Robert Lowe

Robert Lowe
University of Gothenburg | GU · Department of Applied Information Technology

BSc Psychology, MSc Computer Science, PhD Embodied Affective Systems

About

81
Publications
20,812
Reads
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762
Citations
Additional affiliations
April 2015 - present
University of Gothenburg
Position
  • Lecturer
Description
  • I carry out empirical research and computational modelling in emotions.
February 2007 - February 2017
University of Skövde
Position
  • Professor (Assistant)
Description
  • I work on empirical research and computational modelling in emotions.
February 2007 - present
University of Skövde
Position
  • Professor (Associate)
Description
  • PhD supervision: 2 completions (main), and 1 licentiate (main); Lecturing: Adaptive Robotics (co-ordinator), Neural Networks (co-ordinated), Cognitive Science Seminars (co-ordinated), Affective Interaction, Statistics, Visual Design, Cognitive Abilities.
Education
January 2003 - February 2007
University of Hertfordshire
Field of study
  • Adaptive Systems Research
September 2001 - September 2002
University of Hertfordshire
Field of study
  • Computer Science
September 1995 - September 1998
University of Reading
Field of study
  • Psychology

Publications

Publications (81)
Article
Full-text available
Joint Action is typically described as social interaction that requires coordination among two or more co-actors in order to achieve a common goal. In this article, we put forward a hypothesis for the existence of a neural-computational mechanism of affective valuation that may be critically exploited in Joint Action. Such a mechanism would serve t...
Article
The findings of differential outcomes training procedures in controlled stimulus-response learning settings have been explained through theorizing two processes of response control. These processes concern: i) a stimulus-response route, and, ii) an outcome expectancy route through which valuations of stimuli (typically auditory or visual) may be re...
Conference Paper
Full-text available
In the present study, 64 users were asked to convey eight distinct emotion to a humanoid Nao robot via touch, and were then asked to evaluate their experiences of performing that task. Large differences between emotions were revealed. Users perceived conveying of positive/pro-social emotions as significantly easier than negative emotions, with love...
Article
Full-text available
This article adapts an existing experimental protocol for assessing individuals’ ability to transfer knowledge across instrumental and pavlovian learning stages. The protocol (Transfer of Control using differential outcomes learning) is adapted to fit social contexts wherein the pavlovian learning phase is modulated so that individuals are able to...
Article
Full-text available
This article contains performance data, questionnaire ratings, and EEG data from a differential outcomes learning task from two experiments. In both experiments, the standard differential outcomes learning task was extended to involve a social dimension, in order to capture how people can learn from others by observation. In Experiment 1 (N = 20),...
Chapter
Full-text available
The present chapter discusses value-based habitual and goal-directed systems as studied in the animal and human learning literature. It focuses on the means by which these two systems might interact in knowledge transfer, particularly as it applies to social learning. Knowledge is conceived here in terms of types of logic computations as implemente...
Article
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Whether they are considered discrete or dimensional, emotions are ’embodied’ phenomena [...]
Article
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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 m...
Article
Full-text available
Social robots are expected gradually to be used by more and more people in a wider range of settings, domestic as well as professional. As a consequence, the features and quality requirements on human–robot interaction will increase, comprising possibilities to communicate emotions, establishing a positive user experience, e.g., using touch. In thi...
Book
It has been observed how the technological progress of our civilization mimics the extraordinarily smart design choices of the nature surrounding us. As scientists have been inspired by the cosmos to observe and understand its mechanics from the deepest to the smallest particle, so engineers have found inspiration in biological systems to design t...
Article
Full-text available
Affective touch has a fundamental role in human development, social bonding, and for providing emotional support in interpersonal relationships. We present, what is to our knowledge, the first HRI study of tactile conveyance of both positive and negative emotions (affective touch) on the Nao robot, and based on an experimental set-up from a study o...
Article
Full-text available
We here present results and analysis from a study of affective tactile communication between human and humanoid robot (the NAO robot). In the present work, participants conveyed eight emotions to the NAO via touch. In this study, we sought to understand the potential for using a wearable affective (tactile) interface, or WAffI. The aims of our stud...
Article
This special issue presents perspectives on learning and adaptation as they apply to a number of cognitive phenomena including pupil dilation in humans and attention in robots, natural language acquisition and production in embodied agents (robots), human-robot game play and social interaction, neural-dynamic modelling of active perception and neur...
Article
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The partial reinforcement extinction effect (PREE) is an experimentally established phenomenon: behavioural response to a given stimulus is more persistent when previously inconsistently rewarded than when consistently rewarded. This phenomenon is, however, controversial in animal/human learning theory. Contradictory findings exist regarding when t...
Conference Paper
Full-text available
Reinforcement learning algorithms and particularly those based on temporal-difference learning are widely adopted and have been successfully applied to a number of problems as well as used to model animal learning. However, they are based on neural pathways involved in reward-seeking behaviour since little is known about punishment-driven learning...
Article
Full-text available
http://www.mdpi.com/2504-3900/1/3/185/pdf
Chapter
Full-text available
In this chapter, different notions of allostasis (the process of achieving stability through change) as they apply to adaptive behavior are presented. The authors discuss how notions of allostasis can be usefully applied to Cybernetics-based homeostatic systems. Particular emphasis is placed upon affective states – motivational and emotional – and,...
Article
Full-text available
Both nociception and punishment signals have been used in robotics. However, the potential for using these negatively valenced types of reinforcement learning signals for robot learning has not been exploited in detail yet. Nociceptive signals are primarily used as triggers of preprogrammed action sequences. Punishment signals are typically disembo...
Article
Full-text available
In this article we present a novel neural network implementation of Associative Two-Process (ATP) theory based on an Actor–Critic-like architecture. Our implementation emphasizes the affective components of differential reward magnitude and reward omission expectation and thus we model Affective-Associative Two-Process theory (Aff-ATP). ATP has bee...
Chapter
Full-text available
In this chapter, different notions of allostasis (the process of achieving stability through change ) as they apply to adaptive behavior are presented. The authors discuss how notions of allostasis can be usefully applied to Cybernetics-based homeostatic systems. Particular emphasis is placed upon affective states - motivational and emotional - and...
Book
Full-text available
There are many different approaches to understanding human consciousness. By conducting research to better understand various biological mechanisms, these can be redefined and utilized for technological purposes. Advanced Research on Biologically Inspired Cognitive Architectures is an essential reference source for the latest scholarly research on...
Article
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Robots inhabiting human environments need to act in relation to their own experience and embodiment as well as to social and emotional aspects. Robots that learn, act upon and incorporate their own experience and perception of others’ emotions into their responses make not only more productive artificial agents but also agents with whom humans can...
Article
Full-text available
Several simulation theories have been proposed as an explanation for how humans and other agents internalize an “inner world” that allows them to simulate interactions with the external real world – prospectively and retrospectively. Such internal simulation of interaction with the environment has been argued to be a key mechanism behind mentalizin...
Article
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In contrasting an interactiv- ity account alternative to variants on the enactive approach, the authors discuss the role of sense-making. They claim that their interactivity perspective, un- like enactive approaches, accounts for a dependency on “non-local” resources characteristic of many organisms. I draw attention to the cybernetic-enactivist pe...
Article
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The reciprocal coupling of perception and action in cognitive agents has been firmly established: perceptions guide action but so too do actions influence what is perceived. While much has been said on the implications of this for the agent's external behavior, less attention has been paid to what it means for the internal bodily mechanisms which u...
Article
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A technique for simultaneous planning and action based on dynamic field theory is presented. The model builds on previous work on representation of sequential behavior as attractors in dynamic neural fields. Here, we demonstrate how chains of competing attractors can be used to represent dynamic plans towards a goal state. The present work can be s...
Conference Paper
Full-text available
In this chapter, we present a minimalist approach to utilizing the computational principles of affective processes and emotions for autonomous robotics applications. The focus of this paper is on the presentation of this framework in reference to preservation of agent autonomy across levels of cognitive-affective competences. This approach views au...
Conference Paper
Full-text available
In animal and human learning, outcome expectancy is understood to control action under a number of learning paradigms. One such paradigm, the differential outcomes effect (DOE), entails faster learning when responses have differential, rather than non-differential, outcomes. The associative two-process theory has provided an increasingly accepted e...
Conference Paper
Full-text available
Connectionist and bio-inspired approaches to the study of emotional learning and decision making often emphasize, or imply, an executive role for the brain whilst paying only lip service to the role of the non-neural body. In this short paper I will discuss approaches to modelling emotions that have attempted to take into account, in one form or an...
Article
Full-text available
In this article, we propose an architecture of a bio-inspired controller that addresses the problem of learning different locomotion gaits for different robot morphologies. The modeling objective is split into two: baseline motion modeling and dynamics adaptation. Baseline motion modeling aims to achieve fundamental functions of a certain type of l...
Article
Full-text available
The implementation of sequence learning in robotic platforms o ers several challenges. Deciding when to stop one action and continue to the next requires a balance between stability of sensory information and, of course, the knowledge about what action is required next. The work presented here proposes a starting point for the successful execution...
Conference Paper
In this paper is investigated the problem of managing limited resources in human-robot interaction with a computational architecture of emotion. The architecture is based on the appraisal theory of affect and an ethological motivational model of task selection. Key variables and performance criteria for robotic energy autonomous behaviour in intera...
Conference Paper
A computational architecture of emotion is presented which grounds a component of an appraisal cognitive model into the homeostatic processes of a humanoid robot. The focus of the present work entails a ‘grounding’ of the arousal component of the PAD emotion space into the electrical energy processes of an iCub robot. Key variables and performance...
Article
Abstract We present a novel example of a biomechatronic hybrid system. The living component of the system, embedded within microbial fuel cells, relies on the availability of food and water in order to produce electrical energy. The latter is essential to the operations of the mechatronic component, responsible for finding and collecting food and w...
Article
Full-text available
The identification of learning mechanisms for locomotion has been the subject of much research for some time but many challenges remain. Dynamic systems theory (DST) offers a novel approach to humanoid learning through environmental interaction. Reinforcement learning (RL) has offered a promising method to adaptively link the dynamic system to the...
Conference Paper
Full-text available
In this paper we review the functional role that reinforcement plays in notions of affective computation and emotion. We consider three core components of emotional activity - emotion triggers, actions and action tendencies, and feeling states - and evaluate each component in relation to reinforcement learning and behaviour theory as well as with r...
Conference Paper
Full-text available
We present a reinforcement learning algorithm based on Dyna-Sarsa that utilizes separate representations of reward and punishment when guiding state-action value learning and action selection. The adoption of policy meta-learning optimized by a genetic algorithm is explored and results in the context of a two-armed bandit goal-navigation task in a...
Conference Paper
We argue that emotions play a central role in human cognition. It is therefore of interest to researchers with an aim to create artificial systems with human-level intelligence (or indeed beyond) to consider the functions of emotions in the human cognition whose complexity they aim to recreate. To this end, we review here several functional roles o...
Conference Paper
Full-text available
In this work, we present a neurocomputational model for auditory-cue fear acquisition. Computational fear conditioning has experienced a growing interest over the last few years, on the one hand, because it is a robust and quick learning paradigm that can contribute to the development of more versatile robots, and on the other hand, because it can...
Conference Paper
In this article, we use a recurrent neural network including four-cell core architecture to model the walking gait and implement it with the simulated and physical NAO robot. Meanwhile, inspired by the biological CPG models, we propose a simplified CPG model which comprises motorneurons, interneurons, sensor neurons and the simplified spinal cord....
Conference Paper
Full-text available
We have created a model of a hybrid system in which a gene regulatory network (GRN) controls the search for resources (fuel/food and water) necessary to allow an artificial metabolic system (simulated microbial fuel cell) to produce energy. We explore the behaviour of simple animats in a two-dimensional simulated environment requiring minimal cogni...
Article
Full-text available
A classical appraisal model of emotions extended with artificial metabolic mechanisms is presented. The new architecture is based on two existing models: WASABI and a model of Microbial Fuel Cell technology. WASABI is a top-down cognitive model which is implemented in several virtual world applications such as a museum guide. Microbial fuel cells p...
Article
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In this article, we review the nature of the functional and causal relationship between neurophysiologically/psychologically generated states of emotional feeling and action tendencies and extrapolate a novel perspective. Emotion theory, over the past century and beyond, has tended to regard feeling and action tendency as independent phenomena: att...
Poster
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Abstract Classical fear conditioning has experienced a growing interest over the last decade. Fear learning mechanisms are a simple and robust learning paradigm that involves sensory and motor areas. We believe that a deeper study of these mechanisms will contribute not only to a better understanding of fear conditioning but also to the development...
Article
Full-text available
In this article, a simple CPG network is shown to model early infant walking, in particular the onset of independent walking. The difference between early infant walking and early adult walking is addressed with respect to the underlying neurophysiology and evaluated according to gait attributes. Based on this, we successfully model the early infan...
Article
Full-text available
in this article, a generic CPG architecture is used to model infant crawling gaits and is implemented on the NAO robot platform. The CPG architecture is chosen via a systematic approach to designing CPG networks on the basis of group theory and dynamic systems theory. The NAO robot performance is compared to the iCub robot which has a different ana...
Conference Paper
The new scenarios of contemporary adaptive robotics seem to suggest a transformation of the traditional methods. In the search for new approaches to the control of adaptive autonomous systems, the mind becomes a fundamental source of inspiration. In this paper we anticipate, through the use of simulation, the cognitive and behavioral properties tha...
Conference Paper
Choice behaviour where outcome-contingencies vary or are probabilistic has been the focus of many benchmark tasks of infant to adult development in the psychology literature. Dynamic field theoretic (DFT) investigations of cognitive and behavioural competencies have been used in order to identify parameters critical to infant development. In this p...
Article
The possible modulatory influence of motivations and emotions is of great interest in designing robotic adaptive systems. In this paper, an attempt is made to connect the concept of periodic behaviour activations to emotional modulation, in order to ...
Article
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 mark...
Conference Paper
p>With the present study we report the first application of a recently proposed model for realistic microbial fuel cells (MFCs) energy generation dynamics, suitable for robotic simulations with minimal and extremely limited computational overhead. A simulated agent was adapted in order to engage in a viable interaction with its environment. It achi...
Conference Paper
Interference between one cognitive behavior or sensory stim-ulus and subsequent behaviors is a commonly observed effect in the study of human cognition and Psychology. Traditional connectionist approaches explain this phenomenon by mutu-ally inhibiting neural populations underlying those behaviors. Here, we present an alternative model, relying on...
Chapter
The coupling between a body (in an extended sense that encompasses both neural and non-neural dynamics) and its environment is here conceived as a critical substrate for cognition. We propose and discuss the plan for a neurocomputational cognitive architecture for robotic agents, so far implemented in its minimal form for supporting the behavior of...
Conference Paper
p>We present an evolutionary robotics investigation into the metabolism constrained homeostatic dynamics of a simulated robot. Unlike existing research that has focused on either energy or motivation autonomy the robot described here is considered in terms of energy-motivation autonomy. This stipulation is made according to a requirement of autonom...
Article
According to Noë's enactive theory of perception sensorimotor knowledge allows us to predict the sensory outcomes of our actions. This paper suggests that tuning input filters with such predictions may be the cause of sustained inattentional blindness. Most models of learning capture statistically salient regularities in and between data streams. S...
Conference Paper
This paper presents a biologically constrained reward prediction model capable of learning cue-outcome associations involving temporally distant stimuli without using the commonly used temporal difference model. The model incorporates a novel use of an adapted echo state network to substitute the biologically implausible delay chains usually used,...
Article
Full-text available
The computational modeling of emotion has been an area of growing interest in cognitive robotics research in recent years, but also a source of contention regarding how to conceive of emotion and how to model it. In this paper, emotion is characterized as (a) closely connected to embodied cognition, (b) grounded in homeostatic bodily regulation, an...
Article
Full-text available
Research on the neural bases of emotion raises much controversy and few quantitative models exist that can help address the issues raised. Here we replicate and dissect one of those models, Armony and colleagues' neurocomputational model of fear conditioning, which is based on LeDoux's dual-route hypothesis regarding the rat fear circuitry. The imp...
Article
Full-text available
The coupling between a body (in an extended sense that en-compasses both neural and non-neural dynamics) and its environment is here conceived as a critical substrate for cognition. We propose and dis-cuss the plan for a neurocomputational cognitive architecture for robotic agents, so far implemented in its minimal form for supporting the be-havior...