scholarly journals Young Infants Process Prediction Errors at the Theta Rhythm

2020 ◽  
Author(s):  
Moritz Köster ◽  
Miriam Langeloh ◽  
Christine Michel ◽  
Stefanie Hoehl
Keyword(s):  
Theta Rhythm ◽  
Time Window ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Unexpected Events ◽  
Young Infants ◽  
Human Brain Development ◽  
Social Events ◽  
Infant Brain ◽  
Process Prediction

AbstractExamining how young infants respond to unexpected events is key to our understanding of their emerging concepts about the world around them. From a predictive processing perspective, it is intriguing to investigate how the infant brain responds to unexpected events (i.e., prediction errors), because they require infants to refine their predictive models about the environment. Here, to better understand prediction error processes in the infant brain, we presented 9-month-olds (N = 36) a variety of physical and social events with unexpected versus expected outcomes, while recording their electroencephalogram. We found a pronounced response in the ongoing 4 – 5 Hz theta rhythm for the processing of unexpected (in contrast to expected) events, for a prolonged time window (2 s) and across all scalp-recorded electrodes. The condition difference in the theta rhythm was not related to the condition difference in infants’ event-related activity on the negative central (Nc) component (.4 – .6 s), which has been described in former studies. These findings constitute critical evidence that the theta rhythm is involved in the processing of prediction errors from very early in human brain development, which may support infants’ refinement of basic concepts about the physical and social environment.

scholarly journals Young Infants Process Prediction Errors at the Theta Rhythm

NeuroImage ◽  
2021 ◽  
pp. 118074
Author(s):  
Moritz Köster ◽  
Miriam Langeloh ◽  
Christine Michel ◽  
Stefanie Hoehl
Keyword(s):  
Theta Rhythm ◽  
Prediction Errors ◽  
Young Infants ◽  
Process Prediction

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 Predictive neural processing in adult zebrafish depends on shank3b

2019 ◽  
Author(s):  
Kuo‐Hua Huang ◽  
Peter Rupprecht ◽  
Michael Schebesta ◽  
Fabrizio Serluca ◽  
Kyohei Kitamura ◽  
...  
Keyword(s):  
Neuronal Activity ◽  
Sensory Input ◽  
Motor Behavior ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Neuronal Responses ◽  
Adult Zebrafish ◽  
Disease Phenotypes ◽  
Intelligent Behavior ◽  
Widespread Phenomenon

SummaryIntelligent behavior requires a comparison between the predicted and the actual consequences of behavioral actions. According to the theory of predictive processing, this comparison relies on a neuronal error signal that reflects the mismatch between an internal prediction and sensory input. Inappropriate error signals may generate pathological experiences in neuropsychiatric conditions. To examine the processing of sensorimotor prediction errors across different telencephalic brain areas we optically measured neuronal activity in head-fixed, adult zebrafish in a virtual reality. Brief perturbations of visuomotor feedback triggered distinct changes in swimming behavior and different neuronal responses. Neuronal activity reflecting sensorimotor mismatch, rather than sensory input or motor output alone, was prominent throughout multiple forebrain areas. This activity preceded and predicted the transition in motor behavior. Error signals were altered in specific forebrain regions by a mutation in the autism-related gene shank3b. Predictive processing is therefore a widespread phenomenon that may contribute to disease phenotypes.

scholarly journals Constructing the hierarchy of predictive auditory sequences in the marmoset brain

2021 ◽  
Author(s):  
Yuwei Jiang ◽  
Misako Komatsu ◽  
Yuyan Chen ◽  
Ruoying Xie ◽  
Kaiwei Zhang ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Time Scale ◽  
Essential Feature ◽  
Predictive Coding ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Predictive Processing ◽  
Short Time Scale ◽  
Prediction Errors ◽  
Surface Areas

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

scholarly journals Young Children Integrate Current Observations, Priors and Agent Information to Build Predictive Models of Others’ Actions

2018 ◽  
Author(s):  
E. Kayhan ◽  
L. Heil ◽  
J. Kwisthout ◽  
I. van Rooij ◽  
S. Hunnius ◽  
...  
Keyword(s):  
Young Children ◽  
Bayesian Network ◽  
Computational Model ◽  
Predictive Models ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Model Based ◽  
Pupillary Responses ◽  
Causal Bayesian Network ◽  
Processing Framework

AbstractFrom early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agent’s preferences from systematic violations of random sampling. We investigated how young children build and update models of an agent’s sampling actions over time, and whether a computational model based on the causal Bayesian network formalization of predictive processing can explain this process.We formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to build predictive models of others’ actions. We measured pupillary responses as a behavioral marker of ‘prediction errors’ (i.e., the perceived mismatch between what one’s model of an agent predicts and what the agent actually does), as described in the predictive processing framework. Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to generate predictive models of agents and their actions.These findings shed light on the mechanisms behind toddlers’ inferences about agent-caused events. To our knowledge, this is the first study in which young children’s pupillary responses are used as markers of prediction errors, and explained by a computational model based on the causal Bayesian network formalization of predictive processing. We argue that the predictive processing framework provides a promising explanation of the way in which young children process other persons’ actions.HighlightsWe present three formalized hypotheses on how young children generate predictive models of others’ sampling actions.We measured pupillary responses of children as a behavioral marker of prediction errors as described in the predictive processing framework.Results showed that young children integrated information about current observations, prior probabilities and agents to generate predictive models about others’ actions.A computational model based on the causal Bayesian network formalization of predictive processing can explain this process.

scholarly journals Adolescent motherhood in rural Nepal: passive sensing data illuminates daily experiences in maternal lives

2020 ◽  
Author(s):  
Ashley Hagaman ◽  
Damaris Lopez Mercado ◽  
Anubhuti Poudyal ◽  
Dorte Bemme ◽  
Clare Boone ◽  
...  
Keyword(s):  
Adolescent Pregnancy ◽  
Adolescent Mothers ◽  
Low Income ◽  
Time Window ◽  
Well Being ◽  
Comparative Approach ◽  
Behavioral Patterns ◽  
Young Infants ◽  
Passive Sensing ◽  
Passive Data

Abstract Background: Adolescent pregnancy, particularly in low-income settings, is associated with adverse health and social outcomes for both mother and child. Nepal has the second highest rate of adolescent pregnancy in South Asia alongside high rates of maternal depression and suicide. While the maternal morbidity risks of adolescent pregnancy are well researched, impacts on everyday lives, including behaviors and predictable patterns are less well-known. Passive sensing (geographic movement, physical activity, and proximity to infants using Bluetooth technology) is an emerging technology that can enhance the detection of behavior patterns. Given the risk of the postpartum period in LMIC settings, we sought to characterize normal behavioral patterns via passive sensing technology. Methods: We collected passive data over a two-week period with 22 mothers using phone-based GPS, accelerometry, and Bluetooth technologies. Passive data was aggregated for each mother, collapsed into hourly readings, and descriptively summarized. We triangulated this information in a constant comparative approach with a range of qualitative data including multiple in-depth interviews, a daily diary, and systematic fieldnotes. Results: Passive data illuminated a range of behaviors that varied across our participants. Women, during the average time window of 4am and 8pm, spent more than 80% of the day with their infants, were detected as ‘active’ 10-20% of the time in any given hour with peaks in the morning and mid-afternoon, and traveled fewer than 1675 meters from their homes. Household work, instrumental childcare, and household support, paired with the infant’s age, appeared to drive activity patterns. Women with higher amounts of activity and GPS movement had more household support for chores and childcare. Women with young infants had smaller amounts of activity and GPS movement. Women that had nearly no time away from their infant expressed overwhelming responsibilities and increased stress, but also role fulfillment in that time with their infant was an indicator of good mothering. Conclusion: We reveal typical behavioral patterns of rural adolescent mothers and highlight opportunities for integrating this information to improve health and well-being.

scholarly journals Focus of attention modulates the heartbeat evoked potential

2018 ◽  
Author(s):  
Frederike H. Petzschner ◽  
Lilian A. Weber ◽  
Katharina V. Wellstein ◽  
Gina Paolini ◽  
Cao Tri Do ◽  
...  
Keyword(s):  
Evoked Potential ◽  
Time Window ◽  
Predictive Coding ◽  
Sensory Information ◽  
Attentional Focus ◽  
The Body ◽  
Self Report ◽  
Prediction Errors ◽  
Theoretical Frameworks ◽  
Heartbeat Evoked Potential

AbstractTheoretical frameworks such as predictive coding suggest that the perception of the body and world – interoception and exteroception – involve intertwined processes of inference, learning, and prediction. In this framework, attention is thought to gate the influence of sensory information on perception. In contrast to exteroception, there is limited evidence for purely attentional effects on interoception. Here, we empirically tested if attentional focus modulates cortical processing of single heartbeats, using a newly-developed experimental paradigm to probe purely attentional differences between exteroceptive and interoceptive conditions in the heartbeat evoked potential (HEP). We found that the HEP is significantly higher during interoceptive compared to exteroceptive attention, in a time window of 520-580ms after the R-peak. Furthermore, this effect predicted self-report measures of autonomic system reactivity. This study thus provides direct evidence that the HEP is modulated by attention and supports recent interpretations of the HEP as a neural correlate of interoceptive prediction errors.

scholarly journals Decision making in auditory externalization perception

2020 ◽  
Author(s):  
Robert Baumgartner ◽  
Piotr Majdak
Keyword(s):  
Hearing Aids ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Model Framework ◽  
Sound Sources ◽  
Perceptual Inference ◽  
Inference Problem ◽  
Processing Theory ◽  
Hearing Devices ◽  
Error Accumulation

AbstractUnder natural listening conditions, humans perceptually attribute sounds to external objects in their environment. This core function of perceptual inference is often distorted when sounds are produced via hearing devices such as headphones or hearing aids, resulting in sources being perceived unrealistically close or even inside the head. Psychoacoustic studies suggest a mixed role of various cues contributing to the externalization process. We developed a model framework able to probe the contribution of cue-specific prediction errors and to contrast dynamic versus static decision strategies underlying externalization perception. The model was applied to various acoustic distortions with constant reverberation. Our results suggest that the decisions follow a static, weighted accumulation of prediction errors for both monaural and interaural spectral shapes, without a significant contribution of other auditory cues. The weighted error accumulation supports generalizability of predictive processing theory to the perceptual inference problem of spatial hearing.Impact StatementA static rather than dynamic weighting of sensory prediction errors explains the inability to attribute auditory sensations to external sound sources.

scholarly journals The Predictive Dynamics of Happiness and Well-Being

2021 ◽  
pp. 175407392110638
Author(s):  
Mark Miller ◽  
Erik Rietveld ◽  
Julian Kiverstein
Keyword(s):  
Mental Health ◽  
Well Being ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Affective States ◽  
Human Flourishing ◽  
Making Sense ◽  
The Difference ◽  
Momentary Happiness ◽  
Error Dynamics

We offer an account of mental health and well-being using the predictive processing framework (PPF). According to this framework, the difference between mental health and psychopathology can be located in the goodness of the predictive model as a regulator of action. What is crucial for avoiding the rigid patterns of thinking, feeling and acting associated with psychopathology is the regulation of action based on the valence of affective states. In PPF, valence is modelled as error dynamics—the change in prediction errors over time . Our aim in this paper is to show how error dynamics can account for both momentary happiness and longer term well-being. What will emerge is a new neurocomputational framework for making sense of human flourishing.

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