Modeling the Absence of Framing Effect in an Experience-based Covid-19 Disease Problem

Prior research in decisions from experience (DFE) has investigated people’s consequential decisions after information search both experimentally and computationally. However, prior DFE research has yet to explore how computational cognitive models and their mechanisms could explain the effects of problem framing in experience. The primary objective of this paper is to address this literature gap and develop Instance-based Learning Theory (IBLT) models on the effects of problem framing. Human data was collected on a modified form of the Asian disease problem posed about the COVID-19 pandemic across two between-subject conditions: gain (N = 40) and loss (N = 40). The COVID-19 problem was presented as “lives saved” in the gain condition and “lives lost” in the loss condition. Results revealed the absence of the classical framing effect, exhibiting no preference reversal between gain and loss conditions in experience. Next, an IBL model was developed and calibrated to the data obtained in the gain and loss problems. The calibrated model was generalized to the non-calibrated conditions (gain to loss and loss to gain). An IBL model with ACT-R default parameters was also generalized. Results revealed that the IBL model with calibrated parameters explained human choices more accurately compared to the IBL model with ACT-R default parameters. Also, participants showed greater reliance on recency and frequency of outcomes and less variability in their choices across both gain and loss conditions. We highlight the main implications of our findings for the cognitive modeling community.