Predictive learning with uncertainty estimation for modeling infants' cognitive development with caregivers: A neurorobotics experiment

Murata, Satoshi; Tomioka, Saki; Nakajo, Ryoichi; Yamada, Tatsuro; Arie, Hiroaki; Ogata, Tetsuya; Sugano, Shigeki

doi:10.1109/devlrn.2015.7346162

Cited by 4 publications

(4 citation statements)

References 25 publications

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“…This minimal approach stands in direct contrast to similar approaches towards embodied (socially-)adaptive models. Most notable are the more recent approaches that are built on the predictive processing (or active inference) framework (for example, ( Ahmadi and Tani, 2019 ; Murata et al, 2015 ; Schillaci et al, 2020 ; Park et al, 2017 )) that often focus on the modelling of higher-order cognitive function. Such approaches often take computationally-expensive approaches (i.e.…”

Section: Discussionmentioning

confidence: 99%

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

Khan

Cañamero

2022

Front. Robot. AI

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In dynamic (social) environments, an affective state of “stress” can be adaptive and promote agent wellbeing, but maladaptive if not appropriately regulated. The presence of (and interactions with) affect-based social support has been hypothesised to provide mechanisms to regulate stress (the “social buffering” hypothesis), though the precise, underlying mechanisms are still unclear. However, the hormone oxytocin has been implicated in mediating these effects in at least two ways: by improving social appraisals and reducing the short-term release of stress hormones (i.e., cortisol), and adapting an agent’s long-term stress tolerance. These effects likely facilitate an agent’s long-term adaptive ability by grounding their physiological and behavioural adaptation in the (affective) social environment, though these effects also appear to be context-dependent. In this paper, we investigate whether two of the hypothesised hormonal mechanisms that underpin the “social buffering” phenomenon affect the long-term wellbeing of (artificial) social agents who share affective social bonds, across numerous social and physical environmental contexts. Building on previous findings, we hypothesise that “social buffering” effects can improve the long-term wellbeing of agents who share affective social bonds in dynamic environments, through regular prosocial interactions with social bond partners. We model some of the effects associated with oxytocin and cortisol that underpin these hypothesised mechanisms in our biologically-inspired, socially-adaptive agent model, and conduct our investigation in a small society of artificial agents whose goal is to survive in challenging environments. Our results find that, while stress can be adaptive and regulated through affective social support, long-term behavioural and physiological adaptation is determined by the contextual perception of affective social bonds, which is influenced by early-stage interactions between affective social bond partners as well as the degree of the physical and social challenges. We also show how these low-level effects associated with oxytocin and cortisol can be used as “biomarkers” of social support and environmental stress. For socially-situated artificial agents, we suggest that these “social buffering” mechanisms can adapt the (adaptive) stress mechanisms, but that the long-term efficacy of this adaptation is related to the temporal dynamics of social interactions and the contextual perception of the affective social and physical environments.

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Section: Discussionmentioning

confidence: 99%

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

Khan

Cañamero

2022

Front. Robot. AI

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“…A similar approach has been presented by Murata et al (2015), who propose an RNN-based model, stochastic continuous-time RNN (S-CTRNN). The framework integrates probabilistic Bayesian schemes in a recurrent neural network.…”

Section: Roboticmentioning

confidence: 98%

Predictive Processing in Cognitive Robotics: A Review

et al. 2021

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Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down, hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes, such as predictive coding, active inference, perceptual inference, and free-energy principle, tend to be used interchangeably. In the field of cognitive robotics, there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this letter, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related nonrobotic models. The analysis suggests that, first, research in both cognitive robotics implementations and nonrobotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in nonrobotics models, it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions. Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors and tasks in cognitive robotics research under the predictive processing framework.

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“…A similar approach is presented by Murata et al (2015), who propose a RNN-based model named stochastic continuous-time RNN (S-CTRNN). The framework integrates probabilistic Bayesian schemes in a recurrent neural network.…”

Section: Robotic Implementationsmentioning

confidence: 99%

Predictive Processing in Cognitive Robotics: a Review

Ciria¹,

Schillaci²,

Pezzulo³

et al. 2021

Preprint

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Predictive processing has become an influential framework in cognitive sciences. This framework turns the traditional view of perception upside down, claiming that the main flow of information processing is realized in a top-down hierarchical manner. Furthermore, it aims at unifying perception, cognition, and action as a single inferential process. However, in the related literature, the predictive processing framework and its associated schemes such as predictive coding, active inference, perceptual inference, free-energy principle, tend to be used interchangeably. In the field of cognitive robotics there is no clear-cut distinction on which schemes have been implemented and under which assumptions. In this paper, working definitions are set with the main aim of analyzing the state of the art in cognitive robotics research working under the predictive processing framework as well as some related non-robotic models. The analysis suggests that, first, both research in cognitive robotics implementations and non-robotic models needs to be extended to the study of how multiple exteroceptive modalities can be integrated into prediction error minimization schemes. Second, a relevant distinction found here is that cognitive robotics implementations tend to emphasize the learning of a generative model, while in non-robotics models it is almost absent. Third, despite the relevance for active inference, few cognitive robotics implementations examine the issues around control and whether it should result from the substitution of inverse models with proprioceptive predictions.Finally, limited attention has been placed on precision weighting and the tracking of prediction error dynamics. These mechanisms should help to explore more complex behaviors

show abstract

Predictive learning with uncertainty estimation for modeling infants' cognitive development with caregivers: A neurorobotics experiment

Cited by 4 publications

References 25 publications

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

The Long-Term Efficacy of “Social Buffering” in Artificial Social Agents: Contextual Affective Perception Matters

Predictive Processing in Cognitive Robotics: A Review

Predictive Processing in Cognitive Robotics: a Review

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