Datasets as Vectors for Intention?

In June 2018, Windbridge Institute, LLC co-founder and researcher, Mark Boccuzzi, presented the poster “Datasets as Vectors for Intention Measuring the Impact of Intention-Entangled Datasets on the Happiness Scores of Online Gamers” at a joint meeting of the Society for Scientific Exploration and the International Association of Remote Viewing (SSE/IRVA). Boccuzzi’s article below is based on that SSE/IRVA presentation. The project discussed was exploratory in nature and this article should not be considered a formal research report. The results presented should not be mistaken for any kind of claim. The article is provided “as is” without any warranties. Always check with your health care provider when considering treatment options. 


Datasets as Vectors for Intention
Measuring the Impact of Intention-Entangled Datasets on the Happiness Scores of Online Gamers: An Exploratory Study
by Mark Boccuzzi

Previously, FieldREG data from random event generators were collected while experienced meditators focused their attention on the feelings of Love and Hate. The data from these sessions were then converted into images using custom-developed visualization software (Boccuzzi, 2015).

Using deep learning-based image feature classification and extraction software, a unique feature was identified in four of the five Hate images. However, analysis of the data sets found no differences between them that would account for these visual feature differences (Boccuzzi, 2016).

This finding raises two questions:
Q1: Is the intention that created each dataset somehow influencing how the data are being interpreted by the visualization software?

Q2: If the data have become a vector for the original intentions, might people who interact with those datasets be influenced by those intentions?

To address these questions, the current exploratory study used the Love and Hate datasets and a new Neutral intention to drive the gameplay of an online app in order to determine if the intention associated with each dataset impacted the mood of the app’s users.

“Crystal Capture” is a web-based game in which players drag a graphic of a crystal onto a rotating pyramid located at the center of the screen.

To control game operation, the values from each of the previously collected Love and Hate FieldREG datasets were added to produce single sum values for each which were then used as seeds for the game’s pseudo-random number generator (PRNG). A Neutral condition seed (Neutral.Seed) was generated by using the website to produce a random value between the Love.Seed and Hate.Seed values.

Gameplay Walkthrough:

1. The player is asked to report their current level of happiness using a visual scale.

2. The game randomly assigns the player to the Love, Hate, or Neutral condition as calculated by a UNIX Epoch time mod 3 operation. The gameplay mechanic for each condition is identical, but game details such as the placement of graphics and the selection of sound effects are controlled by the condition seed value.

3. Upon completing 20 rounds of gameplay, the player again rates their level of happiness.

4. Data from each user session are saved in a MySQL database on the game server.

Pre- and post-happiness score data from 120 players were analyzed.

There was a statistically significant difference between the change percentage for each of the three conditions (p < 0.00001, Kruskal-Wallis test, H(2) = 27.6659). Love condition player scores showed increased happiness (p = 0.0034, two-tailed t-test), while those in the Hate condition showed decreased happiness (p = 0.0004, two-tailed t-test). No difference was found for the pre-post Neutral condition scores.

Data collection was closed after 120 results were collected.

Game conditions (Love, Hate, Neutral) were identified in the database by a code and the experimenter was blinded as to the coding scheme until after all data were collected and analyzed.

While strong conclusions should not be drawn from this exploratory experiment, the data suggest that player mood changed in a manner consistent with the intention-based data that were used to control the game.

In light of these data, it is interesting to consider not only applications for intention-entangled datasets but also that experimenters’ beliefs, desires, and intentions relating to their experiments might have an impact not only on experimental outcomes but also on the attitudes and moods of other researchers who interact with those data.

Future Directions
Future studies will focus on the Love and the Neutral conditions with a larger population and may incorporate additional intentions.

A demonstration version of Crystal Capture can be played online at
No user data are saved in this demo version. Gameplay in this demo version was created using the Neutral.seed. No mood changes should be expected.

This research originally presented at the 37th Annual Meeting of the Society for Scientific Exploration (a joint conference with the International Remote Viewing Association) in Las Vegas, Nevada, June 2018.

Boccuzzi, M. (2015). Visualizing intention: Art informed by science. Tucson, AZ: Windbridge Institute, LLC (Blurb).

Boccuzzi, M. (2016, June). Applying machine learning to psi research: An example of using a deep machine learning image classifier to analyze seemingly random visualized FieldREG data collected during sessions with meditators. 35th Annual Meeting of the Society for Scientific Exploration and 59th Annual Convention of the Parapsychological Association Joint Meeting. Boulder, Colorado.

The information in this publication is provided “as-is.”  In no respect shall the author, the Windbridge Institute, LLC, or any of the Institute’s agents, representatives, or employees incur any liability for any damages, including loss of income, arising out of, resulting from, or in any way connected to the use of the information provided in this publication. Always consult a licensed health care provider when evaluating treatment options or making lifestyle changes.