The Department of Sensory and Sensorimotor Systems (PI Prof. Li Zhaoping) at the Max Planck Institute for Biological Cybernetics and at the University of Tübingen is currently looking for highly skilled and motivated individuals to work on projects aimed towards understanding visual attentional and perceptual processes using fMRI/MRI, TMS and/or EEG methodologies. The framework and motivation of the projects can be found at: https://www.lizhaoping.org/zhaoping/AGZL_HumanVisual.html. The projects can involve, for example, visual search tasks, stereo vision tasks, visual illusions, and will be discussed during the application process. fMRI/MRI, TMS and/or EEG methodologies can be used in combination with eye tracking, and other related methods as necessary. The postdoc will be working closely with the principal investigator and other members of Zhaoping's team when needed. Responsibilities: • Conduct and participate in research projects such as lab and equipment set up, data collection, data analysis, writing reports and papers, and presenting at scientific conferences. • Participate in routine laboratory operations, such as planning and preparations for experiments, lab maintenance and lab procedures. • Coordinate with the PI and other team members for strategies and project planning. • Coordinate with the PI and other team members for project planning, and in supervision of student projects or teaching assistance for university courses in our field. Who we are: We use a multidisciplinary approach to investigate sensory and sensory-motor transforms in the brain (www.lizhaoping.org). Our approaches consist of both theoretical and experimental techniques including human psychophysics, fMRI imaging, electrophysiology and computational modelling. One part of our group is located in the University, in the Centre for Integrative Neurosciences (CIN), and the other part is in the Max Planck Institute for Biological Cybernetics as the Department for Sensory and Sensorimotor Systems. You will have the opportunity to learn other skills in our multidisciplinary group and benefit from interactions with our colleagues in the university as well as internationally. This job opening is for the CIN or the MPI working group. The position (salary level TVöD-Bund E13, 100%) is for a duration of two years, and renewable to additional years. We seek to raise the number of women in research and teaching and therefore urge qualified women to apply. Disabled persons will be preferred in case of equal qualification. Your application: The position is available immediately and will be open until filled. Preference will be given to applications received by November 30th, 2022. We look forward to receiving your application that includes (1) a cover letter, including a statement on roughly when you would like to start this position, (2) a motivation statement, (3) a CV, (4) names and contact details of three people for references, (5) if you have them, transcripts from your past and current education listing the courses taken and their grades, (6) if you have them, please also include copies of your degree certificates, (7) you may include a pdf file of your best publication(s), or other documents and information that you think could strengthen your application. Please use pdf files for these documents (and you may combine them into a single pdf file) and send to jobs.li@tuebingen.mpg.de, where also informal inquiries can be addressed. Please note that applications without complete information in (1)-(4) will not be considered, unless the cover letter includes an explanation and/or information about when the needed materials will be supplied. For further opportunities in our group, please visit https://www.lizhaoping.org/jobs.html

The Department of Sensory and Sensorimotor Systems (PI Prof. Li Zhaoping) at the Max Planck Institute for Biological Cybernetics and at the University of Tübingen is currently looking for highly skilled and motivated individuals to work on projects aimed towards understanding visual attentional and perceptual processes using fMRI/MRI, TMS and/or EEG methodologies. The framework and motivation of the projects can be found at https://www.lizhaoping.org/zhaoping/AGZL_HumanVisual.html. The projects can involve, for example, visual search tasks, stereo vision tasks, visual illusions, and will be discussed during the application process. fMRI/MRI, TMS and/or EEG methodologies can be used in combination with eye tracking, and other related methods as necessary. Responsibilities: • Conduct and participate in research projects such as lab and equipment set up, data collection, data analysis, writing reports and papers, and presenting at scientific conferences. • Participate in routine laboratory operations, such as planning and preparations for experiments, lab maintenance and lab procedures. • Participate in teaching assistance duties for university courses in our field. Who we are: We use a multidisciplinary approach to investigate sensory and sensory-motor transforms in the brain (www.lizhaoping.org). Our approaches consist of both theoretical and experimental techniques including human psychophysics, fMRI imaging, EEG, electrophysiology and computational modelling. One part of our group is located in the University, in the Centre for Integrative Neurosciences (CIN), and the other part is in the Max Planck Institute for Biological Cybernetics as the Department for Sensory and Sensorimotor Systems. You will have the opportunity to learn skills from other members of the group and benefit from multidisciplinary interactions, including with our collaborators locally and internationally. The PhD contract (TVöD-Bund E13, 65%) duration is for 3 years. We seek to raise the number of women in research and teaching and therefore urge qualified women to apply. Disabled persons will be preferred in case of equal qualification. Your application: The position is available immediately and will be open until filled. Preference will be given to applications received by November 30th, 2022. We look forward to receiving your application that includes (1) a cover letter, including a statement on roughly when you would like to start this position, (2) a motivation statement, (3) a CV, (4) names and contact details of three people for references, (5) transcripts from your past and current education listing the courses taken and their grades, (6) if you have them, please also include copies of your degree certificates, (7) if you have them, include a pdf file of your best publication(s), or other documents and information that you think could strengthen your application. Please use pdf files for these documents (and you may combine them into a single pdf file) and send to jobs.li@tuebingen.mpg.de, where also informal inquiries can be addressed. Please note that applications without complete information in (1)-(5) will not be considered, unless the cover letter includes an explanation and/or information about when the needed materials will be supplied. For further opportunities in our group, please visit https://www.lizhaoping.org/jobs.html

Progress in deep learning has spawned great successes in many engineering applications. As a prime example, convolutional neural networks, a type of feedforward neural networks, are now approaching – and sometimes even surpassing – human accuracy on a variety of visual recognition tasks. In this talk, however, I will show that these neural networks and their recent extensions exhibit a limited ability to solve seemingly simple visual reasoning problems involving incremental grouping, similarity, and spatial relation judgments. Our group has developed a recurrent network model of classical and extra-classical receptive field circuits that is constrained by the anatomy and physiology of the visual cortex. The model was shown to account for diverse visual illusions providing computational evidence for a novel canonical circuit that is shared across visual modalities. I will show that this computational neuroscience model can be turned into a modern end-to-end trainable deep recurrent network architecture that addresses some of the shortcomings exhibited by state-of-the-art feedforward networks for solving complex visual reasoning tasks. This suggests that neuroscience may contribute powerful new ideas and approaches to computer science and artificial intelligence.

Common factors are omnipresent in everyday life, e.g., it is widely held that there is a common factor g for intelligence. In vision, however, there seems to be a multitude of specific factors rather than a strong and unique common factor. In my thesis, I first examined the multidimensionality of the structure underlying visual illusions. To this aim, the susceptibility to various visual illusions was measured. In addition, subjects were tested with variants of the same illusion, which differed in spatial features, luminance, orientation, or contextual conditions. Only weak correlations were observed between the susceptibility to different visual illusions. An individual showing a strong susceptibility to one visual illusion does not necessarily show a strong susceptibility to other visual illusions, suggesting that the structure underlying visual illusions is multifactorial. In contrast, there were strong correlations between the susceptibility to variants of the same illusion. Hence, factors seem to be illusion-specific but not feature-specific. Second, I investigated whether a strong visual factor emerges in healthy elderly and patients with schizophrenia, which may be expected from the general decline in perceptual abilities usually reported in these two populations compared to healthy young adults. Similarly, a strong visual factor may emerge in action video gamers, who often show enhanced perceptual performance compared to non-video gamers. Hence, healthy elderly, patients with schizophrenia, and action video gamers were tested with a battery of visual tasks, such as a contrast detection and orientation discrimination task. As in control groups, between-task correlations were weak in general, which argues against the emergence of a strong common factor for vision in these populations. While similar tasks are usually assumed to rely on similar neural mechanisms, the performances in different visual tasks were only weakly related to each other, i.e., performance does not generalize across visual tasks. These results highlight the relevance of an individual differences approach to unravel the multidimensionality of the visual structure.