An Account of Interference in Associative Memory: Learning the Fan Effect

Area of Science:

• Cognitive Psychology
• Computational Neuroscience
• Memory Research

Background:

• Associative learning influences human cognition, affecting memory recall through priming and interference.
• Existing models capture asymmetric item-to-item associations over time.

Purpose of the Study:

• To extend existing associative learning models by incorporating link maturation.
• To balance long-term stability with short-term variability in associations.
• To predict human response times and error rates in the fan effect.

Main Methods:

• Developed a computational model of associative learning with link maturation.
• Integrated the model with existing accounts of activation strengthening and decay.
• Tested the model's predictive accuracy against human data for the fan effect.

Main Results:

• The enhanced model successfully predicts human response times and error rates.
• Link maturation contributes to balancing short-term variability and long-term stability.
• The model accounts for performance on both target and foil stimuli in the fan effect.

Conclusions:

• The extended associative learning model provides a more comprehensive account of memory processes.
• Link maturation is a crucial factor in understanding the dynamics of associative memory.
• This framework advances computational models of human cognition and memory recall.