Knowledge-based Vision Lab


feature encoding

What we know is shaped by our perceptual experiences. But how does our knowledge shape perception? To resolve this outstanding question, our research sheds light on how visual attention and visual expectations affect:

1) the efficiency with which our brain encodes natural image information

2) the speed with which our brain translates image information into meaningful concepts

3) the flow of visual information across the human cerebral cortexTo address these open questions, we deploy a variety of multivariate analysis techniques to fMRI and EEG data, including a multivariate measure of brain connectivity.

Furthermore, we use advanced psychophysical methods, facilitated by trained deep convolutional neural networks, to create natural images for which we can quantify how informative each image detail is for image recognition.

Coding efficiency in the human visual cortex

To reveal how knowledge allows our brain to better process sensory information, our objective is to determine if attention and expectation increase the efficiency with which image information is encoded in the human visual cortex. To this end we use trained deep constitutional neural networks to create 'feature-reduce' versions of natural images, and combine advanced psychophysical and fMRI methods to determine if expectation and attention increase the encoding of the most informative image features

Speeding up information processing in the human brain

To reveal how knowledge allows our brain to faster process sensory information, we will measure 'brain-speed' as the time it takes to transform visual sensations into meaningful concepts of what we are looking at (semantics). This we will realize by synergizing state of the art knowledge on 'semantic distances' between images and multivariate analysis of EEG response patterns.

Changing information flow between brain areas

To gain more insights into how knowledge changes perception, we will unravel how expectations and attention change the way in which information flows between brain areas. To this end, we will measure how these two processes alter the similarity with which brain areas encode a wide range of images. This we realize by using fMRI and pattern analysis to measure 'representational geometry' for all cortical brain areas. Furthermore, we will combine this data with EEG data to determine the direction of observed effects.

  • Staff
    • Visual neuroscience
    • Expectation
    • Attention
    • Image recognition

  • Alink, A., & Blank, H. (2021). Can expectation suppression be explained by reduced attention to predictable stimuli? NeuroImage, 231, 117824

    Tiedemann, L. J., Alink, A., Beck, J., Büchel, C., & Brassen, S. (2020). Valence encoding signals in the human amygdala and the willingness to eat. Journal of Neuroscience, 40(27), 5264-5272.

    Alink, A. & Charest (2020). Clinically relevant autistic traits predict greater reliance on detail for image recognition. Scientific reports 10(1), 1-9-

    Alink, A., Abdulrahman, H., & Henson, R. N. (2018). Forward models demonstrate that repetition suppression is best modelled by local neural scaling. Nature communications, 9(1), 1-10.

    Wimber, M., Alink, A., Charest, I., Kriegeskorte, N., & Anderson, M. C. (2015). Retrieval induces adaptive forgetting of competing memories via cortical pattern suppression. Nature Neuroscience, 18(4), 582.

    Alink, A., Krugliak, A., Walther, A., & Kriegeskorte, N. (2013). FMRI orientation decoding in V1 does not require global maps or globally coherent orientation stimuli. Frontiers in Psychology, 4,

    Carlson, T., Tovar, D. A., Alink, A., & Kriegeskorte, N. (2013). Representational dynamics of object vision: the first 1000 ms. Journal of vision, 13(10), 1-1.

    Alink, A., Euler, F., Kriegeskorte, N., Singer, W., & Kohler, A. (2012). Auditory motion direction encoding in auditory cortex and high‐level visual cortex. Human brain mapping, 33(4), 969-978.

    Staresina, B. P., Henson, R. N., Kriegeskorte, N., & Alink, A. (2012). Episodic reinstatement in the medial temporal lobe. Journal of Neuroscience, 32(50), 18150-18156.

    Alink, A., Schwiedrzik, C. M., Kohler, A., Singer, W., & Muckli, L. (2010). Stimulus predictability reduces responses in primary visual cortex. Journal of Neuroscience, 30(8), 2960-2966.

  • EU - ERC consolidator Grant: 2023 - 2028

    EU - Marie Skłodowska-Curie fellowship:2017 - 2019

    British Academy - Postdoctoral fellowship: 2013 - 2016

    Dutch Research Counsil (NWO) - Rubicon fellowship: 2010- 2012

  • We are currently hiring postdocs, PhD students and student assistants (e.g. Master Thesis)