Discriminating between models of processing in multi-attribute choice
Throughout the day, many of our choices integrate information from multiple attributes about an item we are considering. How do people process information about multiple attributes and choose whether to select a presented option? In the simplest scenario, for one option and two attributes, the decision to either accept or reject the option is based on combinations of the two attributes. Our model represents the evidence from each attribute towards accepting or rejecting the option as an accumulation process. We can model how the participant could combine this information into the final choice as combinations of these racing accumulators. For example, people may reject an option based on a single poor attribute but only accept the option if both attributes are highly valued. We constructed five different processing architectures and integrated them into a latent mixture modelling process to select between them. We use a hierarchical Bayesian approach to estimate individual participant processing architectures and overall group trends. I will show an initial assessment of our modelling framework using data simulated from the five processing architectures. I will also discuss an experimental task where participants viewed a series of hotel options that differ on two attributes - price and hotel rating. In this task, participants received instructions on how to combine the attribute information for their decisions. The modelling framework recovered the expected processing architectures for the different instruction manipulations, demonstrating good selective influence. Understanding consumer attribute processing helps us present information in such a way as to keep consumers as informed as possible about the consequences of their choices.
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