How long does a spiking signal last?

How long does a spiking signal last?

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It is surprisingly hard to find information about the timing of neurons, in particular how long an action potential can contribute to the summation of a neuron. Is it on the order of milliseconds or seconds, i.e. for a neuron to fire, do many of the presynaptic neurons fire about at the same time?

Many of the neurons in the auditory system have voltage-gated low threshold potassium channels which allow neurons to maintain high firing rates and high temporal acuity. This makes the auditory system a good candidate for finding "fast" neurons. McGineley and Oertel (2006) looked at temporal integration in the ventral cochlear nucleus of the auditory system and found that the integration window was as low as 1.4 ms for octopus cells and of "unlimited" duration for T stellate cells.

I think, but I do not know how to calculate an integration window, that the cells of the Medial Superior Olive are even faster. These cells need to encode differences in timing between the ears on the order of 10s of microseconds.

Physical symptoms as an expression of emotional dysfunction

The development of new symptoms in a patient whose condition had previously been stable may indicate that the patient is emotionally distressed. The exacerbation of an established illness, such as the development of pseudoseizures in a patient with epilepsy, may indicate adjustment difficulties, depression, or complex social or relationship problems. The key symptoms of the so-called somatoform disorders are physical complaints, excessive fear of physical illness, or the excessive and unwarranted pursuit of medical or surgical treatments. 19 Despite their frequency in general medical practice, these problems can be some of the most difficult for doctors to manage, and doctors report finding patients with these disorders among the most difficult to help. 20 A doctor's use of effective patterns of communication 21 can reduce the risk of chronic morbidity and disability. 22


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Scaling Methods∗

Frederick A.A. Kingdom , Nicolaas Prins , in Psychophysics (Second Edition) , 2016

8.2.2 The Dipper Function

In visual psychophysics , “dipper functions” describe a well-known class of discrimination threshold function (see review by Solomon, 2009 ). They are best associated with discrimination thresholds for contrast. If one measures JNDs as a function of baseline, or “pedestal” contrast, say for a grating patch, you obtain a function that invariably looks like the figure on the right of Figure 8.4 . The “dipper” refers to the finding that as you increase pedestal contrast from zero, JNDs first decline to a minimum—the “dipper”—then rise steeply. Although there is more than one explanation as to why one obtains the dipper ( Solomon, 2009 ), a popular explanation is that it reflects an accelerating transducer function near threshold ( Nachmias and Sansbury, 1974 ). In the previous section we showed how one can infer the shape of a perceptual scale from the discrimination function known as Weber's Law. Here we will do the opposite: we will begin with the perceptual scale and see how it predicts the dipper function.

Figure 8.4 . Left: Perceptual scale derived from Eqn (8.7) for the contrast range C = 0–0.1, i.e., one-tenth of the full contrast range. The black horizontal lines show the scale divided into equal intervals, the vertical lines the corresponding intervals in contrast. Middle: the contrast intervals from the left figure, ΔC, plotted as a function of C. Right: same as the middle figure except plotted over the entire range of C, i.e., 0–1 in log-log coordinates.

According to Legge and Foley (1980) the perceptual scale for contrast can be described by


In the 1800s, studies of eye movement were made using direct observations. For example, Louis Émile Javal observed in 1879 that reading does not involve a smooth sweeping of the eyes along the text, as previously assumed, but a series of short stops (called fixations) and quick saccades. [1] This observation raised important questions about reading, questions which were explored during the 1900s: On which words do the eyes stop? For how long? When do they regress to already seen words?

Edmund Huey [2] built an early eye tracker, using a sort of contact lens with a hole for the pupil. The lens was connected to an aluminum pointer that moved in response to the movement of the eye. Huey studied and quantified regressions (only a small proportion of saccades are regressions), and he showed that some words in a sentence are not fixated.

The first non-intrusive eye-trackers were built by Guy Thomas Buswell in Chicago, using beams of light that were reflected on the eye and then recording them on film. Buswell made systematic studies into reading [3] and picture viewing. [4]

In the 1950s, Alfred L. Yarbus [5] did important eye tracking research and his 1967 book is often quoted. He showed that the task given to a subject has a very large influence on the subject's eye movement. He also wrote about the relation between fixations and interest:

"All the records . show conclusively that the character of the eye movement is either completely independent of or only very slightly dependent on the material of the picture and how it was made, provided that it is flat or nearly flat." [6] The cyclical pattern in the examination of pictures "is dependent on not only what is shown on the picture, but also the problem facing the observer and the information that he hopes to gain from the picture." [7]

In the 1970s, eye-tracking research expanded rapidly, particularly reading research. A good overview of the research in this period is given by Rayner. [13]

In 1980, Just and Carpenter [14] formulated the influential Strong eye-mind hypothesis, that "there is no appreciable lag between what is fixated and what is processed". If this hypothesis is correct, then when a subject looks at a word or object, he or she also thinks about it (process cognitively), and for exactly as long as the recorded fixation. The hypothesis is often taken for granted by researchers using eye-tracking. However, gaze-contingent techniques offer an interesting option in order to disentangle overt and covert attentions, to differentiate what is fixated and what is processed.

During the 1980s, the eye-mind hypothesis was often questioned in light of covert attention, [15] [16] the attention to something that one is not looking at, which people often do. If covert attention is common during eye-tracking recordings, the resulting scan-path and fixation patterns would often show not where our attention has been, but only where the eye has been looking, failing to indicate cognitive processing.

The 1980s also saw the birth of using eye-tracking to answer questions related to human-computer interaction. Specifically, researchers investigated how users search for commands in computer menus. [17] Additionally, computers allowed researchers to use eye-tracking results in real time, primarily to help disabled users. [17]

More recently, there has been growth in using eye tracking to study how users interact with different computer interfaces. Specific questions researchers ask are related to how easy different interfaces are for users. [17] The results of the eye tracking research can lead to changes in design of the interface. Yet another recent area of research focuses on Web development. This can include how users react to drop-down menus or where they focus their attention on a website so the developer knows where to place an advertisement. [18]

According to Hoffman, [19] current consensus is that visual attention is always slightly (100 to 250 ms) ahead of the eye. But as soon as attention moves to a new position, the eyes will want to follow. [20]

We still cannot infer specific cognitive processes directly from a fixation on a particular object in a scene. [21] For instance, a fixation on a face in a picture may indicate recognition, liking, dislike, puzzlement etc. Therefore, eye tracking is often coupled with other methodologies, such as introspective verbal protocols.

Thanks to advancement in portable electronic devices, portable head-mounted eye trackers currently can achieve excellent performance and are being increasingly used in research and market applications targeting daily life settings. [22] These same advances have led to increases in the study of small eye movements that occur during fixation, both in the lab and in applied settings. [23]

In the 21st century, the use of artificial intelligence (AI) and artificial neural networks has become a viable way to complete eye-tracking tasks and analysis. In particular, the convolutional neural network lends itself to eye-tracking, as it is designed for image-centric tasks. With AI, eye-tracking tasks and studies can yield additional information that may not have been detected by human observers. The practice of deep learning also allows for a given neural network to improve at a given task when given enough sample data. This requires a relatively large supply of training data, however. [24]

The potential use cases for AI in eye-tracking cover a wide range of topics from medical applications [25] to driver safety [24] to game theory. [26] While the CNN structure may fit relatively well with the task of eye-tracking, researchers have the option to construct a custom neural network that is tailored for the specific task at hand. In those instances, these in-house creations can outperform pre-existing templates for a neural network. [27] In this sense, it remains to be seen if there is a way to determine the ideal network structure for a given task.

Eye-trackers measure rotations of the eye in one of several ways, but principally they fall into one of three categories: (i) measurement of the movement of an object (normally, a special contact lens) attached to the eye (ii) optical tracking without direct contact to the eye and (iii) measurement of electric potentials using electrodes placed around the eyes.

Eye-attached tracking Edit

The first type uses an attachment to the eye, such as a special contact lens with an embedded mirror or magnetic field sensor, and the movement of the attachment is measured with the assumption that it does not slip significantly as the eye rotates. Measurements with tight-fitting contact lenses have provided extremely sensitive recordings of eye movement, and magnetic search coils are the method of choice for researchers studying the dynamics and underlying physiology of eye movement. This method allows the measurement of eye movement in horizontal, vertical and torsion directions. [28]

Optical tracking Edit

The second broad category uses some non-contact, optical method for measuring eye motion. Light, typically infrared, is reflected from the eye and sensed by a video camera or some other specially designed optical sensor. The information is then analyzed to extract eye rotation from changes in reflections. Video-based eye trackers typically use the corneal reflection (the first Purkinje image) and the center of the pupil as features to track over time. A more sensitive type of eye-tracker, the dual-Purkinje eye tracker, [29] uses reflections from the front of the cornea (first Purkinje image) and the back of the lens (fourth Purkinje image) as features to track. A still more sensitive method of tracking is to image features from inside the eye, such as the retinal blood vessels, and follow these features as the eye rotates. Optical methods, particularly those based on video recording, are widely used for gaze-tracking and are favored for being non-invasive and inexpensive.

Electric potential measurement Edit

The third category uses electric potentials measured with electrodes placed around the eyes. The eyes are the origin of a steady electric potential field which can also be detected in total darkness and if the eyes are closed. It can be modelled to be generated by a dipole with its positive pole at the cornea and its negative pole at the retina. The electric signal that can be derived using two pairs of contact electrodes placed on the skin around one eye is called Electrooculogram (EOG). If the eyes move from the centre position towards the periphery, the retina approaches one electrode while the cornea approaches the opposing one. This change in the orientation of the dipole and consequently the electric potential field results in a change in the measured EOG signal. Inversely, by analysing these changes in eye movement can be tracked. Due to the discretisation given by the common electrode setup, two separate movement components – a horizontal and a vertical – can be identified. A third EOG component is the radial EOG channel, [30] which is the average of the EOG channels referenced to some posterior scalp electrode. This radial EOG channel is sensitive to the saccadic spike potentials stemming from the extra-ocular muscles at the onset of saccades, and allows reliable detection of even miniature saccades. [31]

Due to potential drifts and variable relations between the EOG signal amplitudes and the saccade sizes, it is challenging to use EOG for measuring slow eye movement and detecting gaze direction. EOG is, however, a very robust technique for measuring saccadic eye movement associated with gaze shifts and detecting blinks. Contrary to video-based eye-trackers, EOG allows recording of eye movements even with eyes closed, and can thus be used in sleep research. It is a very light-weight approach that, in contrast to current video-based eye-trackers, requires only very low computational power works under different lighting conditions and can be implemented as an embedded, self-contained wearable system. [32] [33] It is thus the method of choice for measuring eye movement in mobile daily-life situations and REM phases during sleep. The major disadvantage of EOG is its relatively poor gaze-direction accuracy compared to a video tracker. That is, it is difficult to determine with good accuracy exactly where a subject is looking, though the time of eye movements can be determined.

The most widely used current designs are video-based eye-trackers. A camera focuses on one or both eyes and records eye movement as the viewer looks at some kind of stimulus. Most modern eye-trackers use the center of the pupil and infrared / near-infrared non-collimated light to create corneal reflections (CR). The vector between the pupil center and the corneal reflections can be used to compute the point of regard on surface or the gaze direction. A simple calibration procedure of the individual is usually needed before using the eye tracker. [34]

Two general types of infrared / near-infrared (also known as active light) eye-tracking techniques are used: bright-pupil and dark-pupil. Their difference is based on the location of the illumination source with respect to the optics. If the illumination is coaxial with the optical path, then the eye acts as a retroreflector as the light reflects off the retina creating a bright pupil effect similar to red eye. If the illumination source is offset from the optical path, then the pupil appears dark because the retroreflection from the retina is directed away from the camera. [35]

Bright-pupil tracking creates greater iris/pupil contrast, allowing more robust eye-tracking with all iris pigmentation, and greatly reduces interference caused by eyelashes and other obscuring features. [36] It also allows tracking in lighting conditions ranging from total darkness to very bright.

Another, less used, method is known as passive light. It uses visible light to illuminate, something which may cause some distractions to users. [35] Another challenge with this method is that the contrast of the pupil is less than in the active light methods, therefore, the center of iris is used for calculating the vector instead. [37] This calculation needs to detect the boundary of the iris and the white sclera (limbus tracking). It presents another challenge for vertical eye movements due to obstruction of eyelids. [38]

Infrared / near-infrared: bright pupil.

Infrared / near-infrared: dark pupil and corneal reflection.

Visible light: center of iris (red), corneal reflection (green), and output vector (blue).

Eye-tracking setups vary greatly: some are head-mounted, some require the head to be stable (for example, with a chin rest), and some function remotely and automatically track the head during motion. Most use a sampling rate of at least 30 Hz. Although 50/60 Hz is more common, today many video-based eye trackers run at 240, 350 or even 1000/1250 Hz, speeds needed in order to capture fixational eye movements or correctly measure saccade dynamics.

Eye movements are typically divided into fixations and saccades – when the eye gaze pauses in a certain position, and when it moves to another position, respectively. The resulting series of fixations and saccades is called a scanpath. Smooth pursuit describes the eye following a moving object. Fixational eye movements include microsaccades: small, involuntary saccades that occur during attempted fixation. Most information from the eye is made available during a fixation or smooth pursuit, but not during a saccade. [39]

Scanpaths are useful for analyzing cognitive intent, interest, and salience. Other biological factors (some as simple as gender) may affect the scanpath as well. Eye tracking in human–computer interaction (HCI) typically investigates the scanpath for usability purposes, or as a method of input in gaze-contingent displays, also known as gaze-based interfaces. [40]

Interpretation of the data that is recorded by the various types of eye-trackers employs a variety of software that animates or visually represents it, so that the visual behavior of one or more users can be graphically resumed. The video is generally manually coded to identify the AOIs(Area Of Interests) or recently using artificial intelligence. Graphical presentation is rarely the basis of research results, since they are limited in terms of what can be analysed - research relying on eye-tracking, for example, usually requires quantitative measures of the eye movement events and their parameters, The following visualisations are the most commonly used:

Animated representations of a point on the interface This method is used when the visual behavior is examined individually indicating where the user focused their gaze in each moment, complemented with a small path that indicates the previous saccade movements, as seen in the image.

Static representations of the saccade path This is fairly similar to the one described above, with the difference that this is static method. A higher level of expertise than with the animated ones is required to interpret this.

Heat maps An alternative static representation, used mainly for the agglomerated analysis of the visual exploration patterns in a group of users. In these representations, the ‘hot’ zones or zones with higher density designate where the users focused their gaze (not their attention) with a higher frequency. Heat maps are the best known visualization technique for eyetracking studies. [41]

Blind zones maps, or focus maps This method is a simplified version of the Heat maps where the visually less attended zones by the users are displayed clearly, thus allowing for an easier understanding of the most relevant information, that is to say, we are informed about which zones were not seen by the users.

Saliency maps Similar to heat maps, a saliency map illustrates areas of focus by brightly displaying the attention-grabbing objects over an initially black canvas. The more focus is given to a particular object, the brighter it will appear. [42]

Eye-trackers necessarily measure the rotation of the eye with respect to some frame of reference. This is usually tied to the measuring system. Thus, if the measuring system is head-mounted, as with EOG or a video-based system mounted to a helmet, then eye-in-head angles are measured. To deduce the line of sight in world coordinates, the head must be kept in a constant position or its movements must be tracked as well. In these cases, head direction is added to eye-in-head direction to determine gaze direction.

If the measuring system is table-mounted, as with scleral search coils or table-mounted camera (“remote”) systems, then gaze angles are measured directly in world coordinates. Typically, in these situations head movements are prohibited. For example, the head position is fixed using a bite bar or a forehead support. Then a head-centered reference frame is identical to a world-centered reference frame. Or colloquially, the eye-in-head position directly determines the gaze direction.

Some results are available on human eye movements under natural conditions where head movements are allowed as well. [43] The relative position of eye and head, even with constant gaze direction, influences neuronal activity in higher visual areas. [44]

A great deal of research has gone into studies of the mechanisms and dynamics of eye rotation, but the goal of eye- tracking is most often to estimate gaze direction. Users may be interested in what features of an image draw the eye, for example. It is important to realize that the eye-tracker does not provide absolute gaze direction, but rather can measure only changes in gaze direction. In order to know precisely what a subject is looking at, some calibration procedure is required in which the subject looks at a point or series of points, while the eye tracker records the value that corresponds to each gaze position. (Even those techniques that track features of the retina cannot provide exact gaze direction because there is no specific anatomical feature that marks the exact point where the visual axis meets the retina, if indeed there is such a single, stable point.) An accurate and reliable calibration is essential for obtaining valid and repeatable eye movement data, and this can be a significant challenge for non-verbal subjects or those who have unstable gaze.

Each method of eye-tracking has advantages and disadvantages, and the choice of an eye-tracking system depends on considerations of cost and application. There are offline methods and online procedures like AttentionTracking. There is a trade-off between cost and sensitivity, with the most sensitive systems costing many tens of thousands of dollars and requiring considerable expertise to operate properly. Advances in computer and video technology have led to the development of relatively low-cost systems that are useful for many applications and fairly easy to use. [45] Interpretation of the results still requires some level of expertise, however, because a misaligned or poorly calibrated system can produce wildly erroneous data.

Eye-tracking while driving a car in a difficult situation Edit

The eye movement of two groups of drivers have been filmed with a special head camera by a team of the Swiss Federal Institute of Technology: Novice and experienced drivers had their eye-movement recorded while approaching a bend of a narrow road. The series of images has been condensed from the original film frames [47] to show 2 eye fixations per image for better comprehension.

Each of these stills corresponds to approximately 0.5 seconds in realtime.

The series of images shows an example of eye fixations #9 to #14 of a typical novice and an experienced driver.

Comparison of the top images shows that the experienced driver checks the curve and even has Fixation No. 9 left to look aside while the novice driver needs to check the road and estimate his distance to the parked car.

In the middle images, the experienced driver is now fully concentrating on the location where an oncoming car could be seen. The novice driver concentrates his view on the parked car.

In the bottom image the novice is busy estimating the distance between the left wall and the parked car, while the experienced driver can use his peripheral vision for that and still concentrate his view on the dangerous point of the curve: If a car appears there, he has to give way, i. e. stop to the right instead of passing the parked car. [48]

More recent studies have also used head-mounted eye tracking to measure eye movements during real-world driving conditions. [49] [23]

Eye-tracking of younger and elderly people while walking Edit

While walking, elderly subjects depend more on foveal vision than do younger subjects. Their walking speed is decreased by a limited visual field, probably caused by a deteriorated peripheral vision.

Younger subjects make use of both their central and peripheral vision while walking. Their peripheral vision allows faster control over the process of walking. [50]

A wide variety of disciplines use eye-tracking techniques, including cognitive science psychology (notably psycholinguistics the visual world paradigm) human-computer interaction (HCI) human factors and ergonomics marketing research and medical research (neurological diagnosis). [51] Specific applications include the tracking eye movement in language reading, music reading, human activity recognition, the perception of advertising, playing of sports, distraction detection and cognitive load estimation of drivers and pilots and as a means of operating computers by people with severe motor impairment. [23]

Commercial applications Edit

In recent years, the increased sophistication and accessibility of eye-tracking technologies have generated a great deal of interest in the commercial sector. Applications include web usability, advertising, sponsorship, package design and automotive engineering. In general, commercial eye-tracking studies function by presenting a target stimulus to a sample of consumers while an eye tracker is used to record the activity of the eye. Examples of target stimuli may include websites television programs sporting events films and commercials magazines and newspapers packages shelf displays consumer systems (ATMs, checkout systems, kiosks) and software. The resulting data can be statistically analyzed and graphically rendered to provide evidence of specific visual patterns. By examining fixations, saccades, pupil dilation, blinks and a variety of other behaviors, researchers can determine a great deal about the effectiveness of a given medium or product. While some companies complete this type of research internally, there are many private companies that offer eye-tracking services and analysis.

One field of commercial eye-tracking research is web usability. While traditional usability techniques are often quite powerful in providing information on clicking and scrolling patterns, eye-tracking offers the ability to analyze user interaction between the clicks and how much time a user spends between clicks, thereby providing valuable insight into which features are the most eye-catching, which features cause confusion and which are ignored altogether. Specifically, eye-tracking can be used to assess search efficiency, branding, online advertisements, navigation usability, overall design and many other site components. Analyses may target a prototype or competitor site in addition to the main client site.

Eye-tracking is commonly used in a variety of different advertising media. Commercials, print ads, online ads and sponsored programs are all conducive to analysis with current eye-tracking technology. One example is the analysis of eye movements over advertisements in the Yellow Pages. One study focused on what particular features caused people to notice an ad, whether they viewed ads in a particular order and how viewing times varied. The study revealed that ad size, graphics, color, and copy all influence attention to advertisements. Knowing this allows researchers to assess in great detail how often a sample of consumers fixates on the target logo, product or ad. As a result, an advertiser can quantify the success of a given campaign in terms of actual visual attention. [52] Another example of this is a study that found that in a search engine results page, authorship snippets received more attention than the paid ads or even the first organic result. [53]

Yet another example of commercial eye-tracking research comes from the field of recruitment. A study analyzed how recruiters screen Linkedin profiles and presented results as heat maps. [54]

Safety applications Edit

Scientists in 2017 constructed a Deep Integrated Neural Network (DINN) out of a Deep Neural Network and a convolutional neural network. [24] The goal was to use deep learning to examine images of drivers and determine their level of drowsiness by "classify[ing] eye states." With enough images, the proposed DINN could ideally determine when drivers blink, how often they blink, and for how long. From there, it could judge how tired a given driver appears to be, effectively conducting an eye-tracking exercise. The DINN was trained on data from over 2,400 subjects and correctly diagnosed their states 96%-99.5% of the time. Most other artificial intelligence models performed at rates above 90%. [24] This technology could ideally provide another avenue for driver drowsiness detection.

Game theory applications Edit

In a 2019 study, a Convolutional Neural Network (CNN) was constructed with the ability to identify individual chess pieces the same way other CNNs can identify facial features. [26] It was then fed eye-tracking input data from thirty chess players of various skill levels. With this data, the CNN used gaze estimation to determine parts of the chess board to which a player was paying close attention. It then generated a saliency map to illustrate those parts of the board. Ultimately, the CNN would combine its knowledge of the board and pieces with its saliency map to predict the players' next move. Regardless of the training dataset the neural network system was trained upon, it predicted the next move more accurately than if it had selected any possible move at random, and the saliency maps drawn for any given player and situation were more than 54% similar. [26]

Assistive technology Edit

People with severe motor impairment can use eye tracking for interacting with computers [55] as it is faster than single switch scanning techniques and intuitive to operate. [56] [57] Motor impairment caused by Cerebral Palsy [58] or Amyotrophic lateral sclerosis often affects speech, and users with Severe Speech and Motor Impairment (SSMI) use a type of software known as Augmentative and Alternative Communication (AAC) aid, [59] that displays icons, words and letters on screen [60] and uses text-to-speech software to generate spoken output. [61] In recent times, researchers also explored eye tracking to control robotic arms [62] and powered wheelchairs. [63] Eye tracking is also helpful in analysing visual search patterns, [64] detecting presence of Nystagmus and detecting early signs of learning disability by analysing eye gaze movement during reading. [65]

Aviation applications Edit

Eye tracking has already been studied for flight safety by comparing scan paths and fixation duration to evaluate the progress of pilot trainees, [66] for estimating pilots’ skills, [67] for analyzing crew’s joint attention and shared situational awareness. [68] Eye tracking technology was also explored to interact with helmet mounted display systems [69] and multi-functional displays [70] in military aircraft. Studies were conducted to investigate the utility of eye tracker for Head-up target locking and Head-up target acquisition in Helmet mounted display systems (HMDS). [71] Pilots' feedback suggested that even though the technology is promising, its hardware and software components are yet to be matured. [ citation needed ] Research on interacting with multi-functional displays in simulator environment showed that eye tracking can improve the response times and perceived cognitive load significantly over existing systems. Further, research also investigated utilizing measurements of fixation and pupillary responses to estimate pilot's cognitive load. Estimating cognitive load can help to design next generation adaptive cockpits with improved flight safety. [72] Eye tracking is also useful for detecting pilot fatigue. [73] [23]

Automotive applications Edit

In recent time, eye tracking technology is investigated in automotive domain in both passive and active ways. National Highway Traffic Safety Administration measured glance duration for undertaking secondary tasks while driving and used it to promote safety by discouraging the introduction of excessively distracting devices in vehicles [74] In addition to distraction detection, eye tracking is also used to interact with IVIS. [75] Though initial research [76] investigated the efficacy of eye tracking system for interaction with HDD (Head Down Display), it still required drivers to take their eyes off the road while performing a secondary task. Recent studies investigated eye gaze controlled interaction with HUD (Head Up Display) that eliminates eyes-off-road distraction. [77] Eye tracking is also used to monitor cognitive load of drivers to detect potential distraction. Though researchers [78] explored different methods to estimate cognitive load of drivers from different physiological parameters, usage of ocular parameters explored a new way to use the existing eye trackers to monitor cognitive load of drivers in addition to interaction with IVIS. [79] [80]

Entertainment applications Edit

The 2021 video game Before Your Eyes registers and reads the player's blinking, and uses it as the main way of interacting with the game. [81] [82]

Trend Trading with Relative Strength Index (RSI) Support and Resistance Levels

Learn the different RSI support and resistance levels to watch for during uptrends and downtrends. Then, use these RSI support and resistance levels to help determine the strength of the current trend. When the RSI breaks these support and resistance levels it often indicates a trend reversal is occurring.

The Relative Strength Index (RSI) is a technical analysis indicator that oscillates between 0 and 100. When the RSI is moving up the price gains are outpacing price losses over the look-back period, and when the RSI is moving down the price losses are outpacing gains over the look-back period. The look-back period is how many many price bars the indicator uses to make its current calculation. The typical setting is 14, meaning the indicator will look at gains and losses over the prior 14 price bars. Traders can use any setting they wish, though. Each new price bar produces a new calculation based on the prior 14 price bars. Thus, the RSI forms a continuous line over time.

When we look at the how the RSI behaves during a trend, we often find that it moves within a defined area for an uptrend and a different defined area for a downtrend. A deviation from this tendency can signal a change in trend. These levels or areas, discussed below, may vary slightly by market or asset.

Trend Trading with RSI

The first thing we need to know is that the RSI moves within support and resistance channels during price trends. The levels the RSI ranges between indicates the strength of the trend and the trend direction.

  • In an uptrend, the RSI range should stay above 30, and often hit 70 or higher.
  • In a downtrend, the RSI will generally stay below 70, and often hit 30 or lower.

This can let us know if a trend is reversing, as a drop below the 30 level on an RSI is rare in an uptrend. If the RSI drops below 30 during an uptrend, or fails to recover above 70, the uptrend could be in trouble.

These levels may vary slightly depending on the market or time frame you&rsquore trading, so look at a historical chart of whatever you are trading to see the RSI levels that are important for the trends in that asset or that time frame (weekly charts, hourly charts, etc.).

In the chart above, 30 acts as a support level for the uptrend, while it was breached for a day or two on a number of occasions the RSI quickly bounced and moved back up to the 70+ level, confirming the uptrend was still in place.

Below is an example of how the RSI acts in a downtrend. Notice how the price is continually reaching below 30 on the RSI (often hitting 20), and doesn&rsquot move above 70. Toward the right side of the chart, the RSI definitively breaks above 70 and reaches near 90. This ended up being a turning point for the stock as the price didn&rsquot reach the April low again. The RSI also helps confirm the ensuing uptrend. The moves up on the RSI often hit 70 or higher and moves down stay above 30.

The next sign of trouble in the stock didn&rsquot occur until years later. In mid-2015 the RSI drops below 30 and doesn&rsquot recover back above 70. A several month downtrend ensues. The RSI then spikes above 70 in April. This marks a potential transition back into an uptrend. The RSI swiftly drops back below 30, though, so we don&rsquot have confirmation that an uptrend has started yet. But, if we look at the price, notice it is barely making lower lows. The downtrend is losing steam, which is why the RSI was able to spike above 70. In August the RSI spikes above 70 again, and then manages to stay above 30 on the oscillations that follow. At this point, the price is also making higher swing highs and higher swing lows. The downtrend is over and the new uptrend is confirmed.

When the RSI moves above 70 and then quickly drops below 30 right after (or vice versa) you can be certain there are likely a lot of traders confused, because the price likely just had a big move up followed by a big move down. Such events often occur at trend transition points (uptrend to downtrend, or downtrend to uptrend) when emotions are high and prone to causing big swings in both directions.

The RSI bouncing between above 70 and below 30 could also signal the start of a big consolidation phase. We see this in the chart below. The RSI helped confirm both the uptrend and downtrend, but then notice how the up moves in the RSI become the same size as the downside RSI waves. The RSI is not showing a bias to toward higher levels or lower levels, it is just ranging&hellipand so is the price. If the RSI is oscillating an equal distance (above and below) 50, chances are the price is in a range, and looking at the price will confirm that.

On the right of the chart above, the most recent move was above 70, and the RSI hasn&rsquot dropped below 30. That favors an uptrend. A drop below 30 on the RSI would point toward a continued ranging environment or a downtrend. Also, whenever a big range or chart pattern develops, watch the price for a breakout or false breakout, as they also provide trading opportunities and analytical insight.

How This Can Actually Help With Your Trading

Trend trading with RSI support and resistance levels can help confirm trends and isolate when the market is shifting direction. That is all well and good, but it is not a crystal ball and doesn&rsquot tell you when to enter and exit trades. For example, you wouldn&rsquot want to trade off RSI support and resistance levels alone, even though just looking at the RSI levels can provide us with a good indication of when the trend is healthy and when it may be reversing.

Using the RSI in this way is only a trend confirmation tool. For example, assume you have a trend trading strategy that just gave you a buy signal. Since you are a trend trader, you want to only be buying if you have a healthy uptrend. Pull up a chart of the asset you are considering a trade in and look at the RSI levels. If the RSI is staying above 30 and routinely reaching above 70, that&rsquos a positive sign for the uptrend. You will likely want to proceed with acting on the buy signal produced by your strategy. If the RSI is dipping below 30 and not reaching 70 that isn&rsquot a strong uptrend. You will probably want to skip acting on that buy signal produced by your strategy.

Also, if an asset is in an uptrend but then the RSI drops below 30, or is in a downtrend and then rallies above 70, that could be the start of a reversal. These are opportunities that may benefit from a reversal strategy. We don&rsquot take trades based on the RSI hitting these levels, but these changes in RSI levels can alert us to potential reversal opportunities. The RSI is letting us know a reversal may be taking shape. The next step is to watch the price action for an entry. To see what to watch for, read Strong Trend Reversal Strategy.

Don&rsquot buy simply because the RSI is above 30 and regularly moving above 70. That just tells us the uptrend is likely in decent shape. We still need a strategy that tells us precisely when to get into trades, and when to get out. The same goes for shorting during a downtrend. These RSI support and resistance levels are just a confirmation tool, and not trade signals.

When the RSI is whipsawing between 70 and 30, or has equal oscillations on either side of 50, the price is likely in a big consolidation/ranging phase. In this case, trend trading strategies may not be as effective. Utilize range trading strategies, or look at the bigger picture and implement a front-running strategy. A front-running strategy takes advantage of the size of the range and the breakout that will inevitably occur down the road.

Final Word on Using RSI Support and Resistance Levels

I rarely use RSI support and resistance levels anymore. Indicators are just interpretations of price data, so often the price itself will tell you all you need to know about which direction to trade in, and when to step aside when the trend is unclear. That said, when I was learning how to read price action,I did find the RSI support and resistance levels useful. I still do refer to them on occasion.

The RSI may help some traders spot trends and reversals&mdashjust like they used to help me &ndashand can be used as a confirmation tool. During an uptrend on the daily chart, the RSI of the asset should be staying above 30 and regularly moving above 70. When this is occurring, the uptrend is likely in decent shape and buy signals based on your strategies can be taken.

During a downtrend, the RSI will often move below 30 and stay below 70. When this is occurring, avoid long trades and consider taking short trades based on your strategies.

Watch for deviations to indicate trend changes. The RSI moving above 70 in a downtrend could signal a reversal into an uptrend, for example, especially if the RSI stays above 30 on the next wave down.

I have not found this RSI technique particularly useful for day trading. While it may confirm trends and reversals, intra-day trends don&rsquot often last long enough for the indicator to be of much value.

On weekly charts, this confirmation method can be quite effective. Look at a historical chart of the asset to see which RSI levels are important for marking uptrends and downtrends.

Power consumption and battery life will of course vary based on the load that the hub is driving. Philo has an extensive analysis for motor power consumption, so I'll just look at what the hub itself consumes while running a program to come up with a baseline battery life estimate.

I've uploaded a test Python program to my hub that gently blinks a heartbeat LED:

In order to measure the current draw, I first wired up the battery to the hub with the battery outside the hub:

I then connected a basic multimeter up in series with the hub as a current sensor:

We can see that the Hub with the simple program running consumes about 150mA of current. Assuming ideal usage of the included 2100mAh battery, the hub should be able to run for approximately 2100mAh / 150mA = 14 hours while not driving any peripherals.

Peripherals such as motors (and even the LED display) quickly dwarf the power consumption of the hub itself, but under normal usage we should expect to see several hours of battery life.

What is sustained attention?

Sustained attention comes into play during many of your daily activities. It’s important for processes related to vigilance or supervision. In order for vigilance to be effective, you have to maintain your attention, which requires a certain level of activation.

Sustained attention also plays a role in learning-related processes. Students in a classroom have to try pretty hard to pay attention to what the teacher is saying. Sometimes, sustained attention mixes with selective attention. In other words, not only do you have to pay attention, but you also have to stay focused on a particular thing while you filter out other distractions.

Sustained attention comes into play when you trigger mechanisms and processes through which your body can stay focused and alert to particular stimuli during relatively long periods of time.

“You were not formed to live like brutes but to follow virtue and knowledge.”

-Dante Alighieri-


The neurotransmitter most often associated with EPSPs is the amino acid glutamate, and is the main excitatory neurotransmitter in the central nervous system of vertebrates. [2] Its ubiquity at excitatory synapses has led to it being called the excitatory neurotransmitter. In some invertebrates, glutamate is the main excitatory transmitter at the neuromuscular junction. [3] [4] In the neuromuscular junction of vertebrates, EPP (end-plate potentials) are mediated by the neurotransmitter acetylcholine, which (along with glutamate) is one of the primary transmitters in the central nervous system of invertebrates. [5] At the same time, GABA is the most common neurotransmitter associated with IPSPs in the brain. However, classifying neurotransmitters as such is technically incorrect, as there are several other synaptic factors that help determine a neurotransmitter's excitatory or inhibitory effects.

The release of neurotransmitter vesicles from the presynaptic cell is probabilistic. In fact, even without stimulation of the presynaptic cell, a single vesicle will occasionally be released into the synapse, generating miniature EPSPs (mEPSPs). Bernard Katz pioneered the study of these mEPSPs at the neuromuscular junction (often called miniature end-plate potentials [6] ) in 1951, revealing the quantal nature of synaptic transmission. Quantal size can then be defined as the synaptic response to the release of neurotransmitter from a single vesicle, while quantal content is the number of effective vesicles released in response to a nerve impulse. [ citation needed ] Quantal analysis refers to the methods used to deduce, for a particular synapse, how many quanta of transmitter are released and what the average effect of each quantum is on the target cell, measured in terms of amount of ions flowing (charge) or change in the membrane potential. [7]

EPSPs are usually recorded using intracellular electrodes. The extracellular signal from a single neuron is extremely small and thus next to impossible to record in the human brain. However, in some areas of the brain, such as the hippocampus, neurons are arranged in such a way that they all receive synaptic inputs in the same area. Because these neurons are in the same orientation, the extracellular signals from synaptic excitation don't cancel out, but rather add up to give a signal that can easily be recorded with a field electrode. This extracellular signal recorded from a population of neurons is the field potential. In studies of hippocampal long-term potentiation (LTP), figures are often given showing the field EPSP (fEPSP) in stratum radiatum of CA1 in response to Schaffer collateral stimulation. This is the signal seen by an extracellular electrode placed in the layer of apical dendrites of CA1 pyramidal neurons. [8] The Schaffer collaterals make excitatory synapses onto these dendrites, and so when they are activated, there is a current sink in stratum radiatum: the field EPSP. The voltage deflection recorded during a field EPSP is negative-going, while an intracellularly recorded EPSP is positive-going. This difference is due to the relative flow of ions (primarily the sodium ion) into the cell, which, in the case of the field EPSP is away from the electrode, while for an intracellular EPSPs it is towards the electrode. After a field EPSP, the extracellular electrode may record another change in electrical potential named the population spike which corresponds to the population of cells firing action potentials (spiking). In other regions than CA1 of the hippocampus, the field EPSP may be far more complex and harder to interpret as the source and sinks are far less defined. In regions such as the striatum, neurotransmitters such as dopamine, acetylcholine, GABA and others may also be released and further complicate the interpretation.

How the brain remembers fearful experiences

Understanding how the brain remembers can one day shed light on what went wrong when memory fails, such as it occurs in Alzheimer's disease. Researchers at Baylor College of Medicine and Rice University reveal for the first time the specific patterns of electrical activity in rat brains that are associated with specific memories, in this case a fearful experience. They discovered that before rats avoid a place in which they had a fearful experience, the brain recalled memories of the physical location where the experience occurred. The results appear in Nature Neuroscience.

"We recall memories all the time," said senior author Dr. Daoyun Ji, associate professor of molecular and cellular biology at Baylor. "For example, I can recall the route I take from home to work every morning, but what are the brain signals at this moment when I hold this memory in my mind?"

Studying the workings of the brain in people is difficult, so scientists have turned to the laboratory rat. They have learned that when the animal is in a particular place, neurons in the hippocampus, appropriately called place cells, generate pulses of activity.

"A number of place cells generates electrical activity called a 'spiking pattern,'" Ji said. "When the rat is in a certain place, a group of neurons generates a specific pattern of spikes and when it moves to a different place, a different group of neurons generates another pattern of spikes. The patterns are very distinct. We can predict where the animal is by looking at its pattern of brain activity."

But, are these spiking patterns involved in memory?

How to know what a rat is thinking

"Our laboratory rats cannot tell us what memory they are recalling at any particular time," Ji said. "To overcome that, we designed an experiment that would allow us to know what was going on in the animal's brain right before a certain event."

In the experiment, conducted by first author Chun-Ting Wu, graduate researcher at the Ji lab, a rat walked along a track, back and forth. After a period of rest, the rat walked the same track again, but when the animal approached the end of the track, it received a mild shock. After it rested again, the rat was placed back on the track. This time, however, when it approached the end of the track where it had received the mild shock before, the rat stopped and turned around, avoiding crossing the fearful path.

"Before a rat walked the tracks the first time, we inserted tiny probes into its hippocampus to record the electrical signals generated by groups of active neurons," Ji said. "By recording these brain signals while the animal walked the track for the first time we could examine the patterns that emerged in its brain -- we could see what patterns were associated with each location on the track, including the location where the animal later got shocked."

"Because the rat turns around and avoids stepping on the end of the track after the shocks, we can reasonably assume that the animal is thinking about the place where it got shocked at the precise moment that it stops walking and turns away," Ji said. "Our observations confirmed this idea."

When the researchers, in collaboration with co-author Dr. Caleb Kemere at Rice University, looked at the brain activity in place neurons at this moment, they found that the spiking patterns corresponding to the location in which the rat had received the shock re-emerged, even though this time the animal was only stopping and thinking about the location.

"Interestingly, from the brain activity we can tell that the animal was 'mentally traveling' from its current location to the shock place. These patterns corresponding to the shock place re-emerged right at the moment when a specific memory is remembered," Ji said.

Future directions

The next goal of the researchers is to investigate whether the spiking pattern they identified is absolutely required for the animals to behave the way they did.

"If we disrupt the pattern, will the animal still avoid stepping into the zone it had learned to avoid?" Ji said. "We are also interested in determining how the spiking patterns of place neurons in the hippocampus can be used by other parts of the brain, such as those involved in making decisions."

Ji and his colleagues are also planning on exploring what role spiking patterns in the hippocampus might play in diseases that involve memory loss, such as Alzheimer's disease.

"We want to determine whether this kind of mechanism is altered in animal models of Alzheimer's disease. Some evidence shows that it is not that the animals don't have a memory, but that somehow they cannot recall it. Using our system to read spiking patterns in the brains of animal models of the disease, we hope to determine whether a specific spiking pattern exists during memory recall. If not, we will explore the possibility that damaged brain circuits are preventing the animal from recalling the memory and look at ways to allow the animal to recall the specific activity patterns, the memory, again."


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