ACT-R Model of Cognitive Arithmetic: Simulation Data and Analysis

Christian Lebière's thesis at Carnegie Mellon University presents an integrated computational model of cognitive arithmetic using the ACT-R architecture. The model simulates human error and latency patterns in arithmetic tasks, analyzing results through separate simulations, a lifetime learning simulation, and formal parameter analysis. It provides a unifying account of cognitive arithmetic results and explores implications for teaching.

Use Cases

• Modeling human error patterns in arithmetic based on the described simulation of cognitive arithmetic.
• Analyzing the dynamics of lifetime arithmetic learning based on the described Bayesian learning mechanisms.
• Investigating parameter sensitivity in cognitive models based on the described formal and empirical parameter analyses.
• Benchmarking general-purpose cognitive architectures like ACT-R based on the described task of cognitive arithmetic.

Strengths

• Model is based on the ACT-R architecture, a well-established general-purpose cognitive modeling framework.
• Analysis includes multiple approaches: separate result simulations, a lifetime learning simulation, and formal parameter analysis.
• Thesis provides a unifying account of main results in the field of cognitive arithmetic.

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Carnegie Mellon University

Collection Method

Computational modeling and simulation using the ACT-R architecture.