Experiment 3 –Results.

<p>The charts represent the d’ prime (A) and response bias (B) results, see details <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173199#pone.0173199.t003" target="_blank">Table 3</a>.</p

LSF power analysis.

<p>A. The charts represent the LSF power for each expressive feature. B. The scatterplots the LSF power against d’ for each target feature.</p

experiment 1 –Stimuli and trial procedure.

<p>A. The eight different stimuli types that were used in the experiments. B. The trial sequence. We note, that in the real experiment we used the faces of Ekman and Friesen series, here for descriptive purposes we provide an example of a face that is not part of this series. The individual in this manuscript has given written informed consent to publish these case details.</p

The perceptual saliency of fearful eyes and smiles: A signal detection study

<div><p>Facial features differ in the amount of expressive information they convey. Specifically, eyes are argued to be essential for fear recognition, while smiles are crucial for recognising happy expressions. In three experiments, we tested whether expression modulates the perceptual saliency of diagnostic facial features and whether the feature’s saliency depends on the face configuration. Participants were presented with masked facial features or noise at perceptual conscious threshold. The task was to indicate whether eyes (experiments 1-3A) or a mouth (experiment 3B) was present. The expression of the face and its configuration (i.e. spatial arrangement of the features) were manipulated. Experiment 1 compared fearful with neutral expressions, experiments 2 and 3 compared fearful versus happy expressions. The detection accuracy data was analysed using Signal Detection Theory (SDT), to examine the effects of expression and configuration on perceptual precision (d’) and response bias (c), separately. Across all three experiments, fearful eyes were detected better (higher d’) than neutral and happy eyes. Eyes were more precisely detected than mouths, whereas smiles were detected better than fearful mouths. The configuration of the features had no consistent effects across the experiments on the ability to detect expressive features. But facial configuration affected consistently the response bias. Participants used a more liberal criterion for detecting the eyes in canonical configuration and fearful expression. Finally, the power in low spatial frequency of a feature predicted its discriminability index. The results suggest that expressive features are perceptually more salient with a higher d’ due to changes at the low-level visual properties, with emotions and configuration affecting perception through top-down processes, as reflected by the response bias.</p></div

Results of Experiment 1, detecting fearful versus neutral eyes (N = 22).

<p>Results of Experiment 1, detecting fearful versus neutral eyes (N = 22).</p

Results of Experiment 3, detecting fearful and happy eyes; fearful and happy mouth.

<p>Results of Experiment 3, detecting fearful and happy eyes; fearful and happy mouth.</p

Experiment 3 –Stimuli example.

<p>An example of happy feature expressions and their filtered version. For descriptive purposes we provide an example of a face that is not part of this series. The individual in this manuscript has given written informed consent to publish these case details.</p

Experiment 1 –Results.

<p>The charts represent the d’ prime (A) and response bias (B) results, see details <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0173199#pone.0173199.t001" target="_blank">Table 1</a>. Scr, scrambled configuration, B eyes on the bottom stripe; M, eyes on the middle stripe, T, eyes on the top stripe.</p

Results of Experiment 2, detecting fearful versus happy eyes.

<p>Results of Experiment 2, detecting fearful versus happy eyes.</p

We Don’t Know What You Did Last Summer. On the Importance of Transparent Reporting of Reaction Time Data Pre-processing

  In behavioral, cognitive, and social sciences, reaction time measures are an important source of information. However, analyses on reaction time data are affected by researchers' analytical choices and the order in which these choices are applied. The results of a systematic literature review, presented in this paper, revealed that the justification for and order in which analytical choices are conducted are rarely reported, leading to difficulty in reproducing results and interpreting mixed findings. To address this methodological shortcoming, we created a checklist on reporting reaction time pre-processing to make these decisions more explicit, improve transparency, and thus, promote best practices within the field. The importance of the pre-processing checklist was additionally supported by an expert consensus survey and a multiverse analysis. Consequently, we appeal for maximal transparency on all methods applied and offer a checklist to improve replicability and reproducibility of studies that use reaction time measures.  </p