We here present the development and validation of the Verbal Affective Memory Test-24 (VAMT-24). First, we ensured face validity by selecting 24 words reliably perceived as positive, negative or neutral, respectively, according to healthy Danish adults' valence ratings of 210 common and non-taboo words. Second, we studied the test's psychometric properties in healthy adults. Finally, we investigated whether individuals diagnosed with Seasonal Affective Disorder (SAD) differed from healthy controls on seasonal changes in affective recall. Recall rates were internally consistent and reliable and converged satisfactorily with established non-affective verbal tests. Immediate recall (IMR) for positive words exceeded IMR for negative words in the healthy sample. Relatedly, individuals with SAD showed a significantly larger decrease in positive recall from summer to winter than healthy controls. Furthermore, larger seasonal decreases in positive recall significantly predicted larger increases in depressive symptoms. Retest reliability was satisfactory, rs ≥ .77. In conclusion, VAMT-24 is more thoroughly developed and validated than existing verbal affective memory tests and showed satisfactory psychometric properties. VAMT-24 seems especially sensitive to measuring positive verbal recall bias, perhaps due to the application of common, non-taboo words. Based on the psychometric and clinical results, we recommend VAMT-24 for international translations and studies of affective memory.

 The effect of colours on attention is discussed.  Previous research have not considered attentional effects of individual colours.  A visual search experiment is conducted to test the attentional guidance of colours.  Significant differences are found between individual colours.  The effects are shown to increase with complexity of the display.

BackgroundWe have previously identified an inverse relationship between cerebral serotonin 4 receptor (5‐HT 4R) binding and nonaffective episodic memory in healthy individuals. Here, we investigate in a novel sample if the association is related to affective components of memory, by examining the association between cerebral 5‐HT 4R binding and affective verbal memory recall.MethodsTwenty‐four healthy volunteers were scanned with the 5‐HT 4R radioligand [11C]SB207145 and positron emission tomography, and were tested with the Verbal Affective Memory Test‐24. The association between 5‐HT 4R binding and affective verbal memory was evaluated using a linear latent variable structural equation model.ResultsWe observed a significant inverse association across all regions between 5‐HT 4R binding and affective verbal memory performances for positive (p = 5.5 × 10−4) and neutral (p = .004) word recall, and an inverse but nonsignificant association for negative (p = .07) word recall. Differences in the associations with 5‐HT 4R binding between word categories (i.e., positive, negative, and neutral) did not reach statistical significance.ConclusionOur findings replicate our previous observation of a negative association between 5‐HT 4R binding and memory performance in an independent cohort and provide novel evidence linking 5‐HT 4R binding, as a biomarker for synaptic 5‐HT levels, to the mnestic processing of positive and neutral word stimuli in healthy humans.

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Data‐driven process monitoring and control techniques and their application to industrial chemical processes are gaining popularity due to the current focus on Industry 4.0, digitalization and the Internet of Things. However, for the development of such techniques, there are significant barriers that must be overcome in obtaining sufficiently large and reliable datasets. As a result, the use of real plant and process data in developing and testing data‐driven process monitoring and control tools can be difficult without investing significant efforts in acquiring, treating, and interpreting the data. Therefore, researchers need a tool that effortlessly generates large amounts of realistic and reliable process data without the requirement for additional data treatment or interpretation. In this work, we propose a data generation platform based on the Tennessee Eastman Process simulation benchmark. A graphical user interface (GUI) developed in MATLAB Simulink is presented that enables users to generate massive amounts of data for testing applicability of big data concepts in the realm of process control for continuous time‐dependent processes. An R‐Shiny app that interacts with the data generation tool is also presented for illustration purposes. The app can visualize the results generated by the Tennessee Eastman Process and can carry out a standard fault detection and diagnosis studies based on PCA. The data generator GUI is available free of charge for research purposes at https://github.com/dtuprodana/TEP.