spreadr: An R package to simulate spreading activation in a network.

Area of Science:

• Cognitive Science
• Computational Linguistics
• Psychology

Background:

• Spreading activation is a key cognitive science concept, but computational tools are limited.
• Existing models often lack accessible implementations for network-based activation spread.

Purpose of the Study:

• Introduce the spreadr R package for computational spreading activation.
• Demonstrate its utility in modeling cognitive phenomena like word recognition and memory.
• Encourage explicit consideration of network structures in cognitive models.

Main Methods:

• Implemented spreading activation using a fixed cognitive resource model.
• Utilized the spreadr R package for simulations.
• Validated against prior findings on clustering effects and extended to false memory and semantic priming.

Main Results:

• Successfully reproduced clustering coefficient effects in spoken word recognition.
• Extended the model to explain higher false alarm rates for low clustering coefficient words in false memory tasks.
• Applied spreading activation to a semantic network to account for semantic priming effects.

Conclusions:

• The spreadr package provides a viable tool for implementing and testing spreading activation models.
• Spreading activation mechanisms can account for diverse cognitive effects across different tasks.
• Highlights the interplay between cognitive system structures and activation spread processes.