Using neurophysiological signals that reflect cognitive or affective state: Six recommendations to avoid common pitfalls

Estimating cognitive or affective state from neurophysiological signals and designing applications that make use of this information requires expertise in many disciplines such as neurophysiology, machine learning, experimental psychology, and human factors. This makes it difficult to perform research that is strong in all its aspects as well as to judge a study or application on its merits. On the occasion of the special topic “Using neurophysiological signals that reflect cognitive or affective state” we here summarize often occurring pitfalls and recommendations on how to avoid them, both for authors (researchers) and readers. They relate to defining the state of interest, the neurophysiological processes that are expected to be involved in the state of interest, confounding factors, inadvertently “cheating” with classification analyses, insight on what underlies successful state estimation, and finally, the added value of neurophysiological measures in the context of an application. We hope that this paper will support the community in producing high quality studies and well-validated, useful applications

Testing the Applicability of a Checklist-Based Startle Management Method in the Simulator

Several checklist-based methods have been proposed to help pilots manage startle in unexpected situations. In the current experiment, we tested how pilots reacted to using such a method, which featured the mnemonic COOL: Calm down – Observe – Outline – Lead. Using a motion-based simulator outfitted with a non-linear aerodynamic model of a small twin-propeller aircraft, twelve pilots practiced using the COOL method before performing four test scenarios involving startling events. Application of the full method in the test scenarios was high (90-100%), and pilots rated the method on average as useful (4 on a 1-5 point Likert scale). The first two steps of the method were seen as the “core” of the method. However, pilots also displayed difficulty with prioritizing dealing with immediate threats over executing the method. The results are promising, but they also warn us to be cautious when introducing a startle management method

The absence of an auditory-visual attentional blink is not due to echoic memory.

Als binnen een halve seconde twee visuele items in een serieel aangeboden stroom moeten worden geselecteerd, is de prestatie voor het tweede item vaak relatief slecht (er treedt een “attentional blink” op); wanneer het eerste echter item auditief wordt aangeboden, verdwijnt de blink meestal. We hebben aangetoond dat dit laatste niet wordt veroorzaakt doordat proefpersonen hun echoïsch geheugen gebruiken om de verwerking van het auditieve item uit te stellen tot na het einde van de visuele stroom

Type of activity and order of experimental conditions affect noise annoyance by identifiable and unidentifiable transportation noise

Previous studies have shown that identifiability of sound sources influence noise annoyance levels. The aim of the present experiment was to additionally study the effects of actively performing a task versus a less active pastime on noise annoyance. This was done by asking participants to perform a task (task condition) or read a magazine of their choice (no-task condition), while listening to identifiable and unidentifiable samples of transportation noise at varying sound exposure levels (55-85 ASEL). Annoyance was higher for identifiable samples (recordings) than for unidentifiable transformed samples (with equal spectral energy and envelope). Although there was no main effect of activity type on noise annoyance, for the transformed samples, an interaction was found between activity type and sound exposure levels: annoyance started lower in the no-task condition, but rose more steeply with ascending exposure levels than was the case during task performance (large effect). When assessing order effects, it was found that annoyance was higher when the task condition came first, especially for lower sound exposure levels (large effects). It is therefore concluded that the type of activity and the condition order do influence noise annoyance but in interaction with exposure levels, the type of noise and habituation

Annoyance by transportation noise: The effects of source identity and tonal components

Aircraft noise is consistently rated as more annoying than noise from other sources with similar intensity. In three experiments, it was investigated whether this penalty is due to the source identity of the noise. In the first experiment, four samples were played to participants engaged in a working memory task: road traffic noise, an Airbus 320 flyover, and unidentifiable, transformed versions of these samples containing the same spectral content and envelope. Original, identifiable samples were rated as more annoying than the transformed samples. A second experiment tested whether these results were due to the absence of tonal components in the transformed samples. This was partly the case: an additional sample, created from the A320 flyover by filtering out major tonal components, was rated as less annoying than the original A320 sample, but as more annoying than the transformed sample. In a third experiment, participants either received full disclosure of the generation of the samples or no information to identify the transformed samples. The transformed sample was rated as most annoying when the A320 identity was disclosed, but as least annoying when it was not. Therefore, it was concluded that annoyance is influenced by both identifiability and the presence of tonal components

Results of Experiment 1.

<p>Panel (a) Percentage correctly identified letters in the dual-task condition. T1-identification in the dual-task condition was significantly lower for lag 0 ms than for all other lags. Panel (b) T2 detection performance in percentage correct in the single- and dual-task conditions. Dual-task results are based on trials with correctly identified letters. T2-detection in the dual-task condition is significantly lower for lags 0 ms, 160 ms and 320 ms than in the single-task condition. Stars mark significant differences between single- and dual-task condition per lag (p<0.05). Note that in this figure chance level is 50%. Error bars reflect 95% confidence intervals in both panels.</p

Results of Experiment 2.

<p>Panel a: Dual-task Gabor detection performance (OT). Panel b: Single- and dual-task performance given correct OT for letter identification (LT). The solid line represents the single-task condition. Stars mark significant differences between single- and dual-task conditions (<i>p</i> < 0.001).</p

Timeline of Experiment 1.

<p>Every letter is presented for 83 ms, followed by a 50 ms blank. The OT search screen and the consecutive mask are presented for 166 ms.</p

Schematic overview of trial timeline from left to right.

<p>Letters were presented for 33 ms alternated by 50-ms blanks. The white target letter (T1) was followed by a lag of 0, 160, 320, or 640 ms, after which a ring of Gabor patches (containing T2) was presented. The ring of Gabor patches and mask screen were on screen for a duration of 160 ms each. Letter size is enhanced for illustration purposes.</p