Computer Animation Technology in Behavioral Sciences: A Sequential, Automatic, and High-Throughput Approach to Quantifying Personality in Zebrafish (<i>Danio rerio</i>)

Fangmeier, Melissa L; Noble, Daniel W. A.; Usui, Takuji; Lagisz, Malgorzata; Hesselson, Daniel; Nakagawa, Shinichi

doi:10.1089/zeb.2017.1532

Cited by 12 publications

(15 citation statements)

References 19 publications

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“…Repeatability partitions variance into within-individual (residual) and between-individual components. Biologically, the repeatability of a trait indicates the amount of observed variance that is due to individuals sustaining trait differences between each other (Nakagawa and Schielzeth, 2010), but estimates can be inflated by measurement errors and experimental confounds (Dohm, 2002;Niemelä and Dingemanse, 2017).…”

Section: Introductionmentioning

confidence: 99%

“…To reduce this knowledge gap, we quantified the repeatability of aversive learning behaviour in zebrafish (Danio rerio), a popular model organism in cognitive science (Gerlai, 2016;Norton and Bally-Cuif, 2010). Zebrafish exhibit a range of distinct behaviours that can be measured in previously established assays (Fangmeier et al, 2018;Meshalkina et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

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Low repeatability of aversive learning in zebrafish (Danio rerio)

Mason

Zajitschek

Anwer

et al. 2021

Journal of Experimental Biology

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Aversive learning – avoiding certain situations based on negative experiences – can profoundly increase fitness in animal species, yet no studies have systematically quantified its repeatability. Therefore, we assessed the repeatability of aversive learning by conditioning approximately 100 zebrafish (Danio rerio) to avoid a colour cue associated with a mild electric shock. Across eight different colour conditions, zebrafish did not show consistent individual differences in aversive learning (R=0.04). Within conditions, when zebrafish were conditioned to the same colour, blue conditioning was more repeatable than green conditioning (R=0.15 and R=0.02). Overall, aversive learning responses of zebrafish were weak and variable. We speculate that the effect of aversive learning might have been too weak to quantify consistent individual differences, or directional selection might have eroded additive genetic variance. We also discuss how confounded repeatability assays and publication bias could have inflated estimates of repeatability in the literature.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Low repeatability of aversive learning in zebrafish (Danio rerio)

Mason

Zajitschek

Anwer

et al. 2021

Journal of Experimental Biology

Self Cite

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show abstract

“…To reduce this knowledge gap, we quantify the repeatability of aversive learning behaviour in zebrafish (Danio rerio), a popular model organism in cognitive science (Gerlai, 2016; Norton & Bally-Cuif, 2010). Zebrafish exhibit a range of distinct behaviours that can be measured in previously established assays (Fangmeier et al, 2018; Meshalkina et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Low Repeatability of Aversive Learning in Zebrafish (Danio rerio)

Mason

Zajitschek

Anwer

et al. 2020

Preprint

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Aversive learning – avoiding certain situations based on negative experiences – can profoundly increase fitness in animal species. The extent to which this cognitive mechanism could evolve depends upon individual differences in aversive learning being stable through time, and heritable across generations, yet no published study has quantified the stability of individual differences in aversive learning using the repeatability statistic, R (also known as the intra-class correlation). We assessed the repeatability of aversive learning by conditioning approximately 100 zebrafish (Danio rerio) to avoid a colour cue associated with a mild electric shock. Across eight different colour conditions zebrafish did not show consistent individual differences in aversive learning (R = 0.04). Within conditions, when zebrafish were twice conditioned to the same colour, blue conditioning was more repeatable than green conditioning (R = 0.15 and R = 0.02). In contrast to the low repeatability estimates for aversive learning, zebrafish showed moderately consistent individual differences in colour preference during the baseline period (i.e. prior to aversive conditioning; R ~ 0.45). Overall, aversive learning responses of zebrafish were weak and variable (difference in time spent near the aversive cue <6 seconds per minute), but individual differences in learning ability did not explain substantial variability. We speculate that either the effect of aversive learning was too weak to quantify consistent individual differences, or directional selection might have eroded additive genetic variance. Finally, we discuss how confounded repeatability assays and publication bias could have inflated average estimates of repeatability in animal behaviour publications.Summary StatementZebrafish exhibit low repeatability (intra-class correlation) in an aversive learning assay possibly due to past selection pressure exhausting genetic variance in this learning trait.

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“…While this is the first robotics-based setup for zebrafish learning, a few previous efforts have explored other automation techniques. For example, Pather and Gerlai (2009) utilized computer-animated images of zebrafish as rewarding stimuli in a shuttle box task, while (Hicks et al, 2006) used real-time video tracking to deliver rewarding or punishing stimuli, in the form of a change in illumination and brief electric shock, Gerlai et al (2009) showed that zebrafish react to computerized images of a predator, and Fangmeier et al (2018) demonstrated the possibility to use automated video stimulus to quantify behavioral traits in zebrafish. Here, we took a significant step forward by combining engineered stimuli with real-time control, affording the possibility of maneuvering them in the entire experimental tank.…”

Section: Discussionmentioning

confidence: 99%

A Comparison of Individual Learning and Social Learning in Zebrafish Through an Ethorobotics Approach

et al. 2019

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Social learning is ubiquitous across the animal kingdom, where animals learn from group members about predators, foraging strategies, and so on. Despite its prevalence and adaptive benefits, our understanding of social learning is far from complete. Here, we study observational learning in zebrafish, a popular animal model in neuroscience. Toward fine control of experimental variables and high consistency across trials, we developed a novel robotics-based experimental test paradigm, in which a robotic replica demonstrated to live subjects the correct door to join a group of conspecifics. We performed two experimental conditions. In the individual training condition, subjects learned the correct door without the replica. In the social training condition, subjects observed the replica approaching both the incorrect door, to no effect, and the correct door, which would open after spending enough time close to it. During these observations, subjects could not actively follow the replica. Zebrafish increased their preference for the correct door over the course of 20 training sessions, but we failed to identify evidence of social learning, whereby we did not register significant differences in performance between the individual and social training conditions. These results suggest that zebrafish may not be able to learn a route by observation, although more research comparing robots to live demonstrators is needed to substantiate this claim.

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Computer Animation Technology in Behavioral Sciences: A Sequential, Automatic, and High-Throughput Approach to Quantifying Personality in Zebrafish (Danio rerio)

Cited by 12 publications

References 19 publications

Low repeatability of aversive learning in zebrafish (Danio rerio)

Low repeatability of aversive learning in zebrafish (Danio rerio)

Low Repeatability of Aversive Learning in Zebrafish (Danio rerio)

A Comparison of Individual Learning and Social Learning in Zebrafish Through an Ethorobotics Approach

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