Abstract

A total of 2,040 children were tested in a school environment with a computer software program designed to measure the intuitive ability of predicting a future, randomly selected target. This ‘precognition’ test consisted of 30 trials. Comparisons between gender and four categories of age groups ranging from 3-19 years of age were examined. Females scored significantly overall (z = 1.75, p = .04), whereas males did not (z = 0.39, n.s.). The only highly significant finding in age categories came from the youngest age group of children 3-6 years of age (males z = 2.29, p = .01; females z = 2.13, p = .02; overall z = 3.13, p = .001).


Introduction

Intuition is a mental faculty not well understood by science. The researcher defines intuition as the ability for obtaining immediate or direct knowledge. Intuition helps one recognize a pattern beyond that which is visible to the senses (Gerrity, 1987) by accessing information of a fact or truth as a whole independently of the linear reasoning process (Rew, 1986). In a study with college students, Bowers et al. (1990) concluded that intuitive hunches often guided students to accurately guess the correct solutions when they were uncertain of the coherent patterns they could not identify during verbal and non-verbal tasks. Schlitz and Honorton (1992) suggest that intuition may be related to creativity. In their study with a small population of artistically focused individuals, they found significantly higher scores than with a non-artistic population (Schlitz and Honorton, 1992). Kennedy and Kanthamani (1995) found a positive correlation between intuitive experiences and expressing artistic creativity. Intuition may have a physiological interface with the heart (McCraty et al., 2004), pineal body (Roney-Dougal, 1993) and right cerebral hemisphere (Alexander et al., 1998). However, these findings have yet to be replicated by other researchers.

One of the more well-studied aspects of intuition is known as precognition, which literally means ‘future knowledge.’ Precognitive experiences, especially in the dream state, are the most common psychic events to occur in the lifetime of the average individual (Rhine, 1954). In a meta-analysis done by Honorton and Ferari (1989) of 309 precognition experiments spanning from 1935 to 1989, conducted by 62 investigators involving over 50,000 participants contributing more than two million trials, they discovered thirty percent of these studies were statistically significant in showing that people can describe future events, where only five percent would be expected by chance. The odds against this being a chance finding are 1 in a number so large that it has 10 with 20 zeros after it.

Studies with Children

Minimal research of measuring intuition in children has been done in comparison to adults (Bourgeois and Palmer, 2002). Several factors need to be considered upon making the decision to work with children. They may be hesitant to trust researchers and could have difficulty understanding the given task, and designing experiments that are fun and engaging is also a challenge (Anderson, 1956; Anderson and McConnell, 1962; Anderson and White, 1958; Drewes and Drucker, 1991; Johnson et al., 1972; Kanthamani et al., 1986).

An earlier study indicated that children demonstrate more intuition than adults (Botrill, 1969), but it is premature to make any strong claims since there have not been enough studies comparing children with adults. Due to the lack of exercise and support, intuition, like any other functioning ability or skill, may become repressed (Ehrenwald, 1972) and through disuse, decline with age. Later studies suggest that children at early developmental stages tend to score higher than children at later stages (Drucker and Rubin, 1975; Drucker and Drewes, 1976; Drucker et al., 1977; Kanthamani et al., 1986). Two subsequent studies support these findings (Shargal, 1987; Spinelli,1987).

Pratt (1961) suggests conducting research in an environment (such as school or home) that is both familiar and comfortable to a child will likely produce better results. J.G. Van Busschbach (1953; 1955; 1956; 1959; 1961) conducted research measuring children's intuitive functioning in different schools in the United States and the Netherlands and found significant results in both countries. He also found that younger children scored significantly higher than older children in grade school (Van Busschbach, 1959). Drucker and Rubin (1975) measured a smaller population and found that children tested at home scored higher than those tested at school. A follow up study by Drucker et al. (1977) found similar results.

A popular belief in Western culture is that females are more likely to trust their intuition than males although there is no substantial evidence to support this claim. Van Busschbach (1956; 1959) and Freeman (1962; 1963; 1965; 1970) have conducted most of the research comparing gender differences. Earlier studies by Van Busschbach (1956; 1959) found that females scored significantly higher than males for both studies combined. Freeman (1962; 1963; 1965; 1970) found that the type of targets used during the experiments made a significant difference in scoring results between the two genders. For instance, females scored higher when the choices of targets were of a similar type from trial to trial, and males scored better when the choices of targets were different from trial to trial.

Three very simple hypotheses were formulated based on these earlier studies:

1. Females will score higher than males on average.
2. The youngest age group of children will score highest on average and there will be a decline in average scores with age.
3. There will be a positive average for all participants combined.


METHODS

According to Goldberg (1983), intuition has six functional applications, including one to make future predictions. Cappon (1993) identified twenty different intuitive skills including foresight, which is used to anticipate or predict future events. Based on these two sources, the researcher in the present study was interested in investigating precognition. Experiments by Winkelman (1981) indicate that precognition scores correlated negatively with math ability, years of schooling, and age.

Over a period of four school years (September 1999 to June 2003), a total of 2040 subjects (1020 males and 1020 females) from ages 3-19 participated in the study. Sixty males and sixty females were tested at each of these age groups. These numbers are recorded in Table 1. For each age in the range of 3-19 I tested 60 boys and 60 girls, totaling 2040 (1020 boys and 1020 girls). Previous studies with children cited above included fewer participants. Testing a larger population size would give scientists a better indication of how intuition is generally distributed among different genders and age groups. All participants were selected from different schools located in Minneapolis, St. Paul, and the surrounding metropolitan area. It began with the researcher establishing contact with principals and teachers of all the schools informing them of the study and requesting permission to test the children. Informed consent was obtained from both parents and school administrators to solicit every student for the study. With the option to decline, all students who agreed to participate completed the test. I selected the first 60 boys and 60 girls who agreed to complete the test. Very few children turned down the invitation to play who were granted permission. I would say less than a dozen. On the flipside, many children wanted to participate but didn’t because the quota for their age group had been filled.

A computer software program designed by Vaughan and Houck called Psychic Reward was downloaded on a Macintosh PowerBook 145 laptop computer as the testing device. The program itself contains one game, which was designed as both a testing device and a training tool for intuition, particularly precognition. An example of what it appears like on the computer screen is shown in Figure 1. A mouse was plugged into the laptop computer and sat on a mouse pad as an optional means of making selections from the screen. Upon arrival, the researcher set up the computer in a particular classroom corner where teachers and students were present, or in an isolated separate room.

Each game consists of 30 trials as indicated in the upper left-hand screen. All targets are selected by the computer, which operates like a random number generator (RNG). The computer’s internal clock freshly seeds the computer’s pseudo-random number generator (PRNG) for a new precognitive target after each trial. Extensive testing by Vaughan and Houck (2000) has demonstrated that the random selection process used in the program is adequately random. The researcher also tested the program’s randomness in each site location for a total of 64,800 trials and obtained an average score of 1000.03, which is very close to the theoretical mean chance expectation (MCE) of 1000 previously determined by Vaughan and Houck. All random tests were conducted both before and after participants were tested to make sure that the program was not inherently biased.

The challenge for each participant is to anticipate which of the wheel’s lettered slots from “a-z” the computer will randomly select. Psychic Reward automatically records the score after each trial, and one game is completed after 30 trials. The possibility of cheating on Psychic Reward is virtually nil. Once a score is recorded on the file, it cannot be tampered with because the program is designed to eliminate any possibility of erasing the score by starting over again.

Participants were instructed to select a letter around the circle either by typing it in on the keyboard or using the mouse to click their choice. Every participant was told that the purpose of the game was to win the jackpot as many times as possible in thirty tries. All participants completed one game of 30 trials. A jackpot occurs when the participant is able to predict the correct letter. The probability of choosing the correct target is 1/26 for each trial. Psychic Reward provides weighted feedback- meaning the closer one picks to the target, the higher the score. For example, a direct hit= $10,000, one off the mark on either side of a direct hit= $4,000, two off the mark= $2,000, three off= $750, four off= $500, etc. Although it is not shown in figure 1, the program does provide both auditory (a sound), and visual feedback (e.g. lower right hand corner will read: $10,000 indicating a jackpot) immediately after each trial. The sound produced for auditory feedback ranges from a fraction of a second to several seconds in length depending on how close the prediction is to the target. For example, a jackpot produces the most exciting and lengthiest sound. Tart (1977) postulates that the decline effect found in earlier studies were due to no immediate feedback. By providing immediate feedback following each trial could help the ability to make correct decisions (Tart, 1977). Both grand total points earned and the average score are also shown in the lower right hand corner of the screen, which provides each participant additional visual feedback after each trial.

RESULTS

Differences between Age Groups

According to Vaughan and Houck (1992) the following equation is used to calculate z scores:

z = (average – 1000)n / 2110.345

The standard deviation (SD) is calculated at 2110.345, which was confirmed in 62,000 Monte-Carlo iterations (Vaughan and Houck, 1992). All z scores are computed by subtracting the MCE (1000) from the average raw score then multiplying it with the square root of the number of trials (n) divided by the SD. Psychic Reward uses this equation to automatically calculate z scores cumulatively after each trial. For a more detailed understanding of the program itself, examine all previous studies of Vaughan and Houck in the references. In Table 1, z-score calculations are found for all four age groups. These were estimated again by using the equation above.

Group A (3-6 years of age) participants of preprimary and kindergarten children had the highest overall average z scores of any of the four age groups in the study. Males (z = 2.29, p = .01) and females (z = 2.13, p = .02) scored well above chance expectation. Together they scored (z = 3.13, p = .001) highly significantly above chance. There were 480 (240 males and 240 females) participants in this group. Males scored slightly higher than females in this age group.

Group B (7-11 years of age) students in elementary school (grades 1-5) registered the lowest z scores in the study (males, z = -0.84; females, z = -0.31; overall z = -0.81). In this group there were 600 (300 males and 300 females) participants. Females scored non-significantly higher than males.

The z scores of Group C (12-14 years of age) students in middle school/junior high (grades 6-8) show that males (z = 0.41) and females (z = 1.23) both scored above chance. Together they scored above chance (z = 1.16, n.s.), but not significantly. Females scored non-significantly higher than males in this age group. There were 360 (180 males and 180 females) participants in this age group.

The combined results of Group D (15-19 years of age) students in high school (grades 9-12) and college are essentially flat (males z = -0.82, females z = 0.73, and overall z = -0.06). Like the results of Groups B and C, females in Group D scored non-significantly higher than males. There were 600 (300) males and 300 females) participants in this group.

Gender Differences

Graph 1 is a plot of the complete raw scores for males and females of each age studied. Each bar consists of 1800 trials for a total of 3600 trials per age group. The theoretical MCE is 1000. Although males (z = 0.39) and females (z = 1.75) in the present study both scored positively overall, only the females scored significantly positively (p = .04).

In table 2 is an analysis of variance (ANOVA). Significant effects were found in both combined (p = .037) scores and age group differences (p = .025). There was a non-significant (p = .359, n.s.) effect between males and females.

DISCUSSION

Each interaction between researcher and participant was uniquely different based on the multiple age levels, cultural backgrounds, and personal understanding of the English language. For instance, group A participants had a limited understanding of the English language, being so young. At this age group, very little verbal interaction between researcher and participant occurred once the game was underway. These participants were often excited to just have an opportunity to play on a computer. It is doubtful that most of them understood what they were trying to do, but they rarely asked questions. Group B participants displayed more complex behaviors than group A, including asking more questions about the game. A number of participants in this age group showed signs and symptoms of mild distress, helplessness, hopelessness, shyness and even confusion. Group C and D participants were curious about what the study was for and asked more inquisitive questions. Many participants of groups B-D attempted to find sequential patterns in the game, but quickly learned there were none. A few participants who were insistent the game must have some kind of logic became very frustrated. Overall, there were a number of participants who spoke English as a second or third language, so additional instructions needed to be given. No interpreter was used in the study.

Building upon previous studies outlined earlier in this paper, the current study provides support that successful intuitive functioning demonstrates an overall decline in children as they get older. Comparing scores from group A to B indicate a significant drop-off in both genders combined from ages 3-10 (r = -0.80, p = .02, two-tailed). Intuition seems to be a natural ability people are born with, as evidenced by the youngest age group scoring the highest in the study. What happens as they get older? Perhaps it may be a combination of school teachers and parents/guardians not giving children the opportunity to exercise their intuitive talents or providing them enough positive encouragement for using this type of aptitude. Another hypothesis is that intuitive abilities are related to both cognitive development and volition. A follow up study could examine these possibilities more closely.

Previous studies by Tart (1977), and Vaughan and Houck (1992; 2000) mentioned earlier show indications of learning. It would be interesting to measure how children learn to develop their intuition by allowing them to continue playing Psychic Reward over an extended period. Because this game for the most part is based purely on chance, it would be of interest to examine whether children's understanding of the probabilities of randomness correlates in any way with the results. When making predictions under uncertainty, Kahneman and Tversky (1982) found that peoples’ intuitions were influenced by faulty heuristics, which may explain why the older children in this particular study were not able to obtain significance. That is, it may be that logic (whether accurate or faulty) overrides precognitive intuition. In the present study, many group B participants who contributed to the lowest z scores overall demonstrated a persistence in the use of logic even though immediate feedback indicated a lack of success in their reasoned strategies. Groups C and D participants showed these tendencies as well, but were a bit quicker than group B participants in learning to abandon their logic as the game progressed. Cosmides and Tooby (1996) indicate that people do possess intuition about probability but the instruction given by teachers needs to be changed to cultivate this skill and to facilitate learning. Children who received instruction on probability showed improvement in their intuitive predictions of outcomes (Fischbein, 1987). Another hypothesis that could be tested in a follow up study is that children given instruction on probability will likely produce more significant results, especially in the ages from 7-19.

What are the applications of intuition in the field of medicine? There is a growing body of literature under the title “Intuition Medicine” that discusses the importance for health and wellness professionals to use this ability as an effective means for improving client/patient care. Shealy and Myss (1988) documents how a medical intuitive diagnosis can be more accurate than using conventional technologies for testing. With the high costs of doing such tests, intuition could provide a cost effective way for improving the quality of care in the long run. One prime example of how intuition could be directly applied in medical school would be in the courses related to bedside manner. All medical students have to complete such courses now to finish school. Children who choose to become health care professionals after high school could be better prepared if they learned how to develop and exercise various intuitive abilities and skills during their developmental stages of growth. Parents, educators and healthcare professionals should consider this important ability.


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Christian J Hallman, Ph.D. is the founder of Psionics Research Education & Development, an organization that focuses on the application of mental and spiritual abilities, skills and talents. He spent a year in the Middle East from 2004-2005, which included both Kuwait and Iraq, as a medical officer in the United States Army providing counseling and therapy for people suffering from psychological trauma. During sessions he utilizes a combination of different mind-body techniques such as: relaxation, visualization, contemplation, meditation and cognitive behavioral training. When not being deployed, he maintains his own consulting business called Quality Health Fitness & Wellness that specializes in teaching workshops on stress management, conflict resolution, suicide/homicide awareness & prevention, anger management, drug/alcohol awareness & prevention, tobacco cessation, Yoga, Qigong and other forms of meditation.