A rich research tradition in sociology, social psychology and economics has demonstrated how concern for status strongly motivates human behaviour. Happiness and well-being are strongly affected by the comparison between the individual’s own income and the income of others. Individuals in dominant position use their status to secure privileged access to resources, such as food and mates. Therefore, social comparison is important for monitoring one’s social status and might emerge early during development. Recent research in cognitive neuroscience suggests that counterfactual and social comparison rely on different brain mechanisms and that the latter induces competition. Emotional responses elicited by social comparison (envy and gloating) engage the reward system as well as social cognition areas more than their private counterparts (regret and relief). We propose to investigate the developmental trajectory of social comparison and competitive behaviour. You will use monetary tasks/games, combined with neuroimaging methods to link interindividual differences in cortical development with attitudes toward social comparison and cooperative/competitive behaviour. Some experience with, or at least strong willingness to learn, computer programming (e.g. matlab, R, Python) is essential for this project. The use of computational models of decision making will be possible (and supported) depending on the student’s interest. You will have access to the school lab and to the brand new state-of-the-art human neuroimaging facility (Brain Research & Imaging Centre
) of the University.
Computational models are powerful tools for understanding human cognition, and their use has led to new, often counterintuitive, theoretical insights. Projects are available that combine computational modelling with behavioural experimentation to investigate the relation between explicit (conscious) and implicit (unconscious) memory. Although the traditional view of explicit and implicit memory is that they are driven by distinct memory systems in the brain, numerous lines of research have converged on the view that memory systems may not divide so sharply on consciousness. Indeed, computational modelling approaches have shown that an alternative, single-system model explains numerous key findings thought to be indicative of distinct systems; it also makes predictions that can be verified empirically. This type of project would suit someone who has experience or interest in programming and has strong statistical/research methods skills. Applicants are advised to make contact to discuss the specific direction of the project before applying.
Humans are acutely aware of their own physical appearance, as well as the appearances of others. Our perceptions of physical attractiveness can significantly influence our thoughts and behaviours towards others. Despite extensive research on the perception of attractiveness, the vast majority of studies have focused on natural variations in intact human body configurations. As a result, there has been little investigation into how individuals with physical disabilities are perceived in terms of their attractiveness and personality, and even less research on the impact of prosthetics on how disabled individuals are judged. Therefore, the goal this project is to examine the perception of physical attractiveness and personality of individuals with disabilities, including those with and without prosthetics. This research has the potential to enhance the social circumstances of individuals who have physical disabilities by offering factual data about the impact of non-organic body parts (such as prosthetics) on disabled individuals' perceptions of their own attractiveness, as well as how others perceive them. This project utilizes eye-tracking and virtual reality to address its research questions.
The comfort distance between humans and robots is a critical element in human-robot interactions. However, due to theoretical and methodological constraints, there is a lack of systematic research in this area. Although prior studies have examined the human-robot comfort distance using robots, they do not offer a comprehensive and systematic comparison between humans and robots as agents in an ecologically valid setting. This research examines human-robot interactions through virtual reality and investigates how the comfort distance from robots is affected by both robot appearance and individual differences.
Recognizing and perceiving faces are essential for social interactions in humans. Faces can convey information about an individual's genetics, underlying physiology, and health status, all of which contribute to our perception of attractiveness. Individuals with high levels of autistic traits often struggle to interpret nonverbal social cues conveyed through faces. Previous research has extensively identified the contributors of perceived facial attractiveness for neurotypical individuals. In this project, we examine whether individuals with high levels of autistic traits exhibit a distinctive preference for facial attractiveness and measure their gaze behaviour when assessing attractiveness.
Whilst autism and schizophrenia are easily distinguishable, they share remarkable similarities in the social difficulties that they encounter. However, it is not clear if the mechanisms that cause this are the same, or whether there are distinct causes that coincidently elicit the same difficulties. Recent predictive coding accounts of perception, whereby the brain generates predictions of what we will see to compare to what we actually observe, suggest that the predictions made by those with autism and schizophrenia may be different, and help us distinguish between them. Furthermore, making predictions about other people facilitates social interactions, and a different style of prediction may explain why people with autism and schizophrenia feel more comfortable interacting with others with the same condition. The aim of this project is therefore to employ predictive models of social perception to devise a behavioural test that is sensitive enough to easily distinguish between autism and schizophrenia, and to assess their social capacities in a more ecologically valid environment of real-world social situations that takes into account the people they are interacting with.
Practice improves perception, even for basic visual tasks such as discriminating the tilt of a line (is it clockwise or counterclockwise?). Such improvements, termed perceptual learning, tell us that sensory brain regions are malleable long into adulthood. Nearly everyone improves with practice, but some people don’t, and others reach near-perfect performance with little practice. Furthermore, performance at baseline may differentiate what people learn in a task (e.g., stimulus-specific vs. non-specific information). This project will examine individual differences in perceptual learning, with attention to the stimulus- and task components of learning, and predictors of the time-course and overall amount of learning in perceptual tasks. The goal is to understand how people vary in the information they select during perception, and how practice alters that selection. Strong analytical and programming skills desired.
Envisioning the future – understanding the function of component processes. Dr Julie Ji
The human capacity to envision novel future experiences via episodic simulation (sensory mental imagery-based mental representations of events) is theorised to confer evolutionary advantages due to its capacity to motivate and guide goal-directed behaviour as it allows us to predict what might happen, how we might feel about it, and how to approach/avoid desirable/undesirable future outcomes. However, key questions remain about the functional implications of its core components. This multi-component project aims to understand the impact of the following components on emotion, evaluative judgment, and motivation: a) self-generation (vs. viewing); b) temporal location (future vs. atemporal); c) perceptual vividness vs. episodic detail.
When one event precedes another, we learn this relationship so that we can behave appropriately. A common assumption is that this learning is caused by prediction error, or the difference between our expectations and reality, with more prediction error resulting in more learning. However, recent data from experiments conducted in our lab cast doubt on this idea. In our experiments we changed the outcomes that followed certain cues. According to prediction error, learning should be greatest for cues whose outcomes changed the most. However, we observed the opposite result. Our results are more consistent with the idea of stubbornness, or ‘theory protection’, than with prediction error. We propose that, once participants learn what follows a cue, they are resistant to changing their beliefs. They therefore attribute unexpected outcomes to the cues that are most consistent with those outcomes, even though these will often be the cues that have the smallest prediction error. This project will examine this theory protection principle, to discover the circumstances in which it applies. This work is expected to have implications for a wide range of fields that use prediction error to explain how we understand the world.
The human face is an extremely rich source of information – just by looking at someone’s face, we can extract information about their age, gender, mood and even their personality. More importantly, we use faces to recognise the people we know as well as to prove our own identity to unfamiliar observers (such as passport control officers at airports). Research has demonstrated that familiar and unfamiliar face recognition are fundamentally different processes – while we can recognise familiar identities somewhat automatically and with no effort whatsoever, unfamiliar face recognition is a surprisingly error-prone task. There have been many attempts to find ways to improve unfamiliar face recognition accuracy by providing feedback, targeted training or by using multiple images of the same person. While successful, to an extent, these approaches have important limitations. Previously, I have identified an easy and consistent route to recognition accuracy improvement – to simply smile. When we compare two smiling images, compared to two neutral images, we are more likely to accurately determine whether they belong to the same person or to two different people. This project aims to explore this smiling effect further by identifying the reasons why we find smiles so beneficial – this might be because when we smile, further idiosyncratic information is available, because a smile increases motivation to perform well or because we spend more time looking at smiling rather than neutral images. The project will also focus on the extent of the smiling advantage by increasing the difficulty of the task, introducing an age gap between the images or with other-race faces.
Habits can be very useful. For example, an experienced car driver can change gear habitually, leaving plenty of mental capacity to monitor complex traffic conditions. However, habits sometimes to lead to errors, such as picking up a chocolate bar in the Newsagent when trying to lose weight. These kinds of errors – where our learning leads us to do things that we would prefer not to, and which feel outside of our control – allow important insights into our psychology. Important questions remain as to why this kind of automatic behaviour occurs, the situations in which it is most likely to be observed and who is most likely to be susceptible. This project would suit a student who has an interest in learning, memory or attention.
Autism spectrum conditions have been associated with a constellation of strengths and weaknesses within the visuospatial domain. One component that has received relatively little scientific attention is the difficulty that many individuals can have with everyday spatial navigation. Some empirical reports have identified the potential cognitive bases of these individual differences, although they have generally been based on relatively simple screen-based virtual environments. In this project, we will use state-of-the-art Immersive Media technologies to comprehensively explore navigation in autism. The work will focus on the documentation of realistic models of the built environment, including models from actual buildings and spaces constructed using long-range 3D scanning technologies. These environments will not only be used to provide a realistic and valid platform to experimentally characterise the range of abilities that contribute to differences in daily navigation, but also to test more applied questions of whether learning in realistic immersive environments can transfer to the real world. This will carry important ramifications for supporting difficulty and improving quality of life for some individuals. The project will also make use of brand-new facilities in Plymouth’s flagship Brain Research and Imaging Centre to examine neural connectivity (include diffusion imaging and white-matter tractography) in relation to navigational performance.
Our health and wellbeing are dependent on our ability to set goals for ourselves, and to achieve those goals. For example, suppose you have secured a new job that is in a different part of town from where you have worked for the last five years. It is important that you are able to navigate your way efficiently to that new workplace on the first day. One way in which this might go wrong is that (anxious and distracted on your first day) you may accidentally drive to your old workplace. That is, a habit that has developed over five years might undermine your goal. We have recently developed a procedure to produce habitual behaviour of this kind in the laboratory. The current project is to extend the examination of these habitual “action slips” to the domain of navigation. Students will create virtual computer-based environments through which participants will be required to navigate to achieve certain goals. The research will examine when and why we are sometimes not in control of our behaviour, as a consequence of learned habits. The project will also relate navigational errors to the contents of the environment (e.g. landmarks, boundaries) in order to examine whether some environments or routes are more likely to engender habitual errors than others. This will have implications for the treatment of navigational impairments found in typical (e.g. ageing) and atypical (e.g. dementia, developmental conditions) populations.
Foraging is a fundamental behaviour for many species. In humans, it has even been typified as the context of our cognitive evolution, and many societies today still subsist on hunting and gathering. However, foraging behaviour is present in all societies, from searching a supermarket shelf to scouring your home for a lost set of keys. This activity is supported by a variety of psychological functions that include, perception, attention, memory, and decision making. Traditionally, psychologists have studied human search behaviour using the visual search paradigm, although this tends to constrain our understanding to simple two-dimensional spaces presented on a monitor. Advances in methodology now present exciting opportunities to create controlled three-dimensional search spaces for participants to explore, and this project will examine the psychological factors that support efficient environmental search behaviour. This can include explorations of environmental structure (e.g. shape, landmarks), statistical properties of the array (e.g. fruiting patterns, spatial likelihoods), and the individual differences that underlie search (e.g. working memory, autistic traits). Experiments could make use of Plymouth’s world-class environmental simulation capabilities, and there may also be the opportunity to address some of these issues in patients who have sustained neurological damage, and to look at changes in search behaviour associated with typical ageing.
Theories of memory have traditionally viewed forgetting as a negative consequence of limitations of the memory system. Anderson’s (2003) retrieval inhibition theory proposes that, on the contrary, forgetting is adaptive and the ability to suppress certain memories is beneficial to the normal function of the memory system. This research will use a range of empirical paradigms and quantitative modelling techniques to investigate the factors that contribute to forgetting, including interference from other memories, conscious inhibition, and context change. Although the focus is on basic research, there is scope for investigating the implications of inhibition and forgetting in applied areas. For example, are emotional or traumatic memories more difficult or easy to suppress? Does suppressing irrelevant information facilitate problem-solving? In revising educational materials, does the strategic inhibition of knowledge actually, improve long-term learning?
Effective learning through testing: The testing effect in basic and applied research. Dr Michael Verde
A great deal of recent interest has focused on the role of testing in learning. Both basic and applied research suggests that revising information through active retrieval is one of the most effective ways to promote long term retention (Roediger & Karpicke, 2006; Roediger & Pyc, 2012). This research project has two goals. The first is to investigate the factors that make testing such an effective method of revision. We will consider theories of associative strengthening, information integration, and contextual reinstatement. The second goal is to apply our findings to ecologically valid materials and settings such as science education. This project has strong potential for interdisciplinary work with researchers in education and biology.
When we think about past events, we often reflect on how things might have happened differently, for example, if I had left home earlier, I might have caught the train. This mental simulation of alternatives to the past is known as counterfactual thinking. The aim of this project is to examine the idea that this process may also be an important part of how people generate lies. Like counterfactual thinking, lying involves making minimal changes to past events and these changes should be consistent with other events that have happened. Lying therefore also requires keeping in mind what the listener knows to be true. The project will examine the processes underlying the generation of lies and whether as a result, some lies are easier than others to generate.
When we reflect on past events, we frequently reflect on how things might have happened differently. We might imagine that things could have turned out better, for example, if I had been paying attention, I wouldn’t have tripped or instead that they could have turned out worse, e.g., I am lucky that I didn’t break my arm. These alternatives shape how we feel about events that have happened and the judgements that we make about them, for example, where we place responsibility or blame. They can also influence decisions about how to behave in the future. This project will therefore examine the types of thoughts that people imagine and their consequences.
Since around 2014, there have been substantial advances in Artificial Intelligence, with cutting-edge machines now able to classify objects with a level of accuracy that some engineers describe as 'human like'. How well founded are these claims of human-level performance on such tasks? And to what extent are these machines – whose designs are often inspired by neuroscience – good models of human behaviour? Some experience with, or at least strong willingness to learn, computer programming (e.g. R, Python, or C++) is essential for this project. Use of neuroscience methodologies (e.g. eye-tracking, EEG, fMRI) may be possible, depending on your interests.
In psychology, we typically assume that the average behaviour of a group of people is representative of a common set of underlying cognitive processes. In reality, we’ve known for some time that the group average can be unrepresentative of the vast majority of individuals that make it up. In a recent experiment, Lenard Dome (one of my current Ph.D. students) found only 3% of participants showed the group-level result! How can we build better theories of human learning, memory, and decision-making that capture the variety of behaviours exhibited within a group (and only the range observed)? Such would be the topic of your Ph.D. Some experience with, or at least strong willingness to learn, computer programming (e.g. R, Python, or C++) is essential for this project. Use of neuroscience methodologies (e.g. eye-tracking, EEG, fMRI) may be possible, depending on your interests.
Climate change is an unprecedented global threat and understanding the dynamics involved in climate change is cognitively challenging. This project sets out to explore climate change cognition such as understanding exponential dynamics, social dilemmas, and their interplay using a game-based approach. It focuses on how people make decisions in a complex environment and which interventions can support mitigating actions. We have recently used a similar game-based approach to study decision making in a pandemic-like simulation (Woike et al., 2022
). This project is ideal for someone who enjoys the conceptual and technical development of online games and has experience with statistical analyses.
Many of the decisions we make and the actions we take or fail to take impact others directly or indirectly. Whether intended or not, our actions may help or harm others and have the potential for shaping reactions and even complex chain reactions. How do we navigate complex social environments, how do we motivate and justify decisions with consequences for others? Experimental ethics in this project combines empirical methods from experimental economics and experimental philosophy to study decision making and interaction with moral implications. Possible topics include: consequences of cooperation and competition, distributive justice, honesty and cheating, promises and threats, social dilemmas, collective action, rivalry, negotiation and conflict escalation. Programming skills would be an asset, and the project would be ideal for students with an interest in (1) developing and analyzing interactive online games and experiments and (2) engaging with literature across different disciplines (psychology, philosophy, and economics).