ME 224 EXPERIMENTAL ENGINEERING

Effects of Flashing Lights and Beeping Tones on Subjective Time

Estimation ME 224 Final Project Report

Aamir Habib, Yoke Peng Leong, Yuchen Yang

12/3/2011

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Contents Abstract ...................................................................................................................................... 2

Introduction ................................................................................................................................ 2

Methods ..................................................................................................................................... 3

Equipment .............................................................................................................................. 3

Parameters.............................................................................................................................. 3

Procedures .............................................................................................................................. 3

MATLAB Code ......................................................................................................................... 3

Results ........................................................................................................................................ 5

Discussions.................................................................................................................................. 8

Difficulties during Experiment ................................................................................................. 8

Result Analysis and Interpretations ......................................................................................... 9

Future Work .......................................................................................................................... 11

Conclusion ................................................................................................................................ 12

Appendix ................................................................................................................................... 13

A. Trials Setup .................................................................................................................... 13

B. MATLAB Script ............................................................................................................... 14

C. Experiment Results ........................................................................................................ 15

D. Plots of Experiment Results ........................................................................................... 17

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Abstract In our experiment, we wish to test the hypothesis that the ability to estimate time is heavily

affected by cognitive processes, which, when subjected to audio-visual stimuli, will distort a

subject’s ability to estimate time and have either a constricting or dilating effect upon said time-

estimation ability.

Introduction Established models for time estimation within people rely on one of two fundamental precepts

regarding the mechanism for estimation – one is based on the use of an internal clock, limited in

interaction with any external stimuli, which allows us to measure time. The other supported

mechanism for time estimation in people relies suggests that cognitive processes are the direct

actor in determining how much time has passed.

Models developed on the basis of the latter theory can ostensibly be divided into two categories

– that focusing on a time lapse will have a time dilation effect, and the other is that the impact

of stimuli upon the senses of the observer will cause some level of ‘interference’ with the time

estimation of the subject, suggesting differences in estimated time based on the stimuli.

In our experiment, we wish to subject an observer to a combination of audio-visual signals

operating at slow and fast speeds – the qualification of the speed as fast and slow are subject to

their respective difference from the standard one-second interval.

Our primary hypothesis is that the typically ‘slow’ stimuli will have a time-dilation effect on

one’s time estimation, while the ‘fast’ stimuli will have a time constriction effect on the

observer’s time estimation.

The secondary purpose of the experiment is to establish which of the stimuli (audio or visual)

will have a greater interference effect (as measured in variation from a time-estimation control)

on the subject.

A third, yet smaller goal, is to determine the level of time-estimation ‘strain’ that mixed-speed

dual signals will have on time estimation. Our belief is that they will contribute to a greater

variation from the baseline, although we cannot postulate whether they will have a dilation or

constriction effect.

References:

1. Effects of Rhythmic Sound Rates on a Visual Counting Task, Kristina Davis, Stephen F.

Austin University, 2001

2. Effects of attention and external stimuli on duration estimation under a prospective

paradigm, Kojima et Matsuda, Hiroshima University, Higashi-Hiroshima, Japan, 2000

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Methods This section describes the equipment, the parameters, the procedures, and the MATLAB codes

used in this experiment.

Equipment - 2 computers with MATLAB & Excel

- 1 breadboard

- 1 bright LED

- 1 pair of headphones

- 1 scope (to block external light)

- 1 press-button

- Wires

Parameters In this experiment, we decided to use the time ranges of 5, 15, 30 and 60 seconds. The

frequency option for both LED (flashing) and speaker (beeping) is 0, 0.6 or 1.8 Hz. The possible

combinations of LED & speaker are (0,1), (1,0), (1,1), (0,0) with “1” meaning “on” and “0”

meaning “off”. By considering all of the possibilities, the expected number of trials per subject is

36 and the estimated time to finish a complete round is around 45 minutes. We have three

subjects for this experiment.

Refer to Appendix A for a full list of trial combinations and randomized sequence used.

Procedures 1. Randomize the 36 trials in Excel.

2. Register the trials in MATLAB.

3. Researcher #1 informs the subject the desired time to count to.

4. Researcher #2 runs the program in MATLAB.

5. Subject starts the current trial by pressing the button. Button should be kept pushed

until the subject has counted to the desired time.

6. Repeat procedure 3 – 5 for the next 35 trials.

7. One round ends.

MATLAB Code Three MATLAB scripts are created for this experiment:

1. Data acquisition and light control (refer to Appendix B for codes)

2. Beeps control (refer to Appendix B for codes)

3. Data filter and analysis (refer to Appendix B for codes)

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Data Acquisition and Light Control Script

This MATLAB script tells the DAQ to collect data and control the light. Following is the flow of

the script:

1. Initialize the DAQ, and create an array of time and light settings each corresponding to

the 36 randomized trials.

2. Wait for user input to start collecting data.

3. Start sampling data at a rate of 1000Hz for a desires amount of time.

4. Turn on and off the LED at an appropriate frequency.

5. Save data for current trial at the end of data sampling.

6. Repeat 2-5 for the rest of the 35 trials.

7. Export workspace data into a .mat file for data analysis later.

Beeps Control

This MATLAB script controls the sound card to the computer to produce beeps at a desired

frequency. Following is the flow of the script:

1. Create an array of beep settings corresponding to the 36 randomized trials.

2. Wait for user input to start.

3. Turn on and off the tone at an appropriate frequency.

4. Repeat 2-3 for the rest of the 35 trials.

Data Filter and Analysis

This MATLAB script filters the data collected and computes the time for each trial. When the

button is pressed, the recorded voltage will be at around 5.3V. When the button is released, the

recorded voltage will be at around 0V. This MATLAB script searches for samples which are above

4V and compute the total time based on the number of samples which are above the threshold.

The result is then saved in an array. In addition, this script reorganizes the array of results into

convenient forms for curve fitting and plotting, and computes the normalized errors.

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Results

Tabulated Results

Table 1 is the tabulated average time estimation results from the experiment. Table 2 is the

average normalized errors in time estimation for all three subjects as compared to the control

time (estimated time when there are no external stimuli).

Actual Time (s)

None (Control)

Lights Tones Lights-Tones

0.6 Hz 1.8 Hz 0.6 Hz 1.8 Hz 0.6-0.6 1.8-1.8 0.6-1.8 1.8-0.6

5 4.375 4.759 4.454 5.153 4.087 5.013 4.511 4.880 5.496

15 14.331 15.176 13.436 13.357 11.315 15.196 13.420 14.256 17.451

30 26.650 26.832 25.871 30.766 28.895 28.578 30.386 30.815 31.072

60 57.305 58.831 53.754 60.624 58.072 57.243 54.493 65.018 61.835 Table 1 Average Time Estimation Resultsi

Actual Time (s)

Noneii

(Control)

Lights Tones Lights-Tones Average

0.6 Hz 1.8 Hz 0.6 Hz 1.8 Hz 0.6-0.6 1.8-1.8 0.6-1.8 1.8-0.6

5 -12.49% 8.77% 1.98% 17.82% -6.54% 14.55% 3.12% 10.52% 25.38% 7.01%

15 -4.46% 6.04% -5.67% -6.12% -20.89% 6.97% -5.88% -0.66% 22.11% -0.95%

30 -11.17% -0.13% -2.25% 16.07% 16.06% 7.44% 17.64% 17.31% 17.35% 8.70%

60 -4.49% 2.03% -4.70% 6.74% 1.92% 0.12% -1.32% 17.01% 11.13% 3.16%

Average -8.15% 4.18% -2.66% 8.63% -2.36% 7.27% 3.39% 11.04% 18.99% Table 2 Average Normalized Errors in Time Estimation from Control Timei, iii

Note:

i. For time estimation results and normalized errors of individual subject, refer to Appendix C.

ii. Percentages for control are errors from the actual time. The rest are error from the control time.

iii. Positive percentages denote estimated time is longer than the control time. Negative percentages denote

estimated time is shorter than the control time.

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Plots of Experiment Results

(Refer to Appendix D for plots of individual subjects.)

Figure 1 Time Comparison for Trials using Only Audio Tone

Subjects typically experienced time-dilation effects when only listening to the slow tone at 0.6Hz.

Conversely, when listening at 1.8Hz, subjects were noted to under-estimate the given time.

Figure 2 Time Comparison for Trials using Only Flashing Lights

A similar effect was observed with the lights, with slow lights (0.6Hz) contributing to a slight

over-estimation of time, while fast lights (1.8Hz) contributed to under-estimation. It is important

to note that, in comparing this trial with the previous one, audio tones appeared to have a

greater interference effect than simply lights.

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Figure 3 Time Comparison for Trials using Mixed Stimuli

Mixed stimuli-mixed speeds: The greatest interference effect was observed when users were

subjected to mixed trials – fast-tones/slow-lights or vice versa. Notably, fast-lights/slow-tones

were seen to experience time-dilation in the shorter duration region, whereas the opposite

situation demonstrated time-dilation for longer durations.

Mixed stimuli-same speeds: Across subjects, same speeds of both stimuli had fairly

unpredictable effects over the full-time trends, contributing to an average that seems to agree

with the general trend of the control line.

Discussions

Difficulties during Experiment Throughout the experiment, we encountered a few problems.

External Noises

One major problem was the disruptions caused by external noise. Besides the beeping sound

generated by MATLAB, subjects occasionally heard other noticeable sounds in the mechatronics

lab.

Loss of Concentration and Careless Mistakes

Due to the large amount of trials, subjects sometimes miss counted the desire times due to loss

of concentration. Other situations involved the release of the press-button carelessly before the

desired time was reached.

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Result Analysis and Interpretations Based on table 2, in average, people tend to estimate a shorter time than the actual time when

there is no external stimulus. People also tend to be more accurate (estimated time is close to

the control time) when the time to estimate it 15s and 60s.

The estimated times are shorter than the control times when the flashing lights is at 1.8Hz, and

the beeps is at 1.8Hz respectively. These results are as expected for fast flashing lights and

beeps stimuli.

Note that the tones seem to have a greater effect than flashing lights. However, we have to be

careful to draw the conclusion that beeps have a greater interfering effect than flashing lights,

because the tones heard are very distinct and unavoidable. But, the flashing lights can be easily

ignored or blurred out of focus by our eyes even when we are looking at it.

For the other cases, the estimated times are longer than the control times.

Lights 0.6 Hz Lights 1.8 Hz

4.18% -2.66%

Tones 0.6 Hz 8.63% 7.27% 18.99%

Tones 1.8 Hz -2.36% 11.04% 3.39% Table 3 Effects of Double Stimuli on Time Estimation

For the combination of both flashing lights and beeps at 1.8Hz, we expect a person to estimate a

shorter time than the control time, and the estimated times for both flashing lights and beeps at

1.8Hz respectively. However, referring to Table 3, the result proves otherwise. This might be

because the subject is confused by the flashing lights and beeps, which are not fully in sync

causing them to pause and estimate a longer time.

When both flashing lights and beeps are at 0.6 Hz, the estimated time is longer than the control

time, and when both are at 1.8 Hz. This result is as expected. However, the double stimuli with

0.6Hz did not cause the estimated time to be significantly longer than when there is only one

stimulus.

When flashing lights and beeps are at different frequency, the estimated times are more than 10%

longer then the control time. These results might be because out of sync stimuli confuse a

subject causing them to pause and estimate a longer time.

In addition to studying the effect of external stimuli on subjective time estimation, we also

found interesting trend on the normalized error across trials from the beginning to the end of

experiment.

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Figure 4 Normalized Error across Trials (Linear Fit)

Figure 5 Normalized Error across Trials (Spline Fit)

Figure 4 and Figure 5 shows that as the experiment proceeds, the normalized errors decrease

and converge. The variances in errors decrease as more trials were conducted. This might be

because the subjects experienced learning, and thus they became better at estimating time. The

effects of external stimuli became smaller as the experiment proceeds.

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Future Work In order to avoid above mentioned problems and to have more accurate and representative

data and results, we recommended a few of the following modifications and extensions to our

current experiment.

Separate Control Experiment

In future experiment, control should be conducted separately at the beginning to obtain the

estimated time by a subject when the subject is fresh and new to the experiment. We will then

be able to distinguish more accurately if the long experiment results in more variance towards

the end of the experiment due to weariness or if the long experiment results in less variance

towards the end of the experiment due to learning.

Stimuli Control

Double stimuli with same frequency should be in sync to avoid unnecessary external effects due

to the out of sync stimuli.

Experiment Location

In the future, the experiment should be conducted in a private room which can isolate external

sounds. Light should also be turned off so that only the LED light is exposed. This also eliminates

the use the scope.

Larger Number of Subjects

More subjects should be tested to give a more confident and representative results.

Interesting Extensions to Current Experiment

We can investigate the effects of phase shift when there are two stimuli. In addition, more

options of frequencies could be added to make the trend more apparent.

We have observed the effects of flashing light and beeping sound on subjective time estimation.

We can also add other elements such as a moving object to see how the speed of a moving

image affects the time estimation. We would assume that as the speed increases, the counting

speed increases as well.

Brightness of the LED and the volume of the speaker might also be interesting parameters to

adjust.

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Conclusion Based on the results of the above, we conclude that our first hypothesis, that slower-than-

second stimuli will dilate and faster-than-second stimuli will constrict time estimation, is correct.

Additionally, the data from the lights-only and tone-only experiments seems to suggest that the

frequency of audio stimuli has a greater effect on one’s time estimation than the frequency of

visual stimuli.

The wide level of variation (and notably, dilation) experienced during the mixed-speed mixed

trials seems to suggest that the so-called conflicting stimulus has a greater interference effect

on the subject than same-speed trials or single-stimuli trials.

Lastly, there seems to be learning effect throughout the experiment causing the variations to

decrease as the experiment proceeds.

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Appendix

A. Trials Setup Trial Time (s) Lights (Hz) Tones (Hz)

1 5 0 0

2 15 0 0

3 30 0 0

4 60 0 0

5 5 0 0.6

6 15 0 0.6

7 30 0 0.6

8 60 0 0.6

9 5 0 1.8

10 15 0 1.8

11 30 0 1.8

12 60 0 1.8

13 5 0.6 0

14 15 0.6 0

15 30 0.6 0

16 60 0.6 0

17 5 0.6 0.6

18 15 0.6 0.6

19 30 0.6 0.6

20 60 0.6 0.6

21 5 0.6 1.8

22 15 0.6 1.8

23 30 0.6 1.8

24 60 0.6 1.8

25 5 1.8 0

26 15 1.8 0

27 30 1.8 0

28 60 1.8 0

29 5 1.8 0.6

30 15 1.8 0.6

31 30 1.8 0.6

32 60 1.8 0.6

33 5 1.8 1.8

34 15 1.8 1.8

35 30 1.8 1.8

36 60 1.8 1.8 Table 4 Time, Light & Tones Combinations in Each Trial

Trial Time (s) Lights (Hz) Tones (Hz)

10 15 0 1.8

25 5 1.8 0

21 5 0.6 1.8

11 30 0 1.8

6 15 0 0.6

36 60 1.8 1.8

34 15 1.8 1.8

19 30 0.6 0.6

33 5 1.8 1.8

26 15 1.8 0

14 15 0.6 0

35 30 1.8 1.8

29 5 1.8 0.6

32 60 1.8 0.6

5 5 0 0.6

24 60 0.6 1.8

2 15 0 0

17 5 0.6 0.6

31 30 1.8 0.6

23 30 0.6 1.8

8 60 0 0.6

12 60 0 1.8

27 30 1.8 0

9 5 0 1.8

15 30 0.6 0

28 60 1.8 0

7 30 0 0.6

4 60 0 0

22 15 0.6 1.8

3 30 0 0

1 5 0 0

20 60 0.6 0.6

16 60 0.6 0

30 15 1.8 0.6

18 15 0.6 0.6

13 5 0.6 0 Table 5 Randomized Trial Sequence

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B. MATLAB Script

a) Data Acquisition and Light

Control

- datacollection.m

b) Beeps Control

- beep.m

c) Data Filter and Analysis

- timefilter.m

- dataanalysis.m

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C. Experiment Results

Actual Time (s)

None (Control)

Lights Tones Lights-Tones

0.6 Hz 1.8 Hz 0.6 Hz 1.8 Hz 0.6-0.6 1.8-1.8 0.6-1.8 1.8-0.6

5 4.465 4.705 4.005 4.787 4.057 4.877 4.528 4.442 6.468

15 15.761 17.451 13.732 14.345 10.783 14.615 14.323 16.741 19.69

30 30.112 32.241 27.096 35.487 24.761 33.693 28.393 33.946 36.534

60 66.073 70.348 51.839 61.759 64.259 64.292 51.208 64.250 59.112 Table 6 Subject A Time Estimation Results

Actual Time (s)

None (Control)i

Lights Tones Lights-Tones

0.6 Hz 1.8 Hz 0.6 Hz 1.8 Hz 0.6-0.6 1.8-1.8 0.6-1.8 1.8-0.6

5 -10.70% 5.38% -10.30% 7.21% -9.14% 9.23% 1.41% -0.52% 44.86%

15 5.07% 10.72% -12.87% -8.98% -31.58% -7.27% -9.12% 6.22% 24.93%

30 0.37% 7.07% -10.02% 17.85% -17.77% 11.89% -5.71% 12.73% 21.33%

60 10.12% 6.47% -21.54% -6.53% -2.75% -2.70% -22.50% -2.76% -10.54% Table 7 Subject A Normalized Errors in Time Estimation from Control Timeii

Actual Time (s)

None

(Control)

Lights Tones Lights-Tones

0.6 Hz 1.8 Hz 0.6 Hz 1.8 Hz 0.6-0.6 1.8-1.8 0.6-1.8 1.8-0.6

5 4.375 5.078 4.444 5.775 4.073 5.561 4.601 5.318 5.273

15 12.847 14.681 13.348 14.168 9.481 15.463 13.462 13.06 17.242

30 22.034 20.167 22.904 27.637 33.029 24.695 34.937 30.597 28.537

60 47.115 44.99 50.315 52.945 50.728 47.946 65.198 73.979 68.388 Table 8 Subject B Time Estimation Results

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Actual Time (s)

None (Control)i

Lights Tones Lights-Tones

0.6 Hz 1.8 Hz 0.6 Hz 1.8 Hz 0.6-0.6 1.8-1.8 0.6-1.8 1.8-0.6

5 -12.50% 16.07% 1.58% 32.00% -6.90% 27.11% 5.17% 21.55% 20.53%

15 -14.35% 14.28% 3.90% 10.28% -26.20% 20.36% 4.79% 1.66% 34.21%

30 -26.55% -8.47% 3.95% 25.43% 49.90% 12.08% 58.56% 38.86% 29.51%

60 -21.48% -4.51% 6.79% 12.37% 7.67% 1.76% 38.38% 57.02% 45.15% Table 9 Subject B Normalized Errors in Time Estimation from Control Timeii

Actual Time (s)

None (Control)

Lights Tones Lights-Tones

0.6 Hz 1.8 Hz 0.6 Hz 1.8 Hz 0.6-0.6 1.8-1.8 0.6-1.8 1.8-0.6

5 4.286 4.495 4.914 4.897 4.132 4.600 4.405 -iii 4.747

15 14.386 13.395 13.229 11.558 13.682 15.509 12.474 12.966 15.422

30 27.805 28.087 27.614 29.174 -iii 27.345 27.827 27.902 28.144

60 58.728 61.154 59.109 67.168 59.228 59.491 47.072 56.824 58.004 Table 10 Subject C Time Estimation Results

Actual Time (s)

None (Control)i

Lights Tones Lights-Tones

0.6 Hz 1.8 Hz 0.6 Hz 1.8 Hz 0.6-0.6 1.8-1.8 0.6-1.8 1.8-0.6

5 -14.28% 4.88% 14.65% 14.26% -3.59% 7.33% 2.78% -iii 10.76%

15 -4.09% -6.89% -8.04% -19.66% -4.89% 7.81% -13.29% -9.87% 7.20%

30 -7.32% 1.01% -0.69% 4.92% -iii -1.65% 0.08% 0.35% 1.22%

60 -2.12% 4.13% 0.65% 14.37% 0.85% 1.30% -19.85% -3.24% -1.23% Table 11 Subject C Normalized Errors in Time Estimation from Control Timeii

Note: i. Percentages for control are errors from the real time. The rest are error from the control time. ii. Positive percentages denote estimated time is longer than the control time. Negative percentages denote estimated time is shorter than the control time. iii. Missing data.

17

D. Plots of Experiment Results

Audio Tones Only

Figure 6

Figure 7

18

(The outlier at 30s for tone at 1.8Hz is due to missing data point)

Figure 8

Flashing Lights Only

Figure 9

19

Figure 10

Figure 11

20

Mixed Stimuli

Figure 12

Figure 13

21

(The outlier at 5s for tone at 1.8Hz and light at 0.6Hz is due to missing data point) Figure 14