P is for percent

We've used ggplots throughout this blog series, but today, I want to introduce another package that helps you customize scales on your ggplots - the scales package. I use this package most frequently to format scales as percent. There aren't a lot of good ways to use percents with my dataset, but one example would be to calculate the percentage each book contributes to the total pages I read in 2019.

library(tidyverse)

## -- Attaching packages ------------------------------------------- tidyverse 1.3.0 --

## <U+2713> ggplot2 3.2.1     <U+2713> purrr   0.3.3
## <U+2713> tibble  2.1.3     <U+2713> dplyr   0.8.3
## <U+2713> tidyr   1.0.0     <U+2713> stringr 1.4.0
## <U+2713> readr   1.3.1     <U+2713> forcats 0.4.0

## -- Conflicts ---------------------------------------------- tidyverse_conflicts() --
## x dplyr::filter() masks stats::filter()
## x dplyr::lag()    masks stats::lag()

reads2019 <- read_csv("~/Downloads/Blogging A to Z/SaraReads2019_allrated.csv",
                      col_names = TRUE)

## Parsed with column specification:
## cols(
##   Title = col_character(),
##   Pages = col_double(),
##   date_started = col_character(),
##   date_read = col_character(),
##   Book.ID = col_double(),
##   Author = col_character(),
##   AdditionalAuthors = col_character(),
##   AverageRating = col_double(),
##   OriginalPublicationYear = col_double(),
##   read_time = col_double(),
##   MyRating = col_double(),
##   Gender = col_double(),
##   Fiction = col_double(),
##   Childrens = col_double(),
##   Fantasy = col_double(),
##   SciFi = col_double(),
##   Mystery = col_double(),
##   SelfHelp = col_double()
## )

reads2019 <- reads2019 %>%
  mutate(perpage = Pages/sum(Pages))

The new variable, perpage, is a proportion. But if I display those data with a figure, I want them to be percentages instead. Here's how to do that. (If you don't already have the scales package, add install.packages("scales") at the beginning of this code.)

library(scales)

## 
## Attaching package: 'scales'

## The following object is masked from 'package:purrr':
## 
##     discard

## The following object is masked from 'package:readr':
## 
##     col_factor

reads2019 %>%
  ggplot(aes(perpage)) +
  geom_histogram() +
  scale_x_continuous(labels = percent, breaks = seq(0,.05,.005)) +
  xlab("Percentage of Total Pages Read") +
  ylab("Books")

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

You need to make sure you load the scales package before you add the labels = percent attribute, or you'll get an error message. Alternatively, you can tell R to use the scales package just for this attribute by adding scales:: before percent. This trick becomes useful when you have lots of packages loaded that use the same function names, because R will use the most recently loaded package for that function, and mask it from any other packages.

This post also seems like a great opportunity to hop on my statistical highhorse and talk about the difference between a histogram and a bar chart. Why is this important? With everything going on in the world - pandemics, political elections, etc. - I've seen lots of comments on others' intelligence, many of which show a misunderstanding of the most well-known histogram: the standard normal curve. You see, raw data, even from a huge number of people and even on a standardized test, like a cognitive ability (aka: IQ) test, is never as clean or pretty as it appears in a histogram.

Histograms use a process called "binning", where ranges of scores are combined to form one of the bars. The bins can be made bigger (including a larger range of scores) or smaller, and smaller bins will start showing the jagged nature of most data, even so-called normally distributed data.

As one example, let's show what my percent figure would look like as a bar chart instead of a histogram (like the one above).

reads2019 %>%
  ggplot(aes(perpage)) +
  geom_bar() +
  scale_x_continuous(labels = percent, breaks = seq(0,.05,.005)) +
  xlab("Percentage of Total Pages Read") +
  ylab("Books")

As you can see, lots of books were binned together for the histogram. I can customize the number of bins in my histogram, but unless I set it to give one bin to each x value, the result will be much cleaner than the bar chart. The same is true for cognitive ability scores. Each bar is a bin, and that bin contains a range of values. So when we talk about scores on a standardized test, we're really referring to a range of scores.

Now, my reading dataset is small - only 87 observations. What happens if I generate a large, random dataset?

set.seed(42)

test <- tibble(ID = c(1:10000),
               value = rnorm(10000))

test %>%
  ggplot(aes(value)) +
  geom_histogram()

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

See that "stat_bin()" warning message? It's telling me that there are 30 bins, so R divided up the range of scores into 30 equally sized bins. What happens when I increase the number of bins? Let's go really crazy and have it create one bin for each score value.

library(magrittr)

## 
## Attaching package: 'magrittr'

## The following object is masked from 'package:purrr':
## 
##     set_names

## The following object is masked from 'package:tidyr':
## 
##     extract

test %$% n_distinct(value)

## [1] 10000

test %>%
  ggplot(aes(value)) +
  geom_histogram(bins = 10000)

Not nearly so pretty, is it? Mind you, this is 10,000 values randomly generated to follow the normal distribution. When you give each value a bin, it doesn't look very normally distributed.

How about if we mimic cognitive ability scores, with a mean of 100 and a standard deviation of 15? I'll even force it to have whole numbers, so we don't have decimal places to deal with.

CogAbil <- tibble(Person = c(1:10000),
                  Ability = rnorm(10000, mean = 100, sd = 15))

CogAbil <- CogAbil %>%
  mutate(Ability = round(Ability, digits = 0))

CogAbil %$%
  n_distinct(Ability)

## [1] 103

CogAbil %>%
  ggplot(aes(Ability)) +
  geom_histogram() +
  labs(title = "With 30 bins") +
  theme(plot.title = element_text(hjust = 0.5))

## `stat_bin()` using `bins = 30`. Pick better value with `binwidth`.

CogAbil %>%
  ggplot(aes(Ability)) +
  geom_histogram(bins = 103) +
  labs(title = "With 1 bin per whole-point score") +
  theme(plot.title = element_text(hjust = 0.5))

(Now, there's more that goes into developing a cognitive ability test, because the original scale of the test (raw scores) differ from the standardized scale that is applied to turn raw scores into one with a mean of 100 and standard deviation of 15. That's where an entire field's worth of knowledge (psychometrics) comes in.)

This is not to say histograms lie - they simplify. And they're not really meant to be used the way many people try to use them.

Purchases and Happiness

I've heard it said before that it's better to spend your money on experiences than materials. While I appreciate the sentiment - memories last longer than stuff - something about that statement has always bothered me and I couldn't put my finger on what. But a recent study in Psychological Science, "Experiential or Material Purchases? Social Class Determines Purchase Happiness," helped shed some light on when that sentiment might not be true.

The study is a meta-analysis of past research, as well as a report of 3 additional studies performed by the authors, to determine whether social class determines happiness with experiential versus material purchases. Their hypothesis was that experiences are more valuable to people with higher socioeconomic status - that is, because their material needs have been met, they can focus on higher needs - while materials would be more valuable to people with lower socioeconomic status - people who may struggling with basic needs like food and clothing. They confirmed their hypothesis, not only when examining participants' actual SES, but also when it was experimentally determined, by asking participants to imagine their monthly income had been increased or decreased. As they sum up in the article:

We argue that social class influences purchase happiness because resource abundance focuses people on internal states and goals, such as self-development, self-expression, and the pursuit of uniqueness (Kraus et al., 2012; Stephens et al., 2012; Stephens et al., 2007), whereas resource deprivation orients people toward resource management and spending money wisely (Fernbach et al., 2015; Van Boven & Gilovich, 2003). These fundamentally different value orientations translate into different purchase motives held by people from higher and lower classes (Lee, Priester, Hall, & Wood, 2018).

Psychology for Writers: Your Happiness Set-Point

A few years ago, a former colleague was chatting with someone about the work she did with Veterans who had experienced a spinal cord injury. The person she was chatting with talked about how miserable she would be if that happened to her, even implying that she'd rather be dead than have a spinal cord injury. Many of us who have worked with people experiencing trauma probably have had similar conversations. People believe they would never be able to happy again if they experienced a life-changing event like a traumatic injury.

On the other hand, we've also heard people talk about how unbelievably happy they would be if they won the lottery or came into a great deal of money in some way.

But you might be surprised to know that researchers have been able to collect data from people both before and after these types of events - simply because they've recruited a large number of people for a study and some people in the sample happened to experience one of these life-changing events. And you might be even more surprised to know that people were generally wrong about how they would feel after these events. People who had experienced a traumatic injury had a dip in happiness but returned to approximately the same place they were before. And people who won the lottery had a brief lift in happiness followed also by a return to baseline.

These findings offer support for what is known as the set-point theory of happiness. According to this theory, people have a happiness baseline and while events may move them up or down in terms of happiness, they'll eventually return to baseline. Situationally influenced emotions are, for the most part, temporary. You might be sad about that injury, or breakup, or financial problem, or you might be elated about that promotion, or lottery win, or new relationship for a little while, but eventually, you revert to your usual level of happiness. Everyone has their own level. Some people are happier on average than others.

This theory also explains why people who are prone to depression need to seek some kind of treatment, often in the form of therapy and medication - interventions to increase your baseline level of happiness. Money or love or new opportunities may help in the short run, but what needs to be targeted is your baseline itself. Obviously events can target your baseline - a person may have an experience that changes their way of looking at the world, for better or worse. But events that only affect your mood and don't affect your thought process or reaction are unlikely to have any lasting effects.

This is important to keep in mind when writing your characters. Situations and events can obviously push their current mood around. But for a change to be permanent, it has to do more than simply make the person happy or sad - it has to change their mindset. A divorce might make a person sad. A divorce that changes how secure a person feels in relationships or leads them to distrust others might result in a permanent change. A lottery win might make a person happy. A lottery win that helps them become financially independent, get out of a bad situation, and completely change their way of life might result in a permanent change.

Think about how your character is changing and why, to make sure it's a believable permanent change and not just a temporary happiness shift. And if it's just temporary, know that it's completely believable for your character to work back to baseline on his or her own.

Today in "Evidence for the Dunning-Kruger Effect"

A new study shows that watching videos of people performing some skill can result in the illusion of skill acquisition, adding yet more evidence to the "how hard can it be?" mindset outlined in the Dunning-Kruger effect:

Although people may have good intentions when trying to learn by watching others, we explored unforeseen consequences of doing so: When people repeatedly watch others perform before ever attempting the skill themselves, they may overestimate the degree to which they can perform the skill, which is what we call an illusion of skill acquisition. This phenomenon is potentially important, because perceptions of learning likely guide choices about what skills to attempt and when.

In six experiments, we explored this hypothesis. First, we tested whether repeatedly watching others increases viewers’ belief that they can perform the skill themselves (Experiment 1). Next, we tested whether these perceptions are mistaken: Mere watching may not translate into better actual performance (Experiments 2–4). Finally, we tested mechanisms. Watching may inflate perceived learning because viewers believe that they have gained sufficient insight from tracking the performer’s actions alone (Experiment 5); conversely, experiencing a “taste” of the performance should attenuate the effect if it is indeed driven by the experiential gap between seeing and doing (Experiment 6).

In the experiments, participants watched videos of the tablecloth trick (pulling a tablecloth off a table without disturbing dishes; experiments 1 and 5), throwing darts (experiment 2), doing the moonwalk (experiment 3), mirror-tracing (tracing a path through a maze displayed at the top of the screen in a blank box just below it; experiment 4), and juggling bowling pins (experiment 6). Through their research, the authors isolated the missing element in learning by watching - feeling the actual performance of the task. In the 6th experiment, simply getting a taste of the feelings involved - holding the pins that would be used in juggling without attempting to juggle themselves - changed ratings of skill acquisition.

During the Olympics, when you watch athletes at the top of their game performing tasks almost effortlessly, it's easy to think the tasks aren't as challenging as they actually are. Based on these study results, even having people simply put on a pair of ice skates or stand on a snowboard might be enough for them to realize just how difficult skating can actually be.

Just to help put things into perspective, here's a supercut of awesome stunts followed by a person demonstrating why you should not try them at home:

Statistics Sunday: What is Bootstrapping?

Last week, I posted about the concept of randomness. This is a key concept throughout statistics. For instance, I may have mentioned before but many statistical tests assume that the cases used to generate the statistics were randomly sampled from the population of interest. That, of course, rarely happens in practice, but this is a key concept in what we call parametric tests - tests that compare to an assumed population distribution.

The reason for this focus on random sampling goes back to the nature of probability. Every case in the population of interest has a chance of being selected - an equal chance in simple random sampling, and unequal but still predictable chances when more complex sampling methods are used, like stratified random sampling. It's true that you could, by chance, draw a bunch of really extreme cases. But there are usually fewer cases in the extremes.

If you look at the normal distribution, for instance, there are so many more cases in the middle that you have a much higher chance of drawing cases that fall close to the middle. This means that, while your random sample may not have as much variance as the population of interest, your measures of central tendency should be pretty close to the underlying population values.

So we have a population, and we draw a random sample from it, hoping that probability will work in our favor and give us a sample data distribution that resembles that population distribution.

But what if we wanted to add one more step, to really give probability a chance (pun intended) to work for us? Just as cases that are typical of the population are more likely to end up in our sample, cases that are typical of our sampling distribution are more likely to end up in a sample of the sample. (which we'll call subsample for brevity's sake) And if we repeatedly drew subsamples and plotted the results, we could generate a distribution that gets a little closer to the underlying population distribution. Of course, we're limited by the size of our sample, in that our subsamples can't exceed that size, but we can bypass that by random sampling with replacement. That means that after pulling out a case and making a note of it, we put it back into the mix. It could get drawn again. This gives us a theoretically limitless sample from which to draw.

That's how bootstrapping works. Bootstrapping is a method of generating unbiased (though it's more accurate to say less biased) estimates. Those estimates could be things like variance or other descriptive statistics, or it could be used in inferential statistical analyses. Bootstrapping means that you use random sampling with replacement to estimate values. Frequently, it means using your observed data as a sort of population, and repeatedly drawing large samples with replacement from that data. In our Facebook use study, we used bootstrapping to test our hypothesis.

To summarize, we measured Facebook use among college students, and also gave them measures of rumination (tendency to fixate on negative feelings), and subjective well-being (life satisfaction, depression, and physical symptoms of ill health). We hypothesized that rumination mediated the effect of Facebook use on well-being. Put in plain language, we believed using Facebook made you more likely to ruminate, which in turn resulted in lower well-being.

The competing hypothesis is that people who already tend to ruminate use Facebook as an outlet for rumination, resulting in lower well-being. In this alternative hypothesis, Facebook is the mediator, not rumination.

Testing mediation means testing for an indirect effect. That is, the independent variable (Facebook use) affects the dependent variable (well-being) indirectly through the mediator (rumination). We used bootstrapping to estimate these indirect effects; we took 5000 random samples of our data to generate our estimates. Just as we're more likely to draw cases typical of our sample (which are hopefully typical of our population), we're more likely to draw samples that (hopefully) have the typical effect of our population. The resulting indirect effects we get from bootstrapping won't be the same as a simple analysis of our observed data. We're using probability to remove bias from our estimates.

And what did we find in our Facebook study? We found stronger support for our hypothesis than the alternative. That is, we had stronger evidence that Facebook use leads to rumination than the alternative that rumination leads to Facebook use. If you're interested in finding out more, you can read the article here.

Statistical Sins: Hello from the 'Other' Side

I'm currently analyzing data from our job analysis survey (in fact, look for a post in the near future about why it's important for psychometricians to remember how to solve systems of equations), and saved the analysis of demographics for last. Why? Because I'm fighting a battle with responses in the 'Other' category for some of these questions. I think I'm winning. Maybe.

I've blogged about survey design before. But I've never really discussed this concept of having an 'Other' category in your items. You assume when you write a survey that people will find a selection that matches their situation and for those few cases where no options match, you have 'Other' to capture those responses. You then ask people to specify why they are 'Other' so you can create additional categories to capture them in tables.

Or, you know, you could have what people actually do and end up with a bunch of people whose situation perfectly fits one of the existing categories and instead selects 'Other,' then writes an almost word-for-word version of an existing category in the specify box. For instance, in one item on our survey, we had 36 people select 'Other'. After I had looked at their responses and placed the ones that fit an existing category into that box, I had 7 'Other's left.

Actually, that's the second best outcome to hope for when allowing for 'Other.' More often, you get vaguely worded responses that could fit in any number of existing categories, if you only had the proper details. For example, in another item on our survey, I have 17 'Others.' I have no idea where to put two-thirds of them because they lack enough detail for me to choose between 2-3 existing options.

Fortunately, those two items are the standouts, and for remaining questions with 'Other' options, only 4-5 people selected them. Even for those few other responses that are gibberish, I'm not losing a lot of cases by calling them unclassifiable. But still, going through other responses is time consuming and requires a lot of judgement calls.

Obviously, what you want to do is minimize the number of 'Other' responses from the beginning. I know this is far easier said than done. But there are some tricks.

Get experts involved in the development of your survey. And I don't just mean experts in survey design (yes, those too, but...); I mean people with expertise in the topic being surveyed. Ask them what terms they use to describe these categories. And ask them what terms their coworkers and subordinates use to describe these categories. Find potential responses that are widely used and as unambiguous as possible. You'll still have a few stragglers who don't know the terms you're using, but you'll hopefully minimize your stragglers.

If possible, pilot test your survey with people who work in the field. And if your survey is very complex, consider doing cognitive interviews (look for a future blog post on that).

Find a balance in the number of options. What this really comes down to is balancing cognitive effort. You want to have enough to cover relevant situations, because that requires less cognitive effort from you when analyzing your data. You just run your descriptives and away we go.

But you also need to minimize response options to a number people can hold in memory at one time. The question above with the 17 other responses was also the question with the most response options. More isn't always better. Sometimes it's worse. I think for this item, we just got greedy about how much information and delineation we wanted in our responses. But if your response options become TL;DR, you'll get people skipping right to 'Other' because that requires less cognitive effort from them.

Balancing cognitive effort won't be 50/50. Someone is always going to pay with more cognitive effort than they'd like to exert and that someone should almost always be you. If you instead make your respondents pay with more effort than they'd like to exert, you end up with junk data or no data (because people stop completing the survey).

And of course, decide whether you care about other at all. If there are only a fixed number of situations and you think you have all of them addressed, you could try just dropping the other category altogether. Know that you'll probably have people skip the question as a result, if their situation isn't addressed. But if you only care to know if X, Y, or Z situations apply to respondents, that might be okay. This comes down to knowing your goal for every question you ask. If you're not really sure what the goal is of a question, maybe you don't need that question at all. As with number of response options, you also want to minimize the number of questions, by dropping any items that aren't essential. It's better to have 100 complete responses on fewer questions than 25 complete and 75 partial responses on more questions.

Mental Illness and Art

By now, you've probably at least heard of, if not watched, 13 Reasons Why, a series on Netflix that chronicles a set of tapes created by Hannah Baker to explain why she committed suicide. These tapes make it to our protagonist, Clay Jensen, a friend of Hannah's, and we learn about the events taking place prior to and after her suicide. It was a difficult series for me to watch - I had a cousin who committed suicide after his 30th birthday party, which coincidentally was also the day I graduated college. I awoke the next day to the news. A friend of mine who also experienced a family suicide mentioned that she was meaning to watch the show, and I passed on some trigger warnings for her. It's a difficult show to watch for anyone, but for people who have experienced firsthand the grief displayed by the characters of the show, especially Hannah's parents, it can bring back many conflicting emotions.

I do mean to sit down and write a review of the show. I think I need a little more distance, because I know I'm still feeling through many of emotions the show triggered. One thing the show has done is, it has started to get people talking about mental illness. In fact, that's what good art does - gets people thinking and talking about the human condition. In fact, two articles have crossed my path today, dealing with negative emotions more broadly and mental illness specifically.

The first is an interview with psychologist Susan David, whose book Emotional Agility deals with the importance of negative emotions, including in workplace settings. She argues that negative emotions should not be suppressed, because they can provide important information for the feeler and his/her coworkers:

A core part of emotional agility is the idea that our emotions are critical; they help us and our organizations. For example, if a person is upset that their idea was stolen at work, that’s a sign that they value fairness. Instead of being good or bad emotions, we should see emotions as containing useful data.

Our moods can provide us with important information - in fact, we refer to this in psychology as the "mood as information effect." If we realize we're in a negative mood, we analyze the situation to see what the cause could be. This mood is an indicator that something isn't right. Of course, Dr. David is going beyond mood as information and discussing how those emotions could inform others about what the person values. Further - and I'm sure she goes into this in her book - suppressing emotions can lead to thought suppression effects, where the suppressed emotions become stronger and more salient. The cognitive stress of suppressing would also make it more difficult for a person to do their job, especially jobs that require more critical thinking.

The other article deals with mental illness among artists, and asks whether pain is necessary to create great art:

Artists are masochists. We revel in the beauty of pain more than any other profession in the world. It's an experience we create for our viewers that is almost palpable. And it is in this experience that we connect to each other, creating everlasting bonds with our audience.

Some of the world's greatest artists have documented their own struggles with mental health. From depression and anxiety to a wide range of psychological disorders, these are all real themes that will always remain in art.

The article, written by artist Melody Nieves, who has struggled with depression herself, includes many great works of art, some familiar and some likely not, that deal with different aspects of mental illness and emotional pain:

Age Peaks

IFLScience just posted this infographic showing the ages at which you peak at various skills, experiences, and characteristics:

I'm experiencing both fascination and slight existential crisis. Thankfully, today is my Friday.

The Truth About Your Brain

I've blogged in the past about Ben Carson, and all the problems when he tries to demonstrate expertise in an area that isn't neuroscience or medicine more generally. So I'm surprisingly not surprised by this speech where he got a lot of things wrong about the human brain:

It remembers everything you’ve ever seen. Everything you’ve ever heard. I could take the oldest person here, make a hole right here on the side of the head, and put some depth electrodes into their hippocampus and stimulate, and they would be able to recite back to you verbatim a book they read 60 years ago. It’s all there; it doesn’t go away.

Ben, please stop. You're making everyone with a doctorate look bad.

Memory is tricky. Your brain isn't a recorder or a computer that commits everything that ever happens to you to some storage compartment. An electrode or hypnosis or sharp blow to the head won't suddenly make these instances come flooding back, and even if they did, those instances would probably be horribly inaccurate. Your brain is a complex organ inside an unbelievably complex system that allows us to navigate the world and have a semblance of self by actively interpreting what we encounter. It isn't the book we read that gets committed to memory - it's our brain's interpretation of it and how we connect it to previously learned information that (sometimes, not always) gets written to memory.

In fact, all our memories are interpretations, with our brain filling things in with previous experience and expectations. And it isn't just during encoding that mistakes can be introduced; it's during retrieval as well. Have you ever remembered a time in your past and somehow remember a person being there you didn't even know at the time? This happens to me a lot. There's no way I could have even known about that person then, let alone remember seeing them. But my present life gets mixed up with the past. It's a little like writing a paper and saving it to your computer only to find years later that it was accidentally merged with newer files. That's what your brain is like.

And a lot of research has shown how easy it is to implant false memories that feel just as - maybe more - real than actual memories.

Carson's speech was apparently extemporaneous, but still, when talking about something you've dedicated your life to, you should be able to talk off-the-cuff without resorting to misinformation and tropes from bad movies:

Top-Down, Bottom-Up, and the ABCD of Personality

I've blogged many times about the human brain, taking time to discuss the various brain regions and what behaviors and processes they control. Your brain is an amazing demonstration of evolution in action, even in terms of its structure.

The lowest parts of the brain (the hindbrain - the cerebellum, pons, and medulla oblongata) control the basics of life: breathing, heartbeat, sleep, swallowing, bladder control, movement, etc. The midbrain/forebrain* controls processes that rank a little higher on the continuum, but still not what we'd consider high-level processing: emotion, sleep-wake cycle and arousal, temperature regulation, and the transfer of short-term to long-term memory (the very basics of learning), among other things.

Finally, the cerebral cortex, the outer-most part of the brain that developed last evolutionarily speaking; it is responsible for what we call consciousness, and this part of the brain in particular is responsible for many of the traits that differentiate humans from other animals - memory, attention, language, and perception. Other animals have a cerebral cortex as well but not nearly as developed as our own.

These various brain structures work together, and sometimes a lower part of the brain will take over for the higher parts of the brain, especially when there is some kind of disorder of higher brain function. Sandeep Gautam over at The Mouse Trap discusses the work of Paul McClean, and refers to activity coming from the lower brain areas as "bottom-up" and activity from the higher brain areas as "top-down." In his post, he discusses the ABCDs - affect (emotion), behavior, cognition (thought), and desire - and links these bottom-up/top-down processes to different personality traits, offering an eight-part structure of personality: a bottom-up and top-down trait for each of the ABCDs:

• Affective
• Bottom-Up: How we respond to stimuli, specifically Introversion/Extroversion
• Top-Down: Analyzing the situation for things that require increased vigilance and potentially anxiety, a trait called Neuroticism (aka: Emotionality)
• Behavioral
• Bottom-Up: Basic response to stimuli, Impulsivity or Impulsive Sensation Seeking
• Top-Down: A more thoughtful response to stimuli, including considering how that response might impact oneself and others, which could lead to inhibition. This trait is known as Conscientiousness
• Cognition
• Bottom-Up: Degree of distractibility or focus when encountering new things, which manifests as the trait Openness to Experience
• Top-Down: Making connections between concepts, a trait known as Imagination
• Desire/Drives
• Bottom-Up: Degree of aggression in one's reactions, a trait known as Agreeableness
• Top-Down: A process driven by expectation, which impacts one's desire to help or hurt others. He refers to this trait as the Honesty-Humility dimension

This structure is a departure from the Big Five personality traits. Obviously, it includes those 5 (Extroversion, Neuroticism, Conscientiousness, Openness to Experience, and Agreeableness), but adds 3 more (Impulsivity, Imagination, and Honesty-Humility). As I've mentioned before, I'm a big fan of the Big Five (more on that here), so I find this new structure interesting but a little strange. Probably what is strangest to me is that 3 of the Big Five are considered bottom-up processes, rather than the more thoughtful, controlled top-down. I would have thought Agreeableness and Openness to Experience were the result of higher-level processing.

It's a somewhat artificial divide of course. Except in the case of injury to a higher-level part of the brain, even bottom-up processes are going to be shaped by higher-level thinking. Your degree of Introversion/Extroversion, for instance, may influence your most basic response to social stimuli, but it's going to take higher-level processing to understand how best to handle that reaction and also determine what you need in that situation (that is, I'm feeling X, so do I need alone time or time with others?).

What do you think about this new taxonomy?



*These two areas tend to be differentiated from each other, but I was always taught about them in combination, under the title "midbrain." The forebrain includes structures like the amygdala, hippocampus, and so on. They rank higher up than the hindbrain, but are still considered "subcortical."

The Truth, The Whole Truth, and Nothing But the Truth

Today has been dubbed the "Day of Facts," and people and organizations around the world are participating:

A relevant fact is a powerful thing. In that spirit, Friday, Feb. 17, has been dubbed the “Day of Facts” and 270 cultural institutions in the United States and 13 other countries have signed up to use Twitter, Facebook and other social media to share important facts.

“The idea is for libraries and museums and archives across the country and around the world to post mission-related content as a way of reassuring the public that, as institutions, we remain trusted sources of knowledge,” said Alex Teller, director of communications at the Newberry Library. “It reflects recognition among a number of different institutions that while our missions haven’t changed, they’ve taken on a new significance in an era of alternative facts.”

Here's one of the contributions from Robert Martin, emeritus curator of the Integrative Research Center at the Field Museum:

Obviously, spreading misinformation is bad, and we should always strive to only share things that are true, but as we know, that doesn't always happen. The problem is that, even people with the best of intentions, who repeat the misinformation in order to correct it and offer the truth, can still misinform people. People will often remember things they read, but not necessarily the source, and occasionally, if they read something that repeats a myth (stating explicitly that it's a myth), people will sometimes just remember that portion. So they walk away from an article intended to dispel that myth with a stronger belief that it is true. This results in misinformation continuing to be spread. I know I've done the same thing even here on this blog, and it pains me to think anyone would walk away from something I wrote with only the falsehood. So here's a list of psychology myths rewritten as facts:

• You use 100% of your brain, and depend on both sides equally.
• Memory is incredibly malleable, even being changed by the present. Every memory you have is likely inaccurate in small or big ways.
• Déjà vu is a perfectly normal, non-clairvoyant experience.
• There is little support that people have unique "learning styles."
• Mental illness is likely to be caused by a combination of environmental factors and physiological factors.
• The most subliminal messages can do is affect your mood, and there is a tenuous connection between mood and behavior.
• Classical music might make your baby a music snob, but not a genius.
• Lie detector tests measure physiological arousal only. The results have to be interpreted by a person, and people are really bad at guessing whether a person is lying.
• You're more likely to be attracted to people who are similar to you.
• Increases in the prevalence of autism are likely due to a better understanding of the disorder, resulting in better diagnosis (and less misdiagnosis). We're also more aware of it now, so it could just feel more prevalent than it used to be.

For more of today's activities, check out the Day of Facts hashtag on Twitter.

On Economics, the Golden Globes, and Geographic Clustering

The recent presidential election is still an important topic for discussion among my friends, and obviously others as well, as Meryl Streep demonstrated during her Golden Globes speech last night while accepting the Cecil B. DeMille Lifetime Achievement Award:

Streep argues that she and other celebrities need to use their power and privilege to fight Trump, who himself used power and privilege to bully. This group of wealthy celebrities who vehemently oppose Trump are perhaps one of the many groups people have used to argue that Trump's election was not about economics. However, Ben Casselman from FiveThirtyEight insists that we stop saying Trump's election had nothing to do with economics: it did, just not in the way people initially thought:

Economic hardship doesn’t explain Trump’s support. In fact, quite the opposite: Clinton easily won most low-income areas. But anxiety is a different story. Trump, as FiveThirtyEight contributor Jed Kolko noted immediately after the election, won most counties — and improved on Romney’s performance — where a large share of jobs are vulnerable to outsourcing or automation. And while there is no standard measure of economic anxiety, a wide range of other plausible proxies shows the same pattern. According to my own analysis of voting data, for example, the slower a county’s job growth has been since 2007, the more it shifted toward Trump. (The same is true looking back to 2000.) And of course Trump performed especially strongly among voters without a college degree — an important indicator of social status but also of economic prospects, given the shrinking share of jobs (and especially well-paying jobs) available to workers without a bachelor’s degree.

The role of economic anxiety becomes even clearer in the data once you control for race. Black and Hispanic Americans tend both to be poorer and to face worse economic prospects than non-Hispanic whites, but they also had strong non-economic reasons to vote against Trump, who had a history of making racist comments. Factoring in the strong opposition to Trump among most racial and ethnic minorities, Trump significantly outperformed Romney in counties where residents had lower credit scores and in counties where more men have stopped working.

The list goes on: More subprime loans? More Trump support. More residents receiving disability payments? More Trump support. Lower earnings among full-time workers? More Trump support. “Trump Country,” as my colleague Andrew Flowers described it shortly after the election, isn’t the part of America where people are in the worst financial shape; it’s the part of America where their economic prospects are on the steepest decline.

This morning, a friend with whom I've discussed the election a great deal, sent me an article published in the Economist several years ago, which offers another - though not mutually exclusive - explanation, and may explain why myself and others who did not support Trump were so surprised by the number of votes Trump was able to earn. It's a similar phenomenon to what playwright Arthur Miller commented on in 2004: "How can the polls be neck and neck when I don't know one Bush supporter?" The answer is geographic clustering:

Americans are increasingly forming like-minded clusters. Conservatives are choosing to live near other conservatives, and liberals near liberals.

A good way to measure this is to look at the country's changing electoral geography. In 1976 Jimmy Carter won the presidency with 50.1% of the popular vote. Though the race was close, some 26.8% of Americans were in “landslide counties” that year, where Mr Carter either won or lost by 20 percentage points or more.

The proportion of Americans who live in such landslide counties has nearly doubled since then. In the dead-heat election of 2000, it was 45.3%. When George Bush narrowly won re-election in 2004, it was a whopping 48.3%.

Where you live is partly determined by where you can afford to live, of course. But the “Big Sort” does not seem to be driven by economic factors. Income is a poor predictor of party preference in America; cultural factors matter more. For Americans who move to a new city, the choice is often not between a posh neighbourhood and a run-down one, but between several different neighbourhoods that are economically similar but culturally distinct.

For example, someone who works in Washington, DC, but wants to live in a suburb can commute either from Maryland or northern Virginia. Both states have equally leafy streets and good schools. But Virginia has plenty of conservative neighbourhoods with megachurches and Bushites you've heard of living on your block. In the posh suburbs of Maryland, by contrast, Republicans are as rare as unkempt lawns and yard signs proclaim that war is not the answer but Barack Obama might be.

WTF Is Up with Swearing

For a few years, a good friend of mine has given up swearing for Lent. It apparently takes a lot of conscious effort and there are certainly slip-ups, where profanities come out before he has the chance to suppress them. An article in Time explores the scientific research around swearing. Not only can it help us to deal with experiences like pain, it also appears to be somewhat involuntary:

When researchers observed how people dealt with the pain of submerging their hands in icy water, they found that people could withstand more discomfort if they repeated a swear word, rather than a non-swear word. Scientists have also found that unlike most sounds we utter, cussing can happen in both voluntary and involuntary ways. The latter—like when we drop our keys in the snow and yell “F-ck” without consciously deciding to—offer evidence that language isn’t just produced one way in the brain. That has clinical and research implications, says Bergen, and it may tell us something about why we came to communicate as we do.

It also suggests that these emotionally charged words can become so deeply ingrained in us that uttering them toes the line of being a physical act rather than a symbolic one, more like a sneeze than a sentence. “When you say them,” [psychologist Timothy] Jay says, “you feel something.”

We've all probably had the experience of uttering a swear word involuntarily, often in situations where we really shouldn't swear. And many of my friends with kids have discussed times their young children have sworn after dropping something. In fact, we probably all remember this scene from A Christmas Story:

In his research, Jay has recorded and analyzed clips of people swearing, to try to understand why we do it. Swearing offers us a release of our emotions, and elicits a physical response, not unlike fight or flight. Benjamin Bergen, another researcher interviewed for the article, has even written a book on swearing: What the F: What Swearing Reveals About Our Language, Our Brains, and Ourselves.

I've recently become more conscious of swearing in my writing. Before, I would try to avoid it as much as possible - my mom was a children's writer and very strongly dislikes bad language - but I felt that it left my characters too wooden. If they spoke more like I did, and the way many of my friends do, they would be a bit more crass. Now I just let fly in my writing. None of my readers have commented on it, positively or negatively, which is probably the best reaction.

It's All Millennials' Fault

Millennials get a pretty bad rap. And now they're being blamed for more - this time, a slump in fabric softener sales. Procter and Gamble thinks it's because they just don't know how to use it or don't recognize its benefits.

In fact, they've suspected this for a little while and have been posting videos on their YouTube page showing those poor helpless millennials how to use fabric softener (as in the video above). How kind of them...

I'm guilty of cracking many jokes about millennials, though many of my friends strongly identify as such. (I'm on the cusp, and really think of myself more as an X-er, but whatever.) Still, the whole "oh you don't do this? maybe you don't know how" bit has been true in many different contexts. Everything from pushing for better hand hygiene to giving a better job interview has been responded to with education. And it's possible that lack of knowledge is part of the reason millennials are turning their nose up at fabric softener, just like a lack of knowledge may explain why people don't wash their hands as well or as often as they should. But dissemination theory provides many reasons innovations aren't taken up. In fact, one of my favorite theories about diffusion of innovations comes from the work of Everett Rogers, who offers a set of variables that determine whether an innovation is taken up into practice:

So millennials may not use fabric softener because they don't see the benefit (which could be responded to with education), but the issue may also lie in the softener itself - they may not like using additional chemicals with their clothes, and therefore see it as incompatible with their values. Or maybe they have used it and didn't notice a difference, making results unobservable.

For me personally - I have really sensitive skin and I find fabric softener very irritating. (I also have to be careful what kind of detergent I use, as well as what fabrics I wear.) And the smell of fabric softener is way too strong for me. I can smell it on my clothes all day and it makes me gag. Considering the increase in allergies, which has been attributed to increased use of antibiotics - allergic reactions are an immune response, and paradigmatic changes in immune function could definitely explain why we're seeing more food and environmental allergies - it could be that many millennials have more sensitivities as well.

More On Polling and Probability

Shortly after the election, I wrote a post with my thoughts on what happened with the polls, and why they failed to predict that Trump would win. One thing I mentioned is the tenuous connection between behavioral intention (what you plan to do) and behavior (what you actually do). That is, people who said they were planning to vote for Clinton changed their minds and voted for Trump instead. And some new work by Dan Hopkins and Diana Mutz suggests this may have been the case:

Our October 2016 wave was conducted with nationally sampled adults over age 26 between Oct. 14 and Oct. 24, meaning that it ended soon after the third Clinton-Trump debate. At the time, Clinton was riding high in the polls — and 43 percent of our panelists in that wave expressed support for Clinton, as opposed to 36 percent for Trump. By way of benchmarking, this same group of panelists had gone for President Obama over Mitt Romney 46 percent to 39 percent in October 2012.

And while most people’s support remained the same, the changes we did observe were consequential. Consider the table below, showing panelists’ support in the October 2016 poll versus their support in the post-election poll, which took place from Nov. 28 to Dec. 7. Eighty-nine percent of the 1,075 American adults reported the same preference in both waves, whether it was for Clinton (38.0 percent), Trump (35.2 percent) or neither (15.8 percent). But among those who did move, Trump had the advantage. While no one moved from Trump to Clinton, 0.9 percent of our respondents moved from Clinton to Trump. Although that 0.9 percent isn’t a lot, those changes are especially influential, since they simultaneously reduce Clinton’s tally and add to Trump’s. If there were a comparable swing in the national electorate, 1.2 million votes would move to Trump.

In all, Trump picked up 4.0 percentage points among people who hadn’t been with him in mid-October, and shed just 1.7 percentage points for a net gain of 2.3 points. Clinton picked up a smaller fraction — 2.3 points — and shed 4.0 points for a net loss of 1.7 points. That’s certainly consistent with Trump gaining steam in the race’s final weeks. Seeing as the 2016 election was held on the latest possible day given the mandate to hold it on the first Tuesday after the first Monday in November, we might just add the 2016 leap year to the ever-growing list of reasons why Trump prevailed.

So what could have changed voters' minds at the last minute, causing them to shift their support? Hmm, I might have some ideas.

New Book on Kahneman and Tversky from the Author of "Moneyball"

A few days ago, I posted the Riddler's Puzzle and discussed some related concepts, including Tversky and Kahneman's concept of loss aversion, where we place more value on (and work harder to avoid) a loss of a certain magnitude than a gain of the same magnitude. Today I learned that Michael Lewis, author of Moneyball has written a book about these two men and their unlikely friendship, called The Undoing Project. Their work is considered the foundation of the field of behavioral economics, and in fact, Kahneman won the Nobel Prize in economics in 2002. Lewis offers some background on these two men:

During their joint waking hours, they could usually be found together. Danny was a morning person, and so anyone who wanted him alone could find him before lunch. Anyone who wanted time with Amos could secure it late at night. In the intervening time, they might be glimpsed disappearing behind the closed door of a seminar room they had commandeered. From the other side of the door you could sometimes hear them hollering at each other, but the most frequent sound to emerge was laughter. Whatever they were talking about, people deduced, must be extremely funny. And yet whatever they were talking about also felt intensely private: Other people were distinctly not invited into their conversation. If you put your ear to the door, you could just make out that the conversation was occurring in both Hebrew and English. They went back and forth—Amos, especially, always switched back to Hebrew when he became emotional.

Danny was always sure he was wrong. Amos was always sure he was right. Amos was the life of every party; Danny didn’t go to the parties. Amos was loose and informal; even when Danny made a stab at informality, it felt as if he had descended from some formal place. With Amos you always just picked up where you left off, no matter how long it had been since you last saw him. With Danny there was always a sense you were starting over, even if you had been with him just yesterday. Amos was tone-deaf but would nevertheless sing Hebrew folk songs with great gusto. Danny was the sort of person who might be in possession of a lovely singing voice that he would never discover. Amos was a one-man wrecking ball for illogical arguments; when Danny heard an illogical argument, he asked, What might that be true of? Danny was a pessimist. Amos was not merely an optimist; Amos willed himself to be optimistic, because he had decided pessimism was stupid. When you are a pessimist and the bad thing happens, you live it twice, Amos liked to say. Once when you worry about it, and the second time when it happens.

The article chronicles many of the milestones in the friendship of these two men: from rivalry early in their careers, to collaborators, to the time they served together in the Israeli army, and beyond. I'm definitely adding this book to my reading list!

Commentary on the American Divide from David Myers

If you took Introductory Psychology or Social Psychology, it's quite possible your textbook was by David Myers, who in addition to writing textbooks and researching psychological topics, maintains a blog called "Talk Psych." Yesterday, Dr. Myers discussed recent Gallup poll results showing that 77% of Americans perceive our nation to be divided:

All major subgroups of Americans share the view that the nation is divided, though Republicans (68%) are less likely to believe this than independents (78%) and Democrats (83%). That is consistent with the findings in the past two polls, conducted after the 2004 and 2012 presidential elections, in which the winning party's supporters were less likely to perceive the nation as divided.

Americans are split about evenly on whether Trump will do more to unite the country (45%) or do more to divide it (49%). These views largely follow party lines, with 88% of Republicans believing Trump will do more to unite the country and 81% of Democrats saying he will do more to divide it. Independents predict Trump will do more to divide (51%) than to unite the country (43%).

In fact, the most recent time that Americans believed America was more united than divided was following the 9/11 attacks, which psychological theorists argued prompted unity through a concept known as terror management theory: we respond to existential threat (reminders that we are mortal and will die, known as mortality salience) by strengthening our ties to others and reaffirming our identities. This was how many explained, for instance, the upswing in flag display and other symbols of our country (in fact, see a previous post on these concepts).

David Myers offers a social psychological explanation for this "record-high" in perceptions of division:

A powerful principle helps explain today’s deep divisions: The beliefs and attitudes we bring to a group grow stronger as we discuss them with like-minded others. This process, known to social psychologists as group polarization, can work for good. Peacemakers, cancer patients, and disability advocates gain strength from kindred spirits. In one of my own studies, low-prejudice students became even more accepting while discussing racial issues. But group polarization can also be toxic, as we observed when high-prejudice students became more prejudiced after discussion with one another. The repeated finding from experiments on group interaction: Opinion-diversity moderates views; like minds polarize further.

Group polarization feeds extremism. Analyses of terrorist organizations reveal that the terrorist mentality usually emerges slowly, among people who share a grievance. As they interact in isolation (sometimes with other “brothers” and “sisters” in camps or in prisons), their views grow more extreme. Increasingly, they categorize the world as “us” against “them.” Separation + conversation = polarization.

The Internet offers us a connected global world without walls, yet also provides a fertile medium for group polarization. Progressives friend progressives and share links to sites that affirm their shared views and that disparage those they despise. Conservatives connect with conservatives and likewise share conservative perspectives.

The very forces and media intended to bring us together can actually drive us apart.

Today's Riddler Puzzle and the Prisoner's Dilemma

I've been following FiveThirtyEight's Riddler Puzzles for a little while now. Each puzzle deals with probability. Today's puzzle has a little game theory mixed in:

Consider the following war game: Two countries are eyeing each other’s gold. At the beginning of the game, the “strength” of each country’s army is drawn from a continuous uniform distribution and lies somewhere between 0 (very weak) and 1 (very strong). Each country knows its own strength but not that of its opponent. The countries observe their own strength and then simultaneously announce “peace” or “war.”

If both announce “peace,” then they each stay quietly in their own territory, with their own gold, which is worth $1 trillion (so each “wins” $1 trillion).

If at least one announces “war,” then they go to war, and the country with the stronger army wins the other’s gold. (That is, the stronger country wins $2 trillion, and the other wins $0.)

What is the optimal strategy of each country (declaring “peace” or “war”) given its strength?

So we have a continuous uniform distribution to determine country's strength. This means that any value of strength between 0 and 1 is equally likely. The problem discusses only one distribution, and doesn't give any information on what would happen if two country's tied in terms of strength, suggesting that the situation is set up so that each country will have a different strength rating - that is, once one value is selected from the distribution for one country, it cannot be selected again for the other country (random sampling without replacement). The only information a country has, then, is its own strength rating, which it can then use to determine the probability that the other country is stronger or weaker.

If we use the self-interested approach: If a country has a high strength rating (more likely that the other country is weaker instead of stronger), it should select "war." If a country has a lower strength rating (more likely that the other country is stronger instead of weaker), it should select "peace." Of course, it is still possible to win with a low strength rating or lose with a high strength rating; such is the nature of probability - unlikely is not the same thing as impossible.

This particular situation involves a zero-sum game. At least, that's how I conceptualize it, even though the problem talks about the possibility that each country could "win" $1 trillion. Each country already has $1 trillion of gold. If both countries choose peace, they get to keep their money (0 gain for either). If a country chooses war, the winner gets the other's gold ($1 trillion gain), leaving the other country with nothing ($1 trillion loss). So a player in a zero-sum game can only benefit due to the loss of the other. So the situation above is not a true prisoner's dilemma, which is a non-zero-sum game:

Frankly, in a zero-sum game, the best approach would be select "peace" every time; everyone gets to keep their money and no one gets hurt. But I'm a pacifist, so what do I know? :) And in fact, the work of Amos Tversky and Daniel Kahneman on loss aversion suggests that people will work harder to avoid a loss than obtain a gain of the same magnitude, suggesting that each country would be more hesitant to lose $1 trillion than to gain $1 trillion. In that case, they would probably only select "war" if they perceive it to be a sure thing - that is, they have a very high strength rating and the probability that the other country has a higher rating is very low. But this problem is purely probabilistic, and I'm sure they don't want social psychology mixing in.

The way to solve it, then, is to figure out the combinations that maximize a country's winnings - which in some cases would be to do nothing and accept a gain of 0. (I'm not actually going to solve the problem here; mostly just using it as an excuse to discuss some of my favorite topics. Yep, I'm using it as a teachable moment - I'm evil.)

Oversharing in the Age of Social Media

Self-disclosure is how we build strong relationships with others. By telling someone else about your inner world, you are showing that you trust them and can build a deeper connection with them, especially when they, in turn, show you their inner world back (which people are generally inclined to do, through a concept known as reciprocity). In fact, there are many explanations for why self-disclosure improve bonds - not just the trust it displays, it also often provides information that may show you have something else in common with someone (putting them more strongly in your in-group, which you are biased to evaluate positively). Sharing also increases the perception that a person is trustworthy. In fact, research has shown that people who self-disclose are evaluated more positively than people who withhold information, even when the information disclosed is really terrible (like failing-to-tell-someone-you-have-an-STD terrible).

The advent of Facebook and other social media sites provide many opportunities to share information about oneself, but as many have observed, it also provides an avenue to overshare. Now any thought you had, any activity you did, can be broadcast to all of your friends, or an even larger audience if a post is "public." And people use social media to share things with the world that they would never dare shout in a crowded room, even though it's basically the same thing (and arguably worse unless someone in that crowded room is recording the whole thing). But why? Researchers offer some answers, and they're pretty much what you would expect (if you know about social psychology):

Social scientist and author Sherry Turkle thinks we’re losing a healthy sense of compartmentalization. Last year, researchers at Harvard found that the act of sharing our personal thoughts and feelings activates the brain’s neurochemical reward system in a bigger way than when we merely report the attitudes and opinions of others. Meanwhile, Elizabeth Bernstein of the Wall Street Journal asked around and concluded that our newfound urge to disclose is partially due to not only the erosion of private life through the proliferation of reality TV and social media, but also due to our subconscious attempts at controlling anxiety.

“This effort is known as ‘self regulation’ and here is how it works,” she writes. “When having a conversation, we can use up a lot of mental energy trying to manage the other person’s impression of us. We try to look smart, witty, and interesting, but the effort required to do this leaves less brain power to filter what we say and to whom.”

Another crucial ingredient encouraging online exhibitionism is, as stated by [Russell W.] Belk, [chair in marketing at York University in Toronto], the “tension between privacy and potential celebrity.” For some people, the longing to be popular far outweighs the longing to be respected, and their social media accounts can verify this.

I would also add that many people don't think through the gravity of what they share online. They're in a situation where they can impulsively share whatever they want, but don't think through the potential consequences. For instance, I would assume most people know not to let all their friends take a picture of their credit card, while adding, "BTW, the code on back is...", and yet, there are countless people who have posted a picture of their credit card on their social media (along with, "The code on back is... Why is everyone asking me that?").

A few years ago, my mom posted an unedited picture of her passport online, because she was happy to finally have one. Since I get notifications when she posts something, and I know her username/password (guess who set it up for her), I was able to login and delete it only a few minutes after it was posted. My mom is one of the smartest, most logical people I know, who would absolutely know better than to let a stranger take a picture of her passport. I don't think she made the connection that posting it on social media is basically the same thing. (A public post, no less! We had a convo about privacy settings after that.)

And I think the reason for that is because we perceive social media as a toy, something for entertainment value. We know better than to do some of these things in the real world, because that's our life, but Facebook is just something we do for fun. So while Sherry Turkle above may say we're losing a sense of compartmentalization, I would argue we're too compartmentalized in how we perceive social media. We're creating a division between real life with real consequences and online life for play, failing to realize that our social media activity also can have real consequences: having your credit card info "stolen" (or rather, giving it away without realizing it), losing a job, being put on a no fly list, all things that have happened.

Several years ago, I did a study on the potential negative effects of Facebook on mental health. When people asked me if the point of my study was to tell people not to use Facebook, my response was this: Facebook is a tool. A tool isn't inherently good or bad. It's a matter of how it's used. A hammer can help you build a house (good) or smash your finger (bad). The problem isn't the hammer, it's how you used it.

But when you're using a tool, it's important to know how to use it properly and safely. We might get that lesson from someone the first time we pick up a hammer. But no one gives us that lesson the first time we log on to Facebook. And maybe they should.

On Digital Media, Fluency, and Fact-Checking

Blogger changed their format for viewing the blogs I follow as well as accessing my own blog.

It used to be that my blog was listed at the top, with a few buttons underneath it, to add a new post, access previous posts, and view the blog itself. Underneath that was my blog list, an RSS feed of recent posts from the blog I follow.

Now, the default screen is the "posts" view with a button to write a new post, and a button to access my reading list. I usually log on to Blogger a few times a day to look at my RSS feed, and will frequently do so even if I'm not planning to write a post. So the new format is great for people who only use Blogger for blogging, but not so great for people like me who use it to track favorite blogs.

My initial response is that I don't like it, but I know that's because it's different and therefore, it's taking me a little bit longer to access things I used to be able to access with little thought. I've blogged before about what happens when "thinking feels hard" - in cognitive psychology, we refer to the ease or difficulty of thinking as "processing fluency" and we refer to the conclusions we draw from monitoring our thinking as "metacognition." So I'm completely aware that's the reason for my initial, knee-jerk reaction. I prefer to put the thinking into my posts themselves, as opposed to getting to a blank post template.

Fluency can explain a lot of reactions to information. Information that is easy to read, makes us feel good, or aligns with our preconceived notions is more likely to be believed. This could be why various sites, such as Facebook, are trying to limit posts from fake news sources. And a recent study out of Stanford University offers some support for these steps:

Some 82% of middle-schoolers couldn’t distinguish between an ad labeled “sponsored content” and a real news story on a website, according to a Stanford University study of 7,804 students from middle school through college. The study, set for release Tuesday, is the biggest so far on how teens evaluate information they find online. Many students judged the credibility of newsy tweets based on how much detail they contained or whether a large photo was attached, rather than on the source.

More than two out of three middle-schoolers couldn’t see any valid reason to mistrust a post written by a bank executive arguing that young adults need more financial-planning help. And nearly four in 10 high-school students believed, based on the headline, that a photo of deformed daisies on a photo-sharing site provided strong evidence of toxic conditions near the Fukushima Daiichi nuclear plant in Japan, even though no source or location was given for the photo.

Obviously, a better step would be to teach critical thinking skills, so that kids can determine for themselves what information should be trusted. But, as the article points out, fewer schools have librarians who would teach students research skills, and increases in standardized curriculum and assessment to ensure students are performing at grade level means there is no longer extra class time that could be spent on media literacy and critical thinking skills. This places the burden of that instruction on parents, who may not be any better at recognizing fake v. legitimate news.

This is the reason for post-its on our parents' computers that simply say, "Check Snopes first."