The perception of emotion in artificial agents

Given recent technological developments in robotics, artificial intelligence and virtual reality, it is perhaps unsurprising that the arrival of emotionally expressive and reactive artificial agents is imminent. However, if such agents are to become integrated into our social milieu, it is imperative to establish an understanding of whether and how humans perceive emotion in artificial agents. In this review, we incorporate recent findings from social robotics, virtual reality, psychology, and neuroscience to examine how people recognize and respond to emotions displayed by artificial agents. First, we review how people perceive emotions expressed by an artificial agent, such as facial and bodily expressions and vocal tone. Second, we evaluate the similarities and differences in the consequences of perceived emotions in artificial compared to human agents. Besides accurately recognizing the emotional state of an artificial agent, it is critical to understand how humans respond to those emotions. Does interacting with an angry robot induce the same responses in people as interacting with an angry person? Similarly, does watching a robot rejoice when it wins a game elicit similar feelings of elation in the human observer? Here we provide an overview of the current state of emotion expression and perception in social robotics, as well as a clear articulation of the challenges and guiding principles to be addressed as we move ever closer to truly emotional artificial agents

IMPULSE BUYING: HOW DIGITAL MARKETING IS INFLUENCING THE MILLENNIAL GENERATION’S IMPULSIVE SPENDING

Impulse buying, in its most simplest form, is the process of making a purchase one had not intended to make (Cruze, 2020). Impulse buys can be small, they can also include larger items such as cars and computers. Millennials, currently aged between 26 and 41 and a population of 72.1 million, make up the largest segment of the American workforce (Pastore, 2020). Not only does this population make up a majority of the American workforce, they are also the highest spending generation and the generation that the media is most easily able to influence (Mullen, 2020). Among millennials, 82 percent buy a product they like the first time they see it, 70 percent admitted to often regretting purchases they made, and 64 percent reported they often make impulse buys (Mullen, 2020). The purpose of this research is to analyze what forms of digital marketing are able to influence the millennial population the most. This research will also attempt to examine what demographics influence consumers to purchase an expensive or luxury product that they had not intended to buy. The demographics that will be included are age, race, gender, general income, and household type. Information will be contributed to this research through surveys submitted anonymously from participants aged 25 to 40 years old. By analyzing the results from the surveys, there will be more insight on what forms of digital marketing are most successful in achieving an impulse purchase by millennials

Analysing and controlling the self-assembly of Gelation

We describe new methods to analyse and control the self-assembly of gelation leading to exciting new soft materials. These materials have been shown to be of use to a wide range of applications including antimicrobial coatings, OPV devices, thermochromic materials and biomedical materials. Many of the described methods are novel or go beyond the state of the art. One of the analytical methods probes the surface chemistry of self-assembled hydrogel fibres to determine their pKa. This method not only determines the gel’s pKa but whether indeed a gel would form from a small molecule and what its rheological stiffness would be. This is the first incident of electrochemistry being used to determine the rheology of gels. Furthermore, a method to probe in the real time the self-assembly kinetics of a gelator to form a gel using multiple pulse amperometry is described. This method is expanded to complex multicomponent systems. Electrochemically fabricated hydrogels are developed and for the first time we show how the rheological properties can be controlled by controlling the electrochemical parameters. In addition to controlled rheological properties, the gels formed have unique mesh sizes and thermochromic properties. We introduce a new gelation trigger method for low molecular weight hydrogels. Dopamine autoxidation can be used to control the self-assembly of small molecules to form gels and we go on to describe how these gels can be used for antimicrobial purposes. To expand on the electrochemically fabricated hydrogels, we propose the oxidation of dopamine as a new electrochemical trigger. We describe how the rheological properties of the gels can be controlled and how they are potentially suitable for biomedical applications. Finally, we describe a method to control the self-assembly of both single and multi-component gel networks by temperature and use an array of analytical techniques to show this. We expand on this work to show how the temperature control can form gels with varying networks which lead directly to changes in the efficiency of electron transfer

Investigating The Discrepancy Between Theory And Empirical Research In Knowledge Transfer And Innovation

The aim of this study is to explore the relationship between knowledge transfer and innovation. The literature documents that increasing knowledge transfer can both increase and decrease an organisation's capability to innovate. This study proposes a model that integrates these two theoretical standpoints and in doing so hypothesizes a non-linear (parabolic) relationship between knowledge transfer and innovation. Empirical research to date, including the work of Storey and Kelly (2002), document a linear relationship between knowledge transfer and innovation. In order to investigate the possibility of a non-linear relationship this study proposes and tests refinements to Storey and Kelly's methodology. A refined survey questionnaire was sent to all the local government councils within two Australian states and both linear and quadratic statistical analyses were performed. The results of this pilot study revealed linear relationships between four of the six knowledge transfer questions and the innovation measurement question. A further refinement to the methodology is recommended before a non-linear relationship between knowledge transfer and innovation is rejected

Dance training shapes action perception and its neural implementation within the young and older adult brain

How we perceive others in action is shaped by our prior experience. Many factors influence brain responses when observing others in action, including training in a particular physical skill, such as sport or dance, and also general development and aging processes. Here, we investigate how learning a complex motor skill shapes neural and behavioural responses among a dance-naïve sample of 20 young and 19 older adults. Across four days, participants physically rehearsed one set of dance sequences, observed a second set, and a third set remained untrained. Functional MRI was obtained prior to and immediately following training. Participants’ behavioural performance on motor and visual tasks improved across the training period, with younger adults showing steeper performance gains than older adults. At the brain level, both age groups demonstrated decreased sensorimotor cortical engagement after physical training, with younger adults showing more pronounced decreases in inferior parietal activity compared to older adults. Neural decoding results demonstrate that among both age groups, visual and motor regions contain experience-specific representations of new motor learning. By combining behavioural measures of performance with univariate and multivariate measures of brain activity, we can start to build a more complete picture of age-related changes in experience-dependent plasticity

From automata to animate beings: the scope and limits of attributing socialness to artificial agents

Understanding the mechanisms and consequences of attributing socialness to artificial agents has important implications for how we can use technology to lead more productive and fulfilling lives. Here, we integrate recent findings on the factors that shape behavioral and brain mechanisms that support social interactions between humans and artificial agents. We review how visual features of an agent, as well as knowledge factors within the human observer, shape attributions across dimensions of socialness. We explore how anthropomorphism and dehumanization further influence how we perceive and interact with artificial agents. Based on these findings, we argue that the cognitive reconstruction within the human observer is likely to be far more crucial in shaping our interactions with artificial agents than previously thought, while the artificial agent's visual features are possibly of lesser importance. We combine these findings to provide an integrative theoretical account based on the “like me” hypothesis, and discuss the key role played by the Theory‐of‐Mind network, especially the temporal parietal junction, in the shift from mechanistic to social attributions. We conclude by highlighting outstanding questions on the impact of long‐term interactions with artificial agents on the behavioral and brain mechanisms of attributing socialness to these agents

Decreased reward value of biological motion among individuals with autistic traits Cognition

The Social Motivation Theory posits that a reduced sensitivity to the value of social stimuli, specifically faces, can account for social impairments in Autism Spectrum Disorders (ASD). Research has demonstrated that typically developing (TD) individuals preferentially orient towards another type of salient social stimulus, namely biological motion. Individuals with ASD, however, do not show this preference. While the reward value of faces to both TD and ASD individuals has been well-established, the extent to which individuals from these populations also find human motion to be rewarding remains poorly understood. The present study investigated the value assigned to biological motion by TD participants in an effort task, and further examined whether these values differed among individuals with more autistic traits. The results suggest that TD participants value natural human motion more than rigid, machine-like motion or non-human control motion, but this preference is attenuated among individuals reporting more autistic traits. This study provides the first evidence to suggest that individuals with more autistic traits find a broader conceptualisation of social stimuli less rewarding compared to individuals with fewer autistic traits. By quantifying the social reward value of human motion, the present findings contribute an important piece to our understanding of social motivation in individuals with and without social impairments

Let's Talk About It! Subjective and Objective Disclosures to Social Robots

This study aims to test the viability of using social robots for eliciting rich disclosures from humans to identify their needs and emotional states. Self-disclosure has been studied in the psychological literature in many ways, addressing both peoples' subjective perceptions of their disclosures, as well as objective disclosures evaluating these via direct observation and analysis of verbal and written output. Here we are interested in how people disclose (non-sensitive) personal information to robots, in an aim to further understand the differences between one's subjective perceptions of disclosure compared to evidence of disclosure from the shared content. An experimental design is suggested for evaluating disclosure to social robots compared to humans and conversational agents. Initial results suggest that while people perceive they disclose more to humans than to humanoid social robots or conversational agents, no actual observed differences in the content of the disclosure emerges between the three agents

Using guitar learning to probe the Action Observation Network's response to visuomotor familiarity

Watching other people move elicits engagement of a collection of sensorimotor brain regions collectively termed the Action Observation Network (AON). An extensive literature documents more robust AON responses when observing or executing familiar compared to unfamiliar actions, as well as a positive correlation between amplitude of AON response and an observer's familiarity with an observed or executed movement. On the other hand, emerging evidence shows patterns of AON activity counter to these findings, whereby in some circumstances, unfamiliar actions lead to greater AON engagement than familiar actions. In an attempt to reconcile these conflicting findings, some have proposed that the relationship between AON response amplitude and action familiarity is nonlinear in nature. In the present study, we used an elaborate guitar training intervention to probe the relationship between movement familiarity and AON engagement during action execution and action observation tasks. Participants underwent fMRI scanning while executing one set of guitar sequences with a scanner-compatible bass guitar and observing a second set of sequences. Participants then acquired further physical practice or observational experience with half of these stimuli outside the scanner across 3 days. Participants then returned for an identical scanning session, wherein they executed and observed equal numbers of familiar (trained) and unfamiliar (untrained) guitar sequences. Via region of interest analyses, we extracted activity within AON regions engaged during both scanning sessions, and then fit linear, quadratic and cubic regression models to these data. The data best support the cubic regression models, suggesting that the response profile within key sensorimotor brain regions associated with the AON respond to action familiarity in a nonlinear manner. Moreover, by probing the subjective nature of the prediction error signal, we show results consistent with a predictive coding account of AON engagement during action observation and execution that also takes into account effects of changes in neural efficiency