Welcome to the fruendlab

This page is currently under construction.
For now, please visit my old website before working at York University

Research

My previous research has combined behavioural and neural measurements with computational modelling and quantitative characterizations of the environment in which an organism acts. A central motive of my work is the interplay between higher level visual processes and low level feature encoding. This process often operates in a recurrent fashion.

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Teaching

In the fall term 2017, I will be teaching "Sensation and Perception" (PSYC 2220) and "Intermediate research methods" (PSYC 3010). More detail will follow closer to September.

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Contact

Rm 003F, Lassonde Bldg
4700 Keele St
Toronto, ON
M3J 1P3

Office: +1 416 736 2100 ext. 22932

Research

My previous research has combined behavioural and neural measurements with computational modelling and quantitative characterizations of the environment in which an organism acts. A central motive of my work is the interplay between higher level visual processes and low level feature encoding. This process often operates in a recurrent fashion. For example, I showed that paying attention to different task requirements can modulate some of the first cortical responses to a visual stimulus (e.g. Fründ et al, 2008), or that the temporal context in which a simple visual stimulus is presented influences how an observer responds to that stimulus (Fründ et al, 2014). Stimulus representations at intermediate levels of the visual hierarchy are ideal to study this interplay of low level feature encoding and high level visual cognition. I was able to demonstrate that the visual system of primates is remarkably well adapted to the statistical properties of curvature in natural images (Fründ & Elder, 2013, 2014). Yet, a coherent shape is more than a lose collection of curved line segments and it is likely that recurrent processing is a core neural mechanism for representing the complex and highly non-linear inter-dependencies in natural images that we perceive as objects and shapes.

Computations in human perception

Understanding the human visual system becomes harder and harder as tasks and stimuli become more complex. Despite limited understanding of the human visual system, recent advances in machine vision have allowed artificial systems to achieve impressive performance in complex visual tasks such as large scale categorization or image captioning. This is made possible by using a class of very flexible machine learning algorithms referred to as artificial neural networks, and in particular by developments that allow these algorithms to mimic some of the hallmarks of processing in the brain, such as recurrent processing, attention and working memory.

My current research focusses on the question what human perception can learn from the success of these models and how learnings from these models can be applied to gaining a deeper understanding of human perception and the neural processes underlying it.

Shape perception

If necessary, the perception of objects from simple shapes can be very quick. As part of my doctoral thesis, I observed that even one of the very first responses of the human electroencephalogram discriminates between meaningful and meaningless shapes.

However, what exactly makes a "shape"? What features do observers use to answer this question? I address these questions in a current collaboration with James Elder. With James, we developed a class of generative statistical models for shapes that occur in natural images, such as photographs. We can adapt these models to match natural shapes with respect to a well defined set of features and be maximally random otherwise. Being generative, these models allow us to generate synthetic shapes, that match natural shapes with respect to the features represented by the distribution. Using psychophysical measurements and ideal observer modeling, we could show that humans are sensitive to local contour properties of shapes but most likely also use global properties of shapes to discriminate between different shapes and to segment coherent shapes from random backgrounds.

Modeling behavioral nonstationarity

Behavioral studies of visual perception typically present a sequence of images to an observer (by observer, vision scientists mean either a human or potentially also an animal). Observers often report that they adapt their response behavior over the time course of a psychophysical experiment. In other words, the response of an observer depends on the image that the observer currently sees and and things that happened so far—including other responses—in the experiment.

This differs from one of the standard assumptions in virtually all models for visual perception: These models assume that all responses in an experiment are *independent* realizations of a respective random variable. We used one of the simplest models for visual perception to study the impact of the violation of this independence assumption. The psychometric function is routinely used in psychophysical studies of visual perception to quantify sensitivity or bias of observers. We showed that these violations may indeed result in incorrect inference on psychometric functions and we propose a very generic way to correct for these errors.

More recently, we built a model that combines both, the current stimulus and events on previous trials. This model allows us to tear apart the effects from the current stimulus and events on previous trials. We observe that effects from previous trials are very heterogeneous but at the same time very strong: On difficult trials, the recent experimental history is nearly as good a predictor as the current trial. This contradicts the naive assumption that our perception is mainly a representation of the environment. It suggests that our perception is rather a combination of the world around us and our own assumptions and expectations of this world.

Teaching

In the fall term 2017, I will be teaching "Sensation and Perception" (PSYC 2220) and "Intermediate research methods" (PSYC 3010). More detail will follow closer to September. I have received emails asking about the suggested reading for the "Sensation and Perception" class. That class will mostly be based on the 10th edition of the book "Sensation and Perception" by Goldstein.

Office hours by appointment.