Evaluating the Generalizability of Diverse Models of Interference Effects

Uncertainty can lead to violations of both “rational” decision-making (Tversky & Kahneman, 1974) and the laws of classical probability (CP; Busemeyer et al., 2011). One type of violation – interference effects – occurs when a marginal decision distribution depends on the presence or absence of a preceding category judgment (Wang & Busemeyer, 2016). Interestingly, recent studies have shown that interference effects emerge under some preceding categorization conditions but not others, resulting in a critical asymmetry (Busemeyer et al., 2009; Wang & Busemeyer, 2016). Models based on the formalism of quantum probability (QP) provide a good accounting of this unusual pattern in the data (Busemeyer et al., 2009; Wang & Busemeyer, 2016). More recently, we have defined models based on CP augmented with ancillary mechanisms that can account for interference effects in the data, in general, but not necessarily the critical asymmetry (anonymous 1; anonymous 2). Given these varied resolutions to violations of CP, an important question warrants further investigation: what is the generalizability of these models and the efficacy of their mechanisms under conditions of novel stimulus types and decisions? In the current study, we probe questions about generalizability by using non-human-stimuli to control for the potential confounding effects of pre-existing associations with human faces within the categorization-decision paradigm used by Wang and Busemeyer (2016). We further assess the influence of bias on interference effects by leveraging a pre-existing bias based on preferences for sophisticated, in comparison to simplistic, mind-types (Almaraz et al, 2018; Dennett, 1996; Epley & Eyal, 2019; Waytz et al., 2010) and a resource allocation task with a clear link to mind-type preferences (Dennett, 1996; Waytz et al., 2010). We expect our study to identify the extent to which interference effects, along with the critical asymmetry, emerge in the new data reflecting a pre-existing bias, and to determine whether three proposed models (using quantum cognition, ACT-R, and a multinomial processing tree) can account for the new findings. Ultimately, we expect our study to inform the degree to which a set of current models of interference effects generalize for biased beliefs and, by extension, to provide insights into processes underlying interference effects in other contexts.