Continuous Self-Monitoring in Remote Viewing

Abstract

The present study used the continuous self-monitoring method to identify and classify the scenes in remote viewing from 2015 to 2019, emphasising data analysis dissertation help. This method has been successfully implemented to study instances of remote viewing. The viewer was able to classify the scenes from 2015 to 2019 into two categories: first, a scene as a picture, and second, a scene as a scenario. Along with using self-monitoring, the viewer was able to classify scenes as a picture into two subtypes: scenes with either one image or two. As for scenes as a scenario, these were further classified into two sections. In addition, by using self-monitoring, the viewer was able to reach results regarding the period between the time of viewing and the time, when the scene became true. The resulting duration was difficult to determine, as it became clear that the period of time in each instance was different; some were days, some weeks, and some were over a year.

Introduction

People often predict the future, for example, exam results, natural disasters, crime, diseases and death. The individuals do that based on their prior experiences or knowledge of shortcomings that may inform what they predict or influence their ability to analyse the events. Sometimes, these predictions are fulfilled, but the individuals often attribute these predictions to their experiences and not paranormal phenomena. That phenomenon is the focus of this article. The ability to predict and describe future events is based on one of these paranormal abilities, which is called remote viewing, and this can also be referred to as remote sensing, visual clarity, telesthesia, time travel and clairvoyance. Remote viewing is the term, which is most widely used by experts (Brown, 2005). This term is used in this article, because it is the most accurate in describing this ability, which can be defined as the ability to see events, people or places as a series of scenes outside the scope of normal vision and without any support from the five physical senses (Targ and Katra, 2000).

By the early 1980s, their experiments were demonstrating that remote viewing could be both reasonably and consistently successful and repeatable (Smith et al., 2014). Experiments developed by scientists such as Ingo Swann at the Stanford Research Institute in 1972 contributed to the research of remote viewing (Targ and Katra, 2000). However, there is still a lack of studies and scientific research that can assist the viewers in understanding what they see. There is also a minority of viewers, who write their experiences in detail and publish them as articles, either because they feel anxious or are afraid of criticism or accusations of lying. There are also only a few centres that specialise in remote viewing, which contain a database of the viewers’ experiences for the viewers and researchers to study and compare the results. According to Dunne and Jahn (2003), the practical applications for remote viewing are still in need for further development. Accordingly, this study is important, as it focuses on using the method of continuous self-monitoring to classify scenes in remote viewing from 2015 to 2019.

According to Nelson and Hayes (1981), ‘[s]elf-monitoring is the procedure, by which individuals record the occurrences of their own target behaviours.’ Self-monitoring received wide attention from the experts and was widely used in regard to the social skills (Seligson, 2004), paranoid ideations (Williams, 1976) and as a treatment for depression and inactivity (Harmon et al., 1980). However, in relation to supernatural abilities, especially remote viewing, it has not been similarly utilised. Therefore, the main objective of this study is to classify scenes in remote viewing using self-monitoring. This aim considers the following core research objectives:

1) To detect the differences between the scenes in remote viewing

2) To determine the time period between the time of viewing and the time the event occurred

Basic concepts in this paper:

The viewer: a person who can see events that will happen in the future through the use of remote viewing

Scene: a view or picture of a place, event or activity that is seen by the viewer during instances of remote viewing

Time of viewing: the time when the viewer saw the scene.

The scene becomes true: when the scene seen by the viewer has happened in real life

The period of occurrence: the period between the time of viewing and the time when the scene becomes true

A scene as a picture: a static, motionless scene

A scene as a scenario: an animated, reactive scene (people, animals, cars, etc. move)

Method

To reach the objective of the research, the steps presented in Figure 1are followed.

As illustrated in Figure 1, there are six stages. Each stage is explained in detail below.

- Drawing the scene: Draw everything that can be observed in the scene (the number of people, means of transportation, nature, buildings, road shape, sun, clouds, etc.). It is preferable to write directly on the drawing and explain the characteristics that cannot be drawn, such as words that were heard or specific feelings.

- Scene time and date: This is important because this will help the viewer determine the length of time before the scene materialised.

- Scene classification: the classification of scenes based on the number of images in the scene and the presence of movement. Are there elements in the scene that move, and what is the location of the viewer in the scene? Is the scene physically tangible or only visual?

- Has the prediction occurred: monitor all events, whether local or international, and continue to evaluate the scenes that occurred while ignoring unfulfilled scenes?

- How long it took to occur: this can be reached by the following equation:

The date on which the prediction was achieved-the scene's date

- Which elements in the scenes did or did not match with the event occurring in real life: the number of elements in the scene that did or did not match

Results and Discussion

Through the continuous self-monitoring method, the remote viewers can determine the categories of scenes and define all their characteristics. In this study, scenes from 2015 to 2019 were drawn and recorded in detail in a special notebook. It is preferable that, the drawing be created during the viewing, while writing the date and time of viewing afterwards. Then, the scenes were classified into two classes, as shown below based on the characteristics of the scenes that were seen (the movement in the scene, the number of pictures in one scene and the viewer's location at the scene). After documenting the details, all scenes, were not achieved and those that the viewer was unable to prove were excluded. Moreover, the viewer focused on the scenes, which were able to be verified by the news of its occurrence, either locally or globally.

Classification

Scenes that became true were classified into two categories: first, a scene as a picture, and second: a scene as a scenario.

First: a scene as a picture

A scene as a picture is static, motionless scene. From the results of self-monitoring from 2015 to 2019, a scene as a picture was divided into two subtypes: scenes containing only one static image – for example, seeing a dead person in a desert area – and scenes that contain two pictures, all of which are static – for example, a picture of a dead person in a desert and a picture of a known building or the flag of a specific country. The next section discusses the features of images that are reached through self-monitoring.

A description of the images

1. Clarity:

A picture as a mirage or imagination or as a ‘semi-hazy spectrum’ (I did not find a suitable explanation for this description). All elements of the images can be distinguished.

2. Arrangement of the pictures:

If the scene consists of two pictures, it would be seen consecutively and quickly, over less than a second where the viewer was able to see the first picture, followed by the second picture.

3. Colours:

All pictures are colourful, where the viewer was able to distinguish all the elements.

4. Image format:

All pictures do not appear completely, meaning that these are neither square nor rectangular like a usual image; instead, these are shaped rather irregularly.

5. Image size:

There is no specific size, but it is generally described as rather small.

6. Dimensions:

The viewer was able to see image in two dimensions. Descriptions can include the weight and height of an individual, the number of floors in the building, etc.

7. Direction:

The viewer was able to notice that pictures appears in a certain direction, for example, on the right side of the right eye, and felt compelled to look at the right side. Even when the viewer remembered these pictures, these are forced to look in the same direction in which the picture was first seen.

Method of interpretation

The viewer was able to notice that the two pictures are related to each other, meaning that the symbols in the first image are related to the symbols in the second image. For example, in a scene consisting of two pictures, the first image depicted a dead policeman in the desert and a man standing behind the body in front of a police car. The second picture appears immediately after the first image. The second picture depicted the dead policeman, but the police car and the other man are not present.

The interpretation: there is a man who will kill the policeman, and a police car will be used to escape. The viewer was able to feel what will happen in this scene. The policeman was stabbed, even though the actual moment of the attack is not present in the scene.

From the observations of the viewer: first, writing and drawing directly after viewing the scene is very important. The viewer noticed that often when attempting describe the scene, it is difficult to repeat all of what was written ('I do not know why the viewer feels tired'?) after writing and drawing the scene. Second, writing and drawing is much easier than directly explaining to a specific person what was seen.

Second: a scene as a scenario

A scene as a scenario is an animated scene that depicts a story (like a dream). From the results of continuous self-monitoring from 2015 to 2019, a scene as a scenario was further classified into two types:

1. A scene is both visible and tangible, meaning the viewer can move inside the scene. For example, in one scene, the viewer saw a picture of a personal office, two people talking and a box. The viewer walked in the office and went to the box. They saw what was in the box and saw the papers on the table but did not read what is written. All this happened while the viewer was sitting alone. The viewer also heard what the two people said, meaning the viewer heard the words as symbols. For example, the viewer heard a word of fire and the name of a country. In addition, the viewer felt what those two people were thinking, such as whether they were considering killing a certain person. Also, in another scene, the viewer was so close to someone in the scene that the viewer felt afraid. If they saw a muscular person giving orders to a group and the viewer is afraid of that character,

2. The viewer saw a moving scene but could not interact with it and instead followed it from afar, meaning that the viewer saw everything that happened as if the viewers were watching a film. For example, in the scene, the viewer saw a large public square, a speeding car, people running through the square, someone sitting beside the road crying and a person shouting at everyone. At the same moment, the viewer saw a picture of a familiar flag, emblem or symbol. The duration of the vision did not exceed five seconds.

Has the prediction occurred?

The following method can be used to prove whether the scene has occurred in real life: after writing down the details of the scene, the viewer then follows the international and local news since most of the scenes were depicted as global bad events.

To clarify the idea, the viewer saw a scene depicting a fire burning in a place that resembles a house or a room. The viewer wrote all the details about the scene and then began to follow international and local news to determine if similar news appeared. The viewer also began inspecting their house, taking the necessary precautions and inspecting the entrances to facilitate escape in the event of a fire. Suddenly, after about four days, the viewer heard fire trucks next door and saw smoke billowing out of the house. In addition, the viewer's neighbour moved from the house as a result of the fire’s devastation. Then the viewer wrote in their notebook that the scene became true.

One of the important findings reached by the viewer in this section is that there were some scenes that were judged to have no occurrence and thus it can be ignored in the notebook. This is due to their inability to follow all the news in the world. The viewer thinks that reliance on continuous self-monitoring is very difficult forth viewers and it leads some scenes to be judged as not being true.

How long it took to occur

The viewer used the equation shown below to calculate the period between the viewing and the time when the scene became true, as well as to compare all the scenes to find commonalities between the periods of occurrence and the types of scenes. This will help in the future to predict the time of occurrence after viewing. The result reached by the viewer indicated that there is no clear link between the time of occurrence and the type of scenes due to the extreme variation in duration. Some took days, some months, and some took over a year to occur. For example, across all instances of scenes as scenarios, the period between the time of viewing and the time when the scene became true was different.

Which elements in the scenes did and did not match the event occurring in real life?

The viewer reached the following main results:

-There are elements in some scenes that did not correspond to the real life, such as distinguishing between night and day. In some of scenes, the events took place at night, but when the scene became true, it occurred during the daytime.

-There were some scenes that were expected to occur in a particular country, but when the scene became true, it occurred in a different country. However, if there were any symbol indicative of a country, such as a flag, this means that scene will occur in that country.

- There was a scene containing characters, who were speaking in a language other than Arabic, but the viewer could understand what was being said even though they cannot speak the language.

- In some scenes, identifying a character's name was difficult, but it can be distinguished whether a person is a man or a woman.

Conclusion

This study has proven that, continuous self-monitoring is very effective in classifying scenes and determining their characteristics, and it is an excellent way in remote viewing. One of the most significant results of using continuous self-monitoring is the realisation that there is no clear relationship between the period of occurrence and the type of scene. The viewer explained that, several scenes of the same type could have vastly different periods of occurrence. The viewer also found that, self-monitoring can be difficult to use in determining whether the scene has occurred in real life.

Continue your exploration of Continuous Professional Development with our related content.

References

Brown, C. (2005) Remote Viewing: The Science and Theory of Nonphysical Perception, Farsight, Inc.‏

Dunne, B. J. and Jahn, R. G. (2003) ‘Information and uncertainty in remote perception research’, Journal of Scientific Exploration, vol. 17, no. 2, pp. 207–241.

Harmon, T. M., Nelson, R. O. and Hayes, S. C. (1980) ‘Self-monitoring of mood versus activity by depressed clients’, Journal of Consulting and Clinical Psychology, vol. 48, pp. 30–38.

Nelson, R. O. and Hayes, S. C. (1981) ‘Theoretical explanations for reactivity in self-monitoring’, Behavior Modification, vol. 5, no. 1, pp. 3–14.‏

Seligson, E. C. (2004) ‘Effects of Training, Prompting, and Self-Monitoring on Staff Behavior in a Classroom for Students with Varied Exceptionalities’.

Smith, C. C., Laham, D. and Moddel, J. (2014) ‘Stock market prediction using associative remote viewing by inexperienced remote viewers’, Journal of Scientific Exploration, vol. 28, pp. 7–16.‏

Targ, R. and Katra, J. E. (2000) ‘Remote viewing in a group setting’, Journal of Scientific Exploration, vol. 14, no.1, pp. 107–114.‏

Williams, J. E. (1976) ‘Self-monitoring of paranoid behavior’, Behavior Therapy, vol. 7, p. 562