Open Research Repository
The Open Research Repository (ORR) is the official institutional repository of the Central European University. The repository provides access to the research output of the CEU community by collecting open access versions of scholarly works authored or co-authored by CEU faculty and students.
For more information, please contact us at: scholcom@ceu.edu
Recent Submissions
Item Navigating decision space:Causal structure improves performance in a branching choice task(2025-12)Previous research has shown that the causal structure of events influences how well they are recalled in episodic memory later on. Here, we aimed to investigate whether these effects apply not only to events that are passively observed but also situations directly shaped by an individual’s decisions. We designed a task in which participants had to traverse decision trees of varying causal structure: ‘Coherent’ trees where each decision followed from the consequences of the preceding decision, and ‘fragmented’ trees where each subsequent decision was only statistically (but not causally) contingent on the preceding decision. In a between-subjects experiment, participants first completed an exploration phase in which they had to explore the decision trees without a specific goal; in a subsequent search phase, they had to reach a target outcome in as few attempts as possible. Analyses of participants’ performance showed that those in the coherent group required significantly fewer attempts to reach a correct outcome than those in the fragmented group. A follow-up experiment surprisingly found that the advantage of causal structure does not depend on episodic memory: Removing the exploration phase barely diminished the positive effect causal coherence had on participants’ performance. In further follow-up experiments without an exploration phase, neither the additional removal of ‘process images’ that show how a choice leads to an outcome, nor the removal of text labels describing decisions, was individually sufficient to equalize performances. Only when both were eliminated at once did participants perform equally well on coherent and fragmented trees. This indicates that cues relating to causal mechanisms (images) and predictive cues (text) each facilitate goal-directed decision making without relying on extensive learning, and that only the absence of both is sufficient to suppress the advantage causal structure provides.Item Neighbourhood topology unveils pathological hubs in the brain networks of epilepsy-surgery patients(2025)Pathological hubs in the brain networks of epilepsy patients are hypothesized to drive seizure generation and propagation. In epilepsy-surgery patients, these hubs have traditionally been associated with the resection area (RA): the region removed during the surgery with the goal of stopping the seizures, and which is typically used as a proxy for the epileptogenic zone. However, recent studies hypothesize that pathological hubs may extend to the vicinity of the RA, potentially complicating post-surgical seizure control. Here we propose a neighbourhood-based analysis of brain organization to investigate this hypothesis. We exploit a large dataset of pre-surgical magnetoencephalography-derived whole-brain networks from 91 epilepsy-surgery patients. Our neighbourhood focus is 2-fold. Firstly, we propose a partition of the brain regions into three sets, namely resected nodes, their neighbours and the remaining network nodes. Secondly, we introduce generalized centrality metrics that describe the neighbourhood of each node, providing a regional measure of hubness. Our analyses reveal that both the RA and its neighbourhood present large hub status, but with significant variability across patients. For some, hubs appear in the RA; for others, in its neighbourhood. Moreover, this variability does not correlate with surgical outcome. These results highlight the potential of neighbourhood-based analyses to uncover novel insights into brain connectivity in brain pathologies, and the need for individualized studies, with large enough cohorts, that account for patient-specific variability.Item The networks of ingredient combinations as culinary fingerprints of world cuisines(2025-12)Investigating how different ingredients are combined in popular dishes is crucial to uncover the principles behind food preferences. Here, we use data from public food repositories and network analysis to characterize and compare worldwide cuisines. Ingredients are first grouped into broader types, and each cuisine is then represented as a network in which nodes correspond to ingredient types and weighted links describe how frequently pairs of types co-occur in recipes. Cuisines differ not only in the popularity of ingredient types and range of recipe sizes, but also in the structural organization of ingredient-type combinations. By analyzing these networks, we uncover distinctive patterns of type associations that serve as culinary fingerprints. For example, European cuisines typically distribute ingredients across different types, whereas certain Asian and South American traditions emphasize one dominant type, such as vegetables or spices. The essence of these patterns is well captured by the networks’ maximum spanning trees, which offer a simplified yet representative backbone for each cuisine. We demonstrate that both these full and simplified network representations enable machine learning models to identify cuisines from subsets of recipes with very high accuracy. Networks of ingredient combinations also cluster global cuisines into meaningful geo-cultural groups, reflecting shared patterns in culinary traditions. More broadly, our study offers novel insights into the structure of world cuisines, enabling data-driven approaches to their characterization, cross-cultural comparison, and potential adaptation.Item Hungary’s Dubious Loyalty:Orbán’s Regime Strategy in the Russia-Ukraine War(Central European University Press, 2023)Item The Historical Construction of National Consciousness:Selected Writings(Central European University Press, 2022)A long essay entitled Three Historical Regions of Europe, appearing first in a samizdat volume in Budapest in 1980, instantly put its author into the forefront of the transnational debate on Central Europe, alongside such intellectual luminaries as Milan Kundera and Czesław Miłosz. The present volume offers English-language readers a rich selection of the depth and breadth of the legacy of Jenő Szűcs (1928-1988). The selection documents Szűcs's seminal contribution to many contemporary debates in historical anthropology, nationalism studies, and conceptual history. It contains his key texts on the history of national consciousness and patterns of collective identity, as well as medieval and early modern political thought. The works published here, most of them previously unavailable in English, provide a sophisticated analysis of a wide range of subjects from the myths of origins of Hungarians before Christianization to the political and religious ideology of the Dózsa peasant uprising in 1514, the medieval roots of civil society, or the revival of ethnic nationalism during the communist era. The volume, with an introduction by the editors locating Szűcs in a transnational context, offers a unique insight into the complex and sensitive debate on national identity in post-1945 East Central Europe.
Communities in ORR
Select a community to browse its collections.