Research in schizophrenia has tended to emphasize deficits in higher cognitive abilities, such as attention, memory, and executive function. Here we provide evidence for dysfunction at a more fundamental level of perceptual processing, temporal integration. On a measure of flicker fusion, patients with schizophrenia exhibited significantly lower thresholds than age and education matched healthy controls. We reasoned that this finding could result from a longer window of temporal integration or could reflect diminished repetition suppression: if every frame of the repeating stimulus were represented as novel, its perceived duration would be accordingly longer. To tease apart these non-exclusive hypotheses, we asked patients to report the number of stimuli perceived on the screen at once (numerosity) as they watched rapidly flashing stimuli that were either repeated or novel. Patients reported significantly higher numerosity than controls in all conditions, again indicating a longer window of temporal integration in schizophrenia. Further, patients showed the largest difference from controls in the repeated condition, suggesting a possible effect of weaker repetition suppression. Finally, we establish that our findings generalize to several different classes of stimuli (letters, pictures, faces, words, and pseudo-words), demonstrating a non-specific effect of a lengthened window of integration. We conclude that the visual system in schizophrenics integrates input over longer periods of time, and that repetition suppression may also be deficient. We suggest that these abnormalities in the processing of temporal information may underlie higher-level deficits in schizophrenia and account for the disturbed sense of continuity and fragmentation of events in time reported by patients.

Download full-text PDF

Source
http://dx.doi.org/10.1016/j.neuropsychologia.2012.11.008DOI Listing

Publication Analysis

Top Keywords

temporal integration
16
repetition suppression
12
integration schizophrenia
8
processing temporal
8
longer window
8
window temporal
8
integration
5
schizophrenia
5
patients
5
lengthened temporal
4

Similar Publications

Machine learning-based assessment of morphometric abnormalities distinguishes bipolar disorder and major depressive disorder.

Neuroradiology

January 2025

Department of Radiology, The First Affiliated Hospital of Chongqing Medical University, Chongqing, 400016, China.

Introduction: Bipolar disorder (BD) and major depressive disorder (MDD) have overlapping clinical presentations which may make it difficult for clinicians to distinguish them potentially resulting in misdiagnosis. This study combined structural MRI and machine learning techniques to determine whether regional morphological differences could distinguish patients with BD and MDD.

Methods: A total of 123 participants, including BD (n = 31), MDD (n = 48), and healthy controls (HC, n = 44), underwent high-resolution 3D T1-weighted imaging.

View Article and Find Full Text PDF

Heterogeneous fault architecture affects crustal seismotectonics and fluid migration. When studying it, we commonly rely on static conceptual models that generally overlook the absolute time dimension of fault (re)activation. Heterogenous faults, however, represent the end-result of protracted, cumulative and intricate deformation histories.

View Article and Find Full Text PDF

Large Language Models (LLMs) have shown success in predicting neural signals associated with narrative processing, but their approach to integrating context over large timescales differs fundamentally from that of the human brain. In this study, we show how the brain, unlike LLMs that process large text windows in parallel, integrates short-term and long-term contextual information through an incremental mechanism. Using fMRI data from 219 participants listening to spoken narratives, we first demonstrate that LLMs predict brain activity effectively only when using short contextual windows of up to a few dozen words.

View Article and Find Full Text PDF

This paper aims to construct a green environmental protection system by advancing database energy-saving techniques and optimizing the energy-saving mechanism against the backdrop of blockchain integration. The protocol classification of wireless sensor networks is examined within the context of the rapid growth of information technology. The analysis draws upon the database storage and sharing model and recent research examples that connect blockchain and database technology.

View Article and Find Full Text PDF

Working-memory load decoding model inspired by brain cognition based on cross-frequency coupling.

Brain Res Bull

January 2025

School of Life and Health Information Science and Engineering, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; College of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Institute for Advanced Sciences, Chongqing University of Posts and Telecommunications, Chongqing 400065, China; Guangyang Bay Laboratory, Chongqing Institute for Brain and Intelligence, Chongqing 400064, China. Electronic address:

Working memory, a fundamental cognitive function of the brain, necessitates the evaluation of cognitive load intensity due to limited cognitive resources. Optimizing cognitive load can enhance task performance efficiency by preventing resource waste and overload. Therefore, identifying working memory load is an essential area of research.

View Article and Find Full Text PDF

Want AI Summaries of new PubMed Abstracts delivered to your In-box?

Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!