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Cognitive Learning01:21

Cognitive Learning

230
Cognitive learning is based on purposive behavior, incidental learning, and insight learning.
E. C. Tolman's theory of purposive behavior emphasizes that much behavior is goal-directed. He argued that to understand behavior, we must look at the entire sequence of actions leading to a goal. For instance, high school students study hard, not just due to past reinforcement but also to achieve the goal of getting into a good college.
Tolman introduced the idea that behavior is influenced by...
230
Naturalistic Observations02:30

Naturalistic Observations

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If you want to understand how behavior occurs, one of the best ways to gain information is to simply observe the behavior in its natural context. However, people might change their behavior in unexpected ways if they know they are being observed. How do researchers obtain accurate information when people tend to hide their natural behavior? As an example, imagine that your professor asks everyone in your class to raise their hand if they always wash their hands after using the restroom. Chances...
15.4K
Decision Making: Traditional Method01:14

Decision Making: Traditional Method

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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.
First, a specific claim about the population parameter is decided based on the research question and is stated in a simple form. Further, an opposing statement to this claim is also stated. These statements can act as null and alternative hypotheses, out of which a null hypothesis would be a...
4.0K
Problem-Solving01:29

Problem-Solving

148
Effective problem-solving consists of two steps: 1. identifying the problem and 2. selecting the appropriate problem-solving strategy (i.e., a plan of action used to find a solution). Humans use four problem-solving strategies:
148
Strategies for Assessing and Addressing Confounding01:25

Strategies for Assessing and Addressing Confounding

86
Confounding is a critical issue in epidemiological studies, often leading to misleading conclusions about associations between exposures and outcomes. It occurs when the relationship between the exposure and the outcome is mixed with the effects of other factors that influence the outcome. Given that, addressing confounding is of high importance for drawing accurate inferences in research.
Confounding can be addressed at both the design phase of a study and through analytical methods after data...
86
Trial and Error and Algorithm01:12

Trial and Error and Algorithm

103
A problem-solving strategy is a plan of action used to find a solution. Different strategies have distinct action plans. Trial and error involves trying different solutions until one works. For instance, to fix a broken printer, you might check ink levels, ensure the paper tray isn't jammed, and verify the printer's connection to your laptop. This method can be time-consuming but is commonly used. Thomas Edison, for example, used trial and error to find a suitable filament for the light...
103

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Related Experiment Video

Updated: Jun 13, 2025

Author Spotlight: Investigating the Effects of Mind-Body-Movement Practices on Brain Function
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Curious Explorer: A Provable Exploration Strategy in Policy Learning.

Marco Miani, Maurizio Parton, Marco Romito

    IEEE Transactions on Pattern Analysis and Machine Intelligence
    |September 16, 2024
    PubMed
    Summary
    This summary is machine-generated.

    Curious Explorer enhances policy gradient methods by improving state space coverage, crucial for optimal performance in challenging exploration tasks. This strategy boosts convergence and sample efficiency for algorithms like REINFORCE.

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    Area of Science:

    • Reinforcement Learning
    • Artificial Intelligence
    • Robotics

    Background:

    • Policy gradient methods critically rely on state coverage assumptions for optimal performance.
    • Unfeasible coverage assumptions in environments like online learning or fixed-state restarts hinder classical algorithms (e.g., REINFORCE).
    • This leads to poor convergence and sample efficiency in challenging exploration scenarios.

    Purpose of the Study:

    • To introduce Curious Explorer, an iterative pure exploration strategy designed to improve state space coverage.
    • To address the limitations of policy gradient methods when coverage assumptions are violated.
    • To enhance the performance of reinforcement learning agents in hard-exploration tasks.

    Main Methods:

    • Curious Explorer utilizes a restart distribution (ρ) and intrinsic rewards to generate increasingly exploratory policies.
    • It iteratively refines policies to improve coverage based on state visitation distributions.
    • Theoretical bounds are derived for optimal policy state visitation and REINFORCE's return error without coverage assumptions.

    Main Results:

    • A theoretical upper bound on optimal policy visits to under-explored states was established.
    • A bound on the error of REINFORCE's return without coverage assumptions was determined.
    • Ablation studies demonstrated Curious Explorer's ability to improve performance of REINFORCE and TRPO on hard-exploration tasks.

    Conclusions:

    • Curious Explorer effectively improves state space coverage for policy gradient methods.
    • The strategy enhances convergence and sample efficiency, particularly in environments with challenging exploration requirements.
    • It offers a viable solution for improving diverse policy gradient algorithms in complex scenarios.