Dr Massimiliano Di Luca

Supervisor Details

Contact Details

School of Psychology, University of Birmingham

Scientific Background

Research Interests

Dr Di Luca investigates the mechanisms of human perception using psychophysical methods and computational modelling. The goal of his research is to understand how the brain progressively builds the perception of the environment by picking up information through actions and by integrating sensations over time.

Scientific Inspiration

Leonardo Da Vinci because he combined art and scientific inquiry with an interdisciplinary approach.

Research Groups

Virtual Reality Lab

Augmented Reality Music Ensemble (ARME) Project

Supervision Style

In three words or phrases: Motivation, guided autonomy, inspiration.

Provision of Training

Your training will be supervised by both me, Mark Elliot, my experienced postdocs and RAs.

Progression Monitoring and Management

Because your project will be connected with a larger project, you will take part in a weekly meeting where you will be able to discuss the updates of your project with the other project members.

I prefer to have weekly 1:1 to provide each project member with advice and guidance and I’m happy to schedule ad-hoc meetings.

Communication

I am known to email at odd hours but I never expect a quick reply. The team has a WhatsApp group chat to ask urgent questions and to organise social events.

PhD Students can expect scheduled meetings with me:

In a group meeting

At least once a week

In year 1 of PhD study

At least once a week

In year 2 of PhD study

At least once a week

In year 3 of PhD study

At least once a week

These meetings will be a mixture of face to face or via video chat or telephone. I am usually contactable for an instant response 3 or 4 days per week.

Working Patterns

The timing of work in my lab is flexible, and other than attending pre-arranged meetings, I expect students to manage their own time.

Notice Period for Feedback

I need at least 1 week's notice to provide feedback on written work of up to 5000 words.

Project Details

Dr Di Luca is the supervisor on the below project:

Ensemble music synchronization

Secondary Supervisor(s): Dr Mark Elliott

University of Registration: University of Birmingham

BBSRC Research Themes: Understanding the Rules of Life (Neuroscience and Behaviour)

No longer accepting applications

Project Outline

The project is linked with the EPSRC-funded http://arme-project.co.uk which aim is to create a virtual music ensemble.

In musical ensembles (such as piano duets, jazz trios, string quartets, rock groups, samba bands, and drum circles), there is no stable reference for performance timing, and musicians must time and synchronise their performance with each other. This project will expand the neuroscientific understanding of how humans synchronise with each other during ensemble music production and according to the underlying type of musical rhythm (e.g., Jacoby, McDermott, 2017). It will involve both an experimental component and a machine learning component to generate an agent-based computational synchronisation model. Taking inspiration from the discrete timing models of Wing and Kristofferson (1973) and the linear correction model of Vorberg and Schultz (2002) developed in finger-tapping performance, the student will build a synchronisation model (e.g., using continuous probability distributions and statistically optimal estimators). The student will have access to a database of hundreds of recorded performances of quartets, trios, and duets provided by a commercial partner in the project. The student will further be able to collect data by running a small number of performance recordings to tackle specific theoretical questions.

References

Wing, Alan M., and Alfred B. Kristofferson (1973). Response delays and the timing of discrete motor responses. Perception & Psychophysics 14.1 (1973): 5-12.

Vorberg, D., & Schulze, H.-H. (2002). Linear phase-correction in synchronization: Predictions, parameter estimation, and simulations. Journal of Mathematical Psychology, 46(1), 56–87.

Jacoby, Nori, and Josh H. McDermott (2017). Integer ratio priors on musical rhythm revealed cross-culturally by iterated reproduction. Current Biology 27.3 (2017): 359-370.

Techniques

Experimental psychology (Behavioural testing with humans, statistical analysis of behavioural data).
Music analysis (Digital signal processing).
Computational modelling.
Machine learning / Artificial intelligence.

Previous Projects

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