scholarly journals Right on time: prediction-errors bidirectionally bias time perception

2019 ◽  
Author(s):  
Ido Toren ◽  
Kristoffer Aberg ◽  
Rony Paz
Keyword(s):  
Time Perception ◽  
Prediction Error ◽  
Discrimination Task ◽  
Internal Representation ◽  
Time Estimation ◽  
Duration Discrimination ◽  
Main Task ◽  
Prediction Errors ◽  
Time Duration ◽  
Negative Prediction

SummaryThe brain updates internal representation of the environment by using the mismatch between the predicted state/outcome and the actual one, termed prediction-error. In parallel, time perception in the sub-second range is crucial for many behaviors such as movement, learning, memory, attention and speech. Both time-perception and prediction-errors are essential for everyday life function of an organism, and interestingly, the striatum was shown to be independently involved in both functions. We therefore hypothesized that the putative shared circuitry might induce behavioral interaction, namely that prediction-errors might bias time perception. To examine this, participants performed a time-duration discrimination task in the presence of positive and negative prediction-errors that were irrelevant and independent of the main task. We find that positive/negative prediction-errors induce a bias in time perception by increasing/decreasing the perceived time, respectively. Using functional imaging, we identify an interaction in Putamen activity between encoding of prediction-error and performance in the discrimination task. A model that accounts for the behavioral and physiological observations confirms that the interaction in regional activations for prediction-errors and time-estimation underlies the observed bias. Our results demonstrate that these two presumably independent roles of the striatum can actually interfere or aid one another in specific scenarios.

scholarly journals Dopamine reward prediction error coding

2016 ◽  
Vol 18 (1) ◽  
pp. 23-32 ◽  
Keyword(s):  
Prediction Error ◽  
Dopamine Neurons ◽  
Prediction Errors ◽  
Reward Prediction Error ◽  
Reward Prediction ◽  
Negative Prediction ◽  
Baseline Activity ◽  
Error Coding ◽  
Reward Value ◽  
Dopamine Signal

Reward prediction errors consist of the differences between received and predicted rewards. They are crucial for basic forms of learning about rewards and make us strive for more rewards—an evolutionary beneficial trait. Most dopamine neurons in the midbrain of humans, monkeys, and rodents signal a reward prediction error; they are activated by more reward than predicted (positive prediction error), remain at baseline activity for fully predicted rewards, and show depressed activity with less reward than predicted (negative prediction error). The dopamine signal increases nonlinearly with reward value and codes formal economic utility. Drugs of addiction generate, hijack, and amplify the dopamine reward signal and induce exaggerated, uncontrolled dopamine effects on neuronal plasticity. The striatum, amygdala, and frontal cortex also show reward prediction error coding, but only in subpopulations of neurons. Thus, the important concept of reward prediction errors is implemented in neuronal hardware.

From Feedback- to Response-based Performance Monitoring in Active and Observational Learning

2014 ◽  
Vol 26 (9) ◽  
pp. 2111-2127 ◽  
Author(s):  
Christian Bellebaum ◽  
Marco Colosio
Keyword(s):  
Active Learning ◽  
Negative Feedback ◽  
Observational Learning ◽  
Prediction Error ◽  
Performance Monitoring ◽  
Learning Performance ◽  
Behavioral Adaptation ◽  
Prediction Errors ◽  
Error Related Negativity ◽  
Negative Prediction

Humans can adapt their behavior by learning from the consequences of their own actions or by observing others. Gradual active learning of action–outcome contingencies is accompanied by a shift from feedback- to response-based performance monitoring. This shift is reflected by complementary learning-related changes of two ACC-driven ERP components, the feedback-related negativity (FRN) and the error-related negativity (ERN), which have both been suggested to signal events “worse than expected,” that is, a negative prediction error. Although recent research has identified comparable components for observed behavior and outcomes (observational ERN and FRN), it is as yet unknown, whether these components are similarly modulated by prediction errors and thus also reflect behavioral adaptation. In this study, two groups of 15 participants learned action–outcome contingencies either actively or by observation. In active learners, FRN amplitude for negative feedback decreased and ERN amplitude in response to erroneous actions increased with learning, whereas observational ERN and FRN in observational learners did not exhibit learning-related changes. Learning performance, assessed in test trials without feedback, was comparable between groups, as was the ERN following actively performed errors during test trials. In summary, the results show that action–outcome associations can be learned similarly well actively and by observation. The mechanisms involved appear to differ, with the FRN in active learning reflecting the integration of information about own actions and the accompanying outcomes.

scholarly journals Learning prediction error neurons in a canonical interneuron circuit

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Loreen Hertäg ◽  
Henning Sprekeler
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Prediction Error ◽  
Sensory Information ◽  
Prediction Errors ◽  
Negative Prediction ◽  
Inhibitory Plasticity ◽  
The Brain ◽  
Shed Light ◽  
Model Circuit

Sensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.

scholarly journals The Neural Correlates of Cued Reward Omission

2021 ◽  
Vol 15 ◽  
Author(s):  
Jessica A. Mollick ◽  
Luke J. Chang ◽  
Anjali Krishnan ◽  
Thomas E. Hazy ◽  
Kai A. Krueger ◽  
...  
Keyword(s):  
Prediction Error ◽  
Conditioned Inhibition ◽  
Brain Regions ◽  
Dopamine Neurons ◽  
Sensory Stimulus ◽  
Prediction Errors ◽  
Significant Activity ◽  
Lateral Habenula ◽  
Negative Prediction ◽  
Reward Delivery

Compared to our understanding of positive prediction error signals occurring due to unexpected reward outcomes, less is known about the neural circuitry in humans that drives negative prediction errors during omission of expected rewards. While classical learning theories such as Rescorla–Wagner or temporal difference learning suggest that both types of prediction errors result from a simple subtraction, there has been recent evidence suggesting that different brain regions provide input to dopamine neurons which contributes to specific components of this prediction error computation. Here, we focus on the brain regions responding to negative prediction error signals, which has been well-established in animal studies to involve a distinct pathway through the lateral habenula. We examine the activity of this pathway in humans, using a conditioned inhibition paradigm with high-resolution functional MRI. First, participants learned to associate a sensory stimulus with reward delivery. Then, reward delivery was omitted whenever this stimulus was presented simultaneously with a different sensory stimulus, the conditioned inhibitor (CI). Both reward presentation and the reward-predictive cue activated midbrain dopamine regions, insula and orbitofrontal cortex. While we found significant activity at an uncorrected threshold for the CI in the habenula, consistent with our predictions, it did not survive correction for multiple comparisons and awaits further replication. Additionally, the pallidum and putamen regions of the basal ganglia showed modulations of activity for the inhibitor that did not survive the corrected threshold.

scholarly journals Learning prediction error neurons in a canonical interneuron circuit

2020 ◽  
Author(s):  
Loreen Hertäg ◽  
Henning Sprekeler
Keyword(s):  
Visual Cortex ◽  
Primary Visual Cortex ◽  
Prediction Error ◽  
Sensory Information ◽  
Prediction Errors ◽  
Negative Prediction ◽  
Inhibitory Plasticity ◽  
The Brain ◽  
Shed Light ◽  
Model Circuit

AbstractSensory systems constantly compare external sensory information with internally generated predictions. While neural hallmarks of prediction errors have been found throughout the brain, the circuit-level mechanisms that underlie their computation are still largely unknown. Here, we show that a well-orchestrated interplay of three interneuron types shapes the development and refinement of negative prediction-error neurons in a computational model of mouse primary visual cortex. By balancing excitation and inhibition in multiple pathways, experience-dependent inhibitory plasticity can generate different variants of prediction-error circuits, which can be distinguished by simulated optogenetic experiments. The experience-dependence of the model circuit is consistent with that of negative prediction-error circuits in layer 2/3 of mouse primary visual cortex. Our model makes a range of testable predictions that may shed light on the circuitry underlying the neural computation of prediction errors.

scholarly journals Valence biases factual and counterfactual learning in opposite directions

2016 ◽  
Author(s):  
Stefano Palminteri ◽  
Germain Lefebvre ◽  
Emma J. Kilford ◽  
Sarah-Jayne Blakemore
Keyword(s):  
Reinforcement Learning ◽  
Computational Model ◽  
Prediction Error ◽  
Prediction Errors ◽  
Learning Tasks ◽  
Negative Prediction ◽  
Account Information ◽  
Factual Learning

AbstractPrevious studies suggest that factual learning, that is, learning from obtained outcomes, is biased, such that participants preferentially take into account positive, as compared to negative, prediction errors. However, whether or not the prediction error valence also affects counterfactual learning, that is, learning from forgone outcomes, is unknown. To address this question, we analysed the performance of two cohorts of participants on reinforcement learning tasks using a computational model that was adapted to test if prediction error valance influences learning. Concerning factual learning, we replicated previous findings of a valence-induced bias, whereby participants learned preferentially from positive, relative to negative, prediction errors. In contrast, for counterfactual learning, we found the opposite valence-induced bias: negative prediction errors were preferentially taken into account relative to positive ones. When considering valence-induced bias in the context of both factual and counterfactual learning, it appears that people tend to preferentially take into account information that confirms their current choice

scholarly journals A Time Duration Discrimination Task for the Study of Elapsed Time Processing in Rats

BIO-PROTOCOL ◽  
2021 ◽  
Vol 11 (6) ◽  
Author(s):  
Sarah Tenney ◽  
Eleftheria Vogiatzoglou ◽  
Deena Chohan ◽  
Annette Vo ◽  
Thomas Hunt ◽  
...  
Keyword(s):  
Discrimination Task ◽  
Duration Discrimination ◽  
Time Duration ◽  
Time Processing ◽  
Elapsed Time ◽  
Duration Discrimination Task

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

scholarly journals Gender differences in refraction prediction error of five formulas for cataract surgery

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Yibing Zhang ◽  
Tingyang Li ◽  
Aparna Reddy ◽  
Nambi Nallasamy
Keyword(s):  
Gender Differences ◽  
Cataract Surgery ◽  
Prediction Error ◽  
Statistical Difference ◽  
Independent Predictor ◽  
Study Data ◽  
Prediction Errors ◽  
Optical Biometry ◽  
Iol Power ◽  
Lens Power

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.

scholarly journals A Location Map Reduced Reversible Data Hiding Method Using Prediction Error Modification

2012 ◽  
Vol 6-7 ◽  
pp. 428-433
Author(s):  
Yan Wei Li ◽  
Mei Chen Wu ◽  
Tung Shou Chen ◽  
Wien Hong
Keyword(s):  
Image Quality ◽  
Data Hiding ◽  
Prediction Error ◽  
Reversible Data Hiding ◽  
Experimental Results ◽  
Cover Image ◽  
Prediction Errors ◽  
Secret Data ◽  
Location Map ◽  
Comparable Image Quality

We propose a reversible data hiding technique to improve Hong and Chen’s (2010) method. Hong and Chen divide the cover image into pixel group, and use reference pixels to predict other pixel values. Data are then embedded by modifying the prediction errors. However, when solving the overflow and underflow problems, they employ a location map to record the position of saturated pixels, and these pixels will not be used to carry data. In their method, if the image has a plenty of saturated pixels, the payload is decreased significantly because a lot of saturated pixels will not joint the embedment. We improve Hong and Chen’s method such that the saturated pixels can be used to carry data. The positions of these saturated pixels are then recorded in a location map, and the location map is embedded together with the secret data. The experimental results illustrate that the proposed method has better payload, will providing a comparable image quality.

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