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Gallen, School of Finance. David C. Waller, Dorsey, Robert E. Windows update 1709 download manuella allen – windows update 1709 download manuella allen Kryvobokov, Xiaolong Liu, Tracking spatial location of clusters of geographically weighted regression estimates of price determinants ,” Land Use PolicyElsevier, vol.
More about this item Keywords Real estate market ; Automated valuation models ; Dowjload ; Geocoding ; French cities ; Machine learning ; Artificial intelligence ; All these keywords. Statistics Access and download statistics Corrections All msnuella on this site has been provided by the respective publishers and authors.
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This paper reviews real estate price estimation in France, a market that has received little attention. We compare seven popular machine learning techniques by proposing a different approach that quantifies the relevance of location features in real estate price alen with high and fine levels of granularity.
We manue,la advantage of a newly available windows 10 21h2 download iso 64-bit dataset provided by the French government that contains 5 years of historical data of real estate transactions. At a low level of granularity, we use geocoding to add precise geographical location features to the machine learning algorithm inputs. Our results updahe reveal that neural networks and random forest techniques particularly outperform other methods when geocoding features are not accounted for, while random forest, adaboost and gradient boosting perform well when geocoding features are considered.
For identifying opportunities in the real estate market through real estate price prediction, our results can be of particular interest. They can also serve as a basis for price assessment in revenue management for durable and non-replenishable products such as real estate. Handle: RePEc:spr:annopr:vyid Most related items Windows update 1709 download manuella allen – windows update 1709 download manuella allen are the items that most often cite the same works as this one and are cited by the same works as this one.
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A comparison of acoustic cues in music and speech for three dimensions перейти affect. Pupil dilation is calculated as a percentage of the mean pupil diameter observed http://replace.me/1359.txt the ms before the onset baseline. Pupillary responses in normal subjects following downkoad stimulation. Individuality in harpsichord performance: disentangling performer- and piece-specific influences on interpretive choices. Mas-Herrero, E. Judd, C. Ahern, S.
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Real estate price estimation in French cities using geocoding and machine learning. This paper reviews real estate price estimation in France, a market that has received little attention. We compare seven popular machine learning techniques by proposing a different approach that quantifies the relevance of location features in real estate price estimation with high and fine levels of granularity.
We take advantage of a newly available open dataset provided by the French government that contains 5 years of historical data of real estate transactions. At a low level of granularity, we use geocoding to add precise geographical location features to the machine learning algorithm inputs. Our results also reveal that neural networks and random forest techniques particularly outperform other methods when geocoding features are not accounted for, while random forest, adaboost and gradient boosting perform well when geocoding features are considered.
For identifying opportunities in the real estate market through real estate price prediction, our results can be of particular interest. They can also serve as a basis for price assessment in revenue management for durable and non-replenishable products such as real estate. Handle: RePEc:spr:annopr:vyid Most related items These are the items that most often cite the same works as this one and are cited by the same works as this one. Statistics Access and download statistics.
Corrections All material on this site has been provided by the respective publishers and authors. The computer was an Apple Mac Mini 4.
The mean intensity across all excerpts was 70 dB SPL, based on audiometric measurements taken at the headphones using a Voltcraft SL decibel meter that was calibrated immediately prior to usage.
The stimuli were presented and the experiment was controlled using Psychtoolbox Participants first signed the informed consent form and filled out the MDBF mood questionnaire. They were then seated in a comfortable chair with their head stabilized in a chin rest, facing the computer monitor at a distance of 60 cm, in a quiet, moderately lit room ambient light levels of lux as measured just below the forehead support using an X-Rite i1Pro lux meter. A randomized target order 5-point HV5 calibration routine was performed 5-point calibration was deemed sufficient since pupil diameter was the only measurement of interest and participants were asked to continuously fixate the area corresponding to the center of the screen , followed by a separate validation using the EyeLink software.
Participants were asked not to move their head during the experiment and to look at the fixation cross located at the center of the screen and try to avoid blinking when it was displayed they were shown an image of the cross.
The cross color was dark gray RGB: 75,75, The smiley face was the same color as the cross and approximately the same size. As with the rating experiment, participants first practiced with three excerpts not included in the actual stimulus set and were then exposed to all 80 excerpts from the stimulus set.
For each excerpt, the fixation cross was first shown for 2 s, then the music played for 6 s, then the cross was displayed for another 2 s, for a total of 10 s of recording of the pupillary response per trial. After 40 stimuli midway through the experiment , participants were allowed to take a pause. Upon resuming the experiment, calibration correction was performed complete calibration was performed if necessary. Once all excerpts had been played, participants were invited to fill in a post-experiment paper questionnaire about their socio-demographic background and musical interests.
Participants also completed the SRS Schulz et al. Finally, participants were paid 5 Euros for their participation, thanked, and debriefed. The entire experiment lasted approximately 30 min. Gaze coordinates were also recorded in order to track the gaze position and exclude samples for which the participants did not fixate the screen area corresponding to the center cross.
Blinks were identified by the proprietary algorithm of the Eyelink eye-tracking system, using default settings. Data samples from 50 ms before the beginning of blinks to 50 ms after the end of blinks were discarded to exclude pre- and post-blink artifacts 1. In addition, given that pupil size estimation is less accurate when participants are not fixating the center of the screen Gagl et al.
A total of 50 of trials 2. Frequency responses in pupil size variation that occur at rates faster than 2 Hz are considered to be noise Richer and Beatty, ; Privitera and Stark, Accordingly, pupil diameter data were low-passed using a fourth-order Butterworth filter with a cutoff frequency of 4 Hz. The baseline pupil diameter was measured as the average pupil diameter for a period of ms immediately preceding the stimulus onset. Baseline-corrected pupil diameters were computed by subtracting the baseline pupil diameter from the raw pupil diameter after stimulus onset.
To allow for comparisons between participants and to correct for possible tonic changes in pupil diameter over the course of the experiment, raw pupil diameters were converted into relative pupil diameter by expressing them as a proportional difference from the baseline diameter van Rijn et al.
Subscale scores were analyzed using a MANOVA design, with experimental group subjective ratings versus pupillary response as a between-subject factor. The overall mean familiarity rating for the 80 excerpts was 2.
To evaluate whether participants rated the excerpts in a consistent manner, inter-rater reliability was assessed by computing the average measure intraclass correlation coefficient ICC using the ICC 2, k form Shrout and Fleiss, , which corresponds to a two-way random effects model for consistency McGraw and Wong, The ICC values obtained for arousal and pleasantness were nearly identical to those reported in Gingras et al.
Moreover, the mean arousal and pleasantness ratings obtained here were also consistent with those obtained on the same excerpts, but with different participants Gingras et al. Mean subjective arousal, tension, and pleasantness ratings for 80 six-second excerpts selected from Romantic piano trios.
The numbers identify the excerpts for a complete listing of the excerpts, see Appendix in the Supplementary Material.
The full scale for all three ratings ranged from 1 to 7, but a restricted range is displayed here to facilitate viewing. Figure 1 shows the two-dimensional emotion spaces corresponding to the set of 80 excerpts, displaying the mean arousal, tension, and pleasantness ratings obtained on each excerpt.
Mean pleasantness ratings range: 3. Mean arousal and mean tension ratings range: 2. A SRS total scores Schulz et al. To visualize whether the time course of pupillary responses is similar for low- and high-arousing stimuli, we categorized the excerpts into low- and high-arousal brackets.
The time course displayed a similar pattern for the 40 excerpts rated as most arousing and the 40 rated as least arousing, although the relative dilation was larger for the high-arousing excerpts Figure 3.
A sharp increase in pupil size occurs about ms after the stimulus onset. The peak dilation is reached around 1. A small dilation occurs ms after the offset, followed by a rapid constriction. These observations are in line with earlier investigations of pupillary responses to affective sounds Partala and Surakka, Time course of the pupillary response for high- and low-arousing excerpts.
Pupil dilation is calculated as a percentage of the mean pupil diameter observed during the ms before the onset baseline. High-arousing excerpts correspond to the 40 excerpts rated as most arousing, whereas low-arousing excerpts are the 40 rated as least arousing. Because the subjective ratings obtained on the excerpts were retrospective ratings of the entire excerpts, pupillary responses were averaged over the entire 6-s duration of the excerpts Partala and Surakka, in order to allow a meaningful investigation of the association between ratings and pupillary responses.
As a preliminary analysis of this association, we first computed the correlations between the mean pupillary responses observed for each excerpt and the mean subjective ratings obtained for each excerpt treating each excerpt as the unit of analysis on the one hand Table 1 , and between the mean pupillary responses observed for each participant and the participant-specific features i.
These analyses showed that mean subjective arousal and tension ratings were positively correlated with the mean pupillary response observed for each excerpt Table 1. TABLE 1. Correlations computed over the mean values obtained for each music excerpt. TABLE 2. Correlations computed over the values obtained for each participant. In doing so, we sought to quantify the contribution of excerpt-specific affective characteristics and listener-specific traits to the observed variance in pupillary response among excerpts and participants using maximum-likelihood linear mixed models.
Given that each excerpt was heard by each participant, excerpts and participants were treated as fully crossed random effects Baayen et al. Here, we began with a full model including all fixed and random effects of interest, and implemented a backward stepwise model selection procedure. Hence, our initial model included arousal, pleasantness, and familiarity ratings as excerpt-specific features tension ratings were not included to reduce multicollinearity , and gender, mood subscales, SRS scores, and attitudes toward music role of music, liking for the excerpts, and frequency of felt emotions as listener-specific features.
Additionally, all two-way interactions between each excerpt-specific feature and listener-specific trait were considered i. Participant, excerpt, and gender were coded as categorical factors, whereas all other predictors were treated as covariates and grand mean centered Enders and Tofighi, According to the model, males were predicted to show stronger pupillary dilations than females 1.
Moreover, each additional unit increment in the mean arousal ratings predicted an increase of 0. However, the effect of arousal was much weaker for listeners who liked the excerpts greatly, with a Spearman correlation coefficient between arousal ratings and pupillary responses of 0. An analogous model was obtained when predicting pupillary responses using tension ratings instead of arousal ratings, with significant effects of gender, reported role of music, tension ratings, and a significant interaction between tension and overall liking for the excerpts.
The coefficients and statistical tests also yielded very similar values to those obtained for the arousal model, which is to be expected considering the very high correlation between arousal and tension ratings.
Pupillary responses to musical stimuli have rarely been investigated. In this study, we collected pupillary responses of non-musicians to a set of 80 six-second music excerpts for which we separately obtained subjective ratings of felt arousal, pleasantness, tension, and familiarity.
A correlational analysis showed that, as predicted, arousal and tension ratings were significantly correlated with mean pupillary response. A linear mixed model analysis including both music- and listener-specific features resulted in a best-fitting model with gender, role of music and arousal ratings as predictors of the pupillary response.
Furthermore, an interaction between arousal ratings and liking was found. In general, these results are in line with the hypothesized contribution of excerpt-specific and listener-specific characteristics to pupillary responses to music. However, contrary to our predictions, female participants showed smaller pupillary dilations than males, even though male and female listeners did not significantly differ in their attitude toward music or in their scores on the subscales of the MDBF mood questionnaire.
Taken together, these results lend support to models that predict that responses to music depend on characteristics of the listener as well as on the music itself Hargreaves et al. Regarding excerpt-specific features, it is worth noting that pleasantness was not significantly correlated with pupillary responses.
This is in agreement with previous reports indicating that pupillary responses are determined by emotional arousal, independently of the perceived pleasantness of the stimuli Bradley et al. Furthermore, we note that pleasantness ratings are not as consistent across participants as arousal and tension ratings, and are also more difficult to predict from the acoustical features of the stimuli Schubert, ; Eerola, ; Gingras et al. Sound intensity, which is one of the main predictors of music-induced subjective arousal, is known to be correlated with physiological responses such as skin conductance Gomez and Danuser, However, our findings not only suggest that the range of subjective music-induced arousal ratings is largely unaffected by amplitude normalization Gingras et al.
More generally, because personality traits, such as neuroticism, have been shown to predict pupillary responses to sound stimuli Antikainen and Niemi, , future research in this domain should consider the role of personality traits in greater depth. The larger pupil dilations observed for male listeners stand in contrast to earlier studies reporting stronger psychophysiological, but not psychological, responses to high-arousing, unpleasant music in females compared to males Nater et al.
This discrepancy with earlier results may be due to the fact that our musical stimuli were not selected to induce high levels of unpleasantness, which is supported by the fact that stress reactivity was not a significant predictor of pupil dilation. Moreover, in contrast to Nater et al. Although we controlled for the potential effect of familiarity by selecting music excerpts from a little-known genre, we observed a positive but non-significant correlation between familiarity and pupil dilation.
Because the range of familiarity ratings was very restricted, we may suppose that the effect of familiarity and exposure on pupillary responses would be more evident with a set of music excerpts ranging from unfamiliar to very familiar. This is supported by recent findings showing that repeated exposure to unfamiliar music significantly increased skin conductance a marker of emotional arousal and that self-reported familiarity ratings were positively related to skin conductance van den Bosch et al.
The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. We thank Andreas Gartus for his technical assistance and Helmut Leder for providing laboratory space for collecting subjective ratings. Ahern, S. Pupillary responses during information processing vary with scholastic aptitude test scores. Science , — Andreassi, J. Hillsdale, NJ: Erlbaum.
Google Scholar. Antikainen, J. Neuroticism and the pupillary response to a brief exposure to noise. Baayen, R. Mixed-effects modeling with crossed random effects for subjects and items.
Bates, D. Fitting linear mixed-effects models using lme4. Beatty, J. Cacioppo, L. Tassinary, and G. Berlyne, D. Aesthetics and Psychobiology. Bigand, E. Categorization of extremely brief auditory stimuli: domain-specific or domain-general processes?
Blood, A. Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions. Bradley, M. Emotion and motivation II: sex differences in picture processing. Emotion 1, — Technical Report B The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45, — Bradshaw, D. Effects of music engagement on responses to painful stimulation.
Pain 28, — Brainard, D. The Psychophysics Toolbox. Brieber, D. Art in time and space: context modulates the relation between art experience and viewing time. Cornelissen, F. Methods Instrum. Cummings, A. Dabbs, J. Testosterone and pupillary response to auditory stimuli. Eerola, T. Are the emotions expressed in music genre-specific? An audio-based evaluation of datasets spanning classical, film, pop and mixed genres. New Music Res. A comparison of the discrete and dimensional models of emotion in music.
Note that, whereas participants who rated the excerpts for arousal and valence in Gingras et al. All participants for both experiments had less than 3 years of musical training, were not musically active at the time of the experiment, and reported normal hearing and no history of hearing disorders. Participants in the pupillary response experiment had normal or corrected-to-normal vision. All experiments conformed to the institutional guidelines of the University of Vienna for experiments with human subjects.
Written informed consent was given by all participants who could withdraw at any time during the experiment without further consequences. All experimental data were collected between November and July The procedure for the subjective rating experiment was identical to the procedure described in Gingras et al.
Briefly, participants first filled out the MDBF mood questionnaire short form version A and were instructed to rate their familiarity with the musical excerpts, as well as their felt arousal, felt tension, and felt pleasantness, using 7-point scales. In order to familiarize participants with the procedure, they first practiced with three excerpts not included in the actual stimulus set and were then exposed to all 80 excerpts from the stimulus set. The order of presentation of the excerpts was randomized.
Ratings were entered on the computer by clicking on ordered icons on the screen corresponding to the scale ratings only once the entire excerpt was played. After all ratings were entered, there was a 5-s delay before the next excerpt began playing.
The entire experiment lasted approximately 45 min. The EyeLink head-supported infrared optical eye-tracking system SR Research, Ottawa, ON, Canada , which includes a Hz infrared camera, illuminator, and proprietary software running on a custom workstation, was used to collect pupil data. The screen used for the experiment was a Samsung SyncMaster The background color of the screen was gray, RGB ,, , following Kuchinke et al.
The computer was an Apple Mac Mini 4. The mean intensity across all excerpts was 70 dB SPL, based on audiometric measurements taken at the headphones using a Voltcraft SL decibel meter that was calibrated immediately prior to usage. The stimuli were presented and the experiment was controlled using Psychtoolbox Participants first signed the informed consent form and filled out the MDBF mood questionnaire.
They were then seated in a comfortable chair with their head stabilized in a chin rest, facing the computer monitor at a distance of 60 cm, in a quiet, moderately lit room ambient light levels of lux as measured just below the forehead support using an X-Rite i1Pro lux meter. A randomized target order 5-point HV5 calibration routine was performed 5-point calibration was deemed sufficient since pupil diameter was the only measurement of interest and participants were asked to continuously fixate the area corresponding to the center of the screen , followed by a separate validation using the EyeLink software.
Participants were asked not to move their head during the experiment and to look at the fixation cross located at the center of the screen and try to avoid blinking when it was displayed they were shown an image of the cross.
The cross color was dark gray RGB: 75,75, The smiley face was the same color as the cross and approximately the same size. As with the rating experiment, participants first practiced with three excerpts not included in the actual stimulus set and were then exposed to all 80 excerpts from the stimulus set. For each excerpt, the fixation cross was first shown for 2 s, then the music played for 6 s, then the cross was displayed for another 2 s, for a total of 10 s of recording of the pupillary response per trial.
After 40 stimuli midway through the experiment , participants were allowed to take a pause. Upon resuming the experiment, calibration correction was performed complete calibration was performed if necessary. Once all excerpts had been played, participants were invited to fill in a post-experiment paper questionnaire about their socio-demographic background and musical interests.
Participants also completed the SRS Schulz et al. Finally, participants were paid 5 Euros for their participation, thanked, and debriefed. The entire experiment lasted approximately 30 min. Gaze coordinates were also recorded in order to track the gaze position and exclude samples for which the participants did not fixate the screen area corresponding to the center cross.
Blinks were identified by the proprietary algorithm of the Eyelink eye-tracking system, using default settings. Data samples from 50 ms before the beginning of blinks to 50 ms after the end of blinks were discarded to exclude pre- and post-blink artifacts 1. In addition, given that pupil size estimation is less accurate when participants are not fixating the center of the screen Gagl et al. A total of 50 of trials 2. Frequency responses in pupil size variation that occur at rates faster than 2 Hz are considered to be noise Richer and Beatty, ; Privitera and Stark, Accordingly, pupil diameter data were low-passed using a fourth-order Butterworth filter with a cutoff frequency of 4 Hz.
The baseline pupil diameter was measured as the average pupil diameter for a period of ms immediately preceding the stimulus onset. Baseline-corrected pupil diameters were computed by subtracting the baseline pupil diameter from the raw pupil diameter after stimulus onset. To allow for comparisons between participants and to correct for possible tonic changes in pupil diameter over the course of the experiment, raw pupil diameters were converted into relative pupil diameter by expressing them as a proportional difference from the baseline diameter van Rijn et al.
Subscale scores were analyzed using a MANOVA design, with experimental group subjective ratings versus pupillary response as a between-subject factor. The overall mean familiarity rating for the 80 excerpts was 2. To evaluate whether participants rated the excerpts in a consistent manner, inter-rater reliability was assessed by computing the average measure intraclass correlation coefficient ICC using the ICC 2, k form Shrout and Fleiss, , which corresponds to a two-way random effects model for consistency McGraw and Wong, The ICC values obtained for arousal and pleasantness were nearly identical to those reported in Gingras et al.
Moreover, the mean arousal and pleasantness ratings obtained here were also consistent with those obtained on the same excerpts, but with different participants Gingras et al.
Mean subjective arousal, tension, and pleasantness ratings for 80 six-second excerpts selected from Romantic piano trios. The numbers identify the excerpts for a complete listing of the excerpts, see Appendix in the Supplementary Material.
The full scale for all three ratings ranged from 1 to 7, but a restricted range is displayed here to facilitate viewing. Figure 1 shows the two-dimensional emotion spaces corresponding to the set of 80 excerpts, displaying the mean arousal, tension, and pleasantness ratings obtained on each excerpt.
Mean pleasantness ratings range: 3. Mean arousal and mean tension ratings range: 2. A SRS total scores Schulz et al. To visualize whether the time course of pupillary responses is similar for low- and high-arousing stimuli, we categorized the excerpts into low- and high-arousal brackets.
The time course displayed a similar pattern for the 40 excerpts rated as most arousing and the 40 rated as least arousing, although the relative dilation was larger for the high-arousing excerpts Figure 3. A sharp increase in pupil size occurs about ms after the stimulus onset. The peak dilation is reached around 1. A small dilation occurs ms after the offset, followed by a rapid constriction.
These observations are in line with earlier investigations of pupillary responses to affective sounds Partala and Surakka, Time course of the pupillary response for high- and low-arousing excerpts. Pupil dilation is calculated as a percentage of the mean pupil diameter observed during the ms before the onset baseline.
High-arousing excerpts correspond to the 40 excerpts rated as most arousing, whereas low-arousing excerpts are the 40 rated as least arousing. Because the subjective ratings obtained on the excerpts were retrospective ratings of the entire excerpts, pupillary responses were averaged over the entire 6-s duration of the excerpts Partala and Surakka, in order to allow a meaningful investigation of the association between ratings and pupillary responses.
As a preliminary analysis of this association, we first computed the correlations between the mean pupillary responses observed for each excerpt and the mean subjective ratings obtained for each excerpt treating each excerpt as the unit of analysis on the one hand Table 1 , and between the mean pupillary responses observed for each participant and the participant-specific features i.
These analyses showed that mean subjective arousal and tension ratings were positively correlated with the mean pupillary response observed for each excerpt Table 1. TABLE 1. Correlations computed over the mean values obtained for each music excerpt. TABLE 2. Correlations computed over the values obtained for each participant. In doing so, we sought to quantify the contribution of excerpt-specific affective characteristics and listener-specific traits to the observed variance in pupillary response among excerpts and participants using maximum-likelihood linear mixed models.
Given that each excerpt was heard by each participant, excerpts and participants were treated as fully crossed random effects Baayen et al. Here, we began with a full model including all fixed and random effects of interest, and implemented a backward stepwise model selection procedure. Hence, our initial model included arousal, pleasantness, and familiarity ratings as excerpt-specific features tension ratings were not included to reduce multicollinearity , and gender, mood subscales, SRS scores, and attitudes toward music role of music, liking for the excerpts, and frequency of felt emotions as listener-specific features.
Additionally, all two-way interactions between each excerpt-specific feature and listener-specific trait were considered i. Participant, excerpt, and gender were coded as categorical factors, whereas all other predictors were treated as covariates and grand mean centered Enders and Tofighi, According to the model, males were predicted to show stronger pupillary dilations than females 1.
Moreover, each additional unit increment in the mean arousal ratings predicted an increase of 0. However, the effect of arousal was much weaker for listeners who liked the excerpts greatly, with a Spearman correlation coefficient between arousal ratings and pupillary responses of 0. An analogous model was obtained when predicting pupillary responses using tension ratings instead of arousal ratings, with significant effects of gender, reported role of music, tension ratings, and a significant interaction between tension and overall liking for the excerpts.
The coefficients and statistical tests also yielded very similar values to those obtained for the arousal model, which is to be expected considering the very high correlation between arousal and tension ratings. Pupillary responses to musical stimuli have rarely been investigated. In this study, we collected pupillary responses of non-musicians to a set of 80 six-second music excerpts for which we separately obtained subjective ratings of felt arousal, pleasantness, tension, and familiarity.
A correlational analysis showed that, as predicted, arousal and tension ratings were significantly correlated with mean pupillary response. A linear mixed model analysis including both music- and listener-specific features resulted in a best-fitting model with gender, role of music and arousal ratings as predictors of the pupillary response. Furthermore, an interaction between arousal ratings and liking was found.
In general, these results are in line with the hypothesized contribution of excerpt-specific and listener-specific characteristics to pupillary responses to music. However, contrary to our predictions, female participants showed smaller pupillary dilations than males, even though male and female listeners did not significantly differ in their attitude toward music or in their scores on the subscales of the MDBF mood questionnaire.
Taken together, these results lend support to models that predict that responses to music depend on characteristics of the listener as well as on the music itself Hargreaves et al. Regarding excerpt-specific features, it is worth noting that pleasantness was not significantly correlated with pupillary responses.
This is in agreement with previous reports indicating that pupillary responses are determined by emotional arousal, independently of the perceived pleasantness of the stimuli Bradley et al. Furthermore, we note that pleasantness ratings are not as consistent across participants as arousal and tension ratings, and are also more difficult to predict from the acoustical features of the stimuli Schubert, ; Eerola, ; Gingras et al. Sound intensity, which is one of the main predictors of music-induced subjective arousal, is known to be correlated with physiological responses such as skin conductance Gomez and Danuser, However, our findings not only suggest that the range of subjective music-induced arousal ratings is largely unaffected by amplitude normalization Gingras et al.
More generally, because personality traits, such as neuroticism, have been shown to predict pupillary responses to sound stimuli Antikainen and Niemi, , future research in this domain should consider the role of personality traits in greater depth. The larger pupil dilations observed for male listeners stand in contrast to earlier studies reporting stronger psychophysiological, but not psychological, responses to high-arousing, unpleasant music in females compared to males Nater et al.
This discrepancy with earlier results may be due to the fact that our musical stimuli were not selected to induce high levels of unpleasantness, which is supported by the fact that stress reactivity was not a significant predictor of pupil dilation. Moreover, in contrast to Nater et al. Although we controlled for the potential effect of familiarity by selecting music excerpts from a little-known genre, we observed a positive but non-significant correlation between familiarity and pupil dilation.
Because the range of familiarity ratings was very restricted, we may suppose that the effect of familiarity and exposure on pupillary responses would be more evident with a set of music excerpts ranging from unfamiliar to very familiar.
This is supported by recent findings showing that repeated exposure to unfamiliar music significantly increased skin conductance a marker of emotional arousal and that self-reported familiarity ratings were positively related to skin conductance van den Bosch et al. The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
We thank Andreas Gartus for his technical assistance and Helmut Leder for providing laboratory space for collecting subjective ratings. Ahern, S.
Pupillary responses during information processing vary with scholastic aptitude test scores. Science , — Andreassi, J. Hillsdale, NJ: Erlbaum. Google Scholar. Antikainen, J. Neuroticism and the pupillary response to a brief exposure to noise. Baayen, R. Mixed-effects modeling with crossed random effects for subjects and items. Bates, D.
Fitting linear mixed-effects models using lme4. Beatty, J. Cacioppo, L. Tassinary, and G. Berlyne, D. Aesthetics and Psychobiology. Bigand, E. Categorization of extremely brief auditory stimuli: domain-specific or domain-general processes? Blood, A. Emotional responses to pleasant and unpleasant music correlate with activity in paralimbic brain regions.
Bradley, M. Emotion and motivation II: sex differences in picture processing. Emotion 1, — Technical Report B The pupil as a measure of emotional arousal and autonomic activation. Psychophysiology 45, — Bradshaw, D.
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Event-related skin conductance responses to musical emotions in humans. Kuchinke, L. Additionally, males exhibited larger dilations than females. Accordingly, pupil diameter data were low-passed using a fourth-order Butterworth filter with a cutoff frequency of 4 Hz. Pupil diameter can react to stimulation in as little as 0. Science , — A linear mixed model analysis including both music- and listener-specific features resulted in a best-fitting model with gender, role of music and arousal ratings as predictors of the pupillary response. To our knowledge, the earliest published study on music-induced pupillary responses is that of Slaughter , which used a subjective, observational methodology to determine that stimulative music led to pupil dilation, while sedative music induced pupil constriction.
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