So let’s talk about relevance. Without some way to navigate the big ball of wibbly-wobbly,
timey-wimey stuff that makes up our everyday conversations, there’s got to be guidelines
pointing us in the direction our conversational companions want to go. But if someone asks
you how long a night on Darillium is, how do you know to give her a time and not just answer "yes"?
Since our heads aren’t bigger on the inside, we must know which inferences to make, and
which ones to throw away, picking and choosing only what’s most relevant. But how can we
figure out how we figure stuff out? I’m Moti Lieberman, and this is The Ling Space.
When we communicate, we try to get across a lot more than just the literal meanings
of our words. And we need to look at our speech in multiple ways: semantics studies how we put those
core meanings together, while pragmatics focuses on how we use those meanings in context to convey
all that extra stuff. For example, if someone asked The Doctor “why do your companions leave?",
and The Doctor responded “some of them forget me”, you’d probably assume
that not all his companions forget him, even if logically, he could have meant every
last one. But we figure, if he meant “all”, he’d have said so!
Our ability to make these kinds of inferences follows from what philosopher Paul Grice called
The Cooperative Principle. This Principle explains why conversations are more than just
strings of disconnected statements. Under this idea, some contributions are more appropriate
than others, and some are ruled out completely. Conversations work because each participant
is headed towards the same goal.
To make sure this actually happens, we follow a set of specific rules that make up the principle,
which steer us where we want to go. These are known as conversational maxims. For instance,
we can assume that The Doctor was obeying the Maxim of Quantity, which basically says
“be as informative as you can”. If he had believed that all his past companions
had forgotten him, he’d have used “all”; but since he didn’t, we’re able to work
out that he believes that only some of them did.
But in order for a hypothesis to become a full-fledged scientific theory, it has to
be testable, so like, make predictions which can be confirmed or denied. And for each maxim
to make specific enough predictions, their definitions need to be operationalized. Operationalization
means turning something abstract into something concrete and measurable. So, like, if we wanted to talk
about someone’s health, one thing we might do is draw some blood and check various
serum levels. Health is abstract, but if we can assign some amounts to it, it becomes
easier to talk about.
Beginning in the 1980s, a group of cognitive scientists did just that for the field of
pragmatics, by developing Relevance Theory. Relevance Theory uses Grice’s Maxim of Relevance
as a starting point. But Grice only ever gave a two-word definition: “be relevant”,
and even admitted that it was hard to pin down what this meant precisely. What these researchers
did was define it as a property — not just of sentences, but of all the possible inferences
we might make from those sentences too. That way, it becomes possible to assign different
amounts of relevance to different ways of thinking. And that lets us explain why we
draw certain conclusions, and not others, from what somebody says.
So how do we calculate the relative relevance of all the possible paths of inference that
we could go down? Well, at its heart, relevance is a balance between how effective an inference
is versus how much effort it takes to compute. So, take our earlier example, where somebody
asked The Doctor about why his companions leave, and he said “some of them forget me."
The inference that not all of them did might require a bit of logical reasoning
to get to, but the effect is worth it, because it increases the amount of useful information that
we have. We take the inference to be a relevant one because we recognize that “not all of
them” is logically compatible with “some of them", and that this improves our knowledge of the situation.
You could always compute other possible inferences, like, say, taking the pronoun “them” to
refer to Time Lords. But it would take a lot more effort to get there, because the most
salient topic under discussion is The Doctor’s companions, not his own species; that inference
just wouldn't make sense in the given context. Because of the significant effort required
to make that kind of leap, it’s labelled irrelevant and ignored.
So the idea is that we follow a certain procedure when figuring out what someone’s trying
to tell us. The plan we follow can take us on a trip through disambiguation, pronoun
reference resolution, Gricean implicatures, and the like, travelling along the path of
least resistance, and finally stopping when our expectations of relevance in any given
context are met. But where do those expectations come from? Well, the whole idea can be summed
up in The Communicative Principle of Relevance, which states that each and every thing we
say carries with it a presumption of its own optimal relevance.
That means we assume that everything said is relevant enough for us to process, and
beyond that, it’s the most relevant it can be. So, going back to our example, if we expect
people to communicate as much relevant information as they can, in a way that minimizes effort
while maximizing effect, we’d expect The Doctor to say “all” if he meant it. Since
all his companions are potentially relevant, but he only mentioned some of them, our inference
that not all of them have forgotten him ends up satisfying our expectations. So Relevance
Theory also gives us inroads to the Maxim of Quantity, not just Relevance!
So, how can we see whether all these ideas about relevance are true? Well, since the
1960s, one of the most famous tests used to study the psychology of reasoning has been
the Wason Selection Task. It’s deceptively simple, really! You put four cards on a table
in front of you, with numbers on the face of them, and colours on the back. Two of the
cards are face up, and two are face down. Maybe one of them has a 6, another a 7, and
the other two are red and blue. Your job is just to figure out which cards you need to
turn over to check whether the following rule is true: if a card has a 6 on its face, then
it has a red back. Go ahead, think for a sec.
Okay, so: you can’t just rely on turning over the 6; you also have to turn over the
blue card. Why? Well, if it has a 6 on the other side, it breaks the rule. But only about
10% of people get this right! And because of this, it’s been argued that people aren’t
generally as good at logical reasoning as they might think. What’s even more interesting
is that when the task is framed in more realistic terms — say, where the rule you’re testing
is about the legal drinking age — people work it out way more often, well over 50% of the time!
The classic explanation is that the drinking age version makes people think of rules, instead
of descriptions. So, when people are presented with cards that have drinks on one side and
ages on the other, they know that they can’t just check the beer card to make sure that the
age is over 18, they’ve gotta turn over the card with the 16 on it, too, to make sure
they aren’t drinking beer.
But what the pragmatics researchers showed was that even the results of less realistic
versions of the task can be improved by using Relevance Theory. And this provides us with
evidence that the theory gives a good description of how we reason about these kinds of “if-then” sentences.
Specifically, the researchers found ways to manipulate either the effort or the effect
of the different inferences that people could potentially make, based on the original rule.
By carefully crafting the context, they showed that people typically choose the most relevant
inferences to ground their decisions, junking other inferences that logically follow from
the rule, but have been tagged as irrelevant.
To see exactly how this works, let’s go back in time to look at our original rule.
If cards with 6s on their face have red backs, then one inference that could be made is that
the card with the 6 on it has a red back! That’s pretty obvious. But there are other
inferences we could make, too, like that there shouldn’t be any cards with a 6 and a back that isn't red.
Now, that last inference is crucial to getting the right answer, but it’s pretty hard to
make because there's a couple of no’s in there, and negation comes at a processing
cost. So if people base their decisions about which cards to check on the inferences that
they can derive from the rule, then that last, costly inference will end up low on relevance,
and people won’t check the blue card. But here’s the thing: if we either lower the
effort involved, or boost the effect, we can actually push people to make that last inference
and turn over the blue card to make sure there’s no 6 there.
How? Well, just invent a scenario that explicitly limits the possible colours to red and blue.
With only two colors, that last inference could be reworded as “there are no cards
with a 6 and a blue back”. Since there’s only one “no” in there, it’s a lot easier
to get to, and therefore much more relevant.
The experimenters also created contexts where the effect of that inference was much higher.
Like, tell people that some 6s mistakenly got printed on cards whose backs weren’t
red, thus breaking the rule. In that case, it gets more important — more relevant — for
subjects to check the blue card, and make sure there’s no 6 on it. Sure enough, when
the effort was low and the effect was high, the rate of correct responses skyrocketed
to nearly 60%! But in cases where the experimenters rendered the effort high and the effect low,
fewer than 5% of subjects got it right.
So experiments like these show how these small shifts in the phrasing or the scenario change
how people interact with the language coming their way. The cards may be abstract, but
they show how, just by nudging people one way or another, we can really make it easier
or harder to get the messages we want across. And it's not hard to think of lots of ways that could be relevant.
So, we’ve reached the end of The Ling Space for this week. If you were my companion on
this pragmatic journey, you learned that we need specific enough hypotheses about pragmatics
to test our predictions; that we’re more likely to make an inference if it’s relevant,
meaning that it’s low in effort and high in effect; and that Relevance Theory helps
explain how we reason about what people mean from what they say!
The Ling Space is produced by me, Moti Lieberman, and directed by Adèle-Elise Prévost. This
week’s episode was written by Stephan Hurtubise. Our editor is Georges Coulombe, our music
is by Shane Turner, and our graphics team is atelierMUSE. We’re down in the comments
below, or you can bring the discussion back over to our website, where we’ll have some
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please subscribe. And we’ll see you next Wednesday. Huq ratukama!