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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

The R-rate and A-rate over trials predicted by the IBL and IBL-Inertia models upon their generalization in the competition set. The R-rate and A-rate observed in human data in the competition set are also shown.
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Figure 4: The R-rate and A-rate over trials predicted by the IBL and IBL-Inertia models upon their generalization in the competition set. The R-rate and A-rate observed in human data in the competition set are also shown.

Mentions: Figure 4 shows the R-rate and the A-rate over trials for human data, and how the IBL and IBL-Inertia models generalized in the competition set. The IBL model, upon generalization, underestimates the observed R-rate and overestimates the observed A-rate in the competition set. These patterns of under- and over-estimations are similar to those observed in the model’s predictions in the estimation set in Figure 2. The IBL-Inertia model’s predictions about the human R-rate and A-rate in the competition set, however, were very good with very little under- and over-estimations of the observed R-rate and A-rate curves. Furthermore, because the pInertia parameter (=0.62) is fixed across trials at a high value in the IBL-Inertia model, the model does not alternate as much as humans in the first few trials. As seen in the lower right graph, the IBL-Inertia model’s A-rate starts around 40%, rather than the 85% as observed in human data. Thus, the IBL-Inertia model is not able to account for the initially high A-rate and the rapid decrease in the A-rate in the first few trials compared with the IBL model in its predictions.


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

Dutt V, Gonzalez C - Front Psychol (2012)

The R-rate and A-rate over trials predicted by the IBL and IBL-Inertia models upon their generalization in the competition set. The R-rate and A-rate observed in human data in the competition set are also shown.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 4: The R-rate and A-rate over trials predicted by the IBL and IBL-Inertia models upon their generalization in the competition set. The R-rate and A-rate observed in human data in the competition set are also shown.
Mentions: Figure 4 shows the R-rate and the A-rate over trials for human data, and how the IBL and IBL-Inertia models generalized in the competition set. The IBL model, upon generalization, underestimates the observed R-rate and overestimates the observed A-rate in the competition set. These patterns of under- and over-estimations are similar to those observed in the model’s predictions in the estimation set in Figure 2. The IBL-Inertia model’s predictions about the human R-rate and A-rate in the competition set, however, were very good with very little under- and over-estimations of the observed R-rate and A-rate curves. Furthermore, because the pInertia parameter (=0.62) is fixed across trials at a high value in the IBL-Inertia model, the model does not alternate as much as humans in the first few trials. As seen in the lower right graph, the IBL-Inertia model’s A-rate starts around 40%, rather than the 85% as observed in human data. Thus, the IBL-Inertia model is not able to account for the initially high A-rate and the rapid decrease in the A-rate in the first few trials compared with the IBL model in its predictions.

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