Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task
Our daily interaction with the world is plagued of situations in which we develop expertise through self-motivated repetition of the same task. In many of these interactions, and especially when dealing with computer and machine interfaces, we must deal with sequences of decisions and actions. For i...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2017-09-01
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| Series: | Frontiers in Computational Neuroscience |
| Subjects: | |
| Online Access: | http://journal.frontiersin.org/article/10.3389/fncom.2017.00080/full |
| _version_ | 1811272948026179584 |
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| author | Cristóbal Moënne-Loccoz Rodrigo C. Vergara Vladimir López Vladimir López Domingo Mery Diego Cosmelli Diego Cosmelli |
| author_facet | Cristóbal Moënne-Loccoz Rodrigo C. Vergara Vladimir López Vladimir López Domingo Mery Diego Cosmelli Diego Cosmelli |
| author_sort | Cristóbal Moënne-Loccoz |
| collection | DOAJ |
| description | Our daily interaction with the world is plagued of situations in which we develop expertise through self-motivated repetition of the same task. In many of these interactions, and especially when dealing with computer and machine interfaces, we must deal with sequences of decisions and actions. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion and a specific sequence of choices must be performed in order to produce the expected outcome. But, as we become experts in the use of such interfaces, is it possible to identify specific search and learning strategies? And if so, can we use this information to predict future actions? In addition to better understanding the cognitive processes underlying sequential decision making, this could allow building adaptive interfaces that can facilitate interaction at different moments of the learning curve. Here we tackle the question of modeling sequential decision-making behavior in a simple human-computer interface that instantiates a 4-level binary decision tree (BDT) task. We record behavioral data from voluntary participants while they attempt to solve the task. Using a Hidden Markov Model-based approach that capitalizes on the hierarchical structure of behavior, we then model their performance during the interaction. Our results show that partitioning the problem space into a small set of hierarchically related stereotyped strategies can potentially capture a host of individual decision making policies. This allows us to follow how participants learn and develop expertise in the use of the interface. Moreover, using a Mixture of Experts based on these stereotyped strategies, the model is able to predict the behavior of participants that master the task. |
| first_indexed | 2024-04-12T22:50:20Z |
| format | Article |
| id | doaj.art-8744ba42e6e84cb598e8a4c9930369ab |
| institution | Directory Open Access Journal |
| issn | 1662-5188 |
| language | English |
| last_indexed | 2024-04-12T22:50:20Z |
| publishDate | 2017-09-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Computational Neuroscience |
| spelling | doaj.art-8744ba42e6e84cb598e8a4c9930369ab2022-12-22T03:13:23ZengFrontiers Media S.A.Frontiers in Computational Neuroscience1662-51882017-09-011110.3389/fncom.2017.00080257066Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making TaskCristóbal Moënne-Loccoz0Rodrigo C. Vergara1Vladimir López2Vladimir López3Domingo Mery4Diego Cosmelli5Diego Cosmelli6Department of Computer Science, School of Engineering, Pontificia Universidad Católica de ChileSantiago, ChileFacultad de Medicina, Biomedical Neuroscience Institute, Universidad de ChileSantiago, ChileCenter for Interdisciplinary Neuroscience, Pontificia Universidad Católica de ChileSantiago, ChileSchool of Psychology, Pontificia Universidad Católica de ChileSantiago, ChileDepartment of Computer Science, School of Engineering, Pontificia Universidad Católica de ChileSantiago, ChileCenter for Interdisciplinary Neuroscience, Pontificia Universidad Católica de ChileSantiago, ChileSchool of Psychology, Pontificia Universidad Católica de ChileSantiago, ChileOur daily interaction with the world is plagued of situations in which we develop expertise through self-motivated repetition of the same task. In many of these interactions, and especially when dealing with computer and machine interfaces, we must deal with sequences of decisions and actions. For instance, when drawing cash from an ATM machine, choices are presented in a step-by-step fashion and a specific sequence of choices must be performed in order to produce the expected outcome. But, as we become experts in the use of such interfaces, is it possible to identify specific search and learning strategies? And if so, can we use this information to predict future actions? In addition to better understanding the cognitive processes underlying sequential decision making, this could allow building adaptive interfaces that can facilitate interaction at different moments of the learning curve. Here we tackle the question of modeling sequential decision-making behavior in a simple human-computer interface that instantiates a 4-level binary decision tree (BDT) task. We record behavioral data from voluntary participants while they attempt to solve the task. Using a Hidden Markov Model-based approach that capitalizes on the hierarchical structure of behavior, we then model their performance during the interaction. Our results show that partitioning the problem space into a small set of hierarchically related stereotyped strategies can potentially capture a host of individual decision making policies. This allows us to follow how participants learn and develop expertise in the use of the interface. Moreover, using a Mixture of Experts based on these stereotyped strategies, the model is able to predict the behavior of participants that master the task.http://journal.frontiersin.org/article/10.3389/fncom.2017.00080/fullsequential decision-makingHidden Markov Modelsexpertise acquisitionbehavioral modelingsearch strategies |
| spellingShingle | Cristóbal Moënne-Loccoz Rodrigo C. Vergara Vladimir López Vladimir López Domingo Mery Diego Cosmelli Diego Cosmelli Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task Frontiers in Computational Neuroscience sequential decision-making Hidden Markov Models expertise acquisition behavioral modeling search strategies |
| title | Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task |
| title_full | Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task |
| title_fullStr | Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task |
| title_full_unstemmed | Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task |
| title_short | Modeling Search Behaviors during the Acquisition of Expertise in a Sequential Decision-Making Task |
| title_sort | modeling search behaviors during the acquisition of expertise in a sequential decision making task |
| topic | sequential decision-making Hidden Markov Models expertise acquisition behavioral modeling search strategies |
| url | http://journal.frontiersin.org/article/10.3389/fncom.2017.00080/full |
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