Summary
The localization of sound sources in humans is based on the binaural cues, interaural time and level differences (ITDs, ILDs) and the spectral cues (Blauert 1997). The ITDs relate to the timing of sound arrival at the two ears. For example, a sound located at the right side will arrive at the right ear earlier than at the left ear. The ILDs refer to the difference of sound pressure-level between the two ears. In the example mentioned above, if the sound located at the right has short wavelength then it will arrive at the right ear with higher sound-pressure than at the left ear. This is because a sound with short wavelength cannot bypass the head. In other words, the head creates an obstacle that diffracts the waves and that is why the sound arriving at the ear closer to the sound source will receive the sound with higher sound-pressure. Due to the association of each of the binaural cues with the wavelength of a sound, Rayleigh (1907) proposed the ‘duplex theory’ of sound source localization suggesting that on the azimuth, the ITDs is the main localization cue for low frequency sounds and the ILDs is the main localization cue for high frequency sounds. The spectral cues are based on the shape of the pinna’s folds and they are very useful for sound source localization in elevation but they also help in azimuthal localization (Schnupp et al. 2012). The contribution of the spectral cues on the azimuthal localization arises from the fact that due to the symmetrical position of the ears on the head, the binaural cues vary symmetrically as a function of spatial location (King et al. 2001). Whereas the ITDs have a very symmetrical distribution, the ILDs become more symmetrical the higher the sound frequency is. This way, there are certain locations within the left-frontal and left-posterior hemifield, as well as the right-frontal and the right-posterior hemifield that share the same binaural cues, which makes the binaural cues ambiguous and so the auditory system cannot depend solely on these for sound source localization. To resolve this ambiguity, our auditory system uses the spectral cues that help to disambiguate frontal-back confusion (King et al. 2001, Schnupp et al. 2012). The role of these cues in localizing sounds in our environment is well established. But their role in acoustic motion localization is not yet clear. This is the topic of the current thesis.
The auditory localization cues are processed on the subcortical and cortical level. The ITDs and ILDs are processed from different neurons along the auditory pathway (Schnupp et al. 2012). Their parallel processing stages seem to converge at the inferior colliculus as evidence shows from cat experiments (Chase and Young 2005). But in humans, an electroencephalographic (EEG) study measuring the mismatch negativity (MMN; Schröger 1996) and a study using magnetoencephalographie (MEG; Salminen et al. 2005) showed that these cues are not integrated.
One of the models of the spatial representation of sound sources is Jeffress’ place code (1948). This model suggests that each location of the azimuthal space is encoded differently, thus the name ‘place code’. Evidence in support of this model comes from studies on the cat (Yin and Chan 1990). However, arguments against this model come from studies in gerbils whose results showed that their subcortical neurons respond maximally to locations that are outside the physiological range based on the size of their heads (Pecka et al. 2008). An alternative model of auditory spatial encoding is the hemifield code (von Bekesy 1960). This model proposes that subcortical neurons are separated into two populations, one tuned to the left hemifield and another tuned to the right. Thus, the receptive field of the neurons is wide and the estimation of the sound source location is derived from the balance of activity of these two populations. Evidence from human studies support this model. Salminen and colleagues (2009) employed an adaptation paradigm during MEG recording. They presented sets of adaptor and probe stimuli that either had the same or different spatial location. Their results showed that the response to the probe was more reduced when the adaptor was located at the far left location and not when the adaptor and probe shared the exact same location. Also, an EEG study on auditory motion showed that sounds that move from central to lateral locations elicit higher amplitudes than when the move in the opposite direction (Magezi and Krumbholz 2010). The authors concluded that these results are based on the movement of the sound source towards the location of the maximal activity of the neurons (also in Salminen et al. 2012).
The ability to detect moving objects is well-embedded into our nature. Whereas it enriches predators and prey with the skills to survive, in everyday life it enables us to interact with our environment. For example, the task of crossing a street (without traffic signs) safely is based on the encoding of visual and auditory features of moving vehicles. In the visual modality, the capability of the system to encode motion is based on motion-specific neurons (Mather 2011). In the auditory modality, the debate over whether these sensors exist is still ongoing.
One theory on how the auditory system encodes motion is the ‘snapshot’ theory (Chandler and Grantham 1991, Grantham 1986). In a series of experiments, Grantham (1986) showed that auditory perception was not affected by features of motion such as velocity, but it was more sensitive on distance as a spatial cue. Thus, what he suggested is that the encoding of auditory motion is based on the mechanisms that encode stationary sounds. In other words, when a sound is moving it activates the neurons that correspond to the points that are located along the trajectory of that sound but in a serial manner. This way, the perception of auditory motion is based on ‘snapshots’ instead of processing motion as a complete feature. This mechanism of auditory motion processing corroborates with Jeffress’ place code (1948). Animal studies on monkeys (Ahissar et al. 1992) and owls (Wagner et al. 1994) showed that neurons responded similarly to moving and stationary sounds. Evidence against this theory come from a recent behavioural study that introduced velocity changes within acoustic motion and showed that participants were able to detect them (Locke et al. 2016). The authors concluded that if ‘snapshot’ theory would be true, then these detections of velocity change would not occur.
Another theory of auditory motion was evolved that supports the motion-specific mechanisms in the brain (Warren et al. 2002, Docummun et al. 2004, Poirier et al. 20017). A human study using functional magnetic resonance imaging (fMRI) and positron-emission tomography (PET) showed evidence of a motion-specific cortical network that includes the planum temporale and the parietotemporal operculum (Warren et al. 2002). The authors suggested that these areas are part of a posterior processing stream that is responsible for analysis of auditory moving objects. Moreover, a recent primate fMRI study provided evidence of motion-specificity in the activity of the posterior belt and parabelt regions of the primary auditory cortex (Poirier et al. 2017). The authors contrasted cortical response to auditory motion with stationary and spectrotemporal sounds and found that the aforementioned cortical areas were only activated by moving sounds.
All in all, the neuronal mechanism underlying auditory motion perception has been vaguely described. However, there is an increasing number of evidence that show that specialized motion areas and mechanisms exist in the cortex. To study how exactly these mechanisms function, it is important to know which aspects of the stimulus paradigm affect the response.
Study 1. In this study, I focused on eliciting the cortical motion-onset response (MOR) in the freefield. This specific response is measured with EEG and it is elicited when a sound motion follows a stationary sound without any temporal gaps between them. The stationary part serves as an adaptive sound and the onset of motion provides a release-of-adaptation, which gives rise to the MOR. One of the focus was to investigate the effect on the MOR when the initial part is moving in space instead of being stationary. In addition, a secondary focus was the effect of the stimuli frequency on the MOR. I hypothesized that, due to the adaptation provided by the initial stimulus part, the motion response would be smaller after moving than after stationary adaptation. Also, I expected that the effects of frequency would follow the literature and since the motion response is a late response, the amplitude would be smaller after the high frequency than low frequency stimulus presentation. The results showed that the current paradigm did not elicit the MOR. Comparison of the current experimental settings with those used previously in the literature showed that the MOR is strongly depended on the adaptation time provided by the first part of the stimuli.
Study 2. In this study, the stimulus characteristics were adapted after failing to elicit the response in the previous study. In addition, I employed an active instead of a passive paradigm, since data from the literature show that the motion response is strongly dependent on the allocation of attention on auditory motion. Thus, in this study, the elicitation of the MOR was successful. The current study examines the modulation of the MOR based on the frequency-range of sound stimuli. Higher amplitude on the motion response was expected after the presentation of stimuli with high frequency spectrum. Also, I studied the effects of hemifield presentation and the direction of motion on the MOR. The results showed that the early part of the motion response (cN1) was modulated by the frequency range of the sounds with stronger amplitudes elicited by stimuli with high frequency range.
Study 3. This study is focused on analysis from data collected in the previous study. The focus, however, is on the effects of the stimulus paradigm on the MOR. I hypothesized that after the adaptation provided by an initial moving part, lower amplitude was expected in comparison to the stimuli with an initial stationary part. These responses were also analysed based on the effects of stimulus frequency. The results showed that the stimulus paradigm with the initial moving part elicited a response that resembles the MOR but has lower amplitude. In addition, the effects of stimulus frequency evident from the previous analysis apply here as well, with high frequency stimuli eliciting higher MOR amplitude than low frequency stimuli.
Study 4. This study examined further the effects of stimuli characteristics on the MOR. Since the latency of the MOR in the previous study was a bit later than what is usually reported in the literature, the focus here was to test the effects of motion velocity and adaptation duration on the MOR. The results showed that faster velocity elicited higher amplitudes on the peak-to-peak comparison. Separate analysis on the MOR components, showed that this effect was based on higher cN1 amplitude. A separate analysis between the electrodes over the left and right hemisphere, showed that the peak-to-peak amplitude was stronger on the electrodes over the right hemisphere. Lastly, the strong adaptation created by the long duration of the initial stationary part provided abundant evidence of auditory motion, which led to the separation of the cP2 into its constituent parts.
Study 5. This behavioural study focused on the effect of motion adaptation on the rear field to the presentation of motion in the frontal field. Thus, the presentation of adaptors and probes within the left-frontal and left-rear fields aimed at locations that share the same ITDs and ILDs. The disambiguation of auditory localization of motion is based on how these interaural cues interact with the spectral cues. A moving probe was presented in the left hemifield, following an adaptor that spanned either the same trajectory or a trajectory located in the opposite field (frontal/ rear). Participants had to indicate the direction of the probe. The results showed that performance was worse when adaptor and probe were sharing the same binaural cues, even if they were in different hemifields and their direction was opposite. But the magnitude of the adaptation effect when the pair was in different hemifields was smaller, thus showing motion-direction detection depends on the integration of interaural and spectral cues.
| Identifer | oai:union.ndltd.org:DRESDEN/oai:qucosa:de:qucosa:33724 |
| Date | 10 April 2019 |
| Creators | Sarrou, Mikaella |
| Contributors | Universität Leipzig |
| Source Sets | Hochschulschriftenserver (HSSS) der SLUB Dresden |
| Language | English |
| Detected Language | English |
| Type | info:eu-repo/semantics/updatedVersion, doc-type:doctoralThesis, info:eu-repo/semantics/doctoralThesis, doc-type:Text |
| Rights | info:eu-repo/semantics/openAccess |
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