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関連する概念動画

Cable Subjected to Its Own Weight01:13

Cable Subjected to Its Own Weight

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Overhead power transmission lines rely on cables to carry electricity across large distances. To ensure the stability and functionality of these lines, it is crucial to understand the shape and tension experienced by the cables under the influence of their weight.
A generalized loading function is employed to analyze a cable subjected to its own weight. This function considers the force acting along the cable's arc length rather than its projected length, providing a more accurate...
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Probability Laws01:49

Probability Laws

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Overview
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Probability Distributions01:32

Probability Distributions

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 The probability of a random variable x  is the likelihood of its occurrence. A probability distribution represents the probabilities of a random variable using a formula, graph, or table. There are two types of probability distribution– discrete probability distribution and continuous probability distribution.
A discrete probability distribution is a probability distribution of discrete random variables. It can be categorized into binomial probability distribution and Poisson...
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Probability in Statistics01:14

Probability in Statistics

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Probability is the likelihood of an event occurring. The term event is defined as a collection of results of a procedure. An event is a simple event when an outcome cannot be divided into simpler parts.
An example of a simple event is a coin toss. The result of a coin toss is either a head or a tail. Here, head and tail are two simple events. These two simple events make up the sample space. Further, the probability of an event occurring falls within the range of 0 to 1. The probability of an...
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Probability Histograms01:17

Probability Histograms

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A probability histogram is a visual representation of a probability distribution. Similar a typical histogram, the probability histogram consists of contiguous (adjoining) boxes. It has both a horizontal axis and a vertical axis. The horizontal axis is labeled with what the data represents. The vertical axis is labeled with probability. Each rectangular bar in the histogram is 1 unit wide, which suggests that the area under each bar equals the probability, P(x), where x is 1, 2, 3, and so on.
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lncRNA - Long Non-coding RNAs02:39

lncRNA - Long Non-coding RNAs

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In humans, more than 80% of the genome gets transcribed. However, only around 2% of the genome codes for proteins. The remaining part produces non-coding RNAs which includes ribosomal RNAs, transfer RNAs, telomerase RNAs, and regulatory RNAs, among other types. A large number of regulatory non-coding RNAs have been classified into two groups depending upon their length – small non-coding RNAs, such as microRNA, which are less than 200 nucleotides in length, and long non-coding RNA...
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関連する実験動画

Updated: Feb 10, 2026

Measuring the Subjective Value of Risky and Ambiguous Options using Experimental Economics and Functional MRI Methods
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経済的主観的価値の完全なドーパミンコーディング:効用と加重確率

Simone Ferrari-Toniolo1, Leo Chi U Seak1, Wolfram Schultz1

  • 1Department of Physiology, Development and Neuroscience, University of Cambridge, Cambridge, UK.

Cell reports
|February 8, 2026
PubMed
まとめ

サルのドーパミンニューロンは、効用と加重確率を統合することにより主観的な報酬価値を表し、動物の選択を反映します。これにより、脳が主観的な確率などのコアコンポーネントから価値を計算する方法が明らかになります。

科学分野:

  • 神経科学
  • 意思決定科学
  • 行動経済学

背景:

  • 行動経済学は、主観的な報酬価値が効用と加重確率を統合すると仮定しています。
  • この主観的価値計算の神経基盤は、大部分未知のままです。

研究 の 目的:

  • ドーパミンニューロンシグナルが主観的な報酬価値を表すかどうかを調査する。
  • これらのシグナルが効用と加重確率を統合するかどうかを判断する。

主な方法:

  • 報酬ベースの選択中にサルの集団レベルのドーパミンニューロン活動を記録した。
  • 効用と加重確率のコンポーネントに関連してドーパミンシグナルを分析した。

主要な成果:

  • ドーパミンシグナルは、動物の選択行動と一致する統合された主観的価値を確実に表します。
  • 主観的に加重された確率は、主観的価値のドーパミンシグナルに大きく貢献します。

結論:

  • ドーパミンニューロンは、効用と加重確率を統合することにより、主観的価値計算を実装します。
  • 不確実な報酬をエージェントがどのように評価するかについての神経基盤を実証します。
キーワード:
神経科学選択ドーパミン経済公理報酬効用加重確率

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