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The role of inertia in modeling decisions from experience with instance-based learning.

Dutt V, Gonzalez C - Front Psychol (2012)

Bottom Line: One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE).This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model.The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

View Article: PubMed Central - PubMed

Affiliation: School of Computing and Electrical Engineering and School of Humanities and Social Sciences, Indian Institute of Technology Mandi, India.

ABSTRACT
One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE). A number of computational models based upon the Instance-Based Learning Theory, a theory of DFE, have included different inertia implementations and have shown to simultaneously account for both risk-taking and alternations between alternatives. The role that inertia plays in these models, however, is unclear as the same model without inertia is also able to account for observed risk-taking quite well. This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model. We use two large datasets, estimation and competition, from the Technion Prediction Tournament involving a repeated binary-choice task to show that incorporating an inertia mechanism in an IBL model enables it to account for the observed average risk-taking and alternations. Including inertia, however, does not help the model to account for the trends in risk-taking and alternations over trials compared to the IBL model without the inertia mechanism. We generalize the two IBL models, with and without inertia, to the competition set by using the parameters determined in the estimation set. The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

No MeSH data available.


Related in: MedlinePlus

(A) The R-rate and A-rate across trials observed in human data in the estimation set of the TPT between trial 2 and trial 100. (B) The R-rate and A-rate across trials observed in human data in the competition set of the TPT between trial 2 and trial 100.
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Figure 1: (A) The R-rate and A-rate across trials observed in human data in the estimation set of the TPT between trial 2 and trial 100. (B) The R-rate and A-rate across trials observed in human data in the competition set of the TPT between trial 2 and trial 100.

Mentions: Figure 1 shows the overall R-rate and A-rate over 99 trials from trial 2 to 100 in the estimation and competition sets. As seen in both datasets, the R-rate decreases slightly across trials, although there is a sharp decrease in the A-rate. The sharp decrease in the A-rate shows a change in the exploration (information-search) pattern across repeated trials. Overall, the R-rate and A-rate curves suggest that participants’ risk-taking behavior remains relatively steady across trials, while they learn to alternate less and choose one of the two alternatives more often. Later in this paper, we evaluate the role of inertia mechanism to account for these R- and A-rate curves in Figure 1 in a computational IBL model.


The role of inertia in modeling decisions from experience with instance-based learning.

Dutt V, Gonzalez C - Front Psychol (2012)

(A) The R-rate and A-rate across trials observed in human data in the estimation set of the TPT between trial 2 and trial 100. (B) The R-rate and A-rate across trials observed in human data in the competition set of the TPT between trial 2 and trial 100.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3368322&req=5

Figure 1: (A) The R-rate and A-rate across trials observed in human data in the estimation set of the TPT between trial 2 and trial 100. (B) The R-rate and A-rate across trials observed in human data in the competition set of the TPT between trial 2 and trial 100.
Mentions: Figure 1 shows the overall R-rate and A-rate over 99 trials from trial 2 to 100 in the estimation and competition sets. As seen in both datasets, the R-rate decreases slightly across trials, although there is a sharp decrease in the A-rate. The sharp decrease in the A-rate shows a change in the exploration (information-search) pattern across repeated trials. Overall, the R-rate and A-rate curves suggest that participants’ risk-taking behavior remains relatively steady across trials, while they learn to alternate less and choose one of the two alternatives more often. Later in this paper, we evaluate the role of inertia mechanism to account for these R- and A-rate curves in Figure 1 in a computational IBL model.

Bottom Line: One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE).This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model.The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

View Article: PubMed Central - PubMed

Affiliation: School of Computing and Electrical Engineering and School of Humanities and Social Sciences, Indian Institute of Technology Mandi, India.

ABSTRACT
One form of inertia is the tendency to repeat the last decision irrespective of the obtained outcomes while making decisions from experience (DFE). A number of computational models based upon the Instance-Based Learning Theory, a theory of DFE, have included different inertia implementations and have shown to simultaneously account for both risk-taking and alternations between alternatives. The role that inertia plays in these models, however, is unclear as the same model without inertia is also able to account for observed risk-taking quite well. This paper demonstrates the predictive benefits of incorporating one particular implementation of inertia in an existing IBL model. We use two large datasets, estimation and competition, from the Technion Prediction Tournament involving a repeated binary-choice task to show that incorporating an inertia mechanism in an IBL model enables it to account for the observed average risk-taking and alternations. Including inertia, however, does not help the model to account for the trends in risk-taking and alternations over trials compared to the IBL model without the inertia mechanism. We generalize the two IBL models, with and without inertia, to the competition set by using the parameters determined in the estimation set. The generalization process demonstrates both the advantages and disadvantages of including inertia in an IBL model.

No MeSH data available.


Related in: MedlinePlus