Sudeep Bhatia, University of Pennsylvania, will give a presentation via Zoom in this week’s Social, Economic, and Decision Psychology research seminar (Thursday 1 April, 16:00-17:00).

Process and content in decisions from memory

Information stored in memory influences the formation of preferences and beliefs in most everyday decision tasks. The richness of this information, and the complexity inherent in interacting memory and decision processes, makes the quantitative model-driven analysis of such decisions very difficult. In this paper we present a general framework that can address the theoretical and methodological barriers to building formal models of naturalistic memory-based decision making. Our framework implements established theories of memory search and decision making within a single integrated cognitive system, and uses computational language models to quantify the thoughts over which memory and decision processes operate. It can thus describe both the content of the information that is sampled from memory, as well as the processes involved in retrieving and evaluating this information in order to make a decision. Furthermore, our framework is tractable, and the parameters that characterize memory-based decisions can be recovered using thought-listing and choice data from existing experimental tasks, and in turn be used to make quantitative predictions regarding choice probability, length of deliberation, retrieved thoughts, and the effects of decision context. We showcase the power and generality of our framework by applying it to naturalistic binary choices from domains such as risk perception, consumer behavior, financial decision making, ethical decision making, legal decision making, food choice, and social judgment.

Supporting literature:

Zhao, W. J., Richie, R., & Bhatia, S. (2019). Process and content in decisions from memory.

Die Fachgruppe Psychologie präsentiert: Zoom-Workshop aus der Reihe „Rein in die Praxis„ (13.04.2021 16:15-17:45) 

Link: https://unibas.zoom.us/j/5298344739

Scott Brown, School of Psychology, The University of Newcastle, Australia, will give a presentation via Zoom in this week’s Social, Economic, and Decision Psychology research seminar (Thursday 25 March, 10:15-11:15).

From bench to bedside in cognitive science

The phrase bench to bedside describes research—often medical—which has elements across the continuum from very basic science (bench) through to direct application with end-users (bedside). This approach to research is increasingly demanded by funding agencies and other bodies, at least where I work. I will describe efforts I have made to get a bit closer to the bedside end of the continuum, with applications in clinical populations and with defence force personnel. The applications focus on simple decision-making, and use insights gained from the basic cognitive science and computational modelling work to make real improvements for end users.

Helene Willadsen, University of Copenhagen, will give a presentation via Zoom in this week’s Social, Economic, and Decision Psychology research seminar (Thursday 11 March, 12:00-13:00).

*Falk, A., Becker, A., Dohmen, T., Huffman, D., & Sunde, U. (2016). The preference survey module: A validated instrument for measuring risk, time, and social preferences. IZA Discussion Papers No. 9674.

The first talk in the Social, Economic, and Decision Psychology research seminar will be given at 12:00 on Thursday 4 March by Ori Plonsky of the Technion Israel Institute of Technology. The talk will be totally virtual, streamed live through Zoom.

Similarity-based learning and the wavy recency effect of rare events

Many behavioral phenomena can be the product of a tendency to rely on small samples of past experiences. Previous studies suggest that this can be a product of a cognitively efficient tendency to rely on the most recent outcomes. Congruently, the most popular models of learning assume that people mostly rely on a few recent outcomes. In this talk, I will review research suggesting a very different explanation: People rely on a small set of the most similar past experiences. My investigation explores settings of repeated binary choice with feedback. I will present a model, designed for these settings, that judges similarity as a function of sequential patterns of outcomes. A computational analysis shows this model can be extremely effective across wide classes of dynamic decision settings (more effective than basic reinforcement learning models).It further shows that in static settings with rare events the model predicts a unique wavy recency pattern. Empirical analysis of multiple datasets (including decisions from experience with full or with partial feedback, probability learning tasks, and repeated decisions under risk with feedback) support this wavy recency prediction, a pattern that was ignored by prior research and violates the basic assumption of recency in popular learning models. a pattern that was ignored by prior research and violates the basic assumption of recency in popular learning models. a pattern that was ignored by prior research and violates the basic assumption of recency in popular learning models.

Supporting literature: Plonsky, O., Teodorescu, K., & Erev, I. (2015). Reliance on small samples, the wavy recency effect, and similarity-based learning. Psychological Review, 122(4), 621.