Information

Physiology behind EEG measurements

Physiology behind EEG measurements


We are searching data for your request:

Forums and discussions:
Manuals and reference books:
Data from registers:
Wait the end of the search in all databases.
Upon completion, a link will appear to access the found materials.

Many questions were asked about general procedures of Electroencephalography (EEG), which were phrased in a rather specific context. Not only were these questions hard to find, they might also be difficult to generalize for novices. This was discussed in this meta-post. Providing a more generalized context could make the information more accessible. Therefore:

What is the physiology behind EEG measurements and how is this recorded?


EEG research all started with Hans Berger, who in 1929 reported that brain activity could be recorded by measuring electrical activity on the scalp. Although the notion of 'brain waves' that were found by Berger was controversial, over the years many researchers replicated the results, which led to the acceptance of EEG as a real phenomenon.

Neurons
Before going into all the different ways of analyzing EEG data, let us first try to understand the brain and how brain activity is generated. Let us therefore look at one of the smallest units in the brain: the neuron. Each neuron has a resting potential of 75 millivolts, meaning that the there is a difference in electrical potential of 75 millivolts between the inside of the cell and the outside. This resting potential is maintained by the Sodium-Potassium pump, by ensuring that there is a high concentration of sodium (Na+) outside of the cell, and a high concentration of potassium (K+) inside the cell.

The neuron can be stimulated by an external electrical potential, which will cause the permeability of the cell wall to change. The Na+ and K+ ions are then temporarily free to move through the cell wall, such that the concentration of Na+ and K+ will be the same both inside and outside of the cell. Accompanied with this free flow is a huge increase in voltage, which is also referred to as an action potential. This action potential will be propagated through the axon to the synapses, which will release neurotransmitters outside of the cell. These neurotransmitters will, in turn, attach to a receptor site at the dendrites of a new neuron, thereby changing the permeability of that neuron. Again, charged ions will flow into that cell, which will result in post-synaptic potential. This potential can be either inhibitory or excitatory. Thousands of synapses can be related to one dendrite. When enough excitatory post-synaptic potentials (EPSPs) occur simultaneously from these synapses, a new action potential can occur in the new neuron.

Electrical fields
The influx of ions at the dendritic side of a neuron, as the result of an EPSP, will result in a negatively charged extra-cellular space, relative to the axon-side of the neuron. This is especially interesting for a pyramidal cell, where the axons and dendrites are clearly separated (See Figure 1; left). Because of this orientation, a dipole will come to exist. A dipole is when a positively charged area is close to a negatively charged area which creates an electrical field: a space surrounding an ion in which force is exerted on other ions (See Figure 1; right). “When a large number of small dipoles, such as those generated by activity in a pyramidal cell, are summed together, the resulting electrical potential can be described as a single, larger dipole referred to as an 'equivalent current dipole' (ECD). The ECD has an orientation equal to the average of the orientations of the smaller dipoles” (Dickter and Kieffaber, 2014).

Measuring EEG
The geometry of the pyramidal cells is not all that makes the electrical fields measurable. The orientation is incredibly important. As stated earlier, the electrical fields have the same orientation as the pyramidal neurons (See Figure 1). If the pyramidal cells are perpendicular to the scalp, the electrical fields will be so as well. The electrical fields will then point 'outwards' and thereby affect the potential at the scalp (See Figure 2, right). This is what is recorded with EEG. Conversely, when the pyramidal cells are in parallel with the scalp, the electrical fields will be so as well, and therefore will not reach the scalp (see Figure 2; left) and will not be measureable.

The extent to which the electrical fields are summed, depends on the spatial and temporal contiguity of the pyramidal cells. Neurons that are closer together will have a higher summation than two neurons that are far apart. Although the human body is full of fluids and electrical flow can be conducted throughout it, the effect will diminish when the distance increases.

So now we know where the signal comes from. But how is it recorded? Well, an EEG cap contains electrodes, which are “connected” to the scalp by use of a conductive gel. This gel contains free ions that are able to move freely which, therefore, allows the gel to conduct the biological signal. The signal is then conducted to the metallic electrode through “redox (oxidation-reduction) reactions”, which is the interchange of electrodes between the gel and the metal of the electrodes. When considering Silver-Chloride (AgCl) electrodes, Cl- ions can be removed from the silver and, conversely, Cl- can be bound to free Ag+ ions, thereby creating the compound AgCl. This changes in the metal will then be conducted through the cable to the systems.

Analog Digital Conversion
At this moment, the signal is still analog, that is, a natural occurring signal that can have any natural value. The computer however, is nog capable of measuring analog values. Instead, the analog signal must be converted to digital values, such that it can be represented in bits (ones and zeros). This is done by taking a range of voltages that you expect to be measured, and divide that by the amount of bits that the system can take (see Figure 3)


Extra Note

EEG can only measure the activity of pyramidal cells that are perpendicular to the scalp and at the surface of the cortex. Are the other pyramidal cells then just lost? Not really. Another really neat property of electrical flow is that it creates a magnetic field that is perpendicular to the dipole (See Figure 4). For the magnetic field to “move out of the head”, making it measurable, the pyramidal cells must lie parallel to the scalp. Magnetic fields also do not need conductive tissue to be measured. This can be recorded with magneto-encephalography (MEG). MEG is thus a perfect complement to EEG, although deeper positioned structures can still not be recorded.


References

Luck, S. J. (2014). An introduction to the event-related potential technique. MIT press. ISO 690
Dickter, C. L., & Kieffaber, P. D. (2013). EEG methods for the psychological sciences. Sage.


Tobii Pro Studio integration

Next to physiology, researchers often also collect eye tracking data. Communication between Tobii Pro Studio and The Observer XT now also makes use of the Noldus communication protocol N-Linx. This simplifies start and stop of the eye tracking recording and synchronization with the Event Log in The Observer XT.


3. Study 2: Rate of Chewing, Mood, and Cognition

This experiment examined if rate of chewing could potentially moderate the effects of gum on attention and mood. Participants were filmed while chewing in order to establish the rate of chewing (pilot data indicated good interrater reliability for scoring of number of chews per minute).

3.1. Methods

3.1.1. Participants

Fifty-six adults (42 females, 14 males mean age = 19.6, SD = 1.4) were recruited. Participants were mostly students from the School of Psychology, Cardiff University.

3.1.2. Materials

Chewing Gum. As a moderating effect of flavour was not observed in Study 1, participants were given a choice of flavours for this study, as well as Studies 3 and 4. The following chewing gums were available: Wrigley's spearmint, Wrigley's extra (flavours: spearmint, peppermint, cool breeze, and ice), and Wrigley's airwaves (flavours: cherry, green mint, black mint, menthol, and eucalyptus).

Selective Attention Tasks [27]

(i) Focused Attention Task. In this task target letters appeared as upper case A's and B's in the centre of the screen. Participants were required to identify as quickly and as accurately as possible if the target letter was an A or a B, by pressing A or B with the forefinger of the left or right hand, while ignoring any distracters presented elsewhere on the screen. Before each presentation of the target, three warning crosses were displayed for 500 ms. The middle cross was then replaced by the target, and the outer crosses were replaced by distracters (in the case of trials with distracters). The outer crosses were separated from the middle cross by 1.02° or 2.6°. The target letter was accompanied by nothing, letters which were the same as the target, letters which were different from the target, or asterisks.

Mean reaction time, number of errors, and number of long responses (𾠀 ms) were measured. The threshold for long responses was based on previous research [28]. Breadth of attention was also assessed (the difference in reaction time and accuracy between targets with distracters presented near to the target versus targets with distracters at a further distance from the target). The difference in reaction time between conditions where the target changed from the previous trial and where it remained the same was used as a measure of speed of encoding of new information. Following 10 practice trials, participants completed three blocks of 64 trials. This test lasted approximately 5 minutes.

(ii) Categoric Search Task. This task was similar to the focused attention task previously outlined, including number of practice and experimental trials. However, in this task participants did not know where the target would appear. At the start of each trial, two crosses appeared 2.04° or 5.2° apart or further apart, located towards the left or right extremes of the display. The target then replaced one of these crosses. For half the trials the target was presented alone and for half it was accompanied by a distracter (a digit from 1 to 7).

Mean reaction time, accuracy, and long responses (� ms) were recorded, as well as reaction time and accuracy with which new information was encoded. Differences in reaction time and accuracy for trials where the position of the target stimulus and response key were compatible versus where they were incompatible were used as a measure of response organisation. The effect of the stimulus appearing in a different location versus the same location as the previous trial was measured, as well as the effect of not knowing the location of the target. This task also lasted approximately 5 minutes.

Variable Fore-Period Simple Reaction Time Task [29]. In this task a box was displayed on the screen, followed by a square being presented in the middle of the box. The participant had to press the “Space” button as soon as the square was detected. The period of time elapsed before each appearance of the square varied. This task lasted 3 minutes.

Repeated Digits Vigilance Task [29]. Three-digit numbers were shown on the screen at the rate of 100 per minute. Each number was normally different from the preceding one, but for 8 occasions per minute the number presented was the same as that presented on the previous trial. Participants had to detect these repetitions and respond by hitting the “Space” button as quickly as possible. The number of hits (correctly detected repetitions), reaction time for hits, and number of false alarms were recorded. The task lasted 5 minutes.

3.1.3. Design

Each participant completed both the chewing gum and no-gum control conditions. Similar to previous studies, gum condition was included as a crossover variable to test if any effects of gum would carry over to a no-gum condition (for those who completed the gum condition first).

3.1.4. Procedure

Following informed consent and a familiarisation with the mood and attention tasks, participants completed the mood and attention tasks twice. Participants were instructed to chew two pieces of gum constantly at their own pace during one of these testing sessions and not to chew during the other testing session. Each set of the mood and attention tasks took approximately 25 minutes, and participants completed the second condition immediately after the first. Participants selected a packet of gum just before the chewing condition. They were filmed throughout the chewing session. In order to assess the rate of chewing during each task, notes were taken of when each computerised task began and ended. This timing of the tasks was matched to the footage of the participant completing the task, so that the rate of chewing during each specific task could be calculated. Participants indicated how hard they had been chewing on a scale of 1 (as softly as possible) to 11 (as hard as possible) immediately after the gum condition.

3.1.5. Analysis

Analysis of Footage. The footage was divided into the mood tasks, blocks for the selective attention tasks, and minutes for the simple reaction time task and repeated digits vigilance task, as well as gaps between tasks. Each piece of footage was rated twice, and the intraclass correlation (single measures) was .996, suggesting excellent test/retest reliability for the video rating. The mean of the two scores for each section of the footage was used as the final result.

Statistical Analysis. Mixed ANOVA was used to assess the effect of chewing gum (repeated measures: gum versus no-gum control), order of gum condition (independent measures: gum condition first versus gum condition second), and time-on-task. Time-on-task was entered as a repeated measures variable in the analysis of variables for which time-on-task data was available (i.e., alertness, hedonic tone, and anxiety, categoric search reaction time, focused attention reaction time, simple reaction time, repeated digits hit, false alarms, and reaction time). Time-on-task was defined as pre- versus posttest for reported mood (i.e., before and after the attention tasks) and blocks or minutes for cognitive tasks.

Multiple regressions with forced entry were used to test if the predictors were associated with changes in attention and mood between gum and no-gum conditions. The predictors were rate of chewing, speed of chewing and intensity (how hard gum was chewed), and prior amount of chewing (total count of times chewed this did not apply for pretest mood, when chewing had just begun).

3.2. Results

3.2.1. Chewing Gum and Mood

There was a significant main effect of time and chewing gum on alertness alertness fell between pre- and posttest assessments, F(1,54) = 57.13, P < .001, and partial η 2 = .51, and chewing gum was associated with higher alertness, F(1,54) = 24.62, P < .001, and partial η 2 = .31. There was also an interaction between gum condition and time, F(1,54) = 8.47, P = .005, and partial η 2 = .14 alertness was higher in the gum condition posttest. There was a significant interaction between gum and order of gum condition, F(1,54) = 11.5, P = .001, and partial η 2 = .18. Alertness was improved to a greater extent by chewing gum when it came first (see Figure 2(a) ).

Chewing gum, pre- and posttest mood (Study 2). (a) Alertness. (b) Hedonic tone. (c) Anxiety. Error bars indicate standard error of the mean.

Hedonic tone fell significantly between pre- and posttest, F(1,54) = 62.45, P < .001, and partial η 2 = .54, and hedonic tone was significantly higher in the gum condition, F(1,54) = 6.74, P = .01, and partial η 2 = .11, but there was not a significant interaction between gum and time, F(1,54) = 2.32, P = .13, and partial η 2 = .04. There was a significant interaction between gum and order of gum condition, F(1,54) = 14.43, P < .001, and partial η 2 = .21. Hedonic tone was improved to a greater extent by chewing gum when it came first (see Figure 2(b) ).

There was no significant effect of time on anxiety, F(1,54) = .09, P = .77, and partial η 2 = .002, nor was there a significant main effect of chewing gum, F(1,54) = 2.75, P = .1, and partial η 2 = .05. There was no interaction between gum and time, F(1,54) = 1.4, P = .24, and partial η 2 = .03, and there was no interaction between gum and order of gum condition, F(1,54) = .76, P = .39, and partial η 2 = .01 (see Figure 2(c) ).

3.2.2. Chewing Gum, Time-on-Task, and Cognition

Chewing gum had a significant main effect on categoric search speed of encoding. There was a significant interaction between gum condition and time-on-task for repeated digits reaction time, F(4,216) = 4.22, P = .003, and partial η 2 = .07 (see Figure 3(a) ). Chewing gum lengthened reaction time during the fourth minute, F(1,54) = 13.91, P < .001, and partial η 2 = .21, indicating a negative effect of chewing gum on performance at this time. There was also a main effect of time, F(4,216) = 20.53, P < .001, and partial η 2 = .28, with reaction time lengthening over time, but there was not a main effect of chewing gum, F(1,54) = 1.04, P = .31, and partial η 2 = .02. There was no significant interaction between gum and order of gum condition, F(1,54) = .04, P = .85, and partial η 2 = .001.

Time-on-task trends in chewing gum effects on (a) vigilance reaction time, (b) vigilance false alarms, (c) vigilance hits, and (d) categoric search reaction time (Study 2). Error bars represent standard error of the mean.

There was a gum by time-on-task interaction for false alarms, F(4,216) = 2.25, P = .048, and partial η 2 = .05 chewing gum reduced the number of false alarms during the final minute of the task, F(1,54) = 13.69, P = .001, and partial η 2 = .2 (see Figure 3(b) ), indicating a positive effect on performance during the final minute. There was a significant main effect of time-on-task, F(4,216) = 9.07, P < .001, and partial η 2 = .14, with the number of false alarms falling during later minutes. There was, however, no main effect of chewing gum on false alarms, F(1,55) = 1.52, P = .22, and partial η 2 = .03. There was an interaction between gum and order of gum condition F(1,54) = 6.7, P = .01, and partial η 2 = .11. False alarms were heightened by gum when gum came before the no-gum control.

For vigilance hits, there was no significant main effect of chewing gum, F(1,54) = .91, P = .35, and partial η 2 = .02 or interaction between gum and time-on-task, F(4,216) = .28, P = .89, and partial η 2 = .005 (see Figure 3(c) ). Again, there was a main effect of time-on-task, with percent hits falling during later minutes, F(4,216) = 31.27, P < .001, and partial η 2 = .37. There was an interaction between gum and order of gum condition, F(1,54) = 16.5, P < .001, and partial η 2 = .23. Hits were enhanced by chewing gum when it came before the no-gum control.

There was a gum × time interaction for categoric search reaction time, with gum shortening reaction time, but only during the first block, F(2,108) = 5.76, P = .004, and partial η 2 = .1 (see Figure 3(d) ). This was in the context of a strong main effect of time, F(2,108) = 5.92, P = .004, and partial η 2 = .1, with reaction time significantly shortened during the second block, although there was not a main effect of gum, F(1,55) = .01, P = .95, and partial η 2 < .001.

Gum had a significant main effect on focused attention speed of encoding, with slower encoding of information in the gum condition. There was a significant interaction between chewing gum and order of gum condition for focused attention mean reaction time, errors, speed of encoding, and simple reaction time. For simple reaction time, performance was improved by gum when it came after the control condition, while the opposite was true for focused attention. Results are summarised in Table 1 .

Table 1

Chewing gum, time-on-task, and attention.

GumNo gumResults
Focused attention
 Mean reaction time (ms)Block 1: M = 396.14 (5.89)
Block 2: M = 395.2 (5.38)
Block 3: M = 400.18 (5.31)
Block 1: M = 391.97 (5.85)
Block 2: M = 397.55 (5.26)
Block 3: M = 401.39 (5.23)
Gum: F(1, 54) = .01, P = .94, η p 2 < .001
Time ** : F(2, 108) = 5.82, P = .004, η p 2 = .1
Gum × time: F(1.802, 99.12) = 1.44, P = .24, η p 2 = .03
Gum × order: F(1, 54) = 3.89, P = .054, η p 2 = .06
 Total errors10.18 (1.12)10.15 (1.04)Gum: F(1, 54) = .003, P = .96, η p 2 < .0001
Gum × order †† : F(1, 54) = 5.37, P = .02, η p 2 = .09
 Long responses.27 (.09).45 (.21)Gum: F(1, 54) = .91, P = .35, η p 2 = .02
Gum × order: F(1, 54) = 2.94, P = .09, η p 2 < .05
𠀻readth of attention 1 18.99 (4.71)25.83 (5.39)Gum: F(1, 54) = 1.03, P = .32, η p 2 = .02
Gum × order: F(1, 54) = .78, P = .38, η p 2 = .01
 Speed of encoding 2 25.47 (2.77)24.44 (2.61)Gum: F(1, 54) = .21, P = .64, η p 2 = .004
Gum × order †† : F(1, 54) = 12.15, P < .001, η p 2 = .18
Categoric search
 Total errors11.16 (.8)11.84 (.93)Gum: F(1, 54) = 1.45, P = .23, η p 2 = .03
Gum × order: F(1, 54) = 2.51, P = .12, η p 2 = .04
 Long responses1.66 (.3)1.87 (.36)Gum: F(1, 54) = .45, P = .5  η p 2 = .008
Gum × order: F(1, 54) = .83, P = .37, η p 2 = .02
 Response organisation 3 27.51 (2.54)26.88 (2.53)Gum: F(1, 54) = .05, P = .083  η p 2 = .001
Gum × order: F(1, 54) = .07, P = .79, η p 2 = .001
 Speed of encoding17.69 (2.73)4.77 (2.54)Gum †† : F(1, 54) = 14.3, P < .001, η p 2 = .21
Gum × order: F(1, 54) = .04, P = .84, η p 2 = .001
 Spatial uncertainty 4 105.92 (4.83)116.26 (5.34)Gum: F(1, 54) = 3.28, P = .08, η p 2 = .06
Gum × order: F(1, 54) = .043, P = .52, η p 2 = .008
 Place repetition 5 15.62 (2.56)14 (2.92)Gum: F(1, 54) = .35, P = .56, η p 2 = .006
Gum × order: F(1, 54) = .72, P = .4, η p 2 = .01
Simple reaction timeBlock 1: M = 327.98 (6.39)
Block 2: M = 336.58 (7.14)
Block 3: M = 339.29 (6.68)
Block 1: M = 314.43 (7.32)
Block 2: M = 331.02 (7.44)
Block 3: M = 341.09 (8)
Gum: F(1, 54) = 2.04, P = .16, η p 2 = .04
Time †† : F(2, 108) = 16.09, P < .001, η p 2 = .23
Gum × time: F(2, 108) = 2.07, P = .13, η p 2 = .04
Gum × order *** : F(1, 54) = 22.08, P = .001, η p 2 = .29

Standard errors of the means are in parentheses. 1 Higher score = broader focus of attention. 2 Higher score = slower encoding of information. 3 Higher score = poorer organisation. 4 Higher score = greater uncertainty. 5 Higher score = greater effect of place repetition. ** indicates P < .01, †† indicates P < .001, and *** indicates P = .001. Gum × gum order refers to interaction between gum condition and order in which gum condition appeared.

3.2.3.  Rate of Chewing, Mood, and Cognition

A faster rate of chewing was associated with lengthened simple reaction time (beta = .42, P = .04). Harder chewing was associated with faster encoding of new information on the categoric search task (beta = −.37, P = .02). Greater prior chewing was associated with a higher level of focused attention errors (beta = .32, P = .04). Rate of chewing, force of chewing, and prior chewing did not moderate mood or performance on the repeated digits vigilance task. Results are summarised in Table 2 .

Table 2

Level of chewing and its effect on mood and cognition.

Unstandardised BSE BBetaSignificance R 2 Adjusted R 2
Mood
 Pretest alertness.03−.01
 𠀼onstant26.4518.14.15
  Rate of chewing−.57.62−.13.36
  Intensity𢄡.042.65−.06.7
 Posttest alertness.06.01
 𠀼onstant20.7413.08.12
  Rate of chewing−.9.6−.32.14
  Prior chewing.01.02.12.61
  Intensity2.192.53.13.39
 Pretest hedonic tone.02−.02
 𠀼onstant.569.77.95
  Rate of chewing−.25.33−.11.45
  Intensity1.441.43.15.32
 Posttest hedonic tone.04−.02
 𠀼onstant11.677.37.12
  Rate of chewing−.41.34−.26.23
  Prior chewing.004.01.11.47
  Intensity−.331.43−.04.82
 Pretest anxiety.05.01
 𠀼onstant𢄩.62−.01.11
  Rate of chewing−.006.2−.01.98
  Intensity1.31.85.22.13
 Posttest anxiety.02−.04
 𠀼onstant.24.07.96
  Rate of chewing−.09.19−.11.62
  Prior chewing−.001.005−.04.86
  Intensity.29.79.06.71
Focused attention
 Mean reaction time (ms).02−.04
 𠀼onstant.88.93.93
  Rate of chewing.25.29.14.39
  Prior chewing−.003.01−.04.81
  Intensity𢄡.011.78−.09.57
 Total errors.1.05
 𠀼onstant𢄡.62.09.45
  Rate of chewing.02.07.04.81
  Prior chewing * .01.003.32.04
  Intensity−.15.42−.05.72
 Number of long responses.02−.03
 𠀼onstant.1.58.86
  Rate of chewing.01.02.04.81
  Prior chewing.001.001.12.46
  Intensity−.11.12−.15.34
𠀻readth of attention.11.06
 𠀼onstant�.1519.44.02
  Rate of chewing1.01.63.27.09
  Prior chewing−.02.03−.12.46
  Intensity4.33.87.17.27
 Speed of encoding.03−.03
 𠀼onstant𢄦.537.38.38
  Rate of chewing.13.24.09.58
  Prior chewing.003.01.04.79
  Intensity.691.47.07.64
Categoric search
 Mean reaction time.01−.05
 𠀼onstant𢄡.8612.14.88
  Rate of chewing.01.44.002.99
  Prior chewing.004.02.04.83
  Intensity𢄡.492.42−.1.54
 Total errors.03−.03
 𠀼onstant𢄢.121.72.23
  Rate of chewing−.01.06−.04.83
  Prior chewing−.001.002−.07.72
  Intensity.42.34.19.23
 Long responses.11.06
 𠀼onstant1.84.88.04
  Rate of chewing−.04.03−.22.2
  Prior chewing<.001.001.06.73
  Intensity−.26.18−.23.14
 Response organization.01−.05
 𠀼onstant1.438.79.87
  Rate of chewing.21.32.12.52
  Prior chewing−.003.01−.05.8
  Intensity−.71.75−.06.69
 Speed of encoding.11.05
 𠀼onstant29.759.82.004
  Rate of chewing−.03.36−.02.93
  Prior chewing.02.01.21.24
  Intensity * 𢄤.741.96−.37.02
 Spatial uncertainty.05−.004
 𠀼onstant�.9416.94.35
  Rate of chewing−.88.61−.25.16
  Prior chewing.01.02.09.63
  Intensity3.563.38.16.3
 Place repetition.02−.03
 𠀼onstant𢄢.418.23.77
  Rate of chewing−.1.3−.06.73
  Prior chewing.01.01.18.35
  Intensity.091.64.009.95
Simple reaction time.08.03
𠀼onstant�.0915.47.18
 Rate of chewing * .84.49.42.04
 Prior chewing.007.02.05.7
 Intensity.742.69.04.79
Repeated digits vigilance
 Percent hits.09.04
 𠀼onstant1.411.79.44
  Rate of chewing−.11.05−.32.06
  Prior chewing<.001.002−.03.86
  Intensity.27.33.12.42
�lse alarms.04−.01
 𠀼onstant−.735.31.89
  Rate of chewing.17.16.17.31
  Prior chewing.002.006.06.69
  Intensity𢄡.11.98−.18.26
 Reaction time.06.007
 𠀼onstant�.4924.72.48
  Rate of chewing𢄡.2.75−.27.12
  Prior chewing.02.03.12.41
  Intensity6.894.56.23.14

3.3. Study 2 Discussion

Consistent with previous research as well as Study 1, chewing gum was associated with higher alertness. This might be expected to improve sustained attention performance, although the results indicated lengthened reaction time as well as fewer false alarms as the vigilance task continued, suggesting negative and positive effects on sustained attention performance. Vigilance performance was not moderated by rate of chewing however, although faster chewing was associated with lengthened simple reaction time, harder chewing was associated with faster encoding of new information on the categoric search task, and prior chewing was associated with more errors on the focused attention task. It thus may be useful for researchers to take some measure of how hard and fast participants are chewing in future research.

In order to further examine the effects of chewing gum on performance and reported feelings in a more naturalistic setting, the next studies examined chewing gum over the course of a working day.


Examples of Biosensor Research

What’s remarkable about such studies are the incredibly fine-scale insights into the human emotion that can be gleaned from the minute subconscious or involuntary phenomena.

Consider galvanic skin response or GSR. This is a measure of electrodermal activity: the relative conductance of our skin from perspiration. Sweating is an utterly autonomic operation that, in addition to its role in thermoregulation, is a reaction to arousal, from general excitement to flat-out terror. By measuring sweat production via skin conductance, GSR can reveal evidence for a stimulated, agitated state of being that’s beyond a person’s deliberate control – including arousal too subtle to manifest on the self-aware spectrum.

Electrocardiography (ECG) registers the electrical signature of a heartbeat, revealing intricacies of their rate and variability that, like GSR, can demonstrate physiological, emotional, or psychological arousal.

Then there’s electroencephalography (EEG) , which tracks brainwaves via scalp-affixed electrodes that measure the electrical pulses produced by mass neuron firings. An EEG readout indicates the moment-by-moment “geography” of brain activity – which cortex is excited when, basically – as well as the brain’s overall state at a given time.

Eye tracking , meanwhile, quantifies when and where a subject’s gaze lingers, the rhythm of reading, and other optical minutiae, while facial expression analysis looks up-close at the configuration of the face’s musculature for clues to a person’s emotions.

The information outputted by a single kind of biosensor can be intriguing and useful, but only to a point. For instance, GSR and ECG readings can suggest the condition of arousal, but not its valence, or emotional character. In other words, sweaty palms or a ramped-up heartbeat doesn’t reveal whether we’re dealing with a love-at-first-sight (i.e., a positive stimulus) sort of situation or a figure-looming-out-of-the-shadows (i.e., a negative stimulus) deal.

Integrate those electrodermal and cardiac data with EEG, facial expression analysis, eye tracking, and other analyses, and you’ve got a much more multifaceted picture. That’s what iMotions is all about.


Kill switch: The evolution of road rage in an increasingly AI car culture

Julie Carpenter , in Living with Robots , 2020

Killer app

The idea of an AI kill switch for human control of a car is complicated in its ethical considerations. While it is possible that an autonomous car may be kitted with ways of gauging a user's mental and emotional state via various biofeedback (and even mood predictive) sensors, at best this is a technical intervention, not a solution to the actual issues of human behaviors associated with road rage.

Like some other technologies, autonomous cars are both mediums of social relations and produce social relations. While territoriality behavior research has been used as a lens for traditional automobiles, it is likely that new concepts for car interiors and the roles of the users will change significantly as cars turn to more autonomous processes, relieving the driver of many of the cognitive and social responsibilities of driving. Yet the changing role of the user in the driving process does not remove the human factors altogether, meaning there is still room for disruption in transportation situations, and these will sometimes elicit user frustration, anger, or other negative stress-related feelings and emotions that could further translate to aggressive behaviors.

Szlemko et al. (2008) have provided research about user perceptions of the nonautonomous car as primary territory, predicting the supporting users’ territorial behaviors and the link to attachment theory as a closely related concept to user perceptions about their car as a defendable place.

While these premises have not been tested in longitudinal research situations with autonomous cars to date, this chapter has presented an initial attempt at building a framework for understanding these concepts of territoriality, emotional attachment, and user experience in relation to the new iterations of car roles and designs, although certainly more work is required. Additionally, the possibility of new ways of manifesting user aggressive behaviors raises design questions about the use of kill switches, whether for the user or the car's AI. Major ethical and practical challenges are at stake when discussing an AI kill switch for users who would require the car's AI to have a flawless understanding of human experience and the ability to apply rules and actions in this vein when discussing the AI-triggered kill switch for erratic user driving.

Moreover, including a mandatory human-controlled kill switch for the AI in a consumer vehicle depends upon the human will to resist obstructing the AI controlling the driving, and controlling aggressive driving behaviors. While these possible scenarios raise a multitude of new questions and issues, it is a set of potentially common circumstances worthy of further consideration.


Behavioral neuroscience as a scientific discipline emerged from a variety of scientific and philosophical traditions in the 18th and 19th centuries. In philosophy, people like René Descartes proposed physical models to explain animal as well as human behavior. Descartes suggested that the pineal gland, a midline unpaired structure in the brain of many organisms, was the point of contact between mind and body. Descartes also elaborated on a theory in which the pneumatics of bodily fluids could explain reflexes and other motor behavior. This theory was inspired by moving statues in a garden in Paris. [4] Electrical stimulation and lesions can also show the affect of motor behavior of humans. They can record the electrical activity of actions, hormones, chemicals and effects drugs have in the body system all which affect ones daily behavior.

Other philosophers also helped give birth to psychology. One of the earliest textbooks in the new field, The Principles of Psychology by William James, argues that the scientific study of psychology should be grounded in an understanding of biology.

The emergence of psychology and behavioral neuroscience as legitimate sciences can be traced from the emergence of physiology from anatomy, particularly neuroanatomy. Physiologists conducted experiments on living organisms, a practice that was distrusted by the dominant anatomists of the 18th and 19th centuries. [5] The influential work of Claude Bernard, Charles Bell, and William Harvey helped to convince the scientific community that reliable data could be obtained from living subjects.

Even before the 18th and 19th century, behavioral neuroscience was beginning to take form as far back as 1700 B.C. [6] The question that seems to continually arise is: what is the connection between the mind and body? The debate is formally referred to as the mind-body problem. There are two major schools of thought that attempt to resolve the mind–body problem monism and dualism. [4] Plato and Aristotle are two of several philosophers who participated in this debate. Plato believed that the brain was where all mental thought and processes happened. [6] In contrast, Aristotle believed the brain served the purpose of cooling down the emotions derived from the heart. [4] The mind-body problem was a stepping stone toward attempting to understand the connection between the mind and body.

Another debate arose about localization of function or functional specialization versus equipotentiality which played a significant role in the development in behavioral neuroscience. As a result of localization of function research, many famous people found within psychology have come to various different conclusions. Wilder Penfield was able to develop a map of the cerebral cortex through studying epileptic patients along with Rassmussen. [4] Research on localization of function has led behavioral neuroscientists to a better understanding of which parts of the brain control behavior. This is best exemplified through the case study of Phineas Gage.

The term "psychobiology" has been used in a variety of contexts, emphasizing the importance of biology, which is the discipline that studies organic, neural and cellular modifications in behavior, plasticity in neuroscience, and biological diseases in all aspects, in addition, biology focuses and analyzes behavior and all the subjects it is concerned about, from a scientific point of view. In this context, psychology helps as a complementary, but important discipline in the neurobiological sciences. The role of psychology in this questions is that of a social tool that backs up the main or strongest biological science. The term "psychobiology" was first used in its modern sense by Knight Dunlap in his book An Outline of Psychobiology (1914). [7] Dunlap also was the founder and editor-in-chief of the journal Psychobiology. In the announcement of that journal, Dunlap writes that the journal will publish research ". bearing on the interconnection of mental and physiological functions", which describes the field of behavioral neuroscience even in its modern sense. [7]

In many cases, humans may serve as experimental subjects in behavioral neuroscience experiments however, a great deal of the experimental literature in behavioral neuroscience comes from the study of non-human species, most frequently rats, mice, and monkeys. As a result, a critical assumption in behavioral neuroscience is that organisms share biological and behavioral similarities, enough to permit extrapolations across species. This allies behavioral neuroscience closely with comparative psychology, evolutionary psychology, evolutionary biology, and neurobiology. Behavioral neuroscience also has paradigmatic and methodological similarities to neuropsychology, which relies heavily on the study of the behavior of humans with nervous system dysfunction (i.e., a non-experimentally based biological manipulation).

Synonyms for behavioral neuroscience include biopsychology, biological psychology, and psychobiology. [8] Physiological psychology is a subfield of behavioral neuroscience, with an appropriately narrower definition.

The distinguishing characteristic of a behavioral neuroscience experiment is that either the independent variable of the experiment is biological, or some dependent variable is biological. In other words, the nervous system of the organism under study is permanently or temporarily altered, or some aspect of the nervous system is measured (usually to be related to a behavioral variable).

Disabling or decreasing neural function Edit

    – A classic method in which a brain-region of interest is naturally or intentionally destroyed to observe any resulting changes such as degraded or enhanced performance on some behavioral measure. Lesions can be placed with relatively high accuracy "Thanks to a variety of brain 'atlases' which provide a map of brain regions in 3-dimensional "stereotactic coordinates.
  • Surgical lesions – Neural tissue is destroyed by removing it surgically.
  • Electrolytic lesions – Neural tissue is destroyed through the application of electrical shock trauma.
  • Chemical lesions – Neural tissue is destroyed by the infusion of a neurotoxin.
  • Temporary lesions – Neural tissue is temporarily disabled by cooling or by the use of anesthetics such as tetrodotoxin.

Enhancing neural function Edit

  • Electrical stimulation – A classic method in which neural activity is enhanced by application of a small electric current (too small to cause significant cell death).
  • Psychopharmacological manipulations – A chemical receptor agonist facilitates neural activity by enhancing or replacing endogenous neurotransmitters. Agonists can be delivered systemically (such as by intravenous injection) or locally (intracerebrally) during a surgical procedure.
  • Synthetic Ligand Injection – Likewise, Gq-DREADDs can be used to modulate cellular function by innervation of brain regions such as Hippocampus. This innervation results in the amplification of γ-rhythms, which increases motor activity. [15] – In some cases (for example, studies of motor cortex), this technique can be analyzed as having a stimulatory effect (rather than as a functional lesion). excitation – A light activated excitatory protein is expressed in select cells. Channelrhodopsin-2 (ChR2), a light activated cation channel, was the first bacterial opsin shown to excite neurons in response to light, [16] though a number of new excitatory optogenetic tools have now been generated by improving and imparting novel properties to ChR2 [17]

Measuring neural activity Edit

    Optical techniques – Optical methods for recording neuronal activity rely on methods that modify the optical properties of neurons in response to the cellular events associated with action potentials or neurotransmitter release.
      (VSDs) were among the earliest method for optically detecting neuronal activity. VSDs commonly changed their fluorescent properties in response to a voltage change across the neuron's membrane, rendering membrane sub-threshold and supra-threshold (action potentials) electrical activity detectable. [18] Genetically encoded voltage sensitive fluorescent proteins have also been developed. [19] relies on dyes [20] or genetically encoded proteins [21] that fluoresce upon binding to the calcium that is transiently present during an action potential. is a technique that relies on a fusion protein that combines a synaptic vesicle membrane protein and a pH sensitive fluorescent protein. Upon synaptic vesicle release, the chimeric protein is exposed to the higher pH of the synaptic cleft, causing a measurable change in fluorescence. [22]

    Genetic techniques Edit

      – The influence of a gene in some behavior can be statistically inferred by studying inbred strains of some species, most commonly mice. The recent sequencing of the genome of many species, most notably mice, has facilitated this technique. – Organisms, often mice, may be bred selectively among inbred strains to create a recombinant congenic strain. This might be done to isolate an experimentally interesting stretch of DNA derived from one strain on the background genome of another strain to allow stronger inferences about the role of that stretch of DNA. – The genome may also be experimentally-manipulated for example, knockout mice can be engineered to lack a particular gene, or a gene may be expressed in a strain which does not normally do so (the 'transgenic'). Advanced techniques may also permit the expression or suppression of a gene to occur by injection of some regulating chemical.

    Computational models - Using a computer to formulate real-world problems to develop solutions. [26] Although this method is often focused in computer science, it has begun to move towards other areas of study. For example, psychology is one of these areas. Computational models allow researchers in psychology to enhance their understanding of the functions and developments in nervous systems. Examples of methods include the modelling of neurons, networks and brain systems and theoretical analysis. [27] Computational methods have a wide variety of roles including clarifying experiments, hypothesis testing and generating new insights. These techniques play an increasing role in the advancement of biological psychology. [28]

    Limitations and advantages Edit

    Different manipulations have advantages and limitations. Neural tissue destroyed as a primary consequence of a surgery, electric shock or neurotoxin can confound the results so that the physical trauma masks changes in the fundamental neurophysiological processes of interest. For example, when using an electrolytic probe to create a purposeful lesion in a distinct region of the rat brain, surrounding tissue can be affected: so, a change in behavior exhibited by the experimental group post-surgery is to some degree a result of damage to surrounding neural tissue, rather than by a lesion of a distinct brain region. [29] [30] Most genetic manipulation techniques are also considered permanent. [30] Temporary lesions can be achieved with advanced in genetic manipulations, for example, certain genes can now be switched on and off with diet. [30] Pharmacological manipulations also allow blocking of certain neurotransmitters temporarily as the function returns to its previous state after the drug has been metabolized. [30]

    In general, behavioral neuroscientists study similar themes and issues as academic psychologists, though limited by the need to use nonhuman animals. As a result, the bulk of literature in behavioral neuroscience deals with mental processes and behaviors that are shared across different animal models such as:

    • Sensation and perception
    • Motivated behavior (hunger, thirst, sex)
    • Control of movement
    • Learning and memory
    • Sleep and biological rhythms
    • Emotion

    However, with increasing technical sophistication and with the development of more precise noninvasive methods that can be applied to human subjects, behavioral neuroscientists are beginning to contribute to other classical topic areas of psychology, philosophy, and linguistics, such as:

    Behavioral neuroscience has also had a strong history of contributing to the understanding of medical disorders, including those that fall under the purview of clinical psychology and biological psychopathology (also known as abnormal psychology). Although animal models do not exist for all mental illnesses, the field has contributed important therapeutic data on a variety of conditions, including:


    Artificial intelligence predicts brain age from EEG signals recorded during sleep studies

    Credit: CC0 Public Domain

    A study shows that a deep neural network model can accurately predict the brain age of healthy patients based on electroencephalogram data recorded during an overnight sleep study, and EEG-predicted brain age indices display unique characteristics within populations with different diseases.

    The study found that the model predicted age with a mean absolute error of only 4.6 years. There was a statistically significant relationship between the Absolute Brain Age Index and epilepsy and seizure disorders, stroke, elevated markers of sleep-disordered breathing (i.e., apnea-hypopnea index and arousal index), and low sleep efficiency. The study also found that patients with diabetes, depression, severe excessive daytime sleepiness, hypertension, and/or memory and concentration problems showed, on average, an elevated Brain Age Index compared with the healthy population sample.

    According to the authors, the results demonstrate that these health conditions are associated with deviations of one's predicted age from one's chronological age.

    "While clinicians can only grossly estimate or quantify the age of a patient based on their EEG, this study shows an artificial intelligence model can predict a patient's age with high precision," said lead author Yoav Nygate, senior AI engineer at EnsoData. "The model's precision enables shifts in the predicted age from the chronological age to express correlations with major disease families and comorbidities. This presents the potential for identifying novel clinical phenotypes that exist within physiological signals utilizing AI model deviations."

    The researchers trained a deep neural network model to predict the age of patients using raw EEG signals recorded during clinical sleep studies performed using overnight polysomnography. The model was trained on 126,241 sleep studies, validated on 6,638 studies, and tested on a holdout set of 1,172 studies. Brain age was assessed by subtracting individuals' chronological age from their EEG-predicted age (i.e., Brain Age Index), and then taking the absolute value of this variable (i.e., Absolute Brain Age Index). Analyses controlled for factors such as sex and body mass index.

    "The results in this study provide initial evidence for the potential of utilizing AI to assess the brain age of a patient," said Nygate. "Our hope is that with continued investigation, research, and clinical studies, a brain age index will one day become a diagnostic biomarker of brain health, much like high blood pressure is for risks of stroke and other cardiovascular disorders."

    The research abstract was published recently in an online supplement of the journal Sleep and will be presented as a poster beginning June 9 during Virtual SLEEP 2021.


    The Importance of Psychophysiological Measures in Traffic Safety

    Suboptimal level of cognitive functioning (e.g., inattention, drowsiness) is a key cause of traffic accidents and poor driving performance. According to Traffic Safety Culture Index, 87.5% of drivers identify distracted driving to be a greater concern today than in past years and 87.9% perceive drowsiness as a threat to their safety (AAA Foundation for Traffic Safety, 2018). Traffic safety researchers are constantly working on methods to improve driving performance by assessing cognitive states, such as drivers' workload, inattention, and fatigue. One way to improve the assessment of covert cognitive states is to adopt a multi-method approach to measure changes in central and peripheral nervous system functioning in order to sense near-real time information about cognitive states of motorists. Such assessments of internal states can also promote the development of Advanced Driver Assistance Systems (ADAS) that can predict and augment risky driving behavior.

    Why Adopt Psychophysiological Measures?

    Cognitive states can be assessed using subjective, behavioral, and physiological measures (Mauss and Robinson, 2009 Strayer et al., 2015 Lohani et al., 2018). Subjective measures can be limiting if the assessment is disruptive to the real-time task (i.e., primary task intrusion, see Oɽonnell and Eggemeier, 1986). More importantly, humans may not always be accurate in making judgements about their cognitive states (Schmidt et al., 2009). Motorists can be inaccurate in making judgments about their internal and cognitive states (such as their attention, workload, and drowsiness levels). For instance, motorists were inaccurate at self-assessments of vigilance (Schmidt et al., 2009) even though objective physiological indicators (e.g., heart rate, EEG, and ERPs) suggested poor vigilance levels at the end of a 3-h drive, participants self-reported improved vigilance instead (Schmidt et al., 2009). Such misjudgments in assessment of cognitive states suggest that objective measures are required to assess and augment human behavior in order to reduce risk for traffic safety. While behavioral measures (such as head movement detection to assess distraction) are also useful, given the intent of this review, we will focus on physiological measures. Accuracy in detecting cognitive workload has been found to significantly increase when physiological data was utilized (Lenneman and Backs, 2009, 2010 Solovey et al., 2014 Borghini et al., 2015 Yang et al., 2016). Some work has also found that physiological measures were sensitive to variations in cognitive load during secondary tasks while behavioral driving measures like steering wheel reversals and velocity (Belyusar et al., 2015) and lane-keeping measures (Lenneman and Backs, 2009) were not. Unlike behavioral measures (e.g., verbal and facial behavior), many physiological measures are not under voluntary control of motorists. Moreover, cognitive states such as mental workload are a multi-faceted and dynamic concept and self-report alone cannot be used to operationalize it, but multiple measures (e.g., performance and physiology) are warranted (de Waard and Lewis-Evans, 2014). Thus, inclusion of physiological data can complement and extend behavioral metrics and improve assessments of motorists' state-level changes in cognition (Brookhuis and de Waard, 1993 Mehler et al., 2012).

    As automation is likely to become more prevalent over time, real-time monitoring behaviors required by motorists may decline as they are less involved in the driving process. This is a critical reason why non-behavior-based metrics will become more relevant to incorporate into our understanding of the motorists' cognitive states. Moreover, distracted motorists of a self-driving vehicle compared to manually driving motorists take longer to gain control of the driving task once automation deactivates (Vogelpohl et al., 2018). Intelligent driving assistance systems should be capable of reliably sensing and assessing distraction and drowsiness levels of motorists to be able to augment safe-driving conditions. Building reliable systems to be able to predict decreased levels of vigilance or dangerous levels of fatigue, drowsiness, or workload could help augment them in a timely manner (Balters et al., 2018).

    Cognition in Dynamic Real-World Driving Contexts

    In general, psychophysiological measures can be used to assess degree of arousal or activation (Mauss and Robinson, 2009). Importantly, multiple psychological constructs can influence variations in psychophysiological measures. For instance, heart rate, skin conductance, and electrical activity of the brain are sensitive to many psychological constructs experienced by motorists, such as workload, drowsiness, stress, etc. In the past years, important contributions have reviewed the literature on specific cognitive states, such as workload (Borghini et al., 2014 Costa et al., 2017), distraction (Matthews et al., 2019), drowsiness (Sahayadhas et al., 2012 Borghini et al., 2014), and stress (Rastgoo et al., 2018) in driving research. These reviews provide an understanding of physiological outcomes that can explain variations in specific constructs based on carefully manipulated and well-controlled designs. Unlike highly controlled lab-based settings, where a single construct (e.g., workload) can be successfully manipulated and its effect on psychophysiological measures examined, real-world settings are more dynamic and complex.

    In a real-world setting, the net resulting cognitive state of a motorist is a combination of variation among several interrelated constructs (e.g., attention allocation, stress, workload, fatigue). Broadly speaking, the net cognitive state of a motorist, composed of variation among these many dimensions, can be classified along an arousal-spectrum ranging from lower-arousal and passive states, to a state of optimal performance, to a hyper-aroused or over-active state. Indeed, this concept is not new Yerkes and Dodson (1908) established strong non-linear relationships between arousal-level and performance, and such relationships have since been well-established across many human performance domains (Hebb, 1955 Broadhurst, 1959 Wekselblatt and Niell, 2015). Although these ideas are not new, there has been a recent resurgence in a formal understanding of arousal-performance relationships, including an expanded understanding of the underlying neuromodulatory systems involved in regulating task engagement and optimal performance (e.g., the adaptive-gain control theory, Aston-Jones and Cohen, 2005). Given the recent increase in understanding of the mapping between physiological indices of arousal and human performance in the lab, such models serve as a clear starting point in delineating the predictive capacity of psychophysiological measures for understanding cognitive states and human performance in the vehicle.

    For instance, low-arousal states relevant to the driving task can be driven by a combination of psychological constructs including low workload, reduced stress, and high drowsiness. On the other hand, an over-aroused state could be due to a combination of high workload and high stress in the presence of low drowsiness. Similarly, other combinations of constructs can also lead to changes in general arousal states as well. Given the likely dynamic interplay among these interrelated constructs in applied settings, the current review focuses on psychophysiological measures that can be utilized to capture motorists' states in real-world driving settings. Indeed, one major applied goal of this work is to be able to accurately capture the dynamic and highly variable changes in arousal that occur in ecologically valid driving settings, a goal that is critical for building accurate predictive models (Yarkoni and Westfall, 2017) of individual motorist's states and future driving performance.

    Specifically, there are two novel contributions of this review. First, instead of focusing on a selective construct and related measures of interest, the goal of this current review is to focus on psychophysiological measures that may have the potential to be adopted in real-world and applied settings to measure state level variations in motorists. The paper provides a broad but selective review of a number of psychophysiological measures that we believe show the greatest promise in their utilization to assess low-arousal vs. over-arousal (passive vs. over-active) states in real-world driving environments. The most commonly used physiology-based measures of cognitive states are considered as potential candidates relevant for driving research. The following physiological measures are reviewed (see section “Psychophysiological Measures to Assess Cognitive States” and Tables 1, 2) in assessing arousal state in real-world driving research: electroencephalography and event-related potentials, optical imaging, heart rate, and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. As reviewed in classical contributions by Cacioppo et al. (Cacioppo and Tassinary, 1990 Cacioppo et al., 2007), inference of unique psychological constructs based on physiological indices (one-to-one relation) is still unresolved and is not the aim of this review (see further discussion in section “Research Applicability in Real-World Settings”). However, we discuss how multiple measures (that are sensitive to several interrelated internal states) may be combined to delineate net resulting changes across multiple inter-related cognitive state-level variations. Second, for each measure, we make the distinction between useful research measures and practical measures for real-world application (see section Research Applicability in Real-World Settings and Table 2). Throughout, we have tried to highlight the practical relevance of measures in the driving context. Although this review focuses primarily on on-road and simulated driving contexts, when relevant, we have also drawn research from related contexts (traffic operators, pilots, or ship navigators) to more thoroughly characterize each measure.

    Table 1. Overview of relationships between arousal state and physiological indices in real-world driving.

    Table 2. Tentative framework for considering the research applicability of these measures in lab and real-world settings.


    Handbook of Sleep Research

    Kimberly A. Cote , . Kevin J. MacDonald , in Handbook of Behavioral Neuroscience , 2019

    3 Peripheral Nervous System

    Physiological measures reflecting activity of the ANS are ideal tools to index cognitive and emotional information processing however, surprisingly few studies have applied measures such as pupillography, facial EMG, electrodermal activity (EDA), and heart rate to measure reactivity to emotional stimuli following sleep loss. Such measures are commonly applied in the emotion field. Like cognitive ERPs, ANS activity changes reliably with presentation of emotional stimuli the response is immediate to the onset of stimuli and sustained over the duration of presentation. Importantly, these ANS systems communicate with brain structures in an interactive way and cooperate to control information processing. One study measured changes in pupil size to negative, neutral, and positive IAPS images in a sleep-deprived and a rested control group ( Franzen, Buysse, Dahl, Thompson, & Siegle, 2009 ). The sleep-deprived group demonstrated higher emotional reactivity to negative images they had greater pupil dilation to negative images than either positive or neutral ones, and they also showed greater anticipatory pupil activity between the warning cue and stimulus onset in the block with negative stimuli presented. In a study that used EMG, Schwarz et al. (2013) examined facial expressions of participants viewing emotional images both after a night of sleep and after 4 h of sleep restriction. Participants were asked to respond to a positive or negative IAPS image, or a happy or angry face, with either a facial expression that matched the tone of the image (congruent condition) or a facial expression that did not match the image (incongruent condition). The facial responses could either be positive (i.e., using zygomatic major muscle by smiling) or negative (i.e., using corrugator major muscle by frowning). Reaction times for facial expression responses were significantly slower after sleep restriction compared with the full night of sleep. The findings support an overall dampening in emotional response as a result of sleep deprivation, but do not provide support for reduced affective inhibitory control, since response time was not delayed for the incongruent more than congruent facial expressions.


    Measurement of attentional reserve and mental effort for cognitive workload assessment under various task demands during dual-task walking

    Previous work focused on cognitive workload assessment suggests EEG spectral content and component amplitudes of the event-related potential (ERP) waveform may index mental effort and attentional reserve, respectively. Although few studies have assessed attentional reserve and mental effort during upper-extremity performance, none have employed a combined approach to measure cognitive workload during locomotion. Therefore, by systematically considering ERPs, spectral content and importantly their combination, this study aimed to examine whether concurrent changes in spectral content and ERPs could collectively serve as an index of cognitive workload during locomotion. Specifically, ERP and EEG biomarkers were assessed as participants performed a cognitive task under two levels of difficulty (easy or hard) and two conditions (seated or walking). Changes in attentional reserve and mental effort appeared to collectively index cognitive workload under varying demands due to changes in task difficulty or performance conditions. This work can inform cognitive workload assessment in patient populations with gait deficiencies for future applications.

    Keywords: Attentional reserve Ecologically valid human performance Event-related potentials Locomotion Mental effort and workload Spectral power.


    Examples of Biosensor Research

    What’s remarkable about such studies are the incredibly fine-scale insights into the human emotion that can be gleaned from the minute subconscious or involuntary phenomena.

    Consider galvanic skin response or GSR. This is a measure of electrodermal activity: the relative conductance of our skin from perspiration. Sweating is an utterly autonomic operation that, in addition to its role in thermoregulation, is a reaction to arousal, from general excitement to flat-out terror. By measuring sweat production via skin conductance, GSR can reveal evidence for a stimulated, agitated state of being that’s beyond a person’s deliberate control – including arousal too subtle to manifest on the self-aware spectrum.

    Electrocardiography (ECG) registers the electrical signature of a heartbeat, revealing intricacies of their rate and variability that, like GSR, can demonstrate physiological, emotional, or psychological arousal.

    Then there’s electroencephalography (EEG) , which tracks brainwaves via scalp-affixed electrodes that measure the electrical pulses produced by mass neuron firings. An EEG readout indicates the moment-by-moment “geography” of brain activity – which cortex is excited when, basically – as well as the brain’s overall state at a given time.

    Eye tracking , meanwhile, quantifies when and where a subject’s gaze lingers, the rhythm of reading, and other optical minutiae, while facial expression analysis looks up-close at the configuration of the face’s musculature for clues to a person’s emotions.

    The information outputted by a single kind of biosensor can be intriguing and useful, but only to a point. For instance, GSR and ECG readings can suggest the condition of arousal, but not its valence, or emotional character. In other words, sweaty palms or a ramped-up heartbeat doesn’t reveal whether we’re dealing with a love-at-first-sight (i.e., a positive stimulus) sort of situation or a figure-looming-out-of-the-shadows (i.e., a negative stimulus) deal.

    Integrate those electrodermal and cardiac data with EEG, facial expression analysis, eye tracking, and other analyses, and you’ve got a much more multifaceted picture. That’s what iMotions is all about.


    3. Study 2: Rate of Chewing, Mood, and Cognition

    This experiment examined if rate of chewing could potentially moderate the effects of gum on attention and mood. Participants were filmed while chewing in order to establish the rate of chewing (pilot data indicated good interrater reliability for scoring of number of chews per minute).

    3.1. Methods

    3.1.1. Participants

    Fifty-six adults (42 females, 14 males mean age = 19.6, SD = 1.4) were recruited. Participants were mostly students from the School of Psychology, Cardiff University.

    3.1.2. Materials

    Chewing Gum. As a moderating effect of flavour was not observed in Study 1, participants were given a choice of flavours for this study, as well as Studies 3 and 4. The following chewing gums were available: Wrigley's spearmint, Wrigley's extra (flavours: spearmint, peppermint, cool breeze, and ice), and Wrigley's airwaves (flavours: cherry, green mint, black mint, menthol, and eucalyptus).

    Selective Attention Tasks [27]

    (i) Focused Attention Task. In this task target letters appeared as upper case A's and B's in the centre of the screen. Participants were required to identify as quickly and as accurately as possible if the target letter was an A or a B, by pressing A or B with the forefinger of the left or right hand, while ignoring any distracters presented elsewhere on the screen. Before each presentation of the target, three warning crosses were displayed for 500 ms. The middle cross was then replaced by the target, and the outer crosses were replaced by distracters (in the case of trials with distracters). The outer crosses were separated from the middle cross by 1.02° or 2.6°. The target letter was accompanied by nothing, letters which were the same as the target, letters which were different from the target, or asterisks.

    Mean reaction time, number of errors, and number of long responses (𾠀 ms) were measured. The threshold for long responses was based on previous research [28]. Breadth of attention was also assessed (the difference in reaction time and accuracy between targets with distracters presented near to the target versus targets with distracters at a further distance from the target). The difference in reaction time between conditions where the target changed from the previous trial and where it remained the same was used as a measure of speed of encoding of new information. Following 10 practice trials, participants completed three blocks of 64 trials. This test lasted approximately 5 minutes.

    (ii) Categoric Search Task. This task was similar to the focused attention task previously outlined, including number of practice and experimental trials. However, in this task participants did not know where the target would appear. At the start of each trial, two crosses appeared 2.04° or 5.2° apart or further apart, located towards the left or right extremes of the display. The target then replaced one of these crosses. For half the trials the target was presented alone and for half it was accompanied by a distracter (a digit from 1 to 7).

    Mean reaction time, accuracy, and long responses (� ms) were recorded, as well as reaction time and accuracy with which new information was encoded. Differences in reaction time and accuracy for trials where the position of the target stimulus and response key were compatible versus where they were incompatible were used as a measure of response organisation. The effect of the stimulus appearing in a different location versus the same location as the previous trial was measured, as well as the effect of not knowing the location of the target. This task also lasted approximately 5 minutes.

    Variable Fore-Period Simple Reaction Time Task [29]. In this task a box was displayed on the screen, followed by a square being presented in the middle of the box. The participant had to press the “Space” button as soon as the square was detected. The period of time elapsed before each appearance of the square varied. This task lasted 3 minutes.

    Repeated Digits Vigilance Task [29]. Three-digit numbers were shown on the screen at the rate of 100 per minute. Each number was normally different from the preceding one, but for 8 occasions per minute the number presented was the same as that presented on the previous trial. Participants had to detect these repetitions and respond by hitting the “Space” button as quickly as possible. The number of hits (correctly detected repetitions), reaction time for hits, and number of false alarms were recorded. The task lasted 5 minutes.

    3.1.3. Design

    Each participant completed both the chewing gum and no-gum control conditions. Similar to previous studies, gum condition was included as a crossover variable to test if any effects of gum would carry over to a no-gum condition (for those who completed the gum condition first).

    3.1.4. Procedure

    Following informed consent and a familiarisation with the mood and attention tasks, participants completed the mood and attention tasks twice. Participants were instructed to chew two pieces of gum constantly at their own pace during one of these testing sessions and not to chew during the other testing session. Each set of the mood and attention tasks took approximately 25 minutes, and participants completed the second condition immediately after the first. Participants selected a packet of gum just before the chewing condition. They were filmed throughout the chewing session. In order to assess the rate of chewing during each task, notes were taken of when each computerised task began and ended. This timing of the tasks was matched to the footage of the participant completing the task, so that the rate of chewing during each specific task could be calculated. Participants indicated how hard they had been chewing on a scale of 1 (as softly as possible) to 11 (as hard as possible) immediately after the gum condition.

    3.1.5. Analysis

    Analysis of Footage. The footage was divided into the mood tasks, blocks for the selective attention tasks, and minutes for the simple reaction time task and repeated digits vigilance task, as well as gaps between tasks. Each piece of footage was rated twice, and the intraclass correlation (single measures) was .996, suggesting excellent test/retest reliability for the video rating. The mean of the two scores for each section of the footage was used as the final result.

    Statistical Analysis. Mixed ANOVA was used to assess the effect of chewing gum (repeated measures: gum versus no-gum control), order of gum condition (independent measures: gum condition first versus gum condition second), and time-on-task. Time-on-task was entered as a repeated measures variable in the analysis of variables for which time-on-task data was available (i.e., alertness, hedonic tone, and anxiety, categoric search reaction time, focused attention reaction time, simple reaction time, repeated digits hit, false alarms, and reaction time). Time-on-task was defined as pre- versus posttest for reported mood (i.e., before and after the attention tasks) and blocks or minutes for cognitive tasks.

    Multiple regressions with forced entry were used to test if the predictors were associated with changes in attention and mood between gum and no-gum conditions. The predictors were rate of chewing, speed of chewing and intensity (how hard gum was chewed), and prior amount of chewing (total count of times chewed this did not apply for pretest mood, when chewing had just begun).

    3.2. Results

    3.2.1. Chewing Gum and Mood

    There was a significant main effect of time and chewing gum on alertness alertness fell between pre- and posttest assessments, F(1,54) = 57.13, P < .001, and partial η 2 = .51, and chewing gum was associated with higher alertness, F(1,54) = 24.62, P < .001, and partial η 2 = .31. There was also an interaction between gum condition and time, F(1,54) = 8.47, P = .005, and partial η 2 = .14 alertness was higher in the gum condition posttest. There was a significant interaction between gum and order of gum condition, F(1,54) = 11.5, P = .001, and partial η 2 = .18. Alertness was improved to a greater extent by chewing gum when it came first (see Figure 2(a) ).

    Chewing gum, pre- and posttest mood (Study 2). (a) Alertness. (b) Hedonic tone. (c) Anxiety. Error bars indicate standard error of the mean.

    Hedonic tone fell significantly between pre- and posttest, F(1,54) = 62.45, P < .001, and partial η 2 = .54, and hedonic tone was significantly higher in the gum condition, F(1,54) = 6.74, P = .01, and partial η 2 = .11, but there was not a significant interaction between gum and time, F(1,54) = 2.32, P = .13, and partial η 2 = .04. There was a significant interaction between gum and order of gum condition, F(1,54) = 14.43, P < .001, and partial η 2 = .21. Hedonic tone was improved to a greater extent by chewing gum when it came first (see Figure 2(b) ).

    There was no significant effect of time on anxiety, F(1,54) = .09, P = .77, and partial η 2 = .002, nor was there a significant main effect of chewing gum, F(1,54) = 2.75, P = .1, and partial η 2 = .05. There was no interaction between gum and time, F(1,54) = 1.4, P = .24, and partial η 2 = .03, and there was no interaction between gum and order of gum condition, F(1,54) = .76, P = .39, and partial η 2 = .01 (see Figure 2(c) ).

    3.2.2. Chewing Gum, Time-on-Task, and Cognition

    Chewing gum had a significant main effect on categoric search speed of encoding. There was a significant interaction between gum condition and time-on-task for repeated digits reaction time, F(4,216) = 4.22, P = .003, and partial η 2 = .07 (see Figure 3(a) ). Chewing gum lengthened reaction time during the fourth minute, F(1,54) = 13.91, P < .001, and partial η 2 = .21, indicating a negative effect of chewing gum on performance at this time. There was also a main effect of time, F(4,216) = 20.53, P < .001, and partial η 2 = .28, with reaction time lengthening over time, but there was not a main effect of chewing gum, F(1,54) = 1.04, P = .31, and partial η 2 = .02. There was no significant interaction between gum and order of gum condition, F(1,54) = .04, P = .85, and partial η 2 = .001.

    Time-on-task trends in chewing gum effects on (a) vigilance reaction time, (b) vigilance false alarms, (c) vigilance hits, and (d) categoric search reaction time (Study 2). Error bars represent standard error of the mean.

    There was a gum by time-on-task interaction for false alarms, F(4,216) = 2.25, P = .048, and partial η 2 = .05 chewing gum reduced the number of false alarms during the final minute of the task, F(1,54) = 13.69, P = .001, and partial η 2 = .2 (see Figure 3(b) ), indicating a positive effect on performance during the final minute. There was a significant main effect of time-on-task, F(4,216) = 9.07, P < .001, and partial η 2 = .14, with the number of false alarms falling during later minutes. There was, however, no main effect of chewing gum on false alarms, F(1,55) = 1.52, P = .22, and partial η 2 = .03. There was an interaction between gum and order of gum condition F(1,54) = 6.7, P = .01, and partial η 2 = .11. False alarms were heightened by gum when gum came before the no-gum control.

    For vigilance hits, there was no significant main effect of chewing gum, F(1,54) = .91, P = .35, and partial η 2 = .02 or interaction between gum and time-on-task, F(4,216) = .28, P = .89, and partial η 2 = .005 (see Figure 3(c) ). Again, there was a main effect of time-on-task, with percent hits falling during later minutes, F(4,216) = 31.27, P < .001, and partial η 2 = .37. There was an interaction between gum and order of gum condition, F(1,54) = 16.5, P < .001, and partial η 2 = .23. Hits were enhanced by chewing gum when it came before the no-gum control.

    There was a gum × time interaction for categoric search reaction time, with gum shortening reaction time, but only during the first block, F(2,108) = 5.76, P = .004, and partial η 2 = .1 (see Figure 3(d) ). This was in the context of a strong main effect of time, F(2,108) = 5.92, P = .004, and partial η 2 = .1, with reaction time significantly shortened during the second block, although there was not a main effect of gum, F(1,55) = .01, P = .95, and partial η 2 < .001.

    Gum had a significant main effect on focused attention speed of encoding, with slower encoding of information in the gum condition. There was a significant interaction between chewing gum and order of gum condition for focused attention mean reaction time, errors, speed of encoding, and simple reaction time. For simple reaction time, performance was improved by gum when it came after the control condition, while the opposite was true for focused attention. Results are summarised in Table 1 .

    Table 1

    Chewing gum, time-on-task, and attention.

    GumNo gumResults
    Focused attention
     Mean reaction time (ms)Block 1: M = 396.14 (5.89)
    Block 2: M = 395.2 (5.38)
    Block 3: M = 400.18 (5.31)
    Block 1: M = 391.97 (5.85)
    Block 2: M = 397.55 (5.26)
    Block 3: M = 401.39 (5.23)
    Gum: F(1, 54) = .01, P = .94, η p 2 < .001
    Time ** : F(2, 108) = 5.82, P = .004, η p 2 = .1
    Gum × time: F(1.802, 99.12) = 1.44, P = .24, η p 2 = .03
    Gum × order: F(1, 54) = 3.89, P = .054, η p 2 = .06
     Total errors10.18 (1.12)10.15 (1.04)Gum: F(1, 54) = .003, P = .96, η p 2 < .0001
    Gum × order †† : F(1, 54) = 5.37, P = .02, η p 2 = .09
     Long responses.27 (.09).45 (.21)Gum: F(1, 54) = .91, P = .35, η p 2 = .02
    Gum × order: F(1, 54) = 2.94, P = .09, η p 2 < .05
    𠀻readth of attention 1 18.99 (4.71)25.83 (5.39)Gum: F(1, 54) = 1.03, P = .32, η p 2 = .02
    Gum × order: F(1, 54) = .78, P = .38, η p 2 = .01
     Speed of encoding 2 25.47 (2.77)24.44 (2.61)Gum: F(1, 54) = .21, P = .64, η p 2 = .004
    Gum × order †† : F(1, 54) = 12.15, P < .001, η p 2 = .18
    Categoric search
     Total errors11.16 (.8)11.84 (.93)Gum: F(1, 54) = 1.45, P = .23, η p 2 = .03
    Gum × order: F(1, 54) = 2.51, P = .12, η p 2 = .04
     Long responses1.66 (.3)1.87 (.36)Gum: F(1, 54) = .45, P = .5  η p 2 = .008
    Gum × order: F(1, 54) = .83, P = .37, η p 2 = .02
     Response organisation 3 27.51 (2.54)26.88 (2.53)Gum: F(1, 54) = .05, P = .083  η p 2 = .001
    Gum × order: F(1, 54) = .07, P = .79, η p 2 = .001
     Speed of encoding17.69 (2.73)4.77 (2.54)Gum †† : F(1, 54) = 14.3, P < .001, η p 2 = .21
    Gum × order: F(1, 54) = .04, P = .84, η p 2 = .001
     Spatial uncertainty 4 105.92 (4.83)116.26 (5.34)Gum: F(1, 54) = 3.28, P = .08, η p 2 = .06
    Gum × order: F(1, 54) = .043, P = .52, η p 2 = .008
     Place repetition 5 15.62 (2.56)14 (2.92)Gum: F(1, 54) = .35, P = .56, η p 2 = .006
    Gum × order: F(1, 54) = .72, P = .4, η p 2 = .01
    Simple reaction timeBlock 1: M = 327.98 (6.39)
    Block 2: M = 336.58 (7.14)
    Block 3: M = 339.29 (6.68)
    Block 1: M = 314.43 (7.32)
    Block 2: M = 331.02 (7.44)
    Block 3: M = 341.09 (8)
    Gum: F(1, 54) = 2.04, P = .16, η p 2 = .04
    Time †† : F(2, 108) = 16.09, P < .001, η p 2 = .23
    Gum × time: F(2, 108) = 2.07, P = .13, η p 2 = .04
    Gum × order *** : F(1, 54) = 22.08, P = .001, η p 2 = .29

    Standard errors of the means are in parentheses. 1 Higher score = broader focus of attention. 2 Higher score = slower encoding of information. 3 Higher score = poorer organisation. 4 Higher score = greater uncertainty. 5 Higher score = greater effect of place repetition. ** indicates P < .01, †† indicates P < .001, and *** indicates P = .001. Gum × gum order refers to interaction between gum condition and order in which gum condition appeared.

    3.2.3.  Rate of Chewing, Mood, and Cognition

    A faster rate of chewing was associated with lengthened simple reaction time (beta = .42, P = .04). Harder chewing was associated with faster encoding of new information on the categoric search task (beta = −.37, P = .02). Greater prior chewing was associated with a higher level of focused attention errors (beta = .32, P = .04). Rate of chewing, force of chewing, and prior chewing did not moderate mood or performance on the repeated digits vigilance task. Results are summarised in Table 2 .

    Table 2

    Level of chewing and its effect on mood and cognition.

    Unstandardised BSE BBetaSignificance R 2 Adjusted R 2
    Mood
     Pretest alertness.03−.01
     𠀼onstant26.4518.14.15
      Rate of chewing−.57.62−.13.36
      Intensity𢄡.042.65−.06.7
     Posttest alertness.06.01
     𠀼onstant20.7413.08.12
      Rate of chewing−.9.6−.32.14
      Prior chewing.01.02.12.61
      Intensity2.192.53.13.39
     Pretest hedonic tone.02−.02
     𠀼onstant.569.77.95
      Rate of chewing−.25.33−.11.45
      Intensity1.441.43.15.32
     Posttest hedonic tone.04−.02
     𠀼onstant11.677.37.12
      Rate of chewing−.41.34−.26.23
      Prior chewing.004.01.11.47
      Intensity−.331.43−.04.82
     Pretest anxiety.05.01
     𠀼onstant𢄩.62−.01.11
      Rate of chewing−.006.2−.01.98
      Intensity1.31.85.22.13
     Posttest anxiety.02−.04
     𠀼onstant.24.07.96
      Rate of chewing−.09.19−.11.62
      Prior chewing−.001.005−.04.86
      Intensity.29.79.06.71
    Focused attention
     Mean reaction time (ms).02−.04
     𠀼onstant.88.93.93
      Rate of chewing.25.29.14.39
      Prior chewing−.003.01−.04.81
      Intensity𢄡.011.78−.09.57
     Total errors.1.05
     𠀼onstant𢄡.62.09.45
      Rate of chewing.02.07.04.81
      Prior chewing * .01.003.32.04
      Intensity−.15.42−.05.72
     Number of long responses.02−.03
     𠀼onstant.1.58.86
      Rate of chewing.01.02.04.81
      Prior chewing.001.001.12.46
      Intensity−.11.12−.15.34
    𠀻readth of attention.11.06
     𠀼onstant�.1519.44.02
      Rate of chewing1.01.63.27.09
      Prior chewing−.02.03−.12.46
      Intensity4.33.87.17.27
     Speed of encoding.03−.03
     𠀼onstant𢄦.537.38.38
      Rate of chewing.13.24.09.58
      Prior chewing.003.01.04.79
      Intensity.691.47.07.64
    Categoric search
     Mean reaction time.01−.05
     𠀼onstant𢄡.8612.14.88
      Rate of chewing.01.44.002.99
      Prior chewing.004.02.04.83
      Intensity𢄡.492.42−.1.54
     Total errors.03−.03
     𠀼onstant𢄢.121.72.23
      Rate of chewing−.01.06−.04.83
      Prior chewing−.001.002−.07.72
      Intensity.42.34.19.23
     Long responses.11.06
     𠀼onstant1.84.88.04
      Rate of chewing−.04.03−.22.2
      Prior chewing<.001.001.06.73
      Intensity−.26.18−.23.14
     Response organization.01−.05
     𠀼onstant1.438.79.87
      Rate of chewing.21.32.12.52
      Prior chewing−.003.01−.05.8
      Intensity−.71.75−.06.69
     Speed of encoding.11.05
     𠀼onstant29.759.82.004
      Rate of chewing−.03.36−.02.93
      Prior chewing.02.01.21.24
      Intensity * 𢄤.741.96−.37.02
     Spatial uncertainty.05−.004
     𠀼onstant�.9416.94.35
      Rate of chewing−.88.61−.25.16
      Prior chewing.01.02.09.63
      Intensity3.563.38.16.3
     Place repetition.02−.03
     𠀼onstant𢄢.418.23.77
      Rate of chewing−.1.3−.06.73
      Prior chewing.01.01.18.35
      Intensity.091.64.009.95
    Simple reaction time.08.03
    𠀼onstant�.0915.47.18
     Rate of chewing * .84.49.42.04
     Prior chewing.007.02.05.7
     Intensity.742.69.04.79
    Repeated digits vigilance
     Percent hits.09.04
     𠀼onstant1.411.79.44
      Rate of chewing−.11.05−.32.06
      Prior chewing<.001.002−.03.86
      Intensity.27.33.12.42
    �lse alarms.04−.01
     𠀼onstant−.735.31.89
      Rate of chewing.17.16.17.31
      Prior chewing.002.006.06.69
      Intensity𢄡.11.98−.18.26
     Reaction time.06.007
     𠀼onstant�.4924.72.48
      Rate of chewing𢄡.2.75−.27.12
      Prior chewing.02.03.12.41
      Intensity6.894.56.23.14

    3.3. Study 2 Discussion

    Consistent with previous research as well as Study 1, chewing gum was associated with higher alertness. This might be expected to improve sustained attention performance, although the results indicated lengthened reaction time as well as fewer false alarms as the vigilance task continued, suggesting negative and positive effects on sustained attention performance. Vigilance performance was not moderated by rate of chewing however, although faster chewing was associated with lengthened simple reaction time, harder chewing was associated with faster encoding of new information on the categoric search task, and prior chewing was associated with more errors on the focused attention task. It thus may be useful for researchers to take some measure of how hard and fast participants are chewing in future research.

    In order to further examine the effects of chewing gum on performance and reported feelings in a more naturalistic setting, the next studies examined chewing gum over the course of a working day.


    Handbook of Sleep Research

    Kimberly A. Cote , . Kevin J. MacDonald , in Handbook of Behavioral Neuroscience , 2019

    3 Peripheral Nervous System

    Physiological measures reflecting activity of the ANS are ideal tools to index cognitive and emotional information processing however, surprisingly few studies have applied measures such as pupillography, facial EMG, electrodermal activity (EDA), and heart rate to measure reactivity to emotional stimuli following sleep loss. Such measures are commonly applied in the emotion field. Like cognitive ERPs, ANS activity changes reliably with presentation of emotional stimuli the response is immediate to the onset of stimuli and sustained over the duration of presentation. Importantly, these ANS systems communicate with brain structures in an interactive way and cooperate to control information processing. One study measured changes in pupil size to negative, neutral, and positive IAPS images in a sleep-deprived and a rested control group ( Franzen, Buysse, Dahl, Thompson, & Siegle, 2009 ). The sleep-deprived group demonstrated higher emotional reactivity to negative images they had greater pupil dilation to negative images than either positive or neutral ones, and they also showed greater anticipatory pupil activity between the warning cue and stimulus onset in the block with negative stimuli presented. In a study that used EMG, Schwarz et al. (2013) examined facial expressions of participants viewing emotional images both after a night of sleep and after 4 h of sleep restriction. Participants were asked to respond to a positive or negative IAPS image, or a happy or angry face, with either a facial expression that matched the tone of the image (congruent condition) or a facial expression that did not match the image (incongruent condition). The facial responses could either be positive (i.e., using zygomatic major muscle by smiling) or negative (i.e., using corrugator major muscle by frowning). Reaction times for facial expression responses were significantly slower after sleep restriction compared with the full night of sleep. The findings support an overall dampening in emotional response as a result of sleep deprivation, but do not provide support for reduced affective inhibitory control, since response time was not delayed for the incongruent more than congruent facial expressions.


    Measurement of attentional reserve and mental effort for cognitive workload assessment under various task demands during dual-task walking

    Previous work focused on cognitive workload assessment suggests EEG spectral content and component amplitudes of the event-related potential (ERP) waveform may index mental effort and attentional reserve, respectively. Although few studies have assessed attentional reserve and mental effort during upper-extremity performance, none have employed a combined approach to measure cognitive workload during locomotion. Therefore, by systematically considering ERPs, spectral content and importantly their combination, this study aimed to examine whether concurrent changes in spectral content and ERPs could collectively serve as an index of cognitive workload during locomotion. Specifically, ERP and EEG biomarkers were assessed as participants performed a cognitive task under two levels of difficulty (easy or hard) and two conditions (seated or walking). Changes in attentional reserve and mental effort appeared to collectively index cognitive workload under varying demands due to changes in task difficulty or performance conditions. This work can inform cognitive workload assessment in patient populations with gait deficiencies for future applications.

    Keywords: Attentional reserve Ecologically valid human performance Event-related potentials Locomotion Mental effort and workload Spectral power.


    The Importance of Psychophysiological Measures in Traffic Safety

    Suboptimal level of cognitive functioning (e.g., inattention, drowsiness) is a key cause of traffic accidents and poor driving performance. According to Traffic Safety Culture Index, 87.5% of drivers identify distracted driving to be a greater concern today than in past years and 87.9% perceive drowsiness as a threat to their safety (AAA Foundation for Traffic Safety, 2018). Traffic safety researchers are constantly working on methods to improve driving performance by assessing cognitive states, such as drivers' workload, inattention, and fatigue. One way to improve the assessment of covert cognitive states is to adopt a multi-method approach to measure changes in central and peripheral nervous system functioning in order to sense near-real time information about cognitive states of motorists. Such assessments of internal states can also promote the development of Advanced Driver Assistance Systems (ADAS) that can predict and augment risky driving behavior.

    Why Adopt Psychophysiological Measures?

    Cognitive states can be assessed using subjective, behavioral, and physiological measures (Mauss and Robinson, 2009 Strayer et al., 2015 Lohani et al., 2018). Subjective measures can be limiting if the assessment is disruptive to the real-time task (i.e., primary task intrusion, see Oɽonnell and Eggemeier, 1986). More importantly, humans may not always be accurate in making judgements about their cognitive states (Schmidt et al., 2009). Motorists can be inaccurate in making judgments about their internal and cognitive states (such as their attention, workload, and drowsiness levels). For instance, motorists were inaccurate at self-assessments of vigilance (Schmidt et al., 2009) even though objective physiological indicators (e.g., heart rate, EEG, and ERPs) suggested poor vigilance levels at the end of a 3-h drive, participants self-reported improved vigilance instead (Schmidt et al., 2009). Such misjudgments in assessment of cognitive states suggest that objective measures are required to assess and augment human behavior in order to reduce risk for traffic safety. While behavioral measures (such as head movement detection to assess distraction) are also useful, given the intent of this review, we will focus on physiological measures. Accuracy in detecting cognitive workload has been found to significantly increase when physiological data was utilized (Lenneman and Backs, 2009, 2010 Solovey et al., 2014 Borghini et al., 2015 Yang et al., 2016). Some work has also found that physiological measures were sensitive to variations in cognitive load during secondary tasks while behavioral driving measures like steering wheel reversals and velocity (Belyusar et al., 2015) and lane-keeping measures (Lenneman and Backs, 2009) were not. Unlike behavioral measures (e.g., verbal and facial behavior), many physiological measures are not under voluntary control of motorists. Moreover, cognitive states such as mental workload are a multi-faceted and dynamic concept and self-report alone cannot be used to operationalize it, but multiple measures (e.g., performance and physiology) are warranted (de Waard and Lewis-Evans, 2014). Thus, inclusion of physiological data can complement and extend behavioral metrics and improve assessments of motorists' state-level changes in cognition (Brookhuis and de Waard, 1993 Mehler et al., 2012).

    As automation is likely to become more prevalent over time, real-time monitoring behaviors required by motorists may decline as they are less involved in the driving process. This is a critical reason why non-behavior-based metrics will become more relevant to incorporate into our understanding of the motorists' cognitive states. Moreover, distracted motorists of a self-driving vehicle compared to manually driving motorists take longer to gain control of the driving task once automation deactivates (Vogelpohl et al., 2018). Intelligent driving assistance systems should be capable of reliably sensing and assessing distraction and drowsiness levels of motorists to be able to augment safe-driving conditions. Building reliable systems to be able to predict decreased levels of vigilance or dangerous levels of fatigue, drowsiness, or workload could help augment them in a timely manner (Balters et al., 2018).

    Cognition in Dynamic Real-World Driving Contexts

    In general, psychophysiological measures can be used to assess degree of arousal or activation (Mauss and Robinson, 2009). Importantly, multiple psychological constructs can influence variations in psychophysiological measures. For instance, heart rate, skin conductance, and electrical activity of the brain are sensitive to many psychological constructs experienced by motorists, such as workload, drowsiness, stress, etc. In the past years, important contributions have reviewed the literature on specific cognitive states, such as workload (Borghini et al., 2014 Costa et al., 2017), distraction (Matthews et al., 2019), drowsiness (Sahayadhas et al., 2012 Borghini et al., 2014), and stress (Rastgoo et al., 2018) in driving research. These reviews provide an understanding of physiological outcomes that can explain variations in specific constructs based on carefully manipulated and well-controlled designs. Unlike highly controlled lab-based settings, where a single construct (e.g., workload) can be successfully manipulated and its effect on psychophysiological measures examined, real-world settings are more dynamic and complex.

    In a real-world setting, the net resulting cognitive state of a motorist is a combination of variation among several interrelated constructs (e.g., attention allocation, stress, workload, fatigue). Broadly speaking, the net cognitive state of a motorist, composed of variation among these many dimensions, can be classified along an arousal-spectrum ranging from lower-arousal and passive states, to a state of optimal performance, to a hyper-aroused or over-active state. Indeed, this concept is not new Yerkes and Dodson (1908) established strong non-linear relationships between arousal-level and performance, and such relationships have since been well-established across many human performance domains (Hebb, 1955 Broadhurst, 1959 Wekselblatt and Niell, 2015). Although these ideas are not new, there has been a recent resurgence in a formal understanding of arousal-performance relationships, including an expanded understanding of the underlying neuromodulatory systems involved in regulating task engagement and optimal performance (e.g., the adaptive-gain control theory, Aston-Jones and Cohen, 2005). Given the recent increase in understanding of the mapping between physiological indices of arousal and human performance in the lab, such models serve as a clear starting point in delineating the predictive capacity of psychophysiological measures for understanding cognitive states and human performance in the vehicle.

    For instance, low-arousal states relevant to the driving task can be driven by a combination of psychological constructs including low workload, reduced stress, and high drowsiness. On the other hand, an over-aroused state could be due to a combination of high workload and high stress in the presence of low drowsiness. Similarly, other combinations of constructs can also lead to changes in general arousal states as well. Given the likely dynamic interplay among these interrelated constructs in applied settings, the current review focuses on psychophysiological measures that can be utilized to capture motorists' states in real-world driving settings. Indeed, one major applied goal of this work is to be able to accurately capture the dynamic and highly variable changes in arousal that occur in ecologically valid driving settings, a goal that is critical for building accurate predictive models (Yarkoni and Westfall, 2017) of individual motorist's states and future driving performance.

    Specifically, there are two novel contributions of this review. First, instead of focusing on a selective construct and related measures of interest, the goal of this current review is to focus on psychophysiological measures that may have the potential to be adopted in real-world and applied settings to measure state level variations in motorists. The paper provides a broad but selective review of a number of psychophysiological measures that we believe show the greatest promise in their utilization to assess low-arousal vs. over-arousal (passive vs. over-active) states in real-world driving environments. The most commonly used physiology-based measures of cognitive states are considered as potential candidates relevant for driving research. The following physiological measures are reviewed (see section “Psychophysiological Measures to Assess Cognitive States” and Tables 1, 2) in assessing arousal state in real-world driving research: electroencephalography and event-related potentials, optical imaging, heart rate, and heart rate variability, blood pressure, skin conductance, electromyography, thermal imaging, and pupillometry. As reviewed in classical contributions by Cacioppo et al. (Cacioppo and Tassinary, 1990 Cacioppo et al., 2007), inference of unique psychological constructs based on physiological indices (one-to-one relation) is still unresolved and is not the aim of this review (see further discussion in section “Research Applicability in Real-World Settings”). However, we discuss how multiple measures (that are sensitive to several interrelated internal states) may be combined to delineate net resulting changes across multiple inter-related cognitive state-level variations. Second, for each measure, we make the distinction between useful research measures and practical measures for real-world application (see section Research Applicability in Real-World Settings and Table 2). Throughout, we have tried to highlight the practical relevance of measures in the driving context. Although this review focuses primarily on on-road and simulated driving contexts, when relevant, we have also drawn research from related contexts (traffic operators, pilots, or ship navigators) to more thoroughly characterize each measure.

    Table 1. Overview of relationships between arousal state and physiological indices in real-world driving.

    Table 2. Tentative framework for considering the research applicability of these measures in lab and real-world settings.


    Tobii Pro Studio integration

    Next to physiology, researchers often also collect eye tracking data. Communication between Tobii Pro Studio and The Observer XT now also makes use of the Noldus communication protocol N-Linx. This simplifies start and stop of the eye tracking recording and synchronization with the Event Log in The Observer XT.


    Kill switch: The evolution of road rage in an increasingly AI car culture

    Julie Carpenter , in Living with Robots , 2020

    Killer app

    The idea of an AI kill switch for human control of a car is complicated in its ethical considerations. While it is possible that an autonomous car may be kitted with ways of gauging a user's mental and emotional state via various biofeedback (and even mood predictive) sensors, at best this is a technical intervention, not a solution to the actual issues of human behaviors associated with road rage.

    Like some other technologies, autonomous cars are both mediums of social relations and produce social relations. While territoriality behavior research has been used as a lens for traditional automobiles, it is likely that new concepts for car interiors and the roles of the users will change significantly as cars turn to more autonomous processes, relieving the driver of many of the cognitive and social responsibilities of driving. Yet the changing role of the user in the driving process does not remove the human factors altogether, meaning there is still room for disruption in transportation situations, and these will sometimes elicit user frustration, anger, or other negative stress-related feelings and emotions that could further translate to aggressive behaviors.

    Szlemko et al. (2008) have provided research about user perceptions of the nonautonomous car as primary territory, predicting the supporting users’ territorial behaviors and the link to attachment theory as a closely related concept to user perceptions about their car as a defendable place.

    While these premises have not been tested in longitudinal research situations with autonomous cars to date, this chapter has presented an initial attempt at building a framework for understanding these concepts of territoriality, emotional attachment, and user experience in relation to the new iterations of car roles and designs, although certainly more work is required. Additionally, the possibility of new ways of manifesting user aggressive behaviors raises design questions about the use of kill switches, whether for the user or the car's AI. Major ethical and practical challenges are at stake when discussing an AI kill switch for users who would require the car's AI to have a flawless understanding of human experience and the ability to apply rules and actions in this vein when discussing the AI-triggered kill switch for erratic user driving.

    Moreover, including a mandatory human-controlled kill switch for the AI in a consumer vehicle depends upon the human will to resist obstructing the AI controlling the driving, and controlling aggressive driving behaviors. While these possible scenarios raise a multitude of new questions and issues, it is a set of potentially common circumstances worthy of further consideration.


    Behavioral neuroscience as a scientific discipline emerged from a variety of scientific and philosophical traditions in the 18th and 19th centuries. In philosophy, people like René Descartes proposed physical models to explain animal as well as human behavior. Descartes suggested that the pineal gland, a midline unpaired structure in the brain of many organisms, was the point of contact between mind and body. Descartes also elaborated on a theory in which the pneumatics of bodily fluids could explain reflexes and other motor behavior. This theory was inspired by moving statues in a garden in Paris. [4] Electrical stimulation and lesions can also show the affect of motor behavior of humans. They can record the electrical activity of actions, hormones, chemicals and effects drugs have in the body system all which affect ones daily behavior.

    Other philosophers also helped give birth to psychology. One of the earliest textbooks in the new field, The Principles of Psychology by William James, argues that the scientific study of psychology should be grounded in an understanding of biology.

    The emergence of psychology and behavioral neuroscience as legitimate sciences can be traced from the emergence of physiology from anatomy, particularly neuroanatomy. Physiologists conducted experiments on living organisms, a practice that was distrusted by the dominant anatomists of the 18th and 19th centuries. [5] The influential work of Claude Bernard, Charles Bell, and William Harvey helped to convince the scientific community that reliable data could be obtained from living subjects.

    Even before the 18th and 19th century, behavioral neuroscience was beginning to take form as far back as 1700 B.C. [6] The question that seems to continually arise is: what is the connection between the mind and body? The debate is formally referred to as the mind-body problem. There are two major schools of thought that attempt to resolve the mind–body problem monism and dualism. [4] Plato and Aristotle are two of several philosophers who participated in this debate. Plato believed that the brain was where all mental thought and processes happened. [6] In contrast, Aristotle believed the brain served the purpose of cooling down the emotions derived from the heart. [4] The mind-body problem was a stepping stone toward attempting to understand the connection between the mind and body.

    Another debate arose about localization of function or functional specialization versus equipotentiality which played a significant role in the development in behavioral neuroscience. As a result of localization of function research, many famous people found within psychology have come to various different conclusions. Wilder Penfield was able to develop a map of the cerebral cortex through studying epileptic patients along with Rassmussen. [4] Research on localization of function has led behavioral neuroscientists to a better understanding of which parts of the brain control behavior. This is best exemplified through the case study of Phineas Gage.

    The term "psychobiology" has been used in a variety of contexts, emphasizing the importance of biology, which is the discipline that studies organic, neural and cellular modifications in behavior, plasticity in neuroscience, and biological diseases in all aspects, in addition, biology focuses and analyzes behavior and all the subjects it is concerned about, from a scientific point of view. In this context, psychology helps as a complementary, but important discipline in the neurobiological sciences. The role of psychology in this questions is that of a social tool that backs up the main or strongest biological science. The term "psychobiology" was first used in its modern sense by Knight Dunlap in his book An Outline of Psychobiology (1914). [7] Dunlap also was the founder and editor-in-chief of the journal Psychobiology. In the announcement of that journal, Dunlap writes that the journal will publish research ". bearing on the interconnection of mental and physiological functions", which describes the field of behavioral neuroscience even in its modern sense. [7]

    In many cases, humans may serve as experimental subjects in behavioral neuroscience experiments however, a great deal of the experimental literature in behavioral neuroscience comes from the study of non-human species, most frequently rats, mice, and monkeys. As a result, a critical assumption in behavioral neuroscience is that organisms share biological and behavioral similarities, enough to permit extrapolations across species. This allies behavioral neuroscience closely with comparative psychology, evolutionary psychology, evolutionary biology, and neurobiology. Behavioral neuroscience also has paradigmatic and methodological similarities to neuropsychology, which relies heavily on the study of the behavior of humans with nervous system dysfunction (i.e., a non-experimentally based biological manipulation).

    Synonyms for behavioral neuroscience include biopsychology, biological psychology, and psychobiology. [8] Physiological psychology is a subfield of behavioral neuroscience, with an appropriately narrower definition.

    The distinguishing characteristic of a behavioral neuroscience experiment is that either the independent variable of the experiment is biological, or some dependent variable is biological. In other words, the nervous system of the organism under study is permanently or temporarily altered, or some aspect of the nervous system is measured (usually to be related to a behavioral variable).

    Disabling or decreasing neural function Edit

      – A classic method in which a brain-region of interest is naturally or intentionally destroyed to observe any resulting changes such as degraded or enhanced performance on some behavioral measure. Lesions can be placed with relatively high accuracy "Thanks to a variety of brain 'atlases' which provide a map of brain regions in 3-dimensional "stereotactic coordinates.
    • Surgical lesions – Neural tissue is destroyed by removing it surgically.
    • Electrolytic lesions – Neural tissue is destroyed through the application of electrical shock trauma.
    • Chemical lesions – Neural tissue is destroyed by the infusion of a neurotoxin.
    • Temporary lesions – Neural tissue is temporarily disabled by cooling or by the use of anesthetics such as tetrodotoxin.

    Enhancing neural function Edit

    • Electrical stimulation – A classic method in which neural activity is enhanced by application of a small electric current (too small to cause significant cell death).
    • Psychopharmacological manipulations – A chemical receptor agonist facilitates neural activity by enhancing or replacing endogenous neurotransmitters. Agonists can be delivered systemically (such as by intravenous injection) or locally (intracerebrally) during a surgical procedure.
    • Synthetic Ligand Injection – Likewise, Gq-DREADDs can be used to modulate cellular function by innervation of brain regions such as Hippocampus. This innervation results in the amplification of γ-rhythms, which increases motor activity. [15] – In some cases (for example, studies of motor cortex), this technique can be analyzed as having a stimulatory effect (rather than as a functional lesion). excitation – A light activated excitatory protein is expressed in select cells. Channelrhodopsin-2 (ChR2), a light activated cation channel, was the first bacterial opsin shown to excite neurons in response to light, [16] though a number of new excitatory optogenetic tools have now been generated by improving and imparting novel properties to ChR2 [17]

    Measuring neural activity Edit

      Optical techniques – Optical methods for recording neuronal activity rely on methods that modify the optical properties of neurons in response to the cellular events associated with action potentials or neurotransmitter release.
        (VSDs) were among the earliest method for optically detecting neuronal activity. VSDs commonly changed their fluorescent properties in response to a voltage change across the neuron's membrane, rendering membrane sub-threshold and supra-threshold (action potentials) electrical activity detectable. [18] Genetically encoded voltage sensitive fluorescent proteins have also been developed. [19] relies on dyes [20] or genetically encoded proteins [21] that fluoresce upon binding to the calcium that is transiently present during an action potential. is a technique that relies on a fusion protein that combines a synaptic vesicle membrane protein and a pH sensitive fluorescent protein. Upon synaptic vesicle release, the chimeric protein is exposed to the higher pH of the synaptic cleft, causing a measurable change in fluorescence. [22]

      Genetic techniques Edit

        – The influence of a gene in some behavior can be statistically inferred by studying inbred strains of some species, most commonly mice. The recent sequencing of the genome of many species, most notably mice, has facilitated this technique. – Organisms, often mice, may be bred selectively among inbred strains to create a recombinant congenic strain. This might be done to isolate an experimentally interesting stretch of DNA derived from one strain on the background genome of another strain to allow stronger inferences about the role of that stretch of DNA. – The genome may also be experimentally-manipulated for example, knockout mice can be engineered to lack a particular gene, or a gene may be expressed in a strain which does not normally do so (the 'transgenic'). Advanced techniques may also permit the expression or suppression of a gene to occur by injection of some regulating chemical.

      Computational models - Using a computer to formulate real-world problems to develop solutions. [26] Although this method is often focused in computer science, it has begun to move towards other areas of study. For example, psychology is one of these areas. Computational models allow researchers in psychology to enhance their understanding of the functions and developments in nervous systems. Examples of methods include the modelling of neurons, networks and brain systems and theoretical analysis. [27] Computational methods have a wide variety of roles including clarifying experiments, hypothesis testing and generating new insights. These techniques play an increasing role in the advancement of biological psychology. [28]

      Limitations and advantages Edit

      Different manipulations have advantages and limitations. Neural tissue destroyed as a primary consequence of a surgery, electric shock or neurotoxin can confound the results so that the physical trauma masks changes in the fundamental neurophysiological processes of interest. For example, when using an electrolytic probe to create a purposeful lesion in a distinct region of the rat brain, surrounding tissue can be affected: so, a change in behavior exhibited by the experimental group post-surgery is to some degree a result of damage to surrounding neural tissue, rather than by a lesion of a distinct brain region. [29] [30] Most genetic manipulation techniques are also considered permanent. [30] Temporary lesions can be achieved with advanced in genetic manipulations, for example, certain genes can now be switched on and off with diet. [30] Pharmacological manipulations also allow blocking of certain neurotransmitters temporarily as the function returns to its previous state after the drug has been metabolized. [30]

      In general, behavioral neuroscientists study similar themes and issues as academic psychologists, though limited by the need to use nonhuman animals. As a result, the bulk of literature in behavioral neuroscience deals with mental processes and behaviors that are shared across different animal models such as:

      • Sensation and perception
      • Motivated behavior (hunger, thirst, sex)
      • Control of movement
      • Learning and memory
      • Sleep and biological rhythms
      • Emotion

      However, with increasing technical sophistication and with the development of more precise noninvasive methods that can be applied to human subjects, behavioral neuroscientists are beginning to contribute to other classical topic areas of psychology, philosophy, and linguistics, such as:

      Behavioral neuroscience has also had a strong history of contributing to the understanding of medical disorders, including those that fall under the purview of clinical psychology and biological psychopathology (also known as abnormal psychology). Although animal models do not exist for all mental illnesses, the field has contributed important therapeutic data on a variety of conditions, including:


      Artificial intelligence predicts brain age from EEG signals recorded during sleep studies

      Credit: CC0 Public Domain

      A study shows that a deep neural network model can accurately predict the brain age of healthy patients based on electroencephalogram data recorded during an overnight sleep study, and EEG-predicted brain age indices display unique characteristics within populations with different diseases.

      The study found that the model predicted age with a mean absolute error of only 4.6 years. There was a statistically significant relationship between the Absolute Brain Age Index and epilepsy and seizure disorders, stroke, elevated markers of sleep-disordered breathing (i.e., apnea-hypopnea index and arousal index), and low sleep efficiency. The study also found that patients with diabetes, depression, severe excessive daytime sleepiness, hypertension, and/or memory and concentration problems showed, on average, an elevated Brain Age Index compared with the healthy population sample.

      According to the authors, the results demonstrate that these health conditions are associated with deviations of one's predicted age from one's chronological age.

      "While clinicians can only grossly estimate or quantify the age of a patient based on their EEG, this study shows an artificial intelligence model can predict a patient's age with high precision," said lead author Yoav Nygate, senior AI engineer at EnsoData. "The model's precision enables shifts in the predicted age from the chronological age to express correlations with major disease families and comorbidities. This presents the potential for identifying novel clinical phenotypes that exist within physiological signals utilizing AI model deviations."

      The researchers trained a deep neural network model to predict the age of patients using raw EEG signals recorded during clinical sleep studies performed using overnight polysomnography. The model was trained on 126,241 sleep studies, validated on 6,638 studies, and tested on a holdout set of 1,172 studies. Brain age was assessed by subtracting individuals' chronological age from their EEG-predicted age (i.e., Brain Age Index), and then taking the absolute value of this variable (i.e., Absolute Brain Age Index). Analyses controlled for factors such as sex and body mass index.

      "The results in this study provide initial evidence for the potential of utilizing AI to assess the brain age of a patient," said Nygate. "Our hope is that with continued investigation, research, and clinical studies, a brain age index will one day become a diagnostic biomarker of brain health, much like high blood pressure is for risks of stroke and other cardiovascular disorders."

      The research abstract was published recently in an online supplement of the journal Sleep and will be presented as a poster beginning June 9 during Virtual SLEEP 2021.


      Watch the video: PPA - Strom Power Purchase Agreement für Post-EEG-Anlagen (June 2022).


Comments:

  1. Tupper

    I have thought and have removed this question

  2. Jurn

    I think mistakes are made. I am able to prove it. Write to me in PM, it talks to you.

  3. Micheal

    This topic is simply incomparable :) It is interesting for me.

  4. Brasar

    In fundamentally incorrect information

  5. Vujas

    I print ... on the wall in the most conspicuous place !!!

  6. Rapere

    Really?

  7. Kaedee

    You are mistaken. I can prove it. Write to me in PM, we will communicate.



Write a message