Hamit Başgöl

Hamit Başgöl

Eberhard Karls Universität Tübingen

Experimental Cognitive Science

Sand 6, F239

72076 Tübingen



E-Mail: hamit.basgoel (at) uni-tuebingen.de

If you want to discuss something, just send me an email.

Research Interests

I use machine learning models and psychophysical experiments to understand cognition and behavior. In general, I am interested in the top-down modulation of visual processing. In particular, my current research interest is the effect of pupil-linked arousal modulation on performances in perceptual and cognitive tasks.


I studied Psychology (B.A) at Hacettepe University. Then, I followed my graduate education in Cognitive Science Program at Boğaziçi University under the supervision of Dr. Emre Ugur and Dr. Inci Ayhan. During my M.A, my research focused on investigating time perception in artificial and cognitive systems and exploring event segmentation from the point of view of predictive processing. After completing my thesis entitled "A Computational Model and Psychological Investigation of Event Segmentation and Learning" (please send an email for the full version), I graduated from the program in 2021. I am now a member of the Experimental Cognitive Science group and of the joint project named Robust Vision.

Conference Presentations and Publications

  • H. Basgol, P. Dayan, and V. H. Franz, Investigating Pupil-Linked Arousal to Complex and Statistically Uncertain Auditory Patterns, Tagung experimentell arbeitender Psycholog:innen; Conference of Experimental Psychologists (TeaP2022), 2022, Online [poster presentation]

  • H. Basgol, I. Ayhan, and E. Ugur, Time Perception: A Review on Psychological, Computational and Robotic Models, IEEE Transactions on Cognitive and Developmental Systems, 2021, doi: 10.1109/TCDS.2021.3059045, link [journal publication]

  • H. Basgol, I. Ayhan, and E. Ugur, A Self-Supervised and Predictive Processing-Based Model of Event Segmentation and Learning, the Annual Conference of Cognitive Science Society, 2021, Online [poster presentation]

  • H. Basgol and E. Ugur, Predicting Human Visual Complexity Judgments via Deep Learning, IEEE 28th Signal Processing and Communications Applications Conference (SIU), 2020, doi: 10.1109/SIU49456.2020.9302050, link [conference publication]

  • H. Basgol, I. Ayhan, and E. Ugur, A Computational Model of Event Segmentation and Learning, ISBCS: International Virtual Symposium on Brain and Cognitive Science, Turkey, 2020, link Online [poster presentation]

  • H. Basgol ve E. Ugur, İnsan Görsel Karmaşıklık Kararlarının Derin Öğrenme ile Tahmin Edilmesi, Sinyal ve Iletisim Konferansı, Türkiye, 2020 [conference presentation]

  • H. Basgol ve E. Ugur, Zaman Algısına İlişkin Hesaplamalı Modeller ve Bilişsel Robotbilim Modelleri, Türkiye Robotbilim Konferansı, Türkiye, 2019 [conference presentation]