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Related Concept Videos

Decision Making01:20

Decision Making

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Decision-making is a fundamental cognitive process that involves evaluating alternatives and selecting among them. This process can range from simple choices, such as deciding what to wear, to complex decisions, like choosing a major in college or a career path. The complexity of the decision often dictates the approach we use, which can be broadly categorized into two types: automatic and controlled decision-making.
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Reason and Intuition01:37

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The human brain processes information for decision-making using one of two routes: an intuitive system and a rational system (Epstein, 1994; popularized by Kahneman, 2011 as System 1 and System 2, respectively). The intuitive system is quick, impulsive, and operates with minimal effort, relying on emotions or habits to provide cues for what to do next, while the rational system is logical, analytical, deliberate, and methodical. Research in neuropsychology suggests that the...
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The cerebellum, while traditionally associated with motor control, also plays a crucial role in memory, particularly in procedural memory, which involves learning motor tasks that become automatic through repetition. For example, studies have shown that when the cerebellum is damaged, individuals or animals lose the ability to learn conditioned motor responses, such as the conditioned eye-blink response in classical conditioning experiments with rabbits. This study demonstrates the...
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Decision Making: Traditional Method01:14

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The process of hypothesis testing based on the traditional method includes calculating the critical value, testing the value of the test statistic using the sample data, and interpreting these values.
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Decision Making: P-value Method01:09

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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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Association Areas of the Cortex01:21

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Association areas are regions of the cerebral cortex that do not have a specific sensory or motor function. Instead, they integrate and interpret information from various sources to enable higher cognitive processes such as memory, learning, and decision-making. Some key association areas include the following:
Prefrontal Association Area: This area is located in the frontal lobe and is involved in planning, decision-making, and moderating social behavior. It connects with primary motor areas,...
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Online Repetitive Transcranial Magnetic Stimulation of Dorsomedial and Dorsolateral Prefrontal Cortex in Cognition Decision Making, and Cognitive Dissonance
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Goal-directed decision making in prefrontal cortex: A computational framework.

Matthew Botvinick1, James An2

  • 1Princeton Neuroscience Institute and Department of Psychology, Princeton University, Princeton, NJ 08540, matthewb@princeton.edu.

Advances in Neural Information Processing Systems
|September 27, 2014
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Summary
This summary is machine-generated.

This study presents a new computational framework for goal-directed action control in animals and humans. It unifies existing theories and introduces an algorithm for optimal planning based on neural representations.

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Area of Science:

  • Cognitive Neuroscience
  • Computational Neuroscience
  • Behavioral Neuroscience

Background:

  • Distinction between habit-based and goal-directed action control.
  • Limited computational research on goal-directed control mechanisms.
  • Need for a unified framework for understanding action selection.

Purpose of the Study:

  • To propose a computational framework for goal-directed control in animals and humans.
  • To integrate neural representations of policies and rewards into a unified model.
  • To develop a novel algorithm for optimal planning in goal-directed action selection.

Main Methods:

  • Utilizing empirically motivated premises regarding prefrontal cortex function.
  • Applying principles of structured probabilistic inference.
  • Developing a novel algorithm extending Bayesian inference for Markov decision problems.

Main Results:

  • A computational framework for goal-directed control.
  • A novel algorithm that provably converges on optimal plans.
  • A unifying theory for various forms of goal-directed action selection.

Conclusions:

  • The proposed framework offers a unified understanding of goal-directed action selection.
  • Orbitofrontal reward representations directly influencing policy selection is highlighted.
  • The framework advances computational principles in behavioral neuroscience.