prediction error
Recently Published Documents


TOTAL DOCUMENTS

2435
(FIVE YEARS 786)

H-INDEX

84
(FIVE YEARS 10)

Complexity ◽  
2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Parmod Kumar Paul ◽  
Om Prakash Mahela ◽  
Baseem Khan

For selecting and interpreting appropriate behaviour of proportion between buy/neutral/sell patterns and high/moderate/low returns, the prediction error reduction index is a very useful tool. It is operationally interpretable in terms of the proportional reduction in error of estimation. We first obtain the buy/sell pattern using an Optimal Band. The analysis of the association between patterns and returns is based on the Goodman–Kruskal prediction error reduction index ( λ ). Empirical analysis suggests that the prediction of returns from patterns is more impressive or of less error as compared to the prediction of patterns from returns. We demonstrated the prediction index for Index NIFTY 50, BANK-NIFTY, and NIFTY-IT of NSE (National Stock Exchange), for the period 2010–2020.


Vision ◽  
2022 ◽  
Vol 6 (1) ◽  
pp. 5
Author(s):  
Beatríz Macías-Murelaga ◽  
Gonzaga Garay-Aramburu ◽  
Roberto Bergado-Mijangos ◽  
Daniel Coello-Ojeda ◽  
Itziar Ozaeta ◽  
...  

The aim of this study was to assess the stability and differences between objective (O-Rx) and subjective (S-Rx) refraction for the assessment of the prediction error (PE). A secondary aim was to report the results of a monofocal intraocular lens (IOL). 100 subjects were included for whom S-Rx and O-Rx were obtained for all visits, and for visual performance, posterior capsular opacification incidence and Nd:YAG rates at 12 months. Either S-Rx and O-Rx showed a hyperopic shift from 1 to 6 months (p < 0.05) and stabilization after 6 months. S-Rx was related with the axial length (rho = −0.29, p = 0.007), obtaining a major tendency towards hyperopia in short eyes implanted with high-power IOLs. O-Rx showed a myopic shift in comparison to S-Rx (p < 0.05). This resulted in a decrease of the number of eyes in ±0.50 D and ±1.00 D from 79 to 67% and from 94 to 90%, respectively. The median (interquartile range) uncorrected and corrected visual acuities were 0.1 (0.29) and 0 (0.12) logMAR, respectively, and seven eyes required Nd:YAG capsulotomy at 12 months. Some caution should be taken in PE studies in which O-Rx is used or S-Rx is measured in a 1-month follow-up. Constant optimization should be conducted for this IOL after S-Rx stabilization.


2022 ◽  
Author(s):  
Tobias Kube

When updating beliefs in light of new information, people preferentially integrate information that is consistent with their prior beliefs and helps them construe a coherent view of the world. Such a selective integration of new information likely contributes to belief polarisation and compromises public discourse. Therefore, it is crucial to understand the factors that underlie biased belief updating. To this end, I conducted three pre-registered experiments covering different controversial political issues (i.e., Experiment 1: climate change, Experiment 2: speed limit on highways, Experiment 3: immigration in relation to violent crime). The main hypothesis was that negative reappraisal of new information (referred to as “cognitive immunisation”) hinders belief updating. Support for this hypothesis was found only in Experiment 2. In all experiments, the magnitude of the prediction error (i.e., the discrepancy between prior beliefs and new information) was strongly related to belief updating. Across experiments, participants’ general attitudes regarding the respective issue influenced the strength of beliefs, but not their update. The present findings provide some indication that the engagement in cognitive immunisation can lead to the maintenance of beliefs despite disconfirming information. However, by far the largest association with belief updating was with the magnitude of the prediction error.


2022 ◽  
pp. 1-49
Author(s):  
Tiberiu Teşileanu ◽  
Siavash Golkar ◽  
Samaneh Nasiri ◽  
Anirvan M. Sengupta ◽  
Dmitri B. Chklovskii

Abstract The brain must extract behaviorally relevant latent variables from the signals streamed by the sensory organs. Such latent variables are often encoded in the dynamics that generated the signal rather than in the specific realization of the waveform. Therefore, one problem faced by the brain is to segment time series based on underlying dynamics. We present two algorithms for performing this segmentation task that are biologically plausible, which we define as acting in a streaming setting and all learning rules being local. One algorithm is model based and can be derived from an optimization problem involving a mixture of autoregressive processes. This algorithm relies on feedback in the form of a prediction error and can also be used for forecasting future samples. In some brain regions, such as the retina, the feedback connections necessary to use the prediction error for learning are absent. For this case, we propose a second, model-free algorithm that uses a running estimate of the autocorrelation structure of the signal to perform the segmentation. We show that both algorithms do well when tasked with segmenting signals drawn from autoregressive models with piecewise-constant parameters. In particular, the segmentation accuracy is similar to that obtained from oracle-like methods in which the ground-truth parameters of the autoregressive models are known. We also test our methods on data sets generated by alternating snippets of voice recordings. We provide implementations of our algorithms at https://github.com/ttesileanu/bio-time-series.


2022 ◽  
Author(s):  
Moritz Moeller ◽  
Sanjay Manohar ◽  
Rafal Bogacz

To accurately predict rewards associated with states or actions, the variability of observations has to be taken into account. In particular, when the observations are noisy, the individual rewards should have less influence on tracking of average reward, and the estimate of the mean reward should be updated to a smaller extent after each observation. However, it is not known how the magnitude of the observation noise might be tracked and used to control prediction updates in the brain reward system. Here, we introduce a new model that uses simple, tractable learning rules that track the mean and standard deviation of reward, and leverages prediction errors scaled by uncertainty as the central feedback signal. We provide a normative analysis, comparing the performance of the new model with that of conventional models in a value tracking task. We find that the new model has an advantage over conventional models when tested across various levels of observation noise. Further, we propose a possible biological implementation of the model in the basal ganglia circuit. The scaled prediction error feedback signal is consistent with experimental findings concerning dopamine prediction error scaling relative to reward magnitude, and the update rules are found to be consistent with many features of striatal plasticity. Our results span across the levels of implementation, algorithm, and computation, and might have important implications for understanding the dopaminergic prediction error signal and its relation to adaptive and effective learning.


Author(s):  
Xiao-qi Zhang ◽  
Si-qi Jiang

Storm surge prediction is of great importance to disaster prevention and mitigation. In this study, four optimization algorithms including genetic algorithm (GA), particle swarm optimization (PSO), beetle antenna search (BAS), and beetle swarm optimization (BSO) are used to optimize the back propagation neural network (BPNN), and four optimized BPNNs for storm surge prediction are proposed and applied to Yulin station and Xiuying station at Hainan, China. The optimal model parameter combination is determined by trail-and-error method for the best prediction performance. Comparisons with the single BPNN indicate that storm surge can be efficiently predicted using the optimized BPNNs. BPNN optimized by BSO has the minimum prediction error, and BPNN optimized by BAS has the minimum time cost to reduce unit prediction error.


2022 ◽  
Author(s):  
Joshua Martin

According to the predictive processing framework, perception is geared to represent the environment in terms of embodied action opportunities as opposed to objective truth. Here, we argue that such an optimisation is reflected by biases in expectations (i.e., prior predictive information) that facilitate ‘useful’ inferences of external sensory causes. To support this, we highlight a body of literature suggesting that perception is systematically biased away from accurate estimates under conditions where utility and accuracy conflict with one another. We interpret this to reflect the brain’s attempt to adjudicate between conflicting sources of prediction error, as external accuracy is sacrificed to facilitate actions that proactively avoid physiologically surprising outcomes. This carries important theoretical implications and offers new insights into psychopathology.


Sign in / Sign up

Export Citation Format

Share Document