Brexit or Bremain? A person and social analysis of voting decisions in the EU referendum

The period following UK's European Union referendum in 2016 foreshadows significant social and political change in the UK. The current research draws on social psychological theories to empirically examine the drivers of voting decisions during the referendum. We report the results of a prospective study using structural equation modelling with data (N = 244) collected just before, and self‐reported voting behaviour immediately following (N = 197), the European Union referendum. We employ a person and social approach to examine the additive roles of worldview, conservatism, social identity, and intergroup threat as predictors of voting intentions and behaviour. Results showed that person factors (worldview and conservatism) predicted voting intentions through social factors (European identity and realistic threat) and that intentions predicted behaviour. The results highlight the importance of addressing threat‐based intergroup rhetoric and the potential of common in‐group identity to mitigate psychological threat.     

Beyond the broom cupboard: teaching assistants’ reflections upon the wider impact of their specialist dyslexia training

Whilst the government-commissioned Rose Report (2009) on provision and support for dyslexic learners in England called for the deployment of more specialist dyslexia teachers in mainstream schools, the reality seems to be that the job of teaching literacy to children with dyslexia is falling to teaching assistants (TAs). As a result, specialist training courses for TAs have been developed in England. Using the Coldwell and Simkins Training Impact Framework, the present study examines TAs participants’ perceptions of the impact of two such local authority-based training programmes, not just in terms of the impact upon their individual practice, but also in terms of their perceptions of the wider impact of their status as a trained ‘expert’ upon their schools’ policies, culture and practice. ‘Enabling’ and ‘blocking’ factors influencing this wider impact are identified, and implications for the deployment of specialist TAs and the design of their training courses are discussed.     

Do complex models increase prediction of complex behaviours? Predicting driving ability in people with brain disorders

Prediction of complex behavioural tasks via relatively simple modelling techniques, such as logistic regression and discriminant analysis, often has limited success. We hypothesized that to more accurately model complex behaviour, more complex models, such as kernel-based methods, would be needed. To test this hypothesis, we assessed the value of six modelling approaches for predicting driving ability based on performance on computerized sensory-motor and cognitive tests (SMCTests?) in 501 people with brain disorders. The models included three models previously used to predict driving ability (discriminant analysis, DA; binary logistic regression, BLR; and nonlinear causal resource analysis, NCRA) and three kernel methods (support vector machine, SVM; product kernel density, PK; and kernel product density, KP). At the classification level, two kernel methods were substantially more accurate at classifying on-road pass or fail (SVM 99.6%, PK 99.8%) than the other models (DA 76%, BLR 78%, NCRA 74%, KP 81%). However, accuracy decreased substantially for all of the kernel models when cross-validation techniques were used to estimate prediction of on-road pass or fail in an independent referral group (SVM 73-76%, PK 72-73%, KP 71-72%) but decreased only slightly for DA (74-75%) and BLR (75-76%). Cross-validation of NCRA was not possible. In conclusion, while kernel-based models are successful at modelling complex data at a classification level, this is likely to be due to overfitting of the data, which does not lead to an improvement in accuracy in independent data over and above the accuracy of other less complex modelling techniques.     

Perceptions of stranger and acquaintance rape: the role of benevolent and hostile sexism in victim blame and rape proclivity

In Studies 1 and 2, after reading an acquaintance-rape but not a stranger-rape scenario, higher benevolent sexist but not hostile sexist participants blamed the victim significantly more. In Study 2, higher hostile sexist but not benevolent sexist male participants showed significantly greater proclivity to commit acquaintance (but not stranger) rape. Studies 3 and 4 supported the hypothesis that the effects of benevolent sexism and hostile sexism are mediated by different perceptions of the victim, as behaving inappropriately and as really wanting sex with the rapist. These findings show that benevolent sexism and hostile sexism underpin different assumptions about women that generate sexist reactions toward rape victims.     

Understanding variability in binary and continuous choice

Excessive variability in binary choice (categorical judgment) can take the form of probability matching rather than the normatively correct behavior of deterministically choosing the more likely alternative. Excessive variability in continuous choice (judgment rating) can take the form of underconfidence, understating the probability of highly likely events and overstating the probability of very unlikely events. We investigated the origins of choice variability in terms of noise prior to decision (at the evidence stage) and at the decision stage. A version of the well-known medical diagnosis task was conducted with binary and continuous choice on each trial. Noise at evidence stage was reduced by allowing the subjects to view historical summaries of prior relevant trials, and noise at the decision stage was reduced by giving the subjects a numerical score on the basis of their continuous choice and the actual outcome. Both treatments greatly reduced variability. Cash payments based on the numerical score had a less reliable incremental effect in our experiment. The overall results are more consistent with a Logit model of decision than with a simple criterion (or maximization) rule or a simple probabilitymatching rule.     

Considerations in software character generation

Two concerns about the development of a computer-driven display system to achieve tachistoscopic displays are discussed. Approaches to solving these problems are described.     

A visual display system for reading and visual perception research

The selection of a computer visual display system suitable for word recognition and reading research is described. The software character generation routines permit flexible definition of character sets. The display software permits control of size scaling and point density of characters being displayed as well as control over the temporal microstructure of presenting and refreshing the displayed text.