presentation on life depends upon smile

The Power of a Smile – How Smiling Can Change Your Life and Change the World

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“the expression one wears on one’s face is far more important than the clothes one wears on one’s back.” – Dale Carnegie

If you were to ask someone who knows me to describe who I am, undoubtedly one of the first things that they will say is that I am a continually happy or ‘smiley’ person. This is something that I always laugh about as it has been mentioned many times throughout my life! The good thing is I’m not complaining as a smile has a tonne of benefits to your overall wellbeing.

Smiling is number 2 on the list of the 43 Habits of Absolutely Happy People . If you can turn smiling into a habit, then you can turn happiness into a habit… let’s explore!

1) You’re More Attractive!

Do you want to be more attractive? Simple. Smile! Research has proven that we find others more attractive when they are wearing a smile. This one is a no-brainer, but for the sake of helping it land in your mind, think about someone who you know and think of first when they are down, sad, angry, frustrated, whatever it may be. How attractive are they? Now picture them simply with a smile on their face. What does this do to their attractiveness? If you took the time to do this exercise, I’m sure you would have easily found the smiling version much more attractive, and maybe this even made you feel a happy tingle inside yourself! This leads to my next point.

2) You Can CHANGE THE WORLD! A Smile is Contagious!

Have you ever been in a sour mood and then someone has come along with a huge smile, some laughter or in a really good mood? Perhaps a baby that just looked up at you and smiled? How did this make you feel? When the person you are talking to or the people that surround you are smiling, you won’t be able to help but smile!

A smile has special powers. You can calm fear, insecurity, hurt and anxiety not only in yourself, but in those that are experiencing those feelings. The next time someone is feeling sad, scared, nervous, whatever it may be, smile with them and see how this makes them feel!

Even 2Pac, the rap legend that passed away realised the benefits of smiling, in his lyrics to ‘Power of a Smile’ he raps: The power of a gun can kill, and the power of fire can burn The power of wind can chill, and the power of mind can learn The power of anger, can raise inside until it tears you apart But the power of a smile, especially yours, can heal a frozen heart

A smile is a contagious thing. Give to the world and the world will give back to you. Smile at the world and the world will smile back at you. You will brighten the days of those around and make a difference in their lives… simply by smiling!

3) Grow Old Sexily!

Wrinkles, wrinkles, wrinkles! Something that we all don’t want to have, however it is something that comes to us all leaving permanent marks of our emotional feelings throughout life. Smiling throughout life will NOT mean you won’t get wrinkles… I wish it did! It WILL however mean that you will have permanent marks of happiness and when you speak with those later in life, you will automatically be smiling!

You also use fewer muscles to smile than you do for frowning. Hopefully you’re smiling now as you read this, but if not, smile now! … Now that you have tried smiling, try and frown. Which one do you think is easier? I would definitely say smiling is the easier of the two, and therefore stretching your muscles and skin less throughout life to grow old sexily!

4) Simply Feel Good!

When you’re smiling, no doubt you’re having a much better time than when you’re not! Smiling simply makes you feel better!

Research has shown that smiling releases serotonin – a neurotransmitter that produces feelings of happiness and wellbeing. It’s like a circle of happiness. Smile and you feel happy, you feel happy and you smile! Even when you’re not feeling great, try smiling, genuinely, and see how you feel!

5) Build Better, Faster Relationships!

Smiling is such a key ingredient for establishing healthy and genuine friendships. When someone is smiling at you it indicates that they like you. When someone likes you what do you think of them? Yep, normally you’re thinking “wow, I like this person!” Smiling also offers encouragement to the person that you are talking to. Think about it, if someone is smiling at you while you are talking to them, you feel as though they are totally into what you are saying, encouraging you to keep going!

Smiling is crucial when it comes to first impressions. Smiling when you first meet someone will indicate to the other person that you are genuinely happy to see them and that you are a positive person. These impressions will be lasting on the other person so definitely not one to miss!

6) Enhance your Business!

Business deals can be made simply through smiling. One of the first things that sales people quickly learn is to smile. Who would you rather buy something from? The sales rep that looks bored, down, angry, frustrated or whatever it may be, or from the sales rep that is simply smiling and happy to see you? The messages that they convey in conversation will link back to how they are feeling. A smiling person would be much more enthusiastic for the product and instil positive feelings in the buyer.

Even telesales have got a hold on this. Now sales reps that are on the other side of the phone are encouraged to smile when they talk as the smile is conveyed in how the person is talking!

Action Plan

It can’t be that hard, can it?

2) Visualise your happiest moments

A number of people will say that they find it hard to smile due to circumstances in their life. Research has shown that our thoughts and feelings influence our behaviours. As this is the case, I encourage you to close your eyes and think of the moments that have made you laugh and smile. How does this make you feel? These visualization exercises are great for helping you smile.

3) Just do it!

Further research has now flipped the above completely on its head and suggested that our behaviours can actually influence our thoughts and feelings ! As this is the case, I encourage you to JUST DO IT! Simply forcing a smile will give you the benefits of the above. Stand up straight, work on your posture, smile to the world and ‘fake it till you make it’! It WILL work!

Check out this fantastic video on TED , spoken by Ron Gutman who provides some fascinating insights into the power of smiling!

P.S. Incase you missed it, I have an article on Lifehack which has been the most viewed in the past week . Check it out here and let me know your thoughts!

About the author 

Brendan Baker

Brendan Baker is Australia's leading personal development blogger and and helps people build and grow online businesses based on their passions. He has created the Launch Your Life Academy and Your First 1000 Subscribers . Connect with Brendan: Twitter , Facebook , Google+

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A smile can suppress the greatest of authorities under your will. Simply due to its undeniably overwhelming power. Thus, making you an overpoweringly attractive being.

The continous practice of this concept of consistently smiling has brought me a long way. Although, i almost forgot about it for a while due to certain happenings. But thank goodness i found your post just in time. It has done a splendid job of remindering me of a number of my success tools.

Stay Blessed.

I once tried smiling crazily(I ean really often). and you know what, about several days later people say that I look beautiful even I dont smile at that time when they were talking to me. it means smile can permanently beautify

I used to smile with no reason and l found myself happy, till now it helps me interact with strangers and make them feel as I’m familiar of them. Thanks much bro from the new feeds in your article i need to prove them

Hi Brendan, a great article on the benefits of smiling. Someone once said a “smile is the only thing that has value when its given away” and I actually agreed with that. But then I discovered that the act of smiling releases endorphins in your brain which lightens your mood and also has health benefits. And as you’ve just pointed out, there are many more benefits including, you’re seen as more attractive by those you come into contact with.

Great blog, keep it coming…

Ernest Borgnine was always smiling.

Smiling in the animal world is a sign of aggression but I also thing engagement and acceptance or the start of it. My drivers license used to look like the unabomber photo but then I thought of my wealthy aunt that always smiled. Also, I thought of the people, bosses and directors, in the newspaper that always seemed to be smiling. I also think of Jewel and Tom Cruise and how they used to have crooked teeth. I always think of the large boned Queen Latifah and her permanent smile. It could be psychosomatic or Neoro programming. Best wishes. Laugh lines are com

i dont even know where to start from. smiling has always been my asset. i dont know what happened to me that i decided to google the power of smile. i do laugh a lot too. some people think im crazy some dont seem to understand but im cool with it.. it makes me feel younger at times

Thank 4 dis great impact ,dear

I like this article. This is not an easy do but worth. Don’t need something big to make a big change. Just smile…

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Hi, Everyone I dated in the past left me, because I smile too much. so, you could guess that they stole smile from my face.

I got this web page from my buddy who shared with me on the topic of this site and now this time I am visiting this website and reading very informative articles at this place.

Feel free to visit my blog Kasy fiskalne – o czym pamiętać przy wyborze? ( Leatha )

Loved this article! I always try to smile at people even if I only get strange looks in return lol.

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Love your article! Did you know that research has shown that smiling actually releases chemical hormones that make us feel better? For those who have trouble forcing themselves to smile, another idea is to watch the silliest youtube.com video they can find. After a few minutes it will be much easier to crack a smile!

Best… idea… ever!!! This just made me crack a laugh! http://www.youtube.com/watch?v=kDQwQGzMTKc

Absolutely love it and completely agree. When you are feeling down, smile, it will completely change your state and put your life and life situation into perspective. Smiling rocks!

haha, love it Mark!!

Agree that it helps put things into perspective as well… sometimes people feel unhappy for no reason!

SMILING ROCKS!

I know that smiling can make you feel better. And I know that it’s something you should do when you’re down. But gosh darn it, sometimes I just don’t want to feel better. It makes me feel that, if all it took was a smile to make me feel better, than apparently I had no business being upset in the first place. I will admit that sometimes I like to be in the dark about that fact. I’m not proud of it. But I’m aware of it, and I’ll try to smile more often on purpose.

Hehe, love it Liz! Very true…

Despite what I’m talking about through this site and in the post above, I actually do believe that it is HEALTHY to be down or sad at times and to feel the emotions. You need to feel this in order to appreciate happiness. The key is to be able to manage your emotions and ensure that feeling down is not a lingering feeling!

You have the choice to make that change.

I’ve tried forcing smiles when I’m down… I must admit that it feels really strange and stupid, because I don’t want to be doing it, but you’re right. It does make a difference 🙂

hehe, no doubt about it, Nat!

It’s awesome that your behaviours can influence your mood… it IS hard work at first because you are going completely against what your subconcious thoughts are telling you to do, however it as you start to change your behaviour you start to feel the shift in mood, continually making it easier.

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Smile Power Day

Smile power day presentation, premium google slides theme and powerpoint template.

Not even the strongest humans or machines can match the power of a smile. When smiling, our body overflows with endorphins, the "happiness hormone" (which actually improves the body's reaction to pain). So, on June 15, don't forget to smile because it's Smile Power Day. Download our template and share that happiness with everyone by creating a great presentation about this topic. It includes sticker illustrations and emojis, as well as photos of people smiling.

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4 Reasons You Should Smile More During Your Presentation

June 17, 2015 / Blog

They say it takes forty-three muscles to frown and only seventeen to smile.

Smiling not only keeps us physically and emotionally healthy, but also improves our public speaking.

Display a positive and relaxed mood during your presentation to better handle unexpected events like negative feedback and tough questions.

Get hundreds of PowerPoint slides for free.

Sign up for your free account today., how smiling helps.

According to public speaking coach, Denise Graveline, a smile has numerous benefits . It shows that you’re passionate about giving your presentation. Smiling also builds trust and rapport as it reveals that you appreciate your audience’s presence.

As a presenter, your goal is to connect with your audience while delivering your message. Smiling before you present increases your chances of capturing their attention and engaging them. Take advantage of this body language form to reach your presentation’s highest potential.

Here’s four more reasons you should smile while delivering your speech:

It makes us feel better.

Smiling helps you feel more comfortable and reduces your tension, especially when you’re nervous about delivering your speech. Since it’s contagious, it attracts a positive atmosphere that allows for an engaging discussion.

It hides your true emotions.

Smiling masks your nervous feelings, making you more optimistic.

They say that smiling more can change how you’re actually feeling at that moment, helping you forget any uneasy feelings.

It shows confidence.

Smiling boosts your confidence, making the audience see you as competent and attractive. A strong tone of voice also makes them view you positively.

It increases audience involvement.

Prioritize your audience’s needs. Since your presentation is all about meeting their expectations, your goal is to engage them and make them feel that they’re involved.

Smiling builds positive relationships and gives you the confidence to establish eye contact with them.

Seal It with a Smile

Smiling is one of your greatest assets as a presenter. It’s beneficial for you and your audience because it offers a positive aura, shows confidence, hides fear, as well as increases involvement.

To craft an effective presentation, let our SlideGenius experts help you out.

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“ 7 Secret Advantages of the Speaker Who Smiles .” The Eloquent Woman . Accessed June 17, 2015. “ How to Maximize Eye Contact for Presentations .” SlideGenius, Inc . 2015. Accessed June 17, 2015.

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How a Simple Smile Can Help Change Your Life

Learn to harness the power of your smile..

Posted January 20, 2020

Happiness can literally make you feel lighter inside, a bit like having some helium in your body. It usually only lasts a short time, but reflecting back on it can conjure up at least a ghost of the feeling, if not the full-bodied experience. Though happiness is a filter that makes the world appear to shine, it can also be frustratingly elusive when you need an emotional lift. Still, there are as many ways to rediscover it as there are the kinds of experiences that can potentially make you happy. But one of the ubiquitously available and best ones—because it requires so little from you—is enlisting the help of your smile.

The secret to your smile’s power is in the fact that your body recognizes happiness and smiling as going together. When the chemicals in your brain create the feeling of happiness, you will naturally smile. And fortunately, when you smile, your body often responds by producing those same “happiness hormones .” (In case you are interested, those chemicals are dopamine , serotonin, oxytocin , and endorphins.)

You can unleash the power of your smile in these four ways:

Identify something positive and smile. When you want to feel a bit happier, look for something positive in your environment or within you. It could be noticing the beautiful mountains outside your window or the peaceful moment of awakening early when all the world still seems to be sleeping . Just noticing a positive often makes people more aware, even grateful , but not necessarily feeling happier. So, choose to smile as you focus upon your chosen positive. Then pay attention to what you sense in your body—it might add that missing piece to your intellectual awareness of positives in your life.

Hold a half-smile. Borrowed from Buddhism, the technique of holding a half-smile has been shown effectively increase happiness. Try it for yourself. With your facial muscles generally relaxed, gently turn up the corners of your mouth. If someone were looking at you, they might not even notice it because the half-smile will be so slight. Hold the half-smile for at least ten minutes. Pay attention to whether your body feels even just a little more relaxed and whether you feel any lighter and happier.

Smile when interacting with others. Think of smiling as a social activity. When people see you smiling, something in them (more specifically, mirror neurons in their brains) may resonate, making them feel happier. Then they will likely smile back, and you will—in turn—feel a bit happier. It’s a feedback loop that can both coax warmer feelings from each of you and then help maintain that more positive feeling.

Pay attention to any sense of disconnection or emotional pain you may feel when you smile. Flashing a smile is not the equivalent of waving a happiness wand. So, rather than trying to demand joyful feelings from yourself, respect your natural reaction. Allow for it, consciously connecting with your genuine experience. Only by letting your painful emotions flow through you can you then find true inner peace and happiness.

Whether your smile prompts happiness or is the result of happiness (or both), these are honest experiences that can give you an emotional lift. Think of your smile as a tool that, when used properly, can fill you with some emotional helium, lifting your spirits.

If you would like to learn more about this topic, check out this brief video:

(If the video is not appearing here, you can see it on Youtube: Just the Thing to Help When You're Feeling a Bit Down .)

Making Change blog posts are for general educational purposes only. They may or may not be relevant for your particular situation; and they should not be relied upon as a substitute for professional assistance.

Making change through compassionate self-awareness

Leslie Becker-Phelps, Ph.D., is on the medical staff at the Robert Wood Johnson University Hospital Somerset in New Jersey.

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MY PERSONAL COMPASS ESSAY: The Power of a Smile

• by Clifton B. Parker
• October 07, 2005

I believe in the power of a smile. A smile can be a subtle twitch of the jaw muscles or a large grin with both rows of teeth showing much like a third-grader on picture day.

Smiling is contagious. One of my goals every day is to smile, whether to myself or to someone else, even a complete stranger. I have a neighbor, an elderly Chinese woman, I have no clue as to what her name is, yet every day when I leave my house I see her on her morning walk and we smile to each other. I once was on the W-line bus on my way to campus and I smiled to a person sitting across from me. I later realized the person was in one of my classes and I introduced myself as the guy that smiled to her on the bus. This person and I have become the closest of friends. I recently had a friend pass away, Johnny Napier, and at his funeral his parents had put up his senior portrait from high school. This was no ordinary senior portrait, however; it was Johnny smiling with a set of joke teeth in his mouth. Johnny's parents knew it was the way Johnny would have wanted us to remember him because he had the power to bring a smile to the faces of his family and friends.

Smiling is what grounds me; it stabilizes and makes me feel more in control every day. It reminds me that, despite the fast pace of life, I must always remember to be happy and appreciate the little things in life. Smiling helps me overcome times of fear, anxiety and nervousness. Smiling helps me express to others my proudest moments. Smiling helps me convey love for others. Sometimes, when no words can be spoken, a smile is all that is needed to fill the air.

The smile is a universal sign of happiness. I believe it is the ultimate connection between all humankind. No matter how big or small, if a smile is genuine it creates an ineffable feeling in the atmosphere. I believe in the power of a smile to make the saddest of circumstances a little better. I believe in the power of a smile to enrich the happiest experiences in life. I believe in the power of a smile to transcend all barriers between individuals and to create special moments in life.

Media Resources

Clifton B. Parker, Dateline, (530) 752-1932, [email protected]

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The Oxford Scientist

The University of Oxford's independent science magazine

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A Smile can Change the World

by Aaliah Dhorat, Year 10, Batley Grammar School, West Yorkshire.

“Making one person smile changes the world… maybe not the entire world, but their world.”

Smiling is infectious, you can catch it like the flu; someone smiled at me today, and I started smiling too. Smiling is like a virus; it’s contagious like other viruses, spreading from person to person. It spreads in many forms, even reading, therefore this essay on smiling should improve your mood (thinking of smiling releases endorphins in your brain, so your mood slightly improves). Seriously, while reading this essay try not to smile, not even a little…

People usually smile when feeling pleasant. When experiencing positivity, the neuronal signals travel from your cortex to your brainstem. Then, the cranial muscle carries the signal towards the smiling muscles in your face. Sounds simple? However, that’s only where it starts…once smiling muscles in your face contract there is a positive feedback loop that goes back to your brain and reinforces your good feeling. To put it succinctly: smiles occur when our brain feels good, it sends messages to our facial muscles telling us to smile, and we smile and tell our brain that we feel good, and so forth. Moreover, the chemicals cause you to be healthier, and therefore smiling might just be as good as exercise.

Smiling has already changed the world: The theory of evolution (arguably) proves that humans have descended from apes. When a monkey bares its teeth, flattens its ears and tightens its throat muscles, it’s because they are afraid and bracing for a fight, so want to provoke the same feelings of fear in their opponent. However, when a human bares its teeth, flattens its ears and tightens its throat muscles, it is as a friendly gesture or to show that it’s feeling pleasant emotions. How did this odd evolutionary divergence occur? How did fang-flashing evolve into smiling? The main evidence comes from ‘missing link’ facial expressions from evolution.

What I find baffling about smiling is that people who are born blind show the same smiles as those who are under the same situations as sighted people; it’s surprising considering that they have never actually seen a smile. This proves that smiling has become a natural response to pleasant feelings. In other words, you don’t have to learn how to smile; smiling is pre-programmed behaviour.

Did you know that smiling stimulates our brain’s reward mechanisms in a way that even chocolate, a well-regarded pleasure-giver, cannot match? Another interesting fact is that a recent research concluded that smiling can be as stimulating as receiving up to 16,000 Pounds Sterling in cash; therefore, in theory, those who smile about 62 times a day feel like millionaires!

Have you ever caught yourself having a bad day and being in the worst mood, but then randomly smiling and feeling your mood lift when talking to someone who is smiling, or seeing someone with smile? According to various studies, smiling is ‘contagious’, proving the rumour right. When you see a smile, your orbit-frontal-cortex activates, which processes sensory rewards. Therefore, when you catch another person smiling you feel rewarded, which is a good feeling, causing a smile!

Research also shows that if you smile, even if you are in a bad mood, your mood will improve immediately, because the simple action of smiling is enough to trigger happy chemicals. Don’t believe me? Try it. The next time you are in a serious argument, smile and observe as the other person’s mood change and lighten a bit. The next time you have just received some bad news, smile and patiently wait for a few moments as you trick your brain into improving your mood. The next time you are in tears, simply smile and improve your mood.

To conclude, a smile has already changed the world according to evolution, and will continue changing the world because one of the most powerful things the world shares is the universal smile. A smile can cut through all barriers. It knows no prejudice. No matter who or where you are, your smile will always be understood. A smile represents something bigger. It represents goodwill, affection and openness. It wordlessly communicates happiness and acceptance. It connects and encompasses us all.

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The Power of Smiling: The Adult Brain Networks Underlying Learned Infant Emotionality

Eloise a stark.

1 Department of Psychiatry, University of Oxford, Warneford Hospital, Oxford, UK

Joana Cabral

2 Center for Music in the Brain, Aarhus University, Aarhus, Denmark

3 Life and Health Sciences Research Institute (ICVS), University of Minho, Braga, Portugal

Madelon M E Riem

4 Department of Medical and Clinical Psychology, Tilburg University, Tilburg, The Netherlands

Marinus H Van IJzendoorn

5 Primary Care Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK

6 Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands

7 MRC/Wits Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa

Morten L Kringelbach

The perception of infant emotionality, one aspect of temperament, starts to form in infancy, yet the underlying mechanisms of how infant emotionality affects adult neural dynamics remain unclear. We used a social reward task with probabilistic visual and auditory feedback (infant laughter or crying) to train 47 nulliparous women to perceive the emotional style of six different infants. Using functional neuroimaging, we subsequently measured brain activity while participants were tested on the learned emotionality of the six infants. We characterized the elicited patterns of dynamic functional brain connectivity using Leading Eigenvector Dynamics Analysis and found significant activity in a brain network linking the orbitofrontal cortex with the amygdala and hippocampus, where the probability of occurrence significantly correlated with the valence of the learned infant emotional disposition. In other words, seeing infants with neutral face expressions after having interacted and learned their various degrees of positive and negative emotional dispositions proportionally increased the activity in a brain network previously shown to be involved in pleasure, emotion, and memory. These findings provide novel neuroimaging insights into how the perception of happy versus sad infant emotionality shapes adult brain networks.

Introduction

“The smile is the shortest distance between two persons”. -Victor Borge.

An individual’s temperament refers to individual differences in several biobehavioral domains, spanning activity, emotionality, attention, and self-regulation ( Rothbart and Bates 2006 ; Shiner et al. 2012 ; Nolvi et al. 2016 ). It is not a trait itself, but rather a rubric for a group of related traits ( Goldsmith et al. 1987 ). The characteristics that comprise temperament are thought to be relatively stable over time and consistent across situations ( Sanson et al. 2004 ), but they also develop in interactions with the social environment ( Lee and Bates 1985 ). Emotionality is one aspect of infant temperament, which is often measured on a scale ranging from clear fussing and crying, to neutral, to predominantly smiles and laughter ( Pauli-Pott et al. 2004 ). In order to have optimal and adaptive social behavior, we utilize knowledge from previous experiences of individuals, such as emotionality, to make predictions about the future and minimize the cost of surprise ( Friston et al. 2006 ; Brown and Brüne 2012 ). Therefore, learning about an individual’s predominant emotional dispositions is a key part of human social interaction, in particular parent–infant interaction ( Stark et al. 2019 ).

Infant emotionality has a measurable effect upon early mother–infant bonding. While positive infant emotionality (measured by infant smiling or laughter) relates to better mother–infant bonding, negative infant emotionality (measured by infant distress) relates to lower quality of bonding, while controlling for maternal symptoms of both depression and anxiety ( Nolvi et al. 2016 ). Infant emotionality may also influence the way in which parents respond to their infant. For instance, irritable children who cry frequently may elicit feelings of irritation in parents and subsequent withdrawal of contact ( Putnam et al. 2002 ). One study in the Netherlands reported that 5.6% of parents in their sample recounted smothering, slapping, or shaking their baby due to crying, particularly when they judged the crying to be “excessive” ( Reijneveld et al. 2004 ). Positive or ‘cute’ temperamental factors such as smiling or babbling may conversely elicit interaction and proximity ( Kringelbach et al. 2016 ).

Infants attract our attention ( Kringelbach et al. 2016 ). The unique and instantly recognizable facial configuration of infants is pleasing and rewarding, and an instinctive reaction of adults upon seeing an infant is to smile ( Hildebrandt and Fitzgerald 1978 ). The infant face has a measurable impact upon our perceptions and behavior. Adults prefer infant faces to adult faces ( Brosch et al. 2007 ; Parsons, Young, Kumari, et al. 2011 ) and infant cues spur us to action—both men and women will expend extra effort to look at cute infant faces for longer ( Parsons, Young, Kumari, et al. 2011 ; Hahn et al. 2013 ). Even seeing an infant face briefly before a simple motor task promotes faster reaction times and more sustained engagement with the task ( Proverbio et al. 2011 ). Infant visual cues therefore seem to be one of the most basic but powerful forces shaping our perceptions and behavior ( Kringelbach et al. 2016 ). Importantly, this behavioral impact of the infant face must be linked to changes in brain activity and in fact the infant face has been shown to elicit brain activity on a very fast timescale (<130 ms) in a network including the orbitofrontal cortex ( Kringelbach et al. 2008 ; Parsons et al. 2013 ; Young et al. 2016 ), which may mobilize the perceiver to ready themselves for providing care.

Still, our perception of cuteness and subsequent behavior is dynamic and is strongly influenced by context such as previous interactions mediated by valenced social signals including smiles, laughter, distress, and crying. In all human relationships, the bond between caregiver and child is arguably the strongest of all. For caregivers, learning about their infant’s emotional state helps them to predict how the infant approaches and reacts to the world. Some infants may smile, laugh, and babble contentedly more frequently than others, indicating a positive disposition. On the other hand, all infants cry to signal need, but infants differ from each other in how frequently and intensely they cry. Infants with a temperament characterized by negative disposition cry more often and tend to react to stressors with a high degree of emotionality, including anger, irritability, fear, or sadness ( Rothbart et al. 1994 ).

We were interested in measuring the underlying brain networks for learning of infant emotionality and used our probabilistic social reward task, which allows participants to learn that infants have different emotional dispositions (through varying levels of probabilistic positive and negative feedback) ( Parsons, Young, Bhandari, et al. 2014 ; Parsons, Young, Craske, et al. 2014 ). In the learning phase, participants learn over time, through trial and error, that a given infant is more or less likely to smile and laugh. We have shown that this can significantly shift the perception of cuteness and motivation to view an infant, so that those infants with more positive emotionality are perceived as ‘cuter’ than before the task ( Parsons et al. 2014 ). This demonstrates that the perception of the emotionality dimension of temperament can be changed through a simple behavioral task that shifts the intrinsic reward value of infants.

Here, we investigated the brain networks underlying learning of infant emotional dispositions. In particular, we were interested in capturing the specific functional network (FN) involved in the perception of the learned infant emotional disposition following the successive presentation, inside the MRI scanner, of pictures of infants with neutral facial expressions, whose emotional disposition was previously learned. In order to achieve this, we used a recent neuroimaging analysis method, the Leading Eigenvector Dynamics Analysis (LEiDA; Cabral et al. 2017 ; Figueroa et al. 2019 ; Lord et al. 2019 ), which allows us to detect, at a single-TR (repetition time) resolution, the occurrence of FN from functional MRI (fMRI) data. In this approach, FNs are defined as recurrent BOLD phase-locking patterns, which can be captured with low-dimensionality by considering only the relative phase of BOLD signals (i.e., how all BOLD phases project into their leading eigenvector at each discrete time point). Previous implementations of the LEiDA method have revealed that the probabilities of occurrence of different FN (and their corresponding switching profiles) can show significant differences between participant groups, but these measures were computed over entire resting-state fMRI sessions ( Cabral et al. 2017 ; Figueroa et al. 2019 ).

Here, for the first time, we make use of the high temporal resolution of LEiDA and apply it to a task paradigm, in order to evaluate if the occurrence of specific FN at a precise timing after the stimulus can relate to the learned infant emotionality. This advanced method allows for an unbiased way to investigate learning of infant emotional dispositions and in particular to identify the brain networks linked to infant emotionality along a positive–negative happy versus sad gradient.

Materials and Methods

Participants.

We analyzed neuroimaging data from 47 female participants included in our previous study ( Riem et al. 2017 ) (mean age 19.62 years old, SD = 2.12, all undergraduate students from the Department of Child and Family Studies, Leiden University, >95% born in The Netherlands). None of the participants had children. All participants were screened for MRI contraindications, childhood experiences, psychiatric, or neurological disorders, problems with hearing, pregnancy, and alcohol and drug abuse. The participants gave written informed consent, and permission for the study was obtained from the Leiden University Medical Centre Ethics Committee and from the Leiden Institute for Brain and Cognition Ethics Committee.

The elements of the study used in this analysis consisted of the learning and test phase of a variation of the original probabilistic social reward task ( Parsons et al. 2014 ) shown in Figure 1 ( Riem et al. 2017 ). The 47 participants came to Leiden University Medical Center for the experiment.

Overview of the social reward task. ( A ) Participants were trained outside of the scanner to associate six different infant faces with different emotional dispositions. They were presented with two infant faces and had to choose the top face (pressing “up”) or the bottom face (pressing “down”). They were then exposed to feedback for the chosen face, either positive (smile and laughter) or negative (sad expression and crying). Bottom panel shows the different contingencies for each of the six faces: 80% happy, 20% happy (the easy pair), 60% happy, and 40% happy (the hard pair), and two with no feedback. ( B ) The testing phase was then administered inside the scanner, where participants stated the predominant emotional disposition of each face—happy or sad ( Riem et al. 2017 ). ( C ) Illustration of each of the reward contingences for the six face stimuli. Please note that the faces shown in the figures are not the ones used in the experiment but rather hand drawings of nonexistent infant faces to depict the learning and test phases of the probabilistic social reward task.

Learning Phase

First, participants were trained to learn the emotional dispositions of the infants in the social reward task ( Parsons et al. 2014 ), which was constructed using previous widely used learning paradigms ( Kringelbach and Rolls 2003 ; Frank et al. 2004 ). There were six different infants that varied in their probability of being happy or sad. Faces were presented in pairs. The easy-to-learn pair consisted of a happy infant, which laughed in 80% of trials and cried in the remaining 20%, presented together with a sad infant that laughed in 20% of trials and cried in the remaining 80%. In the difficult-to-learn pair, the happy infant laughed 60% of the time while the sad infant laughed only 40% of the time. There was also a neutral pair where no feedback was given, which participants were told to expect.

The learning phase consisted of two blocks of 60 trials per participant, with each pair of faces being presented 40 times in total (20 times per block). Trials were randomly ordered in each session, as was the order of the blocks. The emotional disposition of the babies (happy, sad, or neutral) was also randomized between participants.

Participants were presented with one pair of babies at a time, both showing a neutral emotional expression (see Fig. 1 ). They selected the ‘up’ key or the ‘down’ key on a keyboard to choose one of the two baby faces (the upper neutral face or the lower neutral face) and this selection prompted feedback on the selected baby’s emotional disposition. On pressing the key, visual feedback for the selected face was presented immediately for 1.5 s accompanied by a 1.5 s vocalization. In the happy condition, they would see the baby smiling and they would hear a happy vocalization. In the sad condition, participants would see a sad facial expression and hear a baby cry. There was a 500 ms gap between the end of the feedback and the next trial beginning, during which a red fixation cross was presented in the center of the screen.

Participants were instructed to discover the emotional disposition of the infant by listening to the vocalizations and viewing the infant’s facial expressions. By means of repeated trials, they could infer how often the baby cried or laughed and decide which one was the happier or the sadder of the two. Participants were told for one block, “In each pair of faces, there is one happy and one sad baby. Like in real life, the happy baby will not always be happy and the sad baby will not always be sad. In each set, your task is to find the happiest baby, the one who smiles most often, and continue to always select this baby even if this baby may sometimes appear sad.” In the other counterbalanced block, participants were instructed to find the saddest baby.

Testing Phase

The second stage of the experiment was the fMRI procedure, where participant learning of the infant emotionality was tested. Participants were briefed on the fMRI procedure and paradigm. It has previously been established that the participants can discriminate between the six infant faces with high accuracy ( Parsons et al. 2014 ; Riem et al. 2017 ). While being scanned, participants were presented with the six infant faces, all of which had neutral facial expressions. Each neutral infant face was presented in the center of the screen, accompanied by the words ‘happy’ and ‘sad.’ Participants were tasked with indicating whether they believed the baby to be happy or sad, based upon the previous training phase, using their right hand to button press. Each face was presented 20 times, for up to 2.6 s, in random order (120 presentations in total). The button press terminated the trial and continued to the next trial, so the task was self-paced. Interstimulus intervals were jittered and calculated using Optseq ( https://surfer.nmr.mgh.harvard.edu/optseq/ ). All tasks were programmed and performed using E-Prime software.

All infant facial images and vocalizations were the same as those used in Parsons et al. (2014) and Bhandari et al. (2014 ). Each of the six babies was aged 3–12 months old, and had a corresponding image for smiling, crying, and neutral conditions. An independent sample of adult females ( n  = 40) was asked to rate the faces from a larger set of 13 stimuli ( Kringelbach et al. 2008 ) as “male,” “female,” or “cannot tell.” The results were then used to select six faces that represented two perceived as female, two as male, and two with ambiguous ratings ( Parsons et al. 2014 ). All images were in grayscale, and were equally sized (300 × 300 pixels), as well as being matched for luminosity.

There were 12 vocalizations: 6 of crying infants, and 6 of laughing infants. Adults unambiguously categorized these as such ( Young et al. 2012 ), and they were taken from a larger database of sounds, the Oxford Vocal (OxVoc) Sounds Database, which is a validated set of nonacted affective sounds from human infants, adults, and domestic animals ( Parsons, Young, Craske, et al. 2014 ; Young et al. 2017 ). All vocalizations were 1.5 s long, free from background noise, and matched for the characteristics of the sounds. Headphones were used to present the vocalizations to participants during the training phase of the social reward task.

Data acquisition with fMRI

All scanning was performed with a standard whole-head coil on a 3-T Philips Achieva TX MRI system (Philips Medical Systems, Best, The Netherlands) in the Leiden University Medical Center. During fMRI, there were a total of 298 T2*-weighted whole-brain echoplanar images acquired (repetition time = 2.2 s; echo time = 30 ms, flip angle = 80°, 38 transverse slices, voxel size 2.75 × 2.75 × 2.75 mm [+10% interslice gap]). Following the fMRI scan, a T1-weighted anatomic scan was acquired (flip angle = 8°, 140 slices, voxel size 0.875 × 0.875 × 1.2 mm).

Preprocessing

The preprocessing of the neuroimaging data was carried out in FSL5.0 ( www.fmrib.ox.ac.uk/fsl ) using high-pass temporal filtering (100 s high-pass filter), motion correction, brain extraction, and finding the linear registration from the EPI images to standard MNI space via the participant’s T1-weighted images. We used this registration matrix to parcellate according to the AAL parcellation ( Tzourio-Mazoyer et al. 2002 ) and generated the average BOLD signal time series for each AAL90 region (cortical and subcortical but not cerebellum regions) by computing the mean over all voxel time-series for each region. We also created participant-specific vectors with the onset of each stimulus presentation for use in the main data analysis.

Data analysis

Transient functional networks.

To assess the FN activated at each instance of time, we applied LEiDA, a data-driven method that focuses on the connectivity patterns captured by the leading eigenvector of the BOLD phase-coherence matrices over time ( Cabral et al. 2017 ).

First, we obtained a time-resolved matrix of functional connectivity, dFC , with size NxNxT , where N  = 90 is the number of AAL90 brain areas and T  = 280 is the total number of recording frames in each scan (timeseries), using the following equation:

where θ ( n , t ) is the phase of the BOLD signal in area n at time t obtained using the Hilbert transform. The first and last epochs of each scan were removed to account for the boundary distortions associated to the Hilbert transform. dFC ( n , p , t ) is positive if two areas n and p have synchronized BOLD signals at time t (phase shift <90°), and dFC ( n , p , t ) is negative if the BOLD signals of areas n and p are more than 90° out of phase at time t .

To assess instantaneous patterns of functional connectivity, LEiDA considers only the leading eigenvector V 1 ( t ) of each dFC ( t ). This simultaneously reduces the dimensionality of the data (one 1 × N vector at a time instead of a N × N matrix at a time) and acts as a denoising procedure since the leading eigenvector V 1 ( t ) captures only the dominant pattern of connectivity of the dFC ( t ) at time t ( Cabral et al. 2017 ). This vector contains N elements (each representing one brain area) and their sign (positive or negative) serves to separate brain areas into communities according to their BOLD-phase relationship. Since V and − V represent the same state, we use a convention ensuring that most elements are negative. When all elements of V 1 ( t ) have the same sign, it means all BOLD signals are evolving in the same direction (within a range of 90°) and are hence considered to be following a single global mode ( Newman 2006 ). If instead V 1 ( t ) has elements of different signs (i.e., positive and negative), it means the BOLD signals can be divided according to their phase into two modes/communities, where one subset of brain areas become coherent forming a FN, which is phase shifted by more than 90° with respect to the other brain areas. Conveniently, FNs can be represented in cortical space, by plotting links between the smaller subset of areas, whose BOLD signal is coherent and phase-shifted from the rest of the brain.

To detect a discrete number of recurrent FC states, we applied a k -means clustering to all leading eigenvectors V 1 ( t ) across all 47 participants (47 × 278 = 13 066 leading eigenvectors in total). The clustering divides the sample into a k number of clusters (each representing a recurrent FC state), with higher k resulting in more fine-grained network configurations. Although there is no consensus regarding the number of FC states revealed by fMRI (and whether FC states can be discretized in the first place), we can explore which partition of the sample allows for a better detection of FN associated with learning of infant emotionality. As such, we varied k (number of clusters) from 2 to 20, and for each k , obtained a repertoire of k FC states. Subsequently, for each FC state, we evaluated whether its probability of occurrence 2TR (TR stands for repetition transition) after the neutral face presentation correlated with the happy–sad gradient of learned infant emotionality (using Pearson correlation and associated P -values) (see Fig. 2 for an overview of the whole analysis process).

Schematic illustration of the LEiDA methodology used to analyze the fMRI data. ( A ) First, we applied LEiDA to the fMRI data and clustered the FC patterns into a given number, k , of FC states, assigning one of these FC state to each TR (represented by shaded colored bars under the BOLD signals). Then, for each infant face, we detect the FC state that is active 2TR after stimulus presentation (to account for the hemodynamic response time). ( B ) For each infant face and for each participant, we obtain a probability distribution of the FC states, which we subsequently correlate with the happy–sad gradient given by the probability of smiling in the training phase.

We used LEiDA methodology to investigate the dynamics of brain networks involved in learning of infant emotionality arising from the probabilistic social reward task (for more details on task, see Fig. 1 and Methods). This allowed us to investigate the probability of occurrence of each state of functional connectivity linked to the positive emotionality score of the infant faces, that is, the probability of smiling and laughter for each of the six infants in the training phase (80/20, 60/40, and 50/50) (see Fig. 2 and Methods for an overview of the data analysis).

Figure 3 shows the repertoire of FC states (for k  = 8) that recurrently emerged over time in the group of 47 participants during the entire fMRI recording sessions, and where the FC states are sorted according to their overall probability of occurrence. As can be seen in Figure 3 , the most prevalent pattern of functional connectivity [FN#1] corresponds to periods where all the BOLD signals are aligned (within a 90° angle), representing a slow global mode of BOLD activity. When this state is dominant, the associated FC pattern (shown in matrix format in Fig. 3B ) shows only positive values. This global mode of BOLD connectivity is consistent with previous reports of a global modulation of BOLD signals in the resting-state. Given its putative neurophysiological value, we opted not to regress it out ( Murphy and Fox 2017 ).

Repertoire of FN states assessed with LEiDA and association to learned emotionality (for k  = 8). The results show that FN#2 is significantly correlated with positive emotional disposition scores for the six infants ( P  < 0.002, highlighted in green box, see row of probabilities), suggesting that this network is important for learning of infant emotionality. The brain network contains regions including the orbitofrontal cortex, amygdala, and hippocampus. The error bars represent the standard error of the mean across all 47 participants. These results are obtained when the dynamic FC is clustered into 8 FC states.

In the remaining seven FC states, we find different subsets of brain regions (FN#2–8) that transiently but consistently desynchronize together from the global mode of BOLD activity. Figure 3 shows each FN in brain space, by plotting red links between the areas that shift away from the global mode (with this convention, the global mode network FN#1 shows no links). This representation in cortical space reveals that each FN state involves functionally different sets of brain areas. For each of the FN, we computed the probability of being active 2 TR after the presentation of each neutral infant face (allowing for the hemodynamic lag). Since each infant face has an associated emotionality score (80%, 60%, 50%, 40%, 20% probability of smiling and laughing), we correlate this probability with the corresponding emotionality score and obtain an associated P -value ( Fig. 3 , lower row), revealing the significance of each FN in predicting the emotionality of the infants. As can be seen, most of the FNs do not encode the emotionality, but FN#2 is clearly significantly linked to the degree of overall happiness ( P  < 0.002) and includes regions of the orbitofrontal cortex, amygdala, parahippocampus, and hippocampus.

We investigated the robustness of this novel finding by investigating the results over a wide range of clusters k between 2 and 20, given that the spatial configuration of the FNs depends on the number of clusters determined in the k -means algorithm, with a higher number of networks generally resulting in more fine-grained (and often less symmetric) networks. Figure 4 shows for each solution with k FNs, the P -value associated with the most significant result. Since a higher number of clusters increases the probability of false positives, we correct the significance threshold as 0.05/ k (green dashed line). We find that the partitions into 5, 8, and 11 FC states each return a very similar FN, whose probability of occurrence significantly correlates with the infant emotionality after correcting by k .

Significance of correlation between FN and infant emotionality over the range of k -means clustering solutions explored. We verified which partition models detected FN, whose probability of occurrence 2 TRs after the presentation of an infant neutral face correlated to its learned emotionality. The figure shows the P -values obtained for all the networks compared. All the P -values represented as dots are above the 0.05 standard threshold (upper dashed line), meaning that no relation was found between the occurrence of the corresponding networks and the infants’ emotionality. To account for the family-wise error rate when performing multiple hypotheses tests, we corrected the standard threshold by the number of independent hypothesis tested in each partition model (0.05/K lower dashed line). As can be seen, for a number of cluster sizes ( K  = 5, 8, 11), we detect a FN, whose probability of occurrence significantly correlates with the learned infants’ emotionality ( P  < 0.01), with statistical significance surviving correction for multiple comparisons.

Figure 5 shows the robustness of the emotionality learning FN for the three different k values ( k  = 5, 8, 11). As can be seen the regions involved in this FN are remarkably similar for different k values (compare the red lines) and significantly correlated with infant emotionality ( P  < 0.007 for k  = 5; P  < 0.002 for k  = 8; and P  < 0.003 for k  = 11). This confirms the robustness of the result of finding a brain network encoding learning of infant emotionality, involving regions such as the orbitofrontal cortex, parahippocampus, hippocampus, and amygdala.

Functional brain networks associated with emotionality learning. The probability of the networks (red links) being active 2TR after stimulus onset increased as the learned emotionality of the infant showed a more positive disposition. In more detail, these networks are considered to be active when the BOLD signals of these areas (red bars) become coherent and phase shifted by more than 90° with respect to the BOLD signals in the rest of the brain (gray bars). An emotionality learning network was found to show significant correlation with learned emotional dispositions over the 47 participants. This solution for k  = 8 (middle) returned the most significant functional network (FN#2) ( P  = 0.0023, uncorrected, P  = 0.0184 after correcting by the number of clusters). This emotionality learning network includes not only the orbitofrontal cortex (which is known to be involved in pleasure and emotion), but also the amygdala (involved in emotional processing) and the hippocampus and parahippocampus (involved in memory). Attesting to the robustness of the results, the other two networks found to relate significantly with the infants’ learned emotionality (for k  = 5 and k  = 11), also include the orbitofrontal cortex, with differences arising in the number of output states constrained by K .

An infant’s temperament is partially comprised of individual differences in their emotionality—whether they are predominantly happy, signaled by smiles and laughter, or sad, signaled by crying and distress cues. We investigated the functional brain networks underlying learning of infant emotionality using neuroimaging in healthy adult participants. We used a probabilistic social reward task allowing participants to learn, through trial and error, the emotional disposition of a group of six infants ( Parsons et al. 2014 ; Riem et al. 2017 ). Through this interactive learning task, the participants learned the probability of each infant showing a positive disposition by smiling and laughing. We have previously shown that this probabilistic social reward task can reliably shift the way infant cuteness is perceived and the motivation to view individual infant faces ( Parsons et al. 2014 ), such that infants previously judged less cute become significantly cuter if they display a positive emotional disposition during the short social reward task. Here, we scanned 47 participants in the testing phase of the social reward task after they had learned the experimentally established infant emotionality. This allowed us to compute the underlying changes in dynamic functional brain connectivity associated with each infant emotional disposition using a novel LEiDA methodology ( Cabral et al. 2017 ).

Our results revealed for the first time a significant brain network exhibiting time-varying activity that significantly correlated with the experimentally established infant emotional disposition, that is, more activity when seeing the infants with most positive emotionality (80% and 60% probability of smiling and laughing) and much less activity when seeing the infants with the most negative emotionality (20% and 40% probabilities of smiling and laughing). Importantly, these experimentally established infant emotionality values were different from the initial cuteness ratings and desire to view the infant face (Parsons et al. 2014), suggesting that this brain network is not encoding simply the cuteness of an infant but this emotional aspect of the learned infant temperament.

Revealing the brain networks engaged in learning about infant emotionality is important given that positive, cute infant cues such as smiles and laughter promote caregiver proximity and care vital for the infant’s survival ( Kringelbach et al. 2016 ). This should be seen in context of the development of three main “parental capacities,” which apply to all caregivers ( Parsons et al. 2010 ; Stein et al. 2014 ). The first parental capacity is the ability to focus attention on the infant’s emotional cues and respond contingently and responsively, which predicts later cognitive development ( Murray et al. 1996 ). The second key parental capacity is emotional scaffolding, which is the ability to perceive changes in emotion and stress in the infant and support them to regulate their emotions, especially when the infant is distressed. The third key parental capacity is sensitivity to an infant’s attachment behaviors, such as eye contact, and to respond appropriately. Previous research has shown that the antecedents to these capacities, particularly attentional focus, are found even in the brain processing of nonparents ( Kringelbach et al. 2008 ; Young et al. 2016 ). Here we demonstrate for the first time the brain networks involved for nonparents in learning about infant emotional dispositions, which are essential for the ability to perceive emotional state, provide emotional scaffolding in instances such as crying, and to hone sensitivity to an infant’s attachment behaviors ( Bornstein 2014 ). A future endeavor for this work is to explore how learning of infant emotional dispositions affects the brain of new parents, perhaps also exploring own-infant versus other-infant processing.

We have identified the brain network encoding learning of infant emotionality consisting of the orbitofrontal cortex, hippocampus, parahippocampus, and amygdala (see Figs. 3 and ​ and5). 5 ). These regions are known to be structurally connected, for example, via the uncinate fasciculus ( Von Der Heide et al. 2013 ). Perhaps the most important region in this emotionality-encoding network is the orbitofrontal cortex: a large heterogeneous brain region with many functions, which has primarily been implicated in emotion and hedonic processing ( Kringelbach and Rolls 2004 ; Kringelbach 2005 ; Kringelbach and Berridge 2009 ). It has a specific role in processing the valence of primary reinforcers including face perception, as patients with lesions to the orbitofrontal cortex struggle to identify emotional facial expressions ( Hornak et al. 1996 ), and similarly face-selective patches have been found in orbitofrontal cortex, primarily in electrophysiology studies using primates ( O'Scalaidhe et al. 1997 ). Previous work has associated infant faces with fast activity in the OFC at around 130 ms ( Kringelbach et al. 2008 ; Parsons et al. 2013 ). Similarly, the orbitofrontal cortex has been involved in the fast processing (<130 ms) of infant auditory stimuli ( Young et al. 2016 ). This processing is present in men and women, parents, and nonparents, and has been theorized to comprise a universal “caregiving instinct” ( Lorenz 1943 ) that may prepare the individual to provide care to the infant by coordinating responsiveness and readiness for sociality ( Kringelbach et al. 2016 ). Importantly, when an infant face is altered, as in the case of cleft lip, which is rated as much less cute than healthy infants ( Parsons, Young, Parsons, et al. 2011b ), the rapid activity in the orbitofrontal cortex is significantly diminished, suggesting that the configuration of the infant face is vital for the perception of a biologically significant infant ( Parsons et al. 2013 ). Perhaps, as our current study demonstrates, positive emotional cues such as laughter and smiles could help to shift individuals’ perception of these infants to perceive them as cuter and facilitate subsequent caregiving.

The amygdala has been shown to be involved in the processing of emotional stimuli ( LeDoux and Phelps 2000 ) and particularly in the recognition of facial emotions ( Adolphs 2002 ). For many years the literature seemed to suggest that the amygdala was mainly involved in processing negative emotions including facial expressions denoting threat (fear or anger), mainly driven by findings in rodents ( LeDoux and Phelps 2000 ). Yet, amygdala activity has also been found for positive stimuli including faces ( Yang et al. 2002 ; Fitzgerald et al. 2006 ). As a result, Pessoa and Adolphs (2010) proposed that the role of the amygdala in visual processing is to coordinate cortical networks during the evaluation of the biological significance of visual stimuli with an affective dimension, like a conductor with an orchestra. Interestingly, it has been proposed that some of the role of the amygdala seen in rodents have been taken over by the orbitofrontal cortex over the course of evolution ( Rolls 1999 ).

Previous research has also shown the detection of biological significance is linked to emotional memory networks, which include the orbitofrontal cortex, amygdala, parahippocampus, and hippocampus ( Berridge and Kringelbach 2015 ; Kringelbach and Berridge 2017 ). Both human and animal research has shown how the amygdala often works in concert with hippocampal regions to lay down emotionally valenced episodic memories ( Phelps 2004 ) ( Stark et al. 2015 ). There is some evidence to suggest that emotional cues are more easily memorized and recalled (Kensinger 2009), which would suggest that imbuing an infant face with an emotional disposition might strengthen the memory and aid recall. An interesting follow up would be to explore valence in greater detail, specifically whether happy or sad emotionality leads to better recall.

Thus, given their roles in processing emotional behaviors, the interaction between the orbitofrontal cortex and the amygdala with memory systems mediated by the hippocampal regions could signal to the attentional systems to dynamically update the reward value of infants and help guide subsequent caregiving. Here, it is important to stress the role of the network rather than the role of individual brain regions. Due the instantaneous nature of the patterns detected with LEiDA, we were able to detect a specific set of regions whose probability to synchronize their BOLD signal phases relates with the learned emotional dispositions associated to the neutral infant faces. Importantly, since successive stimuli were presented <10 s apart, conventional sliding-window analysis used for the evaluation of dynamic functional connectivity would have failed to capture the emotional specificity associated to each face ( Preti et al. 2017 ). Recently, other methodological approaches focusing on BOLD coactivation patterns have been proposed to analyze BOLD connectivity dynamics at high temporal resolution ( Tagliazucchi et al. 2012 ; Liu and Duyn 2013 ; Karahanoglu and Van De Ville 2015 ). However, coactivation approaches (in their variant forms) are only sensitive to simultaneity in the data, whereas phase-coherence techniques can, by definition, capture temporally delayed relationships, which may explain why the LEiDA method appears more sensitive to detect meaningful functional subsystems. We are thus able to expand our previous categorical neuroimaging analysis, which suggested increased amygdala connectivity with frontal regions and the visual cortex during the perception of infants with a happy disposition ( Riem et al. 2017 ). Crucially, however, such categorical analyses rarely provide insights into the spatiotemporal dynamics of network activity. Longer-term, combining these sophisticated unsupervised data analysis methods with whole-brain computational modeling has the potential to show the causal influence of each of the regions in the emotionality-learning network identified here ( Deco and Kringelbach 2014 ; Deco et al. 2018 ).

Another proposed role of the orbitofrontal–amygdala–hippocampus network would be to provide top–down predictions to sensory regions when processing the neutral infant faces. Previous work has shown that one proposed function of the OFC in visual processing is to integrate perceptual representations with top–down expectations activated by contextual or associative detail ( Bar et al. 2006 ). This view corroborates with the concept that the brain is not a passive organ, but is constantly predicting incoming proximate sensory information based upon memories of past experiences ( Vuust et al. 2018 ). In addition to this, a recent study found the hippocampus to encode the identity of a visual stimulus based upon associative predictions from auditory cues ( Kok and Turk-Browne 2018 ). If this network is providing a prediction of the infant’s emotional disposition despite the neutral face presented during scanning, this could provide evidence demonstrating how contextual and trait-related social information is integrated into visual perception.

Finally, it is interesting to consider how the present research may be adapted to explore further the processing of infant emotionality in psychiatric disorders. Research in depressed patients have shown that they are less accurate at discriminating happy facial expressions ( Gur et al. 1992 ; Surguladze et al. 2004 ; Dai et al. 2016 ), which is thought to underlie some of the impaired interpersonal functioning in depression. This interpersonal functioning is vital to the parent–infant relationship, as is sensitivity to infant cues that signal their affective state and also their needs. Research has found impairments in precise, controlled psychomotor performance in adults with depression ( Young et al. 2015 ) and mothers with postnatal depression also show reduced affective touching than healthy mothers ( Young et al. 2015 ). Given that the brain networks in response to infant cues are crucial in triggering behavioral responsivity, it would be of considerable interest to test whether caregivers with depression may show altered brain networks for learning of infant emotionality from facial and vocal cues. Our learning paradigm may also be usefully incorporated in broader interventions, such as the “Video-feedback Intervention to promote Positive Parenting” video feedback approach ( Juffer et al. 2017 ), emphasizing attention to positive emotional signals of the infant in order to more systematically change parental perceptions of their infants’ negative emotionality and trigger less harsh and more sensitive parental interactions.

This work was supported by a Medical Research Council Studentship awarded to EAS; a European Research Council Consolidator Grant to MLK (CAREGIVING, no. 615539); a Wellcome Trust Grant (No. 090139) to AS; funding from the Portuguese Foundation for Science and Technology (CEECIND/03325/2017), Portugal, to JC; and a research award from the Netherlands Organization for Scientific Research (SPINOZA prize), the Gravitation program of the Dutch Ministry of Education, Culture, and Science and the Netherlands Organization for Scientific Research (NWO grant number 024.001.003) to MHvIJ.

We are grateful to Marian Bakermans-Kranenburg, Christine Parsons, and Katie Young for valuable contributions to task design and this manuscript. Conflicts of interest: The authors declare no conflicts of interest.

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Smile Like Your Life Depends On It

At Black Sheep, we talk a lot about rebellion and activism. We’re known to enjoy protesting, wheatpasting and the occasional theft of an inanimate animal.

On an individual level, rebellion becomes a lot trickier and harder to fathom. What can I do that goes against the grain, creates a ripple effect and ultimately benefits myself and others? The answer is a lot simpler than you would imagine and was articulated best when I had the distinct privilege of seeing Jon Stewart perform stand-up.

“Practice the quiet resistance of living pleasantly.”

In the words of Oprah, that was a tweetable moment. When criticism and gossip is our nation’s currency, consider how revolutionary simply being nice can be. As a result, I made a commitment to start my own pleasant resistance by smiling, mainly at strangers.

Here’s the kicker – I don’t smile. I grew up in the DC area, where smiling and making eye contact is more of a threat than a pleasantry. I typically have what they call “resting bitch face” and have been asked frequently if I’m “ok.” And nothing makes me angrier than when a stranger, typically a man, tells me to smile more.

Yet here I am smiling at strangers in my office hallway, cashiers, people walking their dogs on the sidewalk, you name it. Much to my surprise, it’s not terrible! In fact, I feel better just for doing it and really believe so do the people on the receiving end of my smiles. I now have at least one “hallway friend,” am getting to know my neighbors and local service industry better and get to pet so many more strange dogs.

If you don’t believe me, then please watch this TED Talk (because everyone trusts TED) on the hidden power of smiling and consider how you too can fight the power (pleasantly):

Dionella Martinez