24 January 2020

Climate’s Changing, So Are Birds

Welcome back. In an earlier blog post, I wrote that, though I grew to 5 foot 10-3/4 inches (“5-11” for high school basketball and dating), I’ve been getting shorter for years (see I’m Shrinking!).

Shrinking with age is common--spinal discs can compress, loss of bone density can cause spine curvature and collapsed vertebrae, loss of muscle and gain of fat can contribute.

The possible reasons vary, but they don’t include climate change. That can’t be said for North American migratory birds as shown in a study by a team of researchers affiliated with the University of Michigan and The Field Museum, Chicago.

Bird Sample and Measurements

Reflective windows can
be bird magnets. Imagine
how many strike buildings
during bird migrations.
North American migratory birds, whose breeding ranges span North America and winter ranges extend to South America, regularly collide with buildings in Chicago during their fall and spring treks. Since 1978, personnel and volunteers of The Field Museum have retrieved some 87,000 bird carcasses of more than 200 species.

One person (the same person) made the following morphological measurements on each fresh or thawed bird carcass: (1) tarsus (part of lower leg) length and bill length using digital calipers, (2) relaxed wing length using a wing rule and (3) mass using a digital scale. After the measurements, the carcasses were prepared as specimens, and skull ossification (bone formation, indicative of age), fat levels, sex and molt were recorded.

Although the measurements were made to assess annual and seasonal variation of birds, subtle changes in bird size were observed which led to the team’s analysis of longer-term trends and possible causes.

Shortening of tarsus length (millimeters) of two bird species over the years; dashed lines have zero slope, intercepts equal the mean tarsus length for each species and n equals number of specimens sampled (from onlinelibrary.wiley.com/doi/abs/10.1111/ele.13434).
Chief among the possibles was climate change. Body sizes of animals, especially birds and mammals, are often tied to climate. Within a species, individuals living in colder environments tend to be larger than those in warmer areas. The trend, referred to as Bergmann's rule, helps animals in cold places stay warm.

Linking Bird and Climate Changes
The dataset selected for analysis consisted of the measurements of 70,716 birds, collected 1978 to 2016. The birds represented 52 species (11 families, 30 genera), reflecting diverse ranges, habitats, migratory distances, life histories and ecologies.

The researchers employed two statistical modeling approaches (a form of regression and a mixed-effects model) to test hypotheses on causes of change in adult body size and wing length.

Body size changes focused on three indices: tarsus, mass and a statistical combination (first principal component) of tarsus, wing, bill and mass. These were related to species-specific estimates of climatic and environmental variables at the breeding and wintering grounds over time--temperature, precipitation and a proxy for resource availability (Normalized Difference Vegetation Index).

Wrap Up
The researchers found a near-universal reduction in body size for the 52 species over the four decades. Average body mass decreased by 2.6% and tarsus length by 2.4%.

Modeling showed the variable that accounted for the greatest change--by an order of magnitude--was increased summer temperature at the breeding grounds. There was no evidence that precipitation or resource availability drove the trend.

Lines represent all bird species, with group mean centered by bird species (70,716 specimens of 52 species). Mass, tarsus and a statistical combination of tarsus, bill, wing and mass (PC1) all decreased over the years, while wing length increased (from onlinelibrary.wiley.com/doi/abs/10.1111/ele.13434).
While body size decreased, wing length consistently increased across species. Average wingspans grew by 1.3%, a change which was not explained by environmental variables on the breeding or wintering grounds. There is evidence, however, to suggest that increased wing length could be a compensatory adaptation, allowing birds with smaller bodies to produce the energy needed to make the long migrations.

Stay tuned as climate continues changing, and thanks for stopping by.

P.S.
Study of North American migratory bird changes in Ecology Letters journal: onlinelibrary.wiley.com/doi/abs/10.1111/ele.13434
Article on study on EurekAlert! website: www.eurekalert.org/pub_releases/2019-12/fm-bas112719.php
Bergmann's rule: en.wikipedia.org/wiki/Bergmann%27s_rule

17 January 2020

Which Physicians Speed?

Welcome back. Since you’re reading this post and I hope following this blog, you’re no doubt on the upper end of the intelligence spectrum. You know, for example, that you can learn about any topic, even if you can’t remember or never learned the location of the nearest library. You have the internet.

Is that a physician stopped for
speeding? How fast was he going?
What is his medical specialty?

(photo from evidentiarymatters.com/?m=201307).
Of course, some topics, especially those reviewed here, are rather new and require more digging. And then some have just never been addressed. How frustrating when it’s something you’ve always wondered about, such as today’s topic: Do speeding, luxury car ownership and leniency by police differ across physician specialties?

We’ve finally got answers thanks to researchers affiliated with Harvard Medical School, Massachusetts General Hospital and the National Bureau of Economic Research. Their assessment was predicated upon (1) driving behavior and luxury car ownership have been linked to personality, (2) many people believe there’s a link between personality and a physician's specialty and (3) some specialists interact more with police in their work (e.g., emergency medicine), which could influence leniency.

Speeding Ticket Review
The researchers matched publicly available records on speeding tickets and physicians and, for comparison, non-physicians, who were issued one or more speeding tickets in Florida, 2004-2017. Speed was recorded on 38% of the physicians’ tickets and car make on 64%.

They broke the data down by medical specialty and took account of age and sex, which are correlated with specialty. Tickets issued to physicians younger than 30 were excluded as those might have been issued before the drivers became physicians.

The analysis focused on rates of speeding and extreme speeding (exceeding the speed limit by more than 20 mph), luxury car ownership (e.g., Audi, BMW, Ferrari, Maserati and Porsche) and police officer leniency, which was defined as recording speeds just below the threshold at which a larger fine would otherwise be imposed ("speed discounting").

Speeding tickets issued to physicians in Florida, 2004-2017 (from www.bmj.com/content/bmj/367/bmj.l6354.full.pdf).
Do Physicians or Specialties Differ?
The study sample included 5,372 physicians, issued 14,560 tickets, and 19,639 non-physicians, issued 63,382 tickets. The proportion of drivers ticketed for extreme speeding was almost identical for physicians and non-physicians, 26.4% vs. 26.8%, respectively. (Of interest, one third of physicians in the U.S. are female, yet female physicians were issued only 18.5% of tickets for extreme speeding.)

Speeding was broadly similar across medical specialties, and extreme speeding was common, accounting for one quarter of physicians’ tickets. Psychiatrists were the most likely to be ticketed for extreme speeding. (The prize, however, went to one general internist who was ticketed at 70 mph over the limit.)

Unlike speeding, luxury car ownership by physicians who received a speeding ticket was quite different across specialties. Luxury cars were most commonly being driven by cardiologists and least commonly being driven by those in emergency medicine, family practice, pediatrics, general surgery and psychiatry.

Leniency by police was common, but it did not differ by specialty or between physicians and non-physicians.

Wrap Up
The researchers point out that, being an observational study based on speeding in Florida, cause cannot be established, unmeasured factors may have had an influence and the results may vary in areas with different driving cultures, demographics or policing practices.

So, while the study provides answers we’ve always wondered about, there’s no guarantee those answers apply in your or my neck of the woods. Drive safely and thanks for stopping by.

P.S.

Study of physician driving behaviors in The British Journal of Medicine (BMJ): www.bmj.com/content/bmj/367/bmj.l6354.full.pdf
Article on study on EurekAlert! website: www.eurekalert.org/pub_releases/2019-12/b-pml121619.php

10 January 2020

Liars, Lies and Lying

Welcome back. At the close of 2019, the most viewed post of the more than 660 on this blog was Facial Expressions Addendum

That 2015 post described the Facial Action Coding System for human expressions by Paul Ekman and colleagues. Facial micro-expressions usually occur when a person is deliberately or unconsciously concealing a feeling, which might be lying. (I refer you to the TV show Lie to Me, if you can find it.)

Are you a good liar, or does it
show
(from Walt Disney Productions)?
I hope there’s still interest in lying, because that’s the subject of today’s post. A recent study by researchers affiliated with Maastricht University in the Netherlands and the U.K.’s University of Portsmouth examined the association between deception ability and lie prevalence and characteristics as well as how “good liars" use deception strategies.

Lying Survey
The researchers surveyed 194 participants (175 U.S. and 19 Indian citizens) through Amazon Mechanical Turk with a two-part online questionnaire. Definitions were provided.

In Part 1, participants rated how good they were at deceiving others (1-very poor to 10-excellent); they estimated the number of lies they told during the past 24 hours; and they responded to multiple-response questions about those lies: (i) types of lies told (white lies, exaggerations, lies of omission/concealment, lies of commission/fabrications or embedded lies), (ii) to whom they lied (family, friend, employer, colleague, authority figure or other) and (iii) the mediums of deception (face-to-face, phone, social media, text message, email or other).

In Part 2 of the questionnaire, participants explained strategies they use when telling lies; how important they consider verbal and nonverbal strategies are for lying successfully (1–not important to 10–very important); and which verbal strategies they use from a predetermined list (e.g., reporting from previous experience, providing unverifiable details, telling a plausible story).

Lie Prevalence and Characteristics
Using the self-reported ratings of deception ability, the researchers categorized participants as poor liars (51), neutral liars (75) or good liars (68). Of the poor liars, 70% were female, while 53% of the good liars were male. There was no significant association between deception ability and education level.

The participants told an average of 1.6 lies during the last 24 hours (from 0 to 20 lies), but that was highly skewed. The six most prolific liars accounted for nearly 39% of the lies, and 39% of the participants reported telling no lies.

In general, good liars told the most lies per day, mainly white lies and exaggerations to colleagues, friends or others, such as romantic partners, not to family, employers or authority figures. They told most lies face-to-face.

Bar charts showing the participants’ use of different deception types during the past 24 hours; error bars are 95% confidence intervals (from journals.plos.org/plosone/article?id=10.1371/journal.pone.0225566).
Deception Strategies
The only statistically significant association between deception ability and deception strategy was with “No strategy.” Nevertheless, all liars judged behavioral strategies important for deceiving successfully.
Number (N) of surveyed participants using the listed deception strategies (modified from journals.plos.org/plosone/article?id=10.1371/journal.pone.0225566).
Favoring face-to-face deception, good liars commonly used the verbal strategies of embedding lies into truthful information and keeping their statements clear, simple and plausible. Good liars were also more likely to match the amount and type of details in their lies to the truthful part of their story and provide unverifiable details.

Poor liars were more likely to rely on avoidance, being intentionally vague or omitting certain details.

Wrap Up
The study did not survey a statistically random sample of any defined population, yet the results serve well for an exploratory study, as intended, especially in portraying good liars.

Not to spoil the moment, but I can’t leave the topic of lying without commenting on the president…Oh, never mind. Unlike when he says, “Everyone knows that,” everyone does know that. Thanks for stopping by.

P.S.
Study of lying in PLOS One journal: journals.plos.org/plosone/article?id=10.1371/journal.pone.0225566
Article on study on EurekAlert! website: www.eurekalert.org/pub_releases/2019-12/uop-mtt122019.php

03 January 2020

Nonverbal Exclamation Emotions

Happy 2020! And welcome back. I hope you won’t mind if I review a study published about a year ago. It’s not that I just found the study. Well, it is, sort of. The study was buried on my list of possible blog topics. I noticed it while deleting files to prepare for the new year, and I think it’s an ideal kickoff for 2020.

One of the more pleasant
nonverbal exclamations.
The topic is nonverbal exclamations, such as ohhh or oops. They communicate feelings that can be understood immediately. They are essential to recognizing emotion from vocalizations.

A team of researchers, affiliated with the University of California, Berkeley, Washington University in St. Louis and Sweden’s Stockholm University, set out to better define the relationship between these vocal bursts and emotions. For example, how many distinct kinds of emotions can be expressed? Is the recognition of emotion expressions discrete or continuous?

Collection and Initial Assessment of Vocalizations
The researchers recorded 2,032 vocal bursts by 56 male and female professional actors and non-actors from the U.S., India, Kenya and Singapore responding to emotionally evocative scenarios.

They then had more than 1,000 adults (via Amazon's Mechanical Turk) listen to and evaluate the vocal bursts for the emotions and meaning they conveyed, whether the tone was positive or negative and other characteristics.

Statistical analysis placed the vocal bursts into at least two dozen categories, including amusement, anger, awe, confusion, contempt, contentment, desire, disappointment, disgust, distress, ecstasy, elation, embarrassment, fear, interest, pain, realization, relief, sadness, surprise (positive) surprise (negative), sympathy and triumph.

Providing Contexts for Vocal Bursts
The researchers sampled YouTube video clips that evoked the 24 emotions. Vocal bursts extracted from videos (e.g., puppies being hugged, spellbinding magic tricks) were judged by 88 adults and categorized into 24 shades of emotion.

Here’s the best part. They organized all of the data into a natural language semantic space in the form of an online interactive audio map (see P.S. or figure captions for link).

Graphical depiction of online interactive audio map of emotions conveyed by nonverbal exclamations (from www.alancowen.com/vocs).
Enlarged view of top-left section of online interactive audio map; various colored spots provide audio of the gradient mix of emotions (from www.alancowen.com/vocs).
You slide your cursor over any of the categories of emotion and hear the exclamations--surprise (gasp), realization (ohhh), fear (scream). Then you find the categories are linked by gradients with continuously varying meaning. In the map’s embarrassment region, you might find a vocalization recognized as a mix of amusement, embarrassment and positive surprise.

Wrap Up
The researchers suggest that, along with linguistics applications, the map should be useful in helping teach voice-controlled digital assistants and robots to recognize human emotions based on sounds. Another possible application would be helping to identify specific emotion-related deficits in people with dementia, autism or other emotional processing disorders.

The only problem I find is the relative difficulty of examining the map on a smartphone or even a tablet rather than a laptop or desktop computer. Maybe it’s just my devices. I hope you’ll manage; it’s really cool. Thanks for stopping by.

P.S.
Study of emotions conveyed by nonverbal vocalizations in American Psychologist journal: psycnet.apa.org/doiLanding?doi=10.1037%2Famp0000399
Article on study on ScienceDaily website: www.sciencedaily.com/releases/2019/02/190205144343.htm

Interactive audio map of emotions conveyed by nonverbal vocalizations: www.alancowen.com/vocs
The interactive audio map is also included in the UC Berkeley press release: news.berkeley.edu/2019/02/04/audio-map-of-exclamations/

27 December 2019

Biased Memories

Schema (pl. schemata)…mental structures that an individual uses to organize knowledge and guide cognitive processes and behaviour…Schemata represent the ways in which the characteristics of certain events or objects are recalled, as determined by one’s self-knowledge and cultural-political background. (Britannica)

Welcome back. There’s a nice, neat, very cool study by Ohio State University researchers on false information, specifically numbers. It turns out that people misremember numerical statistics to fit their schemata, their beliefs or expectations. To show this, the researchers conducted two experiments.

Misremembering Unexpected Numbers
In the first, they had 110 participants read short descriptions about four issues that contained numerical information. Pre-testing indicated that the numerical information on two of the issues would fit most participants’ expectations, while the numerical information on the other two issues would not.

An example of the former is that people generally expect more Americans to support rather than oppose same-sex marriage, which is true. An example of the latter is that most people believe the number of Mexican immigrants in the U.S. increased between 2007 and 2014, though it actually decreased by 1.1 million.

After reading the four descriptions, the participants were given a pop quiz: Write down the numbers in the descriptions.
 

Misremembering numbers that
don’t fit expectation.
The participants usually got the numerical relationship correct on the two issues that were consistent with how most people viewed the issue, such as same-sex marriage. But on the two issues that did not fit their expectation, such as the trend in Mexican immigrants, participants were much more likely to remember the numbers according to their probable biases rather than the truth. Some remembered the exact numbers, reversed.

As an extension to the first experiment, the researchers used eye-tracking technology to monitor participants when they read the descriptions. The eye responses were very different when reading numerical information that fit and didn’t fit their expectation.

It Gets Worse When Spread
For the second experiment, the researchers tested how social transmission of numerical information can exacerbate the memory errors.

The Telephone Game (photo from:
www.roastbrief.com.mx/2013/11/boca-en-boca-publicidad)
Similar to the Telephone Game, the first participant in a “telephone chain” read the correct statistics about the trend in Mexican immigrants in the U.S. That person wrote down the numbers from memory, and the numbers were passed to the second participant in the chain. That person wrote down the numbers from memory, the numbers were passed to the third participant, and so on down the chain.

The experiment found that, on average, the first participant reversed the numbers, remembering an increase of 900,000 immigrants instead of a decrease of 1.1 million. That error increased to about 4.6 million immigrants by the last participant.

Wrap Up
The study showed that self-generated, numerical misinformation can be as bad as, if not worse than, false information from external sources. Unfortunately, our biases are constant.

The study was published about a week before the Washington Post Fact Checker reported that President Trump made 15,413 false or misleading claims over 1,055 days. Seeing the updated Fact Checker statistics, I wondered if, instead of intentionally stating numerical information wrong, the president was just misremembering to fit his beliefs. Probably not considering the preponderance of non-numerical claims.

Thanks for stopping by.

P.S.
Study of numerical misinformation in Human Communication Research journal: academic.oup.com/hcr/advance-article-abstract/doi/10.1093/hcr/hqz012/5652186?redirectedFrom=fulltext
Ohio State University news release on study: news.osu.edu/you-create-your-own-false-information-study-finds/
Washington Post article on president’s false or misleading claims: www.washingtonpost.com/politics/2019/12/16/president-trump-has-made-false-or-misleading-claims-over-days/