Comment on 'Lack of evidence for associative learning in pea plants'
- The Biological Intelligence (BI) Lab, School of Life and Environmental Sciences, University of Sydney, Australia
- School of Science and Engineering and School of Creative Industries, University of the Sunshine Coast, Australia
- Department of Physiology, Anatomy and Genetics, University of Oxford, United Kingdom
- Institute of Pharmacology and Toxicology, University of Zurich, Switzerland
- Australian Institute of Marine Science, The Oceans Institute, University of Western Australia, Australia
Abstract
In 2016 we reported evidence for associative learning in plants (Gagliano et al., 2016). In view of the far-reaching implications of this finding we welcome the attempt made by Markel to replicate our study (Markel, 2020). However, as we discuss here, the protocol employed by Markel was unsuitable for testing for associative learning.
Introduction
Testing for associative learning relies on the pairing of an unconditioned stimulus (US) with a conditioned stimulus. To be effective, the stimulus used as an US must invariably elicit a response (in Pavlov's classical experiment in dogs the presentation of food elicited invariably salivation). In our study (Gagliano et al., 2016) we used blue light as the US which caused consistently a growth of the plant in the direction of the last presentation of the light (100% phototropic response). This was not the case in the study by Markel, where only a slight bias towards the last presentation of light was obtained (Markel, 2020). Since light was not an effective US in the study by Markel, it is not surprising that no distinct associative learning was observed.
In our study we also encountered conditions in which light was not an effective US. Thus, in the second series of our experiments, we tested the response of the plants in different circadian phases (light, light-dark, dark: see Figure 3 of Gagliano et al., 2016). Whereas the 100% phototropic response was obtained in the light phase, it was attenuated or abolished in the other two protocols. Consequently, no associative learning could be shown in those conditions.
We offer the following potential explanation for the lack of a consistent phototropism in Markel, 2020. Our study was conducted inside a completely dark 5.3 m2 room, where individual Y-mazes were positioned at ample distance (~20 cm radius) from each other. This was necessary to ensure that a plant inside its maze could only receive the blue light we directionally delivered within each maze at set specific times, and was completely shielded from light sources elsewhere. The lack of darkness in the study by Markel (see Figure 1—figure supplement 1C in Markel, 2020) is a major departure from our original design. We surmise that by inadvertently allowing individual plants to be exposed to light arriving from multiple sources within a 1.5 m2 growth cabinet (e.g. light leaking from mazes positioned too close to each other or reflecting from the chamber’s walls), the set up used by Markel could have resulted in random growth patterns, unrelated to the behaviour the experimental treatments were designed to test for, thereby confounding the results.
Data availability
No data was generated for this study.
References
Article and author information
Author details
Vladyslav V Vyazovskiy
Martial Depczynski
Funding
Templeton World Charity Foundation (TWCF0313)
- Monica Gagliano
The funders had no role in study design, data collection and interpretation, or the decision to submit the work for publication.
Copyright
© 2020, Gagliano et al.
This article is distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use and redistribution provided that the original author and source are credited.
Metrics
-
- 2,187
- views
-
- 164
- downloads
-
- 12
- citations
Views, downloads and citations are aggregated across all versions of this paper published by eLife.
Download links
A two-part list of links to download the article, or parts of the article, in various formats.
Downloads (link to download the article as PDF)
Open citations (links to open the citations from this article in various online reference manager services)
Cite this article (links to download the citations from this article in formats compatible with various reference manager tools)
Comment on 'Lack of evidence for associative learning in pea plants'
eLife 9:e61141.
https://doi.org/10.7554/eLife.61141
Further reading
-
- Plant Biology
Gagliano et al. (Learning by association in plants, 2016) reported associative learning in pea plants. Associative learning has long been considered a behavior performed only by animals, making this claim particularly newsworthy and interesting. In the experiment, plants were trained in Y-shaped mazes for 3 days with fans and lights attached at the top of the maze. Training consisted of wind consistently preceding light from either the same or the opposite arm of the maze. When plant growth forced a decision between the two arms of the maze, fans alone were able to influence growth direction, whereas the growth direction of untrained plants was not affected by fans. However, a replication of their protocol failed to demonstrate the same result, calling for further verification and study before mainstream acceptance of this paradigm-shifting phenomenon. This replication attempt used a larger sample size and fully blinded analysis.
-
- Ecology
- Neuroscience
In nature, animal vocalizations can provide crucial information about identity, including kinship and hierarchy. However, lab-based vocal behavior is typically studied during brief interactions between animals with no prior social relationship, and under environmental conditions with limited ethological relevance. Here, we address this gap by establishing long-term acoustic recordings from Mongolian gerbil families, a core social group that uses an array of sonic and ultrasonic vocalizations. Three separate gerbil families were transferred to an enlarged environment and continuous 20-day audio recordings were obtained. Using a variational autoencoder (VAE) to quantify 583,237 vocalizations, we show that gerbils exhibit a more elaborate vocal repertoire than has been previously reported and that vocal repertoire usage differs significantly by family. By performing gaussian mixture model clustering on the VAE latent space, we show that families preferentially use characteristic sets of vocal clusters and that these usage preferences remain stable over weeks. Furthermore, gerbils displayed family-specific transitions between vocal clusters. Since gerbils live naturally as extended families in complex underground burrows that are adjacent to other families, these results suggest the presence of a vocal dialect which could be exploited by animals to represent kinship. These findings position the Mongolian gerbil as a compelling animal model to study the neural basis of vocal communication and demonstrates the potential for using unsupervised machine learning with uninterrupted acoustic recordings to gain insights into naturalistic animal behavior.
