Contextual Sensitivity in Naturalistic Multi-alternative Choice
Decades of research have been dedicated to understanding context effects (attraction, compromise, and similarity) in multi-alternative, multi-attribute choice. Most studies have used laboratory tasks with artificial stimuli. For example, choices among apartments with attributes represented numerically (i.e., 10 miles to work). However, when participants are shown more naturalistic stimuli (e.g., photos of apartments), the effects often disappear (Frederick, Lee, & Baskin, 2014). Thus, researchers have argued that context effects are an artifact of artificial stimuli and do not occur in naturalistic choices (Frederick et al., 2014; Yang & Lynn, 2014; Trendl, Stewart, & Mullett, 2021). However, the absence of context effects does not imply the absence of contextual sensitivity. Context-dependent behavior occurs whenever the evaluation of an option is dependent on the other options, often defined as a violation of simple scalability. We take a joint experimental and computational modeling approach to address whether naturalistic decisions demonstrate contextual sensitivity. One of the critical limitations for using computational cognitive models to study naturalistic decision making is that these models require quantitative representations of stimuli. In the past, obtaining representations of naturalistic stimuli has been challenging. I will describe how machine learning models can be coupled with cognitive models to overcome this limitation and help resolve the issue of contextual sensitivity in naturalistic multi-alternative choice.