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.
Checkout out our new paper on medial image decision-making. In this paper, we use a joint experimental and computational modeling approach to examine the similarities and differences in the cognitive processes of novice participants and experienced participants (pathology residents and pathology faculty) in cancer cell image identification. For this study we collected a bank of hundreds of digital images that were identified by cell type and classified by difficulty by a panel of expert hematopathologists. The key manipulations in our study included examining the speed-accuracy tradeoff as well as the impact of prior expectations on decisions. In addition, our study examined individual differences in decision-making by comparing task performance to domain general visual ability (as measured using the Novel Object Memory Test (NOMT) (Richler et al. Cognition 166:42–55, 2017). Using signal detection theory and the diffusion decision model (DDM), we found many similarities between experts and novices in our task. While experts tended to have better discriminability, the two groups responded similarly to time pressure (i.e., reduced caution under speed instructions in the DDM) and to the introduction of a probabilistic cue (i.e., increased response bias in the DDM). These results have important implications for training in this area as well as using novice participants in research on medical image perception and decision-making.