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関連する概念動画

Reinforcement Schedules01:24

Reinforcement Schedules

398
Positive reinforcement is a powerful method for teaching new behaviors to both animals and humans. B.F. Skinner demonstrated this with his experiments using rats in a Skinner box. When a rat pressed a lever, it received a food pellet. This immediate reward encouraged the rat to repeat the behavior. This method, where a reward follows every instance of the behavior, is known as continuous reinforcement. It is highly effective for establishing new behaviors quickly.
Once a behavior is learned,...
398
Operant Conditioning01:21

Operant Conditioning

2.7K
Operant conditioning, a key concept in behavioral psychology, involves using reinforcement and punishment to alter the likelihood of a behavior being repeated. B.F. introduced this type of conditioning. Skinner focused on voluntary behaviors and the consequences that follow them, influencing whether these behaviors will be strengthened or diminished.
Reinforcement in operant conditioning can be positive or negative, both of which serve to increase the likelihood of a behavior. Positive...
2.7K
Reinforcement01:23

Reinforcement

748
Positive and negative reinforcement are key concepts in operant conditioning, a learning process where the consequences of a behavior affect the likelihood of that behavior being repeated.
Positive reinforcement occurs when a behavior is followed by the presentation of a rewarding stimulus, increasing the frequency of that behavior. For example:
748
Primary and Secondary Reinforcers01:23

Primary and Secondary Reinforcers

769
In psychology, reinforcement is a key concept in behavior modification. B.F. Skinner demonstrated this with his experiments involving rats in what is known as a Skinner box. The rats learned to press a lever to receive food, a primary reinforcer that fulfilled their innate need for nourishment.
Effective reinforcers for humans vary depending on the individual and the context. Primary reinforcers, such as food, water, sleep, shelter, and pleasure, have inherent value and satisfy basic biological...
769
Observational Learning01:12

Observational Learning

751
Albert Bandura's observational learning, also known as imitation or modeling, occurs when a person observes and imitates another's behavior. It is a quicker process than operant conditioning. A well-known example is the Bobo doll study, where children who saw an adult acting aggressively towards the doll were more likely to act aggressively when left alone, compared to those who observed a nonaggressive adult. Many psychologists view observational learning as a form of latent learning...
751
Decision Making: P-value Method01:09

Decision Making: P-value Method

6.7K
The process of hypothesis testing based on the P-value method includes calculating the P- value using the sample data and interpreting it.
First, a specific claim about the population parameter is proposed. The claim is based on the research question and is stated in a simple form. Further, an opposing statement to the claim  is also stated. These statements can act as null and alternative hypotheses:  a null hypothesis would be a neutral statement while the alternative hypothesis can...
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Studying Food Reward and Motivation in Humans
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Studying Food Reward and Motivation in Humans

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ドーパミンベースの補強学習における価値の分布コード

Will Dabney1, Zeb Kurth-Nelson2,3, Naoshige Uchida4

  • 1DeepMind, London, UK. wdabney@google.com.

Nature
|January 17, 2020
PubMed
まとめ
この要約は機械生成です。

ドーパミンベースの補強学習は 報酬を単一の値ではなく 確率分布として表すことができます この研究は 脳の配分強化学習モデルを支持する 神経学的証拠を提供します

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科学分野:

  • 神経科学
  • 計算神経科学
  • 人工知能

背景:

  • ドーパミンの定式報酬予測エラー理論は 脳における報酬と価値表現を説明しています
  • この理論では,報酬予測は単一のスケーラー数量として表され,ストキャスティック結果の平均を表します.

研究 の 目的:

  • 人工知能における分布的強化学習にインスパイアされたドーパミンベースの強化学習の新しい説明を提案し,テストする.
  • 脳が将来の報酬を 単一の平均値ではなく 確率分布として表すかどうかを調べる

主な方法:

  • ネズミの腹部タグメンタル領域からの単一のユニット記録を使用しました.
  • 配分強化学習仮説から派生した経験的予測をテストした.

主要な成果:

  • 分布強化学習のニューラル基盤を支える強力な証拠を提供します.
  • ドーパミンのニューロンが 将来の報酬の分布をコードしていることが示されました

結論:

  • 報酬の脳の表現は 以前考えられていたより複雑で 単一の価値ではなく 配分が関わっています
  • この研究は 補強学習における ドーパミンの役割を理解するための 新しい枠組みを提供し 神経科学と人工知能の進歩を 合わせています