The research focuses on investigating the underlying mechanisms of Cue Approach Training (CAT), particularly how attention and memory influence preference changes.

This study explores the potential of virtual reality (VR) to revolutionize museum experiences by offering personalized, interactive tours that cater to individual visitor preferences. By employing a novel VR application allowing participants to freely navigate a virtual museum space, the research examines the impact of active decision-making on visitor satisfaction. Participants are divided into three groups to compare satisfaction levels based on varying degrees of choice during the tour. Utilizing the Mizne-Blumenthal collection, participants select art features, with the chosen artworks and their sequence remaining consistent across groups to ensure structural validity. Physiological measures, such as facial expressions and eye movements, along with behavioral and satisfaction questionnaires, provide comprehensive data. This approach combines data science with cultural heritage to enhance engagement, optimize museum visits, and address accessibility challenges, ultimately contributing to museums' missions of interpretation and preservation in the digital age.

In this project, we aim to study whether physiological movements in humans can predict their subjective preferences. The goal of this study is to see whether active or passive movements can indicate an individual’s subjective preference of pictures shown in a VR environment setup. By using 3 of the tracking devices useable in the VR headset (eye tracking, face tracking and hand tracking), we want to see how by swiping right (if they like the picture) or left (if they don’t), or just observing the pictures passively, one’s movements can predict what will one prefer.

An end-to-end open-source software project for conducting behavioral XR experiments. ResXR provides a Unity-based experiment template for multimodal data capture (head, hand, eye, and face tracking from Meta’s Quest Pro head mounted display) alongside a 
Python processing pipeline that automates the creation of a standardized data structure, validation, preprocessing, and quality reporting, inspired by established neuroimaging data formats and tools like fMRIPrep. 

This project investigates the relationship between the cognitive phenomenon of "sudden insight" and the subjective "Aha!" experience within the domain of spatial navigation, an area less explored compared to traditional verbal tasks. Building on a successful pilot study utilizing a novel Virtual Reality (VR) maze paradigm, the research aims to elicit and objectively measure how these sudden realizations manifest during spatial problem-solving. Our initial findings indicated a strong link between the self-reported feeling of an "Aha!" moment and immediate, objective improvements in navigation efficiency. We are currently conducting a rigorous, pre-registered (OSF) replication of this work to validate these patterns with expanded analysis. By integrating human introspection with objective behavioral metrics, this work establishes a framework for future cross-species comparisons, aiming to uncover the fundamental mechanisms of how insight drives learning.

In this study, we examine how learned values of unisensory stimuli are integrated during multisensory decision making. Participants learn the value of visual, auditory, and somatosensory stimuli and then evaluate multisensory combinations of these cues. We aim to explore the relative contribution of each modality, the effect of value conflict across modalities on decision difficulty and whether participants evaluate multisensory combinations as the sum of their individual components.

This research focuses on automatic behavior in different environments. I developed experimental paradigms to test how action tendencies are learned and how they respond to changes in context, rules, or outcomes. My work examines both adaptive forms of automaticity, where efficient responding supports performance, and maladaptive forms, where previously learned responses persist despite new goals or altered contingencies. Using virtual reality and laboratory-based behavioral methods, I aim to refine behavioral measures of automaticity and to identify the task and environmental factors that promote flexibility versus rigidity in learned behavio

This project examines how VR modality (level of immersion) and locomotion interfaces influence spatial learning and gaze behavior. By translating a classic spatial learning task across different VR conditions, the work aims to characterize strategy selection and quantify behavioral and eye-tracking markers that predict individual differences in navigation performance.