Dataset Information

Reinforcement Learning With Parsimonious Computation and a Forgetting Process.

ABSTRACT: Decision-making is assumed to be supported by model-free and model-based systems: the model-free system is based purely on experience, while the model-based system uses a cognitive map of the environment and is more accurate. The recently developed multistep decision-making task and its computational model can dissociate the contributions of the two systems and have been used widely. This study used this task and model to understand our value-based learning process and tested alternative algorithms for the model-free and model-based learning systems. The task used in this study had a deterministic transition structure, and the degree of use of this structure in learning is estimated as the relative contribution of the model-based system to choices. We obtained data from 29 participants and fitted them with various computational models that differ in the model-free and model-based assumptions. The results of model comparison and parameter estimation showed that the participants update the value of action sequences and not each action. Additionally, the model fit was improved substantially by assuming that the learning mechanism includes a forgetting process, where the values of unselected options change to a certain default value over time. We also examined the relationships between the estimated parameters and psychopathology and other traits measured by self-reported questionnaires, and the results suggested that the difference in model assumptions can change the conclusion. In particular, inclusion of the forgetting process in the computational models had a strong impact on estimation of the weighting parameter of the model-free and model-based systems.

SUBMITTER: Toyama A

PROVIDER: S-EPMC6520826 | biostudies-literature | 2019

REPOSITORIES: biostudies-literature

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Publications

Reinforcement Learning With Parsimonious Computation and a Forgetting Process.

Toyama Asako A Katahira Kentaro K Ohira Hideki H

Frontiers in human neuroscience 20190509

Decision-making is assumed to be supported by model-free and model-based systems: the model-free system is based purely on experience, while the model-based system uses a cognitive map of the environment and is more accurate. The recently developed multistep decision-making task and its computational model can dissociate the contributions of the two systems and have been used widely. This study used this task and model to understand our value-based learning process and tested alternative algorit ...[more]

PMID: 31143107

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Project description:Prediction problems in biomedical sciences are generally quite difficult, partially due to incomplete knowledge of how the phenomenon of interest is influenced by the variables and measurements used for prediction, as well as a lack of consensus regarding the ideal predictor(s) for specific problems. In these situations, a powerful approach to improving prediction performance is to construct ensembles that combine the outputs of many individual base predictors, which have been successful for many biomedical prediction tasks. Moreover, selecting a parsimonious ensemble can be of even greater value for biomedical sciences, where it is not only important to learn an accurate predictor, but also to interpret what novel knowledge it can provide about the target problem. Ensemble selection is a promising approach for this task because of its ability to select a collectively predictive subset, often a relatively small one, of all input base predictors. One of the most well-known algorithms for ensemble selection, CES (Caruana et al.'s Ensemble Selection), generally performs well in practice, but faces several challenges due to the difficulty of choosing the right values of its various parameters. Since the choices made for these parameters are usually ad-hoc, good performance of CES is difficult to guarantee for a variety of problems or datasets. To address these challenges with CES and other such algorithms, we propose a novel heterogeneous ensemble selection approach based on the paradigm of reinforcement learning (RL), which offers a more systematic and mathematically sound methodology for exploring the many possible combinations of base predictors that can be selected into an ensemble. We develop three RL-based strategies for constructing ensembles and analyze their results on two unbalanced computational genomics problems, namely the prediction of protein function and splice sites in eukaryotic genomes. We show that the resultant ensembles are indeed substantially more parsimonious as compared to the full set of base predictors, yet still offer almost the same classification power, especially for larger datasets. The RL ensembles also yield a better combination of parsimony and predictive performance as compared to CES.

Dataset Information

Reinforcement Learning With Parsimonious Computation and a Forgetting Process.

Publications

Reinforcement Learning With Parsimonious Computation and a Forgetting Process.

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