- 25 Downloads
Adaptive behavior; Affordance; Classical conditioning; Communication; Competition; Ecological psychology; Embodied cognition; Foraging; Habituation; Information processing; Movement; Numerosity; Ocelli; Phenotypic plasticity; Plant behavior; Plant cognition; Plant learning; Plant signaling; Plant vision; Predictive processing; Primary consciousness; Sentience; Swarm intelligence; Tropism
What Is Plant Behavior and Cognition?
A mark of intelligent behavior is the capacity to select actions that allow an organism to achieve its goals. Although there is no universally agreed-upon definition of what behavior is, an operational strategy that applies to both plants and animals is to approach behavior in terms of the “observable consequences of the choices a living entity makes in response to external or internal stimuli” (Cvrčková et al. 2016). Such choices, when performed by animals, usually involve movement. Because plants are sessile, it becomes more difficult to appreciate their behavior. But behavior takes place regardless of the mechanism (e.g., muscle contraction vs. cell elongation) that underlies overt plant and animal responses. Laying the stress upon animal, as opposed to plant behavior, becomes a matter of scale and bias. Compare a plant that grows leaves and orients itself toward the sunlight to a sponge that forages for oxygen and nutrients by pumping water through its body. Plant behavior, understood as phenotypic plasticity in response to environmental contingencies, does take place in the form of growth, for instance (Trewavas 2017). Consider, more dramatically, a climbing plant growing up a tree clinging with its tendrils, twining, and uncoiling around as it forages for light from one tree to the next (Darwin and Darwin 1880).
With regard to cognition, an encompassing biological perspective calls for the manipulation of the environment in order to enable metabolic functioning. This, however, does not entail that all of plant behavior is cognitive. Inflexible, hardwired reactions to environmental stimuli are not interesting from a cognitive science perspective. Non-hardwired strategies appear to be needed for a behavioral pattern to become cognitive. It is the degree of flexibility that can be observed in the behavioral repertoire of plants as they assess, say, potential conditions under pressure that grants the ascription of intelligence to them (Trewavas 2014). Among other things, the list of competencies includes the capacity to integrate sources of information into flexible overt responses and the capacity to anticipate future contingencies and make decisions accordingly as to how to alter its traits in the phenotype (Novoplansky 2016). More generally, cognition would encompass plant behavioral patterns that are adaptive, flexible, anticipatory, and goal-directed (Calvo 2016), that is, those overt patterns which are functionally analogous to purposeful and intentional animal behavior, as increasingly exposed under time-lapse photography in many documentaries, David Attenborough’s style.
The concepts that gave form to the idea of intelligent behavior in plants were already present in the pioneering work of Charles Darwin (1809–1882), Wilhelm Pfeffer (1845–1920), Gottlieb Haberlandt (1854–1945), and Jagdish Chandra Bose (1858–1937), among others. As Darwin foresaw in The Power of Movement in Plants, strictly speaking, if there are no “lower” and “higher” species hierarchically ranked on Scala Naturae, then botany is not subordinate to zoology. Darwin suspected that some form of electrochemical communication was behind the responses of carnivorous plants. Lacking appropriate experimental apparatus, he shared his insights with physiologist Sir John Burdon-Sanderson (1828–1905), at University College London, who, after measuring a voltage difference between the upper and lower surfaces of Venus flytrap leaves, was able to characterize action potentials in plants. Pfeffer had likewise envisioned the similarities with the way plants and animals process information. Haberlandt subsequently proposed the phloem as a nerve-like structure that plays a role analogous to the nervous system of animals for the sake of relaying information.
After the discovery of plant action potentials in the 1870s, Bose investigated the electrophysiology of plants in detail, monitoring electrical activity and the transmission of action potentials. As he urged, there is no doubt about the “nervous character of the impulse transmitted to a distance.” Against conventional wisdom, Bose argued that plants had “a system of nerves that constituted a single organised whole.” In 1926 he published The Nervous Mechanism of Plants, challenging the norm by using the expression ‘plant nerves’ in his work. Working on the sensitive plant Mimosa pudica, Bose showed that leaf folding was likewise triggered electrically as there is “transmission of excitation” between the petiole and the pulvinus that results in loss of turgor and thereby induces folding. In the 1930s plant action potentials were registered by inserting microelectrodes in the giant cells of freshwater algae Chara and Nitella. This served to unearth the mechanisms of cellular excitability. All this happened before action potentials were first recorded in the squid giant axon. Some decades later, pioneering research conducted by 1963 Nobel Prize winners Alan Lloyd Hodgkin and Andrew Fielding Huxley revealed the three-fold phase profile of animal cell excitability. It happened to hold equally for plant cells, with electrical transmission being an all-or-nothing action potential. Only in the last decade, plant electrical excitation transmission has been comprehensively linked to plant behavior (Baluška et al. 2006).
Plant Signaling and Behavior
Plant adaptive behavior requires the coordination of physiological needs among the diverse plant structures. The environment in which plants eke out a living is highly complex, with many vectors, other than light or gravity, to be appropriately navigated. Fine-tuned integration of information signaling across the root and shoot systems to achieve the plants’ overall goals is thus needed (Calvo 2016). Electrical signaling plays a central role in integrating the plant body, as we have learned from plant electrophysiology (Volkov 2006). Despite the lack of neural tissue or synaptic connections, all higher plants regulate physiological processes in part by electrical means. The focus on the electrophysiological properties of plant cellular networks has permitted in the last decade to stress the need of a unified account of integrated signaling and adaptive behavior (Baluška et al. 2006) for the purpose of studying plant behavior and cognition.
In particular, plant anatomy and physiology reveals a highly cross-linked phyto-neurological structure akin to the cellular networks of invertebrate nervous systems. The vascular system of plants, initially thought to mediate exclusively the transport of water and nutrients, allows higher plants to coordinate their behaviors, with electric communication taking place over long distances through vascular bundles (Calvo et al. 2017). Plant science is starting to reveal the analogies in the way sets of behaviors in animals and plants get organized. Animal nervous systems were evolutionarily tailored with the purpose of coordinating free-moving behavior and thereby happen to organize signaling systems differently. Overall, research in plant signaling and behavior aims to unearth the particular way in which plants perceive and act in an integrated and purposeful manner, despite their sessile nature.
Aspects of Plant Cognition
Stripped of anthropocentric interpretations, many features of cognition traditionally taken to be the sole purview of psychology are present in the Plantae kingdom. The list of competencies includes competition and foraging and the capacity to communicate, learn, and memorize, among other aspects of cognition (Trewavas 2017).
Competition and Foraging
Foraging refers to the type of changes in behavior that permit organisms to procure their life-sustaining supply of energy. The term applies to plants insofar as they actively search for light, water, nitrates, phosphates, and other nutrients (Trewavas 2008), selecting the most convenient direction of growth. Resource foraging takes place in highly dynamic and heterogeneous environments, with resources patchily distributed. This requires precise structural changes at the level of morphology, physiology, and phenotype for the sake of optimal energy intake in a context of Darwinian competition. Red/far-red light ratios, for instance, inform plants as to how to grow out of shade and forage more effectively for light.
Competitive foraging takes place in both shoot and root apparatuses. As a result, shoots and roots typically present a branched morphology that allows plants to scan their surroundings more effectively. Leaves, branches, and root tips make decisions on a regular basis, such as accelerating or decelerating their rate of growth along positive or negative resource gradients, be it gradients of light intensity in the case of shoots or gradients of humidity and nutrient concentration, in the case of roots. Optimal foraging requires that all such local information registered at the sensory periphery of the plant body is integrated for the purpose of delivering a global response (De Kroon et al. 2009). Root territoriality, for instance, exploits the geometric structure of the soil itself. This illustrates the need of integration and the idea that the behavior of plants is not guided locally but rather globally.
Plants exchange gases on a regular basis. Over one third of the carbon they assimilate is released back into the atmosphere in the form of volatile organic compounds (VOCs). The release of VOCs is not just a by-product of plant physiology. Plants exude them throughout their whole body to “talk” and “listen” to conspecifics and to members of different species (Dicke et al. 2003). Popularized as “talking trees,” or “eavesdropping,” a tree under stress, for instance, can pass along airborne warning signals, permitting other plants in the vicinity to trigger defense mechanisms ahead of time. Some plants, like Cuscuta pentagona (dodder), unable to photosynthesize carbohydrates, rely on VOCs to twine round host plants and suck their nutrients out of their vascular system. Cuscuta can discriminate between volatiles exuded from both tomato plants and wheat plants and choose to grow selectively toward one or the other (Runyon et al. 2006).
Communication also takes place underground courtesy of networks of mycorrhizae that allow trees to remain in contact with one another. Symbiotic growth of these fungi with the root apparatus of trees results in patterns of flexible behavior, delivering ultimately overall fitness (Gorzelak et al. 2015).
There are other forms of sensory input that plants can rely on for the purpose of communication. Research in plant bioacoustics (Gagliano et al. 2012) informs us that plants can benefit from the perception of sound and vibrations. The munching of caterpillars results in specific vibrational patterns that plants can detect. Arabidopsis thaliana respond by synthesizing toxins to the vibrations produced by munching caterpillars (Appel and Cocroft 2014). Trichomes (hair cells) located on the leaves and stem of Arabidopsis respond to mechanical stimulation, acting as acoustic detectors or “mechanical antennae” (Liu et al. 2017).
Learning and Memory
Plants enhance their foraging behavior for light and nutrients by modifying their behavioral repertoire through learning and memory processes. Although plant learning and memory may be approached at different levels, the field has benefited particularly from the study of learning as practiced by comparative psychologists. Recent research has focused on overt behavior by applying the methodological toolkit of the animal learning literature under neobehaviorist paradigms (Abramson and Chicas-Mosier 2016).
Both nonassociative and associative forms of learning have been documented to take place in plants. Habituation has been observed in members of the carnivorous Droseraceae family (sundew) and Passiflora gracilis (passion flower), with reports dating back to Pfeffer and Darwin. The sensitive plant Mimosa pudica is the best-studied model for nonassociative learning. The most detailed study to date has been done using Mimosa in the context of light foraging and risk predation (Gagliano et al. 2014), having shown that the leaf-folding reflex of Mimosa used for the purpose of defense against herbivory habituates after vertically dropping the plant repeatedly. The learned response lasted up to a month.
Habitation is consistent with an instinctual reading of plant behavior and therefore represents the simplest form of learning. More sophisticated forms of learning include Pavlovian classical conditioning. Classical conditioning has been reported more recently in the garden pea Pisum sativum (Gagliano et al. 2016), where the direction of growth was affected by a neutral stimulus against the naturally occurring phototropic behavior. Conditioning evidences the acquisition of new competencies that can enhance foraging behavior and, together with nonassociative forms of learning, constitute adaptive processes that point to a basic form of memory.
Models of Plant Behavior
Plant behavior could involve the processing of information, depending on whether a computational framework is endorsed or not. Until recently, the study of plant behavior was dominated by information-processing assumptions incorporated from cognitive psychology and artificial intelligence. Alternatively, from an ecological perspective, we may consider the way in which the flexible behavior of plants gets structured as a result of the way internal and environmental factors couple together non-computationally. We can thus explore the guiding role that different models and theoretical frameworks may play.
Anticipatory behavior could rely on estimating the likelihood that one particular state of affairs, and not another, is the source of energy. According to the “predictive processing” hypothesis (Calvo and Friston 2017), plant perception boils down to a process whereby environmental input is matched to predictions generated endogenously by the system. It is the end result of a process whereby, first, top-down predictions of sensory signals – the conditional probabilities of particular features being the cause of stimulation – propagate backward to the effector organs and, then, predicted inputs are compared with incoming inputs: if predictions fail to match the input signals, the mismatch (a prediction error) is propagated upward. To accomplish it all, plants must embody a model of the causes of the inputs in question, a model whose predictions ensure that prediction error is minimized over time.
Plants may be seen as performing Bayesian model selection. Perception would consist in an active process of probabilistic inference with plants behaving like little statisticians that make tacit inferences about their world through changes in their internal states (Calvo et al. 2016). In order to pass predictions and prediction errors around the plant body and minimize prediction error, a hierarchical organization with reciprocally connected cell populations encoding expectations and prediction error is needed. In the case of plants, there is a functional asymmetry between backward predictive signals from deep vascular cell bundles and forward prediction error signals from mechanoreceptors and chemoreceptors located on the periphery of the plant body that could serve to implement predictive processing.
Prediction error may be minimized in two complementary ways: by updating expectations or by re-sampling. In the former case, a plant may adjust its internal states by updating its expectations so as to furnish better predictions (prediction error is minimized by updating expectations in order to bring them into line with actual states). Such process is called “perceptual inference.” In the latter case, a plant may choose to sample the environment more selectively in order to make new samples match prior expectations. This process is called “active inference.” Probably, prediction error minimization is accomplished through a combination of both forms of inference. Overall, we may say that plants engage with their surroundings proactively, sampling the local environment in order to elicit information with an adaptive value.
Contrary to predictive processing, from an ecological point of view, environmental sources of stimulation are not ambiguous, and therefore perception need not be inferentially mediated to enrich an inherently poor landscape. Ecological perception relates to the detection of rich environmental information and is organized around actions. Plants resonate directly with informational invariants that specify opportunities for behavioral interaction whereby an unambiguous environment can be perceived directly (Carello et al. 2012). From the standpoint of ecological psychology, attention is paid to the ecological scale at which interaction takes place. The unit of analysis is the plant-environment system itself. This setting allows ecological psychologists to study plant adaptive behavior in terms of emergence and self-organization.
Consider a coupled system such as a climbing plant and its support or host. According to the ecological psychology framework, a vine would perceive its surroundings in terms of biologically relevant interactions, picking ecological information up as it circumnutates around and explores its surroundings. Opportunities for interaction are then said to be perceived in the form of affordances, properties of objects that specify ways to interact with them, and guide the climbing maneuver in a continuous and cyclic loop of perception and action. The vine does not infer the support’s availability for twining; it rather perceives climbability itself, the possibility to interact with a support that affords climbing.
More generally, a foraging plant growing toward light or a source of nutrients behaves in functionally the same way as an animal that runs toward its prey. The hypothesis is that plants, like animals, resonate to specificational information without cognitive mediation of any sort whatsoever.
Many areas of research in plant signaling and behavior are receiving increasing attention in recent years, spanning from numerosity and swarm intelligence to the very possibility of visual guidance and plant sentience, among others.
Sensitivity to numerosity does not require awareness or symbolic processing. The carnivorous plant Dionaea muscipula (Venus flytrap) exhibits numerosity-related abilities, being able to count to five (Böhm et al. 2016). More specifically, Dionaea is able to keep count of the number of times it fires action potentials in response to stimulation of the trigger hairs in the snap trap. Mechanical stimulation, such as the touching or bending of the trigger hairs, induces an all-or-nothing action potential. This, in orchestration with hormonal signaling, results in the closing of the trap. But for the snap trap to shut, a second stimulation must be repeated within 20 s of the first. If 20 s elapse with no news, short-term memory decays, and the plant forgets the first stimulation and resets – an adaptation that avoids unintended closure due to, say, raindrops or wind. The first stimulation must be somehow stored in the memory, if the plant is to eventually be able to put a further touch in a meaningful context. Once the trap has been shut, Dionaea keeps counting episodes of mechanical stimulation until it reaches five. This is important because after the initial two action potentials, three more are needed before digestive enzymes are synthesized and released.
Members in flocks of birds, schools of fish, or swarms of insects responding to sensory input in the vicinity such as the pattern of navigation of the closest mate are the canonical illustration of swarm or collective behavior. But mechanisms that give rise to adaptive forms of collective behavior are widespread, with recent examples of collective behavior coming from the non-animal literature. Escherichia coli and Salmonella typhimurium, for instance, swarm and spread in colonies. Myxococcus xanthus bacteria exhibit multiple forms of collective behavior. These rod-shaped myxobacteria glide their way through with a smooth continuous motion in swarms known as “wolf packs.” Plants are able to solve problems collectively too (Baluška et al. 2010). Maize roots, for instance, choose direction and rate of growth, reorienting selectively with respect to other roots in the vicinity to respect alignment (Ciszak et al. 2012). They can estimate distance, direction of nutrient gradients, and potential competition and adjust navigation patterns accordingly. By growing together and scanning the soil structure collectively, root systems are able to enhance the detection of nutrient patches.
Recent evidence with the cyanobacterium Synechocystis suggests that directional light sensing is mediated by the use of primitive eyespots equipped with microlenses (Schuergers et al. 2016). We find likewise eukaryotic ocelloids in warnowiid dinoflagellates. These algae are equipped with a lens and a retina-like structure with pigments at the back that permit to tell the direction of light. The conjecture that plants have plant-specific eyes or ocelli first appeared in print at the beginning of the last century with Gottlieb Haberlandt’s Die Lichtsinnesorgane der Laubblät. As Haberlandt argued, leaf epidermis cells could play the role of sense organs functionally equivalent to animal ones. Commenting on Haberlandt’s hypothesis, Francis Darwin subsequently elaborated on the idea that ocelli, in their simplest form, are nothing but an epidermis cell with a dome-like wall. As the cell is illuminated, sunrays effectively get bent, delivering a spot of light on the basal membrane. Plant mimicry in the woody vine Boquila trifoliolata provides the latest case for the study of plant vision. B. trifoliolata mimics the leaves of the host trees it climbs onto, copying not just the shape and color of host tree leaves but also their size, orientation, petiole length, and vein conspicuousness (Gianoli and Carrasco-Urra 2014).
Two hypotheses as to the mechanism that underlies mimicry are volatile organic compound-based airborne communication and horizontal gene transfer, with parasites or microbes acting as vectors for gene transfer in between vine and host tree. Considering that mimicry takes place in the absence of touch, a further hypothesis has been advanced: B. trifoliolata may perceive their surroundings by forming primitive images courtesy of the visual sensory organs located in their leaves (Baluška and Mancuso 2016). This way, the vine could perceive shapes and colors courtesy of some kind of plant-specific vision akin to the ocelloid-based form of vision documented from research on cyanobacteria and some dinoflagellates. The internal structure of leaves, on top of furthering photosynthesis, could serve to bolster light sensitivity beyond mere photoreception. A transversal cut to a leaf reveals the candidate mechanism that can sustain vision. In particular, three main regions that bear vision-related properties have been identified: a cornea-like cuticle, convex or plano-convex lenslike epidermal cells promoting the convergence of light rays, and the retina-like mesophyll tissue.
The evolution of subjective experience has become a hot topic of research in recent years, with different attempts being made to trace subjectivity at various phylogenetic stages. Evolutionary biology has traditionally put the focus on the Cambrian period to understand the origins of sentience, more specifically, with the explosion of land vertebrate life. But animal sentience may also include invertebrates. The degree of sophistication of the perceptual apparatus of insects and their manifest capacity to learn, memorize, make decisions, and communicate appear to license the quest for the origins of mind beyond vertebrates or mammals in particular. If the mammalian midbrain plays a key role in sensing and feeling, the central ganglion of insects plays an analogous role. But the origins of subjectivity could date back to the very origins of life itself, as the cellular basis of consciousness hypothesis sustains (Reber 2018). According to this hypothesis, the issue boils down to biomechanics, endogenous control, and navigation, and not to neural correlates. If the behavioral repertoire of insects hints at their mentality, the same may hold for bacteria, whose wide range of competencies are only beginning to be fully appraised. Bacteria would have their own subjective experience, however basic the ascription of consciousness might be.
One way or another, consciousness may have evolved across evolutionary time a number of times. In recent years, the candidacy of plants for sentience has been given due consideration. If, despite the manifest lack of neural correlates of consciousness, bacteria evolved biomechanical structures that underlie their own subjective experiences, there is no reason to exclude the possibility that plants also evolved subjective experience. Plants lack none of the functional structures allegedly needed. Plant anticipatory behavior is goal-directed and soft-wired. Plants make decisions, solve complex problems, learn, memorize, and communicate. All this requires coordination and integration of information signaling across the whole plant body. Plants act as globally organized and coherent systems, supporting in principle their capacity for subjective experience (Calvo et al. 2017).
- Baluška, F., Mancuso, S., & Volkmann, D. (Eds.). (2006). Communication in plants: Neuronal aspects of plant life. Berlin: Springer.Google Scholar
- Böhm, J., Scherzer, S., Krol, E., Kreuzer, I., von Meyer, K., Lorey, C., Mueller, T. D., Shabala, L., Monte, I., Solano, R., Al-Rasheid, K. A., Rennenberg, H., Shabala, S., Neher, E., & Hedrich, R. (2016). The Venus flytrap Dionaea muscipula counts prey-induced action potentials to induce sodium uptake. Current Biology, 26, 286–295.CrossRefGoogle Scholar
- Reber, A. S. (2018). The first minds: What I learned from conversations with a caterpillar and a few sentient cells. Oxford University Press, Oxford.Google Scholar
- Schuergers, N. et al. (2016). Cyanobacteria use micro-optics to sense light direction. eLife 5, e12620.Google Scholar
- Trewavas, A. (2008). Aspects of plant intelligence: Convergence & evolution. In S. C. Morris (Ed.), The deep structure of biology: Is convergence sufficiently ubiquitous to give a directional signal (pp. 68–110). West Conshohocken: Templeton Press.Google Scholar
- Trewavas, A. (2017). The foundations of plant intelligence. Journal of the Royal Society Interface, 7, 20160098.Google Scholar
- Volkov, A. G. (Ed.). (2006). Plant electrophysiology. Berlin: Springer.Google Scholar