5 Science-Based Ways To Become Mastermind Like Ayanokoji

Summary

The video argues that “mastermind” thinking, represented by Kiyotaka Ayanokoji, is not an inborn gift or supernatural ability. It is a set of probabilistic reasoning skills that people already use intuitively and can improve through deliberate practice.

Effective strategists combine prior expectations with new evidence, infer hidden intentions from behavior, recognize patterns across different situations, and use their limited attention selectively.

Five Ways to Develop This Kind of Thinking

1. Recognize that you already reason probabilistically

People may struggle with explicit probability calculations, yet still use intuitive probability in everyday judgments. Research described in the video found that even preschoolers make choices shaped by likelihood before they can do basic arithmetic.

Treat probabilistic thinking as a skill to train, rather than a rare ability you either have or lack.

2. Improve forecasting by combining expectations with evidence

Predictions draw on two sources: what usually happens in similar situations and what is happening in the present one. Avoid relying only on past experience, which can make you ignore new information. Also avoid reacting to every new signal as if you have no prior knowledge.

Practice: State what you expect before an event, then compare that expectation with what actually happens and update your view.

3. Infer people’s beliefs and goals from their behavior

Bayesian Theory of Mind describes how people infer another person’s hidden beliefs and goals by asking what would explain their actions. Hesitation, avoidance, or unusually quick agreement can all be evidence, but each should lead to a revised hypothesis—not certainty about what someone thinks.

The same reasoning can apply to groups: consider how others may decide and how your own actions might change the group’s behavior.

Practice: Instead of focusing only on what someone said, ask what they would have to believe or want for their behavior to make sense.

4. Look for structural similarities between problems

Analogy can help transfer a solution from one situation to another when their underlying relationships are alike, even if the people, setting, or stakes differ.

The video connects this idea to models of solving Raven’s Progressive Matrices: success may depend on generating plausible rules for a pattern and refining them with evidence.

Practice: When faced with a new problem, identify what it is structurally similar to—how its parts relate—rather than judging only by surface appearance. One suggested exercise is to list three possible analogies.

5. Train deliberately and spend attention selectively

The brain uses approximate, resource-limited inference rather than performing exhaustive calculations. Under pressure, distraction, or fatigue, judgments may become less reliable.

The video says research found that targeted training can improve probabilistic reasoning, with one brain-imaging study measuring changes after training. It also cites 2025 research in which large language models did not meet the cited Bayesian standards without targeted training.

Practice: Build the habits above: record expectations, test interpretations of people’s goals against later behavior, and search for structural analogies. Apply careful reasoning to the people and patterns that matter most instead of trying to analyze everything.

Main Takeaway

The video presents a strategist’s advantage as disciplined, efficient inference—not seeing everything or reading minds. The underlying capacity is ordinary; the difference is whether it is trained and directed toward useful questions.

Speakers and Sources Featured

• Speaker: An unnamed video narrator. No other person is directly quoted in the subtitles.
• Character referenced: Kiyotaka Ayanokoji, used as an example of the “mastermind” archetype; he is not a speaker.
• Research and sources mentioned, without named authors or institutions:
  • Research on intuitive probabilistic reasoning in adults and preschoolers.
  • Predicting the Future as Bayesian Inference.
  • Research on Bayesian Theory of Mind and group decision-making.
  • Research modeling analogy as Bayesian inference over relational structures.
  • Bayesian modeling of rule discovery in Raven’s Progressive Matrices.
  • Research on computation-limited Bayesian updating.
  • A 2025 study of large language models and Bayesian reasoning.
  • A brain-imaging study examining probabilistic-reasoning training.