Biology subjects

Schubotz, R. I.

Publications and source records attributed to Schubotz, R. I..

5 recordsLinked to original sources

bioRxiv2026

A standardized naturalistic audio stimuli database with unsupervised labeling

Research in cognitive neuroscience has relied on simple, highly controlled stimuli due to the difficulty in developing standardized, ecologically valid stimulus sets. However, there is a consensus that using ecologically valid stimuli is imperative to generalize results beyond controlled laboratory settings. The current study introduces a naturalistic audio stimulus database, consisting of short, recognizable, and emotionally rated stimuli. To create such a database, the current study collected 291 audio files from a wide range of sources. 361 participants rated the audio clips on emotionality, arousal, and recognizability, and subsequently freely described the audios by typing what they believed the sound to be. The text responses of the participants were embedded and clustered using an unsupervised machine-learning algorithm to derive a participant-grounded organization of auditory object categories. The results indicate audio clips were easily recognizable, while emotionality and arousal ratings showed broad variability, making the database suitable for diverse experimental needs. Furthermore, the final database comprises 10 distinct semantic categories, providing a diverse set of auditory stimuli.

neuroscience↗

bioRxiv2025

Competition Between Memory Updating and Differentiation Emerges from Intrinsic Network Dynamics

When an event occurs that is similar to a previous experience, the original episodic memory can be modified with new information (updating) or a new memory can be encoded (differentiation). Prediction errors, the deviation between expected and actual stimuli, are believed to mediate the competition between updating and differentiation, but the underlying mechanisms remain unclear. Here, we present a new analysis of experimental studies (Boeltzig, Liedtke, & Schubotz, 2025; Liedtke et al., 2025) that examine recognition memory and cued recall of similar conversations. The original version was recognized more confidently than the modified version, and the recognition confidence for modified versions showed a U-shaped dependence on the prediction error. Furthermore, the larger the prediction error, the more frequently participants retrieved two versions during cued recall. To account for these results, we propose a computational model based on a modified Hopfield network, which encodes the original and modified versions sequentially and weights the encoding of new patterns by the prediction error. The model shows that (1) similar new memories interfere with previous ones (updating) while dissimilar ones are stored separately (differentiation), (2) interference from similar representations leads to reduced memory accuracy and lower-confidence recognition, and (3) the encoding weight must be modulated by the prediction error to account for the experimental data. Our modeling results show that prediction-error-driven competition between updating and differentiation can emerge from intrinsic network dynamics alone.

neuroscience↗

bioRxiv2025

Alterations of Task-based fMRI Topology Underlying Cognitive Flexibility and Stability in Schizophrenia

Recently, it has been suggested that brain dysconnectivity in patients with schizophrenia contributes to the wide-ranging cognitive deficits that characterize the disorder. Graph theoretical analysis offers a unique method for studying how architectural alterations in large- scale brain networks may contribute to cognitive impairments in these patients. Implementing this technique, we analyzed the functional brain activity during a predictive switch-drift task from 21 patients with schizophrenia and 22 matched healthy controls. We specifically calculated task-based global graph measures for the functional networks that were activated during expected events, events requiring a flexible updating of predictions, and events that required the stabilization of predictions. By implementing Bayesian multivariate generalized linear models, we found functional network alterations during all event types, which indicated less centralized, less integrated, and simultaneously less segregated network topology in patients with schizophrenia compared to controls. In addition, the rate of correctly detected switches, requiring flexible updating of internal models, predicted global graph measures differently for patients compared to controls. In particular, lower cognitive flexibility in patients was associated with reduced integration of functional networks. Overall, the results indicate an adaptation of network topologies, resulting in less optimal network organization in patients with schizophrenia compared to healthy controls.

neuroscience↗

bioRxiv2021

What happened when? Brain and behavioral responses to violated expectations about the structure and content of episodic memories

Episodic memories are not static but can be modified on the basis of new experiences, potentially allowing us to make valid predictions in the face of an ever-changing environment. Recent research has identified prediction errors during retrieval as a possible trigger for such changes. In the present study, we used modified episodic cues to investigate whether different types of mnemonic prediction errors modulate brain activity and subsequent memory performance. Participants encoded different episodes which consisted of short toy stories. During a subsequent functional magnetic resonance imaging (fMRI) session, participants were then presented videos showing the original episodes, or modified versions thereof. In modified videos, either the order of two subsequent action steps was changed or an object was exchanged for another. Content modifications recruited inferior frontal, parietal, and temporo-occipital areas reflecting the processing of the new object information, while brain responses to structure modifications in right dorsal premotor and inferior frontal areas remained subthreshold. In a post-fMRI memory test, the participants tendency to accept modified episodes as originally encoded increased significantly when they had been presented modified versions already during the fMRI session. Our study provides valuable initial insights into the neural processing of different types of episodic prediction errors and their influence on subsequent memory.

neuroscience↗

bioRxiv2019

Exploitation of local and global information in predictive processing

While prediction errors have been established to instigate learning through model adaptation, recent studies have stressed the role of model-compliant events in predictive processing. Specifically, so-called checkpoints have been suggested to be sampled for model evaluation, particularly in uncertain contexts.\n\nUsing electroencephalography (EEG), the present study aimed to investigate the interplay of such global information and local adjustment cues prompting on-line adjustments of expectations. Within a stream of single digits, participants were to detect ordered sequences (i.e., 3-4-5-6-7) that had a regular length of five digits and were occasionally extended to seven digits. Across experimental blocks, these extensions were either rare (low irreducible uncertainty) or frequent (high uncertainty) and could be unexpected or indicated by incidental colour cues.\n\nExploitation of local cue information was reflected in significant decoding of cues vs non-informative analogues using multivariate pattern classification. Modulation of checkpoint processing as a function of global uncertainty was likewise reflected in significant decoding of high vs low uncertainty checkpoints. In line with previous results, both analyses comprised the P3b time frame as an index of excess model-compliant information sampled from probabilistic events.\n\nAccounting for cue information, an N400 component was revealed as the correlate of locally unexpected (vs expected) outcomes, reflecting effortful integration of incongruous information. Finally, we compared the fit of a global model (disregarding local adjustments) and a local model (including local adjustments) using representational similarity analysis (RSA). RSA revealed a better fit for the global model, underscoring the precedence of global reference frames in hierarchical predictive processing.

neuroscience↗