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.
Recently, it has been suggested that tonal violations produce greater skin conductance response (SCR) than timbral violations in music listening. However, it is unknown how people focus their attention during musical excerpts. The aim of this study is to replicate previous research considering two psychophysiological mechanisms: prediction error and brain stem reflex. Twenty-seven nonmusicians were instructed to listen six melodies and detect three altered conditions in one note: a dissonance (note out-of-key), a timbral change, and dissonance which changes in timbre and tone ( timdis). Amplitudes of SCR, heart rate (HR), and respiration rate (RSPR) were analyzed. In addition, the frequency of SCR and the percentage of musical events detection were measured. Results showed no significant differences either on amplitude of SCR or on respiratory rate. However, perception of timdis produced an increase in HR higher than dissonance ( p < .05) and the timbre condition had a higher frequency of SCR than dissonance ( p < .05). In addition, participants only detected 59.3% of dissonances but they were aware of 90% of notes in-key ( original melody). Finally, there was no significant correlation between percentage of detection and frequency of SCR. Results are discussed based on the prediction error mechanism, a theoretical model of expectation.
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.
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.
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