Learning is a fundamental source of individuality

  1. Riddha Manna
  2. Johanni Brea
  3. Gonçalo N Vasconcelos Braga
  4. Alireza Modirshanechi
  5. Ivan Tomić
  6. Ana Marija Jakšić

  • Curated by eLife

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    eLife Assessment

    This is a fundamental study of individual variation and the contribution of learning to behavioural individuality. The experimental design of massively parallel behavioural phenotypes is outstanding and the conclusions are supported by a compelling and rigorous analysis across a large number of experiments in thousands of individuals across genotypes and conditions. The dataset further represents an advance in studying visual associative learning thanks to the ability to make longitudinal measurements of many behavioural decisions within the same animals. These results are a major contribution to the understanding of the sources of behavioural individuality.

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Abstract

Learning and memory are essential components of our individuality. While it is established that behaviour can vary across genetically identical individuals, it remains unknown how much of this variation stems from momentary experience during learning compared to genetics and its past interactions with the environment. To address this, we measured behaviour in thousands of flies from 90 genetic backgrounds while they performed tasks in conditions that either did or did not require learning. Flies that were genetically identical, raised under the same conditions, and tested simultaneously in the same environment persistently modified the extent of expressed individuality when they could learn. This learning-induced residual expression of individuality and its dynamics were subdued or absent in innate, learning-independent behaviours. We could quantify and then recreate this phenomenon in computer simulations. The emergence of in silico behaviour individuality was most consistent with the individuality of real flies once we enabled reinforced learning in simulated agents. Moreover, we showed that minor differences in initial conditions of the experiment can exacerbate the expression of individuality within a genotype in a learning-dependent manner. Our results establish that besides the classical genotype × environment (G × E) interactions shared between individuals in the past, learning from individual momentary experience further extends the expression of individuality.

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  1. Version published to 10.7554/elife.111235.3 on eLife
  2. Version published to 10.7554/elife.111235 on eLife
  3. eLife

    eLife Assessment

    This is a fundamental study of individual variation and the contribution of learning to behavioural individuality. The experimental design of massively parallel behavioural phenotypes is outstanding and the conclusions are supported by a compelling and rigorous analysis across a large number of experiments in thousands of individuals across genotypes and conditions. The dataset further represents an advance in studying visual associative learning thanks to the ability to make longitudinal measurements of many behavioural decisions within the same animals. These results are a major contribution to the understanding of the sources of behavioural individuality.

  4. eLife

    Reviewer #1 (Public review):

    "Learning is a fundamental source of individuality," by Manna and colleagues, interrogates different sources of variation in individual behavior. The authors place individual flies in a Y-shaped arena, which is a common design in the field, and illuminate the arms of the Y with blue versus green light. They track the color preference of individual animals and also perform operant conditioning, meaning that they teach the fly to avoid a particular color/arm by generating a foot shock when the fly enters that arm. There are a number of things that are impressive about this setup: The authors are able to collect data on thousands of individual flies of many different strain backgrounds, and they demonstrate a strong change in color preference after conditioning. This is nice, because in past papers visual learning ability has been modest and difficult to study. To put a number on it, in this paper animals on average don't show a color preference at the start of the assay, spending around 30% of their time in the one arm illuminated green, and the remaining time in the two arms illuminated blue. After conditioning, the average animal spends only 23% of its time in the green arm.

    The authors run 64 animals through the assay for each of 88 wild type strains (maybe? see Major Point 1 below) and see considerable strain-specific (genetic) variation in the change in time spent in the shocked color after conditioning. Some strains show no learning, while others spend <10% of their time in the shocked color after conditioning. They also, I believe, see that some strains have more variability across individuals, which would suggest that some strains have stronger canalization at the development or circuit function level than others-i.e. some genotypes produce more consistent copies of the individual, others less consistent copies. (Or, some genotypes produce robust circuits, and others produce noisy circuits.)

    Finally, the authors argue statistically that learning itself increases variability in individual performance. This makes a lot of sense to me intuitively. Learning changes the physical/chemical properties of circuits in the brain, and because it evolves over time and interacts with environmental variables, it seems like it should send different animals down different channels. Or, at a conceptual level, if I learn to play the piano and my sister doesn't (because of some genetic difference between us or something stochastic), this learning experience will cause all sorts of other differences in our behavior as time passes. I also think the authors do have enough data to be able to make this finding. However, the presentation of the argument in this portion of the paper is hard for me to understand, and I am not an expert in statistics, so the strength of the result is difficult for me to evaluate.

    Major points:

    (1) It's difficult to track through the paper the number of animals tested for different assays. At the beginning, it says N=5632, which works out to 64 flies for each of the 88 DGRP strains. 64 happens to be the number of parallel Y arenas they have. Later in the methods, there's description of more variation within the set of 64 for each strain-two different parent sets per strain, different sexes, conditioned and un-conditioned. And, while the results text focuses on the color learning, the methods discuss additional assays (place learning, multi-day learning).

    Given the numbers, does each run of the 64 mazes include all the tested flies of one strain, or are flies of many strains included in each batch? Do different flies do different assays (color, place, multi-day) or do they all do all the assays? Perhaps there is a table including this information already in the supplement, but I recommend making it much clearer in the main results text and methods. While the dataset is large, if it is split over many conditions and/or if batch and genotype confound each other, this will affect the robustness of the results and how strong the conclusions can be.

    (2) The data presentation in Figure 1 is elegant and easy to follow, but getting into Figure 2 and subsequently, I get lost in the statistics and have trouble understanding what is being measured. My understanding of the big picture is that while genetics and individual randomness contribute a lot to behavior, the evidence for learning as an amplifier of individuality is that variance in behavior among animals of the same strain increases over time in the conditioned group (i.e. the group that is doing the most learning, or a specific kind of learning), but not in the control group. This idea is illustrated in the flattening distributions in the cartoons in Figure 1A. The authors should include graphs of the real data that use the same format as in that cartoon. Instead, the graphs present "residuals," and I don't know what those are. I suspect it's "variation left over after accounting for effects of strain and individual stochasticity." I see the residuals being tracked per strain over time in Figure 2H, but I don't see the change over time in other graphs. I'm looking for something simple like, "variation within the strain at the beginning of learning and at later time points in learning." (But I'm not sure exactly what instantaneous measurement would be the focus in longitudinal analyses of learning behavior.)

    (3) Figure 3 is a cool stab at tracking down the precise mechanism by which stochastic environment interacts with learning to send individuals along different behavioral routes. But again, like in Figure 2, I don't have the sophisticated understanding of statistics to understand exactly what the graphs are telling me, or how they relate to the underlying measurements. I'm relying on the results text alone to reach a conceptual understanding and just taking the graphs on trust.

    So, overall, the authors have a very nice body of work here and with the potential to add a new facet to our understanding of the origins of diversity in animal behavior. In addition to the interpretations they focus on here, this dataset also represents an advance in studying visual associative learning in general, and quite an amazing ability to make longitudinal measurements of many behavioral decisions within the same animals. Improving the data presentation to make it easier to follow for a larger swathe of researchers, especially in figures 2 and 3, will increase its potential impact.

    Comment on revised version:

    The authors have addressed my main points, including adding description of their statistical analyses and providing more detail about the different assays run and which animals were included in the same assay batches.

  5. eLife

    Reviewer #2 (Public review):

    Summary:

    The authors set out to test the extent to which differences in learning capacity and experience contribute to behavioural variation in a genetically identical population under identical environmental conditions.

    Strengths:

    The authors developed and used a scaled-up version of a simple two-choice behavioural paradigm allowing them to test thousands of individuals across multiple genotypes. They then deployed clever and powerful statistical analysis methods and provided compelling evidence for a role of variability in learning in the expression of behavioural variation.

    Weaknesses:

    There are no major weaknesses, although some level of longitudinal analysis to strengthen the evidence for a strict definition of individuality would be a welcome extension of a future study. In addition, it would have been very interesting, although understandably beyond the current scope, to delineate a potential source of learning variability in the brain.

  6. eLife

    Author response:

    The following is the authors’ response to the original reviews.

    Public Reviews:

    Reviewer #1 (Public review):

    "Learning is a fundamental source of individuality," by Manna and colleagues, interrogates different sources of variation in individual behavior. The authors place individual flies in a Y-shaped arena, which is a common design in the field, and illuminate the arms of the Y with blue versus green light. They track the color preference of individual animals and also perform operant conditioning, meaning that they teach the fly to avoid a particular color/arm by generating a foot shock when the fly enters that arm. There are a number of things that are impressive about this setup: The authors are able to collect data on thousands of individual flies of many different strain backgrounds, and they demonstrate a strong change in color preference after conditioning. This is nice, because in past papers, visual learning ability has been modest and difficult to study. To put a number on it, in this paper, animals on average don't show a color preference at the start of the assay, spending around 30% of their time in the one arm illuminated green, and the remaining time in the two arms illuminated blue. After conditioning, the average animal spends only 23% of its time in the green arm.

    The authors run 64 animals through the assay for each of 88 wild-type strains (maybe? see Major Point 1 below) and see considerable strain-specific (genetic) variation in the change in time spent in the shocked color after conditioning. Some strains show no learning, while others spend <10% of their time in the shocked color after conditioning. They also, I believe, see that some strains have more variability across individuals, which would suggest that some strains have stronger canalization at the development or circuit function level than others, i.e., some genotypes produce more consistent copies of the individual, others less consistent copies. (Or, some genotypes produce robust circuits, and others produce noisy circuits.)

    Finally, the authors argue statistically that learning itself increases variability in individual performance. This makes a lot of sense to me intuitively. Learning changes the physical/chemical properties of circuits in the brain, and because it evolves over time and interacts with environmental variables, it seems like it should send different animals down different channels. Or, at a conceptual level, if I learn to play the piano and my sister doesn't (because of some genetic difference between us or something stochastic), this learning experience will cause all sorts of other differences in our behavior as time passes. I also think the authors do have enough data to be able to make this finding. However, the presentation of the argument in this portion of the paper is hard for me to understand, and I am not an expert in statistics, so the strength of the result is difficult for me to evaluate.

    Major points

    (1) It's difficult to track through the paper the number of animals tested for different assays. At the beginning, it says N=5632, which works out to 64 flies for each of the 88 DGRP strains. 64 happens to be the number of parallel Y arenas they have. Later in the methods, there's a description of more variation within the set of 64 for each strain, two different parent sets per strain, different sexes, conditioned and unconditioned. And, while the results text focuses on the color learning, the methods discuss additional assays (place learning, multi-day learning).

    Given the numbers, does each run of the 64 mazes include all the tested flies of one strain, or are flies of many strains included in each batch? Do different flies do different assays (color, place, multi-day), or do they all do all the assays? Perhaps there is a table including this information already in the supplement, but I recommend making it much clearer in the main results text and methods. While the dataset is large, if it is split over many conditions and/or if batch and genotype confound each other, this will affect the robustness of the results and how strong the conclusions can be.

    (2) The data presentation in Figure 1 is elegant and easy to follow, but getting into Figure 2 and subsequently, I get lost in the statistics and have trouble understanding what is being measured. My understanding of the big picture is that while genetics and individual randomness contribute a lot to behavior, the evidence for learning as an amplifier of individuality is that variance in behavior among animals of the same strain increases over time in the conditioned group (i.e., the group that is doing the most learning, or a specific kind of learning), but not in the control group. This idea is illustrated in the flattening distributions in the cartoons in Figure 1A. The authors should include graphs of the real data that use the same format as in that cartoon. Instead, the graphs present "residuals," and I don't know what those are. I suspect it's "variation left over after accounting for effects of strain and individual stochasticity." I see the residuals being tracked per strain over time in Figure 2H, but I don't see the change over time in other graphs. I'm looking for something simple like, "variation within the strain at the beginning of learning and at later time points in learning." (But I'm not sure exactly what instantaneous measurement would be the focus in longitudinal analyses of learning behavior.)

    (3) Figure 3 is a cool stab at tracking down the precise mechanism by which a stochastic environment interacts with learning to send individuals along different behavioral routes. But again, like in Figure 2, I don't have the sophisticated understanding of statistics to understand exactly what the graphs are telling me, or how they relate to the underlying measurements. I'm relying on the results text alone to reach a conceptual understanding, and just taking the graphs on trust.

    So, overall, the authors have a very nice body of work here, and with the potential to add a new facet to our understanding of the origins of diversity in animal behavior. In addition to the interpretations they focus on here, this dataset also represents an advance in studying visual associative learning in general, and quite an amazing ability to make longitudinal measurements of many behavioral decisions within the same animals. Improving the data presentation to make it easier to follow for a larger swathe of researchers, especially in figures 2 and 3, will increase its potential impact.

    Reviewer #2 (Public review):

    Summary:

    The authors set out to test the extent to which differences in learning capacity and experience contribute to behavioural variation in a genetically identical population under identical environmental conditions.

    Strengths:

    The authors developed and used a scaled-up version of a simple two-choice behavioural paradigm, allowing them to test thousands of individuals across multiple genotypes. They then deployed clever and powerful statistical analysis methods and provided compelling evidence for a role of variability in learning in the expression of behavioural variation.

    Weaknesses:

    There are no major weaknesses, although some level of longitudinal analysis to strengthen the evidence for a strict definition of individuality would be a welcome extension of a future study. In addition, it would have been very interesting, although understandably beyond the current scope, to delineate a potential source of learning variability in the brain.

    Following our provisional response to the reviewers, we have implemented these additions to the manuscript:

    (1) We have added 7 additional tables (Table 1-6 and table 8) to the supplementary that detail how many individual flies were used in which of the seven separate experiments, how the individuals were distributed across genotypes, replicates and sexes, and how many were filtered out before the final analysis. At the bottom of each table, we added a short description of the type of experiment and a brief explanation of the filtering. The four smaller experiments where we tested the two mutant lines and one wild-type DGRP line were used primarily to test and validate the experimental platform and the behavioural paradigm used for the main experiment. In these four experiments we tested green place learning, blue place learning, green colour learning, blue colour learning in four separate batches of flies. In each of these experiments we used 192 individuals (64 individuals x 3 genotypes x 4 experiments = 768 individuals in total). In the multiday experiment we used 64 flies per genotype per each of the four groups of sequences of learning paradigms, in total 512 individual flies. Here, each of these 512 individuals were retested in different learning paradigms over 4 days (Table 5). The main experiment was the green place learning (Table 6) where we tested all 88 DGRP lines and again the two mutant lines was used to obtain the majority of the main results and conclusions (64 individuals x 90 genotypes = 5760 individuals). Lastly, additional 896 individuals were measured in the experiment using blue place learning paradigm to test the consistency of learning behaviour as opposed to colour bias within genotype (Table 8). In summary, in all experiments, we have always measured behaviour in 64 flies per genotype (full loading of the behavioural platform), and they were distributed almost entirely evenly across replicates, sexes, and conditions (control vs conditioned). No individual was reused across experiments. In most cases, after filtering the data, 60 or fewer individuals were used in final analyses. For the very few deviations from this experimental design (which occurred due to unforeseen events such as dropped/sick vials, flies flying away or accidentally squished during setup, skewed number of males and females etc.) we added a short explanation in the text below the tables. In total, across all reported experiments in this study, we measured behaviour in 7936 individuals.

    (2) We have added a schematic visual representation of classical measurement of individuality (variance of the distribution of behaviour within genotype where genetically identical individuals are raised in the same environment), entropy-based measurement of individuality (residual individuality) and the change in residual individuality, as we use them in this study (Figure 2D). We also provide a list of different DHresid measures and what distributions are being compared across the DHresid in the same figure. We hope this will serve as a more intuitive explanation of individuality and help readers interpret and follow more easily the results that we report after this figure.

    (3) In the same vein, we added another schematic visual representation to Figure 3 (Figure 3F) where we depict how distributions of individual behaviour may change with every decision and how this change translates to (or can be read out from) the change in residual individuality. We have also renamed the X axis of Figure 3E to “DHresid Start”, so that it is clearer what is measured here and matches the explanation in Figure 2D.

    (4) We have added two additional supplementary figures where the reader can inspect in more detail how the distributions of individual behaviour change longitudinally across time for each genotype in control and conditioned (Figure 3 – Figure supplement 2 and Figure 3 – Figure supplement 3). From these figures one can glance how variance as well as the shapes of the distributions change as the flies learn in the conditioned setting, and how they remain largely the same in the control where flies behave spontaneously. We have added a sentence in the main text to introduce these figures: “We found that the distributions of individual behaviour were broader and their shapes changed substantially over the course of the experiment for the conditioned flies, and not for the control flies Figure 3 - figure supplement 2, Figure 3 - figure supplement 3).”

    (5) As noted in the first provisional response to reviewers, we changed the sentence “In every individual, behaviour is shaped by deterministic, genetic factors and by environmental events throughout lifetime, which may be stochastic and can occur at the molecular, cellular, organismal and even population scales.” to “In every individual, behaviour is shaped by fixed genetic factors and by variable environmental events throughout lifetime, which may be stochastic and can occur at the molecular, cellular, organismal and even population scales.”

    (6) Some sentences were edited in the results so that we can correctly refer to the newly added tables and figures. Context, meaning or interpretation of the results in these sentences was not altered.

    (7) While adding the new table references to the text, we noticed a typo that propagated in the previous version where the number of flies used in the main experiment was stated to be N= 5238, when in fact it should have been N=5239. This is now fixed.

    We once again thank the reviewers for their comments and suggestions – we believe their suggestions helped us improve the presentation and interpretability of our study and we hope the reviewers and readers will agree with this as well.

  7. Version published to 10.7554/elife.111235.2 on eLife
  8. Version published to 10.1101/2024.08.30.610528v5 on bioRxiv
  9. eLife

    Author response:

    Reviewer 1:

    Clarification of sample sizes, assay structure, and experimental design.

    Reviewer 1 noted that the number of animals tested across strains, assays, sexes, parent sets, conditioned and unconditioned groups, and longitudinal conditions is difficult to track through the manuscript. Given the extent of the experimental and data processing procedures such as filtering for inactive or injured flies, we agree that a summary table and/or a visual schematic of the full experimental setup would be helpful.

    Importantly, the vast majority of individuals was used for the main experiment where we conditioned the flies to avoid the green arm, and where the colors of the arms were fixed throughout the assat. A smaller number of flies were tested in the validation experiments (such as different types of conditioning). In each experiment, 64 flies were always set up per genotype and their behaviour was measured in parallel. Usually, around ~60 flies passed the filtering step before analysis (filtering due to inactivity or injured flies). Among those 60-ish flies per genotype the distribution of flies of different sex or flies raised in different replicate vials was balanced. Different individual flies were tested across different assays, except in the multiday experiment, where each individual was tested across four different assays.

    We will add a supplementary summary that includes how many flies were tested across assays, how individuals, males, females, replicates and genotype were distributed across batches (and in the multiday experiments how they were distributed across experiments), and how many flies were filtered out from the final analysis.

    Clearer presentation of the statistical argument that learning amplifies individuality.

    Reviewer 1 also noted that the presentation of the statistical analyses, particularly in Figure 2, was difficult to follow (e.g. what is residual individuality, how is it tracked over time, and why not replace it with something simpler like variance?).

    Our experimental design combines multiple, replicated environments and genotypes. For example, genetically identical flies from genotype A, are raised under identical developmental environments that are replicated two times in two vials. The same is true for genotype B. Individuals from both genotypes are then tested under different conditions, i.e. control and conditioned.

    As we saw, combinations of these factors can change both the means and variance of distributions of individual behaviours in both genotype- or environment-specific manner. Normally, variance would be a good estimate for expressed individuality within a genotype, and comparison of variances would be a good estimate of change in individuality due to some factor (genotype, replicate, or type of conditioning).

    However, we saw that the resulting shape of the data in these experiments, (the shape of the distributions) was incompatible with the classical definition of extent of individuality measured by variance. While it would be more intuitive to track variance over time, we found that this measure obfuscates some obvious changes in the normal shape of the distributions of individual behaviours, as can be visually observed for example between conditioned and control experiments. This is why we moved to develop the measure of residual individuality. Residual individuality aims to measure exactly this dimension of individuality that is missed by measures of variance. We will add a schematic presentation of residual individuality in Figure 2 to explain more explicitly and visually what is being measured here, and what residual individuality represents. This should shed more light on how these analyses support the conclusion that learning increases behavioural variability among individuals in both Figure 2 and Figure 3. The schematic should provide more intuition on how to interpret the data to those unfamiliar with some of the statistics. Besides the schematics, we will also add more intuitive visualizations of the behaviour data in the supplementary, including representations of how within-strain distributions of behaviour change before and during learning or in control condition for all strains, so that the reader may inspect them in more detail.

    Improved explanation of Figure 3 and the link between statistical outputs and behavioural measurements.

    Reviewer 1 also noted that the analyses in Figure 3 are difficult to interpret without relying heavily on the Results text. Hopefully the added schematic in Figure 2 that explains what Divergence represents should address this note and make the interpretation of Figure 3 easier. Indeed, upon reflection, we agree that the label “Divergence” is quite vague. The “Divergence” in fact shows again residual individuality, and how it changes with every made decision in the case where we compare distributions of flies that start at green versus the blue arm. We further subset the distributions by clustering flies that share the same individual initial color bias or similar learning score and measure residual individuality for them as well. Here, value 0 means the two distributions have the same shape, and higher values mean the shapes are more different. We will rename Divergence to “Residual individuality Start” to make it clear that this is conceptually the same type of measurement, and revise the figure legends accordingly so that they match the new schematic in Figure 2. This should hopefully clarify what the figures show. We will also add a schematic to depict how change in the shape of the distribution with each decision can affect residual individuality.

    Reviewer 2:

    Clarification of the term “deterministic” when referring to genetic sources of variation.

    Reviewer 2 noted that describing genotype as a deterministic source of variation could be confusing, since gene expression and downstream cellular phenotypes are themselves noisy and stochastic. Indeed, gene expression as a phenotype is noisy, but also at the core it is a result of G x E (albeit the environment at the molecular scale). What we meant to emphasize here is that an individual’s genotype can be considered a fixed variable that determines phenotype expression across environments. The environment also determines the phenotype, again, in concert with genotype, but it will always vary over time. We agree with the reviewer that the wording should be made stricter to avoid confusion.

    We changed this sentence from “In every individual, behaviour is shaped by deterministic, genetic factors and by environmental events throughout lifetime, which may be stochastic and can occur at the molecular, cellular, organismal and even population scales.” to “In every individual, behaviour is shaped by fixed genetic factors and by variable environmental events throughout lifetime, which may be stochastic and can occur at the molecular, cellular, organismal and even population scales.”

    Longitudinal analysis and neural sources of learning variability.

    Reviewer 2 suggested that additional longitudinal analysis could further strengthen the evidence for individuality, and that identifying neural sources of learning variability would be an interesting future direction. We appreciate these suggestions and very much agree with them. But as it was pointed out by the reviewer, this was beyond the scope of this study. Nonetheless, it may be good to note that we have in fact already started this (ongoing and quite extensive) experimental endeavour to identify neural sources of individuality, which we hope will be soon available as a follow-up study.

    Within the current study we were able to track behaviour longitudinally within a 20-minute experiment, and in one case over multiple days, though for only a smaller subset of flies. Broader conclusions on how behaviour would change over longer timeframes (except those already included in the manuscript) could not be made with the current dataset. We have added a figure in the supplement where the reader can visually explore the temporal changes to the distributions of behaviour. More extensive study to see how individuality evolves over longer time frames is indeed planned for the future.

    We thank the reviewers again for their thoughtful and constructive comments. We believe that addressing these points improved the manuscript.

  10. eLife

    eLife Assessment

    This is a fundamental study of individual variation and the contribution of learning to behavioural individuality. The experimental design of massively parallel behavioural phenotypes is outstanding and the conclusions are supported by a compelling and rigorous analysis across a large number of experiments in thousands of individuals across genotypes and conditions. The dataset further represents an advance in studying visual associative learning thanks to the ability to make longitudinal measurements of many behavioural decisions within the same animals. These results are a major contribution to the understanding of the sources of behavioural individuality.

  11. eLife

    Reviewer #1 (Public review):

    "Learning is a fundamental source of individuality," by Manna and colleagues, interrogates different sources of variation in individual behavior. The authors place individual flies in a Y-shaped arena, which is a common design in the field, and illuminate the arms of the Y with blue versus green light. They track the color preference of individual animals and also perform operant conditioning, meaning that they teach the fly to avoid a particular color/arm by generating a foot shock when the fly enters that arm. There are a number of things that are impressive about this setup: The authors are able to collect data on thousands of individual flies of many different strain backgrounds, and they demonstrate a strong change in color preference after conditioning. This is nice, because in past papers, visual learning ability has been modest and difficult to study. To put a number on it, in this paper, animals on average don't show a color preference at the start of the assay, spending around 30% of their time in the one arm illuminated green, and the remaining time in the two arms illuminated blue. After conditioning, the average animal spends only 23% of its time in the green arm.

    The authors run 64 animals through the assay for each of 88 wild-type strains (maybe? see Major Point 1 below) and see considerable strain-specific (genetic) variation in the change in time spent in the shocked color after conditioning. Some strains show no learning, while others spend <10% of their time in the shocked color after conditioning. They also, I believe, see that some strains have more variability across individuals, which would suggest that some strains have stronger canalization at the development or circuit function level than others, i.e., some genotypes produce more consistent copies of the individual, others less consistent copies. (Or, some genotypes produce robust circuits, and others produce noisy circuits.)

    Finally, the authors argue statistically that learning itself increases variability in individual performance. This makes a lot of sense to me intuitively. Learning changes the physical/chemical properties of circuits in the brain, and because it evolves over time and interacts with environmental variables, it seems like it should send different animals down different channels. Or, at a conceptual level, if I learn to play the piano and my sister doesn't (because of some genetic difference between us or something stochastic), this learning experience will cause all sorts of other differences in our behavior as time passes. I also think the authors do have enough data to be able to make this finding. However, the presentation of the argument in this portion of the paper is hard for me to understand, and I am not an expert in statistics, so the strength of the result is difficult for me to evaluate.

    Major points

    (1) It's difficult to track through the paper the number of animals tested for different assays. At the beginning, it says N=5632, which works out to 64 flies for each of the 88 DGRP strains. 64 happens to be the number of parallel Y arenas they have. Later in the methods, there's a description of more variation within the set of 64 for each strain, two different parent sets per strain, different sexes, conditioned and unconditioned. And, while the results text focuses on the color learning, the methods discuss additional assays (place learning, multi-day learning).

    Given the numbers, does each run of the 64 mazes include all the tested flies of one strain, or are flies of many strains included in each batch? Do different flies do different assays (color, place, multi-day), or do they all do all the assays? Perhaps there is a table including this information already in the supplement, but I recommend making it much clearer in the main results text and methods. While the dataset is large, if it is split over many conditions and/or if batch and genotype confound each other, this will affect the robustness of the results and how strong the conclusions can be.

    (2) The data presentation in Figure 1 is elegant and easy to follow, but getting into Figure 2 and subsequently, I get lost in the statistics and have trouble understanding what is being measured. My understanding of the big picture is that while genetics and individual randomness contribute a lot to behavior, the evidence for learning as an amplifier of individuality is that variance in behavior among animals of the same strain increases over time in the conditioned group (i.e., the group that is doing the most learning, or a specific kind of learning), but not in the control group. This idea is illustrated in the flattening distributions in the cartoons in Figure 1A. The authors should include graphs of the real data that use the same format as in that cartoon. Instead, the graphs present "residuals," and I don't know what those are. I suspect it's "variation left over after accounting for effects of strain and individual stochasticity." I see the residuals being tracked per strain over time in Figure 2H, but I don't see the change over time in other graphs. I'm looking for something simple like, "variation within the strain at the beginning of learning and at later time points in learning." (But I'm not sure exactly what instantaneous measurement would be the focus in longitudinal analyses of learning behavior.)

    (3) Figure 3 is a cool stab at tracking down the precise mechanism by which a stochastic environment interacts with learning to send individuals along different behavioral routes. But again, like in Figure 2, I don't have the sophisticated understanding of statistics to understand exactly what the graphs are telling me, or how they relate to the underlying measurements. I'm relying on the results text alone to reach a conceptual understanding, and just taking the graphs on trust.

    So, overall, the authors have a very nice body of work here, and with the potential to add a new facet to our understanding of the origins of diversity in animal behavior. In addition to the interpretations they focus on here, this dataset also represents an advance in studying visual associative learning in general, and quite an amazing ability to make longitudinal measurements of many behavioral decisions within the same animals. Improving the data presentation to make it easier to follow for a larger swathe of researchers, especially in figures 2 and 3, will increase its potential impact.

  12. eLife

    Reviewer #2 (Public review):

    Summary:

    The authors set out to test the extent to which differences in learning capacity and experience contribute to behavioural variation in a genetically identical population under identical environmental conditions.

    Strengths:

    The authors developed and used a scaled-up version of a simple two-choice behavioural paradigm, allowing them to test thousands of individuals across multiple genotypes. They then deployed clever and powerful statistical analysis methods and provided compelling evidence for a role of variability in learning in the expression of behavioural variation.

    Weaknesses:

    There are no major weaknesses, although some level of longitudinal analysis to strengthen the evidence for a strict definition of individuality would be a welcome extension of a future study. In addition, it would have been very interesting, although understandably beyond the current scope, to delineate a potential source of learning variability in the brain.

  13. Version published to 10.7554/elife.111235.1 on eLife
  14. Version published to 10.1101/2024.08.30.610528v4 on bioRxiv
  15. Version published to 10.1101/2024.08.30.610528v3 on bioRxiv
  16. Mentioned by @merianelion.bsky.social on Bluesky

    In case you want to see experimental evidence on how irrelevant genes truly are for defining our individuality compared to lived experience, here’s my lab’s recent study www.biorxiv.org/content/10.1...

  17. Mentioned by @merianelion.bsky.social on Bluesky

    Link to our paper in case you’re curious www.biorxiv.org/content/10.1...

  18. Version published to 10.1101/2024.08.30.610528v2 on bioRxiv
  19. Mentioned by @biorxiv-behav.bsky.social on Bluesky

    Behavioral individuality is a consequence of experience, genetics and learning https://www.biorxiv.org/content/10.1101/2024.08.30.610528v1

  20. Mentioned by @biorxivpreprint.bsky.social on Bluesky

    Behavioral individuality is a consequence of experience, genetics and learning https://www.biorxiv.org/content/10.1101/2024.08.30.610528v1

  21. Version published to 10.1101/2024.08.30.610528v1 on bioRxiv