the noise experiment

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THE NOISE EXPERIMENT

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Lls - introduction to noise.

All pages in this lab

• Low Light Signal Measurements
• Introduction to Equipment (LLS)
• Introduction to Noise
• Measuring the Light Signal from a Diode
• Appendix A: SR760 FFT Interface Program
• Appendix B: SR830 Lock-In Interface Program
• Appendix C: the Remote Control Box
• Appendix D: the Phase Sensitive (Lock-In) Detector
• Appendix E: Interpreting the Data Sheet for the LED  and  Photodiode Data Sheet

Introduction To Noise

Noise plagues everything. Sometimes it can be so small compared to the signal of interest that it doesn't matter. This is not usually the case). Often different potential sources of noise must be taken account of so that noise can be avoided or even eliminated through experimental design or measuring techniques. In this section, you will learn about and explore the following types of noise:

Intrinsic (Random) Noise Sources

• $\frac{1}{f}$ Noise: Higher noise amplitude at low frequencies. This noise makes low frequency measurements more difficult. Its origin is poorly understood. You should learn to recognize it and its effects on measurements.
• Capacitive Coupling Noise: AC voltages from nearby electronics create stray capacitive effects. Careful design can nearly eliminate capacitive noise.
• Microphonic Noise: Mechanical vibrations/movements affect the electronic components. Shaking a cable = microphonic noise.
• Shot Noise: Non-uniformity in the electric current due to the quantization of charge carriers. Since Shot Noise depends on frequency bandwidth, the Lock-in will be able to eliminate most of this noise.
• Input Noise of Instruments: Aggregate noises of many components in an instrument. Proportional to square root of bandwidth.
• Johnson Noise: Voltage noise across a resistor due to thermal fluctuations in the electron density. Called "white noise" because it's the same for all frequencies.

Back To Top

Before you begin this section . . .

Read the blurb on different noise sources in the SR830 Lock-In Operating Manual (starting on page 3-21).

Other references

• Moore, Davis and Coplan, Building Scientific Apparatus . It is a little more detailed than the SR830 Operating manual; a good reference for just about anything, not too advanced.
• M. J. Buckingham, Noise in Electronic devices and Systems . Its treatment is exhaustive and rather advanced. Unless you groove on ugly math, you'll probably only find the semi-qualitative introductions useful.
• Kogan, Electronic Noise and Fluctuations in Solids . It's also quite advanced--yet filled with all sorts of good information.

$\frac{1}{f}$ Noise

• From Noise in Electronic Devices and Systems
• The origin of $\frac{1}{f}$ noise is poorly understood. However, you should learn to recognize it and its effects on measurements.
• Its spectral density (noise power /unit frequency interval) varies as $\frac{1}{f^{\alpha}}$ , where $\alpha$ typically varies from 0.8 to 1.4 (depending on the material) and is more or less constant over large frequency ranges.
• $\frac{1}{f}$ noise has been observed from 10 -6 Hz up to 10 6 Hz and higher.
• $\frac{1}{f}$ noise exists in practically all electronic devices, metal films, whiskers, liquid metals, electrolytic solutions, thermionic tubes, superconductors, and Josephson junctions. Buckingham also claims that it exists in many types of music, the measurements of the flood levels of the river Nile, the normal human heartbeat and neuro-membranes (p. 144).
• Other names include: current noise, excess noise, flicker noise, semiconductor noise, and pink noise (don't ask me...).

Experiment VI

This experiment is mostly qualitative. You're basically going look at the $\frac{1}{f}$ noise across your fingers and say, "That's $\frac{1}{f}$ noise."

• Turn on the SR760 FFT (don't forget to press the back arrow [ $\leftarrow$ ] button so that the SR760 resets to the default settings). We'll be using the FFT so we can verify the $\frac{1}{f^{\alpha}}$ spectrum.
• Start the LowLight FFT Interface program
• The window pops up asking you to chose between the [ SCREEN CAPTURE ] and [ MAKE SETTINGS REMOTELY ]. The first one means that the program will automatically download the data to the computer and the second option will let you choose and specify various parameters. Click on [ MAKE SETTINGS REMOTELY ].
• SPAN = 97.5 Hz
• LINEAR AVERAGING: Under Advanced Options, choose Linear Averaging. (The Fourier components of the $\frac{1}{f}$ noise will fluctuate randomly, so at any instant it won't look like a $\frac{1}{f}$ spectrum. However, if we average several decompositions together, it will make itself apparent.)
• AVERAGING TYPE = RMS
• \# AVERAGES = 1000, ON
• Click [ SET PARAMETERS ] and the window will ask you if you want to save the data. Make your choice and the program will start acquiring the data.
• Connect a cable to INPUT A of SR760. Grab the cable and place your thumb over the end. Hold on until the data have been taken.

• Now do the same thing, only change the Span to 195 Hz.
• As before, to make another data transfer you do not need to restart the program. There are two choices. One, click on the arrow in the upper left corner (note: this arrow only shows up if you have already transferred the data at least once.) Another choice is to choose [ RUN ] from the OPERATE menu option. With either choice you will be asked to chose between [ SCREEN CAPTURE ] or [ MAKE SETTINGS REMOTELY ].
• Now what do you see? At what frequency range does flicker noise seem to dominate? Where does it disappear relative to the noise floor (the background noise at high frequencies)?

Capacitive Coupling Noise

AC voltages from nearby electronics create stray capacitive effects. Careful design can nearly eliminate capacitive noise.

• 60/120 Hz and harmonics
• AM broadcasts at 0.5 to 2 MHz
• CB radio around 30 MHz
• FM and TV bands at around 100 MHz
• Radar and Microwave around 10 GHz
• Other parts of the circuit in question
• Power lines/cords etc.
• Automobile ignition systems
• Microwave ovens
• Electrical discharges
• Electric motors
• Electromechanical switches and relays

Exercise I (From Building Scientific Apparatus)

Estimate the minimum stray capacitance, $C_{min}$ ,needed to induce a 1 m V rms voltage across a 1 M $\Omega$ resistor from a 120 V rms power line ( f = 60 Hz). Hint: Use the model below:

Is this a large capacitance? Keep in mind that the capacitance of one foot of RG/58 coaxial cable is 33 pF.

Experiment VII

Now you get to explore capacitive noise! This is mostly a qualitative experiment in which you play with dangling inputs and see just how much noise can be induced in a typical lab environment. It is at this point that you get to change those annoying 60/120 Hz peaks!

• Turn on the SR760 FFT and start the LowLight SR760 Interface program.
• Pick [ MAKE SETTINGS REMOTELY ]
• Hook up a coaxial cable to INPUT A of the SR760 and let it lie on the table (unterminated).
• Use the default settings of the LowLight SR760 Interface program (i.e. SPAN = 1.56 kHz, etc.) and take a data run. Save the data and include it in your lab write-up.
• What do you see? Explain the existence of the different peaks. Speculate why there are harmonics on the fundamental. Do you see any $\frac{1}{f}$ noise?
• Let's get a better look at that 60 Hz noise. Take another data run, only this time set the SPAN to 97.5 Hz. Include this data run in your lab write-up, also.
• To get an idea as to how much noise is really being induced in the cable, measure and record the height of the peak centered on 60 Hz (you can either do this with the interface program or with the SPIN KNOB on the FFT (make sure you hit the [ MEAS ] button first, though).
• Does the orientation of the cable affect the magnitude of the induced noise? Experiment with different orientations of the unshielded cable (e.g. lying flat on the table vs. hanging straight down, etc). Explain what you see.
• Now coil the cable up. What happens to the 60 Hz peak (Look at the FFT screen.) Stuff the coiled cable into the brass tube provided. Again, what happens to the 60 Hz peak? Measure and record the magnitude of the 60 Hz peak for both cases. (Use either the FFT or the interface program-see (6) above.)
• With the cable stuffed in the brass tube, ground the brass tube (the instrument rack should suffice as a ground). There should be some clips hanging from the ring stand by the computer. What happens? Again, measure the height of the peak . Include in your write-up a plot of the data with the grounded brass tube around the cable.
• Compare the magnitudes of the peaks in units of volts (not dBV). Explain why coiling the wire reduces the noise, why shielding the cable reduces it some more, and why grounding the shield reduces it even further.

Now you see why sensitive experiments are usually locked up in a Faraday Cage. Other common ways to eliminate Capacitive noise include:

• Using tri-axial cable. Tri-axial cable is basically co-axial cable with an extra metal braid around it.
• Removing the annoying noise source
• Using low (high) impedance for voltage (current) measurements. ***Explain why this works***
• Keep lines close to ground and away from fringing fields.

Microphonic Noise

Exercise ii:.

Estimate the magnitude of the Microphonic noise caused by shaking one meter of RG/58 coaxial cable at a frequency of 10 kHz. Assume that:

• The cable has 1 V across it and a capacitance of 33pf/foot.
• It goes into a scope with a 1 M $\Omega$ input impedance
• The shaking causes a change in capacitance ( $\delta C$ ) of 1 pF (does this sound reasonable? What is $\frac{\delta C}{C}$ ?)

Experiment VIII

This experiment is almost completely qualitative. You'll look at some Microphonic noise on the FFT. The source will be a dangling input, which you will shake by hand.

• Since you'll be providing the Microphonic action (i.e. shaking the cable), you probably won't need to look at the frequency spectrum above 100 Hz-unless you're a super-duper tambourine player. So set the SPAN to 97.5 Hz using the Interface program. Also turn LINEAR AVERAGING off, if it is on (you'll want to track the happenings in real time, as the frequency of your shaking will probably vary a little).

Figure 3: Without Shaking the Cable

Figure 4: Shaking the Cable

Notice how large harmonic components appear around the frequency that you're shaking the cable. Why are there multiple peaks? Why are they broad? Notice how a little bit of shaking can produce noise that easily matches the Capacitive pick-up at 60 Hz. Also note how it is harder to isolate non-affected frequencies (i.e. the noise is spread out over a large band of frequencies, not just peaked at 60 Hz or so).

• Explore the Microphonic noise effects using different values of the SPAN. You don't need to use the Interface program to save these data points. Just adjust the SPAN using the SPIN KNOB on the FFT. Comment on anything you find interesting .
• List some ways to avoid Microphonic Noise.
• It is a form of white noise , noise that has the same power density at all frequencies.
• At a given frequency, the voltage fluctuations are gaussian in nature (i.e. if you plotted a histogram of voltage measurements at any specific frequency at a number of equally spaced times, it would be gaussian in shape).
• Note: When using instruments such as the SR830, the bandwidth is given by the ENBW (Effective Noise Bandwidth), specified in the manual (page 3-11 in the SR830 manual). This applies to all forms of Gaussian noise.

Exercise III

Calculate the shot noise for the Pin-10DP Pin Diode in units $\frac{A}{\sqrt{Hz}}$ . These units may seem peculiar, but they are in universal use. We really want the power/unit frequency interval, which is the square of the units that are used. Notice how the shot noise increases with increasing signal current. If we wanted to recover our signal better, and our only concern was shot noise, would we want a higher or lower signal current? For what value of the signal current would the signal and shot noise currents be equal?

At this time, there is no experiment for shot noise.

Instrument Noise

When you stop and think about the many resistors and other components inside instruments, it's not surprising that they are also sources of noise. The noise added to the signal is usually proportional to $\sqrt{\triangle f}$ , where $\triangle f$ is the bandwidth. To get an idea of how big these noise sources are, refer to:

• SR830 Lock-In Manual, p. (3-17). Input noise ~ $\frac{5nV_{rms}}{\sqrt{Hz}}$
• SR570 Current Preamplifier Manual, p. vii. Input noise ranges from $\frac{150pA}{\sqrt{Hz}}$ to $\frac{5fA}{\sqrt{Hz}}$ . Note: to get this as an output noise in Vrms, divide it by the SENSITIVITY.
• Note: When using instruments such as the SR830 , the bandwidth is given by the ENBW (Effective Noise Bandwidth), specified in the manual (page 3-11 in the SR830 manual). This applies to all forms of Gaussian noise.

There is no exercise or experiment for instrument noise.

Johnson Noise

(Read p. 458 of Building Scientific Apparatus )

• It arises from statistical fluctuations in electron motion in a resistor at finite temperature. It can be derived using ideas based on Black Body radiation. For a derivation, see Thermal Physics by Kittel, p. 98.
• At a given frequency, the voltage fluctuations are Gaussian in nature (i.e. if you plotted a histogram of voltage measurements, it would be Gaussian)
• It is white noise -i.e. exists at all frequencies with the same magnitude. So the more frequencies you sample, the more Johnson noise you get.
• Johnson noise also presents itself in the form of a current: $I_{J, rms}^2=\frac{V_{J, rms}^2}{R_{source}^2}=\frac{4kT\triangle f}{R_{source}}$
• It is independent of material-everything with the same resistance has the same Johnson noise.
• The units $\frac{V}{\sqrt{Hz}}$ and $\frac{A}{\sqrt{Hz}}$ appear a lot in the noise business (usually in the context of white noise ). In order to actually get a value for the noise, you'll need to multiply the quoted value by $\sqrt{\triangle f_b}$ , where $\triangle f_b$ is the bandwidth of your measurement.

Exercise IV

Calculate the Johnson current noise for the Pin-10DP Pin diode. Express your answer in $\frac{V}{\sqrt{Hz}}$ . Would you want a current signal to come from a high impedance source or a low impedance source if you were concerned about Johnson noise? What if it were a voltage source? Why?

Experiment IX

In this experiment, you will use the SR830 Lock-In to measure Johnson noise and its dependence on $R_{source}$ and $\triangle f_b$ by hooking up various resistors across the input of the SR830. Note: Compared to the previous small experiments, this is rather involved.

A former student makes these comments:

Reasons for using the spectral analyzer are:

• You get a picture of what the entire noise spectrum looks like, instead of just some number. You aren't taking a "shot in the dark" and can avoid hitting a noise peak. There are other peaks besides the 60 Hz harmonics.
• The SR760 can tell you the noise power at a point as well as within a bandwidth.
• You can press a few buttons and get an answer really fast.
• You get a better "feel" of whether or not your answer makes sense.
• You get better answers because the scope takes more averages.
• The Lock-in Lab-View program occasionally gives totally wacky values.

Reasons for using the Lock-in to take measurements after using the spectrum analyzer to take a look at the noise are:

• You get to use some of the "effective bandwidth" stuff you learned earlier.
• There is slightly more physics involved, as you can actually see some of the voltage fluctuations.
• Turn on the SR830 Lock-In and start the SR830 Lock-In Interface program.
• Via a coaxial cable, hook up a pair of clips to the INPUT A/I of the Lock-In. Make sure that the cable is short and stiff. Why is this important?
• Unplug any input to the REF IN. You will use the Lock-In's internal oscillator as a reference.
• Depress the [ SOURCE ] button by the SPIN KNOB until INTERNAL is chosen. This tells the SR830 that you'll be using the internal oscillator as a reference signal.
• Depress the [ FREQ ] button above the SPIN KNOB. This will allow you to choose the frequency of oscillation.
• Turn the SPIN KNOB until the desired frequency set. Make sure to choose the frequency wisely so as to eliminate as much non-Johnson noise as possible. Record your frequency choice and reasons for choosing it.
• Connect a 100 $k\Omega$ resistor across the clips. Set the TIME CONSTANT to 30 ms and adjust the SENSITIVITY appropriately. Also, select the highest SLOP/OCT rolloff, 24 dB. Use the "Low noise" setting and NOT "High reserve".
• NUMBER OF RUNS: 7 (the more measurements, the better)
• SAVE DATA?: Yes (box checked) (include in your lab write-up the analyzed data in table format and a plot of the raw data)
• SAVE DATA TO: You decide . For your purposes, one file would probably be just fine. It is recommended that you include information relevant to the measurement, such as the value of the resistor, and the SLOP/OCT rolloff.
• GPIB ADDRESS: 8 (That's where the SR830 lives in the wonderful world of GPIB and you do not change it.)
• SAMPLE MODE: BEST CHOICE is recommended. Note: Sometimes taking more than 1200 data points doesn't work. If this happens, consider using the BEST CHOICE mode while selecting the TIME CONSTANT . This way, the computer will choose an appropriate SAMPLE RATE for you. Then switch to CUSTOM mode and adjust the SPAN so as to reduce the TOTAL NUMBER OF DATA POINTS below 1200.
• TIME CONSTANT: 30 ms
• Press [ START ].
• All of your data are zero or some other constant value . A possible problem is that the Sensitivity isn't adjusted correctly or that it is overloaded.
• Some of your data are zero . Perhaps you need to reduce the TOTAL NUMBER OF DATA POINTS below 1200.

Figure 5: The data aren't "spiky" and have quite a few "periods"

• The data don't have many "periods." WHY IS THIS A PROBLEM (this is a question for you to answer)? The SAMPLE RATE may be too high. Decrease it, keeping in mind the tradeoff with "spikiness", above.
• It should take just a minute or two for the Interface program to collect, analyze, and save the data. Open the file " [ base file path ] Analyzed Data (T= [ time constant ] ).xls " in Excel. You should see something like the following (note, this sample only represents 3 data runs using a resistor that was not 100 k $\Omega$ ):

9. Find the average value of your ${\left \langle \triangle R^2 \right \rangle}^{\frac{1}{2}}$ measurements and compare it to the theoretical value (remember: R stands for magnitude of the measurement made by the Lock-In). Is your measurement off by about a factor of $\sqrt{2}$ ? If it is, why ?

10. Repeat the above procedure for TIME CONSTANT values of 100 msec., 300 msec., 1 sec., and 3 sec. Note: taking data at high TIME CONSTANT values will take a long time. You can let it run overnight or while you go to get lunch, if you want.

11. To see how the Johnson Noise varies with resistance, repeat the above procedure using a 10 $k\Omega$ resistor.

12. Compile all your data and plot them. How well do they agree with theory (remember to calculate $\triangle f$ using the ENBW specified on page 3-11 of the SR830 manual)? What would you expect for such a crude experiment? Remember to account for the Lock-In input noise : $\Large V_{Tot, noise}^2=V_J^2+V_{Input}^2$ .

General comments about getting low-noise signals.

There are three techniques that you can use to reduce the amount of noise in your signal. The first is using shorter cables. The shortest "cable" is a male BNC barrel connector. The second is using "differential inputs, which you can read about on pages 3 to 19 in the SR830 manual. In brief, you can use two cables to measure the signal so that the noise common in both cables cancels out. These two techniques reduce the external noise. The third is running your experiment at low temperatures. After finishing this section, you might dunk your resistor in liquid nitrogen to see how it affects the intrinsic (Johnson) noise of the resistor. Just be sure to note R as a function of T, and don't get the inputs on the equipment cold, or water could condense on them.

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• v.7(17); 2019 Sep 15

The Effect of Noise Exposure on Cognitive Performance and Brain Activity Patterns

Mohammad javad jafari.

1 Environmental and Occupational Hazards Control Research Center, School Of Public Health And Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

2 Department of Occupational Health Engineering, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Reza Khosrowabadi

3 Institute for Cognitive and Brain Science, Shahid Beheshti University, Tehran, Iran

Soheila Khodakarim

4 Department of Epidemiology, School of Public Health and Safety, Shahid Beheshti University of Medical Sciences, Tehran, Iran

Farough Mohammadian

Background:.

It seems qualitative measurements of subjective reactions are not appropriate indicators to assess the effect of noise on cognitive performance.

In this study, quantitative and combined indicators were applied to study the effect of noise on cognitive performance.

MATERIAL AND METHODS:

A total of 54 young subjects were included in this experimental study. The participants’ mental workload and attention were evaluated under different levels of noise exposure including, background noise, 75, 85 and 95 dBA noise levels. The study subject’s EEG signals were recorded for 10 minutes while they were performing the IVA test. The EEG signals were used to estimate the relative power of their brain frequency bands.

Results revealed that mental workload and visual/auditory attention is significantly reduced when the participants are exposed to noise at 95 dBA level (P < 0.05). Results also showed that with the rise in noise levels, the relative power of the Alpha band increases while the relative power of the Beta band decreases as compared to background noise. The most prominent change in the relative power of the Alpha and Beta bands occurs in the occipital and frontal regions of the brain respectively.

CONCLUSION:

The application of new indicators, including brain signal analysis and power spectral density analysis, is strongly recommended in the assessment of cognitive performance during noise exposure. Further studies are suggested regarding the effects of other psychoacoustic parameters such as tonality, noise pitch (treble or bass) at extended exposure levels.

Introduction

The influence of noise on human cognitive performance and brain activity has been often neglected [ 1 ]. Noise has different negative effects ranging from interference with cognitive processing to damaging mental and physical health [ 2 ]. The non-auditory effects of noise exposure include perceived disturbance, annoyance, cognitive impairment, cardiovascular disorders and sleep disturbance [ 1 ]. Noise exposure is a problem in many occupational and non-occupational environments. It is estimated that 22 million workers in the United States are exposed to hazardous noise [ 3 ]. It is also reported that 100 million people are exposed to dangerous environmental noise due to traffic, personal listening devices and other sources [ 4 ]. The World Health Organization (WHO) estimates that at least 1 million healthy life-years (disability-adjusted life-years) are lost annually as a result of environmental noise in high income western European nations (with a population of around 340 million) [ 1 ]. In any vital industry, optimising human performance is a key factor in accident prevention. Noise is one aspect of the work environment that affects workplace safety. Workers in vital occupational roles require high levels of cognitive skill and they need to maintain effective performance while exposed to higher levels of noise than Threshold Limit Values (TLV). Studies show that noise causes cognitive impairment and oxidative stress in the brain [ 5 ]. According to Wang et al., with further urbanisation and industrialisation, noise pollution has become a risk factor for depression, cognitive impairment and neurodegenerative disorders [ 5 ]. It has been observed that exposure to noise influences the central nervous system leading to emotional stress, anxiety, cognitive and memory defects [ 6 ]. Previous studies have suggested that the Limbic system in the brain is involved in emotional activities, The Amygdala and the Hippocampus are two of the main parts within the Limbic system that receives sensory information directly and indirectly from the central auditory system. Auditory stimulation itself can directly or indirectly affect these areas.

The active process of cognitive selection is called “attention” [ 7 ]. Attention plays a significant role in daily activities such as physical movements, emotional responses and perceptual and cognitive functions. When quantifiable information processing is limited, the attention system directs human behaviour based on geographic and temporal characteristics. Noise can affect performance either by impairing information processing or causing changes in strategic responses. In particular, noise increases the level of general alertness or activation and attention. Noise can also reduce performance accuracy and working memory performance, but does not seem to affect performance speed. The scope of cognitive and mental function is diverse, encompassing reaction time, attention, memory, intelligence and concentration, to name a few. Altered cognitive function leads to human error and subsequently increases accidents. This can ultimately lead to reduced performance and productivity. Some studies have shown that noise, improves performance, especially in sleep-deprived workers, mainly due to increased arousal. Certain individuals may be sensitive to noise even when it is lower than TLV. Sensitivity to noise which is referred to as environmental intolerance influence attention and recognition. There are conflicting reports regarding the effect of noise on cognitive performance in the relevant literature. The review study by Gawron regarding the effects of noise on cognitive performance revealed that among 58 studies, 29 reported a negative effect, 7 reported a positive effect and 22 reported no effect of noise on cognitive performance [ 8 ]. Noise as a sensory stimulus increases arousal which is believed to cause a reduction in the breadth of attention. In other words, loud noise causes alterations in the performance of attentional functions.

Smith believes that noise characteristics to be one of the influential parameters regarding the effect of noise on cognitive performance [ 9 ]. A study by Hockey showed that loud noise at 100 dBA (compared to 70 dBA) increased central visual stimuli processing but reduced peripheral stimulus processing [ 10 ]. Exposure to noise above 85 dBA intensity leads to many adverse auditory and non-auditory effects. The non-auditory effects of noise exposure depend on exposure duration, type of task, gender, age and sensitivity to noise. Physiological signals are comprised of: a) signals related to the peripheral nervous system, including heartbeat and Electromyogram and b) signals related to the central nervous system including electroencephalography (EEG). In recent years, interesting results have been obtained from the first group of signals, however, few studies have used EEG signals as a valuable tool for cognitive performance evaluation [ 11 ]. Cognitive theory suggests that the brain is highly involved in emotions. Basic emotions use specific cortical and subcortical systems within the brain and are different from the brain’s electrical and metabolic activities. Therefore, EEG is one of the most effective and common methods of brain imaging used for Brain activity processing relating to human stress including noise [ 12 ]. EEG signals measure all fluctuations in the electrical fields resulting from nerve activity in millisecond resolutions. EEG signals are usually evaluated in multiple frequency bands to determine their relationship with stresses. These bands include the Alpha (8-12.5 Hz), Theta (4-8 Hz), Delta (1-4 Hz) and Beta (12.5-30 Hz) bands. Humphreys and Reveille suggest that fluctuations in the Alpha and Beta bands, in particular, are an indication of cognitive function. Increases in the Alpha frequency band along with decreases in the Beta frequency band causes increased cognitive function [ 13 ]. A reduction in the power of the Alpha band along with a rise in the power of the Theta and Beta bands is an indicator of neurological disorders. Marshal et al., have shown a reverse relationship in the prefrontal cortex between the Alpha power rhythm in an EEG and suffering from stressful conditions, meaning that the Alpha rhythm goes down with stress [ 14 ]. Choi demonstrated a positive relationship between the Beta power rhythm in an EEG and suffering from stressful conditions in the temporal lobe [ 12 ]. Other studies have shown a reduction in the relative power of the Alpha band when attention is reduced. Compared to other imaging techniques, Electroencephalography has certain advantages which include being non-invasive, low cost, comfortable, safe, mobile, and having high time resolution. Therefore, EEG can be a great tool not just for detecting stressors in the environment but also for predicting the negative effects of noise exposure.

Because noise level is one of the influencing factors regarding the effects of noise on cognitive function and brain signals, this study focused on 75, 85 and 95 dBA levels. Also, due to the conflicting results in other studies regarding cognitive function and its importance in many tasks and the few studies on the effects of various noise levels on brain activity patterns, this study was designed in two parts. The first part investigates the effects of various noise levels on mental workload and auditory/visual attention. The second part investigates the effects of noise on the relative power of brain frequency bands and their relationship with visual/auditory attention.

Material and Methods

Study subjects and selection criteria.

Study subjects were selected from university student volunteers. The including criteria was 23-33 years of age, normal hearing, no prior cardiovascular disorders, no alcohol and caffeine consumption 12 hours before testing, a BMI index of 18-28, no hypersensitivity to noise and no sleep disorders. After finalising the selection, testing procedures were trained to the study subjects. All participants had to complete ethical consent forms, General Health questionnaires (GHQ) and Weinstein’s Noise Sensitivity questionnaires. The validity and reliability of the Persian version of these questionnaires had been approved in other studies [ 15 ].

Experimental Design

This experimental study was conducted in an acoustically insulated, climate-controlled room (H = 3 m, L = 3.5 m and W = 2.5 m). A total of 54 participants, including 27 males and 27 females, took part in this study. Study subjects were divided into 3 groups, each with 9 males and 9 females. All study groups were exposed to background noise (45 dBA), and three different noise levels (including 75, 85 and 95 dBA). Table 1 shows the experimental design in detail.

The study protocol for each subject included a 10-minute relaxing phase before testing, followed by the Integrated Visual and Auditory Continuous Performance (IVA) test which was accompanied by background noise while EEG signals were being recorded. After a 30-minute rest, the subject was exposed to noise for 15 minutes, and at the 16 th -minute mark, while the subject was being exposed to various noise levels, the IVA test was initiated, and EEG signals were once again recorded ( Figure 1 ).

Study protocols timing

Noise Source and Presentation

In this study, the used noise was recorded in a household appliance factory using a B and K PULSE Multi-Analyzer System Type 3560. The recorded noise was then analysed using a B & K Sound Level Meter Type 2238. To modify the noise and obtain steady noise at 75, 85 and 95 dBA levels, the Gold Wave software version 4.26 was used. Finally, the noise was replayed using two Genius HF-2020 speakers situated on either side of the test table.

NASA-Task Load Index (NASA-TLX) Questionnaire

A NASA-TLX questionnaire is a well-known tool for evaluating subjective mental workload (as perceived by the subject). This multi-dimensional method assigns an overall score for mental load based on average weights obtained from six scales including mental demand, physical demand, temporal demand, effort, performance, and frustration. Every part of the task is assigned to a 100-point rating score. The mental load evaluation process using this indicator is comprised of three stages. In the first stage, the six scales are self-assessed by the study subject. In the second stage, after weighing the load of each scale, it is given a score by the subject. Finally, the score and the weight of the load are obtained, and the total mental load score is determined. The validity and reliability of this questionnaire have been approved by Mohammadi in Iran, and its Cronbach alpha score was 0.83 [ 16 ].

Integrated Visual and Auditory Continuous Performance Test

Integrated Visual and Auditory test, which was designed by Stanford et al., is part of the Continuous Performance Tests (CPTs) and used to evaluate auditory/visual attention [ 17 ]. It consists of a 13-minute continuous auditory and visual test that evaluates two factors of response control and attention. The task involves responding or not responding (response prevention) to 500 test stimuli. Each stimulus is presented for 1.5 seconds. The subject is asked to click once if he/she detects a 1 and not to respond if detecting a 2. This test has an appropriate sensitivity of 92% and a predictive power of 90%. The Persian version of this test has a validity index of 53% to 93% [ 18 ].

EEG Recording and Analysis

The EEG signals were recorded from 16 Ag/AgCl electrodes mounted in an elastic cap with the amplifier bandpass set to 1 – 40 Hz at a sampling rate of 250 Hz. The electrodes were placed at the frontal (Fp1, Fp2, F3, F4, F7 and F8), temporal (T3 and T4), central (Cz, C3 and C4), parietal (Pz, P3 and P4) and occipital (O1 and O2) regions. This is according to the international 10-20 system of electrode placement ( Figure 2 ). The reference electrode was the left mastoid (A1 in Figure 2 ). Impedance was maintained at below 10 KΩ during the experiment. Both in the background noise condition and during exposure to noise levels of 75, 85 and 95 dBA, while the subject was performing the IVA + Plus test, EEG signals were recorded for 10 minutes with the subject’s eyes open. First, the EEG data was pre-processed using an EEGLAB 2013a toolbox [ 19 ]. Then, using Independent Component Analysis (ICA) on each electrode, artefacts about blinking, eye movements or small body movements were eliminated.

Electrode placement

In order to measure relative power, the filtered signals were separated into various frequency bands (Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8-12.5 Hz), Beta (12.5-30 Hz) and Gamma (30 Hz upwards)) based on their power spectral density using the MATLAB software version 2017b. To calculate the relative power of the frequency bands, the following equations were used:

Let x i (n) denote the n th element of i th EEG channel after preprocessing and X = [x 1 , x 2 … x nc ] where NC denotes the number of EEG channel. The Power spectrum of the EEG signal was calculated using Fast Fourier Transform (FFT) which transforms the EEG signal X from the time domain to the frequency domain Z. The FFT of each EEG channel was calculated separately given by the following:

Where f denotes the frequency, N is the sample size; I is the channel number and J is the imaginary unit. Then absolute power spectrum (PSD) of EEG was calculated using the following:

Where k 1 and k 2 denote the frequency range of the selected band. The relative power of the selected band was then calculated by the following:

Statistical analysis of the mental workload and attention data was carried out using the SPSS 22 software solution. Before performing t-tests, data distribution norms were checked using the Kolmogorov–Smirnov test. A p -value of less than 0.05 was considered statistically significant. The Generalized Estimating Equations (GEE) statistical method was applied for data analysis.

Demographic Characteristics of Participants

Table 2 displays the study subjects’ demographic characteristics. A total of 56 individuals, 27 males and 27 females, were enrolled in the study. Average and standard deviation of age and Body Mass Index (BMI) was 26.56 ± 2.45 and 23.81 ± 1.43, respectively.

Study subjects’ demographic characteristics (N = 54)

Effect of Noise levels on Mental Workload

Figure 3 illustrates the effects of various noise levels on average overall mental workload compared to background noise (45 dBA) for study subjects. The results show that 75 and 85 dBA noise levels, as compared to just background noise, does not follow a particular trend and does not cause a considerable change in the average mental workload (P > 0.05). At 70 dBA level, compared to just background noise, the mental workload had decreased while at 85 dBA it had increased. At 95 dBA level, compared to just background noise, the increase in mental workload was statistically significant (P = 0.03).

The effect of noise levels on mental workload. Background noise = 45dB (A)

The Effect of Noise levels on Visual and Auditory Attention

Figure 4 presents the average and standard deviation for the visual and auditory attention score at various levels of noise compared to background noise (45 dBA). The results show that the changes in visual and auditory attention under exposure to various noise levels are very similar in pattern. At 85 dBA levels, average attention scores are reduced, as compared to just background noise, but this is not statistically significant (P > 0.05). But at 95 dBA levels, average attention scores are reduced considerably compared to background noise; this was statistically significant (P < 0.05).

The effect of noise levels on visual and auditory attention

The Effect of Noise levels on EEG Fluctuations

The Kolmogorov – Smirnov test results indicated that the data were distributed normally. Therefore, the t-test was used in this part. The relative power of the intended brain frequency bands was used to analyse brain signals during exposure to various noise levels relative to background noise (45 dBA). The considered frequency bands include the Delta (1-4 Hz), Theta (4-8 Hz), Alpha (8-12.5 Hz), Beta (12.5-30 Hz) and Gamma (30 Hz upwards) bands.

The results show that among the mentioned frequency bands, the Alpha and Beta bands undergo considerable changes, as relative to just background noise, and are being affected by noise. Based on Table 3 , going from 75 dBA to 95 dBA noise level causes a statistically significant average variation in the relative power of the Alpha band for the Fp 1 , F 4 , P 3 , O 1 and O 2 regions of the brain (P< 0 .05). Again, based on Table 3 , at 95 dBA, the largest variation in the relative power of the Alpha band is observed for the O 1 region of the brain (P < 0.001).

Average variation in the relative power of the Alpha band (μV^2) during exposure to noise relative to background noise (45 dBA)

A significant reduction in the relative power of the Alpha band was only observed for the F 3 region (P<0.05), though a slight reduction was observed for the C 4 , F 7 and F 3 regions of the brain also. The most affected areas of the brain when exposed to noise seems to be the Occipital, Prefrontal, Frontal and Parietal regions of the brain. Figure 5A shows the Scalp Topographical mapping.

Topographical mapping of frequency bands’ relative power during exposure to noise as relative to background noise (45 dBA)

Table 4 demonstrates average variation in the relative power of the Beta band during exposure to various noise levels relative to background noise. The results show a reduction in the relative power of the Beta band in all channels as a result of exposure to 75, 85 and 95 dBA noise, although this reduction was most prominent at 95 dBA. Based on table 4 , this reduction is statistically significant (P < 0.05) and the order by which it occurs, and the affected areas are as follows: F8-T3-C4-Cz-O2-Fp1-T4-F3-C3. No significant effect was observed in the other areas of the brain under study (P > 0.05). Also, based on figure 5b , the reduction in the relative power of the beta band as a result of the increase in the level of noise occurs in the Frontal, Temporal, Occipital and Central lobes.

Average variation in the relative power of the Beta band (μV^2) during exposure to noise as relative to background noise

The results of this study showed that as a stressor, noise affects cognitive performance and brain signals. Also, noise pressure level is an important factor regarding impairment of cognitive function and power spectral density of the brain, meaning that low levels noise is not as effective compared to high levels of noise. It can be said that the results of this study are in agreement with the proposal that a relationship exists between low performance and high levels noise [ 20 ]. Previous studies have neglected to investigate cognitive performance during exposure to noise [ 21 ], [ 22 ]. Some studies have used qualitative measurements including subjective responses for the evaluation of the effects of noise exposure on cognitive function. In this study, however, quantitative indicators were used in combination, including the evaluation of mental workload, evaluation of auditory/visual attention and brain signals (power spectral density) analysis.

In a study by Yoorim Choi, EEG signals were used as a new method for environmental stressor analysis. This method is suggested to overcome the limitations in physiological evaluation techniques [ 12 ]. Share et al., also suggest that to improve cognitive and mental stress evaluation, a combination of these tools should be used [ 23 ]. Sabine et al. revealed that Stroop and mental arithmetic performance increased when exposed to 50 dBA levels noise compared to 70 dBA levels noise. Melamed et al. stated exposure to higher than 85 dBA intensity noise causes irritability, fatigue and stress which is consistent with the present study [ 24 ]. In previous studies, the effects of noise exposure on heartbeat and blood pressure at 95 dBA were compared to 75 and 85 dBA [ 25 ]. Elmenhorst et al. demonstrated that noise exposure causes increased reaction times and errors in field and laboratories study [ 26 ]. The result obtained by Patricia Tassi et al. indicated that noise exposure reduces attention in subjects which is also consistent with the present study [ 27 ]. The effects of high levels of noise exposure on cognitive performance can be amended to the Poulton arousal model which states that noise exposure increases cognitive performance at first. The reason for this is an increase in arousal to reduce the effect of noise on cognitive function. But gradually, the effect of arousal wears off, and the negative effects of noise exposure on cognitive function begin to show [ 28 ]. The results in the present study can be explainable using arousal theory. This theory states that the level of central nervous system activity (which alternates between being asleep and awake) regulates human response to stimuli. There is no overall consensus on the validity of this theory at present, and some have suggested that it cannot be used to describe the relationship between noise exposure and cognitive performance. In any case, considering this theory, it can be said that when arousal is high or low, or in other words, in both low stress and high-stress situations, performance is reduced [ 29 ].

There were conflicting results regarding the effects of noise on cognitive function in previous studies. Some studies determined that noise had improved cognitive function [ 30 ]. While others had concluded that noise had reduced cognitive function [ 31 ]. This is part of the reason why, in this study, quantitative measurements were used in combination. The results of the present study reveal that the reduction of cognitive function and brain signals was only significant when exposed to noise at 95 dB level and not at 75 or 85 dBA. This could be due to other psychoacoustic factors such as noise pitch, tonality, exposure duration, and noise type. The importance of noise pitch and its effects on cognitive function and brain activity has been emphasised in other studies. The results of the study by Kazempour et al., showed that “base” noise (low frequency) reduces computational accuracy and performance [ 32 ]. Pawlaczyk et al. observed a higher sensitivity to “base” noise that caused reduced cognitive function as compared to reference noise [ 33 ]. Naserpour et al., also exhibited that “base” noise at 500 Hz caused longer reaction times as compared to “treble” noise at 800 Hz [ 34 ]. The study by Allahverdy and Jafari showed the complexity of brain activity increases at midrange frequencies, showing the effects of the change in frequency on brain activity [ 35 ].

Another effective parameter regarding noise and performance is noise tonality. In the study by Joonhee et al., it was observed that performance was reduced with increasing noise tone strengths [ 36 ]. Type of noise is also important when evaluating the effects of noise on cognitive function. Studies have shown that the effect of fluctuating noise on cognitive function is higher than steady noise [ 37 ]. Steady noise was the only type used in our study. Also, exposure times used were rather short, which may result in a reduced effect of noise on performance and brain signals when exposed to lower than TLV noise. The lesser effect of lower than TLV noise (45, 75 and 85 dBA) on performance and brain activity may also be due to non-psychoacoustic parameters as well. For instance, scope and diversity are influential in the methods used for cognitive function evaluation [ 38 ]. Simplicity or complexity of the task is another example as a complex task cause a greater cognitive dysfunction when compared to simple tasks. Personal characteristics may also be a factor when subjects are exposed to noise. As some may experience reduced cognitive function while others may not, and some may even show increased cognitive function [ 38 ]. These factors may not be as influential in the present study as the subjects were prescreened for mental disorders, cardiovascular disorders and behavioural abnormalities before selection. Many aspects of brain function and behaviour can only be discussed in terms of neurons communicating with each other. All cognitive processes in the brain are carried out through neuronal activity such as synapses and spikes. Orientation and executive function which are involved in the processing of attention are specifically undermined to enable information processing. The disruption of attention likely occurs in subjects whenever there is a need for sustained attention.

Here, Brain signal analysis disclosed that the Alpha and Beta frequency bands were affected by noise. With an increase in noise levels, the relative power of the Alpha and increased while the relative power of the Beta band decreased. Topographical mapping of the scalp shows that all four lobes of the brain are usually affected by noise, but this is more pronounced in the frontal and occipital lobes, which is consistent with the results of other studies [ 39 ]. Other conclusions can be made from this study regarding the relationship between visual / auditory attention and the relative power of the Alpha and Beta bands. In this regard, it can be said that with increasing noise levels, participants’ auditory / visual attention score went down while the relative power of the Alpha and Beta bands increased and decreased respectively. Topographical mapping of the scalp indicates that the area responsible for attention processing is located in the frontal, temporal and occipital regions of the brain which is consistent with the results of Liz et al., [ 40 ]. Therefore, the results of this study suggest that when one is exposed to various noise levels, mental workload, visual / auditory attention and the relative power of the frequency bands follow a similar trend. In studies that pertain to brain signals and cognitive performance, attention to artifacts such as eye and body movement, electrical interference, impedance fluctuations, sleep disorders, personality characteristics, age, sex and race are all important, and this has been reiterated in various studies [ 41 ]. The benefits of using the NASA TLX and IVA +Plus tests along with EEG signal recording in the psychological and neurophysiological evaluation include the ease of administration, non-invasiveness, short evaluation times and low cost. It is suggested that in future studies on the evaluation of the effects of noise, other psychoacoustic parameters such as noise pitch, tonality and also extended periods of exposure be considered. It is also suggested that more than 16 channels be used for the EEG recordings for better and more detailed evaluations of the various brain regions.

In conclusion, noise levels seem not to have the appropriate sensitivity at levels below 85 dBA on cognitive performance. Therefore, other psychoacoustic parameters that influence cognitive function, including noise pitch and tonality are suggested as candidates for future research. Scalp topographic mapping indicates that the frontal and occipital regions along with the Alpha and Beta frequency bands are most affected by exposure to noise considering the influence of task complexity, personality characteristics, the effects of other psychoacoustic parameters on cognitive and neuro-physiological functions, applying new methods such as the use of brain biosignals along with power spectral density in the evaluation of environmental and occupational stress, especially in the case of noise exposure is suggested. It can thus be concluded that the evaluation of mental workload, auditory / visual attention and brain signals (power spectral density) in combination can be considered as a useful indicator for the assessment of the effects of noise exposure on cognitive performance.

Acknowledgements

This research was conducted as a PhD thesis supported by Shahid Beheshti University of Medical Sciences. The researchers thank the authorities of Shahid Beheshti University of Medical Sciences and all participants who kindly helped us to conduct the study.

Funding: This research did not receive any financial support

Competing Interests: The authors have declared that no competing interests exist

Ethics Approval

The Research and Ethics Committee approved the study proposal of Shahid Beheshti University of Medical Sciences (Ethical code. IR. SBMU. PHNS.1396, 63). Written consent was obtained from the participants after the explanation of the purpose and benefits of research.

Pavlov’s Dogs Experiment and Pavlovian Conditioning Response

Saul McLeod, PhD

Editor-in-Chief for Simply Psychology

BSc (Hons) Psychology, MRes, PhD, University of Manchester

Saul McLeod, PhD., is a qualified psychology teacher with over 18 years of experience in further and higher education. He has been published in peer-reviewed journals, including the Journal of Clinical Psychology.

Learn about our Editorial Process

Olivia Guy-Evans, MSc

Associate Editor for Simply Psychology

BSc (Hons) Psychology, MSc Psychology of Education

Olivia Guy-Evans is a writer and associate editor for Simply Psychology. She has previously worked in healthcare and educational sectors.

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Like many great scientific advances, Pavlovian conditioning (aka classical conditioning) was discovered accidentally. Ivan Petrovich Pavlov (1849–1936) was a physiologist, not a psychologist.

During the 1890s, Pavlov researched salivation in dogs in response to being fed. He inserted a small test tube into the cheek of each dog to measure saliva when the dogs were fed (with a powder made from meat).

Pavlov predicted the dogs would salivate in response to the food in front of them, but he noticed that his dogs would begin to salivate whenever they heard the footsteps of his assistant, who was bringing them the food.

When Pavlov discovered that any object or event that the dogs learned to associate with food (such as the lab assistant) would trigger the same response, he realized that he had made an important scientific discovery.

Accordingly, he devoted the rest of his career to studying this type of learning.

Pavlovian Conditioning: Theory of Learning

Pavlov’s theory of learning, known as classical conditioning, or Pavlovian conditioning, posits that behaviors can be learned through the association between different stimuli.

Classical conditioning (later developed by Watson, in 1913) involves learning to associate an unconditioned stimulus that already brings about a particular response (i.e., a reflex) with a new (conditioned) stimulus, so that the new stimulus brings about the same response.

Pavlov developed some rather unfriendly technical terms to describe this process:

• Neutral Stimulus (NS) : A stimulus that initially does not elicit a particular response or reflex action. In other words, before any conditioning takes place, the neutral stimulus has no effect on the behavior or physiological response of interest. For example, in Pavlov’s experiment, the sound of a metronome was a neutral stimulus initially, as it did not cause the dogs to salivate.
• Unconditioned Stimulus (UCS): This is a stimulus that naturally and automatically triggers a response without any learning needed. In Pavlov’s experiment, the food was the unconditioned stimulus as it automatically induced salivation in the dogs.
• Conditioned Stimulus (CS): This is a previously neutral stimulus that, after being repeatedly associated with an unconditioned stimulus, comes to trigger a conditioned response. For instance, in Pavlov’s experiment, the metronome became a conditioned stimulus when the dogs learned to associate it with food.
• Conditioned Response (CR): This is a learned response to the conditioned stimulus. It typically resembles the unconditioned response but is triggered by the conditioned stimulus instead of the unconditioned stimulus. In Pavlov’s experiment, salivating in response to the metronome was the conditioned response.
• Unconditioned Response (UR): This is an automatic, innate reaction to an unconditioned stimulus. It does not require any learning. In Pavlov’s experiment, the dogs’ automatic salivation in response to the food is an example of an unconditioned response.

Pavlov’s Dog Experiment

Pavlov (1902) started from the idea that there are some things that a dog does not need to learn. For example, dogs don’t learn to salivate whenever they see food. This reflex is ‘hard-wired’ into the dog.

Pavlov showed that dogs could be conditioned to salivate at the sound of a bell if that sound was repeatedly presented at the same time that they were given food.

Pavlov’s studies of classical conditioning have become famous since his early work between 1890 and 1930. Classical conditioning is “classical” in that it is the first systematic study of the basic laws of learning (also known as conditioning).

Pavlov’s dogs were individually situated in secluded environments, secured within harnesses. A food bowl was positioned before them, and a device was employed to gauge the frequency of their salivary gland secretions.

The data from these measurements were systematically recorded onto a rotating drum, allowing Pavlov to meticulously monitor the rates of salivation throughout the course of the experiments.

First, the dogs were presented with the food, and they salivated. The food was the unconditioned stimulus and salivation was an unconditioned (innate) response. (i.e., a stimulus-response connection that required no learning).

Unconditioned Stimulus (Food) > Unconditioned Response (Salivate)

In his experiment, Pavlov used a metronome as his neutral stimulus. By itself, the metronome did not elicit a response from the dogs. 

Neutral Stimulus (Metronome) > No Response

Next, Pavlov began the conditioning procedure, whereby the clicking metronome was introduced just before he gave food to his dogs. After a number of repeats (trials) of this procedure, he presented the metronome on its own.

As you might expect, the sound of the clicking metronome on its own now caused an increase in salivation.

Conditioned Stimulus (Metronome) > Conditioned Response (Salivate)

So, the dog had learned an association between the metronome and the food, and a new behavior had been learned.

Because this response was learned (or conditioned), it is called a conditioned response (and also known as a Pavlovian response). The neutral stimulus has become a conditioned stimulus.

Temporal contiguity

Pavlov found that for associations to be made, the two stimuli had to be presented close together in time (such as a bell).

He called this the law of temporal contiguity. If the time between the conditioned stimulus (bell) and the unconditioned stimulus (food) is too great, then learning will not occur.

‘Unconditioning’ through experimental extinction

In extinction, the conditioned stimulus (the bell) is repeatedly presented without the unconditioned stimulus (the food).

Over time, the dog stops associating the sound of the bell with the food, and the conditioned response (salivation) weakens and eventually disappears.

In other words, the conditioned response is “unconditioned” or “extinguished.”

Spontaneous recovery

Pavlov noted the occurrence of “spontaneous recovery,” where the conditioned response can briefly reappear when the conditioned stimulus is presented after a rest period, even though the response has been extinguished.

This discovery added to the understanding of conditioning and extinction, indicating that these learned associations, while they can fade, are not completely forgotten.

Generalization

The principle of generalization suggests that after a subject has been conditioned to respond in a certain way to a specific stimulus, the subject will also respond in a similar manner to stimuli that are similar to the original one.

In Pavlov’s famous experiments with dogs, he found that after conditioning dogs to salivate at the sound of a bell (which was paired with food), the dogs would also salivate in response to similar sounds, like a buzzer.

This demonstrated the principle of generalization in classical conditioning.

However, the response tends to be more pronounced when the new stimulus closely resembles the original one used in conditioning.

This relationship between the similarity of the stimulus and the strength of the response is known as the generalization gradient.

This principle has been exemplified in research, including a study conducted by Meulders and colleagues in 2013.

Impact of Pavlov’s Research

Ivan Pavlov’s key contribution to psychology was the discovery of classical conditioning, demonstrating how learned associations between stimuli can influence behavior.

His work laid the foundation for behaviorism, influenced therapeutic techniques, and informed our understanding of learning and memory processes.

Behaviorism: Pavlov’s work laid the foundation for behaviorism , a major school of thought in psychology. The principles of classical conditioning have been used to explain a wide range of behaviors, from phobias to food aversions.

Therapy Techniques: Techniques based on classical conditioning, such as systematic desensitization and exposure therapy , have been developed to treat a variety of psychological disorders, including phobias and post-traumatic stress disorder (PTSD).

In these therapies, a conditioned response (such as fear) can be gradually “unlearned” by changing the association between a specific stimulus and its response.

• Little Albert Experiment : The Little Albert experiment, conducted by John B. Watson in 1920, demonstrated that emotional responses could be classically conditioned in humans. A young child, “Little Albert,” was conditioned to fear a white rat, which generalized to similar objects. 

Educational Strategies: Educational strategies, like repetitive learning and rote memorization, can be seen as applications of the principles of classical conditioning. The repeated association between stimulus and response can help to reinforce learning.

Marketing and Advertising: Principles from Pavlov’s conditioning experiments are often used in advertising to build brand recognition and positive associations.

For instance, a brand may pair its product with appealing stimuli (like enjoyable music or attractive visuals) to create a positive emotional response in consumers, who then associate the product with it.

Critical Evaluation

Pavlovian conditioning is traditionally described as learning an association between a neutral conditioned stimulus (CS) and an unconditioned stimulus (US), such that the CS comes to elicit a conditioned response (CR). This fits many lab studies but misses the adaptive function of conditioning (Domjan, 2005).

From a functional perspective, conditioning likely evolves to help organisms effectively interact with biologically important unconditioned stimuli (US) in their natural environment.

For conditioning to happen naturally, the conditioned stimulus (CS) can’t be arbitrary, but must have a real ecological relationship to the US as a precursor or feature of the US object.

Pavlovian conditioning prepares organisms for important biological events by conditioning compensatory responses that improve the organism’s ability to cope.

The critical behavior change from conditioning may not be conditioned responses (CRs), but rather conditioned modifications of unconditioned responses (URs) to the US that improve the organism’s interactions with it.

Evidence shows conditioning occurs readily with naturalistic CSs, like tastes before illness, infant cues before nursing, prey sights before attack. This conditioning is more robust and resistant to effects like blocking.

Traditional descriptions of Pavlovian conditioning as simply the acquired ability of one stimulus to evoke the original response to another stimulus paired with it are inadequate and misleading (Rescorla, 1988).

New research shows conditioning is actually about learning relationships between events, which allows organisms to build mental representations of their environment.

Just pairing stimuli together doesn’t necessarily cause conditioning. It depends on whether one stimulus gives information about the other.

Conditioning rapidly encodes relations among a broad range of stimuli, not just between a neutral stimulus and one eliciting a response. The learned associations allow complex representations of the world.

Recently, Honey et al. (2020, 2022) presented simulations using an alternative model called HeiDI that accounts for Rescorla’s findings. HeiDI differs by allowing reciprocal CS-US and US-CS associations. It uses consistent learning rules applied to all stimulus pairs.

The simulations suggest HeiDI explains Rescorla’s results via two mechanisms:

• Changes in US-CS associations during compound conditioning, allowing greater change in some US-CS links
• Indirect influences of CS-CS associations enabling compounds to recruit associative strength from absent stimuli

HeiDI integrates various conditioning phenomena and retains key Rescorla-Wagner insights about surprise driving learning. However, it moves beyond the limitations of Rescorla-Wagner by providing a framework to address how learning translates into performance.

HeiDI refers to the authors of the model (Honey, Dwyer, Iliescu) as well as highlighting a key feature of the model – the bidirectional or reciprocal associations it proposes between conditioned stimuli and unconditioned stimuli.

H – Honey (the lead author’s surname), ei – Bidirectional (referring to the reciprocal associations), D – Dwyer (the second author’s surname), I – Iliescu (the third author’s surname).

• Domjan, M. (2005). Pavlovian conditioning: A functional perspective.  Annu. Rev. Psychol. ,  56 , 179-206.
• Honey, R.C., Dwyer, D.M., & Iliescu, A.F. (2020a). HeiDI: A model for Pavlovian learning and performance with reciprocal associations. Psychological Review, 127, 829-852.
• Honey, R. C., Dwyer, D. M., & Iliescu, A. F. (2022). Associative change in Pavlovian conditioning: A reappraisal .  Journal of Experimental Psychology: Animal Learning and Cognition .
• Meulders A, Vandebroek, N. Vervliet, B. and Vlaeyen, J.W.S. (2013). Generalization Gradients in Cued and Contextual Pain-Related Fear: An Experimental Study in Health Participants .  Frontiers in Human Neuroscience ,  7 (345). 1-12.
• Pavlov, I. P. (1897/1902). The work of the digestive glands. London: Griffin.
• Pavlov, I. P. (1928). Lectures on conditioned reflexes . (Translated by W.H. Gantt) London: Allen and Unwin.
• Pavlov, I. P. (1927). Conditioned Reflexes: An Investigation of the Physiological Activity of the Cerebral Cortex . Translated and edited by Anrep, GV (Oxford University Press, London, 1927).
• Rescorla, R. A. (1988). Pavlovian conditioning: It’s not what you think it is .  American Psychologist ,  43 (3), 151.
• Pavlov, I. P. (1955). Selected works . Moscow: Foreign Languages Publishing House.
• Watson, J.B. (1913). Psychology as the behaviorist Views It. Psychological Review, 20 , 158-177.
• Watson, J. B., & Rayner, R. (1920). Conditioned emotional reactions.  Journal of experimental psychology ,  3 (1), 1.

Further Reading

• Logan, C. A. (2002). When scientific knowledge becomes scientific discovery: The disappearance of classical conditioning before Pavlov. Journal of the History of the Behavioral Sciences, 38 (4), 393-403.
• Learning and Behavior PowerPoint

What was the main point of Ivan Pavlov’s experiment with dogs?

The main point of Ivan Pavlov’s experiment with dogs was to study and demonstrate the concept of classical conditioning.

Pavlov showed that dogs could be conditioned to associate a neutral stimulus (such as a bell) with a reflexive response (such as salivation) by repeatedly pairing the two stimuli together.

This experiment highlighted the learning process through the association of stimuli and laid the foundation for understanding how behaviors can be modified through conditioning.

What is Pavlovian response?

The Pavlovian response, also known as a conditioned response, refers to a learned, automatic, and involuntary response elicited by a previously neutral stimulus through classical conditioning. It is a key concept in Pavlov’s experiments, where dogs learned to salivate in response to a bell.

When did Pavlov discover classical conditioning?

Ivan Pavlov discovered classical conditioning during his dog experiments in the late 1890s and early 1900s. His seminal work on classical conditioning, often called Pavlovian conditioning, laid the foundation for our understanding of associative learning and its role in behavior modification.

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7 Cool Sound Science Experiments for Kids

Nov. 19, 2018

When it comes to science experiments, some of the most enjoyable involve the science of sound. If you’re looking to dazzle your little learner with exciting new experiments, look no further than simple sound science experiments that use everyday household items to bring sound to life. Let’s explore 7 riveting ideas to discover the science behind sound! Watch educational videos with scientific experiments and show them to your child.

The Classic Paper Cup and String Phone

A much-loved childhood project, the paper cup phone is much more than a fun and old-fashioned way for kids to communicate throughout the house. This elementary sound science project shows kids how sound waves can travel through a string and be converted back to audible sound at the opposite end. 

Supplies Needed: 

• 2 paper cups
• Long string, like fishing line, kite string
• A sharp pencil or needle to poke holes in the cups

What to Do: 

1. Start by cutting a long piece of string of at least 50 feet. 

2. Poke a small hole at the bottom of each cup. 

3. Using each end of the string, thread it through the bottoms of the cups, tying a large knot so that the string does not fall out of the cup. If you make the holes too large, use a washer or paper clip to hold the string in place so that it does not pull out of the cup. 

4. Move into position and encourage your child to move away from you so that the string is far enough to make it tight. Be sure that the string does not touch any other object and that it remains suspended in air as you complete the experiment. 

5. Taking turns, talk into the cup, while the other person listens by putting the cup to their ear. Tell your child to repeat what he or she hears after you have spoken and do the same in return! 

After the experiment, explain to your child what is happening: sound waves created by talking through the cup travel through the line to the other end, converting back to sound on the opposite side! 

Make Music with a Straw Pan Flute

Perfect for younger children, the following sound waves experiment not only involves creating a fun musical instrument your child could play with, but teaches kids how length can affect the pitch of sound waves. 

Supplies Needed:  

• At least 9 or 10 straws, more if desired! 
• Clear gift wrap tape

1. Take the straws and line them up side-by-side and cut them at an angle at the top. 

2. Tape the straws together to make a pan flute. 

3. Instruct your child to blow through the straws. Which straws make higher and lower pitches? Why? 

Feel free to use more straws and experiment with different lengths to produce different pitches and sounds! Ask your child to explain what happens to the sound the shorter a straw is cut, and create double pan flutes to make harmonies to further explore how length alters the pitch. 

Listen to Sounds Travel Underwater

Sound travels well through air, but it travels even better through water! This easy sound experiment for kids can be done in a jiffy out on the back porch. 

• A bucket filled with water
• A large plastic water or soda bottle 
• At least 2 kitchen knives
• Scissors or sharp knife to cut the bottle

1. After filling the bucket with water, take a sharp knife or kitchen shears and help your child cut off the bottom of the plastic water bottle. Be sure that the cap is taken off of the bottle. 

2. Instruct your child to place the bottle in the water so that the cut bottom is in the water. Your child will then put his or her ear to the top of the bottle to listen. 

3. Using the kitchen knives, clang them together to make a sound, but do this in the bucket as your child is listening. What does your child hear? 

Your child has probably noted that the sound of the clanging is loud and clear. Water travels faster through water than in the air, and animals that live underwater are able to hear sound clearly. Discuss the results with your child, to teach him or her more about the conduction of sound waves through water. 

See the Sound

Sound vibrations travel through air, water, and even solid objects, but it’s not possible to see the waves. What if we could see the waves in another way? This science of sound experiment makes sound more visible by forcing objects to react to the sound vibrations. 

• Empty clear mixing bowl
• Plastic wrap
• Large rubber band
• Sugar crystals- Sugar in the Raw works great, or make sugar crystals in another science experiment!

1. Wrap a sheet of plastic wrap over the mixing bowl so that it’s taut, and secure with the large rubber band. Be sure that the plastic wrap is tight and does not sag. 

2. Place a few of the sugar crystals on the top of the plastic wrap, placing them in the middle of the wrap. 

3. Instruct your child to get close to the sugar crystal and say something loudly! What happens to the crystals? Do they move? 

4. Experiment with louder and softer words or sentences to watch the sugar crystals react to the sound vibrations! 

While your child might think it’s his or her breath making the crystals jump and move, but it’s actually the sound vibrations. Try different sounds besides ordinary speech and see how the crystals come to life!

Make a Stick Harmonica

Making musical instruments are easy and fun, and they teach kids about sound waves and pitch. This experiment is much like the pan flute above, but kids can alter the pitch by sliding the straws without reassembling the harmonica. 

• 2 large craft sticks
• 1 wide rubber band
• 2 smaller rubber bands 
• 1 plastic drinking straw

1. Using the scissors, cut the straw into 2 one-inch pieces and set aside. 

2. Take the wide rubber band and stretch it length-wise around one of the jumbo craft sticks and place one of the straw pieces under the rubber band, close to the edge on one end.

3. Take the other craft stick and place it directly on top of the craft stick with the rubber band. Secure them together at the ends using the small rubber bands. 

4. Finally, take the last piece of straw and place it in the harmonica between the sticks on the opposite end from the other, but this piece should be fit above the wide rubber band instead of below it. 

5. Encourage your child to play the harmonica by blowing in the center of the harmonica! Explore different pitches by moving the straw pieces!

After playing the harmonica, don’t forget to complete the sound experiment by talking about the mechanics of the harmonica. The vibrating rubber band makes all the noise, and the closer the straw pieces are to the center of the harmonica, the higher the pitch will be due to the shortened length of the band! 

Experimenting with Sound Waves

It might be hard to imagine that sound waves can travel through solid objects as well as through the air. This simple but exciting sound waves science activity will demonstrate for your child how sound can and does indeed travel through solid objects!

• Metal kitchen spoon- a large metal measuring spoon works great! 
• At least 30 inches of kite string

1. Stretch out the string and tie the handle of the spoon in the middle of the string.

2. Take one end of the string and tie around your child’s pointer finger. Do the same using the other end, but tie this string around the pointer finger of your child’s opposite hand. 

3. Instruct your child to put his or her fingers, with the string wrapped around each, into their ears. 

4. Help your child lean over so the spoon dangles and help him or her swing the spoon so it hits a nearby door or wall. 

5. Hit the door or wall again, but this time with more force. What does your child hear? 

Your child should hear a bell-like sound travel up the string from the spoon and into their ears. Discuss with your child how the sound waves created from the spoon hitting the door moves through the string until he or she is able to hear it!  

Xylophone Water Jars

Musical instruments are so much fun to make! This sound activity teaches children how varying levels of water in containers change the pitch of the sound created. 

• 4 empty and clean baby food jars 
• 4 different colors of food coloring

1. Help your child fill each jar with varying amounts of water. 

2. Add a few drops of food coloring to each jar. 

3. Using the mallet, instruct your child to firmly tap the outside of each jar. What sounds are being made? Which jars have the highest or lowest pitch? 

Encourage your child to hypothesize why some jars emit a lower sound, while others are higher. Play around with the water levels in each jar and experiment with pitch! 

Learn Science with Kids Academy Classroom!

Use this interactive Classroom by Kids Academy called Sound is All Around Us to teach first graders the basics of sound science. OPEN THE CLASSROOM . 

After clicking "Next", you'll find a set-up lesson with an educational video, accompanied by practice worksheets and summary quiz to help kids better understand and remember the learned material.

Equipped with our extensive learning resource library, Kids Academy Classroom allows teachers and parents to create lessons and share them with the young smarties in a couple of clicks. 

Go directly to the Classroom page and create a quick classroom on any topic you want! After students complete the lesson, you'll get access to a report about their performance. Check out our  Classroom Guide article  for more information!

Now that you have 7 cool ideas for exciting sound science experiments, it’s time to get started! Your child will love learning all about the science of sound and the endlessly fascinating ways sound waves can travel through air, water, and objects. Don’t forget to check out our science worksheets and activities to supplement your child’s learning in between all your child’s experiments!

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