wcst_engine

Goal

A task engine to evaluate artificial learning agents and learning strategies on the WCST. Allow “users (agents that learn or implement specific strategies” to play the WCST using this engine. This will hopefully enable:

• Studying how artificial learning agents develop their own strategies, and how they use “internal models” to solve the task
• Studying how artificial agents implementing different specific strategies perform in different task contexts, enabling comparison to monkey/human strategy and performance.

Engine Features

• Agents/Users must be able to take certain actions, such as:
  • Make selections, and in turn get rewards
  • Observe cards
  • Report progress
  • (Cheating) Peak at the Rule!
• Implementation is flexible, can adjust:
  • Block switching conditions
  • Reward/Penalty values
  • How next cards are determined
  • How next rules are determined

Differences from WCST in experimental setting

• Modeling Rewards/Timeout as positive or negative value
• No concept of time (yet?)
• No concept of card positioning,
• Agnostic to what the features actually are visually, visual concept of feature dimensions

Usage

See test_wcst_session.ipynb notebook for full demo of usage

from wcst_session import WcstSession
sess = WcstSession(correct_value=10, incorrect_value=-4, random_seed=42)

sess.get_cards_text()
"""
Output:
array([['TRIANGLE', 'YELLOW', 'ESCHER'],
       ['STAR', 'MAGENTA', 'SWIRL'],
       ['SQUARE', 'CYAN', 'RIPPLE'],
       ['CIRCLE', 'GREEN', 'POLKADOT']], dtype='<U8')
"""

sess.make_selection(2)
"""
Output: 
(True, 10)
"""