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Welcome to episode 33 of the Language Neuroscience Podcast.

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I am Stephen Wilson, at the University of Queensland, in Brisbane, Australia.

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After the last couple of episodes about the business of science, I'm excited today to

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get back to doing an episode about a paper on the neuroscience of language.

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I'm joined by Steve Politzer-Ahles and Bernard Jap to talk about their excellent paper,

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‘Can the mismatch negativity really be elicited by abstract linguistic contrasts?’, which

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just came out in Neurobiology of Language, 2024, Volume 5, Issue 4.

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As I'm sure I've mentioned before, I'm on the editorial board of this journal, and

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I think that it's a great journal that is taking many of the right steps to improve academic

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publishing.

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So, I've decided to record some episodes on new papers in the journal that catch my eye.

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And Steve and Bernard's paper, from the most recent issue caught my eye for many reasons,

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as it will become clear in our conversation.

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One quick note, I am actively recruiting for several funded PhD positions in my lab in

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Brisbane.

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If you or someone you know might be interested, please check out langneurosci.org/join.

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Okay, let's get to it.

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Hi Steve, hi Bernard, how are you guys doing today?

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Great, how about you?

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Pretty good.

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It's 9am and sunny in Brisbane, and how about for you guys, where are you at?

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Yeah, I'm in Hong Kong, I'm doing pretty well, thanks.

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Just fighting a bit of a cold, maybe, we can hear this.

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It's actually pretty early here at 7am in Hong Kong.

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Uh-huh, well, good morning and thanks for waking up early for me.

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Yeah, I'm in Kansas, so it's 6pm, starting to thunderstorm, so hopefully my power doesn't

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go out in the middle of this.

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Okay, well that would be kind of atmospheric if we had like a power failure or something,

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but I guess it kind of doesn't really work out for zoom.

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Okay, great.

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So, can you guys, can we start just by like learning a little bit about you guys?

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Because I haven't met either of you before.

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I guess I'll start with you Steve.

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Like, can you just tell me a bit about yourself?

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Like how did you wind up at this point in your life researching the neural processing of

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linguistic contrast?

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Oh, that's a good question.

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I mean, I didn't expect to be doing neuro, so like it, I guess it's weird to say that

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like I figured out my interest when I was in grad school, because ideally that's something

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you'd know beforehand.

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But like, I went to grad school for linguistics.

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I think I actually, in my application, I said I was going to do theoretical syntax, so

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that's what I thought I was interested in.

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I mean, I am interested in it, but I ended up doing neuro.

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I think a lot of it came from like experiences I had taking,

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I took a neuro linguistics class with Rob Fierentino, and that's, I think, what got me

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interested in this, partly because I was just really excited

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in like how, the feeling of like that we were kind of finding linguistic phenomena

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that we could use to answer puzzles and questions.

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So, like throughout that class, we would always be like, oh, here's this thing that happens,

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but like we don't know if it, you know, do people ignore this noun because it's far away

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from the verb or because it's like not in a c-command relationship with the verb?

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And then be like, oh, we can come up with this fun sentence where like it's equidistant,

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but this one's in c-command and this one isn't.

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So, I just really loved that approach of like we find a really concrete question and then

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use our knowledge of language to figure out how to answer it.

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And I think I was also really excited in that class because especially back when I was starting

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to learn it, there was so much like low hanging fruit in neurolinguistics that people would

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always ask a question like, okay, so we learned about this thing about the N400, but what

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if we tried it with speakers of this language?

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Like, what if we tried it with a concurrent working memory task or something?

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And the answer was almost always like, I don't know if anyone's done that.

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Maybe we could do it.

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And so, it just, it made me really feel like this was a field that like I had a place in

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and I could contribute to and that just made me really excited to do neuro and I think

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I've loved doing it ever since.

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Yeah, that's great.

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Where was it that you went to grad school?

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Actually, it was Kansas, which is where I'm teaching now.

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Okay.

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And I was gone for like 10 years in the middle, but I had a great experience there.

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And when I found that there was a position open there, I was like, well, I know I love that

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department.

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So, I'm really happy that I got to come back.

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Are you from Kansas?

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So, did you just move there for the grad school experience?

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Yeah.

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No, I'm from Pennsylvania.

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So through like college and grad school, I kept gradually moving west and then I guess

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I'd gone all the way back around to the west and ended up back here.

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Okay, that's great.

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And how about you, Bernard?

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What was your story to winding up in this place?

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So, I started with my undergraduate degree where I ended up in the English Department of

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University of Indonesia and we had these different streams of cultural studies and literature

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and linguistics.

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I was instantly hooked to linguistics, shout out to the late Mr. Didin who was an amazing

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professor and something just sort of clicked.

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And at one point the following year, I was already an adjunct for morphology and syntax

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for my juniors, which looking back, I'm just sort of, it's just a little bit hilarious.

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I probably don't know what I was talking about back then.

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Yeah.

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Anyways, after graduation, I was fortunate enough to get into these European scholarship

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programs.

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This is called EMCL.

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European Masters for Clinical Linguistics and there I met an aphasiologist.

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So, you probably know her, Roelien Bastiaanse.

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Yeah, right.

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I don't know her personally but know her work very well.

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Yeah.

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And it was my Masters and PhD thesis.

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So, I did my Masters and PhD in the Netherlands, and I started with aphasiology.

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So, we were dissecting animal brains inside the class and then talking about cases of double

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dissociation and the famous sort of brain injury cases.

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And I received quite a bit of experimental training during these programs with EEG and MRI

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and whatnot.

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A lot of my beginning was actually just washing people's hair as an intern in an EEG lab.

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But yeah, I was sort of hooked from there.

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And there's something sort of remarkable, but actually seeing the brain's response to

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language, sort of unfolding in real time.

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I was also sort of passionate given the amount of impact that you can make because for Indonesian,

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for example, we're missing quite a few things.

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For example, in both aphasia, I was also doing a bit of research on dyslexia.

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I found out during my PhD that we didn't even have a test for assessing dyslexia.

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So that was something that we worked on.

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Anyways, that was a long and winded way to say.

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Yeah.

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Yeah.

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Well, that's, no, that's, it's so true.

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Like, it's really become apparent to me in the last few years since we put out the

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quicker aphasia battery.

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And like, people have written to me to ask to translate it into many different languages

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in the world.

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Or I've just heard this story again and again, like there is no aphasia assessment in my language,

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you know, and because it's creative, comments, people can just do what they want with it.

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So, I appreciate what you're saying about like, you know, a major language like Indonesian,

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probably 200 million speakers, right?

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Or something in that ballpark.

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Is that right?

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100 million maybe.

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What is it like?

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It depends how you define speakers like.

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Yeah.

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And it depends on how you define dialect continue.

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Okay, you never should ask a linguist a simple question.

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Okay.

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But, yeah, but you know, it's a very major language and yet basic assessments will be lacking.

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So much work to be done.

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Cool.

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Okay.

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Well, thank you.

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So, as you know, I wrote to you guys about your paper that just came out in the journal,

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Neurobiology of Language.

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And I thought it looked really interesting, and I wanted to kind of focus on, you know,

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new papers from this journal.

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And it is about the mismatched negativity or MMN.

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So, can you start by telling our listeners about the MMN, like, what is it?

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Why is it interesting?

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Yeah.

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So, the MMN, is this brain response that happens when your brain notices the difference

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between one stimulus or category of stimuli and suddenly gets a different stimulus or

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category of stimuli that doesn't fit.

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And I said, I was very intentional in saying when your brain notices it rather than when

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you notice it, because what's cool about the MMN is that this can happen even when you're

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not paying attention.

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Right.

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So, this is the most typical way of getting MMN, is people will hear like, bah, bah, bah,

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bah, bah, bah, bah, bah, bah, dah, bah, bah, bah, dah.

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and so, whenever you get this dah, which doesn't fit in with the bahs that you got used to,

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you got the MMN.

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But the cool thing is, you know, this will happen when people aren't paying attention to

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them.

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So, like, typically you let them watch in nature documentary and they're just having bahs

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and dahs playing into their ear and their brain will get excited every time there's a dah,

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even though you told them, don't worry about those sounds.

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It can even happen when people are asleep, although I haven't done that myself, but there are

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reports of that in the literature.

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So, it's really thought to be the brain's, like response to noticing violations of patterns

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sub-attentively or without your conscious attention.

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And the other thing that's cool about it that we talk about more in the paper is that it

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doesn't have to be something as simple as bah, bah, bah, dah, for your brain to notice it.

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The brain can pick up on pretty complex patterns and notice when things deviate from them.

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So, there are other reports in the literature of things like, you know, not just one tone

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that's different from other tones, but you can have tones that are following a certain

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kind of sequence and then you get another tone that's out of the sequence and you can get

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the MMN.

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So, it's very impressive that like the brain can track so many things without attention or

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conscious awareness of it.

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Uh-huh.

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Cool.

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And then just so that we make sure that our terminology is all on the table for our listeners,

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you have the, you know, the terms that get thrown around a lot in your paper would be standards

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and deviants.

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Can you tell us what standards and deviants are?

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It doesn't.

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It sounds a bit judgmental. (laughter)

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Yeah.

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Yeah. So, the standards are the, the, in our case, sounds, although it could be something else,

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the standards are the stimuli that you're getting a lot of and the deviants, right, is not

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necessarily deviant people that they're the, the stimuli that are rare in this, in this paradigm.

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So, if a person's hearing, bah, bah, bah, bah, bah, dah, the

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bahs are the standards and the dahs are the deviants, but of course the standards don't have

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to be just one sound. You can have, you know, the standards can be a whole bunch of different

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high tones of varying heights and the deviant can be a tone that's lower than all of them.

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Right.

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So, the standards is a category of things that you're hearing, you're hearing this category

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a lot or seeing this category a lot or whatever.

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And the deviance is the category that you're not getting as much of.

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Great.

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Thanks.

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Yep.

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Sorry, just to add, I think it's also important that the deviants are rare enough and the standards

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are frequent enough.

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The ratio that we have on the paper, I think is 1:7 deviants, the standard ratio

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or about 15% to 85%.

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To get a reliable.

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Okay.

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Yeah.

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So, yeah, for deviants to be deviant, then it needs to be something that they're in opposition

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to.

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It's like when Blink-182 became like a major record label, like, you know, like alternative

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rock was over, right?

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I mean, alternative rock could only be alternative rock when there was mainstream rock.

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And when the only thing that there was, was, alternative rock, there was no alternative rock

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anymore.

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I've got to steal that explanation next time I teach MMN. (Laughter)

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And then, you know, I think that thing, Bernard pointed out is really important because

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it, like, there is research finding you only get the MMN when the deviants are deviant enough,

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right?

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Like 15% or less.

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And that can be interesting to tell you that these things, that the standards are that

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your brain considers them to be one category.

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Because if you have, well, it's not only that the deviants have to be 50% but the standards

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have to be like 80%.

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So, if you have 40% of one thing, 40% of another thing and 15% deviants, you won't get

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mismatched negativity for that because you don't have one thing that you have 80% of.

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So mismatched negativity is like, it's not just a cool thing that happens, but it's also

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a useful test for seeing like what things does your brain lump together into one category?

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Right.

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Yeah.

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So, I mean, that's kind of getting to my next question, which is like what's it useful for,

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right?

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And I guess that you use it fundamentally to probe the brain's categorization abilities.

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Is that a fair summary?

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Yeah.

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And I think the goal of our paper was to see, like going back to I said earlier then and

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can pick up on pretty complex patterns and things.

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We wanted to just see how complex and how abstract your brain can get in terms of like how

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it's categorizing things.

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Great.

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And you know, it's called the mismatched negativity.

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So obviously you've defined what mismatch means.

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Can you tell us like, what does it mean to be negativity?

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Like how is this measured, and you know, what's the timing of it?

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Like what it just tell us a little bit about the sort of neuro inside of this component.

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So, usually you measure it by, you record the brain response you get with those deviants

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and you subtract out the brain response you would get from the same things when they're

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not deviants.

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The way that is often done is by you can play bah, bah, bah, dah to

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people.

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So bahs are standards and dahs are deviants.

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But then you can also play dah, dah, dah, dah, bah.

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So, the dahs are standards now and bahs are deviants and they're the exact same physical

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stimuli.

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So, you can record what's the brain response you get when dahs is a deviant minus what's the

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brain response you get when dah is a standard.

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And so presumably any brain response related to that physical stimulus is the same in both

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and gets subtracted out.

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So, what's left over is the mismatched negativity and it's called mismatched negativity because

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that tends to be negative.

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So, the brain response you get to the deviant tends to be more negative than the brain response

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you get to the standard unless you're a little kid.

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Then it's called the mismatched response and sometimes it's positive instead of negative.

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I'm not a child EEG person so I don't know all the ins and outs of that.

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But for adults it tends to be a negativity.

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And it tends to happen pretty early, often around like 170, 200 milliseconds after you hear

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that sound or see that thing that's deviant.

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Although it can happen a little bit later.

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That's one of the conundrums or conundra that comes up in our paper.

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Yes, definitely.

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We're going to talk about that timing issue.

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But yeah, so typical around 170 to 200 milliseconds and you mentioned you're measuring

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with EEG.

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I think people also measure it sometimes with magnetoencephalography and call it the

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MMMN or something.

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But yeah, you guys are doing it with EEG, yeah.

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Yeah, I think MMF I've seen like the mismatched field since there's not necessarily a positive

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or negative.

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Okay, cool.

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So yeah, like you said, your paper is fundamentally about sort of categorization and what can

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be a deviant that's not just dependent on a simple physical stimulus, difference in 

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the stimuli.

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And you give these really nice examples in your intro, which I was hoping you could share

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with our listeners where people's language experience really determines what counts as a

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standard, what counts as a deviant.

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And maybe you could tell us about the paper by Philips or Colin Phillips from 2000 in

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that respect.

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Okay.

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Yeah, that's a great one.

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Yeah, so that paper, so what was done in that paper was instead of just having bah, bah,

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bah, bah, bah, bah, dah, 

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people would hear like, bah, dah, gah, bah dah, gah, gah, pah
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So, they had, oh, hang on.

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I think you're, the part of you thinking that.

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00:17:32,720 --> 00:17:35,480
Okay, so yeah, this is an awkward thing in your paper, right?

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So, we've got Phillips et al., 2000, and the other Phillips et al., 2000.

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So, I'm thinking of the one, because I'm just kind of, I'm trying to build it from simple

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to more complex.

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I'm thinking of the one where they manipulate voice onset time.

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The different VOT.

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Yes, okay.

302
00:17:49,720 --> 00:17:55,200
Yes, that's the other, the bigger, the more famous Phillips et al. paper, I think.

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00:17:55,200 --> 00:18:00,400
Yeah, but yeah, it's, so, yeah, this paper is a really amazing demonstration of that,

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00:18:00,400 --> 00:18:05,760
like, yeah, you're, like you said, Stephen, that language background influences how your brain

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puts things into categories.

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So, you have, they had, da, da, da, da, da, da, da, da, da, da, ta, but then

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these das all have different voice on set times, right?

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So, the voice on set time is, you know, how long is this little puff of air between the

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end of your d and the beginning of your ah.

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And when that gets long, that tends to sound like a T to English speakers, and when it's

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short, it tends to sound like a D, but the exact, you know, where that cut off is, is, different

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for different people and in different languages.

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So, what they did was they had stimuli with a whole bunch of different VOTs, a bunch of

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different voice onset times.

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So, there was no one stimulus that was the standard.

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There were like a bunch of stimuli with a 10 millisecond, voice onset time, a bunch

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with a 15 millisecond, voice onset time, a bunch with a 20 millisecond and whatever.

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But if it turns out that like as an English speaker, my cut off is 30 milliseconds, like

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anything longer than 30 milliseconds sounds like a T to me and anything shorter sounds like

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a D, they took this like, this distribution of sounds with different voice onset times

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00:19:15,040 --> 00:19:21,560
and put it on the side of that cut off so that like 85% of the ones you heard were ones

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that would sound like a D and 15% were ones that would sound like a T. And indeed people

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get a mismatched negativity for that, which is telling you that like your brain took all

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those different sounds and decided these are all T's.

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So even though they're physically different sounds, or no, so I decided they're all D's,

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right?

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So even though they're physically different sound, your brain lumps them into the D category,

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and that can be a standard and that gives you a mismatched negativity.

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Whereas in the second experiment, they took that whole distribution and just shifted everything

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up.

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So instead of 10 milliseconds, now it's 30 milliseconds or whatever.

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And so that made it so the cut off between D and T was right in the middle of the distribution

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now.

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So, they had the exact same variation of voice on set times, but now half of them sound

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like a D and half of them sound like a T and you don't get mismatched negativity anymore,

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which tells us that it's really not the physical variation in the sounds that's giving you

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this mismatched negativity.

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It's the fact that your brain is taking 80% of these sounds and thinking these are all

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D's and the other 15% are T's.

340
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That's what gives the mismatched negativity.

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It's really like how your brain lumps these things into categories.

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Cool.

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00:20:36,360 --> 00:20:39,960
And that's because that's where the typical English speaker puts that boundary between those

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two phonemes.

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And so, you see that it's linguistic knowledge that is shaping the nature of this categorization

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00:20:48,640 --> 00:20:49,640
process.

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00:20:49,640 --> 00:20:54,240
And then you talk a little bit in your paper about, I'm not going to go through all 12 of

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00:20:54,240 --> 00:20:59,320
the papers that you analyze in your intro, but I want to talk about one more, which is

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Kazanina et al., in 2006, where you really get to see the language specificity of this phenomenon.

350
00:21:08,200 --> 00:21:12,680
So, can you talk about what's so neat about that paper?

351
00:21:12,680 --> 00:21:13,680
Yeah.

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00:21:13,680 --> 00:21:18,760
This is one of my favorite papers ever because it took that sort of finding.

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The same paradigm as the Phillips et al. paper we just talked about and then looked

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at how that varies across people with different language backgrounds.

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00:21:26,520 --> 00:21:30,960
And they did essentially that same experiment we just talked about, but they did it with

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Russian speakers, I think, and with Korean speakers.

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00:21:34,840 --> 00:21:40,800
And the reason that's interesting is because in Russian, D&T are different sounds in the

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rule system of Russia, right?

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00:21:42,400 --> 00:21:45,760
Like linguists, we call them, they're different phonemes.

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Whereas in Korean, D&T are not different phonemes, they're just different versions of the

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same sound.

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00:21:53,480 --> 00:21:58,720
So, if you, I can never remember my Korean, but like if one of them is, if you have a certain

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sound at the beginning of a word, it gets pronounced this way, the same sound in the

364
00:22:02,640 --> 00:22:08,000
middle of a word gets pronounced this other way, but like a Korean speaker wouldn't, they

365
00:22:08,000 --> 00:22:10,600
don't give you different words.

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00:22:10,600 --> 00:22:16,960
It's kind of like for English, the sound M, what we call the M, we pronounce it by touching

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00:22:16,960 --> 00:22:20,640
our lips together if it's in a word like mom, but we pronounce it by touching our lips

368
00:22:20,640 --> 00:22:23,360
to our teeth if it's in a word like symphony, right?

369
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So, we don't think of it as a different sound.

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00:22:25,320 --> 00:22:30,480
In the same way, like in Korean, these two sounds are not considered different sounds,

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00:22:30,480 --> 00:22:33,440
they're just different versions of the same sound.

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And so, what they did in this experiment was they did that Phillips et al. experiment,

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once with Russian speakers and once with Korean speakers.

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00:22:42,240 --> 00:22:47,760
And you found that with Russian speakers, there was a mismatch negativity because they

375
00:22:47,760 --> 00:22:50,120
sort these into two different categories.

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00:22:50,120 --> 00:22:55,240
And in Korean speakers, there wasn't because they don't sort them into different categories

377
00:22:55,240 --> 00:22:56,240
along those lines.

378
00:22:56,240 --> 00:22:59,120
Yeah, super cool.

379
00:22:59,120 --> 00:23:03,440
So that really makes it clear that like linguist acknowledge plays a role in generating the

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00:23:03,440 --> 00:23:08,360
MMN and shaping the categories that create the brain response.

381
00:23:08,360 --> 00:23:12,960
But your point in this paper is that even though they're clearly dependent on linguist

382
00:23:12,960 --> 00:23:17,880
knowledge, they're still ultimately generated by physical cues in the acoustic signal, right?

383
00:23:17,880 --> 00:23:22,880
So, in these cases, there's a change in voice onset time that either crosses a boundary

384
00:23:22,880 --> 00:23:28,640
or doesn't, but there's a physical change in the acoustic stimulus that drives that

385
00:23:28,640 --> 00:23:35,480
MMN and you kind of go through the whole literature and show that in all of the previous MMN studies,

386
00:23:35,480 --> 00:23:37,960
there's always a physical cue.

387
00:23:37,960 --> 00:23:44,480
And you are curious whether you can generate an MMN without any reliable physical cue.

388
00:23:44,480 --> 00:23:51,960
Can you tell us, tell me and our listeners, why was that an interesting question for you guys?

389
00:23:51,960 --> 00:23:58,200
Yeah, I think that it, I started wondering about that,

390
00:23:58,200 --> 00:24:03,200
especially when I was doing some MMN work with Kevin Schluter, who he and I were postdocs

391
00:24:03,200 --> 00:24:06,120
together at NYU Abu Dhabi at the time.

392
00:24:06,120 --> 00:24:11,960
And we were starting, like Kevin in particular has had a background in theoretical phenology.

393
00:24:11,960 --> 00:24:18,320
And so, we were interested in how the MMN is related to these abstract things that phonologists

394
00:24:18,320 --> 00:24:19,960
care about.

395
00:24:19,960 --> 00:24:24,120
And so, we were learning this literature and all these different things that are argued

396
00:24:24,120 --> 00:24:29,080
to show that like the MMN is really sensitive to abstract contrast.

397
00:24:29,080 --> 00:24:33,720
And we just started to wonder like how abstract can things really get because we were noticing

398
00:24:33,720 --> 00:24:36,800
this concern that you just mentioned that.

399
00:24:36,800 --> 00:24:41,960
So even in the Russian Korean study we just talked about, it's like a beautiful demonstration

400
00:24:41,960 --> 00:24:45,920
that the MMN cares about language knowledge.

401
00:24:45,920 --> 00:24:51,760
But then we thought what is really showing is the MMN or your language knowledge shapes

402
00:24:51,760 --> 00:24:55,400
which physical cues you pay attention to and how.

403
00:24:55,400 --> 00:25:01,480
But we really wanted to know like can you really get an MMN for like a phonological contrast

404
00:25:01,480 --> 00:25:06,120
that's like just for the abstract phonological contrast, not for the physical contrast that

405
00:25:06,120 --> 00:25:08,120
comes along with that.

406
00:25:08,120 --> 00:25:11,960
And as we kept looking into that, we kept finding like, oh, there's all of these things that

407
00:25:11,960 --> 00:25:16,800
look like they're very interesting abstract phonological contrast, but they're actually

408
00:25:16,800 --> 00:25:19,520
backed up by physical contrast.

409
00:25:19,520 --> 00:25:24,880
And there are also other interesting cases where you find that the MMN can get bigger or smaller

410
00:25:24,880 --> 00:25:28,720
as a result of interesting abstract linguistic things.

411
00:25:28,720 --> 00:25:34,840
So, like real words have bigger MMNs than non-words, phonologically underspecified sounds get

412
00:25:34,840 --> 00:25:35,840
smaller MMNs.

413
00:25:35,840 --> 00:25:37,880
So there are all these interesting things.

414
00:25:37,880 --> 00:25:42,320
But it's still like the physical contrast that's causing the MMN and then the interesting

415
00:25:42,320 --> 00:25:45,840
linguistic stuff that pushes it around to be bigger or smaller.

416
00:25:45,840 --> 00:25:51,400
So, we really just wanted to know like can you get an MMN for a just a purely linguistic

417
00:25:51,400 --> 00:25:52,400
contrast?

418
00:25:52,400 --> 00:25:54,480
All right.

419
00:25:54,480 --> 00:25:59,600
So, I'd like to ask you to describe your stimuli with your conditions.

420
00:25:59,600 --> 00:26:03,400
And I'm going to, I'd like to kind of do it in reverse order than how you do it in the

421
00:26:03,400 --> 00:26:05,960
paper because in the paper you kind of have your main condition that you're interested

422
00:26:05,960 --> 00:26:08,360
in and then you talk about your controls.

423
00:26:08,360 --> 00:26:12,280
But just because for in terms of explaining it to people that are like doing the dishes

424
00:26:12,280 --> 00:26:18,040
or going to work and I think it might be easier if we kind of build it up piece by piece from

425
00:26:18,040 --> 00:26:22,000
like the simplest condition to the most complex.

426
00:26:22,000 --> 00:26:30,200
So could you start by telling us, telling our listeners about your control condition that

427
00:26:30,200 --> 00:26:35,600
you call the aspiration contrast where it's the literally kind of the simplest?

428
00:26:35,600 --> 00:26:37,160
Yeah.

429
00:26:37,160 --> 00:26:40,920
Maybe I can take this one and Bernard can take over what we get to the more complicated

430
00:26:40,920 --> 00:26:45,000
ones that you'll probably remember the details of the stimuli better.

431
00:26:45,000 --> 00:26:47,360
But the control one was the simplest.

432
00:26:47,360 --> 00:26:54,440
This is just like the da da da da ta or ta ta ta da contrast where there really is just

433
00:26:54,440 --> 00:27:00,000
a physical difference between these like the ta has this extra puff of air or aspiration

434
00:27:00,000 --> 00:27:01,680
which da doesn't.

435
00:27:01,680 --> 00:27:05,640
And so, like you don't even need to know language to notice that these are different like

436
00:27:05,640 --> 00:27:09,000
chinchillas and things can get MMNs for these.

437
00:27:09,000 --> 00:27:14,320
So, this is we really had it there to make sure the experiment is working because like as

438
00:27:14,320 --> 00:27:18,000
we're going to see in a moment we had more complicated things where we weren't sure

439
00:27:18,000 --> 00:27:20,480
if we will get an MMN.

440
00:27:20,480 --> 00:27:25,760
And if you don't get an MMN in the complicated condition and you want to say that's interesting

441
00:27:25,760 --> 00:27:30,520
you need to be able to show that like we did the experiment right and we're able to get

442
00:27:30,520 --> 00:27:32,200
MMNs where you should.

443
00:27:32,200 --> 00:27:33,200
So, we have that.

444
00:27:33,200 --> 00:27:34,200
Okay.

445
00:27:34,200 --> 00:27:37,840
So, you got to be able to prove that you're capable of generating a standard MMN and so

446
00:27:37,840 --> 00:27:43,400
you do a very simple condition to establish that.

447
00:27:43,400 --> 00:27:49,640
And then you kind of have this middle of the road condition which is actually a replication

448
00:27:49,640 --> 00:27:51,840
of a study by Monahan et al., 2022,

449
00:27:51,840 --> 00:27:59,240
which is using a phonological category but it's still kind of driven by an acoustic

450
00:27:59,240 --> 00:28:00,240
cue.

451
00:28:00,240 --> 00:28:03,480
So, Bernard do you want to explain that one?

452
00:28:03,480 --> 00:28:04,480
Sure.

453
00:28:04,480 --> 00:28:09,520
So, in the sort of middle block, we have the voicing contrast.

454
00:28:09,520 --> 00:28:20,040
So, we have voiced phonemes like Bah, dah, Gah, vah, Zah and voiceless ones, Pah, Pah, Pah, Pah, Pah,

455
00:28:20,040 --> 00:28:25,880
and each of them act as deviants in one block and standards in another and then we just

456
00:28:25,880 --> 00:28:31,120
compare them as deviants and them as standards.

457
00:28:31,120 --> 00:28:40,120
So, for us to be able to elicit an MMN here it would mean the participants would have to

458
00:28:40,120 --> 00:28:41,120
be able to generalize.

459
00:28:41,120 --> 00:28:42,120
Yeah.

460
00:28:42,120 --> 00:28:46,960
And I'll add like the reason that this, we call this a little more abstract is because like

461
00:28:46,960 --> 00:28:51,760
the difference between voiced and voiceless is different among these.

462
00:28:51,760 --> 00:28:58,120
So, for the stops like Bah, dah, Gah, Pah Tah, Kah, the voiceless stop has aspiration, 

463
00:28:58,120 --> 00:29:00,640
in it and the voiced stop doesn't.

464
00:29:00,640 --> 00:29:07,160
But for the fricatives like fah, sah, vah, zah, the difference between those is whether there's

465
00:29:07,160 --> 00:29:09,640
voicing during the fricative.

466
00:29:09,640 --> 00:29:16,440
So, the acoustic difference here is really different to the point that you don't have like 80%

467
00:29:16,440 --> 00:29:18,440
of your sounds all have a puff of air in them.

468
00:29:18,440 --> 00:29:20,280
That's not the case anymore.

469
00:29:20,280 --> 00:29:24,600
But phonologically these do make categories because like all the voiceless things do the

470
00:29:24,600 --> 00:29:30,360
same things like in English you have a different plural morpheme that goes after voiced versus

471
00:29:30,360 --> 00:29:31,360
voiceless.

472
00:29:31,360 --> 00:29:36,240
So, there's good reason to believe that these do make categories phonologically, but they don't

473
00:29:36,240 --> 00:29:40,920
have like 85%, 15% sort of category in acoustic space.

474
00:29:40,920 --> 00:29:41,920
Yeah.

475
00:29:41,920 --> 00:29:46,160
So, they make a really, so if you were to describe the rule and acoustic terms it would be very

476
00:29:46,160 --> 00:29:51,240
complicated because you'd have to have a different rule for stops and fricatives and that's

477
00:29:51,240 --> 00:29:56,480
what kind of makes it more abstract, although it's still ultimately a physical cue.

478
00:29:56,480 --> 00:30:02,040
You could describe a physical cue that distinguishes the standards and deviants.

479
00:30:02,040 --> 00:30:03,040
Yes.

480
00:30:03,040 --> 00:30:04,040
Okay.

481
00:30:04,040 --> 00:30:10,840
And then you kind of have your top of the pile most important critical condition where you

482
00:30:10,840 --> 00:30:17,480
do a contrast between verbs in the present tense and verbs in the past tense and you do

483
00:30:17,480 --> 00:30:23,720
this to try and create a situation where there are literally no systematic acoustic cues

484
00:30:23,720 --> 00:30:25,640
that could be generating the MMN.

485
00:30:25,640 --> 00:30:30,960
So, can you describe how this condition works and how did you guys come up with this one?

486
00:30:30,960 --> 00:30:31,960
Yeah.

487
00:30:31,960 --> 00:30:41,080
So we wanted to have a contrast with where the critical deviants’ standards have minimal

488
00:30:41,080 --> 00:30:42,880
physical contrast.

489
00:30:42,880 --> 00:30:46,400
It still does have a physical contrast because they're different words.

490
00:30:46,400 --> 00:30:53,200
But we have, for example, in the present tense we have ‘pave’, the verb ‘pave’, ‘get’ and

491
00:30:53,200 --> 00:31:01,600
‘Thank’ and the past sort of block would be ‘gave’, ‘met’ and ‘sank’.

492
00:31:01,600 --> 00:31:04,720
So, they only differ by their first sound.

493
00:31:04,720 --> 00:31:13,000
So, the way we sort of try to fool participants into thinking that this is just a series of words

494
00:31:13,000 --> 00:31:19,480
and they have to sort of abstract the actual tense feature is by also including a bunch of

495
00:31:19,480 --> 00:31:21,120
extra standards.

496
00:31:21,120 --> 00:31:32,280
So, among the block that features present tense, deviants we also featured past tense, deviants

497
00:31:32,280 --> 00:31:38,200
like toes, sang, blood, war and so on.

498
00:31:38,200 --> 00:31:45,280
So yeah, basically there's not really any systematic orthographic or chronological differences

499
00:31:45,280 --> 00:31:47,680
between the stimuli.

500
00:31:47,680 --> 00:31:52,640
I think we went through several iterations of this block because in English it's kind of

501
00:31:52,640 --> 00:32:00,160
difficult to find words that our only verbs, for example, or only nouns that have this sort

502
00:32:00,160 --> 00:32:01,800
of minimal contrast.

503
00:32:01,800 --> 00:32:09,320
And even then, I think in our stimuli we still saw that was like met, could mean different

504
00:32:09,320 --> 00:32:10,720
things to American speakers.

505
00:32:10,720 --> 00:32:14,880
For example, there isn't this, doesn't this mean the museum?

506
00:32:14,880 --> 00:32:16,880
Oh yeah.

507
00:32:16,880 --> 00:32:22,160
So that was also something that we were constantly thinking about.

508
00:32:22,160 --> 00:32:28,080
So, you had to find kind of culturally illiterate Hong Kong speakers of American English. (Laughter)

509
00:32:28,080 --> 00:32:32,280
Okay, so I'm just going to kind of restate this and just tell me if I get it right.

510
00:32:32,280 --> 00:32:38,760
So, like I think in the critical, in the critical direction you go with past tense standards

511
00:32:38,760 --> 00:32:41,400
and present tense deviants, is that correct?

512
00:32:41,400 --> 00:32:42,400
I think that's correct.

513
00:32:42,400 --> 00:32:46,600
I always have to remember which one is the, the under specified one.

514
00:32:46,600 --> 00:32:48,600
I think that's right.

515
00:32:48,600 --> 00:32:49,600
Yeah.

516
00:32:49,600 --> 00:32:53,120
Okay, so the standards would be, so I'm just reading off of your table.

517
00:32:53,120 --> 00:33:01,240
So, the standards would be something like ‘chose’, ‘sang’, ‘bled’, ‘swore’, ‘clung’, ‘plaid’, ‘grew’,

518
00:33:01,240 --> 00:33:10,120
‘drew’, ‘brought’, and then the deviants would be ‘pave’, ‘get’, ‘thank’.

519
00:33:10,120 --> 00:33:14,520
So, the deviants go into the present tense, and you match it perfectly phonologically by

520
00:33:14,520 --> 00:33:21,640
having the reversed version where the, where the deviants are ‘gave’, ‘met’ and ‘sank’, which have

521
00:33:21,640 --> 00:33:29,080
the identical coders to those critical stimuli but are past tense.

522
00:33:29,080 --> 00:33:33,080
And you need to of course use irregular English verbs, right?

523
00:33:33,080 --> 00:33:36,080
Can you explain why it had to be done that way?

524
00:33:36,080 --> 00:33:41,280
Yeah, because we wanted there to be no cue that tells you these are past, right?

525
00:33:41,280 --> 00:33:47,000
Because the goal was for people to hear like a long series of words where 85% of them are

526
00:33:47,000 --> 00:33:52,280
present tense and, and 15% are past tense and you have to figure out the tense to notice

527
00:33:52,280 --> 00:33:53,800
the difference.

528
00:33:53,800 --> 00:33:58,240
So, if we use regular verbs, then all of the past tense have, this ‘dah’ on the end and that would

529
00:33:58,240 --> 00:33:59,920
be a cue.

530
00:33:59,920 --> 00:34:05,840
So, using these irregulars, which like, you know, we're lucky that English is a weird language.

531
00:34:05,840 --> 00:34:12,920
So, we could have people hearing this like, Chose, sang, Bled, Swore, Get, Clung, Pled, Met,

532
00:34:12,920 --> 00:34:13,920
Thanked, right?

533
00:34:13,920 --> 00:34:18,800
So, it's, it's really hard to notice that they're all there like two past tense verbs sprinkled

534
00:34:18,800 --> 00:34:19,800
in there.

535
00:34:19,800 --> 00:34:24,880
So, it's a really different situation than like the, the control, VOT thing we started

536
00:34:24,880 --> 00:34:27,800
with, which is just like, Bah, Bah, Bah, Bah, Dah.

537
00:34:27,800 --> 00:34:32,960
It was really easy to, or I guess Bah, Bah, Bah, Bah, Bah, Pah, which was what it actually was.

538
00:34:32,960 --> 00:34:33,960
Yeah.

539
00:34:33,960 --> 00:34:42,760
We also wanted to keep it one syllable for each and some of the past tense English verbs

540
00:34:42,760 --> 00:34:43,760
would have two.

541
00:34:43,760 --> 00:34:44,760
Yeah.

542
00:34:44,760 --> 00:34:47,560
Sort of limited our option.

543
00:34:47,560 --> 00:34:51,440
So, yeah, you know, so you guys are really relying on this quirky property of English.

544
00:34:51,440 --> 00:34:54,560
So, I mean, do you think you could bond with Steven Pinker over this?

545
00:34:54,560 --> 00:34:58,640
So, over you interest in irregular verbs?

546
00:34:58,640 --> 00:35:02,240
Yeah, and then we'd have to , we'd have to compare regulars and irregulars.

547
00:35:02,240 --> 00:35:03,240
Oh yeah.

548
00:35:03,240 --> 00:35:06,640
If not comparing regular, irregular, he's not going to be interested.

549
00:35:06,640 --> 00:35:07,640
Yeah.

550
00:35:07,640 --> 00:35:13,080
So yeah, so you're really asking the brain to make a very abstract category here, right?

551
00:35:13,080 --> 00:35:16,120
Like you're asking it, and pre-attentively too, right?

552
00:35:16,120 --> 00:35:20,440
So, you're kind of even wondering whether this might happen if, when people are asleep even,

553
00:35:20,440 --> 00:35:27,640
like are people just pre-attentively extracting these syntactic features and to the point

554
00:35:27,640 --> 00:35:32,840
where the brain is capable of generating an MMN when the sequence is violated.

555
00:35:32,840 --> 00:35:35,640
And you're asking a lot of the brain.

556
00:35:35,640 --> 00:35:41,960
Yeah, and I mean, when we did it, we were like, it was like a long shot for this to even

557
00:35:41,960 --> 00:35:45,320
work because I mean, even if you're paying attention, right?

558
00:35:45,320 --> 00:35:53,560
If I say, "Choose, sing, bleed, swear, cling, met, plead, grow, woo, think, thank," right?

559
00:35:53,560 --> 00:35:57,120
It's really hard to notice that there's a past, present, contrast there.

560
00:35:57,120 --> 00:36:00,840
Like even for me having told you there was going to be there, like I have to read off

561
00:36:00,840 --> 00:36:03,560
the paper to do it. It's too hard for me to do it.

562
00:36:03,560 --> 00:36:09,040
So like to think that the brain could do that without paying attention, it's like very far

563
00:36:09,040 --> 00:36:10,040
fetched.

564
00:36:10,040 --> 00:36:15,080
So, we had to do a lot of methodological stuff to like, make sure we would be able to find

565
00:36:15,080 --> 00:36:20,840
an effect if it's there because we expected it to be really subtle if it exists at all.

566
00:36:20,840 --> 00:36:21,840
Right.

567
00:36:21,840 --> 00:36:26,960
Yeah, no, I mean, I'd be kind of surprised if it would come out.

568
00:36:26,960 --> 00:36:33,400
So, you pre-registered this experiment and I find that one of the really interesting aspects

569
00:36:33,400 --> 00:36:36,440
of this paper and I kind of wanted to talk to you guys about it.

570
00:36:36,440 --> 00:36:38,400
So, you didn't just pre-register it.

571
00:36:38,400 --> 00:36:42,480
You submitted it to neurobiology of language as a registered report, which means that you

572
00:36:42,480 --> 00:36:49,040
wrote the intro and methods and kind of like speculative results and what they would

573
00:36:49,040 --> 00:36:55,000
mean and submitted that for peer review before collecting the data.

574
00:36:55,000 --> 00:37:00,560
So, first, could you tell us like, why did you decide to take this tack with this paper?

575
00:37:00,560 --> 00:37:04,560
Yeah, so just a little bit of a context first.

576
00:37:04,560 --> 00:37:12,520
We submitted the registered report in 2021 and that was when I started as these postdoc

577
00:37:12,520 --> 00:37:18,240
and when I arrived in Hong Kong, he was still in Hong Kong, all the labs closed because

578
00:37:18,240 --> 00:37:19,880
of COVID.

579
00:37:19,880 --> 00:37:24,960
So we weren't, I arrived and then we weren't able to do any work at all.

580
00:37:24,960 --> 00:37:30,440
And yeah, so we discussed that this could be sort of a clever way to utilize our time to

581
00:37:30,440 --> 00:37:36,360
just write up maybe one or two registered reports and see how we can do.

582
00:37:36,360 --> 00:37:40,840
And of course, outside of the convenience factors, you know, to do something while the labs

583
00:37:40,840 --> 00:37:48,280
are all closed, it's sort of a way to commit to methodology before seeing any results.

584
00:37:48,280 --> 00:37:52,360
So, instead of the, as you said, instead of the traditional model where you run an experiment

585
00:37:52,360 --> 00:37:57,920
and let the results in submit, you sort of slip that process, you first develop your entire

586
00:37:57,920 --> 00:38:03,280
research plan, your methods, and so on, and sample size justification and submit that for

587
00:38:03,280 --> 00:38:04,600
peer review.

588
00:38:04,600 --> 00:38:10,200
And if you think that what you're proposing is solid and interesting, you might get an

589
00:38:10,200 --> 00:38:13,920
in principle acceptance before collecting any data.

590
00:38:13,920 --> 00:38:19,320
So, that is also an attractive feature of registered reports.

591
00:38:19,320 --> 00:38:25,600
So afterwards you get to run the study exactly as you plan and then you can submit sort

592
00:38:25,600 --> 00:38:30,920
of the second stage of the results for the final review.

593
00:38:30,920 --> 00:38:37,360
I also think that with studies that are as exploratory as this, we don't really know

594
00:38:37,360 --> 00:38:39,760
what to expect in terms of the findings.

595
00:38:39,760 --> 00:38:43,680
Actually, I'm not even sure when I was running the study that we would find the tense

596
00:38:43,680 --> 00:38:47,480
MMN, I think Steve also feels the same way.

597
00:38:47,480 --> 00:38:50,640
And if we didn't find it, would it be interesting enough, for example?

598
00:38:50,640 --> 00:38:52,720
You know, that's another question.

599
00:38:52,720 --> 00:38:54,720
Yeah.

600
00:38:54,720 --> 00:39:00,600
So, I do think this is sort of one of the biggest advantages.

601
00:39:00,600 --> 00:39:07,000
I think we've done two registered reports now and they've been quite interesting experiences.

602
00:39:07,000 --> 00:39:13,600
I also, if I can keep going here, I think we wanted to make sort of the best study

603
00:39:13,600 --> 00:39:21,080
ever by making sure our data is clean, and we have sort of strict inclusion criteria to the

604
00:39:21,080 --> 00:39:29,200
point where we were finding ways every day on the sort of on the road to submitting this

605
00:39:29,200 --> 00:39:33,880
registered report sort of how to make our lives more difficult by making it more and more

606
00:39:33,880 --> 00:39:34,880
big.

607
00:39:34,880 --> 00:39:43,480
I think at one point we had a few, sort of criteria to assess if we throw out a 

608
00:39:43,480 --> 00:39:46,760
participant or not, they have too many bad channels.

609
00:39:46,760 --> 00:39:52,480
If we're excluding too much data during ICA and a few other factors.

610
00:39:52,480 --> 00:39:56,120
And then we were also trying to find out how do we do this objectively.

611
00:39:56,120 --> 00:40:04,000
So, we tried to do an analysis of the signal to noise ratio using a bootstrap method, which

612
00:40:04,000 --> 00:40:07,200
turned out to be a bit strict.

613
00:40:07,200 --> 00:40:08,200
Yeah.

614
00:40:08,200 --> 00:40:16,480
In any case, we also had a few cases, well, at least in this paper where we had to deviate

615
00:40:16,480 --> 00:40:18,520
from the original plan.

616
00:40:18,520 --> 00:40:19,520
Yeah.

617
00:40:19,520 --> 00:40:20,520
Okay.

618
00:40:20,520 --> 00:40:21,520
Yeah.

619
00:40:21,520 --> 00:40:22,520
Okay.

620
00:40:22,520 --> 00:40:23,520
Very, yeah.

621
00:40:23,520 --> 00:40:24,520
That's interesting.

622
00:40:24,520 --> 00:40:30,120
I was going to ask you about that because you, yes, things didn't go exactly as planned.

623
00:40:30,120 --> 00:40:35,000
But when you, so yeah, you've definitely, I've read your pre-registered version, and I see

624
00:40:35,000 --> 00:40:41,080
that it's identical to what you actually published, which is not always the case. (Laughter)

625
00:40:41,080 --> 00:40:45,720
But I'm curious like what the review process was like for the registered report.

626
00:40:45,720 --> 00:40:47,720
Like did you get a lot of feedback?

627
00:40:47,720 --> 00:40:51,240
Did you make any changes to your study plan based on the review process?

628
00:40:51,240 --> 00:40:52,240
Yeah.

629
00:40:52,240 --> 00:40:53,240
Absolutely.

630
00:40:53,240 --> 00:41:02,840
I think the first thing was the biggest sort of suggestion, one that we did not implement

631
00:41:02,840 --> 00:41:11,480
was the suggestion to split the experiment session into two because to have enough power,

632
00:41:11,480 --> 00:41:17,920
our study needed to be, just the experiment session needed to be around three hours.

633
00:41:17,920 --> 00:41:23,360
To around two hours, 45 minutes on average per person, participant, and then the editor,

634
00:41:23,360 --> 00:41:29,480
one of the editors said, this is too long, and you might want to split your session into

635
00:41:29,480 --> 00:41:31,720
two.

636
00:41:31,720 --> 00:41:38,040
We sort of pushed back on that because we know if we did that, there might be differences

637
00:41:38,040 --> 00:41:44,440
in small things like applying the cap and also the participants might not come back if

638
00:41:44,440 --> 00:41:46,200
we let them go.

639
00:41:46,200 --> 00:41:52,560
And I was kind of happy at the end to sort of keep it into just one session because this

640
00:41:52,560 --> 00:41:54,880
is quite long.

641
00:41:54,880 --> 00:41:55,880
And then we all had that.

642
00:41:55,880 --> 00:41:59,440
But there were changes that we did end up making, right?

643
00:41:59,440 --> 00:42:04,000
Some things got suggested, yeah, what were things about the words I remember?

644
00:42:04,000 --> 00:42:06,280
Stimuli.

645
00:42:06,280 --> 00:42:11,600
But yeah, I think, yeah, there were some words we had included that would have been problems.

646
00:42:11,600 --> 00:42:17,400
So, I think one of our present tense verbs was ‘run’ and a reviewer pointed out, oh, but

647
00:42:17,400 --> 00:42:19,920
‘run’, could also be of deverbal noun, right?

648
00:42:19,920 --> 00:42:25,560
I'm going to go for a run and that might break up your present tense verb category.

649
00:42:25,560 --> 00:42:30,800
So, we did a lot of like tweaks to the stimuli through that.

650
00:42:30,800 --> 00:42:37,200
And I think there are like some changes to the like interstimulus interval or things like

651
00:42:37,200 --> 00:42:38,200
that.

652
00:42:38,200 --> 00:42:46,240
So, a lot of sort of nitty-gritty changes, the experiment, which I was really like glad that

653
00:42:46,240 --> 00:42:50,840
we went through that process because it was so hard to find participants for this study.

654
00:42:50,840 --> 00:42:55,320
It would have been a real shame if we like spent a year getting participants and then submitting

655
00:42:55,320 --> 00:43:00,280
it and then submitted it and then found out like, oh, there was some fatal flaw, like a reviewer

656
00:43:00,280 --> 00:43:01,440
noticed.

657
00:43:01,440 --> 00:43:05,120
So, it was good to like get all those pointed out before we did the experiment.

658
00:43:05,120 --> 00:43:10,520
So, then we did the experiment in a way that we knew was like good and going to like not

659
00:43:10,520 --> 00:43:14,960
have problems that we hadn't thought of that other reviewers would have noticed.

660
00:43:14,960 --> 00:43:15,960
Right.

661
00:43:15,960 --> 00:43:22,960
I mean, yeah, ‘run’ is also like a point scored and cricket or you know, I think even the

662
00:43:22,960 --> 00:43:25,920
ladder in your stockings can be called a ‘run’.

663
00:43:25,920 --> 00:43:30,200
English is just so homophonous, you know, it's like, it's really difficult to design an

664
00:43:30,200 --> 00:43:32,200
unambiguous stimuli in English.

665
00:43:32,200 --> 00:43:33,200
Yeah.

666
00:43:33,200 --> 00:43:34,200
Okay.

667
00:43:34,200 --> 00:43:39,600
So, yeah, and you know, like I just think it's very admirable that you've kind of like cut

668
00:43:39,600 --> 00:43:43,200
yourselves off from the temptation to engage in p-hacking, right?

669
00:43:43,200 --> 00:43:47,840
Like you've released it and you're and this is a very detailed pre-registration I have to

670
00:43:47,840 --> 00:43:49,840
share with our listeners.

671
00:43:49,840 --> 00:43:53,720
Like, you know, it really gets it gets, all the methods are there.

672
00:43:53,720 --> 00:43:57,760
Like there's, there's not a whole lot of wiggle room in comparison to some others that I've

673
00:43:57,760 --> 00:44:00,400
seen where it's just like, we'll analyze the data with t-tests.

674
00:44:00,400 --> 00:44:02,920
It's like, what kind of t-tests?

675
00:44:02,920 --> 00:44:03,920
Yeah.

676
00:44:03,920 --> 00:44:09,480
And a lot of that is thanks to like, like Alec was the editor and the reviewers who anywhere

677
00:44:09,480 --> 00:44:13,080
where we weren't detailed, then they were like, you need to say exactly how you're going

678
00:44:13,080 --> 00:44:14,080
to do this.

679
00:44:14,080 --> 00:44:16,840
So, then we had to come back with a revision and say that.

680
00:44:16,840 --> 00:44:17,840
Yeah.

681
00:44:17,840 --> 00:44:22,920
In the context I assume you mean Alec Marantz, or co-author of some of these seminal

682
00:44:22,920 --> 00:44:23,920
MMN studies.

683
00:44:23,920 --> 00:44:27,680
Yeah, I mean, it's great to have a real expert overseeing that process.

684
00:44:27,680 --> 00:44:29,200
Okay, great.

685
00:44:29,200 --> 00:44:30,960
So, let's talk about what you guys found.

686
00:44:30,960 --> 00:44:35,000
And again, just kind of like from, let's go from the simplest condition and build it up

687
00:44:35,000 --> 00:44:36,000
from there.

688
00:44:36,000 --> 00:44:42,440
So, like, what did you see MMN wise for your aspiration contrast where it's just bah, bah, bah, bah, pah?
 
689
00:44:42,440 --> 00:44:45,560
Or maybe it was the other way around, I don't remember.

690
00:44:45,560 --> 00:44:52,520
Yeah, so and that one we got enormous MMN, thankfully, because if we didn't then we would

691
00:44:52,520 --> 00:44:55,640
have to think like we must have forgotten to plug in the cap or something.

692
00:44:55,640 --> 00:44:56,880
So yeah, 60 times. (Laughter)

693
00:44:56,880 --> 00:44:57,880
Yeah.

694
00:44:57,880 --> 00:45:02,080
It would happen, I guess.

695
00:45:02,080 --> 00:45:05,200
But yeah, so the like bah, bah, bah, bah, pah.

696
00:45:05,200 --> 00:45:09,280
No, I think it was the other way around, it was pah, pah, pah, pah, bah.

697
00:45:09,280 --> 00:45:20,440
So, the bah, gives you a way bigger, a way more negative VRP when it is a deviant than it is a standard and like in bah, bah, bah, pah.

698
00:45:20,440 --> 00:45:27,120
And that was around, you know, huge MMN and roughly around when and where it should be.

699
00:45:27,120 --> 00:45:33,080
It was so huge it was kind of all over the head, but it was also frontal which you often

700
00:45:33,080 --> 00:45:35,360
expect MMNs to be.

701
00:45:35,360 --> 00:45:38,640
And the timing was pretty much where you wanted it to be, right?

702
00:45:38,640 --> 00:45:46,200
I was about 300 milliseconds, maybe a little slower than some MMNs might be, but like definitely

703
00:45:46,200 --> 00:45:48,880
in the ballpark of expectations.

704
00:45:48,880 --> 00:45:49,880
Yeah, yeah.

705
00:45:49,880 --> 00:45:56,640
So, it's definitely a little bit slow, although it could also be like, you know, I'm trying

706
00:45:56,640 --> 00:45:59,400
to remember where we had actually time locked things too.

707
00:45:59,400 --> 00:46:03,120
So, if we had time locked things to like, we might have time locked to the beginning of the

708
00:46:03,120 --> 00:46:08,400
syllable and then, you know, in pah, like however many milliseconds later that you realize

709
00:46:08,400 --> 00:46:10,960
how long the aspiration is.

710
00:46:10,960 --> 00:46:13,160
But yeah, like, looking at the graph now,

711
00:46:13,160 --> 00:46:15,520
it's like a teeny bit before 300 milliseconds.

712
00:46:15,520 --> 00:46:20,680
So, it's later than the classic MMN, but it's still like within the ballpark of what I

713
00:46:20,680 --> 00:46:22,960
think you'd be willing to call an MMN.

714
00:46:22,960 --> 00:46:23,960
Yeah.

715
00:46:23,960 --> 00:46:24,960
And it's actually, I'm looking at it now.

716
00:46:24,960 --> 00:46:28,600
It's like definitely it's deviating clearly by 200.

717
00:46:28,600 --> 00:46:33,480
So, it's well on its way to being an MMN at that point.

718
00:46:33,480 --> 00:46:34,480
That's true. The huge effect is so 

719
00:46:34,480 --> 00:46:35,480
Yeah.

720
00:46:35,480 --> 00:46:39,640
attention grabbing, that I look at like the peak latency, but I should actually be thinking

721
00:46:39,640 --> 00:46:41,240
of the onset latency.

722
00:46:41,240 --> 00:46:42,240
Right.

723
00:46:42,240 --> 00:46:43,240
Okay. Cool.

724
00:46:43,240 --> 00:46:44,240

725
00:46:44,240 --> 00:46:47,880
So, that one came out as expected, and that just kind of confirms that you're able to generate

726
00:46:47,880 --> 00:46:51,560
an MMN and everything's kind of in order.

727
00:46:51,560 --> 00:46:57,720
And then you next, let's look next at the middle contrast, which is the replication of Monahan

728
00:46:57,720 --> 00:47:05,040
et al., which is the sort of voicing violation where you've got standards that share a voicing

729
00:47:05,040 --> 00:47:10,280
category, and then the deviant is crosses that has the opposite value for that feature.

730
00:47:10,280 --> 00:47:12,880
So, what did you see for that one?

731
00:47:12,880 --> 00:47:13,880
Yeah.

732
00:47:13,880 --> 00:47:18,240
And there we, we still get an MMN, but it's now much weaker.

733
00:47:18,240 --> 00:47:20,040
Of course, I'm doing this.

734
00:47:20,040 --> 00:47:24,080
I always want a gesture with my hands, but I have to describe it verbally, right?

735
00:47:24,080 --> 00:47:30,280
But with the, with the, the, the, bah, bah, bah, pah, you have like an obvious huge spike

736
00:47:30,280 --> 00:47:31,280
here, right?

737
00:47:31,280 --> 00:47:36,480
One condition that has a giant peak sticking way up above the other ones.

738
00:47:36,480 --> 00:47:40,920
Here like both conditions are, you know, they're these lines and one is just a tiny bit higher

739
00:47:40,920 --> 00:47:42,720
than the other.

740
00:47:42,720 --> 00:47:47,880
So, we're getting an MMN, and it was statistically significant, according to all the tests that

741
00:47:47,880 --> 00:47:50,880
we pre-registered and said we were going to do.

742
00:47:50,880 --> 00:47:56,000
But it's much smaller like to the extent that if we haven't pre-registered it, like, you

743
00:47:56,000 --> 00:47:59,000
know, people might not trust is that really an effect there?

744
00:47:59,000 --> 00:48:00,000
Yeah.

745
00:48:00,000 --> 00:48:01,000
Okay.

746
00:48:01,000 --> 00:48:03,480
Let's see how the pre-registration is so key, right?

747
00:48:03,480 --> 00:48:07,200
Because there's like many different decision points there, like you could have had a different

748
00:48:07,200 --> 00:48:10,720
epoch, you could have had different plan stats.

749
00:48:10,720 --> 00:48:11,720
You're only looking at it.

750
00:48:11,720 --> 00:48:14,640
I mean, I don't want to get into this detail too much, but like you're only looking at one

751
00:48:14,640 --> 00:48:19,760
direction because of like the, the, the possibly that like markedness has a different effect

752
00:48:19,760 --> 00:48:20,760
than marked.

753
00:48:20,760 --> 00:48:25,520
And so like, you know, that, if that wasn't pre-specified as a reader, I'd be like, "Ah,

754
00:48:25,520 --> 00:48:26,520
that's interesting."

755
00:48:26,520 --> 00:48:27,720
That you decided to do that.

756
00:48:27,720 --> 00:48:30,280
But I know that it's there in advance.

757
00:48:30,280 --> 00:48:31,280
Okay.

758
00:48:31,280 --> 00:48:37,600
So, yeah, you've got this, you've got this effect that meets the criteria that you had set

759
00:48:37,600 --> 00:48:39,720
out in advance.

760
00:48:39,720 --> 00:48:45,840
And so, you've got the MMN and you're replicating that prior finding by Monahan and colleagues.

761
00:48:45,840 --> 00:48:46,840
Okay.

762
00:48:46,840 --> 00:48:53,040
And then finally, your critical manipulation of your novel tense contrast, what did you

763
00:48:53,040 --> 00:48:54,040
find for that one?

764
00:48:54,040 --> 00:48:57,840
Drum roll, like, we got MMN for that too.

765
00:48:57,840 --> 00:48:58,840
So, yeah.

766
00:48:58,840 --> 00:49:07,160
So, this is that like, give, pave, met, thank, sink, whatever, like somehow people, people's

767
00:49:07,160 --> 00:49:12,120
brains noticed when a past, when a present tense verb was thrown in among a bunch of past tense

768
00:49:12,120 --> 00:49:13,360
verbs.

769
00:49:13,360 --> 00:49:18,800
And there was a slight MMN there when they heard the present tense verb, which like again,

770
00:49:18,800 --> 00:49:21,560
it was a very weak MMN.

771
00:49:21,560 --> 00:49:27,200
Like, only, I can only live with myself calling it significant because we have that pre-registration

772
00:49:27,200 --> 00:49:30,040
otherwise, I don't know.

773
00:49:30,040 --> 00:49:32,680
But yeah, so we got this MMN.

774
00:49:32,680 --> 00:49:39,360
It was, I mean, actually, honestly, both this one and the phonological one were, they happened

775
00:49:39,360 --> 00:49:45,240
towards kind of the back of the head, which is not typically where you see MMNs come up.

776
00:49:45,240 --> 00:49:50,480
And they were also pretty late, like it was around 300-ish milliseconds that they were

777
00:49:50,480 --> 00:49:52,480
starting to happen.

778
00:49:52,480 --> 00:49:58,640
So, we got an effect, which was further back and later than MMNs tended to be, but it

779
00:49:58,640 --> 00:50:06,120
was elicited by the kind of contrast or by the kind of experiment design that is known

780
00:50:06,120 --> 00:50:07,920
to elicit MMNs.

781
00:50:07,920 --> 00:50:10,320
Yeah.

782
00:50:10,320 --> 00:50:11,320
Very cool.

783
00:50:11,320 --> 00:50:15,720
And I really think that the pre-registration was just so critical to enabling you to have

784
00:50:15,720 --> 00:50:19,000
that confidence.

785
00:50:19,000 --> 00:50:26,840
But as you said, the timing of the MMNs was a little unexpected, especially for the two

786
00:50:26,840 --> 00:50:29,600
more sort of speculative cases.

787
00:50:29,600 --> 00:50:34,040
And then you have this very nice plot in the paper that readers can listen, can look at

788
00:50:34,040 --> 00:50:39,920
if they want to, figure four, where you kind of show like which electrodes were significant

789
00:50:39,920 --> 00:50:42,600
when for the different conditions.

790
00:50:42,600 --> 00:50:47,120
And I think what's important is that the aspiration MMNs, so the basic, you know, the really

791
00:50:47,120 --> 00:50:53,520
kind of standard, easy one, kind of like everything's like significant about 200 milliseconds

792
00:50:53,520 --> 00:50:54,520
on, right?

793
00:50:54,520 --> 00:50:56,280
They start being significant.

794
00:50:56,280 --> 00:51:00,160
But for the other two, like for the voicing one, which is the second one we talked about,

795
00:51:00,160 --> 00:51:05,280
it really doesn't become significant until about 430 milliseconds.

796
00:51:05,280 --> 00:51:07,000
And then the tense one is actually a bit early than that.

797
00:51:07,000 --> 00:51:09,400
It's like about 330.

798
00:51:09,400 --> 00:51:12,960
So then later, and it's interesting because then you realize you go back and look at the

799
00:51:12,960 --> 00:51:17,040
plots and you realize, oh, yeah, that actual peak difference is not what's driving the

800
00:51:17,040 --> 00:51:21,320
significance for those more experimental conditions.

801
00:51:21,320 --> 00:51:27,240
It's really the later, like there's this sort of later longer lived, mushier negativity that

802
00:51:27,240 --> 00:51:29,320
actually, is driving the significance.

803
00:51:29,320 --> 00:51:31,800
So obviously you guys talk about all of them in the paper.

804
00:51:31,800 --> 00:51:37,360
I mean, how did that, how did that additional context like change how you felt about your

805
00:51:37,360 --> 00:51:39,360
results?

806
00:51:39,360 --> 00:51:40,360
Yeah.

807
00:51:40,360 --> 00:51:45,520
It was a little bit; it made us feel a little bit confused because once we were looking

808
00:51:45,520 --> 00:51:50,880
at them, our first reaction was like I just said, like so excited, yeah, we got an MMN.

809
00:51:50,880 --> 00:51:54,400
And then when we were looking at it more, we were like, wait, did we get an MMN? (Laughter)

810
00:51:54,400 --> 00:51:55,400
What is this?

811
00:51:55,400 --> 00:51:58,680
And then we thought, well, we had pre-registered this plan.

812
00:51:58,680 --> 00:52:01,760
If it comes out this way, we're going to call it an MMN.

813
00:52:01,760 --> 00:52:05,880
And then we thought in retrospect, maybe we should have said, like if all that happens

814
00:52:05,880 --> 00:52:10,840
and it's before 300, but we're like, well, that's not what we pre-registered and it wouldn't

815
00:52:10,840 --> 00:52:13,800
be fair to change our mind about things now.

816
00:52:13,800 --> 00:52:18,080
But we did have to have some thinking about is this really an MMN or is it something else

817
00:52:18,080 --> 00:52:23,880
happening later that we just happened to catch in our statistics?

818
00:52:23,880 --> 00:52:30,360
And we did end up, what we ended up arguing was that, well, we can nitpick about the timing

819
00:52:30,360 --> 00:52:37,840
and the place, but there's already existing research, including like, you know, Bornkessel-

820
00:52:37,840 --> 00:52:44,120
Schlesewsky’s model of like, predictive coding that argues that like, these same things might

821
00:52:44,120 --> 00:52:48,600
cause later or further back effects because it takes your brain longer to figure out the

822
00:52:48,600 --> 00:52:52,120
information that it needs to know this is a deviant.

823
00:52:52,120 --> 00:52:56,920
But we thought what's more important than the timing or the place is just the fact that

824
00:52:56,920 --> 00:53:01,880
this was elicited without people's attention because they, you know, we put them in a design

825
00:53:01,880 --> 00:53:05,560
where they're getting a bunch of past tense verbs and sometimes the present tense, we

826
00:53:05,560 --> 00:53:09,320
had them watch a nature documentary so they weren't paying attention to it.

827
00:53:09,320 --> 00:53:13,000
So, we figured like, that's really the hallmark of the MMN and more than anything else.

828
00:53:13,000 --> 00:53:16,880
It's like what, what kinds of things cause it to happen?

829
00:53:16,880 --> 00:53:20,480
And so here we felt that our is like, even if it's a little bit late and a little bit farther

830
00:53:20,480 --> 00:53:23,600
back, it's still caused by the things that are known to cause MMN.

831
00:53:23,600 --> 00:53:24,600
Right.

832
00:53:24,600 --> 00:53:25,600
Yeah.

833
00:53:25,600 --> 00:53:27,600
I mean, I, I, I see what you're saying.

834
00:53:27,600 --> 00:53:32,560
And I, I think that maybe the, thinking about the amount of processing that has to happen

835
00:53:32,560 --> 00:53:36,280
before you could legitimately get an MMN and it's, is maybe very key, right?

836
00:53:36,280 --> 00:53:41,080
Like, I mean, if you're just talking about like a really simple acoustic change, like, like

837
00:53:41,080 --> 00:53:46,760
hope, low and high tones, that information is going to make it to cortex in well less

838
00:53:46,760 --> 00:53:51,680
than a hundred milliseconds, at which point whatever changed detection mechanism can, can

839
00:53:51,680 --> 00:53:52,680
play out.

840
00:53:52,680 --> 00:54:00,440
But if you're talking like the syntactic one in particular, I mean, where's the, where's

841
00:54:00,440 --> 00:54:03,880
the disambiguation point of those words relative to other possible words?

842
00:54:03,880 --> 00:54:10,440
Like, at what point is that word able to be, you know, recognized as a present tense or

843
00:54:10,440 --> 00:54:12,200
past tense verb?

844
00:54:12,200 --> 00:54:14,560
It's certainly not at zero milliseconds.

845
00:54:14,560 --> 00:54:15,560
That's for sure.

846
00:54:15,560 --> 00:54:16,560
Right.

847
00:54:16,560 --> 00:54:17,840
It's, it's well into the syllable.

848
00:54:17,840 --> 00:54:22,800
And so is that inherently going to squish your MMNs later and, and probably spread them

849
00:54:22,800 --> 00:54:27,720
out a bit because it's probably not an identical disambiguation point across all your

850
00:54:27,720 --> 00:54:30,840
stimuli, even though its monosyllabic, right?

851
00:54:30,840 --> 00:54:31,840
Yeah.

852
00:54:31,840 --> 00:54:36,760
And so that's, I think, part of the reason why like our MMN for pah, pah, pah, bah, was really

853
00:54:36,760 --> 00:54:37,760
peaky.

854
00:54:37,760 --> 00:54:40,080
Like, there's a really sharp point where it happens.

855
00:54:40,080 --> 00:54:44,840
Whereas the ones for these other, they're kind of smeared out because, yeah, like, it's

856
00:54:44,840 --> 00:54:49,400
hard to know when exactly within that word, people are recognizing it.

857
00:54:49,400 --> 00:54:54,640
And so, in those situations, you get, you know, this is again, where I'm wanting to gesture,

858
00:54:54,640 --> 00:55:00,560
you get like a peak, a peak at 100 milliseconds and a peak at 120 and a peak at 130 and your

859
00:55:00,560 --> 00:55:06,320
average them together, they become a low smear across a long amount of time.

860
00:55:06,320 --> 00:55:11,400
But it's really hard to isolate when people are figuring that out.

861
00:55:11,400 --> 00:55:16,000
I had a, in a previous study, I tried like doing a, a gating task to figure out like, how

862
00:55:16,000 --> 00:55:20,400
many milliseconds into the word do people recognize which word it is?

863
00:55:20,400 --> 00:55:24,800
It's actually very, very hard and it varies across people.

864
00:55:24,800 --> 00:55:28,760
And even with this one, like, what we're really interested in is not when do they realize

865
00:55:28,760 --> 00:55:33,080
this is the word paved, but when do they realize this is a past tense verb?

866
00:55:33,080 --> 00:55:35,080
And that might be even different.

867
00:55:35,080 --> 00:55:39,800
So, we, that's another reason I think we really had to pre-register was because we were like,

868
00:55:39,800 --> 00:55:41,920
we don't have an easy solution to this problem.

869
00:55:41,920 --> 00:55:44,400
Let's just roll the dice and hope it works out.

870
00:55:44,400 --> 00:55:45,400
Yeah. (Laughter)

871
00:55:45,400 --> 00:55:47,400
So, we better pre-register it before we try that.

872
00:55:47,400 --> 00:55:51,840
Yeah. And you pre-registered, you basically said that you're going to look at the whole epoch

873
00:55:51,840 --> 00:55:58,600
and you just, and you described, I think, I think it was 700 milliseconds that you're going

874
00:55:58,600 --> 00:55:59,600
to look at.

875
00:55:59,600 --> 00:56:02,960
So yeah, you've kind of like committed yourself to that.

876
00:56:02,960 --> 00:56:07,720
And it's kind of fascinating, isn't it, that like even though you had a clear, even though

877
00:56:07,720 --> 00:56:12,080
you pre-registered and had a clear plan and you stuck to it, it still didn't really end

878
00:56:12,080 --> 00:56:14,520
up resolving the ambiguity of the data, right?

879
00:56:14,520 --> 00:56:17,600
The data, the data just never play nice, do they?

880
00:56:17,600 --> 00:56:22,760
They just never, they never really do what you wanted, even when you did everything right.

881
00:56:22,760 --> 00:56:23,760
No.

882
00:56:23,760 --> 00:56:27,240
That is so true.

883
00:56:27,240 --> 00:56:33,440
What did you think about this Bernard when the timing, when the timing was raised all these

884
00:56:33,440 --> 00:56:34,440
extra issues?

885
00:56:34,440 --> 00:56:41,240
Well, I'm just thinking about how to interpret them and sort of justify our interpretation.

886
00:56:41,240 --> 00:56:50,360
I think the negativity, especially in around the 400 millisecond mark is in the sort of

887
00:56:50,360 --> 00:56:52,600
central posterior area.

888
00:56:52,600 --> 00:56:58,880
If people don't know the context for this study, they might see this as an N400, but we know

889
00:56:58,880 --> 00:57:03,380
from several studies that N400 are modulated by attention.

890
00:57:03,380 --> 00:57:07,800
So, it doesn't really appear when people aren't paying attention.

891
00:57:07,800 --> 00:57:12,600
This is probably one of our sort of defense against it.

892
00:57:12,600 --> 00:57:21,640
We actually have another paper on this abstract mismatch negativity, and the negativity is also

893
00:57:21,640 --> 00:57:24,040
a bit later and coincides with the N400

894
00:57:24,040 --> 00:57:30,080
time window, and this is also again the way we interpret the component.

895
00:57:30,080 --> 00:57:34,760
Yeah, but you haven't manipulated attention, but I guess your people can't possibly be paying

896
00:57:34,760 --> 00:57:36,440
attention to this for three hours.

897
00:57:36,440 --> 00:57:43,720
I think you can just rely on human nature to guarantee that they were paying attention.

898
00:57:43,720 --> 00:57:50,040
That is one of my things I would like to do in the future is this and several other things

899
00:57:50,040 --> 00:57:54,520
that I've done with MMN where these questions have come up where I'm like, I'm not sure if

900
00:57:54,520 --> 00:57:59,320
this is actually MMN or if it's some other overlapping component that got smaller and

901
00:57:59,320 --> 00:58:01,680
made the MMM look bigger.

902
00:58:01,680 --> 00:58:05,640
That's something I've wanted to do is do some experiments where you do manipulate attention

903
00:58:05,640 --> 00:58:11,200
and have a block where they're told press the button every time you hear a past tense verb

904
00:58:11,200 --> 00:58:14,120
and another one where they're just watching a movie.

905
00:58:14,120 --> 00:58:18,040
Of course, the challenge is it took three and a half hours to do it this way.

906
00:58:18,040 --> 00:58:21,600
Am I going to get someone to do a seven-hour experiment where there's two blocks in

907
00:58:21,600 --> 00:58:22,600
it now?

908
00:58:22,600 --> 00:58:23,600
Yeah.

909
00:58:23,600 --> 00:58:27,520
So yeah, it's someday, hopefully I'll figure out a way to make it work, but that's my dream

910
00:58:27,520 --> 00:58:31,480
is to do these things and systematically manipulate attention.

911
00:58:31,480 --> 00:58:36,040
Yeah, but I think you'd have to do it in a way that didn't, that have to be a little bit

912
00:58:36,040 --> 00:58:37,920
orthogonal to the MMN itself, right?

913
00:58:37,920 --> 00:58:42,560
So, I don't think you could, if you probe them on the actual thing you care about, like that

914
00:58:42,560 --> 00:58:48,240
tense feature, I think that would really cause it to not even be in MMN generating situation.

915
00:58:48,240 --> 00:58:52,440
Like you probably would have to have them attend to a different aspect of the stimuli

916
00:58:52,440 --> 00:58:55,920
and throw away those trials and yeah, I'm guessing.

917
00:58:55,920 --> 00:59:01,480
Like every time it starts with an S or like one of these like P300 paradigms where they've

918
00:59:01,480 --> 00:59:04,240
got a standard deviant and a target.

919
00:59:04,240 --> 00:59:13,040
Yeah, so if we, leaving aside our quibbles about the timing and going with your conclusion

920
00:59:13,040 --> 00:59:19,280
that you've demonstrated MMNs for this very abstract contrast, what does that tell you

921
00:59:19,280 --> 00:59:22,160
about the MMN that we didn't know previously?

922
00:59:22,160 --> 00:59:26,640
How does this paper move the field forward?

923
00:59:26,640 --> 00:59:31,760
I mean, honestly, I'm not sure it tells us things we didn't know previously.

924
00:59:31,760 --> 00:59:37,160
The way I think of it is it tells us things that we had assumed but had not actually demonstrated.

925
00:59:37,160 --> 00:59:44,200
So, like if we had not gotten an MMN that would have been sad for me as a linguist, but

926
00:59:44,200 --> 00:59:48,640
it actually would have really changed what we know about the MMN because we've spent

927
00:59:48,640 --> 00:59:53,840
however, many decades thinking the MMN is sensitive to these abstract contrasts and that would

928
00:59:53,840 --> 00:59:56,600
have shown that it actually isn't.

929
00:59:56,600 --> 01:00:01,320
I think what this has shown us is that like the MMN is actually sensitive to the things

930
01:00:01,320 --> 01:00:07,520
that we've been saying it's sensitive to but haven't really proven and like now we've

931
01:00:07,520 --> 01:00:12,800
hopefully demonstrated that for at least one really abstract thing you really can get

932
01:00:12,800 --> 01:00:17,080
the MMN without needing like a physical correlate to it.

933
01:00:17,080 --> 01:00:24,760
So, it shows that the brain is like accessing these very abstract representations and generating

934
01:00:24,760 --> 01:00:32,280
categories based purely on abstract information even before you're paying attention to it.

935
01:00:32,280 --> 01:00:35,120
Yeah, great.

936
01:00:35,120 --> 01:00:36,120
Yeah, I think it's a step forward.

937
01:00:36,120 --> 01:00:42,000
I mean, I think there's a big difference between like actually demonstrating something in

938
01:00:42,000 --> 01:00:48,120
a really robust way versus just kind of thinking it because you had experimentally pointed

939
01:00:48,120 --> 01:00:53,720
in that direction that weren't really but had been open to other interpretations.

940
01:00:53,720 --> 01:00:57,400
And would you, what was it like when you went back with your data to the reviewers?

941
01:00:57,400 --> 01:01:01,600
Was it easy to get the final version published compared to how it might have been coming in

942
01:01:01,600 --> 01:01:02,600
fresh?

943
01:01:02,600 --> 01:01:10,800
I think one sort of possible issue was the fact that we could not fully follow our stage

944
01:01:10,800 --> 01:01:12,840
one.

945
01:01:12,840 --> 01:01:19,280
If we did follow, I think we ended up, if we follow everything to a P, including the exclusion

946
01:01:19,280 --> 01:01:26,000
criteria out of 70 something participants, I think we would end up with only 15 or 16

947
01:01:26,000 --> 01:01:28,960
because it was extreme restrict.

948
01:01:28,960 --> 01:01:39,720
So, we had to justify these changes and sort of mention why and also do separate sub analysis,

949
01:01:39,720 --> 01:01:47,160
including just these participants who would be included in our previously split criteria.

950
01:01:47,160 --> 01:01:51,200
But I think outside of that, I think it was quite straightforward.

951
01:01:51,200 --> 01:01:52,200
Don’t you think?

952
01:01:52,200 --> 01:01:59,520
Yeah, I think like, yeah, we had to have a little extra step of sort of get making actually

953
01:01:59,520 --> 01:02:02,400
even before we did the paper.

954
01:02:02,400 --> 01:02:06,040
I think we had checked with the editor and reviewers like, are you okay with us doing

955
01:02:06,040 --> 01:02:08,920
the analysis in this different way?

956
01:02:08,920 --> 01:02:14,280
And then, yeah, other than that, the second stage of review for a registered report is really

957
01:02:14,280 --> 01:02:19,200
painless because they're not judging, is it a good paper or anything?

958
01:02:19,200 --> 01:02:23,760
They're just judging, did you follow what you said you would follow and are your conclusions

959
01:02:23,760 --> 01:02:26,560
consistent with what you said they would be?

960
01:02:26,560 --> 01:02:31,600
And so, it was like, probably the most painless review process I've ever had.

961
01:02:31,600 --> 01:02:38,360
Thanks to all the previous work that had been put in at the original registration time.

962
01:02:38,360 --> 01:02:42,680
So, would you do it again, registered report?

963
01:02:42,680 --> 01:02:46,880
That's a good, we've talked, Bernard and I have talked about that a lot.

964
01:02:46,880 --> 01:02:51,080
So, there are a lot of benefits to it that we talked about.

965
01:02:51,080 --> 01:02:55,880
And we did, we have another one that I think is just about to come out.

966
01:02:55,880 --> 01:03:00,680
But we also felt like there are some situations, it's not for everything, right?

967
01:03:00,680 --> 01:03:03,400
Not every paper has to be a registered report.

968
01:03:03,400 --> 01:03:08,160
So, I remember Bernard, there were a few issues we had talked about for what were they

969
01:03:08,160 --> 01:03:11,440
like situations where it might actually not be the best thing?

970
01:03:11,440 --> 01:03:19,240
Yeah, I think for early career researchers, if you do stage one, it's a bit time consuming.

971
01:03:19,240 --> 01:03:25,080
For this paper, the stage one was admitted in 2021 and then we only got the paper out by

972
01:03:25,080 --> 01:03:26,880
mid-2024.

973
01:03:26,880 --> 01:03:32,840
So, it is quite a long process, though admittedly, a lot of that is us recruiting and testing

974
01:03:32,840 --> 01:03:39,920
participants, but still, it is, it takes much longer than a standard paper that you would

975
01:03:39,920 --> 01:03:41,200
do.

976
01:03:41,200 --> 01:03:49,240
And also, after you've done all this stage one work, which arguably could be more or might

977
01:03:49,240 --> 01:03:54,480
take more time than the stage two actually, you don't really have anything to show for

978
01:03:54,480 --> 01:03:58,240
it as a sort of early-stage researcher.

979
01:03:58,240 --> 01:04:02,400
So, you're just, you just have, sort of, end up with nothing.

980
01:04:02,400 --> 01:04:08,120
Although this is different from journal to journal, some journals do publish the stage one as registered

981
01:04:08,120 --> 01:04:10,880
report protocols.

982
01:04:10,880 --> 01:04:21,720
And also, I do feel as though for these registered reports, if it is something that is sort of well

983
01:04:21,720 --> 01:04:28,080
founded and quite straightforward, it might be worth just doing straight on without the stage

984
01:04:28,080 --> 01:04:29,080
one process.

985
01:04:29,080 --> 01:04:30,080
Yeah.

986
01:04:30,080 --> 01:04:35,320
And the other thing we had talked about was like, there, I think there's a lot of benefit

987
01:04:35,320 --> 01:04:40,640
to like register report for the kind of thing we did where we are like we have a really

988
01:04:40,640 --> 01:04:43,880
specific prediction and we want to see it.

989
01:04:43,880 --> 01:04:48,200
But sometime, like there's some research where you just need to be able to like chase the

990
01:04:48,200 --> 01:04:52,000
rabbit and not expecting where things are going to go.

991
01:04:52,000 --> 01:04:57,320
So, like my first MMN paper, which was with Kevin Schluter, like was kind of like that,

992
01:04:57,320 --> 01:05:02,000
we did an experiment trying to test one thing and this unrelated weird other thing happened

993
01:05:02,000 --> 01:05:03,920
and we were like, why did that happen? (Laughter)

994
01:05:03,920 --> 01:05:07,560
And so, we did three more experiments trying to figure out what happened.

995
01:05:07,560 --> 01:05:11,320
And it was like some of the most fun I've ever had doing science.

996
01:05:11,320 --> 01:05:15,240
And I like it couldn't really have been read pre-registered because it was like we were

997
01:05:15,240 --> 01:05:18,960
coming up with questions as each new set of data came out.

998
01:05:18,960 --> 01:05:23,880
So, I feel like it's got to be like a rich tapestry, like some research is really amenable

999
01:05:23,880 --> 01:05:29,160
to register reports, but there's also a lot of valuable to like doing exploration as things

1000
01:05:29,160 --> 01:05:30,480
are coming.

1001
01:05:30,480 --> 01:05:31,720
I definitely agree with that.

1002
01:05:31,720 --> 01:05:33,440
I think that we need both.

1003
01:05:33,440 --> 01:05:34,440
We need both.

1004
01:05:34,440 --> 01:05:37,400
Yeah, I'm definitely a rabbit chaser.

1005
01:05:37,400 --> 01:05:39,840
I don't, I rarely have hypotheses.

1006
01:05:39,840 --> 01:05:43,160
Like my whole life is just a fishing expedition.

1007
01:05:43,160 --> 01:05:45,440
And that's just the way I do science.

1008
01:05:45,440 --> 01:05:49,920
And like it definitely like, but rub some people the wrong way, but like, I don't know,

1009
01:05:49,920 --> 01:05:51,640
like there is room for all, right?

1010
01:05:51,640 --> 01:05:58,440
And I think especially when it comes to clinical applications, like I've started to be very

1011
01:05:58,440 --> 01:06:02,560
really feel strongly that like any clinical claims need to be pre-registered just because

1012
01:06:02,560 --> 01:06:06,200
the temptation to p-hack is just too great.

1013
01:06:06,200 --> 01:06:10,760
And if you know, if it's really, you know, going to apply to healthcare and you're making

1014
01:06:10,760 --> 01:06:15,680
healthcare decisions based on it, then I want to know that it was done in a really rigorous

1015
01:06:15,680 --> 01:06:16,680
way.

1016
01:06:16,680 --> 01:06:21,040
So, I think that there's, I think you make good points and there's really a whole landscape

1017
01:06:21,040 --> 01:06:22,800
of different kinds of studies.

1018
01:06:22,800 --> 01:06:30,400
Oh, I think one other thing that we wanted to share was also the fact that some editors and

1019
01:06:30,400 --> 01:06:37,720
reviewers might not be as familiar with the registered report process and on how to review

1020
01:06:37,720 --> 01:06:39,880
stage ones and stage two.

1021
01:06:39,880 --> 01:06:45,640
So, we had really good experience with Neurobiology of Language, but in other journals, this might

1022
01:06:45,640 --> 01:06:50,760
this might be sort of unfamiliar format and your stage two might be reviewed just like a traditional

1023
01:06:50,760 --> 01:06:51,760
paper.

1024
01:06:51,760 --> 01:06:57,120
So, after you report this, after your stage one is accepted, your stage two, you might still

1025
01:06:57,120 --> 01:07:03,680
get comments from reviewers on design, for example, which is probably not justified at that

1026
01:07:03,680 --> 01:07:04,680
stage.

1027
01:07:04,680 --> 01:07:12,000
So that is also another sort of challenge if you want to try your hand and do register reports.

1028
01:07:12,000 --> 01:07:14,480
Yeah, that would be very problematic.

1029
01:07:14,480 --> 01:07:16,800
I'm glad that you didn't have that experience here.

1030
01:07:16,800 --> 01:07:22,440
I mean, I think that because Neurobiology of Language is trying to be a more forward looking

1031
01:07:22,440 --> 01:07:27,520
journal, hopefully anybody that's going to be editing one of those would shut down any

1032
01:07:27,520 --> 01:07:32,000
reviewer that tried to go in that direction and you'd never even see that.

1033
01:07:32,000 --> 01:07:34,960
I would hope that you would never even see that review or comment, right?

1034
01:07:34,960 --> 01:07:40,200
It should be filtered out by the editor, but I'm glad it didn't go that way.

1035
01:07:40,200 --> 01:07:41,200
Yeah.

1036
01:07:41,200 --> 01:07:42,560
All right, great.

1037
01:07:42,560 --> 01:07:47,520
Well, thank you guys so much for taking the time to chat with me about this paper.

1038
01:07:47,520 --> 01:07:51,520
I really enjoyed reading it and I really enjoyed talking to you guys about it.

1039
01:07:51,520 --> 01:07:53,040
Thank you for having us.

1040
01:07:53,040 --> 01:07:54,040
Thanks for having us.

1041
01:07:54,040 --> 01:07:59,040
Yes, great experience to be on this and I listened to a lot of other great discussions

1042
01:07:59,040 --> 01:08:01,000
on this, so I'm glad we can be part of it.

1043
01:08:01,000 --> 01:08:02,000
Oh, thank you.

1044
01:08:02,000 --> 01:08:03,000
I'm glad you've enjoyed it.

1045
01:08:03,000 --> 01:08:04,000
All right.

1046
01:08:04,000 --> 01:08:05,000
Have a good rest of your day.

1047
01:08:05,000 --> 01:08:06,000
I'll see you guys later.

1048
01:08:06,000 --> 01:08:07,000
Thanks.

1049
01:08:07,000 --> 01:08:08,000
We'll see you.

1050
01:08:08,000 --> 01:08:09,000
Have a good one.

1051
01:08:09,000 --> 01:08:10,000
Bye-bye.

1052
01:08:10,000 --> 01:08:11,000
Bye.

1053
01:08:11,000 --> 01:08:13,000
That's it for episode 33.

1054
01:08:13,000 --> 01:08:16,720
Thanks very much, Steve and Bernard for joining me from Kansas and Hong Kong.

1055
01:08:16,720 --> 01:08:17,720
Very much appreciated.

1056
01:08:17,720 --> 01:08:21,600
I've linked to the paper we discussed in the show notes and on the podcast website at

1057
01:08:21,600 --> 01:08:24,560
langneurosci.org/podcast

1058
01:08:24,560 --> 01:08:28,600
Thanks, as always to Marcia Petyt for her tireless work on transcribing the podcast, which I really 

1059
01:08:28,600 --> 01:08:32,240
appreciate as it brings out discussions to a wider audience.

1060
01:08:32,240 --> 01:08:33,480
Bye for now and see you next time.