SAVE THE DATE: Aenne Brielmann (MPI für biologische Kybernetik) Gastvortrag
A computational model of sensory valuation
Where do you want to live? With whom? Do you like that image or swipe it away? Numerous decisions, big and small, partly depend on options’ sensory appeal. Yet, we have a poor understanding of how sensory experiences gain value and how these values influence our decisions. We propose that sensory value serves as a signal towards the goal of maintaining and adapting the states of their cognitive-sensory system in order to process stimuli effectively now and in the future. Two interlinked components generate an object’s sensory value: 1) processing fluency – the likelihood of a stimulus given an observer's state; 2) the change in fluency with which likely future stimuli will be processed – the change in the average likelihood of expected future stimuli.
A simple realization of this theory represents current and expected stimulus likelihoods as n-dimensional Gaussians. We test this model with an Instagram-like experiment where people both rate and browse through images. Our model can predict individual aesthetic judgments as well as their changes over time. What is more, our model can achieve this based on stimulus representations that are directly taken from convolutional neural networks. I will discuss the implications of these findings for the role of sensory valuation in shaping our sensory-cognitive system.
Zoom link: https://tinyurl.com/ncctalks
Aenne Brielmann is a postdoctoral researcher at the Max-Planck Institute for Biological Cybernetics in Tübingen, Germany. She received her BSc and MSc in Psychology from the University of Konstanz (2015) and her PhD from New York University (2020). Her doctoral thesis focused on explaining the experience of beauty from a quantitative, experimental perspective. Her current work tackles the even deeper question of why and how people value sensory experiences. At the moment, she develops a computational model of sensory value that consolidates theories and empirical data from psychology with reward-learning algorithms from machine learning.