Exact solutions to Bayesian and maximum likelihood problems in facial identification when population and error distributions are known

The reliability of traditional photogrammetric identification techniques using a small number of facial landmarks has recently come in for criticism. However, the transformation of parameters into a new face space in which the error distributions are orthogonal, yields a maximum likelihood solution to the problem of identifying a photographed face from a small, known, population which, in a simulated example, raises the success rate from 20% to 93%. A full transformation yielding simultaneously independent population and error distributions can be derived from raw population and error data using a straightforward computer procedure. Such a transformation facilitates computations for the situation where a single suspect is held in custody and the likelihood ratio of his being identical with a photograph is desired. It seems premature to condemn photogrammetry until the more efficient data-analysis approach outlined in this paper has been applied and tested

Clairvoyance in cats: or eight good reasons to publish before writing up your thesis

After a hard day testing participants and worrying about how to analyze the data, the last thing most of us want to think about is generating more work. Here we are, wanting to make the process of becoming “Dr X” as short as possible. Surely a straight line is the shortest distance between two points? Yet sometimes things are not so simple. It could seem like a diversion of time and effort from the direct path to a PhD, but writing up findings for journal publication can bring a lot of benefits

Breaking the F-barrier: How the use of a visual representation of Fisher’s F-ratio can aid student comprehension of orthodox statistics

Universal laws are notoriously hard to discover in the social sciences, but there is one which can be stated with a fair degree of confidence: “all students hate statistics”. Students in the social sciences often need to learn basic statistics as part of a research methods module, and anyone who has ever been responsible for teaching statistics to these students will soon discover that they find it to be the hardest and least popular part of any social science syllabus. A typical problem for students is the use of Fisher’s F-test as a significance test, which even in the simple case of a one-factor analysis of variance (ANOVA) presents difficulties. These are two in number. Firstly, the test is presented as a test of the null hypothesis, that is, that there is no effect of one variable (the independent variable, IV) on the other, dependent variable (DV). This highlights the opposite of what one generally wants to prove, the experimental hypothesis, which is usually that there is an effect of the IV on the DV. Students, if they think about the question at all, may be tempted to ask “why not try to prove the experimental hypothesis directly rather than using this back-to-front approach?” Secondly, the F-ratio itself is presented in the form of an algebraic manipulation, involving the ratio of two mean sums of squares, and these means are themselves moderately complicated to understand. Even students specializing in mathematics often find algebra difficult, and to non- athematicians this formula is simply baffling. Instructors do not usually make a serious attempt to remedy this confusion by attempting to explain what the F-ratio is attempting to measure, and when they do, the explanation is not usually very enlightening. Students may struggle with the statement that the F-ratio is the ratio of “two different estimates of the variance of the population being sampled from, under the null hypothesis”. So what? The result is that students frequently end up applying statistical analysis programs such as SPSS and R, without having the faintest understanding of how the mathematics works. They use the results in a mechanical way, according to a procedure learned by rote memory, and may overlook different tests which might be more appropriate for their data. This might be called the cookbook approach to data analysis, and it is the opposite of the ultimate aim of high quality teaching, which is to provide a deep understanding of principles, which will allow the student to use these principles flexibly in real life challenges, without violating the assumptions of the statistical tests being employed

Understanding the t-test as a variance ratio test, and why t-squared = F.

This unpublished report explains why the t-test is statistically equivalent to a variance ratio test, on the same basis as Fisher's F-test used in the analysis of variance, and shows why in the case of two groups the F statistic reduces to the square of the t statistic, and why the formula for t takes the form it does in cases where the variance in the groups has to be calculated separately for the two groups (the Behrens-Fisher problem). Since writing this paper, I have developed these ideas into a radical new approach to statistical hypothesis testing, based on model comparison. This is set out in detail in my book, Statistics and Experimental Design for Psychologists: a model comparison approach, published in October 2017 and now available on Amazon. The book’s website, which has a sample chapter and a complete set of worked examples free to download, is at: http://www.worldscientific.com/worldscibooks/10.1142/q001

Scales of ability: autism, music, and the need for flexibility in doctoral research.

Working towards a Ph.D. can be a very diverse experience. Not only do people differ greatly from one another in their individual research fields, but one’s whole method of working changes at different stages for the same individual. Looking back as I end my time as a postgrad student, only one thing has remained constant: the future is always unpredictable. The Ph.D. process is like a military operation, and it is said that no military plan survives first contact with the enemy. This is just as true when applied to a research plan and its contact with scientific reality. Threatened by all this uncertainty, one piece of advice that I found helpful was, to find an area in which you can start doing original work at an early stage. Ph.D. examiners are looking for work of publishable quality, and besides, it is very motivating to know that you are exploring a new piece of science. Finding such an area may appear daunting, but I have found that this impression can be mistaken. However, it is necessary to be prepared for the possibility that your first effort may be a false start, and to change direction if necessary. Science, like warfare, requires flexibility and the humility sometimes to admit defeat: a strategic withdrawal, or at least a change of direction, may be essential

Finding the words for it: how alexithymia can account for apparent deficits in the ability of an ASD group to describe their emotional responses to music

The basis for a talk delivered at the 11th International Conference on Music Perception and Cognition (ICMPC11), 23-27 August 2010, Seattle, Washington

A Comparative Study of the Effects of Music on Emotional State in the Normal and High-functioning Autistic Population

It has been assumed that the social deficits inherent in autism imply that individuals with the condition will be unable fully to appreciate the emotional content of music. My aim was to test this assumption, and to explore more widely the similarities and differences between the experience of music in the normal population and those with autism. My first study used musically-induced mood changes and a behavioural measure to show that music does indeed have a more than superficial effect on cognitive processes in a control group. The second study focused on high-functioning adults on the autism spectrum, using semi-structured interviews to investigate the part that music played in their everyday lives, concluding that autism is no bar to full appreciation of the emotional uses of music, though suggesting a degree of impoverishment in the language they used to describe the emotions. The final set of experiments compared control and autism group directly, using physiological (GSR) measures of arousal together with self-report of the emotions evoked by a set of musical items. Standardised questionnaires were used to measure alexithymia (difficulty in identifying and describing feelings) in individuals. Although the autism group experienced comparable levels of physiological arousal to music, they used fewer words than the control group to describe their emotional responses, a difference which correlated strongly with their level of alexithymia. My results are consistent with the hypothesis that in autism, the basic physiological and emotional component of their reactivity to music is functioning normally, but that their ability to translate these reactions into conventional emotional language is reduced, precisely in line with the extent of their alexithymia. These results suggest that the preserved ability of music to generate emotional arousal in autism may lead to clinical applications for the treatment of alexithymia in autism and other conditions

A new power calculator

Describes a new on-line statistical tool for calculating power in simple experimental designs. Power is an important concept in statistics, if only for the very practical reason that many grantgiving bodies now require a minimum power of 80 per cent to be built into the design of any study eligible for funding. At the same time, though the idea is essentially quite simple, standard textbooks often make the calculations appear mysterious. The provision of computer programs or tables to enable the necessary calculations, though functional, provides no insight into what is going on. The development of a visual aid or ‘nomogram’ by Douglas Altman was a major step forward, making the calculation – at least for a typical independent groups scenario – almost instantaneous. But Altman’s version used only two alpha levels (both 2-tailed), and given the design, the use of even two levels made the diagram somewhat overcrowded. Moreover, it provided no insights into the underlying principles on which it operated

Autism, music, and the therapeutic potential of music in alexithymia

It has been argued, in view of the social evolutionary origins of music and the social deficits found in autism, that individuals with autism will be emotionally unresponsive to music. However, a recent study of high-functioning adults with autism has shown that they appear to have a range of responses to music similar to typically developing people, including the deliberate use of music for mood management. In examining why these responses appear unaffected in autism, we explore possible mechanisms for musical mood induction in listeners, hypothesizing that the simulation theory of empathy may illuminate current controversies over the nature of emotion in music. Drawing on these ideas, we put forward suggestions for using a simple associative learning process between musically induced emotions and their cognitive correlates for the clinical treatment of alexithymia, a disorder that is common in autism and characterized by an absence of cognitive insight into one’s emotions