Cohort 3 (2021-2025)
Roberto Luciani
My academic journey began in earnest at Abertay University, where I had the opportunity to volunteer as a research assistant. In the 4th year, my dissertation allowed me to indulge my interest in imagination: I looked at the relationship between people’s self-reported vividness of imagery (how clearly an image forms in their head when they will it to) and the performance of their verbal memory when they were encouraged to imagine elements of a list visually. Through this project, my curiosity grew ever stronger.
After graduating from Abertay University with a BSc in Psychology and Human Resource Management, I secured a PhD position in Social AI CDT. In my PhD, I will be once again studying the nature of imagination. The aim of the project is to understand what areas of the brain are involved in imagination, and to model their function with the help of machine learning. We will be testing participants using established tests of visual imagery and using fMRI to measure their brain activity while performing certain tasks.
It is difficult to understand what it must be like to imagine differently – the experience of reading a book, listening to a rousing speech, appreciating visually evocative poetry: these are some of the things we all experience differently according to the vividness of our visual imagination.
To say I am excited to be given the opportunity to further study this phenomenon is an understatement – I believe that, in its own invisible way, imagination has a big impact on how we interact with each other and the world around us. I hope that, by studying how different we are, we might come to understand each other better in the end.
Towards modelling of biological and artificial perspective taken
Context and objectives
Visual imagery, e.g. the ability to form a visual representation of unseen stimuli, is a fundamental developmental step in social cognition. Being able to take the perspective of another observer is the focus of classic paradigms in theory of mind research such as Piaget’s landscape task: overturning an egocentric world view is reached around the age of 4 when children learn to simulate another person’s perspective towards a visual screen and imagine what is in sight of that person (Piaget, 2013).
Visual imagery might be one of the cognitive processes supported by extensive feedback connections from higher order areas and other modalities to the visual system (Clavagnier et al., 2004), as evidenced by the fact that sound content can be decoded from brain activity patterns in the early visual cortex of blindfolded participants (Vetter et al. 2014). Preliminary data from Muckli’s lab also suggests that this result cannot be reproduced in aphantasic participants who report an inability to generate visual imagery (Zeman, Dewar and Della Sala, 2015).
Our project aims to further explore the neural correlates of visual imagery and aphantasia by using neural decoding techniques, which allow the reconstruction of perceived features from human magnetic resonance imaging (fMRI) data (Raz et al, 2017). This method will allow us to detect shared representation networks between visual imagery and actual visual perception of the same objects, whether these networks are shared across participants, and whether they differ between aphantasics and non-aphantasics.
Proposed methods and expected results
We will use Ultra High Field fMRI to read brain activity while participants (aphantasics and non-aphantasics) are presented with either single-sentence descriptions of object categories (e.g. “a red chair”) or different visual exemplars from the same categories.
Our hypotheses are that, in the visual system, representations of the same categories: (1) will be generalizable between the auditory and visual conditions for the non-aphantasic group, but not for the aphantasic group, (2) will be less generalizable across aphantasics than non-aphantasics in the auditory condition, (3) that the previous two points will allow us to discriminate between aphantasics and non-aphantasic participants.
In Human-Computer Interaction (HCI), we recently developed computational models capable of representing physical and virtual space, solving the problems of how to recognise virtual spatial regions starting from the detected physical position of the users (Benford et al., 2016). We used the models to investigate cognitive dissonance, namely the inability or difficulty to interact with the virtual environment. In this project, we will adapt these computational models and apply them to cognition processes to test hypotheses 1-3. The end goal is to embed them within AI agents to enable empathic-seeming behaviours.
Impact for artificial social intelligence
Our proposal is relevant for the future development of creating and contrasting artificial agents with and without imagery, not only making AI more human-like, but adding the layer of complexity that is imagery-based representations. We outline a number of key questions where we hypothesize imagery has a function in social cognition, and where imagery-based artificially intelligent machines can be applied to social phenomena. To what extent is visual imagery in social AI an advantage? Simulate the perspective another agent has on a view and being able to match the perspective.
References
- Clavagnier, S., Falchier, A. & Kennedy, H. (2004) “Long-distance feedback projections to area V1: Implications for multisensory integration, spatial awareness, and visual consciousness”, Cognitive, Affective, & Behavioral Neuroscience 4, 117–126
- Piaget, J. (2013). Child’s Conception of Space: Selected Works vol 4 (Vol. 4). Routledge.
- Vetter, P., Smith, F.W., and Muckli, L. (2014), “Decoding Sound and Imagery Content in the Early Visual Cortex”, Current Biology, 24, 1256-1262.
- Zeman, A., Dewar, M., and Della Sala, S. (2015) “Lives without imagery — Congenital aphantasia”, Cortex, 73, 378-380.
- Raz, G., Svanera, M., Singer, N., Gilam G., Bleich, M., Lin, T., Admon, R., Gonen, T., Thaler, A., Granot, R.Y., Goebel, R., Benini, S., Valente, G. (2017) “Robust inter-subject audiovisual decoding in functional magnetic resonance imaging using high-dimensional regression”, Neuroimage, 163, 244-263
- Benford, S., Calder, M., Rodden, T., & Sevegnani, M. (2016). On lions, impala, and bigraphs: Modelling interactions in physical/virtual spaces. ACM Transactions on Computer-Human Interaction (TOCHI), 23(2), 9.