- •Preface
- •Acknowledgments
- •Contents
- •1 Introduction
- •1.1 Auditory Temporal and Spatial Factors
- •1.2 Auditory System Model for Temporal and Spatial Information Processing
- •2.1 Analysis of Source Signals
- •2.1.1 Power Spectrum
- •2.1.2 Autocorrelation Function (ACF)
- •2.1.3 Running Autocorrelation
- •2.2 Physical Factors of Sound Fields
- •2.2.1 Sound Transmission from a Point Source through a Room to the Listener
- •2.2.2 Temporal-Monaural Factors
- •2.2.3 Spatial-Binaural Factors
- •2.3 Simulation of a Sound Field in an Anechoic Enclosure
- •3 Subjective Preferences for Sound Fields
- •3.2.1 Optimal Listening Level (LL)
- •3.2.4 Optimal Magnitude of Interaural Crosscorrelation (IACC)
- •3.3 Theory of Subjective Preferences for Sound Fields
- •3.4 Evaluation of Boston Symphony Hall Based on Temporal and Spatial Factors
- •4.1.1 Brainstem Response Correlates of Sound Direction in the Horizontal Plane
- •4.1.2 Brainstem Response Correlates of Listening Level (LL) and Interaural Crosscorrelation Magnitude (IACC)
- •4.1.3 Remarks
- •4.2.2 Hemispheric Lateralization Related to Spatial Aspects of Sound
- •4.2.3 Response Latency Correlates of Subjective Preference
- •4.3 Electroencephalographic (EEG) Correlates of Subjective Preference
- •4.3.3 EEG Correlates of Interaural Correlation Magnitude (IACC) Changes
- •4.4.1 Preferences and the Persistence of Alpha Rhythms
- •4.4.2 Preferences and the Spatial Extent of Alpha Rhythms
- •4.4.3 Alpha Rhythm Correlates of Annoyance
- •5.1 Signal Processing Model of the Human Auditory System
- •5.1.1 Summary of Neural Evidence
- •5.1.1.1 Physical Characteristics of the Ear
- •5.1.1.2 Left and Right Auditory Brainstem Responses (ABRs)
- •5.1.1.3 Left and Right Hemisphere Slow Vertex Responses (SVRs)
- •5.1.1.4 Left and Right Hemisphere EEG Responses
- •5.1.1.5 Left and Right Hemisphere MEG Responses
- •5.1.2 Auditory Signal Processing Model
- •5.2 Temporal Factors Extracted from Autocorrelations of Sound Signals
- •5.3 Auditory Temporal Window for Autocorrelation Processing
- •5.5 Auditory Temporal Window for Binaural Processing
- •5.6 Hemispheric Specialization for Spatial Attributes of Sound Fields
- •6 Temporal Sensations of the Sound Signal
- •6.1 Combinations of Temporal and Spatial Sensations
- •6.2 Pitch of Complex Tones and Multiband Noise
- •6.2.1 Perception of the Low Pitch of Complex Tones
- •6.2.3 Frequency Limits of Missing Fundamentals
- •6.3 Beats Induced by Dual Missing Fundamentals
- •6.4 Loudness
- •6.4.1 Loudness of Sharply Filtered Noise
- •6.4.2 Loudness of Complex Noise
- •6.6 Timbre of an Electric Guitar Sound with Distortion
- •6.6.3 Concluding Remarks
- •7 Spatial Sensations of Binaural Signals
- •7.1 Sound Localization
- •7.1.1 Cues of Localization in the Horizontal Plane
- •7.1.2 Cues of Localization in the Median Plane
- •7.2 Apparent Source Width (ASW)
- •7.2.1 Apparent Width of Bandpass Noise
- •7.2.2 Apparent Width of Multiband Noise
- •7.3 Subjective Diffuseness
- •8.1 Pitches of Piano Notes
- •8.2 Design Studies of Concert Halls as Public Spaces
- •8.2.1 Genetic Algorithms (GAs) for Shape Optimization
- •8.2.2 Two Actual Designs: Kirishima and Tsuyama
- •8.3 Individualized Seat Selection Systems for Enhancing Aural Experience
- •8.3.1 A Seat Selection System
- •8.3.2 Individual Subjective Preference
- •8.3.3 Distributions of Listener Preferences
- •8.5 Concert Hall as Musical Instrument
- •8.5.1 Composing with the Hall in Mind: Matching Music and Reverberation
- •8.5.2 Expanding the Musical Image: Spatial Expression and Apparent Source Width
- •8.5.3 Enveloping Music: Spatial Expression and Musical Dynamics
- •8.6 Performing in a Hall: Blending Musical Performances with Sound Fields
- •8.6.1 Choosing a Performing Position on the Stage
- •8.6.2 Performance Adjustments that Optimize Temporal Factors
- •8.6.3 Towards Future Integration of Composition, Performance and Hall Acoustics
- •9.1 Effects of Temporal Factors on Speech Reception
- •9.2 Effects of Spatial Factors on Speech Reception
- •9.3 Effects of Sound Fields on Perceptual Dissimilarity
- •9.3.1 Perceptual Distance due to Temporal Factors
- •9.3.2 Perceptual Distance due to Spatial Factors
- •10.1 Method of Noise Measurement
- •10.2 Aircraft Noise
- •10.3 Flushing Toilet Noise
- •11.1 Noise Annoyance in Relation to Temporal Factors
- •11.1.1 Annoyance of Band-Pass Noise
- •11.2.1 Experiment 1: Effects of SPL and IACC Fluctuations
- •11.2.2 Experiment 2: Effects of Sound Movement
- •11.3 Effects of Noise and Music on Children
- •12 Introduction to Visual Sensations
- •13 Temporal and Spatial Sensations in Vision
- •13.1 Temporal Sensations of Flickering Light
- •13.1.1 Conclusions
- •13.2 Spatial Sensations
- •14 Subjective Preferences in Vision
- •14.1 Subjective Preferences for Flickering Lights
- •14.2 Subjective Preferences for Oscillatory Movements
- •14.3 Subjective Preferences for Texture
- •14.3.1 Preferred Regularity of Texture
- •15.1 EEG Correlates of Preferences for Flickering Lights
- •15.1.1 Persistence of Alpha Rhythms
- •15.1.2 Spatial Extent of Alpha Rhythms
- •15.2 MEG Correlates of Preferences for Flickering Lights
- •15.2.1 MEG Correlates of Sinusoidal Flicker
- •15.2.2 MEG Correlates of Fluctuating Flicker Rates
- •15.3 EEG Correlates of Preferences for Oscillatory Movements
- •15.4 Hemispheric Specializations in Vision
- •16 Summary of Auditory and Visual Sensations
- •16.1 Auditory Sensations
- •16.1.1 Auditory Temporal Sensations
- •16.1.2 Auditory Spatial Sensations
- •16.1.3 Auditory Subjective Preferences
- •16.1.4 Effects of Noise on Tasks and Annoyance
- •16.2.1 Temporal and Spatial Sensations in Vision
- •16.2.2 Visual Subjective Preferences
- •References
- •Glossary of Symbols
- •Abbreviations
- •Author Index
- •Subject Index
282
Fig. 15.14 Flow of EEG alpha waves across both hemispheres as revealed through crosscorrelation comparisons between signals at occipital location O1 and six other locations. The EEG signals were generated in response to variation of flicker period under a preferred condition. (a) The averaged value of maximal crosscorrelation value
|φ(τ )|max for the six pairs of electrode sites, an indication of how similar the alpha band signals are to each other. (b) The median (50%) value of interelectrode signal delays
τ m(ms) between the six pairs of electrode sites, an indication of the propagation time of the waves
15 EEG and MEG Correlates of Visual Subjective Preferences
a
b
15.2 MEG Correlates of Preferences for Flickering Lights
The MEG was recorded during presentations of the most preferred and less preferred flickering lights alternately during change of the temporal factor. Results showed that (1) the effective duration of the ACF, τe, was longer during the preferred condition than during the less preferred conditions and (2) such results were significant in the left hemisphere when the temporal factor was varied.
15.2.1 MEG Correlates of Sinusoidal Flicker
To investigate human cortical responses that correspond with subjective preference and hemispheric specialization for visual stimulus, the ACF of the MEG in relation
15.2 MEG Correlates of Preferences for Flickering Lights |
283 |
Fig. 15.15 Scale preference values of flicker period T for different subjects. Different symbols represent preferences of different subjects. The thick black line shows averaged scale values of preference for all subjects tested. This reconfirmed the applicability of Equation (15.2)
to the period of a flickering light was analyzed (Soeta et al., 2002). The scale values of individual preference obtained by the PCT together with the averaged scale values of eight subjects are shown in Fig. 15.15. Effects of period on the scale values of preference were examined for all eight subjects by using one-way ANOVA. Results clearly indicated that the effects of period were significant (p < 0.01), and the most preferred period, [T]p, for each subject was estimated by fitting a suitable polynomial curve to a graph on which the scale values were plotted as shown in Fig. 15.16. The preferred period ranged from 0.6 to 2.0 s, and its averaged value was roughly 1.0 s.
Figure 15.17 shows an example of a recorded MEG alpha wave. We selected 16 channels for the ACF analysis that were located around the occipital area and analyzed each response to the single stimulus in the pair for each subject. The selected
Fig. 15.16 An example of obtaining the most-preferred period [T]p and less preferred periods of the flickering light. [T]0.5 ≈ 0.71, [T]1.0 ≈ 2.22, and [T]1.5 ≈ 0.48 (s). (Suffix number of [T] denotes the differences between scale values of preference)
284 |
15 EEG and MEG Correlates of Visual Subjective Preferences |
Left area |
Right area |
Central area
Fig. 15.17 Examples of recorded MEG alpha rhythms. Response durations were 2.5 s
16 channels were divided into three areas as shown in this figure. Table 15.5 shows the results of the two-way ANOVA for τe, (0), and φ1 values of the alpha wave obtained for the eight subjects. Significant effects of preference were found on τe and φ1 values under all conditions tested. It is clear that the values of τe correlated with the value of φ1 (r = 0.75, p < 0.01). The values of τe and φ1 for the most preferred stimuli were larger than those for less preferred stimuli for all subjects, as shown in Figs. 15.18 and 15.19, respectively. There were no clear differences, however, in the value of (0) as shown in Fig. 15.20. Such a difference of averaged values of τe and φ1 from the left area was significantly larger than those from the central and right areas (p < 0.01). The preferred period of the flickering light clearly induces a much longer τe in the alpha wave than that of the less preferred ones. This
Table 15.5 Results of two-way ANOVA on the values of τe, (0), and φ1 under three conditions
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Factor |
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τe |
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(0) |
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φ1 |
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Difference of scale |
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value of preference |
Factor |
F value |
Significance level |
F value |
Significance level |
F value |
Significance level |
||||
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(1) 1.5 |
Preference |
7.9 |
< 0.01 |
0.7 |
0.40 |
4.1 |
< 0.05 |
||||
|
Measurement position |
31.8 |
< 0.001 |
187.1 |
< 0.001 |
48.2 |
< 0.001 |
||||
|
Preference and measurement position |
0.5 |
0.61 |
0.2 |
0.81 |
0.2 |
0.80 |
||||
(2) 1.0 |
Preference |
9.6 |
< 0.005 |
0.6 |
0.43 |
4.9 |
< 0.05 |
||||
Measurement position |
26.6 |
< 0.001 |
166.0 |
< 0.001 |
26.9 |
< 0.001 |
|||||
|
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|
Preference and measurement position |
0.1 |
0.88 |
0.5 |
0.61 |
0.5 |
0.63 |
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(3) 0.5 |
Preference |
6.9 |
< 0.01 |
8.1 |
< 0.005 |
15.7 |
< 0.001 |
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Measurement position |
19.1 |
< 0.001 |
139.7 |
< 0.001 |
37.9 |
< 0.001 |
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Preference and measurement position |
1.5 |
0.23 |
0.0 |
0.99 |
1.1 |
0.31 |
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Lights Flickering for Preferences of Correlates MEG 2.15
285
286 |
15 EEG and MEG Correlates of Visual Subjective Preferences |
Fig. 15.18 Effective durations of averaged MEG alpha wave rhythms in response to flickering light for the different occipital sensor regions shown in Fig. 15.17. Error bars represent 95% confidence. The difference of the scale value of preference was (a) 1.5. (b) 1.0. (c) 0.5. ◦ : Higher preference; •: lower preference
tendency for longer τe for the preferred period of the flickering light rather than φ1 in the alpha wave was also found in a Section 15.1 on EEG. The fact that the brain repeats a similar rhythm under preferred conditions was reconfirmed.
The ratio of high to low preference in terms of the averaged value of τe obtained here was small in the range 1.01–1.06, but the difference is significant. This is much smaller than that derived in the study on EEG, which was 1.18–1.74. The EEG results from extracellular volume currents triggered mainly by the postsynaptic potential. MEG signals are thought to arise from the intracellular currents that flow from dendritic trees to cell bodies in large numbers (> 50,000) neurons. The MEG and EEG field distributions are mutually orthogonal. Only the current that has a component tangential to the surface of a spherically symmetric conductor produces an exterior magnetic field; the radial source is thus externally silent. Therefore, it one could think that the radial source might more directly reflect the neural activity patterns associated with preferred visual stimuli. Differential results from EEG and MEG for the analysis of human cognition have been discussed previously (Eulitz et al., 1997). Also, numerous studies have reported relationships between EEG and MEG coherence and mental processes (Rappelsberger and Petsche, 1988;
15.2 MEG Correlates of Preferences for Flickering Lights |
287 |
Fig. 15.19 Values of MEG alpha rhythm regularity φ1 in response to flickering light for the three occipital sensor regions shown in Fig. 15.17. Error bars represent 95% confidence. The difference of the scale value of preference was (a) 1.5. (b) 1.0. (c) 0.5. ◦ : Higher preference; •: lower preference
Hinrichs and Machleidt, 1992; Petsche, 1996). Those studies concentrated on the interchannel relations, for example, synchronization of alpha frequency rhythms.
Here, we found that the values of τe and φ1 from the left occipital area were significantly larger than those from the central and right occipital areas. Such a clear tendency was not found in the previous study on EEG mentioned above. The significant values τe and φ1 in the left hemisphere may reflect the specialization of the human brain, reconfirming specifically the left-hemisphere dominance for the temporal factors. Average alpha band signal amplitudes (0) from the right occipital area were significantly larger than those from the left occipital area. It is remarkable that the ratio of high to low preference in terms of τe is greater than that of the value of φ1. This indicates that the efffective duration τe of alpha waves reflects subjective preference better than does alpha rhythm regularity φ1.
These MEG experiments investigated human cortical responses corresponding to subjective preferences for flickering lights. The conclusions that we drew from these experiments are:
1.We reconfirmed that the most preferred flicker periods [T]p for individuals were 0.6 – 2 s, with an average value around 1 s.
