Who or what originally inspired you to work in your field of research?

I’ve always been interested in how we share our ‘selves’ with the world. Having started as an undergraduate in linguistics, I initially looked at how our language use interacts with our self-identity. Since then, my research has become more focused not on what you say, but how you say it, and how your voice itself is an important and powerful medium of self-representation.

What do you work on?

I’m interested in voices and self-processing. My research asks questions about how your voice makes you, ‘you’ and recently I’ve been asking whether people can be given a new voice and incorporate it into their self-concept.

What did you do using Gorilla, and what did you find?

We know that information associated with ourselves is perceptually prioritised over information associated with other people. That is, we tend to be faster and more accurate in perceiving a self-related stimulus than an other-related stimulus, which is known as the self-prioritisation effect. This effect has previously been researched by Sui et al. (2012) using a perceptual matching paradigm and, with Gorilla, we were here able to create an online version of this paradigm and test self-prioritisation in voices for the first time.

In our tasks, people heard three unfamiliar voices and learned who they belonged to by being told, for instance, ‘This voice belongs to you’, ‘This voice belongs to a friend’, and ‘This voice belongs to a stranger’. Once they’d learned the correct pairings, we presented different combinations of a voice with an identity label, and asked participants to judge whether it was a correct pairing, or not. If there is a self-prioritisation effect, we’d expect to see that the voice belonging to them, the self-voice, would be judged more quickly and accurately, relative to either the friend-voice or the other-voice (Experiment 1).

Take a look at our study on Gorilla Open Materials.

And that’s exactly what we found! It’s important to remember, however, that it’s not their real self-voice! All the voices used in this paradigm are unfamiliar to them before they start so this prioritisation effect shows that the new self-voice has been perceived as more relevant to them than the others despite being equally unfamiliar.

To test what other factors could influence this prioritisation, we ran two further versions of the same experiment on Gorilla. In Experiment 2, we gave participants a new self-voice that was more similar to their real voice (i.e. gender-matched) while, in Experiment 3, we asked participants to first choose which voice they wanted to become their new self-voice.

We found a self-prioritisation effect in all three experiments. The impact of choosing your voice (Experiment 3) had a greater effect on self-prioritisation than the similarity of physical properties between voice and the participant (Experiment 2).

Did you include any special features in your study to ensure good data quality? If so, what did you do?

We used a headphone screening (designed by Woods, Siegel, Traer & McDermott, 2017) to check people were using headphones while doing the study remotely and so ensure the auditory stimuli were being heard properly. We didn’t use attention checks this time but did use exclusion criteria based on performance.

Has this study been published?

Our paper has been submitted to a journal for peer review and a pre-print is currently available.

What real-world problem do you see that your research could impact?

If you lose the ability to use your own voice (i.e. patients with motor neurone disease) it can be damaging to your sense of self and make it hard to maintain a social identity. By investigating what factors might aid the incorporation of a new, externally-generated, voice into the self-concept, we hope to show that an alternative voice can quickly become self-relevant – especially when it has been chosen – and be subsequently prioritised in perception. Our findings therefore have implications on the design and selection of individuated synthetic voices that are used with assistive communication devices.

How do you think online research is going to change your field?

Quicker testing with higher recruitment numbers. Plus, as long as you take some data quality assurance measures, such as attentional checks, an exclusion criteria and piloting, you can get really good quality data, fast.

What is the biggest advantage of online research methods?

It’s so quick to collect data; once the study is live I can recruit and collect data from all participants within a few hours!

Why did you choose Gorilla?

I’d first been introduced to Gorilla during my MSc and knew it was really flexible, available for use with auditory stimuli, and ideal for linking to an online recruitment platform.

The flexibility that Gorilla provides with such a simple build process is astounding! It’s so adaptable to your requirements and you’re limited only by what you haven’t yet realised you can do. If you ever come unstuck though, I’ve also found the Gorilla Support team to be remarkably quick to help and provide solutions.

How did Gorilla make your life or research better, easier or faster?

I really appreciated how easy it was to share the experiment with others. It was handy for me to send it within Gorilla to colleagues who could contribute to its construction, to send it as an email link for people to pilot or, at test, to share it via a recruitment platform for quick data collection.

On a personal level, what are you most proud of?

I’ve always wanted to do a PhD and have the opportunity to research something I’m so interested in. I’m very proud (and very grateful) to be where I am and also to be enjoying it thanks to such a great lab team!

What advice would you give to someone starting out in behavioural science/research?

Learn to code! I thought coding was a really niche skill, but it’s so widely applicable. Being able to code improved my data analysis, and gave me a valuable transferable skill for industry.

I got started in R while analysing Gorilla data. Having designed every element of my Gorilla experiment, it’s was far easier to understand what my data should look like and helped me understand how the code was manipulating my data. I also tweaked my Gorilla set up using JavaScript, as I needed to delay the autoplay function of an auditory stimulus. So I used the Gorilla scripting tools within the task builder.

I’ve learnt not to be intimidated by coding; turns out it’s fun once you’ve got started!