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Related Concept Videos

Cognitive Learning01:21

Cognitive Learning

196
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...
196
Associative Learning01:27

Associative Learning

278
Associative learning is a fundamental concept in behavioral psychology, wherein a connection is established between two stimuli or events, leading to a learned response. This process is critical in understanding how behaviors are acquired and modified. Conditioning, the mechanism through which associations are formed, can be divided into two main types: classical conditioning and operant conditioning, each elucidating different aspects of associative learning.
Classical conditioning, also known...
278
Root Loci for Positive-Feedback Systems01:23

Root Loci for Positive-Feedback Systems

88
The Hartley oscillator is a positive feedback system that sustains oscillations by feeding the output back to the input in phase, thereby reinforcing the signal. Positive feedback systems can be viewed as negative feedback systems with inverted feedback signals. In these systems, the root locus encompasses all points on the s-plane where the angle of the system transfer function equals 360 degrees.
The construction rules for the root locus in positive feedback systems are similar to those in...
88
Basic Continuous Time Signals01:22

Basic Continuous Time Signals

181
Basic continuous-time signals include the unit step function, unit impulse function, and unit ramp function, collectively referred to as singularity functions. Singularity functions are characterized by discontinuities or discontinuous derivatives.
The unit step function, denoted u(t), is zero for negative time values and one for positive time values, exhibiting a discontinuity at t=0. This function often represents abrupt changes, such as the step voltage introduced when turning a car's...
181
Purposive Learning01:22

Purposive Learning

97
E. C. Tolman emphasized the purposiveness of behavior — the idea that much of our behavior is goal-directed. For instance, employees who aim for a promotion work diligently to meet their targets. Tolman argued that when classical conditioning and operant conditioning occur, the organism acquires certain expectations. In classical conditioning, a child might fear a dog because they expect it to bite. In operant conditioning, a person might consistently work overtime because they expect a...
97
Multi-input and Multi-variable systems01:22

Multi-input and Multi-variable systems

93
Cruise control systems in cars are designed as multi-input systems to maintain a driver's desired speed while compensating for external disturbances such as changes in terrain. The block diagram for a cruise control system typically includes two main inputs: the desired speed set by the driver and any external disturbances, such as the incline of the road. By adjusting the engine throttle, the system maintains the vehicle's speed as close to the desired value as possible.
In the absence...
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Related Experiment Video

Updated: May 26, 2025

Measuring Statistical Learning Across Modalities and Domains in School-Aged Children Via an Online Platform and Neuroimaging Techniques
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Less is more: Local focus in continuous time causal learning.

Victor Btesh1, Neil R Bramley2, Maarten Speekenbrink1

  • 1Department of Experimental Psychology, University College London.

Journal of Experimental Psychology. Learning, Memory, and Cognition
|February 24, 2025
PubMed
Summary
This summary is machine-generated.

Humans actively learn causal structures by focusing on interventions and their immediate effects, simplifying complex data. This frugal strategy optimizes learning efficiency and reduces computational effort.

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Area of Science:

  • Cognitive Science
  • Psychology
  • Machine Learning

Background:

  • Human causal learning is complex, especially with continuous data.
  • Understanding how humans simplify complex causal structures is crucial for cognitive modeling.

Purpose of the Study:

  • Investigate human causal learning in continuous time and space.
  • Explore computational strategies for mitigating data complexity.
  • Examine the role of domain-specific priors in causal inference.

Main Methods:

  • Experimental investigation of human causal learning.
  • Computational modeling of learning strategies.
  • Analysis of data focusing on interventions and downstream effects.

Main Results:

  • Participants are capable active causal structure learners.
  • A task decomposition strategy, focusing on interventions, enhances learning efficiency.
  • Domain-specific priors improve accuracy by mitigating inference errors.

Conclusions:

  • Humans employ frugal, intuitive active learning strategies.
  • Combining systematic interventions with focused attention optimizes learning.
  • Prior knowledge significantly influences causal structure inference.