BACHELOR THESIS

Title:

Acoustic factors of looming perception

Pursued academic degree:

Bachelor of Science (BSc.)

Author: Adrian Josef Fleisch

Enrolment Number: 01507822

Field of Study: Bachelor’s Degree Programme Physics

Supervisor: Robert Baumgartner, PhD.

Examiner: Doz. Dr. Peter Balazs

Vienna, April 15th 2019

Plagiarism Disclaimer I hereby declare that this thesis is my own and autonomous work. All sources and aids used have been indicated as such. All texts either quoted directly or para-phrased have been indicated by in-text citations. This work has not been submitted to any other examination authority.

1

Table of contents

Abstract 2

1 Introduction 3

1.1 Auditory Looming Bias 3 1.2 Head Related Transfer Functions 4 1.3 Event-Related Potentials 4 2 Materials and Methods 5

2.1 Subjects 5 2.2 Stimuli 6 2.3 Recording of the EEG Data 6 2.4 Experimental Procedure 7 2.5 Post Processing 7 2.6 Apparatus 8 3 Results 10

3.1 Behavioural Results 10 3.2 EEG Results 13 4 Discussion 20

4.1 Behavioural Results 20 4.2 EEG Results 20 4.3 Conclusio 22 5 References 24

2

Abstract The so called looming bias is a psychoacoustic phenomenon emphasizing ap-

proaching sounds towards receding ones both in brain activity and behaviourally.

A sound appearing to approach – i.e. getting louder – is perceived stronger than

one seeming to recede. Previous studies have shown that looming bias is induced

by sounds with tonal structure (Neuhoff, 1998). However, it could not be verified

for random noises, in case of this pilot-study, gaussian white noise.

A grave difference to previous studies is the presentation of the sounds. Like this

one most studies used volume change to illicit the impression of an approaching

sound. They used a gradual increase or decrease slope of volume. Instead, in this

experiment an instantaneous change of volume was used which – if working –

would have some advantages towards a sloped change.

To naturalize the sounds filtering with Head Related Transfer Functions (HRTF)

was used. It should give the sounds played via headphones a spacial source ex-

perienced outside the listeners head, as it would be perceived listening to the

sounds via a speaker or another sound source located in front of the listener. Usu-

ally, HRTF are used to simulate three-dimensional hearing on headphones, for this

experiment they were only used for one angle directly in front of the listener.

In total, three differences were compared: Looming versus receding, noises versus

tonal sounds and sounds with HRTF filtering versus sounds without HRTF filtering

resulting in eight different stimuli. For each stimulus 100 repetitions were measured

so in total each participant had to listen to 800 single sound bits.

Two things are topic of research: First behaviourally if looming sounds would be

perceived more consistently as such than receding ones, second via electro en-

cephalography (EEG) we would look whether brain activity would indicate looming

bias. Behaviourally it turned out that a volume change of ±1 dB as it was used in

the beginning for the first four participants was not perceptible for all. This appears

to be the reason why, for two of the four participants tested, the EEG-data did not

show any phenomenon occurring at the volume shift with ±1 dB change.

The experiment could not deliver reliable data proving looming bias for noises as

well as any impact of HRTF based naturalization of sounds. However, it showed

looming bias occurring for at least one subject therefore revealing an interesting

topic for follow up studies.

3

1 Introduction 1.1 Auditory Looming Bias

In a study from 1998 it was shown that listeners would overestimate the change in

loudness for sounds increasing in volume compared to sounds decreasing. The

listeners would state their impression of loudness by placing a cursor on an ana-

logue scale. However, the bias would only be certifiable for sounds with tonal struc-

ture, e.g. simple sinus sounds or complex tones. For white noise the results would

not be reliable suggesting looming bias was not evoked (Neuhoff, 1998).

Looming bias is an effect noticeable in all primates. In experiments on rhesus mon-

keys it was shown that behaviourally, their attention towards rising-intensity com-

plex tones was consistently longer than to receding ones. Also here for white noise

the result was inconsistent, implying no looming bias elicited. (Ghazanfar, Neuhoff,

& Logothetis, 2002). An invasive experiment with rhesus monkeys accordingly

showed a neural bias towards looming complex tones. Auditory cortical activity

was shown to be biased towards looming complex tones, while white noise stimuli

did not. They elicited different brain pattern responses. (Maier & Ghazanfar, 2007)

In an experiment using magnetoencephalography (MEG) to measure the neural

responses of human subjects to complex tones or white noise the looming bias

was proven consistently to the prior stated studies. The neural response for com-

plex sounds is changing in intensity nearly linearly in contrast to overall intensity

changes for white noise stimuli. (Bach, Furl, Barnes, & Dolan, 2015)

Not only change in intensity but also change in spectral cues could elicit looming

bias, as a study from 2017 shows. To achieve that, the subject’s Head Related

Transfer Functions (HRTF) are used to create naturalized, externalized sounds.

By spectral filtering of the sound it was possible to achieve the impression of loom-

ing and receding sounds without changing the general intensity, therefore showing

that looming bias is actually correlated to motion sensitivity and not to changes in

intensity. In this experiment instant sound changes were used instead of sloped

ones (Baumgartner et al., 2017).

4

According to current scientific knowledge it is fair to assume that white noise can-

not to elicit looming bias, it may even be incapable to trigger motion sensitivity in

general. An evolutionary psychological explanation for this is simple to find:

Sounds associated to a threat to our stone-age ancestors all had tonal structure,

for example the growl or approaching steps of a dangerous animal, an avalanche

or a nearby thunder. Our brain generally perceives white noise as what it mostly

is: Disturbance. Therefore it would not highlight it with a psychological reflex like

the auditory looming bias.

1.2 Head Related Transfer Functions

Sounds presented over headphones have one big difference to other sounds with

regards to three-dimensional hearing: They don’t have to travel any distance nor

face any barrier until reaching the inner ear. For sounds within a three-dimensional

auditory space this is not the case. HRTF simulate the acoustic barriers imposed

by the listeners body a sound has to pass through to get to the inner ear. With

these – for each volunteer individually measured – functions a sound could be

shaped in a way so it has a distinctive place outside the listeners head, inside of

which normally without any filtering would be the perceived place of a sound played

with headphones.

The hypothesis of this study is, that with the method of filtering white noise with

HRTF and therefore giving it some structure it could be possible to elicit a meas-

urable auditory looming bias.

1.3 Event-Related Potentials

Event-Related Potentials (ERP) are characteristic neural responses to sensory

stimuli. Two ERP components are interesting for this experiment: The negative

onset N1 and the positive onset P2.

The auditory N1 wave actually consists of several components from 75 ms to 150

ms after the stimulus. It is associated with discriminative processing in the brain.

The positive P2 wave follows it with its peak around 200 ms. These two peaks are

also called the vertex potentials (Luck, 2005, 37, 39 f.).

5

Figure 1: Event related potentials. The two components observed in this experiment are N1 and P2.

The other components usually occur as well, but are not further investigated.

Especially for EEG measurements, precisely definable points in time for events of

interest are favourable. The measurement performed in this experiment looked for

ERP to quantify the neural response of the brain to different stimuli. Especially for

a procedure like this or similar it would be significantly easier and more stable, if it

was possible to elicit a looming bias by an instant volume shift to minimize the

possibility of offsets in neural responses.

2 Materials and Methods 2.1 Subjects

There were five volunteers aged between 14 and 28 tested (M=22, SD=5.0), one

of which was myself. Due to the young age of all participants it was assumed that

none of them has hearing loss in an extent relevant for the experiment. In the con-

text of this bachelor thesis it was not possible to pay them allowance therefore all

the participants were either connected to the Acoustic Research Institute or to the

experimenter.

6

2.2 Stimuli

The sounds, gaussian white noise and a complex tonal signal, are sampled with a

sampling rate of 48 kHz in a length of 1200 ms. At 600 ms, the volumes of the

samples either increased or decreased instantaneously by ± 1dB. Due to feedback

by the participants that they could not hear the volume shift, for the last participant

it was raised to ±3 dB. So that the volume shift itself would not be too present on

the EEG-data we tried to keep it as small as possible while still audible.

The same thing was done for the stimuli filtered with the HRTF with both azimuth

and elevation angle of 0°, meaning that the sound source appears to be directly

in front of the listener. In total eight different stimuli were generated, all of which

were played to each participant 100 times.

2.3 Recording of the EEG data

The EEG System consisting of a Cap(Brain Vision actiCap®) and an Amplifier with

Battery (Brain Vision actiCHamp®) was used in a 32 channel setup as depicted in

Figure 4, where one (channel TP9) worked as reference channel to level out global

impedance changes. For participant 4 and 5 this channel was not attached to the

cap but with a particular clip to the earlobe. The inbuilt active filtering system was

used. Electric contact between the electrodes and the scalp was made with con-

ductive gel, which was inserted through a hole in the electrodes into the small gap

between scalp and electrode. With the headphone amplifier connected to the EEG

interface, the software marked the time of the beginning of each stimulus.

Figure 2: Timeline of the stimuli.

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2.4 Experimental procedure

After the EEG system was set up, the participants were asked to use the number

pad in front of them to answer if the sounds they hear is „becoming louder“ or

„becoming quieter“. By pressing 5 they could answer that the sound was getting

louder, by pressing any of the buttons 4, 7, 8, 9 or 6 they would answer it was

getting quieter.

Before starting the EEG measurement, the volunteers were asked to take a few

test rounds. Then the experiment was started. The participants were to look at a

cross displayed on the monitor in front of them and blink as little as possible. Eye

or head movement and blinking produce large artefacts in the EEG measurement

and are therefore to be avoided as much as possible. The participants could al-

ways take short breaks in between the display of stimuli by not answering right

away. After 80 stimuli presented – about three to four minutes – they were forced

to take at least a short break, but with option of pausing as long as they wanted to.

2.5 Post processing

All the post processing was done using Matlab. For analysing the behavioural part

of the pilot study a small program was written, which corresponds the answers to

the stimuli. The results from the software ExpSuite were saved in a simple .csv

table and therefore are easy to work with.

For processing the EEG-data the eeglab-toolbox for Matlab (Delorme & Makeig,

2004) was used. On its GUI interface the data was resampled to 100 Hz and, with

a Finite Response filter (FIR), reduced to a bandwidth between 0.5 Hz and 20 Hz.

An independent component analysis (ICA) (Jutten & Herault, 1991) was conducted

and between one and three components were excluded from the data to eliminate

artefacts from eye blinks, head movements and the like. This was done by manu-

ally discarding components with suspicious topography. In Figure 3 the compo-

nents of participant 3 are depicted. Components 1-3 were rejected. The compo-

nents 1 and 2’s topography are presumably eye blink artefacts, as the only chan-

nels showing potential changes are the frontal ones. Component 3 is a global ar-

tefact.

8

The central channels Cz, CP1, and CP2 (see Figure 2.) were extracted and

epoched by a small self-written Matlab program. All epochs exceeding thresholds

of ±80 µV were disregarded.

2.6 Apparatus

For each volunteer the individual HRTF were either already available or measured.

The measurement was done using the Multiple Exponential Sweep Method

(Majdak, Balazs, & Laback, 2007).

The experiment itself took place in a sound isolated chamber. To further minimize

background noise the internal active filtering system of the EEG cap was used.

The participant’s heads were measured and a cap with the according size

equipped with 33 electrodes, two of which are ground and reference channel TP9,

which are not shown in Figure 4. The ground is located in between Cz and Fz. For

the first four participants, the reference channel used to cancel out global changes

in impedance, was fixed on the cap behind the left ear, for the last participant it

Figure 3: Topographies of the independent components from participant 3 to exclude artefacts.

9

was fixed to the left earlobe instead to further minimize the impact of brain activity

or muscle movement for the reference.

With a program written especially for the lab environment (ExpSuite, SpExCue),

the Sounds are generated and played in random order. The program also regis-

tered and saved the behavioural responses given by the participants with a number

pad.(“ExpSuite,” n.d.) On a second computer the EEG data was collected.

With an analogue cable a single pulse signal was sent from the headphone ampli-

fier to the EEG system whenever a sound was played to later correspond the EEG

measurement with the sounds. Air tube headphones (Etymotic Research ER2)

were used to minimize electromagnetic interferences from the loudspeakers to the

EEG cap. The ear tips were first connected to the air tube and then carefully in-

serted in the participants ear by the experimenter to ensure their correct place-

ment.

Figure 4: Locations of the electrodes on the scalp.

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3 Results 3.1 Behavioural Results

The behavioural part of the experiment could show us a few things, most im-

portantly if the participants could discriminate the sounds getting louder from the

ones getting quieter at all. This basic necessity was not satisfied by two of the

participants. Figure 6 shows the percentage of correct and wrong responses by

two participants, which apparently could not perceive the change in volume:

Figure 5: Scheme of the setup.

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Figure 6: Behavioural responses of two participants with very low correct responses.

Both volunteers have a general hit rate of about 50% corresponding to just guess-

ing. The spike for complex sounds for participant 4 suggests that the participant

generally assumed the structured sound to be looming without actually regarding

the change in volume. Participant 2 would discriminate between the sounds filtered

with HRTF and the unfiltered sounds, as shown in Figure 7:

Figure 7: Behavioural responses of one participant without and with HRTF-filtered

stimuli.

The volunteer tended to answer looming whenever she*he heard a non-filtered

sound and reversely answered more often receding for a HRTF-filtered stimulus.

For this reason, for the fifth participant we raised the volume shift to ±3 dB to ensure

she*he would hear the difference. The other two participants, one of them being

me, were able to hear the difference.

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In Figure 8, the responses from the three participants sensitive to the volume shift

are represented. There appears to be a slight bias towards looming sounds for the

structured complex tones, which is stronger for the non-filtered sounds than for the

HRTF-filtered sounds. For the white noise sounds exactly the opposite is apparent.

There are more correct answers for receding sounds. Also there is no significant

change between the signals filtered with HRTF and not filtered ones.

Figure 8: Behavioural responses of the accepted participants without and with HRTF-filtered

stimuli, standard error of the means (N=3)

In Figure 9 the results from myself are depicted. I had very high hit rates compared to

the other Participants:

Figure 9: Behavioural responses of Participant 1 – myself – without and with HRTF-filtered

stimuli.

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3.2 EEG Results

Figure 10: Depiction of the whole epoch for all 8 conditions.

In Figure 10, the whole epoch from the beginning of the stimulus marked with the

line at 0 s to its end at 1.2 s is shown. The ERP at the start of the stimulus are

clearly visible with the negative spike N1 at around 0.12 s and the positive spike

P2 at around 0.2 s. Important is the events after the volume shift at 0.6 s, marked

by the second line. By excluding the two participants not sensitive to the volume

change we got the results shown in Figure 11 and Figure 12. On the right side the

mean of the areas corresponding to N1 and P2 are depicted. For N1 the area is

between 80 ms and 120 ms after the stimulus as in previous studies (Baumgartner

et al., 2017), so for this example it is the area from 680 ms to 720 ms from the

epoch. P2 is evaluated in the area between 120 ms and 280 ms corresponding to

720 ms – 880 ms from the epoch. The areas are shown in Figure 11 and Figure

12 by the dotted lines.

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Figure 11: EEG data of the 3 accepted participants for the noise stimuli. On the right hand side the averages of the values in the areas of N1 and P2 are depicted for looming and receding stimuli.

Figure 12: EEG data of the 3 accepted participants for the tonal stimuli.

The windows for N1 and P2 in Figure 11 and 12 are not corresponding with any

peaks and therefore not giving meaningful results regarding the ERP components.

For that reason the mean curve over all accepted participants and stimuli shown

in Figure 12 was used to set working windows.

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Figure 13: Mean curve over all participants and stimuli. The lines marking the borders of the win-dows for N1 and P2. The first two lines are located at the zero crossing at 0.71 s and 0.81 s, the

third one at a local minimum at 0.92 s.

The new windows borders for N1 are located at the zero crossing of the mean

curve. N1 now is defined as the mean of all graph points within 0.71 s and 0.81.

For P2 because there is no zero crossing after the peak the local minimum at

0.92 s was used as rear border. Therefore P2 is defined as the mean of the graph

points between 0.81 s and 0.92 s. In Figure 14 and 15 the results for all valid par-

ticipants is shown. In the sub plots on the right side the N1 and P2 values are

depicted:

Figure 14: EEG data of the accepted participants for noise stimuli with shifted windows with depic-

tion of N1 and P2.

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Figure 15: EEG data of the accepted participants for tonal stimuli with shifted windows with depic-

tion of N1 and P2.

In Figure 14 and 15 all the negative peaks N1 are centrally placed in their window.

The peaks for P2 also are clearly within its windows. The mean over the area of

P2 now reliably is always higher than the mean over the area of N1 for every signal,

as can be seen in the side plot. For the tonal sounds as well as the noise stimuli

the Amplitude for N1 is larger (e.g. the Voltage is lower) for sounds getting louder,

however for white noise the difference is negligible.

The neural responses vary strongly between the participants, so a representation

of all of them together is biased in favour of those subjects neurally reacting

stronger. For discussion the more sensible representation is to show each Subject

individually and then show if there is any consistency throughout them. In Figures

16 – 21 the measurement Results of the three accepted Subjects are displayed:

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Figure 15: EEG data of participant 1 for noise stimuli with shifted windows with depiction of N1 and

P2.

Figure 16: EEG data of participant 1 for tonal stimuli with shifted windows with depiction of N1 and

P2.

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Figure 17: EEG data of participant 3 for noise stimuli with shifted windows with depiction of N1 and

P2.

Figure 18: EEG data of participant 3 for tonal stimuli with shifted windows with depiction of N1 and

P2.

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Figure 19: EEG data of participant 5 for noise stimuli with shifted windows with depiction of N1 and

P2.

Figure 20: EEG data of participant 5 for tonal stimuli with shifted windows with depiction of N1 and

P2.

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4 Discussion 4.1 Behavioural results

The data presented in Figure 8 leads to the conclusion that behaviourally no loom-

ing bias could be observed for any stimulus. The data’s uncertainty and variation

is much larger than any tendencies. However, it is important to note that with a

peer group size this small it was unlikely to gain any sound findings. As seen on

the error bars the uncertainty in this data is very high.

Interestingly, I myself as a participant had by far the best hit rates for all the differ-

ent stimuli, with hardly any difference between filtered or non-filtered sounds. In

Figure 9 this results with correct answers at around 80% are shown. The probable

reason for this is that I was subjected to these sounds in many test runs and other

scenarios and had a lot of practice with this exact testing format.

This could lead to the notion that the other participants either were not instructed

well enough or should have been given more time to practice the task. However,

the experiment itself is very tiring to the volunteers and therefore longer exercising

before the actual experiment would possibly tire the participants more than training

them. The testing method chosen is not really comparable to prior studies, as there

the participants were asked on their subjective impression on how loud a stimulus

was. (Neuhoff, 1998) Here the idea was that a due to looming bias louder per-

ceived sound would be recognised as looming more consistently.

4.2 EEG Results

In Figure 10 something very interesting is happening at the onset of the stimulus:

For the complex sound stimuli the EEG shows the ERP spikes as expected. After

about 50 ms the P1 spike, then at 120 ms the clearly visible negative N1 spike and

then at about 200 ms the large P2-spike. For the white noise, however, there is

only one spike around 200 ms.

This could mean that there is less processing involved when listening to noises. If

that is the case it could be an explanation for the impossibility to elicit a looming

bias with white noise. However, as prior studies have shown, with other methods

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eliciting motion perception it was possible to measure looming bias with unstruc-

tured noise stimuli. (Baumgartner et al., 2017)

Another spike is visible in Figure 10 around 1300 ms. This arises from the sound

stopping at 1200 ms and the brain processing this stop.

In the Figures 11 and 12 the results did not show the characteristic ERP For the

volume shift itself. Something appears to happen, but the spikes seem to being

shifted around 50 ms back. With the areas for N1 and P2 used so far the Figures

would not show meaningful data. After aligning the windows as shown in Figure 13

the Results for N1 and P2 looked much more like predicted. Now in Figure 14 and

15 there are significantly stronger negative spikes N1 for the stimuli getting louder

compared to the ones getting quieter which is an indicator for looming bias. The

values for P2 consistently are above the ones for N1. Because, as mentioned ear-

lier, the means over all subjects is strongly biased towards those neuronally react-

ing stronger, looking at each subject individually and comparing the results is more

informative.

For Participant 1, in Figure 15, there are light N1 peaks visible for both noise stimuli

getting louder, with and without HRTF, while there is no N1 peak at all for the

receding ones. In Figure 16 waves with a Frequency of slightly under 10 Hz are

visible overlaying the ERP. These are so called alpha waves, which occur if the

subject gets tired or inattentive. Although the depiction of N1 shows a stronger

negative amplitude for N1 for increasing sounds, which could indicate looming

bias, this is not certifiable by this depiction due to the strong disturbances by alpha

waves. Interestingly Participant 1 – myself – appears to having been tired during

the experiment although being best in the behavioural test.

Participant 3, the next accepted subject, the depictions of N1 and P2 in both Figure

17 and 18 show strange results. This is due to a slight shift of the ERP peaks. Still,

in Figure 17 for the non-filtered noise stimulus with increasing intensity a strong N1

peak is visible. The N1 peak for the HRTF filtered increasing noise stimulus also

is visible, however the P2 peak is not centred in the P2 window. For both intensity-

decreasing stimuli the characteristic ERP are very weak. The filtered decreasing

noise sounds N1 peak is shifted back out of the window centre about 0.05 s. For

non-filtered intensity decreasing noise stimulus the P2 peak is out of the P2 win-

dow. Figure 18 shows no ERP for the complex tone stimuli. It is not clear what

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caused this disturbances. Concluding it is also not possible to show looming bias

for Participant 3.

For the last Participant, on whom we set hope due to the increased volume shift,

the results were more promising: As seen in Figures 19 and 20, due to the in-

creased volume shift the ERP in general are stronger. For the noise stimuli a clear

looming bias is visible. The N1 spike is slightly stronger for the HRTF filtered in-

creasing white noise stimulus compared to the non-filtered one, this difference

however is too small to deduce anything from it. For the complex tonal sounds

seen in Figure 20 interestingly looming bias is seen only for HRTF-filtered stimuli.

This contradicts a prior study having shown looming bias for non-filtered tonal stim-

uli (Neuhoff, 1998). The difference to this particular study is that in its case the

intensity increase was sloped while in this experiment setup it was instantaneous.

Although the test group is too small to state any new insights, the results, especially

the ones from participant 5, would promote that there is stronger perception of

motion for stimuli filtered with HRTF.

Measurements with an EEG-system generally are very time-consuming. For each

participant the whole procedure took around three to three and a half hours. First

their HRTF had to be measured, which took between 45 minutes and one hour,

then the EEG cap had to be set up taking a least half an hour, then the experiment

itself – being only a small part of the time taken from the participants – again took

one hour. Afterwards the participant had to wash her*his hair due to the contact

gel used, which took her*him another 30 minutes. This is the reason it was not

possible to test a larger group in the context of this bachelor thesis.

4.3 Conclusio

The findings of this experiment cannot prove looming bias for any stimulus defi-

nitely. For this neural responses were too inconsistent throughout the participants.

However this was not the goal of this thesis, which should rather be seen as a pilot

study to larger possibly following studies. To soundly prove looming bias for white

noise stimuli and with abrupt volume shifts a larger group would have to be tested.

What this study shows is that looming bias can generally be elicited by noise as

seen in Figure 19 and therefore a follow up large scale study could be fruitful.

23

If a follow up study to this topic was made I would advise to several changes to the

experimental procedure. The volume difference should be at least ± 3 dB. Obvi-

ously the test group would need to be significantly larger – at least 15 to 20 people

to ensure statistical stability. The validity of the behavioural experiment part would

possibly increase significantly if the participants had more time test running the

experiment. Another great improvement would be to give the participants the pos-

sibility to stand up and walk around during breaks. This was not possible in the

setup used as the EEG-measurement was running throughout the participants

breaks.

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Shinn-Cunningham, B. (2017). Asymmetries in behavioral and neural re-

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