Research articlesAdaptive phenotypic plasticity induces individual variability along a cognitive trade-off
Abstract
Animal species, including humans, display patterns of individual variability in cognition that are difficult to explain. For instance, some individuals perform well in certain cognitive tasks but show difficulties in others. We experimentally analysed the contribution of cognitive plasticity to such variability. Theory suggests that diametrically opposed cognitive phenotypes increase individuals' fitness in environments with different conditions such as resource predictability. Therefore, if selection has generated plasticity that matches individuals’ cognitive phenotypes to the environment, this might produce remarkable cognitive variability. We found that guppies, Poecilia reticulata, exposed to an environment with high resource predictability (i.e. food available at the same time and in the same location) developed enhanced learning abilities. Conversely, guppies exposed to an environment with low resource predictability (i.e. food available at a random time and location) developed enhanced cognitive flexibility and inhibitory control. These cognitive differences align along a trade-off between functions that favour the acquisition of regularities such as learning and functions that adjust behaviour to changing conditions (cognitive flexibility and inhibitory control). Therefore, adaptive cognitive plasticity in response to resource predictability (and potentially similar factors) is a key determinant of cognitive individual differences.
1. Introduction
That individuals display different cognitive abilities has been long acknowledged in human psychology (e.g. [1,2]), and evidence is now accumulating for many other animal taxa (e.g. mammals [3]; birds [4]; teleost fish [5]; insects [6]). The pattern of this intraspecific cognitive variability is often quite complex, with individuals excelling at certain cognitive tasks but performing scarcely in others (e.g. [7–12]). Various hypotheses for this cognitive variability have been formulated and tested (e.g. covariation with personality traits [13]; energetic trade-offs [14]); however, we currently do not have a clear explanation.
Intriguingly, the cognitive variability has been observed often in functions that fall into two categories: (i) functions such as learning and memory that are advantageous in predictable environments, addressing consistent patterns and regularities [6,15,16] and (ii) functions (like the executive functions cognitive flexibility and inhibitory control [8,17,18]) that allow individuals to change their behaviour rapidly and are likely advantageous in ever-changing situations [19]. Studies from other research fields have shown that the resource predictability in the environment affects a large number of non-cognitive traits, including foraging behaviour [20–22], aggressive behaviour [23,24], spatial behaviour [25–28], metabolism [21], stress [29], and reproductive and life-history traits [30,31]. For instance, in predictable environments, individuals forage more efficiently [20], are more aggressive [23], occupy smaller territories [26] and display lower levels of stress [29]. If environmental predictability also affects cognitive traits, it might generate interindividual variability along a trade-off between cognitive functions advantageous in highly predictable versus unpredictable environments [32]. Considering that individuals of a species may be subjected to different predictability levels across space and time (e.g. [26,33,34]), a probable mechanism for this effect is plasticity that permits individuals to match their cognitive phenotype to the predictability experienced. This cognitive plasticity would provide a critical contribution to intraspecific variability in cognition.
In our study, we tested the hypothesis that environmental predictability determines cognitive variability via cognitive plasticity. We manipulated the temporal [35] and spatial predictability [20] of foraging resources in experimental populations of guppies, Poecilia reticulata, a teleost fish with heightened cognitive variability (e.g. [18]). The treatment exposed guppies to simulated environments with either a predictable food source available each day in the same place and at the same time or to an unpredictable food source available at a pseudo-random location and time. We then compared guppies exposed to the two treatments using assays for learning, cognitive flexibility and inhibitory control. Based on the aforementioned trade-off hypothesis [32], we predicted a greater learning performance in guppies from the predictable treatment and a greater performance in the cognitive flexibility and inhibitory control tasks in guppies exposed to the unpredictable treatment.
We additionally administered two behavioural tests to the guppies because environmental predictability may affect various behavioural traits in fish (e.g. [21,23,28]), and the behavioural type of a fish often covaries with its cognitive abilities or affects the outcome of some cognitive tests (e.g. [36,37]). Therefore, eventual cognitive differences between the two predictability treatments could be at least in part due changes in guppies' behaviour. By simultaneously characterizing the cognitive and behavioural phenotype of the experimental guppies, we tried to disentangle the mechanism with which predictability affects cognition. We focused on two behavioural traits that have been consistently shown to covary with cognition in guppies: exploration and social behaviour [36,38–40]. Both exploration and sociability are expected to be reduced in the predictable environment [23,26].
2. Materials and methods
(a) Experimental manipulation of resource predictability
The experiments involved naïve, new-born guppies obtained from gravid females in our facility (electronic supplementary material, S1, §a). These subjects underwent exposure to environments with different levels of predictability for 20 consecutive days. We assigned a randomly chosen group of six individuals to each of 12 experimental aquaria (n = 72 guppies overall). Six experimental aquaria were assigned the predictable environment treatment and the remaining six experimental aquaria to the unpredictable environment treatment (n = 6 replicates).
The experimental aquaria were rectangular and contained four separate foraging areas, one in each corner (figure 1a). We administered food to the guppies once per day in one feeding area of the experimental aquarium, with a different schedule for the two treatments. For half of the aquaria assigned to the predictable environment treatment, we provided the food at 11.00 h; for the remaining half in the aquaria of the predictable environment treatment, we provided the food at 15.00 h. Moreover, in the predictable environment treatment, the food was consistently administered in a predetermined foraging area per each aquarium. In the unpredictable environment treatment, we provided the food each day at a random time between 8.00 and 18.00 h and in a foraging area determined according to a pseudo-random scheme. Details of the treatment are provided in electronic supplementary material, S1, §b. At the end of the treatment, four subjects randomly collected from each experimental aquarium were used in the cognitive and behavioural assays, which were administered to a predetermined sequence (electronic supplementary material, S1, §c). We interrupted the testing of one subject because it showed signs of distress in the first assay. Therefore, the sample used in the study was n = 47 guppies, including 23 of the predictable environment treatment and 24 of the unpredictable environment treatment.
Figure 1. Diagram of the apparatuses used in the study. (a) Aquarium in which the subjects were exposed to the predictability treatments, details and lateral view. (b) Experimental apparatus for the learning and the cognitive flexibility assays, detail and top view. (c) Inhibitory control apparatus. (d,e) Apparatuses used in the two behavioural assays: (d) exploration and (e) social behaviour.
(b) Learning assay
The learning assay was based on an established discrimination paradigm in which the guppies had to select a rewarded colour stimulus between two options [41,42]. Briefly, each subject was tested in an experimental apparatus consisting of two chambers connected by a central corridor (figure 1b). The apparatus was maintained under standard conditions and was provided with several enrichments (electronic supplementary material, S1, §d). After a pre-test procedure (electronic supplementary material, S1, §d), each subject underwent 12 colour discrimination trials per day. In each trial, the experimenter inserted two stimulus cards in one of the two chambers of the apparatus. Each card had a circle (∅ 1.8 cm), either yellow or red. For each subject, one of the two colours was considered as the correct stimulus. The correct colour was counterbalanced between the experimental groups and the left-right position of the correct colour was counterbalanced between trials. If the fish approached the correct colour within 15 min, it received a food reward; otherwise, the experimenter removed the cards. As the approach, we considered when the subject swam at 0.5 × body length or less from the stimulus, oriented toward the stimulus. The testing of each guppy continued until it reached a criterion of less than 30% errors 2 consecutive days. In each day of testing, we recorded the number of errors and the number of correct responses of each subject.
(c) Cognitive flexibility assay
After the learning task, we administered a reversal learning task to assess cognitive flexibility following the paradigm of previous studies in teleost fish [42,43]. The apparatus and the procedure were the same as for the test phase of the learning task. However, the experimenter rewarded the choice of the previously unrewarded colour. The reversal learning task started the day after the subject reached the criterion of the learning task. The testing continued until each subject reached a criterion less than 30% errors, and in each day, we collected the number of errors and correct responses, as previously described.
(d) Inhibitory control assay
Following a paradigm implemented in this species [44,45], we assessed inhibitory control as the ability to withhold attempts to capture an unreachable prey behind a transparent barrier. Guppies underwent the inhibitory control assay individually in 4 l experimental aquaria maintained under standard conditions (figure 1c; electronic supplementary material, S1, §e). To perform the assay, the experimenter presented to the subject a laboratory glass tube (∅ 1.2 cm) containing a solution of water and approximately 500 live Artemia salina nauplii. The tube was suspended near one extremity of the apparatus. The guppies were accustomed to A. salina nauplii, as this prey was provided during the maintenance. Moreover, during a pre-test phase, the subjects were trained to feed in correspondence of the same extremity of the apparatus subsequentially used to present the tube (electronic supplementary material, S1, §e). Therefore, most of the subjects rapidly approached the tube and attempted to capture the prey. Guppies’ behaviour was videorecorded for 20 min, allowing the experimenter to record the capture attempts from the recordings played back at a reduced speed. The experimenter recorded as an error each event in which a guppy touched the glass tube with the snout in an attempt to capture a prey. Moreover, the experimenter recorded when the subject approached the stimulus for the first time. Because of an issue with the webcam software, we did not retrieve the recordings of four subjects. Therefore, the sample size of this assay was 43 guppies, 22 of the predictable environment treatment and 21 of the unpredictable environment treatment.
(e) Behavioural tests
First, we conducted a novel environment exploration test in an open-field arena [46–48]. The guppies were observed individually in an unfamiliar, white, empty arena (figure 1d) for 20 min. During this period, using an automatic tracking system (electronic supplementary material, S1, §f), we measured two variables. The first variable was the exploratory activity of the subject as the distance moved. The second variable was the time the subjects spent in the centre of the arena (10 cm from the edges). This latter variable is considered proxy for various behavioural traits. For instance, shyer, more neophobic, and more anxious individuals tend to avoid the centre of the apparatus where they perceive to be more exposed to potential predators (thigmotaxis behaviour [49,50]).
Second, we conducted a social behaviour test [51] in which the guppies were tested in the central compartment of a three-chamber apparatus (figure 1e). The two lateral chambers were divided from the central one by a transparent partition. One lateral chamber contained a shoal of five guppies and the other was left empty. The experimenter released the fish in the apparatus and then recorded its behaviour for 20 min. From the video recordings, the experimenter obtained the time spent by the subject close to the social stimulus (i.e. within 5 cm) and the time spent close to the empty lateral chamber. From these data, we calculated an index of sociability for each subject considering that more social individuals were expected to spend more time close to the stimulus compared to the empty chamber. Further details of this procedure are reported in the electronic supplementary material, S1, §g.
3. Results
(a) Predictable environment enhanced learning
All the subjects tested (n = 47) reached the criterion in the colour discrimination learning task in a relatively short period of time (4.43 ± 2.58 days, mean ± s.d.). The analysis on the number of errors across testing days found a significant decrease (generalized linear mixed-effects model, GLMM: , p < 0.001). This suggested that the subjects progressively learned to choose the correct colour. Critically, the decrease in number of errors was steeper for the guppies of the predictable environment comparing to the guppies of the unpredictable environment (GLMM: , p < 0.001; figure 2a). Therefore, learning was faster for the guppies of the predictable environment.
Figure 2. Results of the learning task and the cognitive flexibility task. (a) Proportion of errors made by the subjects from the two treatments (predictable and unpredictable environment) when learning to discriminate between the two colours, divided per each day of the experiment. (b) Proportion of errors made by the subjects from the two treatments (predictable and unpredictable environment) when reversing the learned choice between the two colours, divided per each day of the experiment. In both panels, points and shaded area represent mean and 95% confidence intervals estimated from the GLMM used in the analysis; dashed lines represent chance level.
(b) Unpredictable environment enhanced cognitive flexibility
All the subjects tested (n = 47) reached the criterion in the reversal learning assay. The number of days to the criterion in the reversal learning assay (8.36 ± 4.06 days) was approximately twice as that observed in the initial learning assay, suggesting greater difficulty of the cognitive flexibility assay for the guppies (paired-samples t-test: t46 = 5.126, p < 0.001).
The number of errors in the reversal learning assay significantly decreased across testing days (GLMM: , p < 0.001), as expected due to subject's learning to handle the reversed reward contingency. Critically, the decrease in number of errors was steeper for the guppies of the unpredictable environment comparing to the guppies of the predictable environment (GLMM: , p < 0.001; figure 2b), suggesting greater cognitive flexibility in the guppies of the former treatment.
(c) Unpredictable environment enhanced inhibitory control
Seventy-seven out of 43 guppies attempted to capture the stimulus prey, on average within the third minute (± 3.88, s.d.) from the beginning of the test. The environment experienced by the subject did not affect whether it approached the prey (predictable environment: 16 out of 22 subjects; unpredictable environment: 11 out of 22 subjects; Fisher exact test: = 0.215). Similarly, the environmental treatment experienced did not affect when the subjects approached the prey (two-sample t-test: t25 = 0.099, p = 0.922; figure 3a).
Figure 3. Results of the inhibitory control task. (a) Time taken by the subjects from the two treatments (predictable and unpredictable environment) to approach the stimulus prey; points and error bars represent means and s.e., respectively. (b) Number of attempts to capture the prey performed by the subjects from the two treatments (predictable and unpredictable environment) in each minute of the test; points and shaded area represent mean and 95% confidence intervals estimated from the GLMM used in the analysis.
Overall, we observed 1661 attempts to capture the prey, with an average of 61.52 ± 63.39 (mean ± s.d.) attempts per subject. The number of attempts was higher at the beginning of the experiment and then, decreased over testing time (GLMM: , p < 0.001). Guppies from the predictable environment displayed a higher number of attempts (predictable environment: 74.69 ± 67.45; unpredictable environment: 42.36 ± 62.87; GLMM: , p = 0.020; figure 3b).
(d) Level of predictability did not affect variance within experimental group
The performance variance observed within each of the two experimental groups of guppies was not significantly different in any of the cognitive tasks (Bartlett tests: number of errors in the learning assay: Bartlett's K2 = 0.273, p = 0.601; number of errors in the cognitive flexibility assay: Bartlett's K2 = 0.033, p = 0.856; number of number of attempts in the inhibitory control assay: Bartlett's K2 = 0.056, p = 0.813).
(e) Predictability affected guppies' activity but this did not explain cognitive plasticity
In the open-field test, the exploratory activity of the subjects decreased significantly across testing time (linear mixed-effects model, LMM: , p < 0.001). Moreover, guppies from the unpredictable environment showed greater activity (LMM: , p = 0.033; figure 4a). The time spent in the centre of the arena (boldness) in the open-field test was significantly affected only by the experimental time (LMM: , p = 0.008), with no changes due to the treatment (figure 4b). In the sociability test, none of the terms in the model, including the treatment, significantly explained the preference for the social stimulus (LMM: all p-values > 0.5; figure 4c).
Figure 4. Results of the behavioural tests. (a) Exploratory activity measured as distance moved in the exploration test. (b) Boldness measured as time spent in the centre of the arena in the exploration test. (c) Sociability as the proportion of time spent close to the social stimulus in the social behaviour test. In all the panels, points and error bars represent mean and 95% confidence intervals estimated from the LMMs used in the analysis.
A regression analysis indicated that activity did not explain performance in the learning (linear regression: t = −0.740, p = 0.463, R2 = 0.012), cognitive flexibility (linear regression: t = −0.363, p = 0.718, R2 = 0.003) and inhibitory control task (linear regression: t = 0.207, p = 0.838, R2 = 0.002). Therefore, the effects of environmental predictability in the cognitive tasks are not explained by the change in behavioural activity detected in the exploration task.
4. Discussion
Our study revealed that guppies can develop a highly diversified cognitive phenotype that matches the resource predictability level experienced in the environment. When food was predictably found in the same spatial location and at the same time of the day, guppies developed greater learning performance. Conversely, when the location and timing of the food were unpredictable, guppies developed greater cognitive flexibility and greater inhibitory control.
We designed the study to investigate the effect of predictability experimentally, and thus, we analysed populations of subjects exposed to very different levels of predictability. In nature, smaller fluctuations in the predictability levels experienced are likely to similarly determine plasticity-mediated cognitive variability between and within populations. The effect of predictability aligns with growing reports of cognitive plasticity in teleost fishes in response to other environmental factors (e.g. environment quality [52]; social environment [53]; enrichment [42]; predation risk [54]). All these forms of cognitive plasticity may interact in nature, thereby determining a broad spectrum of individual phenotypes. Concerning this, it will be important to ascertain whether the cognitive variability due to plasticity is stable across an individual's life or it can be altered if the environment changes. Some forms of cognitive plasticity displayed by fish are likely malleable, such as those determined by factors that vary with the season [55,56], further amplifying the potential of plasticity to produce individual differences in cognition. Notably, while the teleost's brain certainly displays a remarkable level of neural plasticity potential (reviewed in [57]), including extensive neurogenesis capacity in the adult (reviewed in [58]), cognitive plasticity might be also widespread in tetrapod vertebrates (e.g. [59,60]).
The plasticity due to predictability level has differently affected the three cognitive functions investigated in this study, aligning with the trade-off hypothesis proposed by Tello-Ramos et al. [32]. This provides support for the idea that the observed cognitive plasticity is adaptive at least from two points of view. First, the trade-off hypothesis is based on the fact that enhancing functions such as learning and memory should be advantageous when the environmental conditions are predictable because they permit to rapidly and reliably exploit resources that are available consistently with the same spatial and/or temporal pattern. Conversely, executive functions such as cognitive flexibility and inhibitory control are involved in modifying individuals' behaviour [19], which should permit to adjust to resources that vary in space and time. Second, the function specificity per se supports the idea of an adaptive mechanism. A more general, non-adaptive mechanism is indeed expected to determine unidirectional changes (i.e. an increase or decrease) in all the cognitive abilities of an individual, a scenario that contrasts with what was observed in our study. Function specificity has also been reported for other forms of cognitive plasticity. For instance, guppies raised in the presence of biotic and abiotic stimuli developed greater learning ability compared to guppies raised in barren environments, but no differences were observed in inhibitory control and cognitive flexibility [42]. Many earlier investigations on plasticity and adaptive selection have focused on general proxies of cognition, such as brain size (e.g. [61,62]). The findings in relation to function-specific effects suggest the need of a more precise approach that investigates cognition at the level of single functions to depict cognitive adaptation fully.
The unpredictable treatment also increased guppies’ exploratory activity (but did not affect our measures of boldness and social behaviour). Variability in behavioural traits has been linked to cognitive variability [13], including in our study species [11,36,37,63]. However, in our correlation analysis, the observed effect on activity did not emerge as a potential explanation for the differences observed in the cognitive tasks. Unpredictable environmental conditions are also known to increase corticosteroid concentrations in all main vertebrate groups, including teleost fish (review by [64]), and these hormones may affect cognitive abilities in a range of tasks [65–67]. Therefore, corticosteroids are proximate mechanisms worth investigating for the effect observed in guppies.
In conclusion, we demonstrated a form of cognitive plasticity driven by the levels of predictability of resources in an environment. This plasticity determined variability along a trade-off line between functions useful to learn and fixate a specific behaviour and functions that permit changes in behaviour, highlighting that cognitive plasticity might have an important role in determining phenotypic variance. To fully understand cognitive variability in animals, research efforts should be devoted to analyse other forms of cognitive plasticity and their interacting effects on individual's cognition. Altogether, multiple plasticity mechanisms might be responsible for individualities in cognitive abilities.
Ethics
The experiments complied with Directive 2010/63/EU of the European Parliament and of the Council of 22 September 2010 on the protection of animals used for scientific purposes and with Italian law D. Lgs n. 26 of 4 March 2014 ('Attuazione della direttiva 2010/63/UE sulla protezione degli animali utilizzati a fini scientifici'). The procedures were designed following the ASAB/ABS Guidelines for the Use of Animals in Research (https://doi.org/10.1016/j.anbehav.2019.11.002) [68] and were approved by the Ethical committee (OPBA) of University of Ferrara (permit TLX-2022-1).
Data accessibility
The data are provided in the electronic supplementary material [69].
Authors' contributions
T.L.-X.: conceptualization, formal analysis and writing—original draft; G.M.: data curation, formal analysis, investigation, methodology and writing—review and editing; C.B.: conceptualization and writing—review and editing.
All authors gave final approval for publication and agreed to be held accountable for the work performed therein.
Conflict of interest declaration
We declare we have no competing interests.
Funding
Work supported by #NEXTGENERATIONEU (NGEU) and funded by the Ministry of University and Research (MUR), National Recovery and Resilience Plan (NRRP), project MNESYS (PE0000006), 'A multiscale integrated approach to the study of the nervous system in health and disease' (DN. 1553 11.10.2022).
Acknowledgements
We are thankful to Andrea Margutti for building the apparatuses, to Roberto Bianchi for help in testing the subjects and to Elia Gatto for suggestions on the statistical analysis.
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