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Related Concept Videos

Randomized Experiments01:13

Randomized Experiments

The randomization process involves assigning study participants randomly to experimental or control groups based on their probability of being equally assigned. Randomization is meant to eliminate selection bias and balance known and unknown confounding factors so that the control group is similar to the treatment group as much as possible. A computer program and a random number generator can be used to assign participants to groups in a way that minimizes bias.
Simple randomization
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Sampling Plans01:23

Sampling Plans

Sampling is a crucial step in analytical chemistry, allowing researchers to collect representative data from a large population. Common sampling methods include random, judgmental, systematic, stratified, and cluster sampling.
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Avoidance Learning and Learned Helplessness

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Purposive Learning01:22

Purposive Learning

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Observational Learning01:12

Observational Learning

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 because...
Incentive Theory: Pull Theory of Motivation01:18

Incentive Theory: Pull Theory of Motivation

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Related Experiment Video

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Studying Food Reward and Motivation in Humans
12:09

Studying Food Reward and Motivation in Humans

Published on: March 19, 2014

Beyond "incentive hope": Information sampling and learning under reward uncertainty.

Maya Zhe Wang1, Benjamin Y Hayden2

  • 1Department of Brain and Cognitive Sciences and Center for Visual Sciences,University of Rochester,Rochester,NY 14627.

The Behavioral and Brain Sciences
|April 4, 2019
PubMed
Summary

Reward uncertainty drives seeking and consumption, but the "incentive hope" mechanism is not the sole explanation. Naturalistic tasks can clarify motivational processes linking learning and foraging behaviors.

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Area of Science:

  • Behavioral neuroscience
  • Cognitive psychology
  • Animal behavior

Background:

  • Reward uncertainty is a key factor influencing motivated behavior.
  • Existing models, like "incentive hope," offer partial explanations for reward seeking.
  • The interplay between learning, curiosity, and reward seeking requires further elucidation.

Purpose of the Study:

  • To evaluate the necessity and sufficiency of the "incentive hope" mechanism in explaining reward uncertainty's effects.
  • To explore how naturalistic and foraging-like tasks can dissect motivational processes.
  • To identify the neural underpinnings of behaviors bridging learning and foraging.

Main Methods:

  • Theoretical analysis of the "incentive hope" mechanism.
  • Proposal of naturalistic and foraging-like tasks for empirical investigation.
  • Focus on parsing motivational processes and their neural correlates.

Main Results:

  • The "incentive hope" mechanism is sufficient but not necessary to explain reward seeking under uncertainty.
  • Naturalistic tasks offer a promising avenue for understanding complex motivational dynamics.
  • These tasks can help bridge the gap between learning and foraging behaviors.

Conclusions:

  • The "incentive hope" model provides a framework but does not encompass all factors driving reward seeking.
  • Investigating motivational processes through naturalistic tasks is crucial for a comprehensive understanding.
  • Further research is needed to identify the neural basis of these integrated behaviors.