1. Ecology
  2. Plant Biology
Download icon

Comment on 'Lack of evidence for associative learning in pea plants'

  1. Monica Gagliano  Is a corresponding author
  2. Vladyslav V Vyazovskiy
  3. Alexander A Borbély
  4. Martial Depczynski
  5. Ben Radford
  1. The Biological Intelligence (BI) Lab, School of Life and Environmental Sciences, University of Sydney, Australia
  2. School of Science and Engineering and School of Creative Industries, University of the Sunshine Coast, Australia
  3. Department of Physiology, Anatomy and Genetics, University of Oxford, United Kingdom
  4. Institute of Pharmacology and Toxicology, University of Zurich, Switzerland
  5. Australian Institute of Marine Science, The Oceans Institute, University of Western Australia, Australia
  • Cited 1
  • Views 628
  • Annotations
Cite this article as: eLife 2020;9:e61141 doi: 10.7554/eLife.61141

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.

References

  1. 1
  2. 2

Article and author information

Author details

  1. Monica Gagliano

    1. The Biological Intelligence (BI) Lab, School of Life and Environmental Sciences, University of Sydney, Camperdown, Australia
    2. School of Science and Engineering and School of Creative Industries, University of the Sunshine Coast, Maroochydore, Australia
    Contribution
    Writing - original draft, Writing - review and editing
    For correspondence
    monica.gagliano@uwa.edu.au
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-2414-6990
  2. Vladyslav V Vyazovskiy

    Department of Physiology, Anatomy and Genetics, University of Oxford, Oxford, United Kingdom
    Contribution
    Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0002-4336-6681
  3. Alexander A Borbély

    Institute of Pharmacology and Toxicology, University of Zurich, Zurich, Switzerland
    Contribution
    Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
  4. Martial Depczynski

    Australian Institute of Marine Science, The Oceans Institute, University of Western Australia, Crawley, Australia
    Contribution
    Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared
    ORCID icon "This ORCID iD identifies the author of this article:" 0000-0001-8723-0076
  5. Ben Radford

    Australian Institute of Marine Science, The Oceans Institute, University of Western Australia, Crawley, Australia
    Contribution
    Writing - original draft, Writing - review and editing
    Competing interests
    No competing interests declared

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.

Senior Editor

  1. Christian S Hardtke, University of Lausanne, Switzerland

Reviewing Editor

  1. Daeyeol Lee, Johns Hopkins University, United States

Publication history

  1. Received: July 16, 2020
  2. Accepted: September 3, 2020
  3. Version of Record published: September 10, 2020 (version 1)

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

  • 628
    Page views
  • 32
    Downloads
  • 1
    Citations

Article citation count generated by polling the highest count across the following sources: Crossref, PubMed Central, Scopus.

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)

Download citations (links to download the citations from this article in formats compatible with various reference manager tools)

Open citations (links to open the citations from this article in various online reference manager services)

Further reading

    1. Plant Biology
    Kasey Markel
    Short Report

    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.

    1. Ecology
    2. Neuroscience
    Felix JH Hol et al.
    Tools and Resources

    Female mosquitoes need a blood meal to reproduce, and in obtaining this essential nutrient they transmit deadly pathogens. Although crucial for the spread of mosquito-borne diseases, blood feeding remains poorly understood due to technological limitations. Indeed, studies often expose human subjects to assess biting behavior. Here, we present the biteOscope, a device that attracts mosquitoes to a host mimic which they bite to obtain an artificial blood meal. The host mimic is transparent, allowing high-resolution imaging of the feeding mosquito. Using machine learning we extract detailed behavioral statistics describing the locomotion, pose, biting, and feeding dynamics of Aedes aegypti, Aedes albopictus, Anopheles stephensi, and Anopheles coluzzii. In addition to characterizing behavioral patterns, we discover that the common insect repellent DEET repels Anopheles coluzzii upon contact with their legs. The biteOscope provides a new perspective on mosquito blood feeding, enabling the high-throughput quantitative characterization of this lethal behavior.