Funded PhD open to UK-domiciled, home fee applicants of Black African, Black Caribbean or other Black or mixed Black heritage: Digital twin simulations of visual perception to explore animal camouflage

Background:

The core challenge of understanding animal colouration is complex: animals exist in intricate ecosystems that interact differently with various observers, including predators, prey, potential mates and competitors¹. Modelling optimal colouration for concealment or signalling is hard, simply due to the sheer number of variables, including possible colours, patterns, and shapes of animals as well as the environment they inhabit. Studying these systems with biological observers is severely constrained by time, complexity, and ethical considerations^2-4.

Advancements in machine learning enable us to create artificial agents mimicking biological behaviour. Deep neural networks have demonstrated success in automated target detection, but these typically do not integrate biological vision. Networks determine the best way to detect targets limited only by their mathematical complexity rather than biological input constraints⁵.

Our team have previously shown that networks can model human performance in detecting camouflaged targets, effectively replicating aspects of human vision for certain tasks. Networks were used predict human reaction times to colours⁶ and textures⁷, allowing us to test and validate vast parameter spaces to establish optimal concealment. Notably, neural networks were not presented with images containing targets, but only parameters describing those targets, offering the opportunity for practical continuation of the methodology as proposed here.

Aims and objectives:

1.      Create deep neural network models with both targets and their backgrounds included, enabling artificial observers to process the complete visual scene rather than focusing solely on target parameters.

2.      Establish a framework that allows comparisons of how target detection is performed between biological observers (humans) and artificial agents in order to develop valid digital twins. For example, understand which parts of targets the observers detect first.

3.      Extend the methodology to non-human animals with different visual systems, in particular domestic chickens (Gallus domesticus), with a view to estimating optimal presentation contexts⁸ (e.g. lighting conditions)

Methods:

1.      Use texture generating algorithms (e.g. based on reaction-diffusion equations^7,9) to create large pattern spaces and parameterise them to control for visual similarity¹⁰.

2.      Run computer-based psychophysics experiments on human participants to collect reaction time data to a large set of targets, including where they click on the target, with the potential extensibility to eye tracking.

3.      Develop convolutional deep neural network architectures that can predict reaction times to targets presented in particular contexts. Networks will also be able to output the weights of image areas showing their relative importance in target detection (e.g. using Class Activation Mapping¹¹).

4.      Develop and implement a paradigm to present a large number of experimental trials to non-human animals with a focus on domestic chickens.

5.      Implement a system to efficiently validate the predictions using a limited number of biological observers (e.g. using Genetic Algorithms^4,7).

Key references:

1. Cuthill et al. (2017), https://doi.org/10.1126/science.aan0221

2. Bond & Kamil (2002), https://doi.org/10.1038/415609a

3. Bond & Kamil (2006), https://doi.org/10.1073/pnas.0509963103

4. Hancock & Troscianko (2022), https://doi.org/10.1111/evo.14476

5. Talas et al. (2019), https://doi.org/10.1111/2041-210X.13334

6. Fennell et al. (2019), https://doi.org/10.1098/rsif.2019.0183

7. Fennell et al. (2021), https://doi.org/10.1111/evo.14162

8. Lambton et al. (2010), https://doi.org/10.1016/j.applanim.2009.12.010

9. Turing (1952), https://doi.org/10.1098/rstb.1952.0012

10. Talas, Baddeley & Cuthill (2017), https://doi.org/10.1098/rstb.2016.0351

11. Minh (2023), https://doi.org/10.48550/arXiv.2309.14304

Supervisors:

Dr Laszlo Talas (Bristol Veterinary School)

Dr John Fennell (Bristol Veterinary School)

Professor Nick Scott-Samuel (School of Experimental Psychology)

Dr Sarah Lambton (Bristol Veterinary School)

University of Bristol Scholarship - How to apply

You can submit an application via the University of Bristol application portal: Start your application | Study at Bristol | University of Bristol. Select the programme “Veterinary Science (PhD) (4yr)” and see the available start dates. Select ‘Sept 2025’ to begin your application.

In the funding section of the application form, please indicate “University of Bristol Scholarship – Black Heritage”.

In the research section please enter the project title of the scholarship you are applying for along with the supervisor's name. You can upload a blank document instead of the research statement, which is not needed.

We will also be running a pre-application online workshop and Q&A session on how to prepare a PhD application on 5^th December 2024 04:00 PM GMT; if you would like to register for this workshop then please sign up here.

We are keen to support applicants from minority and under-represented backgrounds (based on protected characteristics) and those who have experienced other challenges or disadvantages. We encourage you to use your personal statement to ensure we can take these factors into account.

Candidate requirements: Standard University of Bristol eligibility rules apply. Please visit PhD Veterinary Sciences | Study at Bristol | University of Bristol for more information.

The application deadline is 4pm Monday 6th January 2025.

Contacts: please contact fohs-pgadmissions@bristol.ac.uk with any queries about your application. Please contact the project supervisor for project-related queries: L.Talas@bristol.ac.uk

Bristol PGR scholarships for applicants of Black heritage

As part of our commitment to the Black community, the University of Bristol has launched a number of PGR research scholarships exclusively for students of Black heritage for 2024/25 entry. These are open to UK-domiciled, home fee applicants of Black African, Black Caribbean or other Black or mixed Black heritage.The scholarships aim to address the under-representation of black people in postgraduate research and support our work to improve representation across all levels of study and academia.

We have a wide range of support networks, student societies and community groups for students of Black heritage. These include our Be More Empowered for Success PGR Programme which aims to influence positive change across the themes of access, belonging and empowerment. This year, we are also taking part in the Women’s Higher Education Network 100 Black Women Professors NOW Pipeline Programme, which aims to propel the careers of Black PhD students, through coaching, development, networking and mentoring. WHEN also work with senior leadership to deliver systemic change. We are looking to continue to enhance this support as we continue to strive to make our student communities more inclusive.

If you have any questions on the scheme, you are welcome to contact Professor Stephanie King (stephanie.king@bristol.ac.uk) and Professor Tom Gaunt (Tom.Gaunt@bristol.ac.uk), our Faculty Postgraduate Education Directors for Health and Life Sciences.