January | 2013 | Human Science Explored

Would you trust these eyes?

January 30, 2013

A recent blog post at Science 2.0 reported on a fascinating study by a team of researchers from Charles University in Prague. These researchers published in PLOS One their findings on the relationship between eye colour and perceived trustworthiness of faces.

Which of these two people looks most trustworthy?

Photos by Randen Pederson (L) and “Garrett” (R) – click for details

The team (Karel Kleisner, Lenka Priplatova, Peter Frost, and Jaroslav Flegr) showed photographs of 40 male and 40 female faces to their subjects, and asked them to rate how trustworthy the faces looked. They found a significant relationship – brown-eyed faces were perceived as more trustworthy.

Kleisner, Priplatova, Frost, and Flegr found (after controlling for “dominance” and ”attractiveness”), a very significant (p < 0.001) link between eye colour and perceived trustworthiness, in both male and female faces (image from their paper)

The genius of Kleisner et al., however, was not to leave it at that, but to repeat the experiment after altering the eye colours on the photographs. This revealed that eye colour per se had no effect. Rather, the perceived trustworthiness was linked to aspects of facial shape – aspects that normally correlate with eye colour.

They write: “brown-eyed faces tended to have a rounder and broader chin, a broader mouth with upward-pointing corners, relatively bigger eyes, and eyebrows closer to each other. This was also the pattern of a trustworthy face.”

These results are consistent with earlier work by Alexander Todorov, Sean Baron, and Nikolaas Oosterhof, who found that not only are certain facial shapes perceived as trustworthy, but that these faces generate a measurable response in the Amygdala, detectable with functional Magnetic Resonance Imaging. I’m not sure why this brain response exists, but presumably it is one of the things which confidence tricksters exploit.

Would you trust this brown-eyed man? Frank Abagnale, whose life was portrayed in the film Catch Me If You Can (photo by “Marcus JB”)

– Tony

2 Comments | Human Behaviour, Psychology, Research | Tagged: Amygdala, p-value, Trust | Permalink
Posted by Tony

Vaccination and decision-making

January 26, 2013

Vaccination is important. In Australia, for example, vaccination coverage among 5-year-olds for the terrible disease polio ranges from 93% in Tasmania and the ACT, down to 89.5% in Western Australia (the latter number is a disappointing memorial to the legendary Sugar Bird Lady).

Fortunately, the number of polio cases have been dropping as vaccine coverage improves (source).

Much of the world has now eradicated polio. Afghanistan, Pakistan, and parts of Africa still have poor polio vaccination coverage, and are continuing to see cases. Taliban murders of vaccination workers are a factor in this, sadly.

Polio vaccination coverage worldwide (source)

Frederick Chen, Amanda Griffith, Allin Cottrell, and Yue-Ling Wong, in an interesting paper reported on the Science 2.0 blog, use an online game to investigate vaccination choices. Chen et al. find that “people’s behaviour is responsive to the cost of self-protection, the reported prevalence of disease, and their experiences earlier in the epidemic.”

These results imply that vaccination rates are likely to be disappointing – perhaps dangerously so – for some of the killer diseases which the Western world has half-forgotten. Dana McCaffery and other infants did not need to die of whooping cough, for example.

Whooping cough is deadly. So is diphtheria.

The methods used by Chen et al. are also an interesting way of obtaining human behavioural data in other domains. Here is some related work.

– Tony

1 Comment | Decision Making, Human Behaviour | Tagged: Decision-making, Disease, Society, Vaccination | Permalink
Posted by Tony

Language and Culture

January 20, 2013

Australian PhD student Sara Ciesielski (Graduate School of Humanities & Social Sciences, University of Melbourne) recently had the honour of presenting a “two minute thesis” for PhD Comics, on her project “Language Development and Socialization in Sherpa”:

Novelist Dorothy L. Sayers, in her short story “The Entertaining Episode of the Article in Question” (in Lord Peter Views the Body, 1928), writes about the gender-laden French language: “Now, in France, every male child is brought up to use masculine adjectives about himself. He says: Que je suis beau! But a little girl has it rammed home to her that she is female; she must say: Que je suis belle! … When I am at a station and I hear an excited young woman say to her companion, ‘Me prends-tu pour un imbécile’ – the masculine article arouses curiosity.”

Referring to the same issue is Luce Irigaray’s famous book with an untranslatable title: Ce sexe qui n’en est pas un. The French language also incorporates status differences in the distinction between the pronouns tu and vous. However, other languages, such as Thai, have a far more complex pronoun structure.

Non-verbal Thai communication also requires care

Pioneer linguist Edward Sapir pointed out examples of languages where males and females used quite distinct sublanguages. Japanese is one such language, thus leading to more extreme cases of the issue pointed out by Dorothy Sayers.

Even in British English, studies show that “mauve,” “beige,” “pink,” “maroon,” “lovely,” “nice,” and “cute” are used more often by females than males. However, in 2010, Internet legend XKCD conducted a survey of colour term usage, half-expecting to see this clichéd pattern:

In fact, results were more like this:

More complex patterns of language usage mark professions, ethnic communities, and other subcultures. Writer Annie Dillard once said: “The mind – the culture – has two little tools, grammar and lexicon: a decorated sand bucket and a matching shovel.” The decorations vary from group to group. And occasionally, the shovel is used as a weapon.

– Tony

Leave a Comment » | Human Behaviour | Tagged: Culture, Language, Linguistics, Words | Permalink
Posted by Tony

Emotion and Intelligence

January 14, 2013

A recent blog post in Science 2.0 refers to the 2004 book The First Idea: How Symbols, Language, and Intelligence Evolved from Our Primate Ancestors to Modern Humans by the late Stanley I. Greenspan and by Stuart Shanker. Drawing particularly on personal studies of child development, Greenspan and Shanker claim that “our highest level mental capacities, such as reflective thinking, only develop fully when infants and children are engaged in certain types of nurturing learning interactions.”

They go onto to argue that the various stages of child development involve an intertwined growth of emotional and cognitive skills, and that these cannot be separated.

This raises the question as to whether (strong) Artificial Intelligence is possible. Can an unemotional thinking entity, like Data in Star Trek, actually exist? Such issues are explored further in a 2002 book edited by Robert Trappl, Paolo Petta, and Sabine Payr.

Unemotional thinkers in fiction, like Star Trek’s Data, actually do display various emotions – if not, the reader/viewer would lose interest

Greenspan and Shanker’s theories also have implications for child-rearing. If they are correct, emotionally rich interactions with caregivers are essential for the development of intelligence. For example, they argue (in contrast to Noam Chomsky and Steven Pinker), that language does not develop “spontaneously,” but is critically dependent on those interactions. Greenspan and Shanker write: “A child’s first words, her early word combinations, and her first steps towards mastering grammar are not just guided by emotional content, but, indeed, are imbued with it.”

Emotionally rich interaction (photo: Robert Whitehead, 2006)

– Tony

Leave a Comment » | Human Behaviour, Human Relationships, Psychology | Tagged: Child development, Emotion, Intelligence, Language | Permalink
Posted by Tony

School Shootings – Can Potential Shooter Profiles be Identified?

January 8, 2013

In light of the recent shootings, in Newtown, Connecticut. New debates have been sparked on the idea of gun control and identification of potentially unstable individuals who could commit such crimes.

An article on Science Daily website, School Shootings: What We Know and What We Can Do, highlighted some recent research studying past events and tragedies to accumulate a profile of potential shooters and how these individuals can be identified ahead of time. The article uses research by Dr. Daniel J. Flannery on explaining how shooters demonstrate similar features such as depression, low self-esteem, narcissism and a fascination with death. However these key aspects and similarities across shootings are not strong enough to produce conclusive profiles which could allow for future prevention of such tragedies.

Other research has produced similar findings, Leary, Kowalski, Smith and Philips (2003) analysed multiple shootings between 1995 to 2001. They found that depression, low self-esteem and narcissism were all present in the individuals involved in the shootings. However they all also shared one more common attribute and that was social rejection. Social rejection alone cannot fully explain these acts of violence as most people navigate through life and at some stage are exposed to social rejection. However this social rejection coupled with psychological problems or a fascination with death may lead to acts of violence occurring.

Unfortunately research in this area is inconclusive and therefore specific attributes and characteristics have not been idenitifed to put in place preventative measures to reduce the chances of such tragedies occuring again.

– Stefano

7 Comments | Human Behaviour, Human Factors, Human Relationship, Human Relationships, Individuals, Profiling, Psychology, Psychometrics, Research, Uncategorized | Tagged: Connecticut, Mental health, Research, School shooting, science, Science Daily, Self-esteem, social rejection, videogames | Permalink
Posted by portaluris

Human Sciences, Statistics, and R

January 6, 2013

The use of statistics has long been important in the human sciences. An early example is an analysis by William Sealy Gosset (alias “Student”) of biometric data obtained by Scotland Yard around 1900. The heights of 3,000 male criminals fit a bell curve almost perfectly:

Standard statistical methods allow the identification of correlations, which mark possible causal links:

XKCD teaches us that “Correlation doesn’t imply causation, but it does waggle its eyebrows suggestively and gesture furtively while mouthing ‘look over there.’”

Newer, more sophisticated statistical methods allow the exploration of time series and spatial data. For example, this project looks at the spatial distribution of West Nile virus (WNV) – which disease clusters are significant, and which are merely tragic coincidence:

Distribution of significant clusters of human WNV in the Chicago region, from Ruiz et al.

SPSS has been the mainstay of statistical analysis in the human sciences, but many newer techniques are better supported in the free R toolkit. For example, this paper discusses detecting significant clusters of diseases using R. The New York Times has commented on R’s growing popularity, and James Holland Jones points out that R is used by the majority of academic statisticians (and hence includes the newest developments in statistics), R has good help resources, and R makes really cool graphics.

A really cool graph in R, using the ggplot2 R package (from Jeromy Anglim’s Psychology and Statistics Blog)

An increasing quantity of human-science-related instructional material is available in R, including:

The psych and other R packages supporting the e-book An introduction to psychometric theory with applications in R
An R Companion and datasets supporting the book Statistics for Archaeologists
Notes on the use of R for psychology experiments and questionnaires
R resources at the R psychologist blog
A list of Must-Have R Packages for Social Scientists
Why use R? An (economics) grad student’s 2 cents
The Edinburgh Psychology R-users group

Through the igraph, sna, and other packages (and the statnet suite), R also provides easy-to-use facilities for social network analysis, a topic dear to my heart. For example, the following code defines the valued centrality measure proposed in this paper:

library("igraph")
valued.centrality <- function (g) {
  recip <- function (x) if (x == 0) 0 else 1/x
  f <- function (r) sum(sapply(r, recip)) / (length(r) - 1)
  apply (shortest.paths(g), MARGIN=1, f)
}

This definition has the advantage of allowing disconnected network components, so that we can use these centrality scores to add colour to a standard plot (using the igraph package within R):