scholarly journals “Virus and Epidemic”: Causal Knowledge Activates Prediction Error Circuitry

2010 ◽  
Vol 22 (10) ◽  
pp. 2151-2163 ◽  
Author(s):  
Daniela B. Fenker ◽  
Mircea A. Schoenfeld ◽  
Michael R. Waldmann ◽  
Hartmut Schuetze ◽  
Hans-Jochen Heinze ◽  
...  

Knowledge about cause and effect relationships (e.g., virus–epidemic) is essential for predicting changes in the environment and for anticipating the consequences of events and one's own actions. Although there is evidence that predictions and learning from prediction errors are instrumental in acquiring causal knowledge, it is unclear whether prediction error circuitry remains involved in the mental representation and evaluation of causal knowledge already stored in semantic memory. In an fMRI study, participants assessed whether pairs of words were causally related (e.g., virus–epidemic) or noncausally associated (e.g., emerald–ring). In a second fMRI study, a task cue prompted the participants to evaluate either the causal or the noncausal associative relationship between pairs of words. Causally related pairs elicited higher activity in OFC, amygdala, striatum, and substantia nigra/ventral tegmental area than noncausally associated pairs. These regions were also more activated by the causal than by the associative task cue. This network overlaps with the mesolimbic and mesocortical dopaminergic network known to code prediction errors, suggesting that prediction error processing might participate in assessments of causality even under conditions when it is not explicitly required to make predictions.

2021 ◽  
Vol 12 ◽  
Author(s):  
John Zulueta ◽  
Alexander Pantelis Demos ◽  
Claudia Vesel ◽  
Mindy Ross ◽  
Andrea Piscitello ◽  
...  

Background: Research by our group and others have demonstrated the feasibility of using mobile phone derived metadata to model mood and cognition. Given the effects of age and mood on cognitive performance, it was hypothesized that using such data a model could be built to predict chronological age and that differences between predicted age and actual age could be a marker of pathology.Methods: These data were collected via the ongoing BiAffect study. Participants complete the Mood Disorders Questionnaire (MDQ), a screening questionnaire for bipolar disorder, and self-reported their birth year. Data were split into training and validation sets. Features derived from the smartphone kinematics were used to train random forest regression models to predict age. Prediction errors were compared between participants screening positive and negative on the MDQ.Results: Three hundred forty-four participants had analyzable data of which 227 had positive screens for bipolar disorder and 117 had negative screens. The absolute prediction error tended to be lower for participants with positive screens (median 4.50 years) than those with negative screens (median 7.92 years) (W = 508, p = 0.0049). The raw prediction error tended to be lower for participants with negative screens (median = −5.95 years) than those with positive screens (median = 0.55 years) (W = 1,037, p= 0.037).Conclusions: The tendency to underestimate the chronological age of participants screening negative for bipolar disorder compared to those screening positive is consistent with the finding that bipolar disorder may be associated with brain changes that could reflect pathological aging. This interesting result could also reflect that those who screen negative for bipolar disorder and who engaged in the study were more likely to have higher premorbid functioning. This work demonstrates that age-related changes may be detected via a passive smartphone kinematics based digital biomarker.


2020 ◽  
Author(s):  
Alyssa H. Sinclair ◽  
Grace M. Manalili ◽  
Iva K. Brunec ◽  
R. Alison Adcock ◽  
Morgan D. Barense

AbstractThe brain supports adaptive behavior by generating predictions, learning from errors, and updating memories. Prediction error, or surprise, is a known trigger for memory updating; however, the mechanisms that link prediction error, neural representations, and naturalistic memory updating remain unknown. In an fMRI study, we elicited prediction errors by interrupting familiar narrative videos immediately before an expected conclusion. We found that prediction errors reversed the effect of post-video univariate hippocampal activation on subsequent memory: hippocampal activation predicted false memories after prediction errors, but protected memories from distortion after expected events. Tracking second-by-second neural patterns revealed that prediction errors disrupted the temporal continuity of hippocampal representations. This disruption of signal history led to memory updating after prediction error. We conclude that prediction errors during memory reactivation prompt the hippocampus to abandon ongoing predictions and neural representations. Following prediction error, the hippocampus switches to an externally-oriented processing mode that supports memory updating.


2020 ◽  
Vol 43 ◽  
Author(s):  
Kellen Mrkva ◽  
Luca Cian ◽  
Leaf Van Boven

Abstract Gilead et al. present a rich account of abstraction. Though the account describes several elements which influence mental representation, it is worth also delineating how feelings, such as fluency and emotion, influence mental simulation. Additionally, though past experience can sometimes make simulations more accurate and worthwhile (as Gilead et al. suggest), many systematic prediction errors persist despite substantial experience.


2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yibing Zhang ◽  
Tingyang Li ◽  
Aparna Reddy ◽  
Nambi Nallasamy

Abstract Objectives To evaluate gender differences in optical biometry measurements and lens power calculations. Methods Eight thousand four hundred thirty-one eyes of five thousand five hundred nineteen patients who underwent cataract surgery at University of Michigan’s Kellogg Eye Center were included in this retrospective study. Data including age, gender, optical biometry, postoperative refraction, implanted intraocular lens (IOL) power, and IOL formula refraction predictions were gathered and/or calculated utilizing the Sight Outcomes Research Collaborative (SOURCE) database and analyzed. Results There was a statistical difference between every optical biometry measure between genders. Despite lens constant optimization, mean signed prediction errors (SPEs) of modern IOL formulas differed significantly between genders, with predictions skewed more hyperopic for males and myopic for females for all 5 of the modern IOL formulas tested. Optimization of lens constants by gender significantly decreased prediction error for 2 of the 5 modern IOL formulas tested. Conclusions Gender was found to be an independent predictor of refraction prediction error for all 5 formulas studied. Optimization of lens constants by gender can decrease refraction prediction error for certain modern IOL formulas.


2018 ◽  
Vol 80 (1) ◽  
pp. 219-241 ◽  
Author(s):  
Stephanie C. Gantz ◽  
Christopher P. Ford ◽  
Hitoshi Morikawa ◽  
John T. Williams

2007 ◽  
Vol 18 (4) ◽  
pp. 740-751 ◽  
Author(s):  
N. Canessa ◽  
F. Borgo ◽  
S. F. Cappa ◽  
D. Perani ◽  
A. Falini ◽  
...  

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