Welcome to the fruendlab

Research#

A central motive of my work is the interplay between higher level visual processes and low level feature encoding. I study this by combining behavioural and neural measurements with computational modelling and quantitative characterizations of the environment in which an organism acts.

Recent highlights

Generative adversarial nets and perception#

When performing image manipulations in natural images, the images typically become less natural, making it difficult to relate results to natural behaviour. We solve this by using generative adversarial nets, which allows us to perform experimental manipulations that are constrained to remain within the manifold of natural images (or at least an approximation thereof).

• Fruend, Patel & Stalker (2019): Contrast invariant tuning in human perception of image content. bioRxiv, submitted for publication.
• Fruend (2019): Constrained sampling from deep generative image models reveals mechanisms of human target detection. bioRxiv, in revision.
• Fruend & Stalker (2018): Human Sensitivity to Perturbations Constrained by a Model of the Natural Image Manifold. J Vis, 18(11):20.

Shape perception and perceptual grouping#

Shape is one of the fundamental qualities of our visual experience, but little is known about the mechanisms that underly shape representation in the visual system. In a number of recent studies, we tried to shed light on the mechanisms that mediate human perception of planar shape.

• Wilder, Fruend & Elder (2018): Frequency tuning of shape perception revealed by classification image analysis. J Vis, 18(9) doi:10.1167/18.8.9.
• Elder, Oleskiw & Fruend (2018): The Role of Global Cues in the Perceptual Grouping of Natural Shapes. J Vis, 18, 14.
• Schmidtmann & Fruend (2019): Radial frequency patterns describe a small and perceptually distinct subset of all possible planar shapes. Vision Res, 154, 122-130.

Software#

• We had originally implemented just the extended signal detection theory framework by Sebastian and Geisler, but it now turns out that our python package esdt is a good place to collect a number of different analysis tools for psychophysical data.
• During a hackweek project, we developed vyu, a simple webcam based eye tracker for fixation control. Given that this was done as a one week fun project, the project is currently not really usable for third parties. Yet, that will change in the future.
• Our lab is on github, where we host a number of more or less useful tools and code pieces.

Teaching#

In the Fall term 2019, I am teaching "Sensation and Perception" (PSYC 2220B) and "Intermediate Research Methods" (PSYCH 3010). In the Winter term 2020, I will teach a graduate course on computational neuroscience under the title "Principles of Neural Coding".

Sensation and Perception

In "Sensation and Perception" we study how our mind creates the experience of a coherent world from the physical phenomena that arrive at our receptors. After a brief introduction to perception in general, the course focusses on vision from both a behavioural as well as a neuroscientific perspective.

Assessment of learning progress is achieved through a combination of brief, weekly online tests and a written final exam in which students summarize their learning outcomes.

Intermediate Research Methods

In "intermediate research methods", we address questions about operationalization of theoretical concepts and the formulation of a research question and we discuss aspects of planning an experiment, proper experimental control, and appropriate embedding of a study in the context of prior research. Students practise these skills by designing and executing a small research study of their own.

Assessment of learning progress is achieved through a series of written assignments that cover the study's motivation, design, critique of other students' writing, and discussion of results and limitations.

Principles of Neural Coding

What are the rules that allow our brain the process sensory input, make decisions, and form actions? How can populations of neurons represent and process information? In this course, we study these questions from a computational perspective, asking questions about information processing and computational principles. Students practise these concepts in a series of analytical and programming exercises and they read and discuss recent papers from the field.

People#