Solving the Credit Assignment Problem: The Interaction of Explicit and Implicit Learning with Internal and External State Information

Abstract

In most problem-solving activities, feedback is received at the end of an action sequence. This creates a credit-assignment problem where the learner must associate the feedback with earlier actions, and the interdependencies of actions require the learner to either remember past choices of actions (internal state information) or rely on external cues in the environment (external state information) to select the right actions. We investigated the nature of explicit and implicit learning processes in the credit-assignment problem using a probabilistic sequential choice task with and without external state information. We found that when explicit memory encoding was dominant, subjects were faster to select the better option in their first choices than in the last choices; when implicit reinforcement learning was dominant subjects were faster to select the better option in their last choices than in their first choices. However, implicit reinforcement learning was only successful when distinct external state information was available. The results suggest the nature of learning in credit assignment: an explicit memory encoding process that keeps track of internal state information and a reinforcement-learning process that uses state information to propagate reinforcement backwards to previous choices. However, the implicit reinforcement learning process is effective only when the valences can be attributed to the appropriate states in the system either internally generated states in the cognitive system or externally presented stimuli in the environment.

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Document Details

Document Type
Technical Report
Publication Date
Jan 01, 2006
Accession Number
ADA459074

Entities

People

  • John R. Anderson
  • Wai-tat Fu

Organizations

  • University of Illinois Urbana–Champaign

Tags

DTIC Thesaurus Topics

  • Abstracts
  • Brain
  • Classification
  • Coding
  • Cognition
  • Cognitive Science
  • Environment
  • Feedback
  • Information Processing
  • Learning
  • Office Buildings
  • Probability
  • Psychological Phenomena And Processes
  • Psychology
  • Reinforcement Learning
  • Sequences
  • Universities

Fields of Study

  • Psychology

Readers

  • Neural Network Machine Learning.
  • Systems Analysis and Design
  • Team-Based Human-Centered Cognitive Task Decision Making and Information Performance.

Technology Areas

  • AI & ML
  • AI & ML - Machine Learning Algorithms
  • AI & ML - Neural Networks