MSc Psychological Research PhD - Establishing a role for social robots in healthy independent ageing

The aim of this 1+3 PhD studentship is to address the flexibility and adaptability of older individuals' neurocognitive functioning when interacting with social robots, in order to determine how best to introduce robots to this cohort to maximise uptake, utility, and longstanding use. This is achieved through a combination of questionnaires, long-term training, psychophysiological and brain-based measures with healthy older adults, and a robotics industry research internship.

My task during these 3 years will involve developing sophisticated behavioural and brain-based measures to probe 1) how older individuals perceive and interact with socially assistive robots, and 2) how these perceptions and interactions develop (or break down) over time. In doing so, I will deepen my understanding of eye-tracking and behavioural methods, and acquire expertise with functional neuroimaging and additional electrophysiological techniques (such as electromyography)

The research outcomes from this 1+3 studentship promise to help illuminate how sophisticated new digital technologies that combine AI with social robotics might be designed, introduced, and used, to best help older individuals maintain healthy, independent lives.By gaining a clearer understanding of how older adults perceive and interact with social robots, it should be possible to encourage longer-term acceptance and integration of these agents in the home environment of healthy older individuals. The findings from this project stand to directly inform and advance our understanding of the ways in which digital innovations can help (or hinder) our capacity to maintain independent lives in advanced age. The findings should also provide valuable insights regarding why certain digital innovations may not be accepted by different sectors of general public

Proposed Methodology

To build the best understanding of the attitudes of healthy older adults towards social robots, we will obtain measures of acceptance, trust, and engagement through questionnaires (e.g., Nomura et al., 2008; Bartneck et al., 2009). In doing so, we will shed light on the perceived scope and limits of social robots within the population. Participants will partake in longitudinal training studies (4-8 weeks) wherein they regularly come to the laboratory to partake in interactions with a robot. Through eye-tracking technology, we will gain insight into the attention and engagement of users during human-robot interactions. By employing electromyography (EMG) we can measure implicit emotional responses to the robots (Kirsch et al., 2016). Through the deployment of motor coordination tasks (e.g. mimicry tasks) we will determine the extent to which individuals anthropomorphise the robots (Klapper et al., 2014). Finally, through functional magnetic resonance imaging (fMRI), we will gain insight into brain-based changes that may occur as a result of ongoing social interactions with a robot. Importantly, each of these measures can be used before, during, and after the longterm training/exposure intervention to gain valuable insights to the plasticity and adaptability of social neurocognitive processes in these individuals.