The Cognitive Body: From Dynamic Modulation to Anticipation

Montebelli, Alberto; Lowe, Robert; Ziemke, Tom

doi:10.1007/978-3-642-02565-5_8

Cited by 6 publications

(7 citation statements)

References 36 publications

(54 reference statements)

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“…Further details are provided in Table 1. (Kiryazov et al, 2013;Lowe et al, 2010;Lewis and Cañamero, 2019;Montebelli et al, 2008). The first internal need (which we also call internal variables) is Energy, which is analogous to a physical need to consume physical resources (i.e., to maintain blood glucose levels).…”

Section: Action-selection Architecturementioning

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: Action-selection Architecturementioning

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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“…Such a mechanism permits the integration of systems that operate on different time scales in the service of present and more enduring organism behaviour (see, e.g. Montebelli, Lowe and Ziemke 2009). We feel that integrating such body, brain and behavioural/interactive systems according to different time scales using a cognitive robotics methodology offers a promising approach to understanding how somatic states impact on complex decision-making in a dynamical systems context.…”

Section: Using Cognitive Robotics To Extend the Igt Benchmarkmentioning

confidence: 99%

Towards a cognitive robotics methodology for reward-based decision-making: dynamical systems modelling of the Iowa Gambling Task

Lowe

Ziemke

2010

Connection Science

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The somatic marker hypothesis (SMH) posits that the role of emotions and mental states in decision-making manifests through bodily responses to stimuli of import to the organism's welfare. The Iowa Gambling Task (IGT), proposed by Bechara and Damasio in the mid-1990s, has provided the major source of empirical validation to the role of somatic markers in the service of flexible and cost-effective decision-making in humans. In recent years the IGT has been the subject of much criticism concerning: (1) whether measures of somatic markers reveal that they are important for decision-making as opposed to behaviour preparation;(2) the underlying neural substrate posited as critical to decision-making of the type relevant to the task; and (3) aspects of the methodological approach used, particularly on the canonical version of the task. In this paper, a cognitive robotics methodology is proposed to explore a dynamical systems approach as it applies to the neural computation of reward-based learning and issues concerning embodiment. This approach is particularly relevant in light of a strongly emerging alternative hypothesis to the SMH, the reversal learning hypothesis, which links, behaviourally and neurocomputationally, a number of more or less complex reward-based decision-making tasks, including the 'A-not-B' task -already subject to dynamical systems investigations with a focus on neural activation dynamics. It is also suggested that the cognitive robotics methodology may be used to extend systematically the IGT benchmark to more naturalised, but nevertheless controlled, settings that might better explore the extent to which the SMH, and somatic states per se, impact on complex decision-making.

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“…Sun (2015) has suggested that computational cognitive architectures benefit greatly from incorporating a notion of 'implicit' processing, as part of a structured (dual-process) cognitive architecture. Agents utilizing cognitive architectures in the real world by necessity utilize systems that learn from (distributed) patterns of sensorimotor embodiment (Pfeifer & Scheier 2001;Montebelli et al 2008Montebelli et al , 2010Montebelli et al , 2013Lowe et al 2008) whose interaction with value systems (e.g. pavlovian) may permit emergent activity attributable to 'higher' cognition (Braitenberg 1986, Kiryazov et al 2013Lowe & Kiryazov 2014; Barrett et al 2016).…”

Section: The Adaptive and Cognitive Value Of Associative Processesmentioning

confidence: 99%

Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

Lowe

Almer

Balkenius

2019

Open Information Science

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Connectionist architectures constitute a popular method for modelling animal associative learning processes in order to glean insights into the formation of cognitive capacities. Such approaches (based on purely feedforward activity) are considered limited in their ability to capture relational cognitive capacities. Pavlovian learning value-based models, being not based purely on fully connected feedforward structure, have demonstrated learning capabilities that often mimic those of ‘higher’ relational cognition. Capturing data using such models often reveals how associative mechanisms can exploit structure in the experimental setting, so that ‘explicit’ relational cognitive capacities are not, in fact, required. On the other hand, models of relational cognition, implemented as neural networks, permit formation and retrieval of relational representations of varying levels of complexity. The flexible processing capacities of such models are, however, are subject to constraints as to how offline relational versus online (real-time, real-world) processing may be mediated. In the current article, we review the potential for building a connectionist-relational cognitive architecture with reference to the representational rank view of cognitive capacity put forward by Halford et al. Through interfacing system 1-like (connectionist/associative learning) and system 2-like (relational-cognition) computations through a bidirectional affective processing approach, continuity between Halford et al’s cognitive systems may be operationalized according to real world/online constraints. By addressing i) and ii) in this manner, this paper puts forward a testable unifying framework for system 1-like and system 2-like cognition.

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The Cognitive Body: From Dynamic Modulation to Anticipation

Cited by 6 publications

References 36 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

Towards a cognitive robotics methodology for reward-based decision-making: dynamical systems modelling of the Iowa Gambling Task

Bridging Connectionism and Relational Cognition through Bi-directional Affective-Associative Processing

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