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Basic and Clinical Neuroscience، جلد ۱۶، شماره ۵، صفحات ۰-۰
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| عنوان فارسی |
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| چکیده فارسی مقاله |
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| کلیدواژه‌های فارسی مقاله |
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| عنوان انگلیسی |
Better than Maximum Likelihood Estimation of Model-based and Model-free Learning Styles |
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| چکیده انگلیسی مقاله |
Various decision-making systems work together to shape human behavior. Goal-directed and habitual systems are the two most important systems studied by reinforcement learning (RL) through model-based (MB) and model-free (MF) learning styles, respectively. Human behavior resembles the combination of these two decision-making paradigms, achieved by the weighted sum of the action values of the two styles in an RL framework. The weighting parameter is often extracted by the maximum likelihood (ML) or maximum a-posteriori (MAP) estimation method. In this study, we employ RL agents that use a combination of MB and MF decision-making to perform the well-known Daw two-stage task. ML and MAP methods result in less reliable estimates of the weighting parameter, where a large bias toward extreme values is often observed. We propose the k‑nearest neighbor as an alternative nonparametric estimate to improve the estimation error, where we devise a set of 20 features extracted from the behavior of the RL agent. Simulated experiments examine the proposed method. Our method reduces the bias and variance of the estimation error based on the obtained results. Human behavior data from previous studies is investigated as well. The proposed method results in predicting indices such as age, gender, IQ, the dwell time of gaze, and psychiatric disorder indices which are missed by the traditional method. In brief, the proposed method increases the reliability of the estimated parameters and enhances the applicability of reinforcement learning paradigms in clinical trials. |
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| کلیدواژه‌های انگلیسی مقاله |
Model-based and Model-free combined learning, Modeling different styles of learning, k-nearest neighbors estimation versus maximum likelihood and maximum a‑posteriori estimations, Behavioral observation analysis, Behavioral parameter estimation |
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| نویسندگان مقاله |
| Sadjad Yazdani
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
| Abdol-Hossein Vahabie
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
| Babak Nadjar-Araabi
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
| Majid Nili Ahmadabadi
School of Electrical and Computer Engineering, University of Tehran, Tehran, Iran.
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| نشانی اینترنتی |
http://bcn.iums.ac.ir/browse.php?a_code=A-10-5883-1&slc_lang=en&sid=1 |
| فایل مقاله |
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| کد مقاله (doi) |
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| زبان مقاله منتشر شده |
en |
| موضوعات مقاله منتشر شده |
Computational Neuroscience |
| نوع مقاله منتشر شده |
Original |
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