 2, 3 or 4 (average = 3). The configurations present 6 spatial patterns and for each spatial pattern, there are 6 color patterns (i.e., the order in which the colors are painted on the balls). We use the nomenclature of [3] for the configurations and the patterns. There are 1296 possible tasks, requiring between 0 (trivial tasks) and 8 moves. The number of solutions per task range from 1 to 8. More information about the search space is available at [6]


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

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

Search space of the TOL.A: configurations using nomenclature of [3]. B: search space. The nodes represent the configurations. The edges represent licit moves that transform a configuration into another (bi-directional).
© Copyright Policy
Related In: Results  -  Collection

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

pone-0007263-g001: Search space of the TOL.A: configurations using nomenclature of [3]. B: search space. The nodes represent the configurations. The edges represent licit moves that transform a configuration into another (bi-directional).
Mentions: The search space of the TOL (Figure 1) contains 36 configurations and 108 licit moves (i.e., 36 nodes and 54 bi-directional edges). The number of licit moves from a given configuration (branching factor) is 2, 3 or 4 (average = 3). The configurations present 6 spatial patterns and for each spatial pattern, there are 6 color patterns (i.e., the order in which the colors are painted on the balls). We use the nomenclature of [3] for the configurations and the patterns. There are 1296 possible tasks, requiring between 0 (trivial tasks) and 8 moves. The number of solutions per task range from 1 to 8. More information about the search space is available at [6]

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