Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task

Belkaid, Marwen; Cuperlier, Nicolas; Gaussier, Philippe

doi:10.1371/journal.pone.0184960

Cited by 8 publications

(7 citation statements)

References 36 publications

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“…That is, not only do Locomotors simply get on with things, they also stay on task better once a goal has been initiated. It is a well demonstrated fact that those high in boredom proneness also show impairments of sustained attention ( Damrad-Frye and Laird, 1989 ; Cheyne et al, 2006 ; Carriere et al, 2008 ; Malkovsky et al, 2012 ; Hunter and Eastwood, 2016 ; see Eastwood et al, 2012 for review). That is, highly boredom prone individuals struggle to stay on task.…”

Section: Introductionmentioning

confidence: 99%

A Failure to Launch: Regulatory Modes and Boredom Proneness

2018

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Boredom is a ubiquitous human experience characterized as a state of wanting but failing to engage with the world. Individuals prone to the experience of boredom demonstrate lower levels of self-control which may be at the heart of their failures to engage in goal-directed, meaningful behaviors. Here we develop the hypothesis that distinct self-regulatory profiles, which in turn differentially influence modes of goal pursuit, are at the heart of boredom proneness. Two specific regulatory modes are addressed: Locomotion, the desire to ‘just do it,’ an action oriented mode of goal-pursuit, and Assessment, the desire to ‘do the right thing,’ an evaluative orientation toward goal pursuit. We present data from a series of seven large samples of undergraduates showing that boredom proneness is negatively correlated with Locomotion, as though getting on with things acts as a prophylactic against boredom. This ‘failure to launch’ that we suggest is prevalent in the highly boredom prone individual, could be due to an inability to appropriately discriminate value (i.e., everything is tarred with the same gray brush), an unwillingness to put in the required effort to engage, or simply a failure to get started. In contrast, boredom proneness was consistently positively correlated with the Assessment mode of self-regulation. We suggest that this association reflects a kind of rumination that hampers satisfying goal pursuit.

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Section: Introductionmentioning

confidence: 99%

A Failure to Launch: Regulatory Modes and Boredom Proneness

2018

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“…Prior works related to robot confidence have focused on the allocation of control between human and robot [39], influencing operator behavior [38], or otherwise directly communicating the robot's self-assessed state [40][41][42][43]. Other work is aimed at intrinsic motivations of the robot [49,50] or understanding of its environment [44]. While our model of confidence could drive overt feedback to the operator or be applied only to internal processes of the robot, the implementation presented here is directed at minimally intrusive adjustment of physical behavior to mitigate the challenges of human interaction with multiple mobile robots.…”

Section: Discussionmentioning

confidence: 99%

“…This approach makes selective use of the available information to determine the confidence value. Future work might explore more sophisticated methods such as artificial neural networks and learned behaviors [49,50], hidden Markov models (HMMs) [52], and graph convolution networks [128].…”

Section: Discussionmentioning

confidence: 99%

“…Other works have modeled aspects of human cognition enabling robots to self-assess and adapt their behaviors [46][47][48]. The authors in [49] proposed an artificial neural networkbased model of emotional metacontrol for modulating sensorimotor processes, and used intrinsic frustration and boredom to intervene in a visual search task. The authors in [50] modeled curiosity in an intrinsic motivation system used to maximize a robot's learning progress by avoiding unlearnable situations and shifting its attention to situations of increasing difficulty.…”

Section: Background 21 Robot Confidencementioning

confidence: 99%

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Multirobot Confidence and Behavior Modeling: An Evaluation of Semiautonomous Task Performance and Efficiency

Lucas

Pandya

2021

Robotics

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There is considerable interest in multirobot systems capable of performing spatially distributed, hazardous, and complex tasks as a team leveraging the unique abilities of humans and automated machines working alongside each other. The limitations of human perception and cognition affect operators’ ability to integrate information from multiple mobile robots, switch between their spatial frames of reference, and divide attention among many sensory inputs and command outputs. Automation is necessary to help the operator manage increasing demands as the number of robots (and humans) scales up. However, more automation does not necessarily equate to better performance. A generalized robot confidence model was developed, which transforms key operator attention indicators to a robot confidence value for each robot to enable the robots’ adaptive behaviors. This model was implemented in a multirobot test platform with the operator commanding robot trajectories using a computer mouse and an eye tracker providing gaze data used to estimate dynamic operator attention. The human-attention-based robot confidence model dynamically adapted the behavior of individual robots in response to operator attention. The model was successfully evaluated to reveal evidence linking average robot confidence to multirobot search task performance and efficiency. The contributions of this work provide essential steps toward effective human operation of multiple unmanned vehicles to perform spatially distributed and hazardous tasks in complex environments for space exploration, defense, homeland security, search and rescue, and other real-world applications.

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“…It indicates that first occurrence of an incident will cause high motivation (or be seen as “novelty”). However, when the same accumulated stimulation occurs repeatedly, it tends to be less valuable (“boring”) [21]. Quantitative analysis for understanding motivation in this paper provides a method to measure external stimulus extracted from sensorimotor, driven by the goal priority, action selection, reward, or feedback.…”

Section: Related Workmentioning

confidence: 99%

Reinforcement Emotion-Cognition System: A Teaching Words Task

Xie

Zhang

et al. 2019

Computational Intelligence and Neuroscience

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The goal of this paper is to suggest a system for intelligent learning environments with robots modeling of emotion regulation and cognition based on quantitative motivation. A detailed interactive situation for teaching words is proposed. In this study, we introduce one bottom-up collaboration method for emotion-cognition interplay and behaviour decision-making. Integration with gross emotion regulation theory lets the proposed system adapt to natural interactions between students and the robot in emotional interaction. Four key ideas are advocated, and they jointly set up a reinforcement emotion-cognition system (RECS). First, the quantitative motivation is grounded on external interactive sensory detection, which is affected by memory and preference. Second, the emotion generation triggered by an initial motivation such as external stimulus is also influenced by the state in the previous time. Third, the competitive and cooperative relationship between emotion and motivation intervenes to make the decision of emotional expression and teaching actions. Finally, cognitive reappraisal, the emotion regulation strategy, is introduced for the establishment of emotion transition combined with personalized cognition. We display that this RECS increases the robot emotional interactive performance and makes corresponding teaching decision through behavioural and statistical analysis.

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Emotional metacontrol of attention: Top-down modulation of sensorimotor processes in a robotic visual search task

Cited by 8 publications

References 36 publications

A Failure to Launch: Regulatory Modes and Boredom Proneness

A Failure to Launch: Regulatory Modes and Boredom Proneness

Multirobot Confidence and Behavior Modeling: An Evaluation of Semiautonomous Task Performance and Efficiency

Reinforcement Emotion-Cognition System: A Teaching Words Task

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