Cognitive Science Seminar
The Cognitive Science Seminar is a course of MEi:CogSci study programme that is a legacy of original interdisciplinary Cognitive Science and Artificial Intelligence Seminar, which started in 2015. In winter semester the seminar is oriented mostly for the students of cognitive science, to provide insight into the current research in the field of cognitive science by people from FMPI UK and beyond. In summer semester it is a community activity and focuses on current trends in AI and CogSci. The seminar is open, everybody is welcome to attend the offered lectures.
The seminar is organized by Kristína Malinovská.
Time and place: Tuesday 16:30-18:00 in I9 and MS Teams CogSci seminar
Program for winter semester 2026/2027
29.09.2026 - Kristína Malinovská
Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava
Field: cognitive robotics
Cognitive robotics and bio-inspired neural networks
Cognitive robotics is an interdisciplinary field that brings together research in machine learning, robotics and bio-inspired artificial intelligence, typically artificial neural networks. It focuses on studying and implementing human cognitive abilities in robots, from simple motor skills, sensing and understanding the world, to developing a sense of their own body, acquiring language and the ability to talk to humans about their surrounding world and work with them on collaborative tasks. In connection with the human-robot interaction field, cognitive modeling represents a way to make robots more considerate and aware of people as well as make their behavior more trustworthy, legible and explainable. In the talk, I will present examples of cognitive robotics’ and HRI research of the cognition and neural computation research group, focusing on sensorimotor interaction and cross-modal learning, language and concepts, acquiring a body schema and building a robotic mirror neuron system for understanding of actions. I will also briefly introduce my research on bio-inspired learning in artificial neural networks.
13.10.2026 - Martin Takáč
Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava
Field: artificial intelligence - models of cognition
Computational modeling in psychiatry
I my talk I'll first introduce the emerging field of computational psychiatry and its opportunities. In the second part I'll illustrate the field with the case study of modeling hallucinations and delusions within the paradigm of predictive processing.
27.10.2026 - Tomáš Gál
Department of Applied Informatics, Faculty of Mathematics, Physics and Informatics, Comenius University Bratislava
Field: human-computer interaction
Cognitive Biases in Human–AI Interaction
Human–AI interaction might be perceived as if cognitive biases were located either in the human user or in the artificial system, while in practice many relevant effects emerge from the interaction between both sides and from the interface that connects them. This lecture examines bias across four connected levels. First, it introduces human cognitive biases that shape how users interpret and rely on AI outputs, including confirmation bias, anchoring, automation bias, authority effects, and anthropomorphism. Second, it turns to systematic tendencies of AI systems themselves, such as sensitivity to framing, sycophancy, preference for coherent and complete answers, and other recurring patterns that may resemble human biases without sharing the same underlying mechanisms. Third, it considers the role of interface affordances, including conversational fluency, answer-first design, confidence cues, personalization, and other features that influence perceived competence, trust, and scrutiny. Finally, these layers are brought together in the concept of an emergent Human–AI feedback loop, in which human expectations shape prompts and interpretation, model tendencies shape responses, and interface design influences how these patterns are reinforced or corrected over time. The lecture does not treat cognitive bias simply as an error to be eliminated. Instead, it asks which systematic tendencies become unhelpful in a given context, which can support efficient judgment and decision-making, and how Human–AI interaction can be designed and used to reduce maladaptive biases while preserving, exploiting, or even strengthening useful cognitive heuristics and forms of epistemic support.
03.11.2026 - Dominika Fričová
Faculty of Medicine, Comenius University in Bratislava
Field: neurobiology
Cognition in vitro: human neurons as a model of Parkinson’s Disease in a Petri dish
Cognition is commonly studied through behaviour, neuropsychological assessment, and brain imaging. Yet its cellular and network-level foundations can also be investigated in controlled in vitro models of the human nervous system. Human neurons can be generated from readily accessible cells, such as skin fibroblasts, providing experimentally tractable systems in which to examine neuronal connectivity, communication, and activity. These models also allow us to ask how neuronal function is altered by genetic perturbations or disease-related molecular pathology. Parkinson’s disease is often viewed primarily as a movement disorder, but it can also affect attention, executive function, learning, memory, and decision-making. This lecture will focus on alpha-synuclein, a protein central to Parkinson’s disease pathology, and on how its misfolding and aggregation may impair neuronal function. We will discuss experimental strategies for testing whether pathological alpha-synuclein disrupts neuronal connectivity, synaptic function, and network activity; cellular processes that may contribute to cognitive symptoms in Parkinson’s disease.
24.11.2026 - Adam Kubinec
Department of Behavioural Neuroscience, Centre of Experimental Medicine, Slovak Academy of Sciences
Field: behavioural neuroscience
Neurocognitive mechanisms of semantic memory
Semantic cognition allows us to store, organise, and retrieve knowledge about words, objects, and the relations between them. Retrieving the right concept at the right moment is not always effortless: strongly associated but irrelevant concepts can come to mind automatically and must be suppressed for retrieval to remain flexible and goal directed. Difficulties with this regulation are observed across a range of neurological and psychiatric disorders, yet the underlying cognitive and neural mechanisms remain unclear. This lecture will present results from a series of experiments examining how automatic activation and inhibitory control interact during retrieval from semantic memory. We developed the Associative Dissociative Task (ADT), a paradigm that distinguishes automatic and controlled components of retrieval. Using pupillometry, we traced when control is engaged and introduced a marker of associative intrusion. Using transcranial direct current stimulation, we tested the contributions of the left prefrontal cortex and the right posterior cerebellum. The results are consistent with a prefrontal role in proactive and early reactive control and a cerebellar role in retrieving overlearned associations. I will argue that these findings support a model in which semantic retrieval combines automatic activation with proactive and reactive inhibition acting at the input and target levels, and discuss their potential relevance for understanding retrieval difficulties in clinical populations.
08.12.2026 - Michal Teplan
Institute of Measurement Science, Slovak Academy of Sciences
Field: psychology
The mental state of flow as a research challenge
The phenomenon of mental Flow, characterized by a sense of energized focus, full immersion, and enjoyment, represents a peak state of human performance and well-being. Emerging in modern times through the pioneering work of Csikszentmihalyi, the scientific quest for understanding Flow’s underlying mechanisms remains a challenging and ongoing endeavor. My personal journey and motivation stem from the conviction that this elusive state can be transformed from a random occurrence into a scientifically monitored and intentionally trained skill. Psychologically, Flow is related to concentration on the task in a challenge-skill balance situation, with the loss of self-consciousness. Physiologically, Flow is associated with higher Heart Rate Variability, indicating parasympathetic activity, and lower galvanic skin response, confirming reduced emotional stress. Neuroscientifically, it involves a decrease in EEG powers over the prefrontal cortex pointing to the reduction of the analytical, self-critical mind. In this seminar, the initial phase of our investigation will be introduced, and hypotheses will be formed. The role of mindfulness meditation training and extensions to spirituality will be explained. Basically, we focus on defining ground truth data by combining subjective psychological scales and expert assessment with objective physiological and biomechanical measurements, aiming to create a robust, measurable Flow Index. We intend to leverage biomechanics (GPS trackers, accelerator sensors, and micro-movement analysis) and data science to seek secondary Flow correlates in quantifiable sport performance metrics. Ultimately, our goal is to develop a comprehensive system for the assessment, monitoring, and training of the Flow state, with utilization of real-time biofeedback, providing athletes, managers, and artists with the tools for achieving lifelong excellence and well-being.