This is a reframing of both Wittgenstein’s and Russell’s ideas in a way that brings them into the domain of cognition, neuroscience, and game theory. The key step is to take Wittgenstein’s notion that games are not defined by any one necessary feature but by a "family resemblance" of activities, and then apply this to cognition—specifically, how the brain handles sense data and sensations.
De-externalizing Wittgenstein’s Game Concept
Instead of treating "game-ness" as something external (rules, competition, goals, play, etc.), we look at it as a way of organizing experience within cognition. That is, the brain doesn’t just passively receive Russell’s sense data; it structures, categorizes, and interacts with them in game-like ways to make sense of the world.
Game-like Properties of Cognitive Processing
What does the brain do with sense data that makes it "game-like"? We could break this down into several mechanisms:
Pattern Recognition as Rule Formation
The brain doesn’t just register data—it infers rules from repeated exposure to stimuli.
E.g., a child sees an apple roll off a table and expects another apple to do the same.
These inferred rules are flexible, much like the rules of games—sometimes explicit, sometimes implicit.
Categorization as Game Classification
In games, we classify things into roles: player vs. opponent, goal vs. obstacle, tool vs. useless item.
In cognition, the brain does the same: safe vs. dangerous, edible vs. inedible, self vs. other.
This means that categorization itself is a kind of game, where the brain tests and refines its "rulebook" based on interactions with the world.
Predictions as Gameplay Moves
The brain simulates outcomes based on inputs.
Much like in a game where we imagine possible moves before making them, the brain predicts the consequences of action (or inaction).
This is fundamental to decision-making—choosing "moves" in real life.
Feedback Loops as Game Iteration
Games involve feedback: winning, losing, scoring points, failing, retrying.
Cognition operates similarly: neurons fire in response to stimuli, predictions are tested, and errors refine the system.
Learning is, in a sense, playing the game of adjusting to reality with better strategies.
Memory as Game Replay & Strategy Storage
Memory is not a passive recording device but a storehouse of past “games” played with the world.
It allows us to "replay" strategies, refine them, and use them in similar but novel contexts.
The Practical Cognitive Implication
If "game" means doing something with information that enables us to interact with the world practically, then cognition itself is fundamentally game-like at every level. It does not receive sense data—it plays with it, structures it into meaningful units, and refines its internal rules through experience.
This perspective aligns well with predictive processing models of cognition (Friston, Clark), which suggest the brain is an active "predictive engine" rather than a passive data-processing machine. It also resonates with Piaget’s constructivist view that knowledge itself emerges through active engagement with the world—much like a player learns a game by playing.
Further Implications
Could we design better cognitive models by thinking of perception, memory, and learning explicitly as game mechanics?
Can we structure AI cognition around game-like principles rather than strict logic trees?
Does this mean that play itself is not an addition to cognition but its fundamental mode of operation?
De-externalizing Wittgenstein’s Game Concept
Instead of treating "game-ness" as something external (rules, competition, goals, play, etc.), we look at it as a way of organizing experience within cognition. That is, the brain doesn’t just passively receive Russell’s sense data; it structures, categorizes, and interacts with them in game-like ways to make sense of the world.
Game-like Properties of Cognitive Processing
What does the brain do with sense data that makes it "game-like"? We could break this down into several mechanisms:
Pattern Recognition as Rule Formation
The brain doesn’t just register data—it infers rules from repeated exposure to stimuli. E.g., a child sees an apple roll off a table and expects another apple to do the same. These inferred rules are flexible, much like the rules of games—sometimes explicit, sometimes implicit.
Categorization as Game Classification
In games, we classify things into roles: player vs. opponent, goal vs. obstacle, tool vs. useless item. In cognition, the brain does the same: safe vs. dangerous, edible vs. inedible, self vs. other. This means that categorization itself is a kind of game, where the brain tests and refines its "rulebook" based on interactions with the world.
Predictions as Gameplay Moves
The brain simulates outcomes based on inputs. Much like in a game where we imagine possible moves before making them, the brain predicts the consequences of action (or inaction). This is fundamental to decision-making—choosing "moves" in real life.
Feedback Loops as Game Iteration
Games involve feedback: winning, losing, scoring points, failing, retrying. Cognition operates similarly: neurons fire in response to stimuli, predictions are tested, and errors refine the system. Learning is, in a sense, playing the game of adjusting to reality with better strategies.
Memory as Game Replay & Strategy Storage
Memory is not a passive recording device but a storehouse of past “games” played with the world. It allows us to "replay" strategies, refine them, and use them in similar but novel contexts. The Practical Cognitive Implication If "game" means doing something with information that enables us to interact with the world practically, then cognition itself is fundamentally game-like at every level. It does not receive sense data—it plays with it, structures it into meaningful units, and refines its internal rules through experience.
This perspective aligns well with predictive processing models of cognition (Friston, Clark), which suggest the brain is an active "predictive engine" rather than a passive data-processing machine. It also resonates with Piaget’s constructivist view that knowledge itself emerges through active engagement with the world—much like a player learns a game by playing.
Further Implications
Could we design better cognitive models by thinking of perception, memory, and learning explicitly as game mechanics? Can we structure AI cognition around game-like principles rather than strict logic trees? Does this mean that play itself is not an addition to cognition but its fundamental mode of operation?