WEBVTT

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So I'd like to say thank you and welcome for coming to emotions.

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What are emotions and how we measure them?

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Uh I just want to point out we have some free

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guides on our website if you want more uh further readings.

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And we also have a uh certification for human behavior called iMotions Academy.

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Uh Your two speakers today will be Doctor Brendan

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Murray.

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He's our VP of client enablement services and Jessica Wilson,

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uh also a doctor in neuro uh in Neuroscience,

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senior product specialist at iMotions.

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And uh without further ado, I'll turn it over to our presenters.

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Great.

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Thank you so much Olivia and thank all of you

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so much for being here to attend the webinar today.

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Um As Olivia mentioned, we are going to be discussing

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what our emotions and how do we measure them? Uh

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Just some quick background on myself and my co

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presenter, Doctor Jessica Wilson.

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Uh I am the Vice President of Enablement Services at iMotions.

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Uh My background is I have a phd in cognitive psychology.

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Uh and I've been working in the Behavioral Science

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research space for about 16 years at this point.

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Uh Jessica. I'll let you go ahead and introduce yourself as well.

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Yeah, thanks, Brendan. Hi, everyone. Thank you so much for joining today.

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Uh My name is Jessica Wilson and I'm a senior product specialist here at iMotions.

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Uh My background is in neuroscience and physiology.

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So, a lifetime ago, I did research on sleep and Parkinson's disease and since then,

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have had the opportunity to help deploy these technologies

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um with iMotions clients all over the world.

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So I'm very excited to be here today and to

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partner up with Brendan on this super cool topic.

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Wonderful.

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So a as we jump in, uh this is a topic that's very near and dear to my heart. Uh back

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several years ago when I was still an academic,

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uh my research specialization was in understanding the interaction between

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human emotion and how it influenced uh memory.

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Uh So that specific set of cognitive processes,

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we all uh on this webinar probably have our own conception of

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what emotion is uh or what we mean by the word emotion.

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For many of us that probably calls to mind,

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imagery, imagery of things like romantic comedies, uh taking care of loved ones

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as emotion researchers,

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we use uh simultaneously both a much more specific and a much more broad

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definition of emotion at the same time uh when we use that word.

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So the first thing that

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is helpful to understand is this concept that

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emotion is really everywhere around us,

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it's not just specific to some of those examples that I mentioned up front.

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For example, when we are interacting with another person,

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whether that is uh face to face, whether it's virtually as we're doing right now,

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we take in and we process a lot of information all at once.

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One of the things that we process very quickly

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and virtually automatically is an individual's facial expression.

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This is an evolutionary response that we have as a collectivist species

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that rely on one another for our survival and well being,

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it's very important that we can quickly take a look at someone's face,

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try to make some kind of judgment about what they're thinking and feeling so

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that we can use that information to navigate our social interactions with them.

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And this is not just something that we do with humans.

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Uh As many of you are probably aware,

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we tend to detect faces everywhere and we also

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assign emotional value or emotional responses to those faces.

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It's very common that we'll take a look at our pet or we'll

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be uh encountering animals somewhere and we

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might think something to ourselves like,

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oh, look at the cute happy puppy

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or we see a car and we think to ourselves, oh, that car looks really angry.

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Or even if we're having,

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engaging in a digital communication with somebody and they're sending us emoji,

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we start automatically and very uh

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very quickly and robustly assigning emotional value

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to those emoji that they send us uh interpretations of things like,

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wow, they seem really surprised by whatever it is that I just said

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and this is not just specific and unique to faces. We also do this

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uh automatically with even very low level stimuli.

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So back in the day when I used to uh teach college courses on emotion neuroscience,

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one of the examples that I would like to do which Doctor Wilson,

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I'll ask you to be my guinea pig on this

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uh is to show folks a short video like the one that I'm about to show

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this

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is called the hider symbol uh illusion. And

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what uh what we'll do is I'm just gonna play a short clip of the video and Jessica,

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if you wouldn't mind,

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just kind of narrating what it seems to you like is actually happening in this video,

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please.

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Ok.

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So the red guy is trying to exit the box. Uh It's trapped though.

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Um The blue guy and the pink guy,

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it looks like they're trying to help him get out

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and blue guy is a lot more frantic about it.

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He's like moving around really quickly

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might be antagonizing. The red guy

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looks like they might be fighting and pink is hiding away.

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All right. So in the interest of time, I'll stop you there. That was, that was great.

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Thank you so much for that.

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Uh So you can do this uh with really any number of different people and

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you'll get lots of different stories about

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what these uh different characters are doing,

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what their motivations are, uh what they're trying to accomplish.

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Uh Some folks will think that the

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blue triangle and the pink circle are bullying the red triangle or the

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red triangle is being aggressive to them and they're trying to escape.

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And you come up with these very rich uh emotional stories

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which at the end of the day make absolutely no sense whatsoever because

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these are literally three amorphous shapes just moving around on a screen.

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It's two triangles in a circle.

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They do not have any motivation, they don't have any intent.

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There are no goals that they're trying to accomplish.

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And yet this is a very,

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very natural process for us to start coming up with these

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stories to try to assign emotional value to what we're seeing.

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And as I mentioned before, when we talked about faces,

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this is an evolutionary response.

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Our emotions can do things like help us rapidly detect threats if

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we're out hiking and suddenly a snake pops out of nowhere.

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We have a very quick emotional response to

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that as part of our self preservation instinct,

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uh we'll likely leave immediately.

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Um Maybe even more immediately depending on how afraid of snakes we are.

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And we do this with varying degrees of sensitivity.

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So those of you who may uh be more fearful or more aversive to snakes

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uh will look at this image and probably be able to spot the snake very quickly.

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Whereas others of you may have just noticed the leaves,

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you might have thought that that was a, that that was a branch or part of the foliage.

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And sometimes we do this a bit too sensitively.

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Uh So if there are any,

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uh if there are any folks who are watching and

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I apologize in advance if you're very snake averse.

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Um But you might quickly look at this image and think that

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that piece of straw in the middle is also a snake.

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Um So sometimes we can do this a bit too sensitively.

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Our emotions are the way that we assign labels of

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good and bad uh to things in our world.

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Uh We try a piece of cake and

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that cake makes us feel good and we like the way that it tastes.

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And so now we are motivated to have more of that cake both now and in the future,

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conversely,

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there might be a piece of stinky cheese that we have

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an aversive reaction to and we feel like it's bad and

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we're going to avoid it and we're not gonna want to

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eat foods that maybe uh contain that type of cheese.

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But we can also change our labels of good and bad over

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time uh based on the experiences that we have in our lives.

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So maybe over time, we actually acquire a taste for that stinky cheese.

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And then we start actively seeking it out and cooking with it.

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So all of this is to say that uh when we talk about emotion, again,

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we're not talking about those sort of very specific uh stereotypical

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uh cases of emotion that I mentioned upfront. Things like the romantic comedies.

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We're talking about a much more ubiquitous part of our daily life that helps guide

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uh our behavior in the moment,

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helps guide our behavior in the future and helps us navigate the world that we're in.

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So what exactly are emotions?

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Um I've talked about what those emotions can do for us so far,

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but we haven't quite yet defined what emotions are.

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And if there's one thing that you take

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away from the information in the webinar today,

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this is the piece of information that I I believe is the most important,

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which is that emotions are our way,

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our brain's way of tagging information as being relevant.

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We are constantly bombarded by stimuli in our environment.

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We need to in some way filter out a

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lot of that information that's not immediately important to us

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and have a way to kind of grab onto the things that are important for our survival,

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for our reward, for things that are goal oriented in terms of what we're trying to do.

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Uh And our emotions and our emotional responses are brain's way of making that

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relevance tag so that we know that there's something important to pay attention to.

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And as a very simplified schematic of what this looks like.

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You can imagine that there's some stimulus that we encounter.

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Uh let's say again that it's the snake while we're hiking,

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we have an emotional response in the moment.

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Uh For many of us, if we see a snake while we're hiking,

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that's going to be a fear response.

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Uh And we're going to be looking to remove ourselves from that situation quickly.

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And so in the moment, our behavior is likely going to change based on our emotions.

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But all of this doesn't happen in a vacuum.

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Our brain then actually stores that information so that it can

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help inform our future behavior and our future decision making.

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We're going to avoid that hiking trail in the future, for example,

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so that we can avoid the snakes.

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But also the next time we go hiking somewhere else,

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we're likely to be more vigilant uh for things

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like snakes or other threats while we're out hiking.

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So it's this feedback loop of having a response to something in the moment,

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using that information to guide how we're going to act in the moment

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and then storing that information about that response so that we can

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update or influence our future behavior.

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And these emotional responses that we have are measurable.

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Uh This is, these are not,

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uh this is not just a nebulous uh amorphous term that doesn't really mean

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anything or refers to some concept that we can't put a finger on,

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we can measure the responses that individuals are having

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in the moment to various things in their environment.

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Many theories of emotion, uh that are generally accepted,

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uh tend to operationalize emotion in two dimensions.

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The first being uh emotional arousal or the

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intensity of the response that we're having.

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Uh, you can imagine that depending on your degree of fearfulness for snakes.

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If you see a snake,

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you may have a very strong response to that or a very low level response.

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Uh Or if you're somebody who has a strong affinity for snakes,

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you can have a very intense response that is very positive as opposed to negative.

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And that's the second dimension of emotion

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that we typically think about uh emotional

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valence or the positivity or negativity that's

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associated with the state that we're in

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and these two dimensions up.

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Oh, yeah, please, Jessica,

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sorry to interrupt.

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Um I have a question that actually a lot

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of people have been asking what is the difference

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between emotions and feelings because I think there's a

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lot of overlap between those uh those concepts,

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but they are still a little bit different.

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Yeah, absolutely.

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So when we talk about emotions or emotional responses,

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we are typically talking about something that is right in a specific moment,

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it's the response or reaction

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that we have to a stimulus to a person to something that's in our environment.

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Uh They tend to be very rapid, they tend to be very fleeting

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and again,

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really serving that evolutionary purpose of helping us make a

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decision about what we're going to do right now.

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Uh Things like feelings or mood tend to be spread out over a longer period of time.

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So you can think about watching a television show.

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Uh You're watching your favorite comedy

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and that might put you in a good mood and a good

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state of feeling for the half hour that you're watching the episode.

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But throughout that episode,

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there are going to be specific moments that

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are particularly engaging or funny to you.

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Uh those jokes that really happen to land with you,

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those will be the moments that you're having these emotional responses.

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So easiest way to think about the difference is that emotions

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tend to be a rapid in the moment response to something.

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Whereas feelings, mood, uh uh other uh other similar terms to that,

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those tend to be more kind of states that we're in over a longer period of time.

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Thank you.

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Absolutely.

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So these two dimensions of emotion,

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uh intensity and positivity and negativity are,

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are generally thought of to operate relatively independently of one another.

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I gave the example of a snake before where

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if you're someone who's very fearful of snakes,

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if you see one, you will have a high intensity response, which is also negative.

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If you're someone who has a strong affinity for snakes,

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uh you may have a relatively high intensity but a positive response

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and we can take those two combination or

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the combination of those two dimensions of emotion.

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And we can think about them in this sort of uh two by two display. Uh This is in uh

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emotion literature referred to as the effect of circuplex.

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Um first introduced by Jim Russell uh in the 19 eighties

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with the idea being that any of these responses that we have

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contained some degree of intensity and some degree of positivity and negativity.

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And then by being able to understand those two dimensions,

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we can start to categorize different experiences,

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different things that we encounter as making us feel excited or

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sad or depressed or anxious or have uh having an aversive response

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and as an over,

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yes, please.

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I have a quick question about the circuplex model.

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Um I know this is a very popular way to conceptualize emotion.

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Um But is that sort of the,

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the de facto way that we can think about emotion or are

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there other models in the literature um out there right now,

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it's a really great question and I'll try not to

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take us down too much of a rabbit hole here.

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Uh So this is one of the prevailing and most popular theories of emotion.

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Um But it is one of several theories of emotion that exist. Um

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This uh circuplex model is a part of what is called psychological constructionism,

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which is the idea

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that uh emotional responses that we have are

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constructed from these combinations of intensity and positivity, negativity,

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uh things like excitement or happiness or distress or anxiety.

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Uh Those are not discreet sorts of states or feelings or uh responses that we have.

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But rather those are just kind of convenient verbal labels that we assign to how

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we're feeling based on the intensity and the

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positivity of the response that we're having.

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Uh the one of the other very popular theories

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of emotion is what's called basic emotion theory.

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And basic emotion theory does ascribe to the idea

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that emotions are discrete units uh that something like anger, for example,

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uh has a discrete and preset and automatic set of processes

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that trigger in the brain to give rise to anger.

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In other words, in basic emotion theory,

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those basic emotions, anger, sadness, happiness,

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those are the smallest building blocks of how we feel at a particular moment in time.

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Whereas with uh the circumflex model or psychological construction,

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uh the idea is that those emotions of something

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like anger are not the smallest building blocks,

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but that the smaller building blocks are

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these dimensions of intensity and positivity,

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negativity.

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This is a debate that's been existing in emotion research literature for

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uh literally decades at this point.

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Uh And there still continues to be disagreement

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over whether emotions are very discreet uh and sort

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of innate and prepackage or if they are something

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that arise from these combinations of uh intensity,

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intensity and positivity,

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uh much of the research that, that uh that I've done

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uh both in my academic career and in practice uh in business consulting,

301
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uh have

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00:16:05.510 --> 00:16:09.729
led me to have much more affinity for the circuplex model. Uh

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to give an example of anger again,

304
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if we think of anger as just a discrete

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unit, that kind of happens

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for me,

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that doesn't really fit very well with the fact that uh from one person to the next

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anger can feel very different to two different people or even within ourselves.

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My feeling of anger in one instance can be very

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different from my feeling of anger in a different instance.

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Uh if someone cuts me off on the highway,

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that feels very different from if I stub my toe when I'm walking around at night,

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if that makes sense.

314
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Yeah, that makes a ton of sense. And I think,

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I think it's such a good point to make that there are

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different models and ways to think about emotion out there because often

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when

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people who don't specialize in neuroscience or,

319
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or emotion or psychology want to sort of adapt these concepts to their practice,

320
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it's very easy to fall into a trap of like, oh, ok.

321
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Well, this is what it is according to

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science

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and

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science by nature, there's always going to be a measure of uncertainty,

325
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there's always going to be people arguing about the best way to think about things.

326
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So

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I think for everyone who wants to utilize

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these concepts within their own applications and industries,

329
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you know, be a little bit uncomfortable with that ambiguity and be comfortable,

330
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you know,

331
00:17:22.130 --> 00:17:24.630
with the fact that people are still trying to figure this

332
00:17:24.640 --> 00:17:27.750
out and that's in a way good for you because,

333
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you know, that ensures that what you're working with is sort of the most

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modern up to date,

335
00:17:31.579 --> 00:17:35.359
cutting edge sort of ideas out there that people are still trying to figure this out.

336
00:17:35.369 --> 00:17:38.880
Um So I think it's a really good point to make. Thank you, Brendan Abs

337
00:17:39.000 --> 00:17:40.349
absolutely excellent point.

338
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So

339
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I actually,

340
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I do actually have one other question. Um

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00:17:48.170 --> 00:17:51.469
And this pertains more to your, to your next slide. Uh Someone asked,

342
00:17:51.579 --> 00:17:55.109
is it possible to have multiple emotions at once?

343
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Um And is there a way to have like

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a specific emotion that might eliminate another emotion?

345
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So basically, if you're thinking about those conceptual building blocks,

346
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how might they interact with together?

347
00:18:06.430 --> 00:18:09.880
Absolutely. It's a really good question and I, I will pull up the next slide for that.

348
00:18:09.890 --> 00:18:13.010
Uh Because what I do on this slide is I do show some very

349
00:18:13.020 --> 00:18:17.030
simplified but discreet examples of uh times

350
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that may make us feel specific emotions.

351
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Um

352
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The question of whether or not we can feel two different emotions at once.

353
00:18:24.530 --> 00:18:27.709
Uh It is actually a very loaded question and also one that's

354
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still open um for folks who are familiar with the term ambivalence,

355
00:18:32.030 --> 00:18:35.500
uh that really if you break that word down means and the valence,

356
00:18:35.510 --> 00:18:39.140
you're feeling multiple things at once or having multiple emotions at once

357
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where there is discrepancy or disagreement is the degree to which

358
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those emotions coexist with one another at the exact same time

359
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versus uh having a state where we're actually rapidly

360
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switching back and forth between two different feelings.

361
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Uh If we think about, for folks who are familiar with blackjack, for example,

362
00:18:58.199 --> 00:18:59.670
uh casino card game,

363
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where uh all of the players at the table are playing against the house or the dealer,

364
00:19:05.359 --> 00:19:09.130
but each individual person's actions can influence whether other

365
00:19:09.140 --> 00:19:11.709
players at the table with them win or lose.

366
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And you can find yourself in a situation where

367
00:19:14.265 --> 00:19:16.444
it's right for you to make a certain play

368
00:19:16.675 --> 00:19:21.275
uh in the game of blackjack, that is best for you to maximize your winnings,

369
00:19:21.405 --> 00:19:23.494
but that actually comes at the expense of others.

370
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This is a great example of a time where you might feel mixed emotions or

371
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ambivalent or multiple different emotions at once where

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you're happy for yourself that you're winning.

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Uh But you may also feel some feelings of guilt or sadness for, for others around you.

374
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And it, it's,

375
00:19:38.930 --> 00:19:43.199
it's not a currently settled question as to the degree

376
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to which those things overlap with one another versus you.

377
00:19:47.510 --> 00:19:53.160
I if you were able to really get in tune with yourself at the millisecond level,

378
00:19:53.170 --> 00:19:56.474
is it that you're just going back and forth between multiple states very,

379
00:19:56.484 --> 00:19:57.484
very rapidly.

380
00:19:57.584 --> 00:20:02.074
So certainly, there are times where we can feel mixed emotions or feel ambivalence.

381
00:20:02.244 --> 00:20:03.005
Um The, the,

382
00:20:03.015 --> 00:20:07.435
the examples that are on this slider are much simpler and more discreet than that.

383
00:20:07.444 --> 00:20:09.364
But it's certainly something that's, that's possible.

384
00:20:09.375 --> 00:20:11.515
We can have multiple responses to something.

385
00:20:11.584 --> 00:20:14.474
It's just a question of the timing of when those responses happen.

386
00:20:20.760 --> 00:20:22.060
So perfect. Thank you.

387
00:20:22.910 --> 00:20:23.650
Absolutely.

388
00:20:23.660 --> 00:20:23.729
We,

389
00:20:23.739 --> 00:20:26.300
we've talked to this point about what some

390
00:20:26.310 --> 00:20:29.410
of the evolutionary utility of emotion is.

391
00:20:29.420 --> 00:20:31.569
Why do we have emotional responses to things?

392
00:20:31.579 --> 00:20:35.869
It's essentially to help guide our survival and allow us to navigate our world.

393
00:20:36.239 --> 00:20:39.239
We've talked about how we can potentially break down

394
00:20:39.380 --> 00:20:45.079
uh what emotion means into these discrete axes of intensity and positivity,

395
00:20:45.089 --> 00:20:45.770
negativity.

396
00:20:46.599 --> 00:20:47.859
But it's also important to understand that

397
00:20:47.869 --> 00:20:49.520
we can actually measure these things too.

398
00:20:49.530 --> 00:20:51.680
Again, these are not nebulous concepts.

399
00:20:51.689 --> 00:20:54.630
Uh These are things that we have the tools at

400
00:20:54.640 --> 00:20:56.939
our disposal to be able to measure in the moment.

401
00:20:56.949 --> 00:20:59.660
What is the response that someone is having to,

402
00:20:59.760 --> 00:21:02.459
whatever it is they're encountering or they're presented with

403
00:21:02.839 --> 00:21:05.540
something like galvanic skin response, for example,

404
00:21:05.599 --> 00:21:08.520
uh is a great measure of the intensity of an emotional

405
00:21:08.530 --> 00:21:10.859
response that someone's having at a given moment in time.

406
00:21:11.280 --> 00:21:14.160
Uh galvanic skin response for those who aren't familiar

407
00:21:14.410 --> 00:21:19.260
uh is a measure of the electrical conductivity on the surface of the skin

408
00:21:19.880 --> 00:21:21.839
when we encounter something in our environment

409
00:21:21.849 --> 00:21:23.989
that our brain tags as being relevant.

410
00:21:24.250 --> 00:21:28.140
There's a whole bunch of stuff that happens in, uh in the brain and in our body.

411
00:21:28.170 --> 00:21:31.319
One of the things that happens is that we slightly increase

412
00:21:31.329 --> 00:21:33.619
the amount of sweat that's on the surface of our skin

413
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that changes the electrical properties of our skin.

414
00:21:36.359 --> 00:21:40.099
And you can measure that very easily with uh essentially a simple circuit

415
00:21:40.109 --> 00:21:44.260
to understand when somebody had a response and how intense that response was.

416
00:21:45.430 --> 00:21:49.040
If we want to measure uh the positivity or negativity of

417
00:21:49.050 --> 00:21:51.319
the response that someone's having at a given moment in time,

418
00:21:51.520 --> 00:21:53.199
there are many tools that can do this.

419
00:21:53.209 --> 00:21:56.930
Uh facial expressions can have quite a bit of utility here. For understanding.

420
00:21:56.939 --> 00:22:00.689
Is somebody smiling at a given moment in time? Are they making an angry face?

421
00:22:00.699 --> 00:22:02.969
Are they frowning? Are they looking disgusted?

422
00:22:03.410 --> 00:22:04.810
It doesn't give us perfect,

423
00:22:04.819 --> 00:22:07.430
complete information about what they're actually feeling.

424
00:22:07.439 --> 00:22:09.670
It just tells us what they're displaying on their face.

425
00:22:09.869 --> 00:22:13.150
But again, we talked about the social utility of facial expressions

426
00:22:13.319 --> 00:22:16.510
and that can give us important information about what someone is

427
00:22:16.520 --> 00:22:18.670
feeling or the response that they're having in the moment.

428
00:22:19.510 --> 00:22:20.209
And of course,

429
00:22:20.219 --> 00:22:22.420
many measures of traditional support can help us

430
00:22:22.430 --> 00:22:24.770
get at that uh that dimension of positivity,

431
00:22:24.780 --> 00:22:28.130
negativity asking people how much did you like X?

432
00:22:28.160 --> 00:22:32.050
Uh how likely would you be to do A B or C again in the future?

433
00:22:32.500 --> 00:22:34.869
So these are measurable dimensions of emotion

434
00:22:35.260 --> 00:22:38.280
and that becomes very important because once we can measure

435
00:22:38.319 --> 00:22:40.430
the emotional responses that people are having to things,

436
00:22:40.439 --> 00:22:43.410
we can actually use that to improve our research to be able

437
00:22:43.420 --> 00:22:46.250
to understand and answer the questions that we're looking to answer.

438
00:22:47.959 --> 00:22:48.199
Now,

439
00:22:48.209 --> 00:22:51.310
I think it's worth pointing out here that these are

440
00:22:51.319 --> 00:22:53.989
the most common tools for measuring valance and intensity,

441
00:22:54.000 --> 00:22:56.800
but there are many circumstances or use cases.

442
00:22:56.949 --> 00:22:59.180
Um It depends a lot on context both on the

443
00:22:59.189 --> 00:23:02.010
stimuli that you're using and also the physical setup.

444
00:23:02.020 --> 00:23:03.369
So for example, um you know,

445
00:23:03.380 --> 00:23:06.489
we do a lot of work with virtual reality and it's very hard

446
00:23:06.500 --> 00:23:09.459
to get facial expressions when there's a giant headset in the way.

447
00:23:09.510 --> 00:23:10.660
Um So maybe Brendan,

448
00:23:10.670 --> 00:23:12.790
can you talk a little bit as to sort of the different use

449
00:23:12.800 --> 00:23:16.530
cases and how the tools you might use might differ in those circumstances?

450
00:23:16.939 --> 00:23:20.880
Yeah, absolutely. So something like facial expressivity, for example,

451
00:23:21.170 --> 00:23:24.170
as you would expect you need full view of someone's face

452
00:23:24.180 --> 00:23:27.719
to know what facial expression they're currently displaying at the time.

453
00:23:27.729 --> 00:23:31.959
Uh in a case like a virtual reality headset, as you mentioned, Jessica,

454
00:23:32.380 --> 00:23:35.339
most of your uh most of a respondent's face or a

455
00:23:35.349 --> 00:23:37.599
person's face is going to be obscured by that headset.

456
00:23:37.609 --> 00:23:42.349
And so facial expression analysis may not work uh may not work. Uh In those cases,

457
00:23:42.599 --> 00:23:43.750
this can be a place where you could

458
00:23:43.760 --> 00:23:46.750
leverage asking questions via something like self report

459
00:23:47.150 --> 00:23:49.400
depending on the question that you're trying to answer,

460
00:23:49.410 --> 00:23:52.150
you may also be able to use someone's behavior,

461
00:23:52.160 --> 00:23:54.439
uh how they're acting or engaging with the

462
00:23:54.449 --> 00:23:58.229
virtual environment that uh that they're currently immersed in

463
00:23:58.410 --> 00:24:02.410
to be able to understand how good or bad they're feeling in a particular moment.

464
00:24:02.719 --> 00:24:05.775
Um If they're going through uh and uh

465
00:24:05.994 --> 00:24:09.305
an exercise like a reward based game, for example,

466
00:24:09.535 --> 00:24:11.604
they gravitating more and more towards certain

467
00:24:11.614 --> 00:24:13.665
actions or certain parts of that game

468
00:24:13.795 --> 00:24:16.305
where they're experiencing increasing reward.

469
00:24:16.314 --> 00:24:17.665
Uh That's something that we know is

470
00:24:17.675 --> 00:24:20.854
tied very closely to people's subjective feelings

471
00:24:20.864 --> 00:24:24.265
of how good or bad something was is whether or not they felt rewarded

472
00:24:25.290 --> 00:24:26.609
in other circumstances.

473
00:24:27.239 --> 00:24:29.609
If you want to understand something like

474
00:24:29.619 --> 00:24:32.709
a cognitive behavioral approach versus avoidance motivation,

475
00:24:32.959 --> 00:24:35.819
it's not a pure valence or positive, negative measure.

476
00:24:35.930 --> 00:24:38.689
But that may be the dimension that you care the most about.

477
00:24:38.699 --> 00:24:40.959
Are people more motivated to approach or

478
00:24:40.969 --> 00:24:42.869
avoid something that you're showing to them?

479
00:24:42.880 --> 00:24:45.359
In which case using eeg

480
00:24:45.540 --> 00:24:49.000
in a controlled lab based environment may also be an appropriate measure to

481
00:24:49.010 --> 00:24:52.949
try to get at that uh at that uh axis of positivity,

482
00:24:52.959 --> 00:24:53.619
negativity.

483
00:24:53.880 --> 00:24:57.709
So a lot of it is uh Jessica you mentioned really comes down to both your,

484
00:24:57.719 --> 00:24:58.650
your research and

485
00:24:59.810 --> 00:24:59.819
uh

486
00:25:01.359 --> 00:25:01.430
uh

487
00:25:02.449 --> 00:25:03.500
you're trying to answer

488
00:25:03.760 --> 00:25:06.469
and just having a good understanding of what

489
00:25:06.479 --> 00:25:08.260
would make sense for that horizontal access.

490
00:25:08.270 --> 00:25:10.609
Is it good versus bad? Is it

491
00:25:10.709 --> 00:25:11.790
purchase intent?

492
00:25:11.800 --> 00:25:16.109
Is it uh approach versus avoidance and building your research program?

493
00:25:16.119 --> 00:25:19.810
Uh around that rather than trying to retrofit something, uh

494
00:25:19.910 --> 00:25:24.810
retrofit something into these sorts of presupposed measures.

495
00:25:25.859 --> 00:25:26.849
Thank you for that.

496
00:25:26.859 --> 00:25:30.930
Um There is uh one question that um that has been asked quite often.

497
00:25:31.140 --> 00:25:34.349
So you talk about using biosensor approaches with self

498
00:25:34.479 --> 00:25:35.170
report

499
00:25:35.290 --> 00:25:37.650
um because in a way they do complement each other,

500
00:25:37.660 --> 00:25:40.880
but you might have circumstances where the biometrics are telling

501
00:25:40.890 --> 00:25:43.689
you something different from what they're saying in the survey.

502
00:25:43.949 --> 00:25:46.650
How do you reconcile those sorts of discrepancies?

503
00:25:47.630 --> 00:25:51.060
Uh Those are my favorite results to see because I think that those are the

504
00:25:51.069 --> 00:25:54.890
most interesting uh even though they can be the most work to try to interpret.

505
00:25:55.050 --> 00:26:00.150
So a couple of things to keep in mind uh and we'll cover this in a few slides from now.

506
00:26:00.160 --> 00:26:01.349
Uh this sort of

507
00:26:01.530 --> 00:26:03.089
perceived discrepancy

508
00:26:03.270 --> 00:26:04.050
between

509
00:26:04.260 --> 00:26:09.699
uh biosensor and self reported research. Um and why it's not truly a discrepancy,

510
00:26:09.989 --> 00:26:13.609
but keep in mind first that you're measuring people at different points in time.

511
00:26:14.099 --> 00:26:16.319
Uh Any type of biosensor research,

512
00:26:16.329 --> 00:26:18.630
you're capturing responses from someone in the

513
00:26:18.640 --> 00:26:21.079
moment when they're having an experience.

514
00:26:21.180 --> 00:26:23.439
Whereas measures of self report,

515
00:26:23.449 --> 00:26:27.040
even if you're stopping people during an experience and asking them questions,

516
00:26:27.329 --> 00:26:31.640
that's still after the fact after they had had an experience. And so at that point,

517
00:26:32.060 --> 00:26:36.359
they may not remember all of the things that they felt like they had a reaction to

518
00:26:36.560 --> 00:26:38.949
uh they may minimize in their minds.

519
00:26:38.959 --> 00:26:42.319
The importance of some of the things that they really liked or disliked.

520
00:26:42.439 --> 00:26:43.189
Um,

521
00:26:43.199 --> 00:26:46.699
they may be biased based on the way that you're phrasing the

522
00:26:46.709 --> 00:26:50.119
question or the fact that it's a person asking them a question,

523
00:26:51.109 --> 00:26:54.939
self report after the fact can still give you exceedingly important information

524
00:26:54.949 --> 00:26:58.900
about what people remember from their experience and what the takeaway was.

525
00:26:59.199 --> 00:27:02.719
I usually describe this in two different ways. The first being that

526
00:27:02.890 --> 00:27:05.910
uh biosensor or neuroscience research can do a really

527
00:27:05.920 --> 00:27:09.099
good job of answering what and how questions.

528
00:27:09.109 --> 00:27:11.109
So what do people respond to?

529
00:27:11.119 --> 00:27:13.869
How intense are the responses that they're having, et cetera?

530
00:27:14.300 --> 00:27:18.609
Whereas many self report methodologies are good at answering why questions,

531
00:27:18.780 --> 00:27:24.430
why do people feel like they uh preferred video A over video B?

532
00:27:24.479 --> 00:27:28.810
Why do people think they would be more likely to purchase product A versus product B?

533
00:27:29.489 --> 00:27:35.310
And I usually break that down into thinking that uh the biosensor or neuroscience

534
00:27:35.319 --> 00:27:40.339
results are a good indicator of what that specific person and people like them

535
00:27:40.469 --> 00:27:45.020
uh are likely to do or behave for themselves. And self report can be a really good

536
00:27:45.140 --> 00:27:47.780
indicator of advocacy in the future.

537
00:27:47.969 --> 00:27:52.780
So if people remember something and they share feelings with you through self

538
00:27:52.890 --> 00:27:54.089
reported methodologies,

539
00:27:54.270 --> 00:27:59.219
they're more likely to then take that information back to loved ones, friends,

540
00:27:59.229 --> 00:28:00.719
acquaintances, et cetera.

541
00:28:01.020 --> 00:28:03.069
So when we see those again,

542
00:28:03.079 --> 00:28:07.239
perceived discrepancies between biosensor uh results and self

543
00:28:07.459 --> 00:28:08.300
reported results

544
00:28:08.760 --> 00:28:11.530
that gives you important information on the time

545
00:28:11.540 --> 00:28:13.380
course of the experience that someone had,

546
00:28:13.390 --> 00:28:15.030
how did they respond in the moment

547
00:28:15.329 --> 00:28:16.900
through the biosensor research?

548
00:28:17.020 --> 00:28:21.349
And then what information did they retain from that experience that they had,

549
00:28:21.359 --> 00:28:23.719
which you're capturing through self reported methods?

550
00:28:24.739 --> 00:28:27.060
I think that's such an important distinction to make uh

551
00:28:27.069 --> 00:28:30.170
especially as people become more interested in these tools.

552
00:28:30.239 --> 00:28:34.420
Uh Another common trap to get into is to think that this is going to be the end all,

553
00:28:34.430 --> 00:28:37.949
be all for explaining everything where whereas for each tool,

554
00:28:37.959 --> 00:28:39.609
there's definite restrictions

555
00:28:39.770 --> 00:28:42.439
um to how you can think about and interpret, you know,

556
00:28:42.449 --> 00:28:44.035
the results that come from that, right?

557
00:28:44.045 --> 00:28:44.704
So I think,

558
00:28:44.824 --> 00:28:48.655
you know, really developing that holistic view and using those multiple sensors,

559
00:28:48.665 --> 00:28:52.314
you know, really gives you that nice complementary approach to account for,

560
00:28:52.425 --> 00:28:52.685
you know,

561
00:28:52.694 --> 00:28:57.415
all of these different restrictions with like timeline and physiology versus

562
00:28:57.425 --> 00:28:59.775
the way we think about and want to communicate about things.

563
00:28:59.785 --> 00:29:01.854
So I think this is a really great way to think about that.

564
00:29:03.099 --> 00:29:04.199
Yeah. A absolutely.

565
00:29:04.209 --> 00:29:07.829
And I think that leads nicely into the next section

566
00:29:07.839 --> 00:29:09.319
that we wanted to talk about in the webinar,

567
00:29:09.329 --> 00:29:12.040
which is what can we actually do with emotion measurement?

568
00:29:12.099 --> 00:29:15.000
Um And exactly to your point, Jessica,

569
00:29:15.430 --> 00:29:18.500
uh a lot of uh a lot of research,

570
00:29:18.510 --> 00:29:22.920
particularly commercial based uh neuroscience or Biosensor research that I see

571
00:29:23.349 --> 00:29:27.339
presupposes uh this syllogism that I have on the screen here,

572
00:29:27.349 --> 00:29:29.310
which is that people encounter something.

573
00:29:29.339 --> 00:29:31.989
Uh And there's a set of biologically based

574
00:29:32.000 --> 00:29:34.599
processes or emotional responses that they have to,

575
00:29:34.609 --> 00:29:34.939
that

576
00:29:35.439 --> 00:29:36.819
as we've discussed before,

577
00:29:37.199 --> 00:29:40.079
we have the tools to measure those emotional responses.

578
00:29:40.089 --> 00:29:42.540
And if we can measure their emotional responses,

579
00:29:42.550 --> 00:29:44.979
then we can predict their future behavior.

580
00:29:45.300 --> 00:29:48.040
Um That tends to be the end goal for a lot of folks is

581
00:29:48.050 --> 00:29:50.380
how do I predict what people are going to do in the future?

582
00:29:51.900 --> 00:29:55.310
The challenge with this line of thinking is that

583
00:29:55.319 --> 00:29:57.319
it doesn't take into account the fact that yes,

584
00:29:57.329 --> 00:30:00.699
we do have emotional responses to many different things in the moment

585
00:30:00.709 --> 00:30:03.770
and those things will guide our future behavior as we talked about.

586
00:30:04.199 --> 00:30:06.780
But there's also a whole bunch of other stuff that influences the

587
00:30:06.790 --> 00:30:09.400
way that we're going to act at any given moment in time.

588
00:30:09.410 --> 00:30:13.699
Uh The context that we're in the people that we're with uh time of day,

589
00:30:13.709 --> 00:30:15.500
what we had for breakfast.

590
00:30:15.510 --> 00:30:19.020
Um And whether we've got an upset stomach from it or not,

591
00:30:19.079 --> 00:30:22.739
there are all of these different factors that we can't really measure.

592
00:30:22.750 --> 00:30:26.520
Um And if we tried to, we would just be completely overfitting our research,

593
00:30:26.750 --> 00:30:30.410
um or we would just have no way to, to possibly measure.

594
00:30:30.689 --> 00:30:32.989
So there are so many other things besides our

595
00:30:33.000 --> 00:30:35.660
in the moment responses that can influence our behavior,

596
00:30:36.089 --> 00:30:37.979
that this is not the correct way.

597
00:30:38.170 --> 00:30:41.920
Uh In most cases to think about the types of research that you're doing,

598
00:30:42.180 --> 00:30:45.989
it's not about measuring people to predict what they're going to do in the future.

599
00:30:46.560 --> 00:30:47.140
Instead,

600
00:30:47.150 --> 00:30:49.599
the model that I prefer kind of flips that around

601
00:30:49.609 --> 00:30:52.239
a little bit and is a bit more backward looking.

602
00:30:52.459 --> 00:30:53.229
So

603
00:30:53.650 --> 00:30:55.310
taking observed behavior,

604
00:30:55.319 --> 00:30:58.310
we know that for whatever our research practice

605
00:30:58.319 --> 00:31:00.869
is that there is some way that people behave

606
00:31:00.880 --> 00:31:05.310
in the world and what we are trying to do is explain as best we can,

607
00:31:06.209 --> 00:31:10.859
why they behave the way that they did. What is it that was driving their behavior?

608
00:31:11.239 --> 00:31:15.150
And I typically think of this uh from the frame of mind of

609
00:31:15.369 --> 00:31:18.270
uh there's a lot of variability in how people act.

610
00:31:18.829 --> 00:31:21.150
So uh if we think about product purchases,

611
00:31:21.160 --> 00:31:23.530
some people are gonna buy a specific product,

612
00:31:23.920 --> 00:31:25.800
other people are not going to buy that product

613
00:31:25.810 --> 00:31:27.670
at all of the people that buy that product.

614
00:31:27.680 --> 00:31:31.290
Maybe they're gonna buy a lot of it this week and none of it for the next three weeks,

615
00:31:31.410 --> 00:31:33.910
maybe there are gonna be some people that are gonna buy it every single day.

616
00:31:34.349 --> 00:31:37.770
There's a lot of variability in whatever the behavior is that we care about.

617
00:31:38.119 --> 00:31:41.229
And what we are trying to do is explain as much of that

618
00:31:41.239 --> 00:31:46.390
variability as possible to understand why people are behaving the way that they do

619
00:31:47.959 --> 00:31:50.449
and where this becomes really important to Jessica's point

620
00:31:50.459 --> 00:31:53.579
from both a research design and interpretation standpoint is that

621
00:31:53.939 --> 00:31:57.719
no tool is meant to be a perfect measure of

622
00:31:57.910 --> 00:32:00.180
understanding why people act the way that they do.

623
00:32:00.369 --> 00:32:00.890
Instead,

624
00:32:00.900 --> 00:32:05.719
we're just trying to take out chunks of uh explanatory power so

625
00:32:05.729 --> 00:32:08.900
that we can get the best understanding possible of how people act.

626
00:32:09.359 --> 00:32:11.699
So let's take a product example. Um

627
00:32:12.000 --> 00:32:15.069
Maybe uh I work for a company that is launching a new

628
00:32:15.079 --> 00:32:18.780
product and I wanna know how it's being received by people.

629
00:32:19.130 --> 00:32:20.780
Well, some of the questions that I might

630
00:32:21.000 --> 00:32:23.619
uh I might expect my consumers to be asking

631
00:32:23.630 --> 00:32:25.670
themselves when they see this new product is,

632
00:32:25.739 --> 00:32:28.020
did it motivate me to act in some way?

633
00:32:28.050 --> 00:32:29.500
Uh Do I see this product?

634
00:32:29.510 --> 00:32:34.300
And it's, I'm feeling motivated to approach it or to get it or to get more of it?

635
00:32:35.280 --> 00:32:37.439
Did it connect with me on an emotional level?

636
00:32:37.449 --> 00:32:41.010
Uh Did it elicit that relevance response that we talked about before?

637
00:32:41.550 --> 00:32:42.680
Is it memorable?

638
00:32:42.689 --> 00:32:45.859
Um Is it something again, as I said, uh as I said before,

639
00:32:45.869 --> 00:32:49.969
that I'm likely to remember later that maybe I'll advocate for to others about

640
00:32:50.869 --> 00:32:53.770
and even did I notice whatever the product is to begin with?

641
00:32:53.780 --> 00:32:56.589
Um perception is hugely important because if we don't

642
00:32:56.599 --> 00:33:00.189
actually perceive whatever the thing is that's being measured,

643
00:33:00.260 --> 00:33:01.930
we're not going to have a response to it.

644
00:33:01.939 --> 00:33:03.650
Uh That's the way the perception works.

645
00:33:04.439 --> 00:33:06.060
Uh Did it make me smile?

646
00:33:06.069 --> 00:33:09.310
And then of course, there to Jessica's point before,

647
00:33:09.319 --> 00:33:11.810
there are always going to be a whole host

648
00:33:11.819 --> 00:33:14.790
of unexplained factors that we can't control for that,

649
00:33:14.800 --> 00:33:16.640
we can never hope to measure all of them,

650
00:33:16.650 --> 00:33:20.699
which are also going to drive the way that people pe that people behave and act.

651
00:33:21.339 --> 00:33:24.880
And the reason that I I highlight uh these specific questions

652
00:33:25.010 --> 00:33:27.670
is that these are ones that we have tools to measure

653
00:33:27.969 --> 00:33:31.170
uh something like electroencephalography as I mentioned before.

654
00:33:31.180 --> 00:33:33.739
Uh under the right lab based circumstances can give you a

655
00:33:33.750 --> 00:33:36.469
really good indication of cognitive behavioral

656
00:33:36.479 --> 00:33:38.709
motivation to approach or avoid something

657
00:33:39.160 --> 00:33:40.300
galvanic skin response,

658
00:33:40.310 --> 00:33:44.510
which we've talked about several times is a great measure of emotional relevance.

659
00:33:44.520 --> 00:33:47.300
Or if something connected with me on an emotional level,

660
00:33:48.099 --> 00:33:48.380
self

661
00:33:48.489 --> 00:33:51.680
report, great measure of memorability and, and other things.

662
00:33:51.869 --> 00:33:54.319
Uh if we want to know if people notice something,

663
00:33:54.329 --> 00:33:57.550
eye tracking is a great uh is a great place to go to if

664
00:33:57.560 --> 00:33:58.479
we want to know if somebody was

665
00:33:58.489 --> 00:34:01.380
smiling or frowning using facial expression analysis.

666
00:34:01.520 --> 00:34:02.959
And then, of course, as I've said,

667
00:34:02.969 --> 00:34:06.319
there's always gonna be some chunk of people's behavior that is just

668
00:34:06.329 --> 00:34:09.879
driven by things that we'll just never hope to be able to measure

669
00:34:11.300 --> 00:34:11.918
Brandon.

670
00:34:11.929 --> 00:34:14.320
I, I really love this slide just because, you know,

671
00:34:14.330 --> 00:34:17.679
breaking it down into how each of these different tools

672
00:34:17.688 --> 00:34:20.629
can account for the variance uh within our behaviors.

673
00:34:20.639 --> 00:34:21.790
Uh I think, you know,

674
00:34:21.800 --> 00:34:23.668
one of the trends that I'm seeing a lot uh

675
00:34:23.679 --> 00:34:26.699
in different industries is this interest in emotion A I,

676
00:34:26.800 --> 00:34:29.000
this is of course coined by our, our partners at F

677
00:34:29.120 --> 00:34:31.560
activa who've been really been a driving force in this area.

678
00:34:31.699 --> 00:34:32.458
Um But you know,

679
00:34:32.469 --> 00:34:34.110
this concept where people are trying to use

680
00:34:34.120 --> 00:34:37.679
machine learning to create predictive models of emotion.

681
00:34:37.889 --> 00:34:40.790
Um I was wondering if you could speak a little bit to your experience there.

682
00:34:41.540 --> 00:34:43.879
Yeah, absolutely. Uh So

683
00:34:44.060 --> 00:34:46.610
to take it at its, at its most base level,

684
00:34:46.620 --> 00:34:50.668
I think the underlying question is whether or not that's possible, right?

685
00:34:50.679 --> 00:34:52.080
Is it possible to

686
00:34:52.320 --> 00:34:54.469
uh from these measures of emotion,

687
00:34:54.478 --> 00:34:59.040
be able to create a machine learning or A I models that can then uh

688
00:34:59.050 --> 00:35:04.399
do a good job of predicting how people would respond to XY or Z.

689
00:35:04.719 --> 00:35:08.120
And I think the short answer to that is yes, certainly.

690
00:35:08.129 --> 00:35:11.500
These are, these are measurable responses that people have

691
00:35:11.719 --> 00:35:16.860
and it becomes a question of scope and scale of data and what you're trying to answer

692
00:35:17.239 --> 00:35:19.989
uh to be able to train a machine learning

693
00:35:20.000 --> 00:35:24.100
model on uh on emotion or understanding human emotion.

694
00:35:24.270 --> 00:35:27.139
You need tons and tons of data from many different

695
00:35:27.149 --> 00:35:30.419
examples of many different people in many different environments.

696
00:35:30.689 --> 00:35:33.110
And that can be as broad or as constrained

697
00:35:33.120 --> 00:35:35.030
as you want depending on what you're interested in.

698
00:35:35.350 --> 00:35:39.090
If you want to know uh the degree to which people

699
00:35:39.100 --> 00:35:41.949
uh feel really good when they wake up in the morning.

700
00:35:41.959 --> 00:35:44.239
If they've got a certain color of light in the room

701
00:35:44.250 --> 00:35:47.350
versus uh a a different color of light in the room,

702
00:35:47.820 --> 00:35:51.889
that's something that over time, you could collect enough data from enough people.

703
00:35:51.899 --> 00:35:54.389
If you had enough access to those participants,

704
00:35:54.399 --> 00:35:56.830
uh If you had the tools to be able to do that measurement

705
00:35:57.189 --> 00:35:59.179
and start to build a model of OK.

706
00:35:59.189 --> 00:36:01.800
Here's how people will most likely respond to

707
00:36:01.810 --> 00:36:04.770
different colors within that one specific environment.

708
00:36:05.199 --> 00:36:06.959
If you want to broaden that out to something

709
00:36:06.969 --> 00:36:09.300
like how do people respond to color in general?

710
00:36:09.590 --> 00:36:10.300
That's a much,

711
00:36:10.310 --> 00:36:16.510
much more broad uh research question that again is not impossible but would be very,

712
00:36:16.520 --> 00:36:19.939
very difficult to be able to get enough instances.

713
00:36:19.949 --> 00:36:24.639
Uh um uh of those emotional responses from people to

714
00:36:24.649 --> 00:36:27.010
be able to train a reliable machine learning model.

715
00:36:27.209 --> 00:36:32.419
So certainly something that's possible uh as the ubiquity of self measurement uh

716
00:36:32.429 --> 00:36:37.659
continues to increase in consumer devices that people uh that people own,

717
00:36:37.679 --> 00:36:40.889
this will become easier and easier to gather those large data sets.

718
00:36:41.060 --> 00:36:43.899
Um But right now, it's something that, you know, fit

719
00:36:44.060 --> 00:36:46.669
a for example, has the scope and the scale to do for what,

720
00:36:46.679 --> 00:36:49.149
for what uh for the things that they're interested in.

721
00:36:49.449 --> 00:36:53.110
Um But it's hard to do without a lot of access to a lot of data.

722
00:36:54.800 --> 00:36:55.780
Yeah, I think

723
00:36:55.919 --> 00:36:59.719
I'm really excited to see where this sort of field goes because I mean,

724
00:36:59.729 --> 00:37:01.449
we're seeing it in the market research space,

725
00:37:01.459 --> 00:37:04.709
we're seeing it in the automotive space, definitely in the academic space.

726
00:37:04.719 --> 00:37:09.580
There's so many different applications, health care for sure. Um So yeah, I mean,

727
00:37:09.969 --> 00:37:12.239
like Brendan said there's some caveats about, you know,

728
00:37:12.250 --> 00:37:14.350
what kind of data are you gonna be looking for?

729
00:37:14.360 --> 00:37:16.179
What are your specific metrics?

730
00:37:16.189 --> 00:37:20.080
I think that's so important, like really refine what your variables are and then,

731
00:37:20.090 --> 00:37:23.340
you know, find some way to find your ground truth to really validate those.

732
00:37:23.350 --> 00:37:23.649
But

733
00:37:23.780 --> 00:37:26.270
yeah, I think it's a super cool application.

734
00:37:26.280 --> 00:37:29.429
Uh And FX T has been driving a lot of that, it would be cool to see where this goes.

735
00:37:31.030 --> 00:37:31.850
Absolutely.

736
00:37:33.449 --> 00:37:38.379
So to, to move on a bit just being mindful of, of the time that we have here.

737
00:37:38.389 --> 00:37:41.050
Uh One of the questions that came up earlier was

738
00:37:41.060 --> 00:37:44.360
trying to understand this uh perceived discrepancy between in the

739
00:37:44.370 --> 00:37:47.280
moment measures that you would get from neuroscience or biosensors

740
00:37:47.290 --> 00:37:50.129
uh relative to what people can say after the fact

741
00:37:50.314 --> 00:37:54.094
and experience. And typically this is operationalized as

742
00:37:54.324 --> 00:37:55.405
uh some folks call it.

743
00:37:55.415 --> 00:37:59.885
The can't say, won't say challenge, which is that if you ask people questions,

744
00:38:00.135 --> 00:38:03.405
there are lots of thoughts and feelings and responses that they

745
00:38:03.415 --> 00:38:06.965
can verbalize that they're willing to tell you in the moment.

746
00:38:07.225 --> 00:38:09.264
And then there are also thoughts, feelings, beliefs,

747
00:38:09.274 --> 00:38:13.764
et cetera that they uh that they're not willing or able to verbalize for you.

748
00:38:14.080 --> 00:38:17.459
Um They may withhold information when you're

749
00:38:17.469 --> 00:38:19.239
asking them questions about a product because

750
00:38:19.250 --> 00:38:23.379
they might think that their answer or their feelings are taboo in some way

751
00:38:23.560 --> 00:38:27.209
or they don't want to hurt your feelings or they minimize their own opinion.

752
00:38:27.399 --> 00:38:29.030
The kind of, oh,

753
00:38:29.040 --> 00:38:30.969
I'm not gonna share that because it's probably

754
00:38:30.979 --> 00:38:32.550
stupid and nobody else feels that way,

755
00:38:32.560 --> 00:38:33.620
sort of phenomenon.

756
00:38:33.659 --> 00:38:34.219
Um,

757
00:38:34.270 --> 00:38:37.389
so there are things that people can tell you and then there are things that they are

758
00:38:37.399 --> 00:38:40.550
either unwilling or unable to tell you because they

759
00:38:40.560 --> 00:38:42.479
have thoughts and feelings that they're unaware of.

760
00:38:42.830 --> 00:38:46.040
And then there are also biases that people have, uh,

761
00:38:46.050 --> 00:38:51.719
which are sort of a base level uh set of feelings or beliefs that people hold

762
00:38:51.729 --> 00:38:54.250
about any number of different things that will

763
00:38:54.260 --> 00:38:56.969
in small and large ways drive their behavior.

764
00:38:57.909 --> 00:38:59.239
When we think about this,

765
00:38:59.250 --> 00:39:02.790
uh these two dimensions of measuring people

766
00:39:02.800 --> 00:39:05.330
in the moment through neuroscience or biosensors,

767
00:39:05.340 --> 00:39:07.729
uh and measuring them after the fact through some sort of self

768
00:39:07.830 --> 00:39:08.600
reported measure,

769
00:39:08.949 --> 00:39:12.179
what we're really talking about is uh what's discussed

770
00:39:12.189 --> 00:39:14.919
in cognitive science as a dual process theory,

771
00:39:15.389 --> 00:39:20.489
dual process theory uh is uh uh in it, in its most simple form.

772
00:39:20.500 --> 00:39:25.370
This idea that uh if information comes in, we encounter something,

773
00:39:25.379 --> 00:39:27.889
we see an advertisement, we see an image

774
00:39:28.100 --> 00:39:29.270
uh that,

775
00:39:29.669 --> 00:39:30.199
that

776
00:39:30.360 --> 00:39:33.959
information that we take in can go through one path in our brain

777
00:39:34.260 --> 00:39:37.449
or it can go through another path and those paths may

778
00:39:37.459 --> 00:39:40.229
or may not be separate and distinct from one another.

779
00:39:40.260 --> 00:39:44.129
But both of them can influence the behavior that we take after the fact.

780
00:39:44.770 --> 00:39:47.820
And one of the most probably popular and well known

781
00:39:48.139 --> 00:39:49.139
uh

782
00:39:49.709 --> 00:39:52.939
uh references to dual process theory is this

783
00:39:52.949 --> 00:39:55.139
idea of type one versus type two thinking.

784
00:39:55.149 --> 00:39:56.939
Um For the folks who are familiar

785
00:39:57.060 --> 00:40:00.540
uh type one thinking or cognition is uh often thought of

786
00:40:00.550 --> 00:40:05.139
or called things like automatic and effortless and emotional and fast.

787
00:40:05.679 --> 00:40:09.280
And type two is discussed as being more controlled or effortful.

788
00:40:09.350 --> 00:40:14.500
Uh it's slow, it's more non emotional um or more sort of cognitive in nature.

789
00:40:15.639 --> 00:40:16.899
Uh More popularly,

790
00:40:16.909 --> 00:40:22.989
these have been referred to over the past uh eight or 10 years or so as system one

791
00:40:23.000 --> 00:40:26.310
versus system two thinking system one being that intuitive

792
00:40:26.370 --> 00:40:29.260
uh system and system two being the more rational,

793
00:40:29.270 --> 00:40:31.020
rational or reasoned system.

794
00:40:31.939 --> 00:40:34.389
Uh We'd had a quick poll uh that was in the

795
00:40:34.399 --> 00:40:38.239
webinar for how many people are familiar with Dan Conman's book,

796
00:40:38.250 --> 00:40:39.439
Thinking Fast and Slow.

797
00:40:39.810 --> 00:40:43.879
Uh And I was also curious to know how many folks uh who are familiar

798
00:40:44.280 --> 00:40:46.379
uh with the book have actually read it.

799
00:40:46.389 --> 00:40:49.199
So if you can just take a 2nd 1st and just answer,

800
00:40:49.350 --> 00:40:51.199
have you heard of Thinking Fast and Slow by Dan

801
00:40:51.459 --> 00:40:53.110
Conne? And we'll give a few seconds on that.

802
00:40:56.830 --> 00:41:01.919
All right. So it looks like we've gotten almost even two thirds split.

803
00:41:01.929 --> 00:41:04.929
So two thirds of the folks are familiar with thinking fast and slow.

804
00:41:04.989 --> 00:41:08.689
Uh And about a third of the attendees uh are saying, no, they're not familiar with it

805
00:41:10.350 --> 00:41:13.929
for those of you who uh

806
00:41:14.110 --> 00:41:18.649
who answered that? Yes, you're familiar with it. Um How many of you have actually,

807
00:41:19.189 --> 00:41:22.270
uh, have actually read the book thinking fast and slow.

808
00:41:22.280 --> 00:41:24.610
We'll give a few seconds, uh, on that one as well.

809
00:41:24.770 --> 00:41:27.209
So, if you've heard of it, have you actually read this book?

810
00:41:32.560 --> 00:41:35.100
All right. And we're seeing, uh, about the same.

811
00:41:35.110 --> 00:41:36.580
So about two thirds of folks have,

812
00:41:36.590 --> 00:41:39.330
have said that they have actually read Thinking Fast and slow and

813
00:41:39.340 --> 00:41:41.820
about a third are saying that they haven't at this point.

814
00:41:43.879 --> 00:41:46.860
So the reason that I asked that question is because I, I,

815
00:41:46.870 --> 00:41:48.439
when I do these presentations,

816
00:41:48.449 --> 00:41:50.389
I like to make a couple of what seem

817
00:41:50.399 --> 00:41:54.629
at first to be relatively um controversial statements.

818
00:41:54.639 --> 00:41:58.929
Uh Given the popularity of this system, one versus system two distinction.

819
00:41:59.780 --> 00:42:01.489
The first being that

820
00:42:01.620 --> 00:42:04.600
system one and system two are fictitious characters.

821
00:42:04.610 --> 00:42:07.600
These are not systems that actually exist uh within the brain.

822
00:42:08.649 --> 00:42:11.850
The second being that they're not even systems at all um that

823
00:42:11.860 --> 00:42:14.949
they're not entities that have interacting aspects or parts to them.

824
00:42:15.679 --> 00:42:19.530
And the really important one, there's no part of your brain that is the system,

825
00:42:19.540 --> 00:42:21.629
one part of your brain, there's no part of your brain.

826
00:42:21.639 --> 00:42:23.209
That's the system two part of your brain.

827
00:42:23.239 --> 00:42:25.830
Um And so this system one versus system

828
00:42:25.840 --> 00:42:29.159
two is actually a really meaningless distinction.

829
00:42:29.429 --> 00:42:31.179
And the reason that I say that

830
00:42:31.489 --> 00:42:34.459
uh that I say that these statements might sound controversial is

831
00:42:34.469 --> 00:42:38.580
Daniel Conman is a Nobel Prize winning economist and behavioral scientist.

832
00:42:38.590 --> 00:42:40.379
Uh And I myself am not.

833
00:42:40.800 --> 00:42:43.780
Um But these are not my thoughts or opinions.

834
00:42:43.790 --> 00:42:46.350
These are actually things that Dan Kahneman himself says

835
00:42:46.459 --> 00:42:49.989
at the very beginning of the book, uh on one of the first pages,

836
00:42:50.010 --> 00:42:52.540
these are the statements that he makes is look,

837
00:42:52.550 --> 00:42:55.260
these are not actually systems that exist.

838
00:42:55.270 --> 00:43:00.199
Uh I'm writing a book here. I'm trying to get across a complex cognitive principle.

839
00:43:00.310 --> 00:43:04.879
Um I need to have characters for my book, but this is not actually how the brain works.

840
00:43:05.389 --> 00:43:07.919
The reason I like to bring this up is that this tends to get

841
00:43:07.929 --> 00:43:09.899
glossed over quite frequently when people are

842
00:43:09.909 --> 00:43:12.330
thinking about how they approach the research.

843
00:43:12.340 --> 00:43:14.250
Uh I see this very frequently.

844
00:43:14.379 --> 00:43:18.320
Um even today where folks are concerned about whether or not

845
00:43:18.330 --> 00:43:21.489
their research is geared at measuring system one versus system two.

846
00:43:22.020 --> 00:43:23.510
And the challenge with that is that

847
00:43:23.870 --> 00:43:28.469
it's a relatively meaningless distinction about whether you're measuring

848
00:43:28.479 --> 00:43:31.439
system on system two because those systems don't exist,

849
00:43:31.610 --> 00:43:34.889
it's all kind of one flow of cognitive process.

850
00:43:35.300 --> 00:43:39.020
And so the model that I tend to try to push people more towards,

851
00:43:39.030 --> 00:43:41.729
to update their thinking and change the way that they're

852
00:43:41.739 --> 00:43:45.879
potentially designing their research is more of a process flow.

853
00:43:45.889 --> 00:43:45.949
So

854
00:43:46.050 --> 00:43:48.159
starting with context, which I mentioned earlier,

855
00:43:48.250 --> 00:43:51.600
context is very important for understanding how people are going

856
00:43:51.610 --> 00:43:54.510
to respond and behave to any number of stimuli.

857
00:43:54.590 --> 00:43:55.239
Um

858
00:43:55.590 --> 00:43:59.030
uh This is context is frequently self selected by people.

859
00:43:59.040 --> 00:44:01.439
We decide what situations to put ourselves in.

860
00:44:03.479 --> 00:44:06.110
And then once we know what the context is that people are in,

861
00:44:06.330 --> 00:44:07.870
uh we want to measure a perception.

862
00:44:07.879 --> 00:44:11.620
So whatever it is that we care about measuring people's responses to,

863
00:44:11.639 --> 00:44:14.260
we first need to know, did they actually perceive it?

864
00:44:14.270 --> 00:44:15.639
Um Did they see it?

865
00:44:15.649 --> 00:44:18.780
Did they hear whatever it is that we're, that we're interested in measuring

866
00:44:20.389 --> 00:44:23.989
after that perception occurs, then there is a process uh which is

867
00:44:24.139 --> 00:44:26.030
referred to as cognitive appraisal.

868
00:44:26.260 --> 00:44:29.469
This is pretty rapid and automatic. It's a cascade of

869
00:44:29.790 --> 00:44:32.590
implicit explicit automatic responses,

870
00:44:32.610 --> 00:44:34.649
seeing the snake while you're out hiking

871
00:44:34.659 --> 00:44:37.530
and having that either fear based response.

872
00:44:37.540 --> 00:44:40.449
Or again, if you're someone who really likes snakes, maybe you're excited to,

873
00:44:40.459 --> 00:44:42.850
to see the snake, but all that happens very,

874
00:44:42.860 --> 00:44:47.530
very quickly to help inform and guide how we're going to act right in that moment.

875
00:44:49.120 --> 00:44:51.879
And then the last step is one that's relatively uniquely human,

876
00:44:51.889 --> 00:44:54.979
which is emotion regulation or self regulation.

877
00:44:55.239 --> 00:44:57.800
Uh We are not totally beholden to whatever

878
00:44:57.810 --> 00:44:59.939
that initial appraisal response is that we have

879
00:45:00.169 --> 00:45:04.830
uh we can update that response, we can amplify it in situations that call for it.

880
00:45:05.100 --> 00:45:07.610
Uh or we can suppress it or reframe it

881
00:45:07.760 --> 00:45:11.139
and actually change the way that we're thinking about the situation that we're in.

882
00:45:13.090 --> 00:45:15.649
And I like this process flow because it highlights the fact

883
00:45:15.659 --> 00:45:18.379
that all of these steps feed back into one another.

884
00:45:18.389 --> 00:45:22.379
Um So the appraisal that we have of something that in the moment,

885
00:45:22.389 --> 00:45:23.389
emotional response,

886
00:45:23.399 --> 00:45:25.120
that automatic response that happens when we

887
00:45:25.129 --> 00:45:28.000
encounter something can actually influence our perception.

888
00:45:28.310 --> 00:45:30.729
If we have an emotional response to something,

889
00:45:30.750 --> 00:45:33.620
we're actually more likely to see more of that in

890
00:45:33.629 --> 00:45:36.169
our environment over a short period of time afterwards.

891
00:45:37.479 --> 00:45:39.729
Our appraisal can also influence our context.

892
00:45:39.739 --> 00:45:41.590
If we start to learn over time,

893
00:45:41.600 --> 00:45:45.419
that one particular hiking trail is not a great hiking trail for us,

894
00:45:45.429 --> 00:45:48.949
we'll start to update the context that we're self selecting for ourselves.

895
00:45:48.959 --> 00:45:52.729
And that in turn is then going to affect what we perceive in our new environment,

896
00:45:52.739 --> 00:45:55.129
how we're responding to those things in our new environment.

897
00:45:56.000 --> 00:45:58.899
And our regulation can also affect all of these steps.

898
00:45:58.909 --> 00:46:01.659
Our emotion regulation or self regulation can influence

899
00:46:01.669 --> 00:46:04.179
the future appraisal responses that we have.

900
00:46:04.250 --> 00:46:06.510
Our regulation can influence our perception.

901
00:46:06.520 --> 00:46:09.540
And it can also have an influence on the context that we put ourselves in.

902
00:46:12.090 --> 00:46:15.510
And the reason that I like this is because it nicely packages how we can

903
00:46:15.520 --> 00:46:17.290
measure each of these different steps to

904
00:46:17.300 --> 00:46:19.669
get the best understanding of human behavior.

905
00:46:19.679 --> 00:46:21.870
So context, we can measure through observed behavior,

906
00:46:21.879 --> 00:46:24.280
just seeing what people are doing and where they're going.

907
00:46:24.770 --> 00:46:27.330
Perception can be measured through something like eye tracking.

908
00:46:27.340 --> 00:46:27.909
For example,

909
00:46:27.919 --> 00:46:31.129
if we're interested in visual or physical things

910
00:46:31.139 --> 00:46:32.709
to know if people notice them or not,

911
00:46:33.449 --> 00:46:35.629
those appraisal responses, we can measure through

912
00:46:35.949 --> 00:46:39.540
skin response, electroencephalography, facial expressions, et cetera

913
00:46:40.030 --> 00:46:44.310
and all of those tools can also be used to measure those self-regulation processes.

914
00:46:44.320 --> 00:46:47.469
But this is also an important step where self report comes in as well

915
00:46:47.479 --> 00:46:52.389
because our self-regulation is going to heavily influence what we have to say about

916
00:46:52.520 --> 00:46:56.229
an experience about an object, whatever else after we encounter it.

917
00:46:58.949 --> 00:47:01.360
So I'm gonna wrap up quickly just with a very short

918
00:47:01.370 --> 00:47:04.659
case example of what emotion measurement looks like in practice.

919
00:47:04.669 --> 00:47:07.639
Uh So this is a case example that uh I've presented

920
00:47:07.649 --> 00:47:10.840
on several times over the years uh from a study that

921
00:47:11.030 --> 00:47:14.250
uh iMotions have done in conjunction with Activision Blizzard Media,

922
00:47:14.270 --> 00:47:18.669
uh where the approach that was taken by Activision was more

923
00:47:18.679 --> 00:47:21.800
of this process flow of a motion model that that I discussed

924
00:47:22.239 --> 00:47:27.620
uh Activision for those who aren't familiar is a major global uh gaming company.

925
00:47:27.899 --> 00:47:31.820
And the specific team that we were working with wanted to understand

926
00:47:31.989 --> 00:47:34.040
advertising within mobile games.

927
00:47:34.050 --> 00:47:37.620
And specifically what is the impact of tying some kind of reward

928
00:47:37.629 --> 00:47:41.409
to an advertising experience while people are playing a mobile game.

929
00:47:41.800 --> 00:47:42.820
So for example,

930
00:47:43.169 --> 00:47:44.419
you're playing candy crush,

931
00:47:44.429 --> 00:47:48.540
uh you encounter an ad in the middle of your candy crush experience.

932
00:47:48.889 --> 00:47:53.169
How is it different if you are actually given a reward for watching that ad,

933
00:47:53.179 --> 00:47:54.879
something like you get to skill level,

934
00:47:54.889 --> 00:48:00.669
you get an extra life uh versus an experience in another game or in social media or

935
00:48:00.679 --> 00:48:02.770
on some other digital platform where there's no

936
00:48:02.780 --> 00:48:06.590
inherent reward that's tied to watching the advertisement.

937
00:48:07.979 --> 00:48:09.439
Uh So Activision's hypothesis is,

938
00:48:09.449 --> 00:48:12.489
as you would expect was that reward based advertising would be more

939
00:48:12.500 --> 00:48:16.280
likely to get attention and reduce a version um from users.

940
00:48:16.290 --> 00:48:18.760
Um And might be less likely to detract from

941
00:48:18.770 --> 00:48:21.840
just the overall emotional experience of playing the game.

942
00:48:22.239 --> 00:48:25.120
Uh They tested folks on a variety of different platforms,

943
00:48:25.129 --> 00:48:30.179
um Some with reward based uh gaming ads and other platforms

944
00:48:30.189 --> 00:48:32.729
where there was no reward that was tied to the advertising.

945
00:48:33.110 --> 00:48:33.760
Um

946
00:48:34.429 --> 00:48:38.560
And what they found was that first, if we think about perception,

947
00:48:38.909 --> 00:48:44.959
those premium or reward based ads were viewed for longer than non reward based ads.

948
00:48:44.969 --> 00:48:45.580
Um

949
00:48:45.699 --> 00:48:49.719
And we know that uh all of us are pretty advertising averse.

950
00:48:49.729 --> 00:48:52.719
So to be able to create an environment where people are

951
00:48:52.729 --> 00:48:55.919
more likely to watch ads for more than twice as long

952
00:48:56.030 --> 00:48:58.770
when there's some reward that's tied to them versus not,

953
00:48:58.780 --> 00:49:00.439
is really impactful and powerful

954
00:49:01.929 --> 00:49:05.330
in terms of the emotional response to both the ads themselves

955
00:49:05.340 --> 00:49:08.659
and to the platform that people were experiencing the ads on

956
00:49:08.830 --> 00:49:12.909
what we saw was that individuals who received reward based advertising

957
00:49:13.070 --> 00:49:17.600
uh were more likely to in the moment experience uh positive and more

958
00:49:17.610 --> 00:49:22.780
intense um uh emotions than folks who were not receiving reward based ads

959
00:49:23.179 --> 00:49:25.840
and also the experience of the platform itself.

960
00:49:25.850 --> 00:49:29.780
So physically playing the game, physically using the social media feed,

961
00:49:29.810 --> 00:49:33.360
that was also more emotionally evocative for folks when the

962
00:49:33.370 --> 00:49:35.959
ads had some kind of inherent reward tied to them.

963
00:49:37.479 --> 00:49:40.600
And then in terms of self regulation, what you might expect is that,

964
00:49:40.610 --> 00:49:43.219
uh because people are watching ads for longer,

965
00:49:43.379 --> 00:49:44.570
they may after the fact,

966
00:49:44.580 --> 00:49:48.800
perceive that ad experience is having been more intrusive because after all,

967
00:49:48.810 --> 00:49:51.979
they've spent a greater percentage of their experience time watching

968
00:49:51.989 --> 00:49:55.010
advertising than people who didn't get reward based ads.

969
00:49:55.469 --> 00:49:59.050
Uh But we actually saw the opposite after the fact where people said,

970
00:49:59.149 --> 00:49:59.860
when we were asked,

971
00:49:59.870 --> 00:50:03.899
how intrusive was the ad experience if they received reward based ads?

972
00:50:04.100 --> 00:50:09.129
Uh they were uh much less likely to indicate that they felt that the

973
00:50:09.139 --> 00:50:13.610
ads were intrusive relative to the folks who did not receive reward based ads.

974
00:50:14.429 --> 00:50:15.530
And this was really,

975
00:50:15.540 --> 00:50:20.639
really impactful for uh Activision's uh ad strategy because you have

976
00:50:20.649 --> 00:50:24.399
created an environment where people are having a more enjoyable experience,

977
00:50:24.409 --> 00:50:27.429
they're watching advertising for longer and their perception

978
00:50:27.439 --> 00:50:29.219
after the fact is that the ad experience was

979
00:50:29.229 --> 00:50:31.739
less intrusive for them uh when there was

980
00:50:31.750 --> 00:50:34.449
some kind of reward tied to that advertisement.

981
00:50:37.350 --> 00:50:39.800
So I'll wrap up here with just a quick little plug.

982
00:50:39.810 --> 00:50:43.310
Uh Obviously, uh we who are presenting today.

983
00:50:43.320 --> 00:50:45.050
Uh We all work at I Motions which is

984
00:50:45.060 --> 00:50:47.929
a software company that provides a software platform to

985
00:50:47.939 --> 00:50:50.350
allow you to be able to take the different

986
00:50:50.360 --> 00:50:52.489
types of measures that we've talked about today.

987
00:50:52.820 --> 00:50:55.489
Um We have a quick poll that's up right now about

988
00:50:55.500 --> 00:50:58.040
uh ways in which uh you would like for us to

989
00:50:58.050 --> 00:51:00.939
get in contact with you if you're interested in learning more

990
00:51:01.010 --> 00:51:04.500
about how you can utilize the I motion software platform to

991
00:51:04.709 --> 00:51:08.159
either bring biosensors into the research that you're thinking about doing.

992
00:51:08.360 --> 00:51:12.270
Uh if there are opportunities to consider other biosensors besides ones

993
00:51:12.280 --> 00:51:14.879
or in addition to ones that you may currently be using.

994
00:51:15.300 --> 00:51:18.949
And then we also have a suite of uh support services.

995
00:51:18.959 --> 00:51:23.159
Um We're resellers of the hardware um to be able to

996
00:51:23.169 --> 00:51:25.479
get you doing the best research that you possibly can.

997
00:51:26.120 --> 00:51:30.669
So, thank all of you so much for your time. I really appreciate everybody being here.

998
00:51:30.679 --> 00:51:33.840
Uh and Olivia all it back over to you to wrap us up a bit.

999
00:51:35.610 --> 00:51:39.979
OK? I'll just give a couple more seconds for people to vote. Um

1000
00:51:40.409 --> 00:51:44.020
Thank you guys for attending just so, you know, we do have handouts.

1001
00:51:44.350 --> 00:51:49.580
Uh If you look on the right corner of your screen, there's a little paper icon,

1002
00:51:49.870 --> 00:51:51.479
there's some handouts for you to download.

1003
00:51:51.489 --> 00:51:58.070
And of course, uh we have free uh guides on our website I motions.com/guides,

1004
00:51:58.379 --> 00:52:01.870
uh where there's other um biosensor data and

1005
00:52:01.879 --> 00:52:04.310
also research and also how to write grants.

1006
00:52:04.320 --> 00:52:06.780
For example, if you have for the academics out there,

1007
00:52:07.159 --> 00:52:11.689
a lot of great um free resources uh in our knowledge library.

1008
00:52:13.679 --> 00:52:16.419
Great. We're getting a lot of votes in. Thank you very much.

1009
00:52:16.429 --> 00:52:17.909
I'm gonna close the polls soon

1010
00:52:19.820 --> 00:52:21.870
and we're gonna be taking just one or two

1011
00:52:21.879 --> 00:52:25.020
more questions and whatever questions we don't get to

1012
00:52:25.370 --> 00:52:29.739
please feel free to email us at marketing at I motions.com.

1013
00:52:33.270 --> 00:52:36.800
There are actually a ton of questions which is very exciting.

1014
00:52:37.510 --> 00:52:38.010
Yeah.

1015
00:52:38.139 --> 00:52:41.729
Um Let me lead off with the one of this is,

1016
00:52:41.750 --> 00:52:46.040
is negative emotion weighted heavier than positive

1017
00:52:46.969 --> 00:52:49.429
emotion during an analysis of a task.

1018
00:52:51.739 --> 00:52:52.469
Hm

1019
00:52:55.909 --> 00:52:57.010
Brendan. I think you're muted.

1020
00:52:58.610 --> 00:52:59.790
Sorry. Can you hear me now?

1021
00:53:00.959 --> 00:53:02.399
Yes, perfect.

1022
00:53:02.639 --> 00:53:06.219
Um I it's a really great question and uh Jessica,

1023
00:53:06.229 --> 00:53:08.590
please feel free to jump in on this one as well.

1024
00:53:08.629 --> 00:53:11.689
Um We talked at the beginning of the presentation about

1025
00:53:11.699 --> 00:53:15.560
how emotions really are born out of an evolutionary purpose,

1026
00:53:15.570 --> 00:53:15.959
right?

1027
00:53:15.969 --> 00:53:20.620
Um These have developed over the course of human history to help keep us alive

1028
00:53:20.790 --> 00:53:22.780
um at the most base level,

1029
00:53:23.090 --> 00:53:27.229
uh negative emotions or having a negative emotional response to something

1030
00:53:27.449 --> 00:53:30.949
does a lot of things very differently from having a positive response.

1031
00:53:30.959 --> 00:53:32.899
Um If we think about the n

1032
00:53:33.020 --> 00:53:37.129
the need to act or the immediacy to, to act

1033
00:53:37.560 --> 00:53:39.949
depends of course on the task in the context.

1034
00:53:39.959 --> 00:53:44.189
But generally, there's more immediacy to act if we're feeling negative emotions,

1035
00:53:44.199 --> 00:53:46.189
relative to positive emotions,

1036
00:53:46.199 --> 00:53:49.989
that also does depend on the intensity of the emotion that's being experienced.

1037
00:53:50.590 --> 00:53:52.449
We also know that in general,

1038
00:53:52.459 --> 00:53:57.610
uh negative emotional responses tend to have differing effects on our perception,

1039
00:53:57.620 --> 00:53:58.909
cognition and memory.

1040
00:53:58.989 --> 00:54:01.729
So if there's something negative that's in our environment,

1041
00:54:02.090 --> 00:54:04.320
you may be familiar with the weapon focus effect.

1042
00:54:04.330 --> 00:54:07.340
Uh We tend to focus on whatever the negative thing is

1043
00:54:07.469 --> 00:54:11.370
at the expense of information around it or in the periphery.

1044
00:54:11.689 --> 00:54:14.300
Whereas positive information, um

1045
00:54:14.449 --> 00:54:17.750
probably a, a less well known term than weapon focus effect.

1046
00:54:17.760 --> 00:54:19.030
But with positive emotions,

1047
00:54:19.040 --> 00:54:22.219
you might hear the term broaden and build meaning

1048
00:54:22.229 --> 00:54:24.909
that when we're experiencing something as being positive,

1049
00:54:24.939 --> 00:54:28.090
we tend to sort of uh open our cognition a little

1050
00:54:28.100 --> 00:54:30.350
bit more to the other things that are in the environment.

1051
00:54:30.590 --> 00:54:33.909
And again, both of those you can think of from an evolutionary perspective.

1052
00:54:33.919 --> 00:54:35.409
If there's a threat in our environment,

1053
00:54:35.500 --> 00:54:38.810
it's really critical that we devote our cognitive resources to focusing on

1054
00:54:38.820 --> 00:54:42.699
that thing so that we can preserve ourselves and our survival.

1055
00:54:43.010 --> 00:54:45.520
Whereas if there's something that's positive in our environment, we

1056
00:54:45.780 --> 00:54:48.820
wanna take in as much information as possible about it

1057
00:54:48.959 --> 00:54:51.260
uh And about the environment in the context so

1058
00:54:51.270 --> 00:54:54.020
that we can later seek out similar rewards later.

1059
00:54:54.030 --> 00:54:57.510
Um So that also kind of speaks to the immediacy that I mentioned before as well.

1060
00:54:59.770 --> 00:55:02.350
OK. I think we have time for one more question.

1061
00:55:02.550 --> 00:55:05.270
Uh Someone would like uh you to explain

1062
00:55:05.280 --> 00:55:08.550
about primary and secondary cross cultural emotion.

1063
00:55:10.290 --> 00:55:11.780
Yeah, it's a really good question.

1064
00:55:11.790 --> 00:55:17.159
Um So we know that for uh for many types of emotion measurement,

1065
00:55:17.169 --> 00:55:21.379
whether it's using neuroscience and biosensors, whether it's using self report,

1066
00:55:21.389 --> 00:55:24.479
that there are cultural differences in terms of the responses

1067
00:55:24.489 --> 00:55:27.500
that people have and what it is that they're responding to

1068
00:55:28.040 --> 00:55:29.780
the way that I usually think about it

1069
00:55:29.790 --> 00:55:32.820
is that uh something like galvanic skin response,

1070
00:55:32.830 --> 00:55:34.879
for example, is universal.

1071
00:55:34.889 --> 00:55:36.189
Uh It doesn't matter,

1072
00:55:36.419 --> 00:55:40.699
uh it doesn't matter, culture, it doesn't matter uh biological sex.

1073
00:55:40.709 --> 00:55:42.860
Uh Any of those factors that you can think of

1074
00:55:43.149 --> 00:55:46.370
if somebody has an elevated galvanic skin response,

1075
00:55:46.379 --> 00:55:48.260
that indicates that there is something that is

1076
00:55:48.270 --> 00:55:50.620
relevant that their brain is identifying for them

1077
00:55:51.020 --> 00:55:55.199
where cultural differences or other group differences come in is in

1078
00:55:55.209 --> 00:55:58.610
terms of what it is that people have that response to

1079
00:55:58.939 --> 00:56:04.040
that can differ greatly across cultures, across age, uh across sex.

1080
00:56:04.050 --> 00:56:07.199
Uh Any of those individual difference factors that you can think of.

1081
00:56:07.469 --> 00:56:11.770
Um So the responses themselves generally mean the

1082
00:56:11.780 --> 00:56:14.030
same things regardless of who the people are.

1083
00:56:14.080 --> 00:56:17.040
Uh But it's what you're having that response to

1084
00:56:17.270 --> 00:56:21.010
that can vary quite a bit depending on what the groups are that you're looking at.

1085
00:56:22.780 --> 00:56:25.949
Great. I hope uh that was all the questions we can answer.

1086
00:56:25.959 --> 00:56:29.149
Of course, we're still available at marketing at imotions.com.

1087
00:56:29.159 --> 00:56:30.870
If you have any further questions.

1088
00:56:31.129 --> 00:56:33.209
Thank you so much for joining us. Uh

1089
00:56:33.830 --> 00:56:38.689
Everybody stay safe. Uh Well, wishes from Copenhagen, Boston and Chicago.

1090
00:56:39.770 --> 00:56:41.209
Bye everyone. Thanks very

1091
00:56:41.310 --> 00:56:41.389
much.

1092
00:56:42.600 --> 00:56:42.780
Take.