The HED Task Catalog is under development. IDs are not stable until formal release. Comments are welcome at github.com/hed-standard/hed-task/issues.

Probabilistic Classification Learning Task

HED task ID: hedtsk_probabilistic_classification_learning

Family: Conditioning, reinforcement and implicit learning tasks

Also known as: Weather Prediction, Probabilistic Classification Learning, Weather Prediction Task, WPT

Multi-cue probabilistic prediction of a binary outcome with trial-by-trial feedback; learning curves index gradual procedural category learning.

Description

The Weather Prediction Task presents 1-3 cues from a set of 4 possible cards; participants predict a binary outcome (rain/sunshine). Each cue is probabilistically associated with outcomes (e.g., 75%/25%), requiring participants to learn from statistical patterns. Feedback is provided after each prediction. Critically, amnesic patients with medial temporal lobe damage show intact learning despite impaired conscious awareness of the task structure, implicating basal ganglia and striatal systems in implicit probabilistic learning.

Inclusion test

An experiment is an instance of this task when its procedure matches, it manipulates at least one of the listed variables, and it records at least one of the listed measures.

Procedure	Participants predict an outcome (e.g., rain/sun) based on combinations of cues (cards), where each cue is probabilistically (not deterministically) related to outcomes. Learning is incremental across hundreds of trials.
Manipulation	Cue-outcome probability structure; number of cues per trial; feedback type (corrective, observational); concurrent vs. single task.
Measurement	Accuracy learning curve; optimal response rate; strategy analysis (multi-cue vs. single-cue); comparison to Parkinson patients (basal ganglia involvement).

Variations

Named versions that change what the participant experiences or does. The identifier of a variation is hedvar_<task>__<variation>.

Variation	Description	Justification
Standard Weather Prediction (4 cues, binary outcome) hedvar_probabilistic_classification_learning__standard_weather_prediction_4_cues_binary_outcome	Four cards with probabilistic associations to rain/sun.	Canonical probabilistic categorization with cue combinations
Deterministic Version hedvar_probabilistic_classification_learning__deterministic_version	100% cue-outcome mappings; becomes explicit rule learning.	Cues perfectly predict outcome; removes probabilistic uncertainty
Varying Probability Levels hedvar_probabilistic_classification_learning__varying_probability_levels	Different probabilistic strengths (60/40 to 90/10).	Cue-outcome reliability systematically manipulated; different learning statistics
Information-Integration Category Learning hedvar_probabilistic_classification_learning__information_integration_category_learning	Multi-dimensional continuous stimuli; optimal categorization requires integration across dimensions.	Categories defined by integration of dimensions; different decision rule
Rule-Based Category Learning (comparison) hedvar_probabilistic_classification_learning__rule_based_category_learning_comparison	Stimuli categorizable by single explicit rule; dissociates from implicit systems.	Explicit rule applicable; contrasts implicit vs. explicit learning systems
Feedback vs. Observation Learning hedvar_probabilistic_classification_learning__feedback_vs_observation_learning	Learning from trial-by-trial feedback vs. observing cue-outcome pairings.	Active feedback vs. observational learning; different learning mechanism
Transfer Test Variants hedvar_probabilistic_classification_learning__transfer_test_variants	New cue combinations or reversed contingencies after initial learning.	Probe generalization with novel cue combinations; different test phase

Cognitive processes

This task is designed to engage the following processes:

• Reinforcement learning

• Associative learning

• Categorization

• Strategy use

• Procedural memory

Key references

• Knowlton, B. J., Squire, L. R., & Gluck, M. A. (1994). Probabilistic classification learning in amnesia. Learning & Memory, 1(2), 106-120. (DOI, PubMed)

• Poldrack, R. A., Prabhakaran, V., Seger, C. A., & Gabrieli, J. D. (1999). Striatal activation during acquisition of a cognitive skill. Neuropsychology, 13(4), 564-574. (DOI, PubMed)

• Seger, C. A. (2008). How do the basal ganglia contribute to categorization? Their roles in generalization, response selection, and learning via feedback. Neuroscience & Biobehavioral Reviews, 32(2), 265-278. (DOI, PubMed)

Further references

• Knowlton, B. J., & Patterson, T. K. (2018). Habit formation and the striatum. Current Topics in Behavioral Neurosciences, 37, 275–295. (DOI)

• Meeter, M., Myers, C. E., Shohamy, D., Hopkins, R. O., & Gluck, M. A. (2006). Strategies in probabilistic categorization: Results from a new way of analyzing performance. Learning & Memory, 13(2), 230–239. (DOI, PubMed)

• Price, A. L. (2009). Distinguishing the contributions of implicit and explicit processes to performance of the weather prediction task. Memory & Cognition, 37(2), 210–222. (DOI, PubMed)

• Foerde, K., Knowlton, B. J., & Poldrack, R. A. (2006). Modulation of competing memory systems by distraction. Proceedings of the National Academy of Sciences, 103(31), 11778–11783. (DOI, PubMed)

External links

• Cognitive Atlas: Probabilistic classification task