2 results
External cues improve visual working memory encoding in the presence of salient distractors in schizophrenia
- Catherine V. Barnes-Scheufler, Lara Rösler, Michael Schaum, Carmen Schiweck, Benjamin Peters, Jutta S. Mayer, Andreas Reif, Michael Wibral, Robert A. Bittner
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- Journal:
- Psychological Medicine , First View
- Published online by Cambridge University Press:
- 04 March 2024, pp. 1-10
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- Article
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Background
People with schizophrenia (PSZ) are impaired in attentional prioritization of non-salient but relevant stimuli over salient distractors during visual working memory (VWM) encoding. Conversely, guidance of top–down attention by external predictive cues is intact. Yet, it is unknown whether this preserved ability can help PSZ encode more information in the presence of salient distractors.
MethodsWe employed a visuospatial change-detection task using four Gabor patches with differing orientations in 66 PSZ and 74 healthy controls (HCS). Two Gabor patches flickered which were designated either as targets or distractors and either a predictive or a non-predictive cue was displayed to manipulate top–down attention, resulting in four conditions.
ResultsWe observed significant effects of group, salience and cue as well as significant interactions of salience by cue, group by salience and group by cue. Across all conditions, PSZ stored significantly less information in VWM than HCS. PSZ stored significantly less non-flickering than flickering information with a non-predictive cue. However, PSZ stored significantly more flickering and non-flickering information with a predictive cue.
ConclusionsOur findings indicate that control of attentional selection is impaired in schizophrenia. We demonstrate that additional top–down information significantly improves performance in PSZ. The observed deficit in attentional control suggests a disturbance of GABAergic inhibition in early visual areas. Moreover, our findings are indicative of a mechanism for enhancing attentional control in PSZ, which could be utilized by pro-cognitive interventions. Thus, the current paradigm is suitable to reveal both preserved and compromised cognitive component processes in schizophrenia.
17 - Bits from Brains: Analyzing Distributed Computation in Neural Systems
- from Part V - From Matter to Mind
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- By Michael Wibral, Universities of Cologne and Konstanz, Joseph Lizier, University of Sydney, Viola Priesemann, the Max Planck Institute for Brain Research
- Edited by Sara Imari Walker, Arizona State University, Paul C. W. Davies, Arizona State University, George F. R. Ellis, University of Cape Town
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- Book:
- From Matter to Life
- Published online:
- 02 March 2017
- Print publication:
- 23 February 2017, pp 429-467
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Summary
Artificial computing systems are a pervasive phenomenon in today's life. While traditionally such systems were employed to support humans in tasks that required mere number-crunching, there is an increasing demand for systems that exhibit autonomous, intelligent behavior in complex environments. These complex environments often confront artificial systems with ill-posed problems that have to be solved under constraints of incomplete knowledge and limited resources. Tasks of this kind are typically solved with ease by biological computing systems, as these cannot afford the luxury to dismiss any problem that happens to cross their path as “ill-posed.” Consequently, biological systems have evolved algorithms to approximately solve such problems – algorithms that are adapted to their limited resources and that just yield “good enough” solutions quickly. Algorithms from biological systems may, therefore, serve as an inspiration for artificial information processing systems to solve similar problems under tight constraints of computational power, data availability, and time.
One naive way to use this inspiration is to copy and incorporate as much detail as possible from the biological into the artificial system, in the hope to also copy the emergent information processing. However, already small errors in copying the parameters of a system may compromise success. Therefore, it may be useful to derive inspiration also in a more abstract way, that is directly linked to the information processing carried out by a biological system. But how can can we gain insight into this information processing without caring for its biological implementation?
The formal language to quantitatively describe and dissect information processing – in any system – is provided by information theory. For our particular question we can exploit the fact that information theory does not care about the nature of variables that enter the computation or information processing. Thus, it is in principle possible to treat all relevant aspects of biological computation, and of biologically inspired computing systems, in one natural framework.
In Wibral et al. (2015) we systematically presented how to analyze biological computing systems, especially neural systems, using methods from information theory and discussed how these information-theoretic results can inspire the design of artificial computing systems. Specifically, we focused on three types of approaches to characterizing the information processing undertaken in such systems and on what this tells us about the algorithms they implement.