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I work on a mixture of sociolinguistics and semantics, through the lens of language perception. More specifically, I look at how internal linguistic variation in swearing influences person perception.
I used Gorilla to run a variant categorization task using a mixture of regular words, swearwords and legal non-words all ending with the morpheme ‘-ing’. Tokens of these words were artificially manipulated in a MATLAB based program called TANDEM-STRAIGHT to create phonetic continua between the two common pronunciations of ‘-ing’: the standard [ɪŋ] variant (e.g. in ‘playing’) and the more non-standard [ɪn] variant (e.g. in ‘playin’). The 7‑step continua created some potentially ambiguous tokens of each item.
In a forced-choice variant categorization task, participants heard 30 test stimuli (plus 20 distractors). On each trial, participants had to use their keyboards to select whether they heard an ‘-ing’ word or an ‘-in’ word. I hypothesised that, for swearwords, listeners would be biased to select the more informal [ɪn] variant when the token was acoustically ambiguous – the same bias shouldn’t occur for phonetically matching regular words and legal non-words. This prediction was motivated by the fact that, like the [ɪn] variant, swearwords are also more frequent in informal contexts.
Contrary to this hypothesis, swearwords actually biased listeners towards selecting the [ɪŋ] variant on ambiguous tokens, compared with regular words and non-words. The results suggest that, rather than swearwords cueing the alveolar form, as predicted, swearwords inhibited performance. Swearwords are shown to take up more attentional resources than neutral words, for example in Stroop tasks. With participants’ attention on the ‘-ing’ token, the swearwords distracted them from this task. As a result, they defaulted to the underlying form (the [ɪŋ] variant) as the most likely option. The results have consequences for how sociolinguists interested in cognition think about attentional load in perception.
Yes, you can find them here!
The best aspect of Gorilla is the functionality of the experiment builder for people with little to no coding experience. The process is very simple and allows for a large amount of customization.
Gorilla allowed me to auto-reject people that failed catch trials during my experiment. It was important that participants were able to differentiate between unambiguous tokens of ‘-ing’ and ‘-in’ – to test this I used a series of practice trials. Gorilla then rejected anyone who failed to reach this threshold by including their performance as embedded data.
Previously, most language perception studies required people to come into the lab in person, which was both costly and time-consuming – the same is true of grammaticality judgment tasks.
“Online research means this work can be done much faster and at lower cost.”
In my spare time I work as a special constable in the metropolitan police in London – I spend time policing the South East boroughs of London on a voluntary basis. Apart from that, I am a keen rugby player and I have recently started playing a lot of chess.
I have oddly been interested in swearing for a long time, back to my undergraduate studies. During that time I worked part-time as a nightclub doorman in Southampton, which exposed me to some less-than-savoury language on a fairly regular basis.
“I have a particular anecdote about a time a customer referred to me, in on sentence, using two seemingly contradictory swearwords for the male and female genitalia respectively. This amused me so much that I then decided to start researching swearing in my studies and, well, here we are.”
Some of my work focuses on the grammaticality of swearing. For example, I ran an experiment on Gorilla looking at infixing e.g. fan‑fcking-tastic. According to phonological theory, this is constrained by prosody, meaning that it is ill-formed to say fanta‑fcking-stic.
I think some people take this as me saying that you should always swear according to the grammar. But frankly, I don’t care how you swear – the beauty of swearing, particularly in English, is that is so permissive and so endlessly creative. I often encounter non-linguists more generally that think I am going to be a grammar-nazi, but again, no self-respecting linguist would actually spend their time correcting people – it goes directly against what we stand for.
I’d liked to have interviewed Simone de Beauvoir, for the simple fact that she was a brilliant writer and thinker. I have another project about phrases like ‘the wife’, which is so much more common than ‘the husband’, and I’d be keen to get her take on it.
A previous attempt at running an online experiment had used free software – while cheaper, their server couldn’t sustain the level of activity, to the experiment would regularly crash. I chose Gorilla on advice from my PhD supervisor, another regular user.
I used a headphone check task to ensure that participants were indeed using headphones and not just the speakers on their computers. This task was created by another research group. It is a 3‑AFC “Which tone is quietest?” task with 200Hz pure tones. Unbeknownst to the participant, a random one of the tones is in antiphase across the stereo channels, resulting in heavy attenuation only when heard over loudspeakers (but not over headphones). This results in very poor performance if the task is attempted without headphones.
Ensuring quality results that would match those that you could collect in the lab.
The only way to learn is trial and error. You can read all the open materials and advice columns, but ultimately you won’t learn what works and what doesn’t until you have tried to create an experiment from start to finish. Also, pilot pilot pilot – I can’t stress that enough.
Any meme that exemplifies the struggles of statistics and R in particular.
For anyone interested in semantics (and who can speak German), there is a great series on YouTube by Daniel Gutzmann.
A great new book about linguistics for the general reader is Language Unlimited: The Science Behind Our Most Creative Power by David Adger.
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