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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
Algorithmic indexes.Horizontal: number of moves. Vertical: execution time (normalized). A: unbounded memory, U(N). B: bounded memory, B(N), I(task) (grey zone) and I(N), i.e., average of I(task), C: detail: all indicators together, N< = 4. According to Shallice (1982), with 4 moves or more, the supervisory attentional system is engaged.
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pone-0007263-g002: Algorithmic indexes.Horizontal: number of moves. Vertical: execution time (normalized). A: unbounded memory, U(N). B: bounded memory, B(N), I(task) (grey zone) and I(N), i.e., average of I(task), C: detail: all indicators together, N< = 4. According to Shallice (1982), with 4 moves or more, the supervisory attentional system is engaged.

Mentions: For validation, we computed I(N) as the average of I(task) for the tasks of N moves. We verified that I(N) was similar to B(N). This was expected, because the random algorithm uses bounded memory. Note that I(N) increases slightly slower than B(N), possibly because the algorithm does not examine the paths that contain loops (see exponents in Table 1, bottom right and Figure 2). However this small difference does not justify using I(N) as a separate algorithmic index.


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

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

Algorithmic indexes.Horizontal: number of moves. Vertical: execution time (normalized). A: unbounded memory, U(N). B: bounded memory, B(N), I(task) (grey zone) and I(N), i.e., average of I(task), C: detail: all indicators together, N< = 4. According to Shallice (1982), with 4 moves or more, the supervisory attentional system is engaged.
© Copyright Policy
Related In: Results  -  Collection

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

pone-0007263-g002: Algorithmic indexes.Horizontal: number of moves. Vertical: execution time (normalized). A: unbounded memory, U(N). B: bounded memory, B(N), I(task) (grey zone) and I(N), i.e., average of I(task), C: detail: all indicators together, N< = 4. According to Shallice (1982), with 4 moves or more, the supervisory attentional system is engaged.
Mentions: For validation, we computed I(N) as the average of I(task) for the tasks of N moves. We verified that I(N) was similar to B(N). This was expected, because the random algorithm uses bounded memory. Note that I(N) increases slightly slower than B(N), possibly because the algorithm does not examine the paths that contain loops (see exponents in Table 1, bottom right and Figure 2). However this small difference does not justify using I(N) as a separate algorithmic index.

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