Andrea Liberman received the designation of Highest Honors for her thesis titled “Charitable Giving: How Framing Amount Raised Influences Donations”. Congratulations Andrea!
In a new paper accepted at Psychonomic Bulletin & Review, we examine different types of biases and how they influence intertemporal choices. Dual process theories of intertemporal decision making propose that decision makers automatically favor immediate rewards. In this paper, we use a drift diffusion model to implement these theories, and empirically investigate the role of their proposed automatic biases. Our model permits automatic biases in the response process, in the form of a shifted starting point, as well as automatic biases in the evaluation process, in the form of an additive drift rate intercept. We fit our model to individual-level choice and response time data, and find that automatic biases (as measured though the starting point and drift rate intercept in our model) are prevalent in intertemporal choice, but that the type, magnitude, and direction of these biases vary greatly across individuals. Our results pose new challenges for theories of intertemporal choice behavior.
A new paper accepted at Behavior Research Methods examines parameter recovery for time-variant evidence accumulation models. Evidence accumulation models have been one of the most dominant modeling frameworks used to study rapid decision-making over the past several decades. These models propose that evidence accumulates from the environment until the evidence for one alternative reaches some threshold, typically associated with caution, triggering a response. However, researchers have recently begun to reconsider the fundamental assumptions of how caution varies with time. In the past it was typically assumed that levels of caution are independent of time. Recent investigations have however suggested the possibility that levels of caution decrease over time and that this strategy provides more efficient performance under certain conditions. This paper provides the first comprehensive assessment of this newer class of models accounting for time varying caution to determine how robustly their parameters can be estimated.
Check out the preprint of our new paper on the importance of including response time data in the analysis and comparison of dynamic models of context effects. Using state-of-the-art response time modeling and data from 12 different experiments appearing in 6 different published studies, we compared four previously proposed models of context effects: Multi-alternative Decision Field Theory (MDFT), the Leaky-Competing Accumulator (LCA), the Multi-attribute Linear Ballistic Accumulator (MLBA), and the Associative Accumulation Model (AAM). Our results show that response time data is critical at distinguishing among these models and that using choice data alone can lead to inconclusive results for some datasets. The paper will appear in Psychonomic Bulletin & Review.
Check out our new paper titled “A quantum probability account of individual differences in causal reasoning” in the Journal of Mathematical Psychology:
In this paper, we use quantum probability theory to investigate individual differences in causal reasoning, analyzing datasets from Rehder (2014) on comparative judgments and from Rehder and Waldmann (2016) on absolute judgments. We show that a quantum probability model can both account for individual differences in causal judgments, and why these judgments sometimes violate the properties of causal Bayes nets.