The Bernoulli Network for the Behavioural Sciences is proud to announce that the 9th Bernoulli Lecture for the Behavioral Sciences will be held by Prof. Dr. Angela Duckworth, on the topic of “Strategic Self-control”, and take place October 24th, 2019, 18:15, at the Bernoullianum, Bernoullistrasse 30, Basel.

You can find more information about the event on the Bernoulli Network website as well as a recent interview with Prof. Duckworth here.

This week, Mandy Hütter (Eberhard Karls Universität Tübingen) visits to give a presentation (Thursday 17 October).

A sampling approach to evaluative conditioning (Mandy Hütter & Max Ihmels, Eberhard Karls Universität Tübingen)

A key paradigm in research on the development of preferences is evaluative conditioning (EC). In an EC procedure, a conditioned stimulus (CS) is paired with positive or negative unconditioned stimuli (USs) and consequently acquires US valence. In our sampling approach to EC, we give participants control over which CSs they want to sample (i.e., for what CSs they want to see conditioning trials). To distinguish between the effects of sampled information and sampling decisions, we also introduced a yoked condition, in which participants saw the same information produced by the sampling condition, but did not get to make any decisions. I will present four experiments that established the basic paradigm (Experiments 1 and 2), investigated the effect of not sampling a stimulus on evaluations (Experiment 3), and investigated the role of hedonic versus epistemic motivation on sampling and its effects on evaluations (Experiment 4). These experiments demonstrate several mechanisms via which agency can alter evaluative learning and highlight avenues for future research.

The SWE colloquium talk on Thursday 10 October will be given by

  Stefan Mayer, University of Tübingen, and

  Jan Landwehr, Goethe University Frankfurt.

Capturing determinants of processing fluency using deep neural networks to predict consumer behaviour

In recent years, the processing fluency framework has been proven to be a very powerful theoretical model to understand a wide variety of phenomena, especially in social and consumer psychology. At the same time, the extraction of low-level visual information from images has made significant progress and now allows the extraction of objective measures of determinants of processing fluency such as symmetry, contrast, simplicity, or prototypicality from any digital image. In the present project, we will show how new algorithms based on deep neural networks can improve the measurement of visual similarity and visual typicality. In particular, we show that our new measure can be employed to predict liking judgments and purchasing behaviour of cars in line with the predictions of the processing fluency framework. On this basis, we discuss to what extent deep neural networks are an adequate model of human visual perception and offer the potential to improve our understanding of perceptual processes.