Authentic leadership,followership, and psychological capital as antecedents of work engagement

The present study investigated authentic leadership, psychological capital, and followership behaviour influences on work engagement of employees. Respondents were 901 South African employees within the healthcare industry organisation (n = 647) and mining industry (n = 254). The employees completed questionnaires on authentic leadership, psychological capital, and followership behaviour, and work engagement. Results following structural equation modelling and mediation analysis suggest work engagement to be explained by the psychological capital of the employee rather than by authentic leadership qualities. Improving employee psychological capital has the potential to enhance the levels of work engagement of employees.     

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.     

Contrast effects in typicality judgements: A hierarchical Bayesian approach

We examine the influence of contrast categories on the internal graded membership structure of everyday concepts using computational models proposed in the artificial category learning tradition. In particular, the generalized context model (Nosofsky, 1986), which assumes that only members of a given category contribute to the typicality of a category member, is contrasted to the similarity–dissimilarity generalized context model (SD-GCM; Stewart & Brown, 2005), which assumes that members of other categories are also influential in determining typicality. The models are compared in a hierarchical Bayesian framework in their account of the typicality gradient of five animal categories and six artefact categories. For each target category, we consider all possible relevant contrast categories. Three separate issue are examined: (a) whether contrast effects can be found, (b) which categories are responsible for these effects, and (c) whether more than one category influences the typicality. Results indicate that the internal category structure is codetermined by dissimilarity towards potential contrast categories. In most cases, only a single contrast category contributed to the typicality. The present findings suggest that contrast effects might be more widespread than has previously been assumed. Further, they stress the importance of characteristics particular of everyday concepts, which require careful consideration when applying computational models of representation of the artificial category learning tradition to everyday concepts.     

When and for whom do frequencies facilitate performance? On the role of numerical literacy

The thesis that the mind is better prepared to process frequencies—as compared to other numerical formats—continues to be debated. A recent aspect of this issue is the role of numeracy (numerical literacy; one's ability to understand and work with numerical information) and specifically the argument that individual differences in numeracy interact with numerical formats. This interaction, either that frequencies improve performance only for those of low numeracy or that frequencies work only for those of high numeracy, would suggest that better performance using frequencies could be due to (nonevolutionary) numeracy effects. The three present studies revisited prior work with cumulative probability, Bayesian reasoning, and scenario risk assessments to study the effects of numeracy on frequency facilitation. Results from these experiments consistently failed to replicate previous findings of interactions; however, a more consistent finding emerged of a straightforward frequency effect. The lack of interactions and observations of frequency main effects lend support to the evolutionary explanation of the frequency effect. In addition, some possible statistical processes are proposed to explain the observation of interactions in past studies.     

Attention and memory protection: Interactions between retrospective attention cueing and interference

Visual working memory (VWM) and attention have a number of features in common, but despite extensive research it is still unclear how the two interact. Can focused attention improve VWM precision? Can it protect VWM from interference? Here we used a partial-report, continuous-response orientation memory task to examine how attention and interference affect different aspects of VWM and how they interact with one another. Both attention and interference were orthogonally manipulated during the retention interval. Attention was manipulated by presenting informative retro-cues, whereas interference was manipulated by introducing a secondary interfering task. Mixture-model analyses revealed that retro-cues, compared to uninformative cues, improved all aspects of performance: Attention increased recall precision and decreased guessing rate and swap-errors (reporting a wrong item in memory). Similarly, performing a secondary task impaired all aspects of the VWM task. In particular, an interaction between retro-cue and secondary task interference was found primarily for swap-errors. Together these results suggest that both the quantity and quality of VWM representations are sensitive to attention cueing and interference modulations, and they highlight the role of attention in protecting the feature–location associations needed to access the correct items in memory.     

Inconsistency effects in source memory and compensatory schema-consistent guessing

The attention–elaboration hypothesis of memory for schematically unexpected information predicts better source memory for unexpected than expected sources. In three source-monitoring experiments, the authors tested the occurrence of an inconsistency effect in source memory. Participants were presented with items that were schematically either very expected or very unexpected for their source. Multinomial processing tree models were used to separate source memory, item memory, and guessing bias. Results show an inconsistency effect in source memory accompanied by a compensatory schema-consistent guessing bias when expectancy strength is high, that is, when items are very expected or very unexpected for their source.     

Methodological and empirical developments for the Ratcliff diffusion model of response times and accuracy

Summerfield, Egner, Mangels, and Hirsch (2006) suggested that visual misperception may be triggered by strong yet wrong expectation when sensory data are insufficient. Bar (2003) posits that immediately after stimulus presentation, low frequency spatial information (LFSI) convey the global form of the image which is matched to stored representations in the orbitofrontal cortex to generate predictions of the input. We used signal detection to test whether a strong prediction induced by informative global form, hence reducing the number of possible input matches, would facilitate recognition, but at the price of increasing the rate of misperceptions. Twenty-five participants were presented with target and foil images that shared global form, but differed in details. Pictures with informative global form resulted in higher confidence in recognition and shorter reaction times, but increased false alarms. Predictions of sensory input based on low frequency spatial information allow quick, facilitated recognition. However, the price for speed is the increased rate of misperceptions.     

Further attempts to clarify the importance of category variability for categorisation

The issue of how category variability affects classification of novel instances is an important one for assessing theories of categorisation, yet previous research cannot provide a compelling conclusion. In five experiments we reexamine some of the factors thought to affect participant performance. In Experiments 1 and 2, participants almost always classified the test item as belonging to the high variability category. By contrast, in Experiment 3 we employed an alternative experimental paradigm, where the difference in variability of the two categories was less salient. In that case, participants tended to classify a test item as belonging to the low variability category. Two additional experiments (4 and 5) explored in detail the differences between Experiments 1, 2 on the one hand, and 3 on the other. Some insight into the underlying psychological processes can be provided by computational models of categorisation, and we focus on the continuous version of Anderson's (1991) Rational Model, which has not been explored before in this context. The model predicts that test instances exactly halfway between the prototypes of two categories should be classified into the more variable category, consistent with the bulk of empirical findings. We also provided a comparison with a slightly reduced version of the Generalised Context Model (GCM) to show that its predictions are consistent with those from the Rational Model, for our stimulus sets.