The ability to choose among different and often conflicting options, and predict outcomes, is a fundamental aspect of life1,2,3,4. One form of choice behaviour is based on establishing an association between an occurrence of external events and the opportunity to satisfy internal homeostatic needs, such as hunger, thirst or sleep. The notion that choices are driven by the expectation of their rewarding outcome goes back to Aristotle5 and has been observed extensively across the animal kingdom6,7,8,9. However, it remains unknown whether this is also true for plants.

In the complex photosynthetic world of plants, light plays an especially important role in growth and survival. Its role is dual. On the one hand, light energy is necessary for processes of biosynthesis. On the other hand, light provides a time cue for entrainment of the circadian rhythm to the 24-h cycle, thereby optimizing the adjustment of growth and metabolism to the seasonal variation of the photoperiod10. Therefore, the ability to detect salient cues that increase efficiency in foraging for light is absolutely essential and confers a significant evolutionary advantage. Plants have recently been found to acquire new behaviours to enhance foraging efficiency for light through the non-associative learning process of habituation11, and thus to facilitate photosynthesis and growth. However, it remained unknown whether plants can also learn through forming associations.

To investigate this possibility, we employed a classical conditioning paradigm where a neutral environmental cue (a conditioned stimulus, CS) predicted the occurrence of light, which is biologically significant (an unconditioned stimulus, US). In the first experiment, pea seedlings (n = 45) were entrained to an 8-h light:16-h dark cycle for 5–8 days. In the subsequent 3-d training period, they were kept in darkness with the exception of 1-hour light exposures during the three daily training sessions. Training occurred individually inside a Y-maze, where the airflow produced by a fan ([F] as the CS) and a blue LED light ([L] as the US) were systematically presented according to a specific protocol (Fig. 1 and details in Methods section; see also Extended Data Fig. 1 and Supplementary Video 1).

Figure 1 Training and testing protocol for associative learning in pea seedlings. (A) During training seedlings were exposed to the fan [F] and light [L] on either the same arm (i) or on the opposite arm (ii) of the Y-maze. The fan served as the conditioned stimulus (CS), light as the unconditioned stimulus (US). During testing with exposure to the fan alone two categories of responses were distinguished. Correct response: Seedlings growing into the arm of the maze where the light was “predicted” by the fan to occur [green arrow; iii (corresponding to scenario i) and iv (corresponding to scenario ii)]; Incorrect response: Seedlings growing into the arm of the maze where the light was not “predicted” by the fan to occur (black arrow; iii and iv). (B) Seedlings received training for three consecutive days before testing. Each training day consisted of three 2-h training sessions separated by 1-h intervals. The 90-min CS preceded the 60-min US by 60 minutes so that there was a 30-min overlap. (i). During the 1-day testing session, seedlings were exposed to the fan alone for three 90-min sessions (ii). Seedlings of the control group were left undisturbed (no fan, no light; iii). Full size image

Prior to training, seedlings were randomly assigned to one of 2 experimental groups. In one group exposure to the light and fan was on the same arm of the maze [F + L], whereas in the other group light and fan were on opposite arms [F vs L] (Fig. 1Ai,ii). Accordingly, this design tested for both a positive association of the fan (CS) with light (plant trained to seek out fan as a predictor of light) and a negative association of the fan with light (plant trained to avoid the fan and find light on the side of the tube with no air movement). The protocols were maintained throughout the 3-d training period. However, to render the direction of the incoming light unpredictable, its position with respect to the arm of the maze was re-assigned for each 120-min training session (details in Methods section). During training, the seedlings grew and approached the Y-bifurcation of the maze.

Before the testing day, the seedlings were further subdivided randomly into a test group (n = 26) and a control group (n = 19; the numbers are unequal due to a technical problem). The test group was exposed only to the fan during the three 90-min sessions. In this group, to control for the influence of innate phototropic response, the fan was placed in the arm opposite to last light exposure in the [F + L] group and on the arm of last light exposure in the [F vs L] group. The seedlings of the control group were left undisturbed. On the morning after the testing day, we visually inspected the seedlings and recorded the arm of the maze they had grown into (Fig. 1Aiii,iv).

As expected, we found that all seedlings of the control group grew into the arm of the maze where the blue light had been presented in the last training session (white bars; Fig. 2). This result corroborates the well-known innate phototropic response of seedlings to blue light12. In contrast, in the test group, the majority of seedlings exhibited a conditioned response to the fan (green bars; Fig. 2). In the [F + L] group, 62% of the seedlings grew towards the fan (Fig. 2A), whereas in the [F vs L] group, 69% of the seedlings grew in the direction opposite to the fan (Fig. 2B). Thus, the first experiment has shown that plants are able to form associations to enhance foraging success.

Figure 2 Associative learning in pea seedlings. In the absence of the fan, all control seedlings (100%) directed their growth toward the arm of the maze where the light was last presented (white bars). In the presence of the fan, the majority of seedlings grew toward the arm of the maze that had been associated with light during training ([F + L]: same side; [F vs L]: opposite side), thus exhibiting the conditioned response (green bars). A smaller proportion of seedlings did not show learning, thus exhibiting the innate response (blue bars). The response of the experimental groups was significantly different from controls (Two-tailed Fisher’s Exact Test, P = 0.0027 for [F + L] and P = 0.0017 for [F vs L]). See Data file in Supplementary Information. Full size image

In animals, the circadian system provides a framework for a wide range of behaviours. The ability to anticipate changes in food availability enables efficient interactions of the organism with the environment in a time of day dependent manner, and facilitates learning13,14. Great progress has been achieved in characterizing the molecular and physiological basis of circadian rhythmicity in plants15. However, it remains unknown whether the time of day modulates behavioural processes such as learning, in plants.

In our second experiment, seedlings (n = 83) were trained and tested inside a Y-maze, where temperature and light served as Zeitgebers16 (Fig. 1B; details in Methods section). The training and testing procedure corresponded to that of the first experiment. However, exposure to fan and blue light occurred always on the same arm of the maze ([F + L] condition). The main variable was the phase of the 24-h temperature cycle in which the training and testing sessions occurred.

Prior to training, the seedlings were randomly assigned to one of 3 experimental groups. They were maintained under controlled environmental conditions (12-h light:12-h dark coinciding with high-low temperature 21 °C:17 °C). The temperature cycle served as the Zeitgeber that was maintained throughout the training and testing periods. The timing of the experimental ‘day’ (light +21 °C) in the growth chamber varied between the groups (Fig. 3A): Group 1 experienced ‘day’ from 07:00–19:00 (‘Light’), Group 2 from 01:00–13:00 (‘Light-Dark’) and Group 3 from 19:00–07:00 (‘Dark’). After emergence, each seedling was transferred into its individual Y-maze where one of the arms was used to deliver both the unconditioned stimulus (light, US) and the conditioned stimulus (fan, CS). Since both [F vs L] and [F + L] protocols had been equally effective in the first experiment, only the latter protocol was used here. During the three training days, the seedlings were maintained at their respective temperature regimes in total darkness, except for the three 1-h sessions with blue light exposure paired with the fan (see orange and blue rectangular areas; Fig. 3A). These sessions occurred at the same external clock time in the three groups (10:00, 13:00, 16:00), but at different phases of the 24-h temperature cycle. On the testing day, only CS was delivered in the three 90-min sessions (Fig. 1Bii; see orange rectangular areas only, Fig. 3A). The control groups were left undisturbed on the testing day (Fig. 1Biii).

Figure 3 Circadian effects on behavioural performance of pea seedlings. (A) Seedlings were kept in incubation chambers, where initially both light and temperature were used as Zeitgebers (temperature = dotted line; mean values across all days; light:dark cycles, yellow:grey shaded areas). During three training days, the seedlings were kept in darkness with the exception of the three training sessions, while the temperature cycle was maintained (note: the LD cycle was not maintained during training). The training (orange and blue rectangular areas, indicating the time of exposure to the fan and the blue light respectively) and testing sessions occurred during the former light phase in the Light group (i), and partly or entirely outside the former light phase in Light-Dark (ii) and Dark group (iii), respectively. (B) In the ‘Light’ group (i), the growth response of tested seedlings was significantly different from control seedlings (Two-tailed Fisher’s Exact Test, P = 0.002). All control seedlings grew to the arm of the maze where the blue light had been delivered on the last training day [white bar; (i)], while 61% of tested seedlings grew towards the arm where the fan predicted the blue light to occur [green bar; (i)]. A minority of tested plants (39%) did not form an association [blue bar; (i)]. Under phase-shifted conditions, the tested seedlings did not differ from controls [Two-tailed Fisher’s Exact Test, P = 0.769 for the Light-Dark group (ii); P = 0.653 for the Dark group (iii)]. Phase-shift disrupted the phototropic response of control seedlings [white bars; (ii, iii)], causing only 46% of individuals in the Light-Dark group and 70% in the Dark group to direct their growth towards the side of last light exposure [white bars; (ii, iii)]. Full size image

In the ‘Light’ group, the majority of tested seedlings grew towards the fan, opposite to the arm where the light was delivered during the last training session (green bar; Fig. 3Bi), confirming the results of the first experiment. All control seedlings in this experimental group grew towards the arm of the last light exposure (white bar; Fig. 3Bi), also confirming previous findings. However, this was no longer the case in the ‘Light-Dark’ and ‘Dark’ group, in which training and testing occurred at different phases of the temperature cycle. Under these conditions, learning was not successful (47% and 20% in the ‘Light-Dark’ and ‘Dark’ group respectively; green bars; Fig. 3Bii,iii). Growth towards previous light exposure of undisturbed control seedlings was reduced in the ‘Dark’ Group (white and yellow bars; Fig. 3Biii), and abolished in the ‘Light-Dark’ group. In the latter, the innate phototropic response, which is consistently 100% efficient when exposure to light occurs during daylight hours (white bars; Figs 2A,B and 3Bi), is no longer displayed and growth behaviour appears random (white and yellow bars; Fig. 3Bii).

Thus, our results show that pea seedlings develop an association that facilitates growth towards the light based on the occurrence of a neutral cue. This learned behaviour prevails over innate positive tropism to light, which is thought to be the major determinant of growth direction in plants. In both experiments, the ability of seedlings to anticipate both the imminent arrival of light (“when”) and its direction (“where”) based on the presence and position of the fan indicates that plants are able to encode both temporal and spatial information and modify their behaviour under the control of environmental cues. This form of learning is ubiquitous in the animal kingdom17,18, including all major vertebrate taxa and several invertebrate species19 and can also be implemented in artificial networks and machines20. Whilst the possibility that plants also learn by association has been considered by earlier studies21,22, our current study provides the first unequivocal evidence.

The learning paradigm characterised here opens new and exciting opportunities for examining numerous other forms of associative learning mechanisms as well as phenomena that arise within associative learning in plants. For example, our current experimental design could be easily extended by including an experimental condition, where the US precedes (and then overlaps) the CS. If this too elicits the same conditioned response as CS preceding US, this would provide evidence for a form of associative learning known as conditional sensitization. Similarly by extending our design to test different temporal arrangements between the CS and US in learning acquisition (e.g. the CS and US are presented at widely separated intervals and/or in alternate orders), the role of timing and how spatiotemporal relationships between events are encoded within an association in plants could be readily assessed and then compared to findings in animal studies17,23. Additionally at the ecological level, it would be most interesting to test whether/how plants respond to other, novel neutral stimuli (besides the fan used in our study); this would provide evidence for stimulus generalization, another form of associative learning that occurs when, after a conditioned response has been established to a particular CS, other similar stimuli to the CS will elicit the same response. Because the ability to generalize previous learning and apply it to novel situations endows organisms with the behavioural flexibility to adjust to environmental change without having to learn ‘from scratch’ in each specific situation24,25, this aspect of associative learning is of great ecological relevance. Accordingly, we believe that experimental efforts directed towards its study under natural conditions could make important contributions to our understanding of plant ecology.

Besides opening up new experimental research avenues, our findings raise some obvious questions about the underlying physiological/molecular mechanisms by which plants can integrate environmental and internal cues and coordinate complex patterns of information during associative learning, so that a more effective, even adaptive, behavioural response can be expressed. At the molecular level, the role of epigenetic reprogramming has been identified as a potential candidate mechanism underlying learning processes across taxa26,27, including plants11,28. In multicellular organisms with a nervous system, changes in the synaptic strength between neurons, for example, can be stored as a memory trace that sustain associative learning. In plants and other organisms that do not have a nervous system, modifications of the patterns of interactions between molecules and communication between cells can be stored in a way rather similar to neural networks29. Presumably, then, the mechanisms maintaining associative learning operate in plants as in other organisms on the basis of fundamental ‘rules’ that alter the flow of information by modifying the shape and connections within a network via epigenetic changes26. While the specific mechanisms that underlie associative learning in plants are to be defined and may be quite different from other system, comparative studies of the molecular tools involved in associative learning across the different systems could be useful in revealing such fundamental ‘rules’. Research in invertebrate animals such as Aplysia, for example, represents a remarkable success story and an example of how using a simple behaviour and a non-physiological manipulation resulted in fundamental discoveries going far beyond a specific learning paradigm and species30,31.

While the mechanisms underlying associative learning in plants remain to be determined, there is little doubt that it is likely to enhance opportunities to locate and capture available light in the environment. It has been shown that auxin signalling systems may be involved in the integration between photo- and mechanosensory information, which enables directional growth to optimize photosynthesis32,33. Earlier experiments have demonstrated that young plants can form stable memories pertaining to the direction of a light source34, while a more recent study showed that plants optimize opportunities to access and forage for light by learning to ignore recurrent, but unimportant stimuli in their environment11. Our results extend these findings by showing that plants are able to adapt quickly to changes in the environment and develop anticipatory behaviour, which may play an important role in maintaining metabolic homeostasis.

Consistently, we show in the second experiment that associative learning occurs most readily during the subjective day. This experiment, in which plants were trained at three different time periods within 24 hours, revealed that the learning effect disappears when training occurred during the evening hours when light would not normally be available. This finding is particularly intriguing and bolsters the argument that associative learning is an adaptive response that is only utilized during daylight hours (when it is most useful) via an internal circadian clock. Interestingly, the response of the control group in this experiment was affected by the phase of the temperature cycle. While in the Dark group the majority of seedlings still grew towards the side of the last light exposure, this was not the case for the Light-Dark group. Thus the phototropic response which was 100% efficient in the Light group, proved to be attenuated or abolished in the other groups. Therefore, the main finding of the second experiment is that phototropism of peas is circadian phase-dependent. This could be due to a modulation of light-sensing capacity or of the transmission of light-induced processes to structures affecting growth (see ref. 35 for review of the topic). Given that alignment of the internal circadian phase with external environmental signals is essential to maintain efficient photosynthesis during the day as well as optimal utilization of reserves during the night36, plants are likely to incur an energetic cost when performing light-foraging behaviours at a time that is misaligned with the internal circadian signals. Our results suggest that under these conditions, the expression of both innate and learned behaviours is traded off to meet basic metabolic demands in order to ensure growth and survival.

The emergence of associative learning has been proposed as one of the key biological innovations that powered the Cambrian explosion by driving the evolution of new sensory modalities and hence, altering the life and adaptive possibilities of animals37,38. Our results now show that associative learning is also an essential component of plant behaviour. We propose that the ability to construct, remember and recall new relationships established via associative learning constitutes a universal adaptive mechanism shared by all organisms. The ubiquity of associative learning across taxa, including non-animal groups suggests that the role this learning process plays in nature is thus far underexplored and underappreciated. Our findings raise the possibility that associative learning may have played a similarly important role in the remarkable diversification of the plant kingdom and that this kind of learning emerged in plant and animal groups alike via convergent evolution39.