Research Network member Ian James considers whether some of our language for meat is playing a part in dissociation and the meat paradox, and discusses his experimental research on the topic.
With around 134 billion[i] vertebrates suffering[ii] on factory farms each year, and the devastating environmental and public health consequences this entails,[iii] the consumption of animal products is arguably one of humanity’s most pressing moral and practical concerns.
So how do we persuade 7+ billion people, many of whom are indifferent to the suffering of animals, to start caring about them?
Well, it turns out we don’t need to.
Data suggests that most people are against harm to animals.[iv] Good news. Except that most people also …eat them, which necessitates harm to animals.
This phenomenon has been called the ‘meat paradox.’ And in light of the above, it may well be among humanity’s most damaging blind spots.
Dissonance, dissociation, dinner
In 1957, influential social psychologist Leon Festinger published “A Theory of Cognitive Dissonance.” Cognitive dissonance can be roughly defined as discomfort experienced when noticing an inconsistency in one’s behaviours and/or beliefs.
No one likes cognitive dissonance. But fortunately (or very unfortunately), when it comes to meat-related dissonance, society has an ace up its sleeve: dissociation.
By obscuring the connection between the food product and the animal from which it derived, companies making or selling animal products are able to keep their sales up; society is able to maintain ideas, traditions, and institutions it holds dear (meat and two veg, anyone?); and individuals are able to keep eating the food they like without having to confront self-contradictions.
Of course, everyone (young children aside) knows what meat is. We all know that chicken is the flesh of chickens and beef comes from killing cows. How, then, are so many consumers seemingly able to ignore this knowledge at the supermarket and the dinner table?
First, it should be mentioned that many consumers seem unaware of the horrors farm animals go through.[v] A 2017 study[vi] found 58% of US consumers thought “most farm animals are treated well,” 75% said they usually buy humane animal products, and 49% even supported a ban on factory farming (for context, around 99% of US farm animals live on factory farms[vii]). While motivated reasoning may well be playing a part here,[viii] it’s worth noting that some consumers are genuinely oblivious to the suffering they’re causing.
But what of the myriad meat-eating animal lovers that do know?
Academics have suggested a number of facilitators of dissociation.[ix] Presentation[x] (e.g. removal of heads and other recognisably animal features; absence of source animal pictures),[xi] production (in)visibility[xii] (most people never even see factory farms, let alone see inside them), and strategic ignorance[xiii] (see this article) are three such factors.
And language may be another important facilitator.
Chickens make chicken; cows make beef
The words we use to refer to meat products in English are interesting.
Sometimes they’re the same as the names of the animals they come from; ‘chicken’ comes from chickens and ‘salmon’ from salmon.
More often than not, however, they are less transparent in terms of their animal sources. Take beef, pork, mutton, venison, veal, ham, and so on; these words – often deriving from French following Norman settlement in Britain[xiv] – are distinct from the names of their source animals (cow, pig, etc.). A six-year-old would be forgiven for not knowing where ‘beef’ comes from. Similarly, a 26-year-old could be forgiven for temporarily shelving the knowledge of what that ‘steak’ really is.
You can probably see where this is going. It’s been argued[xv] that this feature of English meat terminology may be helping people to dissociate these products from their animal sources, and serves to “keep our ‘meat’ separated from any idea that she or he was once an animal.”[xvi] And this linguistic smokescreen seems to go far further than just meat referents themselves, as discussed in this excellent article.
Incidentally, it tends to be the animals we consider more similar to ourselves and feel more empathy towards[xvii] which get this linguistic treatment. So we can happily watch that cute pig video while munching down our bacon sarnies.
Evidence…?
So, some believe our meat words help us forget meat’s animal origins. Fine, but where’s the proof?
Kunst & Hohle (2016) found that replacing ‘beef’ and ‘pork’ on menus with ‘cow’ and ‘pig’ led to increased avoidance of these options in favour of meat-free ones (a finding which has been replicated[xviii]). It seems the removal of this linguistic disconnect may serve to disrupt dissociation to such an extent as to dissuade many consumers from ordering these meats.
But what if those dishes were only avoided because ‘cow’ and ‘pig’ on a menu sound weird, and most people don’t want to order weird-sounding food? Do we know it’s because this wording is more likely to shatter dissociation and remind people of animals? Do we know, conversely, that less transparent terms like ‘beef’ are likely to facilitate dissociation?
The theory is that when someone sees, hears, or thinks the word ‘chicken’, there’s a fair chance the source animal (a chicken) will pop into their head. Should this occur, the source is said to have become salient. Conversely, with ‘beef’ and co., by virtue of their linguistic estrangement, there’s a lower chance the source animal will make an appearance in cognition/become salient. Intuitively this seems reasonable enough; perhaps even obvious. But minds are complex, and assumptions are dangerous. So, do we have any empirical evidence?
I looked to experimental psycholinguistics and, perhaps surprisingly, the answer seemed to be no. Which is where MA dissertation comes in.
My study
‘Priming’ is a psychological paradigm by which one thing triggers, or primes, related things in our minds.[xix] For example, hearing the word ‘doctor’ may prime nurse, hospital, and so on, both on a linguistic and conceptual level. It ‘arms’ these related words and ideas in our brains such that if they crop up (e.g. in conversation) they’ll be more readily accessible and more rapidly comprehended. This happens automatically and “is not subject to conscious control.”[xx]
Priming experiments often use response times to assess the degree of relatedness between things in our minds. Continuing the example above, if you show someone the word ‘doctor’ followed by a picture, they’ll generally be quicker to identify the picture if it’s closely related (a nurse) than if it’s not (a potato). Get a computer involved to accurately measure how many milliseconds each response takes – then repeat this procedure with enough stimuli and enough participants – and a pretty reliable picture can be formed of how closely related two concepts, or two types of concepts, tend to be in people’s minds. [xxi]
I did this for my dissertation using a nifty programme called PsychoPy. Participants were shown a word (the prime) quickly followed by a picture, and were asked to ignore the words and instead identify, for each picture, whether or not it was animate (by pressing – as quickly as they could – one of two keys on their keyboard). For example, they might see the word ‘beef’ followed by a picture of a cow. Now, it’s not really about whether the thing pictured is animate; it’s about how long it takes people (in aggregate) to respond, since response speed reflects recognition speed, and faster recognition suggests priming (and therefore relatedness of the two concepts).
Sample screenshot from experiment (picture from the Bank of Standardized Stimuli[xxii])
My hypothesis: the meats that were named differently from their source animals (beef, pork) would be responded to more slowly than those named the same (chicken, lamb). Why? Because ‘chicken’ is more likely to prime the concept of a chicken than ‘beef’ is to prime the concept of a cow. These related pairs were hidden amongst various unrelated pairs (e.g., ‘comb’ followed by a picture of a log), and various other word and picture types, so as to disguise the experiment’s purpose (which if identified might affect responses). And to provide points of comparison, identity primes were included: pairs where the picture matched the word exactly and unambiguously (e.g. ‘teapot’ followed by a teapot).
The experiment consisted of 48 word-picture pairs, with a sample size of 39 participants.
And the results?
The ‘transparent’ meat words like chicken showed a strong priming effect. The average response time for these was almost identical to that of the identity primes, suggesting that when people saw ‘chicken’ on the screen, they thought of a chicken. The ‘opaque’ words, on the other hand, showed no priming effect whatsoever; their average was almost exactly the same as for the unrelated pairs. Thus, it seems that ‘beef’ had no greater benefit in getting participants to think of a cow than ‘comb’ did in getting them to think of a log (for example). Further, the difference between these data sets (transparent versus opaque meat words) was ‘statistically significant’ and therefore unlikely to have occurred by chance.
Overview of statistical analyses |
||
Condition (mean RT, ms) |
Difference in ms |
p-value (repeated measures ANOVA) |
Transparent (564) vs. opaque (617) |
52.3 |
0.017 (0.022 using Wilcoxon signed rank; 0.010 using paired samples T-test following log-transformation) |
Transparent (564) vs. identity (568) |
4.0 |
0.843 |
Opaque (617) vs.unrelated (616) |
0.3 |
0.987 |
To be clear, it’s not that people never think of cows when encountering beef. This experiment only suggests that ‘beef,’ and other such opaque terms, don’t automatically trigger activation of their source animal representations, whereas ‘chicken’ and company do. In the hifalutin words of my report, “the derivationally opaque nature of much of English meat lexis, by circumventing any automatic thought of source animals, may be facilitating meat-SA dissociation for these meats and perhaps in general” (SA = source animal).
Takeaways
This result may be useful for people or organisations working to reduce meat consumption. For example, it may be worth mentioning pigs, or including a picture of one, every time you mention pork, bacon, etc. It might also be relevant to manufacturers of vegan meat-alternatives, who might be prudent to use terms like ‘cow free’ instead of ‘no beef’ in order to counteract dissociation among consumers.
Although there are a lot of factors supporting meat-source animal dissociation, it seems somewhat fragile and vulnerable to disruption.[xxiii] So mention the cow, present the pig, and hopefully, little by little, consumers will start living more empathetically.
Recommended further reading:
Bryant, C. J., Prosser, A. M. B., & Barnett, J. (2022). Going veggie: Identifying and overcoming the social and psychological barriers to veganism. Appetite, 169, 105812. https://doi.org/10.1016/j.appet.2021.105812
Benningstad, N., & Kunst, J. R. (2020). Dissociating meat from its animal origins: A systematic literature review. Appetite, 147, 104554. https://doi.org/10.1016/j.appet.2019.104554
To get in touch with Ian or request a copy of his dissertation, please email him at imj162[at]alumni.bham.ac[dot]uk[UW1]
The views expressed by our Research News contributors are not necessarily the views of The Vegan Society.
[i] Sentience Institute (2019)
[ii] Singer & Mason (2006), Low et al. (2012)
[iii] de Vries & de Boer (2010), Springmann et al. (2018), Graça et al. (2014)
[iv] Allen et al. (2002), Anderson & Tyler (2018)
[v] Cornish et al. (2016), Alonso et al. (2020)
[vi] Reese (2017)
[vii] Sentience Institute (2019)
[viii] Bryant et al. (2022)
[ix] Benningstad & Kunst (2020)
[x] Singer (1995), Rothgerber & Mican (2014)
[xi] Kunst & Hohle (2016)
[xii] Plous (1993), Rothgerber (2014)
[xiii] Onwezen & van der Weele (2016)
[xiv] Morton (2004)
[xv] Serpell (1996), Adams (2010)
[xvi] Adams (2010:13)
[xvii] Allen et al. (2002)
[xviii] Earle et al. (2019)
[xix] Molden (2014)
[xx] Field (2004:218)
[xxi] Shao & Meyer (2018)
[xxii] Brodeur et al. (2010, 2014)
[xxiii] Kunst & Hohle (2016), Tian et al. (2016)