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Cassie Hayward: From the Melbourne School of Psychological Sciences at the University
Cassie Hayward: of Melbourne, this is PsychTalks.
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Nick Haslam: Today we're talking with Dr Frank Mollica. Frank studies the
Nick Haslam: wonders of human language, what it tells us about how
Nick Haslam: we think and how we interact with the world around us.
Nick Haslam: His research crisscrosses numerous cultural and linguistic settings, and it
Nick Haslam: also has a lot to say about the horror of
Nick Haslam: legal language. Let's get into it.
Nick Haslam: Welcome, Frank.
Frank Mollica: Hi, thanks for having me.
Nick Haslam: So, Frank, you describe yourself as a computational cognitive scientist,
Nick Haslam: and now that might be unfamiliar to some of our listeners.
Nick Haslam: So what does it mean? And can you explain what
Nick Haslam: cognitive science is and what's computational about your approach to it?
Frank Mollica: Yeah. So, cognitive science is an interdisciplinary field. It mixes anthropology,
Frank Mollica: computer science, linguistics, neuroscience, philosophy, and of course, psychology. Uh,
Frank Mollica: we study the mind like a computer.
Frank Mollica: Right, uh, what are the mental representations and processes, the
Frank Mollica: computer programme basically, that explains human reasoning?
Frank Mollica: So I'm a computational cognitive scientist, I spend most of
Frank Mollica: my time translating between these two fields and basically taking
Frank Mollica: the theories and the insights from these fields and putting
Frank Mollica: them into math, and then, you know, formalising the math,
Frank Mollica: making nice testable predictions that, you know, I then go
Frank Mollica: out and find collaborators or go into the field and
Frank Mollica: and test with large data normally.
Cassie Hayward: Um, as you've said, the focus of your research is language,
Cassie Hayward: but you talk about it as a cognitive technology. What
Cassie Hayward: do you mean by that? OK.
Frank Mollica: So, a cognitive technology, I guess at the simplest level,
Frank Mollica: it's a tool. For example, uh, language and number are
Frank Mollica: cognitive technologies, right, we invented that, that's our fault, right,
Frank Mollica: like we have littered the world with linguistic structure and
Frank Mollica: numerical structure, and then we learn from it and we
Frank Mollica: use it to achieve these goals.
Frank Mollica: Um, and importantly with these cognitive technologies, we learn them, right?
Frank Mollica: We learn them as kids, we use them to, you know,
Frank Mollica: achieve different goals, whether it's, you know, math or whether
Frank Mollica: it's communication, right, and that leaves extra structure in the
Frank Mollica: world that other people learn from. And additionally, we teach
Frank Mollica: these things, right, we explicitly teach people number, we explicitly
Frank Mollica: teach people, right, like language.
Frank Mollica: Right, and through this repeated pattern of uh learning and teaching,
Frank Mollica: uh we culturally evolve optimal solutions to our problems, right,
Frank Mollica: that help us to achieve our goals, and this actually
Frank Mollica: allows us to flexibly adapt to the environment in ways
Frank Mollica: that biological evolution wouldn't allow us to.
Nick Haslam: This is so interesting because the idea that language is
Nick Haslam: shaped by culture is sort of intuitive to most of us, um,
Nick Haslam: the idea that, you know, different,
Frank Mollica: Languages split up the world in different kind of ways.
Frank Mollica: But I remember back in the dark ages when I
Frank Mollica: was studying language, uh, the emphasis was, was much more
Frank Mollica: on universals, on how all languages are in, if you like,
Frank Mollica: built from the same building blocks.
Nick Haslam: So can you give us an example, um, say, colour words,
Nick Haslam: how do different cultures or languages, if you like, um,
Nick Haslam: break up the colour space?
Frank Mollica: Yeah, so colour is this fun case, right, we all
Frank Mollica: can perceive the same colours if we have normal vision, right,
Frank Mollica: we can distinguish, you know, the eggshell white from the white, um,
Frank Mollica: but different languages of the world, they don't have a
Frank Mollica: vocabulary that's that rich, right? Um, the basic
Frank Mollica: colour terms, the ones that we use every day, right,
Frank Mollica: not the eggshell, off-white, mauve, turquoise, right? There's only a
Frank Mollica: finite inventory of these basic colour terms. Uh, English has
Frank Mollica: about 11, right, um, blue, yellow, pink, purple, um, but
Frank Mollica: other languages have fewer and some languages have more. So
Frank Mollica: for example, Agarabi, uh, a language they speak in Papua
Frank Mollica: New Guinea, uh, that only has about 5 colour terms that,
Frank Mollica: you know, everyone would use, right, they use like blue, green, yellow, red,
Frank Mollica: and dark or black.
Frank Mollica: Um, whereas other languages like uh Mexican Spanish, for example,
Frank Mollica: has two blues, uh, they have a celeste kind of
Frank Mollica: sky blue, and then the rest of what an English
Frank Mollica: person will call blue.
Frank Mollica: Uh, similarly, Russian also has two blues, except they carve
Frank Mollica: it up differently. They have like the blue that you know,
Frank Mollica: generally English people would, you know, agree is blue, but
Frank Mollica: then they carve off a bit that's like a navy blue,
Frank Mollica: it's darker.
Nick Haslam: So interesting, so I guess part of what a computational
Nick Haslam: cognitive scientist might do is see whether there are principles
Nick Haslam: underlying these differences, uh, right? So, uh, one of the
Nick Haslam: ones you refer to, I think when you talk about
Nick Haslam: this stuff is,
Nick Haslam: a principle of efficiency. So what do you mean by
Nick Haslam: efficiency and how do you show that languages are efficient
Nick Haslam: or not?
Frank Mollica: Yeah. So this is the day job, right? Figuring out
Frank Mollica: what are these underlying principles that can explain like all
Frank Mollica: of this variation. We've optimally solved these goals, right? We,
Frank Mollica: we've come up with these efficient structures like language or number, right?
Frank Mollica: But how do we get there? Uh, and so one
Frank Mollica: thing I want to clarify is that goals don't always
Frank Mollica: go in the same direction.
Frank Mollica: Right, so let's take for example, a listener, a listener's
Frank Mollica: goal might be to hear a word and then be
Frank Mollica: able to identify any object in the world, right? So
Frank Mollica: if I give you a word that can point to
Frank Mollica: exactly the object that's intended, an optimal language from a
Frank Mollica: listener's perspective is a language where every possible state of
Frank Mollica: the world has its own unique word, right? So that
Frank Mollica: means we have a unique word for all 621 Marvel
Frank Mollica: universes and every single item inside them.
Frank Mollica: Right, uh, impossible, we can't do that, especially because if
Frank Mollica: you think about what a speaker's goal is, a speaker
Frank Mollica: wants to be able to choose the word as quickly
Frank Mollica: and accurately as possible, that's going to get their listener
Frank Mollica: to understand, right? They don't want to search memory through
Frank Mollica: an infinite, you know, amount of words in order to
Frank Mollica: figure out what's the right word that would get my
Frank Mollica: speaker to understand what I'm talking about. They have only
Frank Mollica: a finite memory, they want us to be quick. A
Frank Mollica: speaker's optimal language would essentially be one word, right, 'ba',
Frank Mollica: and when I say 'ba', it means whatever I intended
Frank Mollica: a speaker to mean.
Frank Mollica: So all of my intentions, just one word, ba, I
Frank Mollica: don't have to think about it, right? Now, of course,
Frank Mollica: neither of these languages actually work, right, we call them
Frank Mollica: degenerate languages, but real languages have to figure out how
Frank Mollica: to trade off these two goals and how they choose
Frank Mollica: to budget these goals defines a sort of efficiency trade-off.
Frank Mollica: Right, it defines a trade-off between these two pressures, one
Frank Mollica: for uh simplicity, we don't want a language that blows
Frank Mollica: up in like a vocabulary, right, but also informativity, we
Frank Mollica: want all of the new words that we add to
Frank Mollica: be useful, right, to help us identify the things that
Frank Mollica: we care about. Uh and so languages of the world
Frank Mollica: tend to efficiently optimise this trade-off, right? Each language gets
Frank Mollica: to choose how it wants to budget and spend on,
Frank Mollica: you know, uh this much complexity for this much informativity.
Frank Mollica: And what's interesting is we can build these computational models
Frank Mollica: off of these two principles that define all of the
Frank Mollica: different ways that you could budget between informativity and uh
Frank Mollica: and simplicity. And when you do that, you get a
Frank Mollica: whole variety of sort of a trade-off Pareto frontier of
Frank Mollica: possible ways that languages could be, right? They could solve
Frank Mollica: this problem.
Frank Mollica: And all of the existing languages for something like colour
Frank Mollica: tend to fall exactly near this boundary, suggesting that every
Frank Mollica: language is indeed efficiently solving this problem. They're balancing their goals,
Frank Mollica: but they all pick how they want to budget, you know, uh, which,
Frank Mollica: which goal do I prefer more or prioritise more differently.
Frank Mollica: And that actually characterises the diversity that we see in
Frank Mollica: the world.
Cassie Hayward: Is it just about efficiency, or does it shape how
Cassie Hayward: we see the world? I had to chuckle when you
Cassie Hayward: talked about the Marvel Universe because I feel like I've
Cassie Hayward: learned a whole new language over the past few years
Cassie Hayward: as my kids become obsessed with soccer. So I've learned
Cassie Hayward: a whole new words, new everything.
Cassie Hayward: Am I changing the way I see the world with that?
Frank Mollica: So I would argue that language definitely changes how you
Frank Mollica: see the world, but it's not in like the Arrival,
Frank Mollica: the movie kind of way where if you learn the
Frank Mollica: alien language, right, suddenly you can time travel, right? And
Frank Mollica: it's also not in the perceptual kind of way, where like,
Frank Mollica: you know, my language has two colours for two colour
Frank Mollica: words for blue, now I can, you know, see blues differently,
Frank Mollica: because we can all see, you know, the shades of
Frank Mollica: blue is the same, right, we can all distinguish between them.
Frank Mollica: Um, but what language does do is it points attention
Frank Mollica: on all of the structure in the world and it
Frank Mollica: highlights what's important, right, what's going to be useful distinctions
Frank Mollica: for later things in learning, right? Language is a primary
Frank Mollica: tool for social transmission, right, this core thing, uh, where
Frank Mollica: if we're going to culturally evolve good optimal solutions for,
Frank Mollica: you know, our different goals, then we need to be
Frank Mollica: able to transmit it. Uh, so for one example, to
Frank Mollica: give you, you have to make this concrete, is if
Frank Mollica: you think about like kinship terms.
Frank Mollica: Right, kinship terms, you know, everybody has a family tree, right,
Frank Mollica: uh kinship terms are things like mother and brother and whatnot.
Frank Mollica: So everyone has a family tree, but it's invisible, you
Frank Mollica: don't see it, right, like it's latent. If you're a
Frank Mollica: kid and you're trying to figure out what your kinship
Frank Mollica: terms are, you have to figure out basically just from
Frank Mollica: the words as highlighting different people, what the underlying relationships are,
Frank Mollica: and these underlying relationships can have different goals that they're
Frank Mollica: made to achieve that you don't actually pay attention to
Frank Mollica: or even see when you're a kid. For example, uh,
Frank Mollica: we can think about the Yanomami tribe, uh, these are
Frank Mollica: people that live in the Amazon rainforest, right?
Frank Mollica: Whereas English doesn't separate uh cousins, your mother's brothers, uh,
Frank Mollica: your father's brothers, your mother's sisters, your father's brother's kids,
Frank Mollica: they all get the same term, they're all your cousin, right?
Frank Mollica: And Yanomami, they actually care about uh whether you're a
Frank Mollica: parallel cousin or a cross cousin. So a parallel cousin
Frank Mollica: is your mother's sister's kid.
Frank Mollica: Your father's brother's kids, right? A cross cousin is your
Frank Mollica: mother's brother's kids or your father's sister's kids, right? Um,
Frank Mollica: and so kids at a very young age have to learn,
Frank Mollica: you know, the difference between these two, different groupings, right? Um,
Frank Mollica: and this for a kid, you know, it can seem
Frank Mollica: very arbitrary, it's a nice genealogical relationship, it exists, you
Frank Mollica: can look at it on the tree, right? But like, why?
Frank Mollica: Um, and it turns out that these kind of relationships
Frank Mollica: actually have more to do, uh, with in this case
Frank Mollica: in mating, for example, where, uh, it's tough to find, uh,
Frank Mollica: you know, a potential mate in these kind of tribal places, right,
Frank Mollica: and so this community solves that by having preferential marriages
Frank Mollica: to cross cousins when they're mate limited. So your cross
Frank Mollica: cousins are potentially future mates, um, but this doesn't matter
Frank Mollica: to a kid, right? Like a kid who's learning this
Frank Mollica: language is not going, who am I mating, they're a kid.
Frank Mollica: Right? Um, so without language, you wouldn't be able to
Frank Mollica: be aware of these kind of distinctions like early on,
Frank Mollica: and these kind of distinctions can highlight or build uh
Frank Mollica: structures that, you know, are useful for other goals later
Frank Mollica: on in life.
Nick Haslam: That's so interesting and so you've given us an explanation
Nick Haslam: for why the Yanomami might have a different, way of
Nick Haslam: dividing up kinship than we do here. What's the reason why,
Nick Haslam: just going back to colour, why Russians would divide blue
Nick Haslam: in a different way from Mexicans and why we English
Nick Haslam: folk don't?
Frank Mollica: Uh, so this is a, a nice question. I don't
Frank Mollica: know if I'm gonna have a satisfying answer, um.
Frank Mollica: The answer that I would uh suggest or I'd go
Frank Mollica: with a hypothesis at the moment is that if we
Frank Mollica: want to communicate about things informatively, right, that means that
Frank Mollica: we need to know how often we need to talk
Frank Mollica: about or this particular tool in this case is word,
Frank Mollica: is going to be useful in achieving our goal. Um,
Frank Mollica: and so if you look at the, the different parts
Frank Mollica: of the colour space, what we want to do is
Frank Mollica: we want to normally identify things by colour.
Frank Mollica: Right, and so if there are things that are important
Frank Mollica: to identify by colour, right, that's how we're going to
Frank Mollica: carve up our colour space so that we can be
Frank Mollica: more precise and have harder edges around those specific cases.
Nick Haslam: So sort of adapted to the local cultural or ecological
Nick Haslam: or something environmental context?
Frank Mollica: Ideally it would be adapted, uh, exactly, it would be
Frank Mollica: adapted to the local context.
Frank Mollica: So for example, if you look at uh places that
Frank Mollica: are tropical, have lots of, uh, you know, rainforests, green vibes,
Frank Mollica: lots of bright, vibrant colours, uh, they tend to split
Frank Mollica: the colour space so that they, they make really clear
Frank Mollica: these bright, you know, uh, colourful, poisonous amphibians that you
Frank Mollica: should not touch, uh, versus, you know, things that are
Frank Mollica: OK to touch and interact with.
Nick Haslam: So another aspect of language you've studied, and you brought
Nick Haslam: this up in relation to the uh kinship terms a
Nick Haslam: little bit, is how language is acquired.
Nick Haslam: And you were involved in some fascinating work again in
Nick Haslam: South America on the development of mathematical, uh, knowledge and
Nick Haslam: number concepts in particular indigenous people, uh, in that region.
Nick Haslam: Can you tell us a bit about that work and
Nick Haslam: what it showed?
Frank Mollica: Yeah. So I was lucky enough to be able to
Frank Mollica: collaborate with some people who are working, uh, in Bolivia
Frank Mollica: with the Tsimane people. Uh, the Tsimane are hunter gatherers,
Frank Mollica: it's a hunter-gatherer society, uh, they live not far from
Frank Mollica: La Paz along a river.
Frank Mollica: So what we're interested in is primarily numerical developments and
Frank Mollica: what's fun is that the Tsimane language has a base
Frank Mollica: 10 counting system, right, so 1-2-3-4-5-6-7-8-9-10, then it sort of resets, right?
Frank Mollica: A lot of other industrialised societies also have, you know,
Frank Mollica: a base 10 counting system, right, any English two-year-old can
Frank Mollica: sing that song for you, they know how to count
Frank Mollica: like 1-2-3-4-5-6-7-8-9-10.
Frank Mollica: The interesting thing though is that while a 2 year
Frank Mollica: old can count that, 2-year-olds don't actually know what those
Frank Mollica: words mean. So if you put a pile of cookies
Frank Mollica: in front of the same 2 year old who just
Frank Mollica: counted 10 for you and ask them, Can I have
Frank Mollica: 3 cookies, right, you're just as likely to get the
Frank Mollica: entire pile of cookies as you are to get however
Frank Mollica: many fit in their hand, and certainly not 3.
Frank Mollica: Um, right, kids learn number words in stages. Uh, first
Frank Mollica: they learn what the meaning of one is, they can
Frank Mollica: hand you one cookie exactly, then they learn what the
Frank Mollica: meaning of two is, right, so they can hand you
Frank Mollica: 1 or 2 cookies, but you ask them for 3,
Frank Mollica: you still get that handful, then they become 3-knowers and
Frank Mollica: they figure out what 3 means. Sometimes you see kids
Frank Mollica: that are 4-knowers, so they know what 4 means, but
Frank Mollica: what's really cool is that we don't have 5-knowers.
Frank Mollica: Right, By the time that a kid would be a 5-knower, right,
Frank Mollica: they just figured it out, they figured out the algorithm,
Frank Mollica: they can now count. So however many numbers that they
Frank Mollica: can count to, they can now accurately, well, as accurately
Frank Mollica: as they can count, they can hand you that many cookies, right?
Frank Mollica: What's really cool is this happens in all industrialised societies
Frank Mollica: that have a base 10 counting system. It takes a
Frank Mollica: couple of years, right, we see this in Japan, we
Frank Mollica: see this in Hebrew, Arabic, um.
Frank Mollica: Every language that we have data for, right, kids go
Frank Mollica: through the stage of development, you know, uh 1-knower, 2-knower, 3-knower, 4-knower.
Frank Mollica: We want to know, hunter-gatherer society, Tsimane kids, do they
Frank Mollica: show the same pattern, right? They also have a base
Frank Mollica: 10 system, but you know, very different from these industrialised
Frank Mollica: societies that we have data for. So, uh, you know, uh,
Frank Mollica: my collaborators basically went and looked at the Tsimane children
Frank Mollica: and we found out that yes, they do go through
Frank Mollica: this exact same pattern. Uh, they go 1-knower, 2-knower, 3-knower, 4-knower,
Frank Mollica: and then eventually they figure out the algorithm. What's really
Frank Mollica: cool is we recently did a meta-analysis, and it turns
Frank Mollica: out that the cardinal principal knowers, the kids that figured
Frank Mollica: out the algorithm.
Frank Mollica: They all have some formal schooling, um, and formal schooling
Frank Mollica: among the Tsimane is actually really recent, um, and it's
Frank Mollica: not formal schooling like an industrialised society, is much more like, um,
Frank Mollica: you know, when the government can provide aid, they have
Frank Mollica: a teacher who comes and they teach the Tsimane kids
Frank Mollica: in Spanish, uh, and so the instruction is in Spanish
Frank Mollica: and it's very recent, um, but only the kids with
Frank Mollica: formal schooling, uh, have, at least in our data set, uh,
Frank Mollica: have acquired that full counting ability.
Frank Mollica: But what's really cool is that there are people, there
Frank Mollica: are Tsimane that are adults that, you know, did not
Frank Mollica: have any of this formal schooling, uh, and they can count, right?
Frank Mollica: And so we wanted to figure out, you know, well,
Frank Mollica: how did they figure it out, right? They didn't have
Frank Mollica: formal schooling, so is formal schooling actually required, right? Um,
Frank Mollica: and so the anthropologist on our team, David O'Shaughnessy, went
Frank Mollica: and actually uh went to go find adults that, you know,
Frank Mollica: might not have had the experience of formal schooling, but
Frank Mollica: they might have had mathematical uh experience elsewhere, they might
Frank Mollica: have done trade.
Frank Mollica: Right, so the Tsimane do trade with uh other people, right,
Frank Mollica: and the trade language is sort of Spanish because that's a,
Frank Mollica: you know, the the lingua franca of Bolivia, right? Um,
Frank Mollica: and what they found, Dave, when, when he went there,
Frank Mollica: he found out that basically
Frank Mollica: these people
Frank Mollica: maybe they, they can't exactly count, but they can do trade,
Frank Mollica: and so he found this really cool uh group of
Frank Mollica: people who could do mathematical computations that were necessary for trade.
Frank Mollica: So for example, uh, in the Tremane society, basically trade
Frank Mollica: works in fives, you have these jatata leaves uh that
Frank Mollica: they trade or sometimes they'll trade like a bunch of
Frank Mollica: uh bananas or plantains if they're in the right sort
Frank Mollica: of amount, like for a cluster. They basically group them
Frank Mollica: in 5 and they do multiples of 5 is like
Frank Mollica: the the trade.
Frank Mollica: So, uh, if you ask these people, you know, 5
Frank Mollica: times 2, 5 times 3, 10 times 4, fine, 10
Frank Mollica: plus 5, fine, 3 plus 4, no idea, wrong, right,
Frank Mollica: 2 plus 5, nope.
Frank Mollica: Um, right, so they figured out enough math, they figured
Frank Mollica: out math that works specifically for them for the goals
Frank Mollica: that they're trying to achieve, right? And I think that's
Frank Mollica: why I guess numerical cognition is such a great idea,
Frank Mollica: such a great example of these cognitive technologies, right, these
Frank Mollica: technologies that are customly adapted to our goals, right, if
Frank Mollica: the only thing that I really need to do is
Frank Mollica: trade in multiples of 5, I'm going to learn math
Frank Mollica: in multiples of 5, right? Like why learn that 1
Frank Mollica: + 1 stuff?
Frank Mollica: Um, and I mean, this isn't the first time an
Frank Mollica: anthropologist has found a result. Um, if you look back, uh,
Frank Mollica: Jeff Sachs has worked with Brazilian candy sellers during periods
Frank Mollica: of inflation, where, uh, if you're trying to sell candy
Frank Mollica: in massive periods of inflation, these like 6 to 10
Frank Mollica: year old boys would basically be trying to sell candy
Frank Mollica: at the public transportation, uh, stops, right, but you wouldn't know,
Frank Mollica: like how much am I actually going to need to
Frank Mollica: cost these things because the, you know, the, the price
Frank Mollica: changes rapidly over the course of a day.
Frank Mollica: Right, so you have to sort of really quickly offhand
Frank Mollica: make some heuristics that in this case, use a magnitude
Frank Mollica: comparisons to figure out how you should price your candy
Frank Mollica: so that you have enough to buy tomorrow's box of
Frank Mollica: candy and that you still make a profit.
Frank Mollica: Right, and when you compare these, you know, like non-schooled
Frank Mollica: Brazilian candy sellers to, you know, people of equivalent socioeconomic
Frank Mollica: status with schooling or even the the kids that have,
Frank Mollica: you know, like a good socioeconomic status and, you know, schooling,
Frank Mollica: they're better at these abstract magnitude comparisons than their peers, right,
Frank Mollica: they can't do the formal counting thing that they would
Frank Mollica: teach you in school, but it's like they can teach
Frank Mollica: you what they needed, right, and they're better at it, um, so.
Frank Mollica: These kind of things really are adapted, cognitive technologies are,
Frank Mollica: are really adapted to what we need to do.
Cassie Hayward: I think they're such fascinating examples of how efficient language
Cassie Hayward: can be, but also so adapted to the goals that
Cassie Hayward: you need in that, in that scenario. But you've also
Cassie Hayward: done research on the type of language that's spectacularly inefficient.
Cassie Hayward: Can you tell us about legalese?
Frank Mollica: Yes, um.
Frank Mollica: Legalese is notorious, right? What do I really have to say? Um,
Frank Mollica: legalese is difficult in so many different ways, um, rather
Frank Mollica: than just saying a clean sentence, legalese likes to take
Frank Mollica: a sentence and throw it in the middle of the
Frank Mollica: other sentence, right? For example, the contractor who knowingly and
Frank Mollica: in sound mind and good conscious entered this employment agreement
Frank Mollica: upon termination of their employment at either the employer or
Frank Mollica: their behest, will be entitled to a one-time payout of
Frank Mollica: $100 million.
Frank Mollica: It's a golden parachute clause that I just made up, um, right.
Frank Mollica: So when you have that clause that that's a big
Frank Mollica: long sentence, but it's actually two sentences where they just
Frank Mollica: threw one right in the middle, right? The outer sentence
Frank Mollica: is that the contractor is entitled to a one-time payout
Frank Mollica: of $100 million right, upon termination of their employment at
Frank Mollica: either their employer or their own behest, right? But then
Frank Mollica: we ended this other sentence, this, the contractor is in
Frank Mollica: sound mind and good conscious and when they entered this agreement.
Frank Mollica: Right? Why did that need to be in the middle
Frank Mollica: of this other sentence? It's memory taxing, legalese isn't fun, uh,
Frank Mollica: it's a tax on memory, but also legalese uses these
Frank Mollica: rare words that we never see anywhere else, um, mens rea,
Frank Mollica: mens sana, um, or even simple things if you look
Frank Mollica: at your, uh, real estate agreement in a lot of
Frank Mollica: different languages, right? Why do we call it lessee instead
Frank Mollica: of renter?
Frank Mollica: Renter is much more friendly when everyone knows what a
Frank Mollica: renter is. What is a lessee?
Cassie Hayward: It, is it all the complexity required because legal concepts
Cassie Hayward: are themselves extremely intricate, or are they just trying to
Cassie Hayward: confuse us?
Frank Mollica: Ah, great question. So, we've actually looked at this, uh,
Frank Mollica: and worked with my grad student Eric Martinez and Ted
Frank Mollica: Gibson at MIT.
Frank Mollica: Uh, we basically asked a whole bunch of lawyers, right, uh,
Frank Mollica: we gave them contracts that were written in sort of
Frank Mollica: plain English, and we gave them contract excerpts that are
Frank Mollica: written in legalese, and we asked them, are they both
Frank Mollica: equally enforceable, right? Do they have the same legal content,
Frank Mollica: do they have the same legal standing, right? Uh, and
Frank Mollica: overwhelmingly they do, right? Plain English equivalents do exist, it's possible.
Frank Mollica: So there's nothing about the concepts that make these things
Frank Mollica: complex because I can easily undo them, uh, just by
Frank Mollica: taking sentences outside of other sentences, using slightly more frequent words.
Nick Haslam: So if language tends to become more efficient over time,
Nick Haslam: surely over time legalese has also become more efficient?
Frank Mollica: Uh, you would think that. Um, no, we've actually also
Frank Mollica: looked at this. So, uh, in a study, I guess
Frank Mollica: last year now, uh, we looked at the entire US
Frank Mollica: legal code, uh, and I should say so far we've
Frank Mollica: only ever looked at US laws, right? It might be
Frank Mollica: different than other laws and we're looking at that now,
Frank Mollica: but so far we've only just looked at
Frank Mollica: US legal code. And if you look at the entirety
Frank Mollica: of the US legal code up to about 2022.
Frank Mollica: Right, and you can find uh other texts that are comparable.
Frank Mollica: We can look at like fiction from the exact same
Frank Mollica: time periods, right, or even academic texts with all of
Frank Mollica: their weird jargon and whatnot, um, from the exact same
Frank Mollica: time period, and we can actually look at, you know,
Frank Mollica: how prevalent are these really hard-to-process linguistic structures, this kind
Frank Mollica: of centre embedding where you take sentences and throw them
Frank Mollica: into other sentences or the frequency of words. Uh, and
Frank Mollica: if we look across all of this time, legalese is
Frank Mollica: always containing
Frank Mollica: more of these difficult to process structures than any of
Frank Mollica: the other control tests. This is despite calls for, you know,
Frank Mollica: reform in like the 1970s and even the Plain Writing Act, uh,
Frank Mollica: the Plain Language Act of 2010.
Nick Haslam: Uh, I do hope, Frank, that we don't get sued
Nick Haslam: for talking about all of this, and, and, uh, probably
Nick Haslam: we should be evenhanded and start talking about that jargon
Nick Haslam: in psychology you mentioned, but if legal language isn't getting
Nick Haslam: more plain and more straightforward over time, what is the obstacle?
Nick Haslam: Uh, and what does it tell us about how we
Nick Haslam: should be trying to increase the use of plain language? Yeah.
Frank Mollica: So, whenever you're trying to change something that exists and
Frank Mollica: has this kind of structural momentum, it's really hard to
Frank Mollica: just overcome momentum. It's so much easier to just copy
Frank Mollica: this template and use it on the next document, right? Similarly,
Frank Mollica: there's uh very few structural changes that uh really incentivise
Frank Mollica: uh using simpler language, right? There's no reason to use
Frank Mollica: legal language is going to change people's financial and goals basically.
Frank Mollica: It's harder to change something that already has momentum when
Frank Mollica: there's no clear incentive to do so.
Frank Mollica: Uh, and so we have to change the incentive structure
Frank Mollica: of institutions if we want to actually see results.
Cassie Hayward: It reminds me how kids have their own language, every
Cassie Hayward: kind of generation have their own words for what's cool
Cassie Hayward: and what's not. Is it just so they have their
Cassie Hayward: own little kind of way of speaking that other people
Cassie Hayward: aren't allowed in to understand?
Frank Mollica: Uh, so when we did that study with the lawyers,
Frank Mollica: we actually asked them this exact same question. We were,
Frank Mollica: you know, interested to see if maybe it's just an
Frank Mollica: in-group bias, so it's like you're going to signal that
Frank Mollica: you're a good lawyer and so you're going to use
Frank Mollica: your jargon to do that. Um, and it turns out
Frank Mollica: it's not, the lawyers would equally hire, equally work with
Frank Mollica: uh people who use the plain language alternatives versus uh
Frank Mollica: legalese alternatives. What we actually think keeps legalese going this way, um,
Frank Mollica: is actually something called performativity.
Frank Mollica: Uh, is this idea that language in legally isn't just
Frank Mollica: describing something. Normally when we use language, we're trying to communicate,
Frank Mollica: we're just trying to describe the state of the world.
Frank Mollica: When we use legalese, we're actually changing the state of
Frank Mollica: the world, right? I am now placing with this legal
Frank Mollica: language an obligation over you or some kind of right
Frank Mollica: or something upon you, right?
Frank Mollica: The same way where you, you know, like crack a
Frank Mollica: wine bottle over a ship, and you say like, I
Frank Mollica: now christen you the whatever and you've now named it,
Frank Mollica: you've changed the state of the world, or when a
Frank Mollica: priest says I now pronounce you man and wife, right,
Frank Mollica: and you're now married, the world has changed and this
Frank Mollica: kind of performativity, right, um, is the same kind of
Frank Mollica: thing that happens in legalese.
Frank Mollica: Uh, and so we see this kind of performativity also
Frank Mollica: in somewhere else, uh, we see this in magic spells, right, uh,
Frank Mollica: magic spells are also supposed to change the world by just,
Frank Mollica: you know, words themselves, and magic spells also use language
Frank Mollica: to signal that they're doing it, right? So your magic
Frank Mollica: spell is supposed to use some archaic language or it's
Frank Mollica: supposed to rhyme, right? That's how you know that it's
Frank Mollica: a magic spell. Well, our current hypothesis is that with
Frank Mollica: legal language, it's basically the same thing, except the structure
Frank Mollica: isn't rhyming or
Frank Mollica: archaic language or maybe it is some archaic words, um,
Frank Mollica: but it's throwing sentences in other sentences and being complicated, uh,
Frank Mollica: and that sets it apart, that gives it sort of
Frank Mollica: legal weight in people's minds, not in any sort of, uh,
Frank Mollica: evaluative body of the law.
Nick Haslam: Fascinating stuff. So look, Frank, on a final note, I've
Nick Haslam: got a bone to pick with you about an old
Nick Haslam: paper of yours, uh, where you said that English speakers
Nick Haslam: have learned only about 1.5 megabytes of linguistic information.
Nick Haslam: which, for listeners of my age, will remember a 5.25
Nick Haslam: inch floppy disc from the eighties, uh, would hold. I mean,
Nick Haslam: surely clever people like Cassie and I know more than that.
Cassie Hayward: I mean, it's true, like, clearly people know more than just,
Cassie Hayward: you know, the save icon worth of information about language. Um,
Cassie Hayward: but when we look at language, we talk about the
Cassie Hayward: linguistic forms of words, right, and how things combine.
Frank Mollica: That's actually really small. One save icon, one floppy disc
Frank Mollica: contains all the information that you actually need about uh
Frank Mollica: language and how it combines the the actual structures and forms.
Frank Mollica: The part that really, you know, takes up most of
Frank Mollica: the space even on the floppy disc is the semantics,
Frank Mollica: what words mean, right? Uh, it's the part that makes
Frank Mollica: the large language models large, they're supposed to be world
Frank Mollica: knowledge or something like that. Um, but that's, you know,
Frank Mollica: separate from language, it's not much knowledge about language, um,
Frank Mollica: and even in our estimate
Frank Mollica: that's the vast majority of the, the information that you
Frank Mollica: need to learn about a language is semantics, it's word meanings.
Cassie Hayward: I feel like I need a whole floppy disc size
Cassie Hayward: memory of the offside rule in soccer, which I will
Cassie Hayward: never understand. But, Frank, the work you do probably doesn't necessarily,
Cassie Hayward: isn't necessarily the first thing people think of when they
Cassie Hayward: think about psychology, but I think it really shines a
Cassie Hayward: light on how we think and learn and speak and interact. Um,
Cassie Hayward: and I just want to thank you so much for
Cassie Hayward: sharing with us today.
Frank Mollica: Thank you so much for being here. It was a pleasure.
Cassie Hayward: And that wraps up this episode of PsychTalks. A big
Cassie Hayward: thank you to our guest Dr Frank Mollica for sharing
Cassie Hayward: his insights. This episode was produced by Carly Godden with
Cassie Hayward: support from Mairead Murray and Gemma Papprill. Sound engineering by
Cassie Hayward: Jack Palmer. Thanks for tuning in. See you next time.