ArticleFrontiers in psychology2012
Predictive coding strategies for developmental neurorobotics.
Article in Frontiers in psychology, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed, 8 citations in OpenAlex.
- Multimodal Representation Learning for Place Recognition Using Deep Hebbian Predictive Coding.Frontiers in robotics and AI · 2021Article
- Neurobiologically Inspired Self-Monitoring Systems.Proceedings of the IEEE. Institute of Electrical and Electronics Engineers · 2020Article
- Neurorobotics-A Thriving Community and a Promising Pathway Toward Intelligent Cognitive Robots.Frontiers in neurorobotics · 2018Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In recent years, predictive coding strategies have been proposed as a possible means by which the brain might make sense of the truly overwhelming amount of sensory data available to the brain at any given moment of time. Instead of the raw data, the brain is hypothesized to guide its actions by assigning causal beliefs to the observed error between what it expects to happen and what actually happens. In this paper, we present a variety of developmental neurorobotics experiments in which minimalist prediction error-based encoding strategies are utilize to elucidate the emergence of infant-like behavior in humanoid robotic platforms. Our approaches will be first naively Piagian, then move onto more Vygotskian ideas. More specifically, we will investigate how simple forms of infant learning, such as motor sequence generation, object permanence, and imitation learning may arise if minimizing prediction errors are used as objective functions.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.