Accumulating advantages: A new conceptualization of rapid multiple choice

Area of Science:

• Cognitive Science
• Computational Neuroscience
• Mathematical Psychology

Background:

• Independent racing evidence-accumulator models are standard for studying rapid decisions, especially binary choices.
• These models typically assume a one-to-one link between evidence accumulators and response options.

Purpose of the Study:

• To introduce and evaluate an alternative independent-race framework where multiple accumulators can link to a single response.
• To demonstrate this framework's ability to model complex decision phenomena beyond traditional models.

Main Methods:

• Utilized Brown and Heathcote's (2008) linear ballistic accumulator (LBA) to create a tractable "advantage LBA" measurement model.
• Applied the model to binary and multiple-choice decision tasks, analyzing benchmark phenomena.

Main Results:

• The advantage LBA model quantitatively accounts for phenomena that challenge traditional accumulator models.
• Successfully modeled effects of additive vs. multiplicative input changes in binary choice.
• Explained multiple-choice phenomena like lure stimulus strength and Hick's law.

Conclusions:

• The "advantage LBA" offers a flexible and mathematically tractable approach to modeling multi-choice decision dynamics.
• This framework provides a new avenue for understanding the nuances of complex decision-making processes.