scholarly journals Similar Effects for Resting State and Unconscious Thought: Both Solve Multi-attribute Choices Better Than Conscious Thought

2018 ◽  
Vol 9 ◽  
Author(s):  
Fengpei Hu ◽  
Xiang Yu ◽  
Huadong Chu ◽  
Lei Zhao ◽  
Uyi Jude ◽  
...  
Keyword(s):  
Resting State ◽  
Unconscious Thought ◽  
Conscious Thought ◽  
Better Than

Unconscious versus conscious thought in creative science problem finding: Unconscious thought showed no advantage!

2019 ◽  
Vol 71 ◽  
pp. 109-113 ◽  
Author(s):  
Ran Ding ◽  
Qin Han ◽  
Ruifen Li ◽  
Tingni Li ◽  
Ying Cui ◽  
...  
Keyword(s):  
Unconscious Thought ◽  
Conscious Thought ◽  
Problem Finding ◽  
Science Problem

scholarly journals S147. FUNCTIONAL BRAIN CONNECTIVITY DATA IMPROVE CLINICAL OUTCOME PREDICTION IN YOUTH AT RISK FOR PSYCHOSIS

2020 ◽  
Vol 46 (Supplement_1) ◽  
pp. S92-S92
Author(s):  
Guusje Collin ◽  
Alfonso Nieto-Castanon ◽  
Martha Shenton ◽  
Ofer Pasternak ◽  
Sinead Kelly ◽  
...  
Keyword(s):  
Prediction Model ◽  
Resting State ◽  
Outcome Prediction ◽  
Brain Connectivity ◽  
Network Connectivity ◽  
Resting State Functional Connectivity ◽  
Clinical Predictors ◽  
Functional Brain ◽  
Improved Outcome ◽  
Better Than

Abstract Background Improved outcome prediction in individuals at high risk for psychosis may facilitate targeted early intervention. Studies suggest that improved outcome prediction may be achieved through the use of neurocognitive or neuroimaging data, on their own or in addition to clinical data. This study examines whether adding resting-state functional connectivity data to validated clinical predictors of psychosis improve outcome prediction in the prodromal stage. Methods This study involves 137 adolescents and young adults at Clinical High Risk (CHR) for psychosis from the Shanghai At Risk for Psychosis (SHARP) program. Based on outcome after one-year follow-up, participants were separated into three outcome categories: good outcome (symptom remission, N = 71), intermediate outcome (ongoing CHR symptoms, N = 30), and poor outcome (conversion to psychosis or treatment-refractory, N = 36). Resting-state fMRI data were acquired for each participant and processed using the Conn toolbox, including rigorous motion correction. Multinomial logistic regression analysis and leave-one-out cross-validation were used to assess the performance of three prediction models: 1) a clinical-only model using validated clinical predictors from the NAPLS-2 psychosis-risk calculator, 2) an fMRI-only model using measures of functional connectome organization and within/between-network connectivity among established resting-state networks, and 3) a combined clinical and fMRI prediction model. Model performance was assessed using the harmonic mean of the positive predictive value and sensitivity for each outcome category. This F1 measure was compared to expected chance-levels using a permutation test with 1,000 sampled permutations in order to evaluate the statistical significance of the model’s prediction. Results The clinical-only prediction model failed to achieve a significant level of outcome prediction (F1 = 0.32, F1-chance = 0.26 □ 0.06, p = .154). The fMRI-only model did predict clinical outcome to a significant degree (F1 = 0.41, F1-chance = 0.29 □ 0.06, p = .016), but the combined clinical and fMRI prediction model showed the best performance (F1 = 0.46, F1-chance = 0.29 □ 0.06, p < .001). On average, positive predictive values (reflecting the probability that an outcome label predicted by the model was correct) were 39% better than chance-level and 32% better than the clinical-only model. Analyzing the contribution of individual predictor variables showed that GAF functional decline, a family history of psychosis, and performance on the Hopkins Verbal Learning Test were the most influential clinical predictors, whereas modular connectome organization, default-mode and fronto-parietal within-network connectivity, and between-network connectivity among language, salience, dorsal attention, cerebellum, and sensorimotor networks were the leading fMRI predictors. Discussion This study’s findings suggest that functional brain abnormalities reflected by alterations in resting-state functional connectivity precede and may drive subsequent changes in clinical functioning. Moreover, the findings show that markers of functional brain connectivity may be useful for improving early identification and clinical decision-making in prodromal psychosis.

scholarly journals Comparison of respiratory protection during workload between different wearing methods of replaceable particulate respirators and powered air-purifying respirators

2021 ◽  
Author(s):  
Hiroka Baba ◽  
Kazunori Ikegami ◽  
Shingo Sekoguchi ◽  
Taiki Shirasaka ◽  
Hajime Ando ◽  
...  
Keyword(s):  
Resting State ◽  
Health Problems ◽  
Protective Equipment ◽  
Respiratory Protection ◽  
The Difference ◽  
Better Than ◽  
Number Of Particles

This study evaluated the difference in respiratory protection between replaceable particulate respirators (RPRs) and powered air-purifying respirators (PAPRs), with different wearing methods during workload. Participants wore RPRs or PAPRs in the ways that workers wore them in actual workplaces. We measured the number of particles inside and outside the respiratory protective equipment (RPE) during workload for each wearing variation. The fit factor (FF) of RPRs in the workload state was significantly lower than that in the resting state, indicating inadequate respiratory protection. In contrast, the FF of PAPRs during workload was significantly lower than that at rest; however, respiratory protection was maintained. PAPR did not show a significant decrease in FF owing to the wearing variations during workload. In conclusion, PAPRs were found to be superior to RPRs in terms of respiratory protection. PAPRs are better than RPRs for workers who have to wear RPE inappropriately due to health problems.

scholarly journals Role of automatic, conscious, and unconscious thought processes in complex decision making

1970 ◽  
Vol 14 (1) ◽  
pp. 33-52
Author(s):  
Draženka Levačić ◽  
Mario Pandžić ◽  
Dragan Glavaš
Keyword(s):  
Decision Making ◽  
Careful Examination ◽  
Automatic Processes ◽  
Thought Processes ◽  
Unconscious Thought ◽  
Conscious Thought ◽  
Complex Decision Making ◽  
Complex Decision

A complex decision is any decision which includes choosing among options with numerous describing attributes. Certain decisions are fast, often guided with automatic processes of thought, while other decisions are made much slower with careful examination of all the factors. These processes can have a significant impact on the quality of decision making. The aim of this research was to investigate the effect of automatic, conscious and unconscious thought processes in the context of decision making. Participants were psychology students aged between 19 to 28 years. First experiment investigated the role of three different thought processes on choosing a subjectively best option, as well as TTB heuristic option. The second experiment investigated metacognitive aspects of decision making, precisely, to determine the differences in feeling of rightness (FOR) as well as the tendency to change the decision, depending on the activated thought processes. Different thought processes determined the choice of the subjectively best option. In the conscious thought condition, participants chose the subjectively best option more often than in the automatic or unconscious thought condition. However, there was no difference between conditions in choosing the TTB heuristic option. The feeling of rightness was significantly higher in conscious thought condition than in automatic or unconscious thought condition, but the two latter conditions did not differ in the judgment of feeling of rightness nor did they differ in the tendency to change the decision.

scholarly journals Evaluating the Benefits of Conscious and Unconscious Thought in Complex Decision Making

2019 ◽  
Vol 6 (1) ◽  
pp. 72-78 ◽  
Author(s):  
Marlène Abadie ◽  
Laurent Waroquier
Keyword(s):  
Decision Making ◽  
Decision Problem ◽  
Multiple Criteria ◽  
Unconscious Thought ◽  
Unconscious Processing ◽  
Conscious Thought ◽  
Complex Decision Making ◽  
Complex Decision ◽  
The Right ◽  
Snap Judgments

Decision-making research reports mixed findings about the best way to make complex decisions involving multiple criteria. While some researchers emphasize the importance of conscious thought to make good decisions, others encourage people to stop thinking and trust their snap judgments. Still others recommend a distracting activity prior to making a choice, assuming that unconscious processing of the decision problem occurs during distraction. We review studies comparing these three decision modes. We show that conscious deliberation helps people to make good decisions when people have in mind precise verbatim information about the exact features of each alternatives. By contrast, a distraction period is more useful when meaning-based gist representations of the alternatives are accessible. Finally, while a period of distraction or deliberation is beneficial for decision making under certain conditions, to blindly follow one’s gut feeling is never the right solution.

Unconscious creativity: When can unconscious thought outperform conscious thought?

2012 ◽  
Vol 22 (4) ◽  
pp. 573-581 ◽  
Author(s):  
Haiyang Yang ◽  
Amitava Chattopadhyay ◽  
Kuangjie Zhang ◽  
Darren W. Dahl
Keyword(s):  
Unconscious Thought ◽  
Conscious Thought

scholarly journals The functional organisation of the hippocampus along its long axis is gradual and predicts recollection

2018 ◽  
Author(s):  
Izabela Przezdzik ◽  
Myrthe Faber ◽  
Guillén Fernández ◽  
Christian F. Beckmann ◽  
Koen V. Haak
Keyword(s):  
Resting State ◽  
Longitudinal Axis ◽  
Resting State Fmri ◽  
Alternative View ◽  
Mapping Data ◽  
Human Hippocampus ◽  
Human Connectome Project ◽  
Individual Variations ◽  
Functional Organisation ◽  
Better Than

AbstractUnderstanding the functional organisation of the hippocampus is crucial for understanding its role in cognition and disorders in which it is implicated. Different views have been proposed of how function is distributed along its long axis: one view suggests segregation, whereas the alternative view postulates a more gradual organisation. Here, we applied a novel ‘connectopic mapping’ data-analysis approach to the resting-state fMRI data of participants of the Human Connectome Project, and demonstrate that the functional organisation of the hippocampal longitudinal axis is gradual rather than segregated into parcels. In addition, we show that inter-individual variations in this gradual organisation predicts variations in recollection memory better than a characterisation based on parcellation. These results present an important step forward in understanding the functional organisation of the human hippocampus and have important implications for translating between rodent and human research.

scholarly journals Temporal Multiple Kernel Learning (tMKL) model for predicting resting state FC via characterizing fMRI connectivity dynamics

2018 ◽  
Author(s):  
Sriniwas Govinda Surampudi ◽  
Joyneel Misra ◽  
Gustavo Deco ◽  
Raju Bapi Surampudi ◽  
Avinash Sharma ◽  
...  
Keyword(s):  
Functional Connectivity ◽  
Resting State ◽  
Temporal Structure ◽  
Multiple Kernel Learning ◽  
Kernel Learning ◽  
Dimensional Manifold ◽  
Grand Average ◽  
Multiple Kernel ◽  
Subject Specific ◽  
Better Than

AbstractOver the last decade there has been growing interest in understanding the brain activity in the absence of any task or stimulus captured by the resting-state functional magnetic resonance imaging (rsfMRI). These resting state patterns are not static, but exhibit complex spatio-temporal dynamics. In the recent years substantial effort has been put to characterize different FC configurations while brain states makes transitions over time. The dynamics governing this transitions and their relationship with stationary functional connectivity remains elusive. Over the last years a multitude of methods has been proposed to discover and characterize FC dynamics and one of the most accepted method is sliding window approach. Moreover, as these FC configurations are observed to be cyclically repeating in time there was further motivation to use of a generic clustering scheme to identify latent states of dynamics. We discover the underlying lower-dimensional manifold of the temporal structure which is further parameterized as a set of local density distributions, or latent transient states. We propose an innovative method that learns parameters specific to these latent states using a graph-theoretic model (temporal Multiple Kernel Learning, tMKL) and finally predicts the grand average functional connectivity (FC) of the unseen subjects by leveraging a state transition Markov model. tMKL thus learns a mapping between the underlying anatomical network and the temporal structure. Training and testing were done using the rs-fMRI data of 46 healthy participants and the results establish the viability of the proposed solution. Parameters of the model are learned via state-specific optimization formulations and yet the model performs at par or better than state-of-the-art models for predicting the grand average FC. Moreover, the model shows sensitivity towards subject-specific anatomy. The proposed model performs significantly better than the established models of predicting resting state functional connectivity based on whole-brain dynamic mean-field model, single diffusion kernel model and another version of multiple kernel learning model. In summary, We provide a novel solution that does not make strong assumption about underlying data and is generally applicable to resting or task data to learn subject specific state transitions and successful characterization of SC-dFC-FC relationship through an unifying framework.

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