What Is Associative Remote Viewing and How Is It Used in Horse Racing?

Discover what Associate Remote Viewing is and how it’s applied in horse racing. Learn how this method works, why some bettors use it, and its potential impact on predictions.

Associative Remote Viewing (ARV) in Horse Racing: What It Is and How It Works

Not everyone knows about Remote Viewing (RV), a method that allows a person to describe objects or events beyond their physical senses, whether distant in space or time. It was developed in the 1970s–80s as part of the Stargate Project, funded by the CIA and U.S. Department of Defense, with research at Stanford Research Institute (SRI International). Many researchers, including Ingo Swann, produced results so striking they couldn’t be explained by chance.

From RV, a technique called Associative Remote Viewing (ARV) emerged. It is used to predict outcomes when multiple options exist, such as the winner of a horse race. ARV leverages the fact that viewers naturally mix target descriptions with personal associations. Experiments with ARV show that human experience is crucial. Accurate predictions require strong remote viewing skills and the ability to correctly interpret results.

For anyone interested in a remote viewing training app, Verevio is one of the most complete training platforms. It’s on both the App Store and Google Play. The app splits practice into focused trainers so you can build skills step by step before moving into full sessions. You don’t need a partner, which makes training easier, and the target pool is huge — over 1,000 targets in the Remote Viewing Quiz (RVQ) trainer alone. A good starting point is beginner-friendly exercises like Lifeforms or First Impressions. This will help you gain practice if you are interested in practicing ARV in horse racing predictions yourself.

Once you have some experience with these exercises, it becomes easier to understand how ARV works in real-world applications.

How It Works

In short, in ARV (Associative Remote Viewing), a bet is placed before the race, and the outcome is “linked” to a future image or object. The photos act as anchors, helping to lock in the result and give the remote viewer a structured way to test accuracy.

Check step by step how it works: 

1. The race has not yet taken place. For example, tomorrow at 15:00 there will be a horse race, and there are three favorites: horse A, horse B and horse C. 
2. The organizer creates a system of associations in advance:
   1. If horse A wins → after the race the remote viewer will receive a photo of Big Ben. 
   2. If horse B wins → after the race he will receive a photo of the Themsa River. 
   3. If horse C wins → after the race he will receive a photo of a football ball. 
3. Even BEFORE the race, the remote viewer is given the task of describing the image of the photo that will be shown to him AFTER the race.
   1. He knows nothing about horses and associations. 
   2. He describes: “I see a tall metal structure, arrows, a sense of the city” → the organizer sees that it is closer to the Big Ben.
4. The organizer knows the association table.
   1. Big Ben = horse A. So, he places a bet on horse A BEFORE the start. 
5. Then the race takes place.
   1. If horse A wins, the viewer after the race receives exactly the photo (the Big Ben) that was “sewn” into the result. 
   2. If the prediction did not match, it means that the image was interpreted incorrectly. 

Note: Multiple experiments have shown again and again that accuracy depends heavily on people — on their skill, experience, and consistency.

Research and Results

Modern researchers, including Debra Lynne Katz, Ph.D., have studied ARV extensively. She conducted a series of experiments and published a book on the subject.

In one project, 86 ARV experiments were conducted, involving 220 transcripts of what viewers reported. Three groups of judges evaluated the results blind, meaning they only worked with the texts and sketches of viewers without knowing the actual events. Their task was to assess how closely the descriptions matched future outcomes and to make predictions. These assessments were then compared to check the level of agreement between judges.

The findings were telling:

• Full agreement among all judges occurred in only 6.9% of cases.
• In 19.7% of cases, a strong majority (8 out of 9 judges) reached the same conclusion.
• Judges with more experience produced statistically more accurate assessments than less experienced ones.
• Results remained consistent regardless of which scoring scale was applied.

The Bottom Line

At the end of the day, it all comes down to human factors. The most consistent results come from those who combine technical skill, domain knowledge, and disciplined interpretation. If you have experience in horse racing and remote viewing, you’re likely to have an advantage when it comes to ARV.