Separate dopaminergic pathways mediate approach and avoidance learning, with biological training via positive reinforcement producing qualitatively different affective profiles than punishment-based training

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• Reinforcement learning acting on individual characteristics affecting their connections to others can result in dynamics that are equivalent to unsupervised learning at the system scale.claim0.783
  Key insight linking individual rewards to system-level learning.
• Plants display context-dependent habituation responses, learn to avoid otherwise neutral stimuli by paired association (classical conditioning), re-orient themselves in anticipation of reinforcements, and evaluate risk to inform game-theoretic decision-making.claim0.770
  Summary of sophisticated plant behaviours that support the inference of cognition.
• Cognition and learning are substrate-independent; principles of basal cognition can be implemented by any network with signals and non-linear responses to weighted inputs.claim0.768
  Core thesis that cognitive principles transcend neural substrates, enabling application to gene-regulatory, ecological, and social networks.
• Associative learning occurs in gene regulatory networks and other non-neural systems, making it substrate-independent.claim0.767
• Progress in machine behavior science and biohybrid engineering breaks down the simplistic dualism of life vs. machine, revealing a continuum of emergence, rational control, and agency.claim0.764
  Second central claim: life and machine form a continuous multidimensional space, not discrete bins
• Interventions along activation manifold M_h yield behavioral trajectories following behavior manifold M_y, and vice versa — bidirectional relationship demonstrated across language models and video world models.finding0.764
  Central empirical result showing causal coupling between representation and behavior geometry across multiple substrates and modalities.
• Midbrain dopamine neurons fire above baseline for rewards better than predicted, at baseline for matching predictions, and below baseline for worse-than-predicted rewards, matching the temporal difference errorfinding0.761
  The foundational finding linking dopaminergic activity to formal RL prediction error
• The collective intelligence of tissues is sophisticated enough to be trainable via reinforcement learning for specific morphological outcomes.hypothesis0.760
  Ongoing test prediction: tissues can associate stimuli with rewards to modify anatomy.