C PLANTS AS INFERENTIAL SYSTEM

 C

Here’s a 3-point summary of your passage on plants, prediction, and the free-energy principle:

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3 Points – Predictive Plants

1. Gradualism in cognition

   • Friston, like Levin, takes a gradualist view, seeing living and nonliving systems on a continuum of goal-directed behavior.

   • The author, however, distinguishes true goals as belonging to mortal, living systems, while machines’ goals are human-imposed.

2. Plants as inferential systems

   • Plants, though brainless, engage in inference and prediction using partial sensory information.

   • Examples: a bean plant predicting and reaching for a pole to access sunlight, or plants detecting insect attacks and producing defensive chemicals.

3. Minimizing surprise and uncertainty

   • Applying the free-energy principle, plants exchange with their environment to minimize surprise and resolve uncertainty.

   • This suggests plants are proactively engaged with their surroundings, not merely reacting instinctively.

Key idea: Plants demonstrate a primitive form of foresight and interaction, challenging the notion that prediction and goal-directed behavior require brains.

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A

In plain English, an inferential system is something that makes guesses or predictions about what’s happening or what will happen next based on the information it has.

• It doesn’t need a brain to do this—it just uses the clues in its environment to “figure out” the best response.

• For example:

  • A plant senses light and the position of a nearby pole, and “predicts” that if it grows toward the pole, it will get more sunlight.

  • A thermostat notices the temperature is too low and “infers” it should turn on the heat to reach the desired level.

So, an inferential system is basically a system that reads signals, makes simple predictions, and acts to reduce surprises or problems.