scholarly journals Making Sense of the World: Infant Learning From a Predictive Processing Perspective

2020 ◽  
Vol 15 (3) ◽  
pp. 562-571 ◽  
Author(s):  
Moritz Köster ◽  
Ezgi Kayhan ◽  
Miriam Langeloh ◽  
Stefanie Hoehl
Keyword(s):  
Social Environment ◽  
Infant Development ◽  
Predictive Processing ◽  
Human Infants ◽  
Making Sense ◽  
Working Principle ◽  
Learning Principles ◽  
Theory Research ◽  
Infant Learning ◽  
Processing Framework

For human infants, the first years after birth are a period of intense exploration—getting to understand their own competencies in interaction with a complex physical and social environment. In contemporary neuroscience, the predictive-processing framework has been proposed as a general working principle of the human brain, the optimization of predictions about the consequences of one’s own actions, and sensory inputs from the environment. However, the predictive-processing framework has rarely been applied to infancy research. We argue that a predictive-processing framework may provide a unifying perspective on several phenomena of infant development and learning that may seem unrelated at first sight. These phenomena include statistical learning principles, infants’ motor and proprioceptive learning, and infants’ basic understanding of their physical and social environment. We discuss how a predictive-processing perspective can advance the understanding of infants’ early learning processes in theory, research, and application.

Priors and Prejudice: How prior knowledge shapes intergroup perception

2021 ◽  
Author(s):  
Hugh McGovern ◽  
Marte Otten
Keyword(s):  
Social Psychology ◽  
Prior Knowledge ◽  
Cognitive Processes ◽  
Social Neuroscience ◽  
Intergroup Bias ◽  
Predictive Processing ◽  
Future Research ◽  
Intergroup Perception ◽  
The Brain ◽  
Processing Framework

Bayesian processing has become a popular framework by which to understand cognitive processes. However, relatively little has been done to understand how Bayesian processing in the brain can be applied to understanding intergroup cognition. We assess how categorization and evaluation processes unfold based on priors about the ethnic outgroup being perceived. We then consider how the precision of prior knowledge about groups differentially influence perception depending on how the information about that group was learned affects the way in which it is recalled. Finally, we evaluate the mechanisms of how humans learn information about other ethnic groups and assess how the method of learning influences future intergroup perception. We suggest that a predictive processing framework for assessing prejudice could help accounting for seemingly disparate findings on intergroup bias from social neuroscience, social psychology, and evolutionary psychology. Such an integration has important implications for future research on prejudice at the interpersonal, intergroup, and societal levels.

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.

Multisite Study of Infants with Single-Suture Craniosynostosis: Preliminary Report of Presurgery Development

2005 ◽  
Vol 42 (4) ◽  
pp. 377-384 ◽  
Author(s):  
Kathleen A. Kapp-Simon ◽  
Brian Leroux ◽  
Michael Cunningham ◽  
Matthew L. Speltz
Keyword(s):  
Life Stress ◽  
Infant Development ◽  
Control Group ◽  
Family Socioeconomic Status ◽  
Mental Development Index ◽  
Preschool Language ◽  
Group Data ◽  
Developmental Index ◽  
Infant Learning ◽  
Single Suture

Objective To evaluate the cognitive and language development of untreated infants with a single suture craniosynostosis at time of diagnosis. Design and Participants The study presents time 1 assessments on the first 100 infants with a single suture craniosynostosis (sagittal, metopic, unilateral coronal, and lambdoid) referred to the Infant Learning Project, a prospective, multisite, longitudinal study evaluating neurocognitive development. Main Outcome Measures Infants were assessed with the mental (Mental Development Index [MDI]) and motor scales (Psychomotor Developmental Index [PDI]) of the Bayley Scales of Infant Development and the Preschool Language Scale receptive (PLS-AC) and expressive (PLS-EC) language scales. Mothers completed the Parenting Stress Inventory, a brief measure of intelligence (maternal IQ [MAT-IQ]), and provided information regarding family socioeconomic status (SES). Results As a group, infants scored significantly lower than the normative data on the MDI (91.9), PDI (83.5), PLS-AC (90.4), and PLS-EC (95.8), and the distribution of scores differed significantly from the expected distribution. MAT-IQ and SES were significantly correlated (.47, p < .01) with mothers of higher IQ coming from higher SES groups. Both MAT-IQ and SES were significantly negatively correlated with life stress events; however, neither MAT-IQ nor SES was correlated with any of the infant development scores. Conclusions There may be a greater developmental risk for infants with a single suture craniosynostosis than previously believed. However, control group data will be necessary to confirm findings.

scholarly journals Explanation Within Arm’s Reach: A Predictive Processing Framework for Single Arm Use in Octopuses

Erkenntnis ◽  
2021 ◽  
Author(s):  
Sidney Carls-Diamante
Keyword(s):  
Nervous System ◽  
Motor Control ◽  
Spatial Representation ◽  
Motor Task ◽  
The Body ◽  
Predictive Processing ◽  
Motor Tasks ◽  
Functional Characteristics ◽  
Somatotopic Map ◽  
Processing Framework

AbstractOctopuses are highly intelligent animals with vertebrate-like cognitive and behavioural repertoires. Despite these similarities, vertebrate-based models of cognition and behaviour cannot always be successfully applied to octopuses, due to the structural and functional characteristics that have evolved in their nervous system in response to the unique challenges posed by octopus morphology. For instance, the octopus brain does not support a somatotopic or point-for-point spatial map of the body—an important feature of vertebrate nervous systems. Thus, while octopuses are capable of motor tasks whose vertebrate counterparts require detailed interoceptive monitoring, these movements may not be explainable using motor control frameworks premised on internal spatial representation. One such motor task is the extension of a single arm. The ability of octopuses to select and use a single arm without the guidance of a somatotopic map has been regarded as a motor control puzzle. In an attempt at a solution, this paper develops a predictive processing account of single-arm extension in octopuses.

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 On the epigenesis of meaning in robots and organisms: Could a humanoid robot develop a human(oid) Umwelt?

2002 ◽  
Vol 30 (1) ◽  
pp. 101-111
Author(s):  
Tom Ziemke
Keyword(s):  
Social Environment ◽  
Humanoid Robot ◽  
Thought Experiments ◽  
Humanoid Robots ◽  
Human Infants ◽  
Humanoid Robotics ◽  
Strong Ai

This paper discusses recent research on humanoid robots and thought experiments addressing the question to what degree such robots could be expected to develop human-like cognition, if rather than being preprogrammed they were made to learn from the interaction with their physical and social environment like human infants. A question of particular interest, from both a semiotic and a cognitive scientific perspective, is whether or not such robots could develop an experiential Umwelt, i.e. could the sign processes they are involved in become intrinsically meaningful to themselves? Arguments for and against the possibility of phenomenal artificial minds of different forms are discussed, and it is concluded that humanoid robotics still has to be considered “weak” rather than “strong AI”, i.e. it deals with models of mind rather than actual minds.

scholarly journals Predictive processing as a systematic basis for identifying the neural correlates of consciousness

2020 ◽  
Vol 1 (II) ◽  
Author(s):  
Jakob Hohwy ◽  
Anil Seth
Keyword(s):  
Brain Function ◽  
Neural Substrates ◽  
Neural Correlates ◽  
Predictive Processing ◽  
Promising Candidate ◽  
Neural Correlates Of Consciousness ◽  
Significant Potential ◽  
Strong Potential ◽  
Processing Framework

The search for the neural correlates of consciousness is in need of a systematic, principled foundation that can endow putative neural correlates with greater predictive and explanatory value. Here, we propose the predictive processing framework for brain function as a promising candidate for providing this systematic foundation. The proposal is motivated by that framework’s ability to address three general challenges to identifying the neural correlates of consciousness, and to satisfy two constraints common to many theories of consciousness. Implementing the search for neural correlates of consciousness through the lens of predictive processing delivers strong potential for predictive and explanatory value through detailed, systematic mappings between neural substrates and phenomenological structure. We conclude that the predictive processing framework, precisely because it at the outset is not itself a theory of consciousness, has significant potential for advancing the neuroscience of consciousness.

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.

Useful misrepresentation: perception as embodied proactive inference

2022 ◽  
Author(s):  
Joshua Martin
Keyword(s):  
Prediction Error ◽  
Predictive Processing ◽  
Predictive Information ◽  
Objective Truth ◽  
Embodied Action ◽  
Processing Framework

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.

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