This study investigates how temperature, inside and outside the classroom, influence teachers’ mood and mental fatigue as well as the perceived students’ behavior. Two daily random measurements of the temperature inside various classrooms were taken for 7 months. Mood, mental fatigue, and perception of students’ behavior were evaluated for the teachers. Daily external temperature data were obtained from the State Agency of Meteorology. Results showed that indoor temperature, indoor humidity, and the difference between outdoor/indoor temperature significantly explain a worse perception of mood of the teachers and a worse perception of students’ behavior that influences perception of students’ behavior.

Performance in competitive situations has been linked to various psychobiological factors such as personality traits (e.g., competitiveness), situational appraisal (e.g., motivation), and cardiovascular response (e.g., heart rate). However, it remains unclear whether these factors can predict competitive success. This paper aims to assess, through discriminant analysis, the predictive capacity of these psychobiological variables regarding the likelihood of winning, ultimately delineating a psychophysiological profile associated with success. Across three distinct studies, a total of 154 participants (66 men) engaged in a face-to-face laboratory competition. Prior to the competition, assessments of competitiveness traits, anxiety, self-efficacy, and motivation were conducted, and heart rate reactivity during the competition was measured. These variables collectively formed the basis for constructing the predictive model. The results of the initial study demonstrated that our model accurately classified 68.8% of the cases. Specifically, high levels of competitiveness, self-efficacy, motivation, and heart rate reactivity, coupled with low anxiety, were predictive of winning. These findings were subsequently replicated in two independent validation samples involving both men and women (studies 2 and 3), thereby reinforcing the robustness of the earlier results. In conclusion, our findings suggest that the psychological state preceding competition, along with cardiovascular reactivity, may serve as predictors for the probability of winning.

The emotional effects of music have a cross-cultural component that can be explained through the tonal and non-tonal properties of musical pieces. To investigate the relationship between music and the emotions it arouses, we have built a composite neural network with the aim of predicting both the emotional categorization and the emotional valence and activation of Vieillard et al.’s (2008) musical stimuli. Our neural network uses two Adalines in the first level of the structure to predict activation and emotional valence from a minimal set of temporal and tonal properties of the stimuli (rhythm, tempo, time signature, mode, absolute tonal range and the frequency of the lowest note). In the second level, the network uses a Self-Organizing Map (SOM) network to classify the stimuli into four emotional categories (calm, happiness, fear and sadness). The results have allowed us to replicate the features of the Circumplex Model of Emotion. The percentage of explained variance obtained for activation is satisfactory and higher than in previous research for emotional valence. The percentage of music pieces correctly classified by the SOM was also very high (87%). We discuss the results in relation to competing models of music and emotion.

The relationship between parameters extracted from the musical stimuli and emotional response has been traditionally approached using several physical measures extracted from time or frequency domains. From time-domain measures, the musical onset is defined as the moment in that any musical instrument or human voice issues a musical note. The onsets’ sequence in the performance of a specific musical score creates what is known as the onset curve (OC). The influence of the structure of OC on the emotional judgment of people is not known. To this end, we have applied principal component analysis on a complete set of variables extracted from the OC to capture their statistical structure. We have found a trifactorial structure related to activation and valence dimensions of emotional judgment. The structure has been cross-validated using different participants and stimuli. In this way, we propose the factorial scores of the OC as a reliable and relevant piece of information to predict the emotional judgment of music.