Planta Sapiens: The New Science of Plant Intelligence

Overview

“Planta Sapiens” explores the fascinating world of plants and challenges our understanding of intelligence, consciousness, and communication in the plant kingdom. Calvo argues that plants possess complex behaviors and abilities that rival those of animals, challenging the traditional view of plants as passive organisms. While the theories are built on compelling arguments, the empirical evidence is still lacking.

Key Concepts

The Intelligence Question — Reframing What Counts

• Intelligence without brains: Calvo’s central provocation is that intelligence should be defined functionally — by what an organism does (sense, integrate, decide, act adaptively) rather than by the substrate it uses (neurons, brains). Plants lack nervous systems entirely, yet they exhibit complex, flexible, goal-directed behaviour under changing conditions
  • Defining intelligence: Calvo proposes that an intelligent system is one that can acquire information about its environment, integrate that information across space and time, and use it to make adaptive decisions under uncertainty — a definition that is substrate-independent and applies across kingdoms of life
• The problem with “tropism”: Classical botany explains plant responses as tropisms — automatic, predetermined responses to stimuli (phototropism, gravitropism, thigmotropism). Calvo argues that framing plant behaviour as purely mechanistic tropisms forecloses inquiry; many plant responses are context-dependent, variable, and involve apparent “choices” between competing options, which tropism language cannot capture

Sensory Capacities — What Plants Can Detect

• Light: Plants possess multiple photoreceptor families — phytochromes (red/far-red ratio, detecting shade from neighbours), cryptochromes (blue light, circadian regulation), phototropins (blue light, directing growth toward light sources), and UVR8 (UV-B, activating protective pigment production). Together these provide a sophisticated light-sensing system with spectral resolution
  • Shade avoidance: When a plant detects a low red-to-far-red ratio (indicating neighbouring plants filtering red light), it triggers stem elongation, altered leaf angle, and accelerated flowering — a competitive strategy that requires integrating light quality information with developmental programming
• Touch and vibration (mechanosensation): Mimosa pudica folds its leaves within milliseconds of touch via rapid changes in turgor pressure; Arabidopsis exposed to the vibrations of caterpillar chewing upregulates defensive chemicals (glucosinolates), but does not respond to wind vibrations of similar amplitude — suggesting frequency-specific discrimination
• Chemical sensing: Plants detect volatile organic compounds (VOCs) released by damaged neighbours and prime their own defences pre-emptively (e.g., wild tobacco upregulating protease inhibitors after receiving airborne methyl jasmonate from clipped sagebrush); roots detect and respond to chemical exudates from neighbouring roots, adjusting growth patterns based on whether the neighbour is kin or non-kin (kin recognition)
• Gravity and proprioception: Statoliths (starch-filled amyloplasts) in root-cap cells sediment under gravity, bending the cell and triggering auxin redistribution — but Calvo argues this is only part of the story; the plant integrates gravitropic signals with light, moisture, and obstacle-detection signals to produce a coherent growth trajectory

Adaptive Decision-Making

• Resource foraging: Root systems exhibit behaviour analogous to animal foraging — when encountering a nutrient-rich patch, roots proliferate locally (exploitation); when nutrients are scarce, roots extend into unexplored soil (exploration). This exploitation/exploration trade-off mirrors optimal foraging theory in animal ecology and can be modelled with the same mathematical frameworks
• Risk sensitivity: Experiments on pea plants (Pisum sativum) by Efrat Dener and colleagues showed that when given a choice between a pot with constant nutrients and a pot with variable nutrients, plants preferentially grew roots into the variable pot when overall nutrient levels were low (risk-seeking) and into the constant pot when levels were high (risk-averse) — matching the predictions of risk-sensitivity theory from behavioural economics
• Memory and learning: Mimosa pudica can habituate to repeated non-harmful dropping — ceasing to fold its leaves — and retain this learned response for weeks without reinforcement, even after being placed in new conditions. Calvo argues this meets the operational definition of learning: a lasting change in behaviour as a result of experience, not explicable by sensory adaptation or fatigue

Predictive Processing Framework

• Plants as predictive systems: Calvo’s most theoretically ambitious claim is that plant behaviour can be understood through predictive processing — the framework (developed for brains by Karl Friston, Anil Seth, and others) in which an organism maintains an internal model of its environment and continuously generates predictions about incoming sensory signals. Behaviour is driven by the minimisation of prediction error — the discrepancy between expected and actual sensory input
  • How this maps onto plants: Circadian anticipation of dawn (opening stomata before sunrise), seasonal anticipation of winter (measuring photoperiod decline via phytochrome ratios), and pre-emptive defence priming (responding to VOC signals of future herbivory) are all instances where plants act on predictions rather than merely reacting to current stimuli
  • Active inference in plants: Under active inference (the action-oriented extension of predictive processing), an organism can minimise prediction error in two ways: (1) update its internal model (perceptual inference — analogous to adjusting gene expression or hormone levels) or (2) act on the world to make it conform to predictions (active inference — analogous to growing toward a light source or extending roots toward water). Calvo argues that plant behaviour fits this scheme naturally
• Connecting to neuroscience: This framework places plant cognition on the same theoretical continuum as animal cognition — not claiming that plants are conscious in the same way, but that the underlying computational logic (prediction, error correction, model updating) may be universal to adaptive biological systems. This connects directly to Anil Seth (Being You), Thomas Parr et al. (Active Inference), and Mark Solms (The Hidden Spring)

Personal Reflection

The most compelling aspect of this book is how it reframes plant behavior through the lens of predictive processing, suggesting that intelligence and consciousness might exist on a spectrum far broader than we traditionally conceive. However, the speculative nature of some claims highlights the need for more rigorous empirical work to substantiate these fascinating theoretical proposals.

Related Books

• The Light Eaters - More empirically-focused exploration of plant senses
• Good Nature - Human interaction with plant systems
• Active Inference - Calvo applies the free energy principle to plant cognition; this provides the formal framework

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Parent: Books