T. Weidemann

Christoph T. Weidemann

Honorary Associate Professor at
Swansea University [link]
Department of Psychology [link]

Currently doing research in industry.
This page features only work I can share publicly.
E-mail: [e-mail]

Research Interests

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Cognitive processes, such as those involved in perception, memory, and decision making, are highly context dependent. Previous experiences, expectations, and goals all shape how sensory input is transformed into percepts, how memories are stored and retrieved and how available information is evaluated to guide behavior. This feature of human information processing is fascinatingly pervasive and can be easily experienced, especially in cases when it leads to errors. For example, it is often difficult to identify a familiar face outside of its usual context ("the butcher on the bus" phenomenon) and the the erroneous repetition of written words often goes unnoticed ("repetition blindness"; an example is embedded in this very sentence). Despite leading to errors in some cases, the integration of context with current processing is integral to cognition because it constitutes the foundation for learning and adaptive behavior. My research investigates how context shapes human information processing. To this end I measure accuracy and speed of overt behavior as well as activity in the human brain as assessed with tools such as electroencephalography (EEG), magnetoencephalography (MEG) and direct recordings from electrodes that are implanted in the brains of neurosurgery patients. I use statistical and computational models to account for overt behavior and brain activity in an effort to precisely characterize cognitive processes.

Brief vita

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July, 2022 – present:
Data Scientist
Meta, New York, NY, USA
August, 2021 – present:
Honorary Associate Professor at the Department of Psychology [link],
Swansea University, Wales, UK
March, 2023 – May, 2023:
Visiting Associate Professor at the Department of Global Studies,
Duke University
December, 2019 – July, 2022:
Associate Reseach Scientist at the Department of Biomedical Engineering [link],
Columbia University, USA
March, 2010 – July, 2021:
Lecturer – Associate Professor at the Department of Psychology [link],
Swansea University, Wales, UK
July, 2019 – December, 2019:
Visiting Scholar at the Computational Memory Lab [link],
Department of Psychology, University of Pennsylvania, USA
August, 2015 – July, 2018:
Visiting Scholar at the Computational Memory Lab [link],
Department of Psychology, University of Pennsylvania, USA
October, 2006 – March, 2010:
Post-doctoral research fellow at the Computational Memory Lab [link],
Department of Psychology, University of Pennsylvania, USA; supervisor: Prof. Michael J. Kahana [link]
August, 2006:
PhD in psychology [link] and cognitive science [link] (minor in neuroscience).
Indiana University, Bloomington, USA; adviser: Prof. Richard M. Shiffrin [link]
Spring & Summer, 2004:
Pre-doctoral research fellow at the Center for Adaptive Behavior and Cognition [link],
Max Planck Institute for Human Development, Berlin, Germany
September, 2002:
Diplom (German degree similar to MS/MA/MSc) in psychology.
University of Bonn, Germany [link]

Publications & Manuscripts

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(my profile on Google Scholar [link])

I had to transfer the copyright for some of the articles listed below to the publishers of the journals in which they appeared. However, I am allowed to distribute copies to individuals for personal and/or research use. Your click on any of the links below constitutes your request to me for a personal copy of the linked article. A detailed copyright notice appears in the articles. Nature's web debates published an interesting relevant article by Richard M. Stallman titled "Science must `push copyright aside´" [link].















Quality links

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Selected colleagues and collaborators:

Great software for science:

Below I am posting links to a few selected programs (not written by me) that I find particularly useful for scientific work. All programs linked below are free in the sense that anyone may download, install, use, modify, and re-distribute them (detailed information can be found on the respective websites linked below). This freedom is particularly valuable for scientific work, because it allows the free sharing of one's work with collaborators, colleagues, students, or anyone else without requiring permission of the copyright holder of the associated program. All the programs linked below run on a variety of platforms such as Linux, Windows, and MacOS.

The Python programming language [link]
A nice object oriented programming language, well suited for scientific computing. Various libraries cover a wide range of possibly applications. Of particular interest are Substantial documentation is available on the Python documentation website [link] and the websites of the respective libraries.
The R project for statistical computing [link]
A powerful software environment for statistical computing and graphics. Relevant related software includes
LaTeX [link]
A high-quality document preparation and typesetting system optimized for technical and scientific documents. Also useful for creating presentations and posters.
Unison [link]
A great file synchronizer. Not directly science related, but useful for anybody who regularly uses more than one computer and wants to keep them synchronized.