Response-time data provide critical constraints on dynamic models of multi-alternative, multi-attribute choice

Area of Science:

• Cognitive psychology
• Neuroscience
• Decision economics

Background:

• Multi-alternative, multi-attribute choice is a key area of study across psychology, neuroscience, and economics.
• Previous research primarily used choice data, yielding inconclusive results for comparing decision models.
• Dynamic process models require evaluation with both choice and response-time data.

Purpose of the Study:

• To quantitatively compare four prominent dynamic process models of multi-alternative, multi-attribute choice.
• To assess the utility of response-time data in distinguishing between these cognitive models.
• To address the inconclusive findings from previous choice-data-only studies.

Main Methods:

• Utilized state-of-the-art response-time modeling techniques.
• Analyzed data from 12 experiments across six published studies.
• Compared four models: multi-alternative decision field theory (MDFT), leaky-competing accumulator (LCA), multi-attribute linear ballistic accumulator (MLBA), and associative accumulation model (AAM).

Main Results:

• Response-time data proved critical for differentiating between the evaluated cognitive models.
• Choice data alone led to inconclusive results for certain experimental datasets.
• The study highlights the limitations of relying solely on choice data for model comparison.

Conclusions:

• Response-time data is essential for robustly evaluating and distinguishing between dynamic process models of decision-making.
• Future research should incorporate response-time data to advance understanding of cognitive choice processes.
• This approach offers a more definitive method for comparing theories in decision science.