Meta-analysis of human prediction error for incentives, perception, cognition, and action

2022 ◽  
Author(s):  
Philip R. Corlett ◽  
Jessica A. Mollick ◽  
Hedy Kober
Keyword(s):  
Prediction Error ◽  
Meta Analysis

scholarly journals Substrates of Human Prediction Error for Incentives, Perception, Cognition, and Action

2021 ◽  
Author(s):  
Philip R. Corlett ◽  
Jessica A Mollick ◽  
Hedy Kober
Keyword(s):  
Artificial Intelligence ◽  
Computational Neuroscience ◽  
Orbitofrontal Cortex ◽  
Prediction Error ◽  
Functional Neuroimaging ◽  
Meta Analysis ◽  
Basic Research ◽  
Preclinical Studies ◽  
Prediction Errors ◽  
Neural Firing

Prediction errors (PEs) are a keystone for computational neuroscience. Their association with midbrain neural firing has been confirmed across species and has inspired the construction of artificial intelligence that can outperform humans. However, there is still much to learn. Here, we leverage the wealth of human PE data acquired in the functional neuroimaging setting in service of a deeper understanding, using meta-analysis. Across 263 PE studies that have focused on reward, punishment, action, cognition, and perception, we found consistent region-PE associations that were posited theoretically or evinced in preclinical studies, but not yet established in humans, including midbrain PE signals during perceptual and Pavlovian tasks. Further, we found evidence for PEs over successor representations in orbitofrontal cortex, and for default mode network PE signals. By combining functional imaging meta-analysis with theory and basic research, we provide new insights into learning in machines, humans, and other animals.

scholarly journals Separate neural representations of prediction error valence and surprise: Evidence from an fMRI meta-analysis

2018 ◽  
Vol 39 (7) ◽  
pp. 2887-2906 ◽  
Author(s):  
Elsa Fouragnan ◽  
Chris Retzler ◽  
Marios G. Philiastides
Keyword(s):  
Prediction Error ◽  
Meta Analysis ◽  
Neural Representations

scholarly journals Accuracy of intraocular lens power calculation formulas after laser refractive surgery in myopic eyes: A meta-analysis

2019 ◽  
Author(s):  
Hongyu Li ◽  
Jun Li ◽  
Hui Song
Keyword(s):  
Intraocular Lens ◽  
Prediction Error ◽  
Refractive Surgery ◽  
Meta Analysis ◽  
Cochrane Library ◽  
Power Calculation ◽  
Intraocular Lens Power Calculation ◽  
Intraocular Lens Power ◽  
Lens Power ◽  
Lens Power Calculation

Abstract Background: To compare the accuracy of intraocular lens power calculation formulas after refractive surgery in myopic eyes. Methods: We searched the databases on the PubMed, EMBASE, Web of science and Cochrane library to select relevant studies published between Jan 1, 2009 and Aug 11, 2019. Primary outcomes were the percentages of refractive prediction error within ±0.5D and ±1.0D. Results: The results of this meta-analysis were investigated from 16 studies, including 7 common methods (Haigis-L, Shammas-PL, Double-K SRK/T, Barrett true K no history, Wang-Koch-Maloney, ASCRS average and OCT formula). ASCRS average yielded significantly higher percentage of refractive prediction error within ±0.5D than Haigis-L, Shammas-PL and W-K-M (P=0.009, 0.01, 0.008, respectively). Barrett true K no history also yielded significantly higher percentage of refractive prediction error within ±0.5D than Shammas-PL and W-K-M (P=0.01, <0.0001, respectively), and the same result was found by comparing OCT formula with Hiaigi-L and Shammas-PL (P=0.03, 0.01, respectively). Only the Haigis-L had significantly higher percentages than W-K-M method in the ±1.0D group (P = 0.04). Conclusion: We suggest that the ASCRS average and Barrett true K no history formula should be used to calculate the IOL power in eyes after myopic refractive surgery.

scholarly journals Accuracy of Intraocular Lens Power Calculation Formulas in Pediatric Cataract Patients: A Systematic Review and Meta-Analysis

2021 ◽  
Vol 8 ◽  
Author(s):  
Yueyang Zhong ◽  
Yibo Yu ◽  
Jinyu Li ◽  
Bing Lu ◽  
Su Li ◽  
...  
Keyword(s):  
Systematic Review ◽  
Intraocular Lens ◽  
Prediction Error ◽  
Meta Analysis ◽  
Power Calculation ◽  
Pediatric Cataract ◽  
Iol Power Calculation ◽  
Iol Power ◽  
The Mean ◽  
Cataract Patients

Background: Among the various intraocular lens (IOL) power calculation formulas available in clinical settings, which one can yield more accurate results is still inconclusive. We performed a meta-analysis to compare the accuracy of the IOL power calculation formulas used for pediatric cataract patients.Methods: Observational cohort studies published through April 2021 were systematically searched in PubMed, Web of Science, and EMBASE databases. For each included study, the mean differences of the mean prediction error and mean absolute prediction error (APE) were analyzed and compared using the random-effects model.Results: Twelve studies involving 1,647 eyes were enrolled in the meta-analysis, and five formulas were compared: Holladay 1, Holladay 2, Hoffer Q, SRK/T, and SRK II. Holladay 1 exhibited the smallest APE (0.97; 95% confidence interval [CI]: 0.92–1.03). For the patients with an axial length (AL) less than 22 mm, SRK/T showed a significantly smaller APE than SRK II (mean difference [MD]: −0.37; 95% CI: −0.63 to −0.12). For the patients younger than 24 months, SRK/T had a significantly smaller APE than Hoffer Q (MD: −0.28; 95% CI: −0.51 to −0.06). For the patients aged 24–60 months, SRK/T presented a significantly smaller APE than Holladay 2 (MD: −0.60; 95% CI: −0.93 to −0.26).Conclusion: Due to the rapid growth and high variability of pediatric eyes, the formulas for IOL calculation should be considered according to clinical parameters such as age and AL. The evidence obtained supported the accuracy and reliability of SRK/T under certain conditions.Systematic Review Registration: PROSPERO, identifier: INPLASY202190077.

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