Biology subjects

Gregorova, K.

Publications and source records attributed to Gregorova, K..

3 recordsLinked to original sources

bioRxiv2026

A self without a "we": Individuals with Borderline personality disorder show signatures of disrupted social basis functions

A capacity to navigate social relationships is fundamental to human wellbeing. However, the cognitive mechanisms underlying social dysfunction in psychiatric conditions remain poorly understood. Using an abstract multi-person decision making task that isolates the relational structure of multi-person interactions, we compare non-clinical controls (NCC, n=51) and individuals with BPD symptoms (n=46), a condition characterized by disturbances in self-other boundaries and chronic feelings of loneliness. Generally, we found a pattern of less self-centered decision-making in BPD. More specifically, individuals with BPD showed signatures of perceiving opponents as a coherent group ("them"), but a markedly reduced tendency to represent themselves as part of their own team ("us"). This pattern of results points towards a disruption of a primary social basis function. Our laboratory findings extend to experienced loneliness in daily life as measured by ecological momentary assessments (EMA). Together, this suggests that BPD is characterized by an unbalanced representation of social structure, with an attenuated "we" alongside a heightened "they".

neuroscience↗

bioRxiv2026

Eye-Tracking-BIDS: the Brain Imaging Data Structure extended to gaze position and pupil data

The Brain Imaging Data Structure (BIDS) is a widely adopted, community-driven standard to organize neuroimaging data and metadata. Although numerous extensions have been developed to incrementally extend coverage to new modalities and data types, an unambiguous, granular specification for eye-tracking recordings is lacking. Here, we present how BIDS will structure data and metadata produced by eye-tracking devices, including gaze position and pupil data. In addition to prescribing the organization of the unprocessed (raw) recordings and associated metadata as produced by the device, BEP20 also resolves gaps in current BIDS specifications beyond the scope of eye tracking. In particular, it adds a mechanism for including asynchronous model parameters and messages, such as contextual information, statuses, and events, such as triggers, generated by the device. BEP20 includes examples that illustrate its applicability in various experimental settings. This BIDS extension provides a robust standard that supports the development of self-adaptive, open, and automated eye-tracking data structures, thereby bolstering transparency and reliability of results in this field.

neuroscience↗

bioRxiv2018

Visual word recognition relies on an orthographic prediction error signal

Most current models assume that the perceptual and cognitive processes of visual word recognition and reading operate upon neuronally coded domain-general low-level visual representations - typically oriented line representations. We here demonstrate, consistent with neurophysiological theories of Bayesian-like predictive neural computations, that prior visual knowledge of words may be utilized to explain away redundant and highly expected parts of the visual percept. Subsequent processing stages, accordingly, operate upon an optimized representation of the visual input, the orthographic prediction error, highlighting only the visual information relevant for word identification. We show that this optimized representation is related to orthographic word characteristics, accounts for word recognition behavior, and is processed early in the visual processing stream, i.e., in V4 and before 200 ms after word-onset. Based on these findings, we propose that prior visual-orthographic knowledge is used to optimize the representation of visually presented words, which in turn allows for highly efficient reading processes.

neuroscience↗