scholarly journals Prediction error induced motor contagions in human behaviors

eLife ◽  
2018 ◽  
Vol 7 ◽  
Author(s):  
Tsuyoshi Ikegami ◽  
Gowrishankar Ganesh ◽  
Tatsuya Takeuchi ◽  
Hiroki Nakamoto
Keyword(s):  
Prediction Error ◽  
Action Observation ◽  
Whole Body ◽  
Prediction Errors ◽  
Action Prediction ◽  
Human Actions ◽  
Human Behaviors ◽  
Motor Contagion ◽  
Arm Reaching

Motor contagions refer to implicit effects on one's actions induced by observed actions. Motor contagions are believed to be induced simply by action observation and cause an observer's action to become similar to the action observed. In contrast, here we report a new motor contagion that is induced only when the observation is accompanied by prediction errors - differences between actions one observes and those he/she predicts or expects. In two experiments, one on whole-body baseball pitching and another on simple arm reaching, we show that the observation of the same action induces distinct motor contagions, depending on whether prediction errors are present or not. In the absence of prediction errors, as in previous reports, participants' actions changed to become similar to the observed action, while in the presence of prediction errors, their actions changed to diverge away from it, suggesting distinct effects of action observation and action prediction on human actions.

scholarly journals Prediction error induced motor contagions in human behaviors

2017 ◽  
Author(s):  
Tsuyoshi Ikegami ◽  
Gowrishankar Ganesh ◽  
Tatsuya Takeuchi ◽  
Hiroki Nakamoto
Keyword(s):  
Prediction Error ◽  
Action Observation ◽  
Prediction Errors ◽  
Action Prediction ◽  
Human Actions ◽  
Human Behaviors ◽  
Motor Contagion

AbstractMotor contagions refer to implicit effects on one’s actions induced by observation of other’s actions. Motor contagions are believed to be induced simply by action observation and cause an observer’s action to become similar to the observed action. In contrast, here we report a new motor contagion that is induced only when the observation is accompanied by prediction errors-differences between actions one observes and those he/she predicts or expects. Moreover, this contagion may not manifest as a similarity between one’s own and observed actions. In our experiment, observation of the same action induced distinct motor contagions, depending on whether prediction errors are present or not. In the absence of prediction errors, similarly to previous reports, participants’ actions changed to become similar to the observed action, while in the presence of prediction errors, their actions changed to diverge away from it. Our results suggest distinct effects of action observation and action prediction on human actions.

scholarly journals Locus Coeruleus tracking of prediction errors optimises cognitive flexibility: an Active Inference model

2018 ◽  
Author(s):  
Anna C Sales ◽  
Karl J. Friston ◽  
Matthew W. Jones ◽  
Anthony E. Pickering ◽  
Rosalyn J. Moran
Keyword(s):  
Locus Coeruleus ◽  
Prediction Error ◽  
Internal Models ◽  
Prediction Errors ◽  
Active Inference ◽  
Behavioural Flexibility ◽  
Action Prediction ◽  
Belief Updating ◽  
State Action ◽  
The Brain

AbstractThe locus coeruleus (LC) in the pons is the major source of noradrenaline (NA) in the brain. Two modes of LC firing have been associated with distinct cognitive states: changes in tonic rates of firing are correlated with global levels of arousal and behavioural flexibility, whilst phasic LC responses are evoked by salient stimuli. Here, we unify these two modes of firing by modelling the response of the LC as a correlate of a prediction error when inferring states for action planning under Active Inference (AI).We simulate a classic Go/No-go reward learning task and a three-arm foraging task and show that, if LC activity is considered to reflect the magnitude of high level ‘state-action’ prediction errors, then both tonic and phasic modes of firing are emergent features of belief updating. We also demonstrate that when contingencies change, AI agents can update their internal models more quickly by feeding back this state-action prediction error – reflected in LC firing and noradrenaline release – to optimise learning rate, enabling large adjustments over short timescales. We propose that such prediction errors are mediated by cortico-LC connections, whilst ascending input from LC to cortex modulates belief updating in anterior cingulate cortex (ACC).In short, we characterise the LC/ NA system within a general theory of brain function. In doing so, we show that contrasting, behaviour-dependent firing patterns are an emergent property of the LC’s crucial role in translating prediction errors into an optimal mediation between plasticity and stability.Author SummaryThe brain uses sensory information to build internal models and make predictions about the world. When errors of prediction occur, models must be updated to ensure desired outcomes are still achieved. Neuromodulator chemicals provide a possible pathway for triggering such changes in brain state. One such neuromodulator, noradrenaline, originates predominantly from a cluster of neurons in the brainstem – the locus coeruleus (LC) – and plays a key role in behaviour, for instance, in determining the balance between exploiting or exploring the environment.Here we use Active Inference (AI), a mathematical model of perception and action, to formally describe LC function. We propose that LC activity is triggered by errors in prediction and that the subsequent release of noradrenaline alters the rate of learning about the environment. Biologically, this describes an LC-cortex feedback loop promoting behavioural flexibility in times of uncertainty. We model LC output as a simulated animal performs two tasks known to elicit archetypal responses. We find that experimentally observed ‘phasic’ and ‘tonic’ patterns of LC activity emerge naturally, and that modulation of learning rates improves task performance. This provides a simple, unified computational account of noradrenergic computational function within a general model of behaviour.

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.

scholarly journals A Homeostatic Model of Subjective Cognitive Decline

2018 ◽  
Vol 8 (12) ◽  
pp. 228 ◽  
Author(s):  
Akiko Mizuno ◽  
Maria Ly ◽  
Howard Aizenstein
Keyword(s):  
Cognitive Decline ◽  
Prediction Error ◽  
Primary Source ◽  
Subjective Cognitive Decline ◽  
Prediction Errors ◽  
Functional Neuroanatomy ◽  
System A ◽  
Prediction Error Minimization ◽  
Breakdown Model ◽  
Normal Cognition

Subjective Cognitive Decline (SCD) is possibly one of the earliest detectable signs of dementia, but we do not know which mental processes lead to elevated concern. In this narrative review, we will summarize the previous literature on the biomarkers and functional neuroanatomy of SCD. In order to extend upon the prevailing theory of SCD, compensatory hyperactivation, we will introduce a new model: the breakdown of homeostasis in the prediction error minimization system. A cognitive prediction error is a discrepancy between an implicit cognitive prediction and the corresponding outcome. Experiencing frequent prediction errors may be a primary source of elevated subjective concern. Our homeostasis breakdown model provides an explanation for the progression from both normal cognition to SCD and from SCD to advanced dementia stages.

Process Mean Shift Detection Using Prediction Error Analysis

1998 ◽  
Vol 120 (3) ◽  
pp. 489-495 ◽  
Author(s):  
S. J. Hu ◽  
Y. G. Liu
Keyword(s):  
Error Analysis ◽  
Prediction Error ◽  
Control Charts ◽  
Mean Shift ◽  
Measurement Data ◽  
Step Function ◽  
False Alarms ◽  
Prediction Errors ◽  
Function Type ◽  
Shift Detection

Autocorrelation in 100 percent measurement data results in false alarms when the traditional control charts, such as X and R charts, are applied in process monitoring. A popular approach proposed in the literature is based on prediction error analysis (PEA), i.e., using time series models to remove the autocorrelation, and then applying the control charts to the residuals, or prediction errors. This paper uses a step function type mean shift as an example to investigate the effect of prediction error analysis on the speed of mean shift detection. The use of PEA results in two changes in the 100 percent measurement data: (1) change in the variance, and (2) change in the magnitude of the mean shift. Both changes affect the speed of mean shift detection. These effects are model parameter dependent and are obtained quantitatively for AR(1) and ARMA(2,1) models. Simulations and examples from automobile body assembly processes are used to demonstrate these effects. It is shown that depending on the parameters of the AMRA models, the speed of detection could be increased or decreased significantly.

The Embodiment of Concepts

2018 ◽  
pp. 640-660
Author(s):  
Michiel Van Elk ◽  
Harold Bekkering
Keyword(s):  
Prediction Error ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Conceptual Representation ◽  
Recurrent Processing ◽  
Different Dimensions ◽  
Prior Models ◽  
Processing Framework

We characterize theories of conceptual representation as embodied, disembodied, or hybrid according to their stance on a number of different dimensions: the nature of concepts, the relation between language and concepts, the function of concepts, the acquisition of concepts, the representation of concepts, and the role of context. We propose to extend an embodied view of concepts, by taking into account the importance of multimodal associations and predictive processing. We argue that concepts are dynamically acquired and updated, based on recurrent processing of prediction error signals in a hierarchically structured network. Concepts are thus used as prior models to generate multimodal expectations, thereby reducing surprise and enabling greater precision in the perception of exemplars. This view places embodied theories of concepts in a novel predictive processing framework, by highlighting the importance of concepts for prediction, learning and shaping categories on the basis of prediction errors.

scholarly journals Prefrontal solution to the bias-variance tradeoff during reinforcement learning

2020 ◽  
Author(s):  
Dongjae Kim ◽  
Jaeseung Jeong ◽  
Sang Wan Lee
Keyword(s):  
Adaptive Control ◽  
Reinforcement Learning ◽  
Prediction Error ◽  
Brain Regions ◽  
Decision Task ◽  
Prediction Errors ◽  
Model Based ◽  
Model Free ◽  
Bias Variance ◽  
The Brain

AbstractThe goal of learning is to maximize future rewards by minimizing prediction errors. Evidence have shown that the brain achieves this by combining model-based and model-free learning. However, the prediction error minimization is challenged by a bias-variance tradeoff, which imposes constraints on each strategy’s performance. We provide new theoretical insight into how this tradeoff can be resolved through the adaptive control of model-based and model-free learning. The theory predicts the baseline correction for prediction error reduces the lower bound of the bias–variance error by factoring out irreducible noise. Using a Markov decision task with context changes, we showed behavioral evidence of adaptive control. Model-based behavioral analyses show that the prediction error baseline signals context changes to improve adaptability. Critically, the neural results support this view, demonstrating multiplexed representations of prediction error baseline within the ventrolateral and ventromedial prefrontal cortex, key brain regions known to guide model-based and model-free learning.One sentence summaryA theoretical, behavioral, computational, and neural account of how the brain resolves the bias-variance tradeoff during reinforcement learning is described.

The Associative Learning Prediction Error Feedback Loop as Reversible Driving Belt of the Bioenergetics of Longevity

2021 ◽  
Author(s):  
Matthew S Price
Keyword(s):  
Associative Learning ◽  
Prediction Error ◽  
Feedback Loop ◽  
Telomerase Activity ◽  
Behavioral Strategy ◽  
Prediction Errors ◽  
Error Feedback ◽  
Treatment Expectations ◽  
Telomere Shortening ◽  
Inhibitory Learning

Leukocyte telomere shortening is a useful biomarker of biological and cellular age that occurs at an accelerated rate in anxiety disorders and posttraumatic stress disorder (PTSD). Intriguingly, inhibitory learning — the systematic exposure to noxious stimuli that serves as a basis for many treatments for anxiety, phobia, and PTSD —reduces relative telomeres attrition rates and increases protective telomerase activity in a manner predictive of treatment response. How does inhibitory learning, a behavioral strategy, modulate organismal chromosomal activity? Inhibitory learning may induce repeated mismatch between treatment expectations, intrasession states, and eventual outcome. Nevertheless, inhibitory learning can incentivize repetition of the behavior. Thus, this paper aims to conceptualize inhibitory learning as involving a ‘prediction error feedback loop’, i.e., a series of self-perpetuating prediction errors — mismatches between expectations and outcomes — that enhances neural inhibitory regulation to effectuate extinction. Inhibitory learning is necessarily predicated upon an opposing process – excitatory learning – that may be conceptualized as a prediction error feedback loop that operates in reverse to inhibitory learning and enhances neural excitability as arousal. Together, excitatory and inhibitory learning may be elements of an associative learning prediction error feedback loop responsible for modulating neural bioenergetic rates, leading to changes in downstream cellular signaling that could explain reduced or increased rates of leukocyte telomere shortening and telomerase activity from each behavioral strategy, respectively.

scholarly journals Can patients with cerebellar disease switch learning mechanisms to reduce their adaptation deficits?

Brain ◽  
2019 ◽  
Vol 142 (3) ◽  
pp. 662-673 ◽  
Author(s):  
Aaron L Wong ◽  
Cherie L Marvel ◽  
Jordan A Taylor ◽  
John W Krakauer
Keyword(s):  
Prediction Error ◽  
Poor Performance ◽  
Visuomotor Adaptation ◽  
Cerebellar Degeneration ◽  
Prediction Errors ◽  
Cerebellar Disease ◽  
Learning Mechanisms ◽  
Visuomotor Rotation ◽  
Age Related ◽  
Sensory Prediction

Abstract Systematic perturbations in motor adaptation tasks are primarily countered by learning from sensory-prediction errors, with secondary contributions from other learning processes. Despite the availability of these additional processes, particularly the use of explicit re-aiming to counteract observed target errors, patients with cerebellar degeneration are surprisingly unable to compensate for their sensory-prediction error deficits by spontaneously switching to another learning mechanism. We hypothesized that if the nature of the task was changed—by allowing vision of the hand, which eliminates sensory-prediction errors—patients could be induced to preferentially adopt aiming strategies to solve visuomotor rotations. To test this, we first developed a novel visuomotor rotation paradigm that provides participants with vision of their hand in addition to the cursor, effectively setting the sensory-prediction error signal to zero. We demonstrated in younger healthy control subjects that this promotes a switch to strategic re-aiming based on target errors. We then showed that with vision of the hand, patients with cerebellar degeneration could also switch to an aiming strategy in response to visuomotor rotations, performing similarly to age-matched participants (older controls). Moreover, patients could retrieve their learned aiming solution after vision of the hand was removed (although they could not improve beyond what they retrieved), and retain it for at least 1 year. Both patients and older controls, however, exhibited impaired overall adaptation performance compared to younger healthy controls (age 18–33 years), likely due to age-related reductions in spatial and working memory. Patients also failed to generalize, i.e. they were unable to adopt analogous aiming strategies in response to novel rotations. Hence, there appears to be an inescapable obligatory dependence on sensory-prediction error-based learning—even when this system is impaired in patients with cerebellar disease. The persistence of sensory-prediction error-based learning effectively suppresses a switch to target error-based learning, which perhaps explains the unexpectedly poor performance by patients with cerebellar degeneration in visuomotor adaptation tasks.

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