{"paper_id":"432ebc0e-c023-48ea-a8b2-ec0d824ea5ae","body_text":"Environmental enrichment selectively enhances learning, but not inhibitory control, in juvenile gilthead seabream (Sparus aurata) | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Environmental enrichment selectively enhances learning, but not inhibitory control, in juvenile gilthead seabream (Sparus aurata) Inês S. Neves, João L. Saraiva, María J. Cabrera-Álvarez This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8960731/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 7 You are reading this latest preprint version Abstract Human-driven environmental change and the expansion of aquaculture have altered the ecological, social, and sensory conditions experienced by fish, raising concerns about how captive environments affect behaviour, cognition, and welfare. Environmental enrichment is widely used to mitigate these impacts and may also enhance welfare and cognitive performance in captive animals. However, its effects on executive functions during early developmental stages remain poorly understood in fish. We investigated inhibitory control and learning in juvenile gilthead seabream ( Sparus aurata ) using the cylinder task, a detour paradigm requiring inhibition of a direct response towards a visible food reward. Fish were housed under enriched and non-enriched conditions and completed a training phase followed by an inhibitory control test. During training, enriched fish showed a significant reduction in the time until task completion across trials, while non-enriched fish did not, indicating differences in learning. In contrast, inhibitory control performance remained low in both treatments, with no effects of enrichment on success rate or response time. Performance variation was instead explained by trial progression, individual behavioural differences, and developmental factors. Fish tested later in the experiment exhibited shorter response times, consistent with ontogenetic changes. Enriched fish displayed higher feeding contact rates, reflecting increased motivation and task engagement rather than improved inhibitory control. Clustering analyses revealed stable individual behavioural profiles predicting task engagement, independently of housing treatment. Overall, environmental enrichment enhances learning and motivation in juvenile seabream but does not improve inhibitory control at least at this developmental stage, highlighting the importance of ontogeny, individual variation, and species-specific ecology when assessing executive functions in fish. Aquaculture Fish cognition Learning Ontogeny Response inhibition Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Figure 7 Figure 8 Figure 9 Figure 10 Figure 11 Figure 12 Figure 13 1. Introduction Human demand for seafood has become a major driver of ecological and environmental transformation worldwide. As global consumption increases, aquatic ecosystems are being reshaped through overexploitation, habitat modification, and the construction of human-engineered environments designed to sustain fish production (Coleman and Williams 2002 ; Clifford and Heffernan 2018 ; Dong et al. 2023 ). These pressures alter the ecological, social, and sensory landscapes in which fish evolved, forcing many species to cope with rapidly changing conditions such as disrupted habitats, altered community structures, and increased interactions with humans and other species. While some fish display behavioural or cognitive flexibility that allows them to adapt to these disturbances, others struggle to function in environments that no longer correspond to their evolved biological and behavioural capacities (Huntingford et al. 2006 ). Within this broader context of human-driven change, aquaculture has become one of the fastest-growing food-producing sectors worldwide and now provides a substantial share of the seafood consumed globally (Barreto et al. 2022 ; FAO 2024 ; Bjørndal et al. 2024 ). Unlike wild systems, aquaculture environments impose a distinct and more controlled set of challenges whose physical and social properties can differ sharply from natural habitats. Farmed fish must cope with ethological constraints (e.g., limited space, altered social dynamics, disrupted reproductive opportunities), environmental and physiological stressors, and a range of human-induced procedures that vary in intensity across species and production systems (Saraiva et al. 2022 ). These conditions shape not only growth and physiology but also behaviour, cognition, and welfare (Conte 2004 ; Ashley 2007 ; Arechavala-Lopez et al. 2022 ). Concerns about stress, restricted environments, and artificial farming conditions have intensified, especially as research continues to demonstrate that fish experience affective states, have complex cognitive abilities, and are capable of behavioural sophistication comparable in many aspects to other vertebrates (Brown and Dorey 2019; Saraiva et al. 2019 ; Sneddon and Brown 2020 ; Lambert et al. 2022 ). One way to improve captive conditions is through environmental enrichment (EE), broadly defined as the addition of structural or sensory complexity to the environment (Näslund and Johnsson 2016 ; Arechavala-Lopez et al. 2022 ). Enriched environments promote exploration, enhance neural plasticity, and improve learning and memory across vertebrates (Braithwaite and Salvanes 2005 ; Barcellos et al. 2018 ; Zhang et al. 2021 ). In fish, EE has been shown to reduce aggression, decrease stress responses, and improve spatial learning, behavioural flexibility, and overall welfare (Salvanes et al. 2007 ; Brydges and Braithwaite 2009 ; Arechavala-Lopez et al. 2019 , 2020 ; Zhang et al. 2021 ). These findings indicate that environmental complexity can play a key role in shaping how fish perceive, learn, and interact with their surroundings. Cognition in fish has historically been underestimated, but research over the past two decades demonstrates that teleosts exhibit impressive long-term memory, social learning, numerical discrimination, spatial mapping, and problem-solving abilities, sometimes comparable to those of non-human primates (Bshary et al. 2002 ; Laland and Hoppitt 2003 ; Griffiths 2003 ; Warburton 2003 ; Broglio et al. 2003 ; Brown et al. 2011; Prétôt et al. 2025 ). These capacities are influenced by ecological factors, developmental conditions, and early-life experiences in teleost fish and other vertebrates (Näslund et al. 2012 ; Ebbesson and Braithwaite 2012 ; Sørensen et al. 2013 ). For example, teleosts reared in structurally complex or variable environments often show enhanced learning performance, greater behavioural flexibility, and improved problem-solving performance compared to individuals reared in barren or socially stressful conditions (Näslund et al. 2012 ; Ebbesson and Braithwaite 2012 ; Sørensen et al. 2013 ). Environmental complexity, in particular, has been shown to promote neural proliferation and brain growth in fish, whereas chronic social stress or impoverished rearing conditions can impair neural development and learning performance (Näslund et al. 2012 ; Ebbesson and Braithwaite 2012 ; Sørensen et al. 2013 ). Within the expansive study of cognition, inhibitory control has attracted considerable interest. It is a core executive function that allows individuals to suppress impulsive responses in favour of more effective strategies (Chudasama 2011 ; Diamond 2013 ). Inhibitory control influences foraging, predator avoidance, and social interactions, and is widely used as a proxy for behavioural flexibility (Band and van Boxtel 1999 ; Shamosh et al. 2008 ). A standard method for assessing this ability is the detour paradigm, or Cylinder Task, in which animals must resist approaching a visible reward directly through a barrier and instead detour to the open sides (MacLean et al. 2014 ; Kabadayi et al. 2016 ). This task has been applied across mammals, birds, and more recently teleost fish, with successful demonstrations in guppies ( Poecilia reticulata ), Nile tilapia ( Oreochromis niloticus ), and zebrafish ( Danio rerio ) (Lucon-Xiccato et al. 2017 ; Santacà et al. 2019 ; Brandão et al. 2019 ). In guppies, individuals were able to learn the detour rule and showed improvement across trials, revealing both learning effects and consistent individual differences in inhibitory control (Lucon-Xiccato et al. 2017 ). Nile tilapia also successfully solved the task, with performance linked to behavioural traits such as boldness and exploratory tendency, suggesting, according to Brandão et al., 2019 , a relationship between inhibitory control and personality. In zebrafish, inhibitory control was demonstrated through consistent detour performance, with variation among individuals indicating that executive functions can be meaningfully assessed in this species (Santacà et al. 2019 ). Gilthead seabream ( Sparus aurata ) is a particularly interesting species for this type of research. It is one of the most economically important marine fish in Mediterranean aquaculture, valued for its robustness, adaptability, and wide environmental tolerance (Jobling 2011 ; Mhalhel et al. 2023 ). Behavioural studies on seabream have examined personality traits, learning abilities, and responses to EE, showing enhanced exploratory behaviour, improved spatial orientation, and reduced aggression in enriched settings (Millot et al. 2009 ; Castanheira et al. 2013 ; Arechavala-Lopez et al. 2019 , 2020 ). Despite its relevance in aquaculture and behavioural research, fundamental executive functions such as inhibitory control have never been tested in this species. In this study, we investigated inhibitory control in juvenile gilthead seabream using the Cylinder Task, a detour paradigm in which individuals must inhibit a direct response toward a visible food reward and instead access it by detouring to an open side. Specifically, the objectives of this study were twofold: first, to determine whether gilthead seabream can successfully perform the detour task, providing insight into their inhibitory control and cognitive flexibility; and second, to assess whether previous exposure to the structural environment influences task performance and learning, thereby shedding light on how experience with complex or altered habitats may shape behavioural responses relevant to coping with human-induced environmental change. 2. Materials and Methods 2.1. Animals and housing Juvenile immature gilthead seabream ( Sparus aurata ) (N = 39; mean ± SD body weight: 24.4 ± 2.0 g; body length: 10.2 ± 0.4 cm) were housed at an indoor laboratory at the University of Algarve (Portugal). Fish were distributed into four 200 L tanks, each divided into two compartments by a perforated opaque partition, forming eight groups of 5 fish per compartment. Two tanks contained EE, consisting of three 13.5 cm plastic plants per compartment, each composed of three green, leaf-bearing branches attached to a white stone, and two tanks were non-enriched (NE), serving as controls (Fig. 1 ). Fish were maintained under controlled conditions (19–20°C, 36 PSU, pH 7.6), with continuous aeration and a 12L:12D photoperiod. Forty individuals were initially tagged (8-mm Trovan Ltd., UK microtransponders) for identification. One fish died before the start of the experiment, and it was not replaced to prevent any further disturbance to the social dynamics of the group, resulting in the final sample size of N = 39. 2.2. Experimental set-up Training and testing were conducted in a separate 320-L tank (200 × 40 × 40 cm) divided into three compartments: (1) a waiting compartment (100 × 40 × 40 cm) where the group of fish to be tested in that session would be resting, (2) a holding compartment (20 × 40 × 40 cm) where the subject fish would be isolated before the testing started, and (3) an experimental compartment (80 × 40 × 40 cm) where the training or testing phase would take place (Fig. 2 ). Opaque partitions visually isolated compartments, whereas transparent partitions allowed visual access to the apparatus when required. Depending on the phase, the experimental compartment contained a white feeding plate (10 × 10 cm), a transparent cylinder (22 cm length, 10 cm diameter), or both (apparatus). 2.3. Experimental design The cylinder task consisted of three training phases (Plate Training, Cylinder Familiarization, and Forced Cylinder) followed by the Inhibitory Control Test. When a reward was used, it consisted of a small piece of shrimp, adjusted to the size of the fish’s mouth to ensure it could be consumed in a single bite. A group of five fish belonging to the same housing group were tested within five to seven days following the schematic represented in Fig. 3 , with testing alternating between two EE and two NE groups, and the whole experiment being conducted over the course of 19 weeks, with a break of 8 weeks in the middle. 2.3.1. Phase 1 – Plate training Each trial commenced with the fish isolated in the holding compartment for 10-min (Fig. 4 ). The opaque and transparent partitions were removed sequentially, providing access to a white plate containing a food reward placed 40 cm from the partition. Trials lasted up to 5 min, and a fish progressed to the next phase after achieving five successes in six consecutive trials. 2.3.2. Phase 2 – Cylinder familiarization A transparent cylinder was placed at the bottom of the housing tanks for 48 h to allow fish to become accustomed to the object. No behavioural data were collected during this phase. 2.3.3. Phase 3 – Forced cylinder The cylinder was attached to a partition containing an entry hole, requiring the fish to pass through the cylinder to access the food reward placed 18 cm beyond the cylinder (Fig. 5 ). A fish completed this phase after passing through the cylinder at least one time in a 5-min trial. 2.3.4. Phase 4 – Inhibitory Control test The cylinder contained the food reward at the bottom of its central section, and was placed centrally within the experimental compartment, oriented perpendicular to the fish’s swimming direction. Fish were required to detour, enter through an open end without touching the cylinder, and eat the food reward within 5 min (Fig. 6 ). Each fish performed 10–30 trials with a 10-min inter-trial interval. Outcomes were scored as: Success (S): detour without touching the cylinder Failure (F): contact with the cylinder Failure followed by success (FS): contact with the cylinder followed by success within the same trial No interaction (NI): absence of contact or approach (trial subsequently repeated) For analyses of first-touch outcomes, FS trials were coded as failures. 2.4. Data processing Variables included treatment (EE, NE), trial number, morphometric measurements (initial and final size and weight), latency to eat the food (phase 1), outcome, and behavioural metrics such as: time to succeed or fail, post-fail (time to succeed after fail), and food bites (number of attempts to reach the food). Derived variables were defined as follows: Learning rate (LR): slope of response time across trials (linear regression for each fish). Latency score: ordinal scale (0–3), where 3 corresponds to faster responses, and 0 represents the failure outcomes based on response-time thresholds. Exploratory behaviour: ordinal score from Phase 3 based on time taken to pass the cylinder. Contact rate: number of contacts with the cylinder per second during test trials. Efficiency: mean time for an individual to succeed in phase 4, classified based on mean response time as either fast (< 100s), moderate (100–199s), or slow (200–299s). 2.5. Statistical analysis Analyses were performed in R (v4.3). Normality and homoscedasticity were evaluated before selecting parametric or non-parametric tests. Training performance was analysed using linear mixed-effects models (LMMs) to account for repeated measures, with trial and treatment included as fixed effects and fish identity included as a random intercept, and with repeated-measures ANOVA to assess overall changes across trials. Inhibitory control performance during the test phase was analysed using generalized linear models (GLMs) and generalized linear mixed-effects models (GLMMs) with a binomial error structure, modelling success (success/failure) as the response variable. Trial number and treatment were included as fixed effects, and fish identity was included as a random intercept when repeated observations were present. To further characterise performance during testing, additional analyses were conducted on the first 10 valid responses per individual using linear mixed-effects models with fish identity as a random effect. Behavioural variables including learning rate, latency, exploratory behaviour, and contact rate were analysed using ANOVA and when the assumptions were violated, non-parametric alternatives were applied, including Wilcoxon rank-sum tests or Kruskal-Wallis tests followed by Dunn’s post hoc comparisons. Variables expressed as proportions were additionally analysed using beta regression models. To characterise individual behavioural profiles, principal component analysis (PCA) was applied to standardised behavioural variables from the training phase. Scores from the principal components were subsequently used in clustering analyses to identify distinct behavioural profiles. Differences among behavioural clusters were tested using one-way ANOVA. Statistical significance was set at α = 0.05. 2.6. Use of AI We employed AI-assisted tools (ChatGPT by OpenAI and DeepL Write by DeepL SE) to facilitate tasks, including grammar refinement, enhancing language clarity, and providing assistance in conducting statistical analysis. 3. Results 3.1. Training performance 3.1.1. Skill acquisition during plate training Fish performance during the plate training phase (Phase 1) improved significantly across trials. Repeated measures ANOVA revealed a significant main effect of trial (F(4,152) = 9.28, p < 0.01, ges = 0.048) with fish completing the task more rapidly in later trials. Post hoc comparisons revealed that fish took significantly more time to complete the task in Trial 1 than in Trials 3, 4, and 5 (all p ≤ 0.013), consistent with a learning or habituation effect (Fig. 7 ). There was no significant main effect of treatment (F(1,37) = 0.12, p = 0.732, ges = 0.002), indicating that overall performance did not differ between treatments. However, the interaction between trial and treatment was significant following Greenhouse–Geisser correction (F(4,148) = 2.69, p = 0.033), suggesting that performance differed between treatments. When analysed separately, EE fish showed a significant improvement across trials (F(4,72) = 8.30, p < 0.001, ges = 0.115), with Post hoc comparisons indicating that the completion times in Trial 1 were significantly longer than those in Trials 3, 4, and 5 (all p = 0.013). In contrast, NE fish did not exhibit a significant change in performance across trials (F(4,76) = 2.05, p = 0.096, ges = 0.013; Greenhouse–Geisser corrected p = 0.133), indicating the absence of measurable improvement with repeated exposure. 3.1.2. Individual variation in task success and efficiency Considerable inter-individual variation was observed during plate training (Phase 1), completion times were positively skewed (mean ± SD: 68.79 ± 94.89 s; median ± SD: 51.38 ± 99.48 s), reflecting a majority of fast-performing individuals and a subset of fish that frequently failed to complete the task within the allowed time. Completion time was strongly correlated with task outcome (Spearman’s ρ = 0.58, p < 0.001), supporting the validity of the efficiency classification used to categorise individuals as fast, moderate, slow, or failing. Despite this variation, no significant differences in median or mean completion times were detected between treatments (all p > 0.20), or across housing groups (all p > 0.14). 3.2. Inhibitory control test performance 3.2.1. Overall task outcomes During the Inhibitory Control Test, success rates were generally low across individuals. Considering “failure followed by success” trials as failures, the mean proportion of successful trials was 3.6% (± 4.3%), while failures were predominant (81.2% ± 19.4%) with timeouts occurring at an intermediate frequency (15.2% ± 19.0%). The distribution of the outcomes differed significantly between treatments (X 2 (2) = 27.66, p < 0.001). Specifically, fish from the EE tanks exhibited a higher proportion of direct failures, whereas fish from the NE tanks showed relatively higher frequencies of “failure followed by success” and no-interaction trials. However, the proportion of successful trials (S) remained low in both treatments (Fig. 8 ). Linear models initially indicated no significant main effects of treatment (p = 0.197), total trial number (p = 0.220), or their interaction (p = 0.105) on success rate. However, when the trial number × treatment interaction was explicitly included in the model, significant effects emerged for treatment (p < 0.001), trial number (p = 0.016), and their interaction (p < 0.001). Beta regression models similarly revealed significant effects of treatment, total trial number, and their interaction on success probability (all p < 0.001). In contrast, failure and timeout rates were primarily explained by trial number rather than treatment: failure rate decreased significantly with increasing trial number (p = 0.006), while timeout rate increased with the number of trials (p = 0.002) (Fig. 9 ). 3.2.2. Response time across treatment and the first 10 valid trials No significant effect of treatment on response time was detected (p = 0.524), and response time remained unchanged across trials (p = 0.841). In addition, the interaction between treatment and trial number was not significant (p = 0.180), indicating that response-time trajectories across trials did not differ between treatments (Fig. 10 ). 3.2.3. Testing period effects and morphometric differences Performance during the Inhibitory Control Test differed significantly along the testing period. Fish tested in the second half of the experiment (N = 19) showed shorter latencies to success than those tested in the first half (N = 20; Wilcoxon rank-sum test: W = 89, p = 0.012). Initial body size did not differ between these two groups, with no significant differences in starting body weight (First half: mean = 14.03 g; Second half: mean = 13.39 g; p = 0.588) or starting length (First half: mean = 8.20 cm; Second half: mean = 7.92 cm; p = 0.349). In contrast, fish tested in the second half of the experiment were significantly larger at the time of testing, with higher final body weight (First half: mean = 27.12 g; Second half: mean = 42.91 g; p < 0.001) and greater final length (First half: mean = 11.14 cm; Second half: mean = 13.06 cm; p < 0.001), reflecting differences in growth at the time of testing and a possible effect in cognitive performance (Fig. 11 ). 3.3. Contact rate and individual behavioural differences Fish housed in enriched environments exhibited significantly higher food contact rate during the test phase than fish housed in NE conditions (Kruskal–Wallis χ² = 15.30, df = 1, p < 0.001). Median contact rate was greater in enriched tanks (median = 42.5, IQR = 36.0–48.0) than in NE tanks (median = 29.0, IQR = 24.0–34.5), indicating higher levels of behavioural engagement in enriched conditions (Fig. 12 ). Exploratory clustering analyses based on training-phase behaviour identified distinct behavioural profiles among individuals. Cluster 1 includes moderate learners with relatively fast responses and low-to-moderate exploratory behaviour; Cluster 2 includes intermediate learners with moderate responsiveness and higher activity levels; Cluster 3 consists of slow learners with delayed responses and high exploratory behaviour; and Cluster 4 includes rapid learners with high exploratory activity. These profiles differed significantly in subsequent contact rate during testing (one-way ANOVA: F(3,31) = 3.32, p = 0.032), with slower learners investing less contact rate than faster-responding individuals (Fig. 13 ). Behavioural cluster membership was not significantly associated with the treatment (Fisher’s exact test, p = 0.199), although Cluster 4 was observed only among EE individuals. 4. Discussion This study investigated whether EE influences learning and inhibitory control performance in juvenile gilthead seabream. During the training phase fish showed clear differences between treatments, with individuals housed in enriched environments exhibiting a more pronounced reduction in completion time, indicating enhanced learning. However, this effect did not extend to the inhibitory control test. Overall success rates during testing were low, and no significant effect of EE was detected on success probability or response time across trials. Instead, inhibitory control performance appeared to be shaped primarily by trial progression, individual variability, and developmental factors rather than housing conditions. However, enriched fish showed higher levels of behavioural engagement, as reflected by increased food contact rates, suggesting that EE can be affecting motivation and activity while not inhibitory control itself. Together, these results suggest that while EE can support learning and engagement, it does not enhance inhibitory control in juvenile seabream, indicating a stronger role of intrinsic and developmental factors in shaping cognitive performance (Diamond 2013 ). The overall success rate observed in the inhibitory control test was very low in juvenile gilthead seabream, indicating that this species may face particular challenges when performing the cylinder task at least at this developmental stage. Low success in this task has been reported in some fish species, but contrasts with higher success rates documented in others, such as guppies and cleaner fish, which have shown more robust inhibitory control under comparable cylinder-task paradigms (MacLean et al. 2014 ; Lucon-Xiccato and Bisazza 2017 ; Lucon-Xiccato and Bertolucci 2020 ; Montalbano et al. 2020 ; Lucon-Xiccato 2024 ). These differences suggest that inhibitory control performance may be strongly shaped by species-specific ecological and behavioural traits rather than reflecting a general cognitive limitation (Kabadayi et al. 2018 ; Santacà et al. 2019 ). Nonetheless, interspecific variation does not operate in isolation from development. Indeed, inhibitory control is known to develop gradually across ontogeny in many taxa, and reduced performance during juvenile stages is widely reported in birds and mammals, including humans (Moffitt et al. 2011 ; Diamond 2013 ; Savaşçı et al. 2021; Vernouillet et al. 2025 ). Accordingly, the low success observed here may largely reflect developmental constraints, as inhibitory control was assessed in juvenile individuals, whereas studies reporting higher performance typically tested adults (MacLean et al. 2014 ; Minter et al. 2017 ; Lucon-Xiccato and Bisazza 2017 ), with some exceptions (e.g., Savaşçı et al. 2021). In our study, developmental factors also played a role, as fish tested later in the experimental period exhibited shorter response times and greater task efficiency. The better performance in older fish (with consequential increased size) may reflect more advanced neural, sensory, or motor development, facilitating faster decision-making and more effective interaction with the task (Brydges et al. 2011 ; Bensky et al. 2017 ). Similar relationships between body size, developmental stage, and cognitive performance have been reported in fish, where ontogenetic changes strongly influence learning ability, behavioural flexibility, and problem-solving performance (Brydges et al. 2011 ; Bensky et al. 2017 ). Together, these findings indicate that inhibitory control performance in juvenile seabream likely reflects ongoing developmental processes operating both across and within ontogenetic stages, underscoring the importance of accounting for growth-related variation when interpreting task outcomes and when assessing executive functions in young fish (Brydges et al. 2011 ; Diamond 2013 ; Bensky et al. 2017 ). Testing adult seabream in similar paradigms would be a crucial next step to fully characterise inhibitory control capacities in this species. Gilthead seabream are benthic, opportunistic foragers that rely heavily on substrate exploration and flexible feeding strategies, rather than rapid visuomotor inhibition in frontal approaches, which may reduce their suitability for tasks requiring direct motor suppression (Arechavala-Lopez et al. 2019 , 2022 ). In addition, seabream are a highly social species, particularly during juvenile stages, and isolation during individual testing may have negatively affected motivation, stress levels, or task engagement, potentially contributing to the low success observed (Montero et al. 2009 ; Arechavala-Lopez et al. 2022 ). Social isolation has been shown to alter behaviour, stress physiology, and cognitive responses in fish, including changes in exploration, attention, and feeding motivation (Montero et al. 2009 ; Saraiva et al. 2018 ). In the present study, the combination of individual testing, early stages of development, and a cognitively demanding task may therefore have contributed to the overall low success rates observed. On the other hand, our findings seem to suggest that inhibitory control tasks developed and validated in other fish species may not fully capture cognitive performance in juvenile seabream, highlighting the importance of considering species ecology, sociality, and developmental stage when interpreting task outcomes (Kabadayi et al. 2018 ; Santacà et al. 2019 ). Inhibitory control is a multidimensional construct that can be assessed using different paradigms, including detour tasks, go/no-go tasks, reversal learning, and delay-of-gratification assays, each of which engages partially distinct cognitive and motivational processes (Diamond 2013 ; MacLean et al. 2014 ). The cylinder task specifically assesses the ability to inhibit a prepotent motor response toward a visible reward, a core component of motor inhibitory control (MacLean et al. 2014 ; Gatto et al. 2018 ; Lucon-Xiccato and Bertolucci 2020 ; Lucon-Xiccato 2024 ). However, the cylinder task was originally developed for terrestrial animals, which typically move in a two-dimensional plane and interact with obstacles using frontal approaches, potentially simplifying the motor inhibition demands compared to aquatic species (MacLean et al. 2014 ; Kabadayi et al. 2018 ; van Horik et al. 2018 ; Griebling et al. 2026 ). Fish, by contrast, move freely in three dimensions and often rely on continuous exploration rather than discrete frontal approaches, which may increase the perceptual and motor complexity of the task (Domenici and Blake 1997 ; MacLean et al. 2014 ; Santacà et al. 2019 ). This requirement may be particularly demanding for species that rely heavily on exploratory, benthic, or substrate-oriented foraging strategies rather than direct frontal approaches to food, such as the gilthead seabream (Arechavala-Lopez et al. 2019 , 2022 ). In contrast, tasks such as reversal learning or go/no-go paradigms, which require suppressing previously learned responses or selectively responding to cues, may better align with the ecological and behavioural traits of some fish species and have been successfully applied in teleosts (Bensky et al. 2017 ; Lucon-Xiccato and Bisazza 2017 ). Adapting inhibitory control assays to species-specific sensory, ecological, and developmental contexts may therefore provide a more accurate assessment of executive function in fish (Lucon-Xiccato and Bisazza 2017 ; Kabadayi et al. 2018 ; Lucon-Xiccato 2024 ). While species-specific ecological traits and social context may partly explain the low overall success observed in the inhibitory control test, the EE had a clear positive effect on learning during the training phase. Fish housed in enriched environments showed a significant reduction in completion time across trials, while fish housed in NE conditions did not exhibit a significant improvement. This finding is consistent with previous studies demonstrating that environmental complexity enhances learning performance in fish by promoting exploration, sensory stimulation, and neural plasticity (Salvanes et al. 2013 ; Näslund and Johnsson 2016 ). In contrast, EE did not increase success probability during the inhibitory control test, or affect response time across trials, indicating that the benefits of enrichment did not contribute to inhibitory control performance. This result suggests that learning efficiency and inhibitory control rely on partially distinct cognitive and motivational processes, with EE primarily enhancing engagement and motivation rather than executive control mechanisms (Diamond 2013 ). Similar patterns have been reported in other fish species, where EE improves learning speed and behavioural flexibility but does not consistently enhance inhibitory control or detour task performance (Bensky et al. 2017 ; Lucon-Xiccato and Bisazza 2017 ; Lucon-Xiccato et al. 2022 , 2023 ; Montalbano et al. 2022 ). Together, these results indicate that while EE can facilitate learning and increase behavioural engagement, it may not be sufficient to improve inhibitory control in juvenile seabream, reinforcing the idea that this cognitive domain is more strongly influenced by intrinsic or developmental factors than by enrichment alone (Savaşçı et al. 2021; Lucon-Xiccato 2024 ). Consistent with the limited effect of EE on inhibitory control performance, substantial inter-individual variation emerged as a key factor shaping behaviour during both the training phase and the inhibitory control test. During training, individuals differed markedly in completion time and efficiency, with some fish consistently performing faster while others frequently failed to complete the task within the allotted time. These differences were not explained by housing conditions, suggesting stable individual variation in learning and task engagement. Similarly, during testing, individuals varied widely in behavioural investment, as reflected by differences in feeding contact rate and overall engagement with the task. Exploratory clustering analyses revealed distinct behavioural profiles that were maintained across experimental phases, indicating consistency in behavioural tendencies across contexts. These findings suggest that stable individual differences in activity, motivation, and task engagement have a strong influence on cognitive performance in juvenile seabream (Carere and Locurto 2011 ; Rowe and Healy 2014 ). The importance of such individual variation has also been demonstrated in other fish species using the cylinder task. For example, Lucon-Xiccato et al. ( 2020a ) and Montalbano et al. ( 2020 ) reported large, repeatable individual differences in inhibitory performance, and studies in guppies, sticklebacks ( Gasterosteus aculeatus ) and mosquitofish ( Gambusia affinis ) show that personality traits such as boldness and exploration are closely linked to inhibitory control measures (Keagy et al. 2019 ; Lucon-Xiccato et al. 2020b ; Wallace et al. 2020 ; Savaşçı et al. 2021; Vinogradov et al. 2022 ). Lucon-Xiccato and Bisazza ( 2017 ) showed that personality traits such as boldness and exploratory tendency strongly affected detour task performance in guppies, with bolder individuals often outperforming shy conspecifics, independent of learning ability. This indicates that performance in inhibitory control tasks may reflect differences in motivation, risk-taking, or exploratory strategies rather than inhibitory control alone (Sih and Del Giudice 2012 ; Rowe and Healy 2014 ; Lucon-Xiccato and Bisazza 2017 ; van Horik et al. 2018 ; Montalbano et al. 2020 ). Together, these findings highlight the importance of accounting for personality traits when assessing cognitive abilities, particularly in species such as gilthead seabream that exhibit high behavioural variability linked to flexible foraging strategies and strong social tendencies (Montero et al. 2009 ; Carere and Locurto 2011 ; Sih and Del Giudice 2012 ; Lucon-Xiccato and Bisazza 2017 ; Arechavala-Lopez et al. 2019 , 2022 ). In juveniles, this variability may further limit the sensitivity of inhibitory control tasks, as performance can be strongly influenced by motivation, exploration, and developmental state rather than inhibitory control alone (Lucon-Xiccato and Bisazza 2017 ; Montalbano et al. 2020 ; Savaşçı et al. 2021; Lucon-Xiccato 2024 ). In general, the present findings indicate that inhibitory control performance in juvenile seabream is shaped by a complex interaction between developmental stage, individual behavioural variation, and experimental context, rather than by EE alone. While EE clearly enhanced learning during training and promoted behavioural engagement, it did not affect inhibitory control during testing. These results emphasise the need to consider species-specific ecology and sociality when applying standard cognitive tasks to fish, and caution against interpreting low performance as evidence of limited cognitive capacity. In conclusion, this study demonstrates that EE enhances learning performance and engagement in juvenile gilthead seabream, but does not improve inhibitory control when assessed by the cylinder task. Instead, inhibitory control was strongly influenced by developmental stage and individual behavioural variation: older, larger and more engaged individuals demonstrated faster responses, independently of the housing condition (EE vs . NE). The overall low success rates observed suggest that inhibitory control tasks developed for other taxa may be particularly challenging for juvenile seabream and may reflect developmental and ecological constraints rather than limited cognitive capacity. Together, these findings highlight the importance of accounting for ontogeny, individual differences, and species-specific ecology when assessing executive functions in fish. Furthermore, they emphasise the necessity to adapt cognitive tasks to better align with the biological and developmental characteristics of the species under study. Declarations Conflict of Interest Statement The authors have no relevant financial or non-financial interests to disclose. Author Contribution I. S. N.: Writing original draft, Writing – review & editing, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation.J. L. S.: Writing – review & editing, Resources, Funding acquisition.M. J. C-A: Writing – review & editing, Resources, Project administration, Methodology, Conceptualization, Supervision, Funding acquisition. Acknowledgement This study was conducted as part of the ManyFishes project, a Big Team Science Initiative that aims to assess the cognitive abilities across fish species through collaborative, multi-laboratory research (https://themanyfishes.github.io/). We thank all the laboratories and researchers involved in the ManyFishes project for their collective efforts and contributions to this collaborative work. Additionally, we are grateful to Gonçalo Oliveira and Nuno Jesus for their invaluable technical assistance and support during the animal husbandry and experimental procedures and Dr. Pedro Guerreiro for his support on tank design. We thank Dr. José Ricardo Paula for insightful discussions and constructive suggestions that helped improve the study. We also thank Dr. Andrea Meloni and Esther Hoyo Alvarez for their assistance and advice on statistical analysis. We thank the Portuguese national funds FCT - Foundation for Science and Technology, for funding the CCMAR research center through projects UIDB/04326/2020 (DOI:10.54499/UIDB/04326/2020) and LA/P/0101/2020 (DOI:10.54499/LA/P/0101/2020). Data Availability All relevant data are available from the authors upon request. 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The tank was divided into three compartments by opaque (black) and transparent (dark grey) partitions (40 × 37.5 cm). 1) Waiting compartment (100 × 40 × 40 cm) where untested fish waited in a group prior to training; 2) Holding compartment (20 × 40 × 40 cm) where the focal fish was placed individually at the beginning of each trial; 3) Experimental compartment (80 × 40 × 40 cm) where training was conducted. In this Phase a white circular plate with a green dot at its centre was placed in the experimental compartment at 40 cm from the transparent partition. The focal fish started each trial in the holding compartment (2), and must eat the food reward positioned directly above the green dot on the plate to complete the task\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image4.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/2acca733c02536d90c2939e3.png\"},{\"id\":104403640,\"identity\":\"2978f73c-11dc-4480-9070-5acf67ae2a3a\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:18:45\",\"extension\":\"png\",\"order_by\":5,\"title\":\"Figure 5\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":74624,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic representation of the Forced Cylinder setup (Phase 3) in the experimental tank (200 × 40 × 40 cm). The tank was divided into three compartments by opaque (black) and transparent (dark grey) partitions (40 × 37.5 cm). 1) Waiting compartment (100 × 40 × 40 cm) where untested fish waited in a group prior to the trial; 2) Holding compartment (20 × 40 × 40 cm) where the focal fish was initially placed individually; 3) Experimental compartment (80 × 40 × 40 cm) where training was performed. A transparent plastic cylinder (22 cm in length and 10 cm in diameter) was attached to the transparent partition separating the holding and experimental compartments, allowing access only through the cylinder. A white square plate with a green dot at its centre and a food reward above it was positioned in the experimental compartment at 40 cm from the transparent partition. The fish must cross the cylinder to complete the task\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image5.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/13171a82e049fa779a3766cc.png\"},{\"id\":104088234,\"identity\":\"fa9e5f23-992f-42dd-9b87-db5cb92110dc\",\"added_by\":\"auto\",\"created_at\":\"2026-03-06 15:46:48\",\"extension\":\"png\",\"order_by\":6,\"title\":\"Figure 6\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":75418,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eSchematic representation of the Inhibitory Control Test setup (Phase 4) in the experimental tank (200 × 40 × 40 cm). The tank was divided into three compartments by opaque (black) and transparent (dark grey) partitions (40 × 37.5 cm). 1)\\u003cstrong\\u003e \\u003c/strong\\u003eWaiting compartment (100 × 40 × 40 cm) where untested fish waited in a group prior to the trial; 2) Holding compartment (20 × 40 × 40 cm) where the focal fish was initially placed individually; 3) Experimental compartment (80 × 40 × 40 cm) where the inhibitory control test was performed. A transparent plastic cylinder (22 cm in length and 10 cm in diameter) was placed horizontally in the experimental compartment, parallel to the rear wall of the tank. A white square plate with a green dot at its centre was positioned under the cylinder at a distance of 40 cm from the transparent partition, with a food reward placed inside the cylinder directly above the green dot. To complete the task, the focal fish started in the holding compartment (2) and was required to detour the transparent cylinder to access the food reward without touching the apparatus\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image6.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/8358f83896d10ec07921ca2a.png\"},{\"id\":104403383,\"identity\":\"66a52955-6db6-47f7-8129-ce0ab1a6e9a5\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:18:13\",\"extension\":\"png\",\"order_by\":7,\"title\":\"Figure 7\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":100303,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eTime to complete Phase 1 across trials for the EE (red) and NE (blue) treatments. The faded thin lines in the background represent individual subjects’ performance across trials, and the bold lines with circles show the group mean time per trial. Error bars indicate the standard error of the mean for each treatment at each trial\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image7.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/9835144876cd9bcf39167d09.png\"},{\"id\":104403254,\"identity\":\"d0b460bb-528c-49ac-b72f-d6e80d60c5c9\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:17:51\",\"extension\":\"png\",\"order_by\":8,\"title\":\"Figure 8\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":57513,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eOutcomes of the tests by treatment, shown as (A) total counts and (B) average percentage of trials per fish. Red bars represent the enriched treatment (EE) and blue bars represent the non-enriched treatment (NE). Outcome possibilities: F, fail; FS, failure followed by a success; S, success; NI, no response within the allotted time\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image8.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/5f2c7aef883621844e8758ac.png\"},{\"id\":104088237,\"identity\":\"8c6ec295-3c26-42e1-8658-a7641e18419f\",\"added_by\":\"auto\",\"created_at\":\"2026-03-06 15:46:48\",\"extension\":\"png\",\"order_by\":9,\"title\":\"Figure 9\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":85221,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eRelationship between the total number of trials and outcome proportions by treatment. A) percentage of no interaction (NI) per total trials. B) percentage of successful trials (S) per total trials. C) percentage of failed responses (F) per total trials. Points represent individual fish, and solid red lines correspond to the enriched treatment, while solid blue lines correspond to the non-enriched treatment. Regression lines indicate the linear trend for each treatment\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image9.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/f2a83bca796091b094316c09.png\"},{\"id\":104403654,\"identity\":\"83195b98-defe-41cc-9c7d-b1abd942d5e7\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:18:46\",\"extension\":\"png\",\"order_by\":10,\"title\":\"Figure 10\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":94097,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eResponse time across the first 10 valid trials in the inhibitory control test for each treatment. Points represent individual fish in each trial, and solid red lines correspond to the enriched treatment, while solid blue lines correspond to the non-enriched treatment. The fitted lines show the linear regression of response time across trials for each treatment\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image10.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/ed22969058da3a02307308c1.png\"},{\"id\":104403181,\"identity\":\"b1834e34-c6ec-4231-9ef3-b90b097106bb\",\"added_by\":\"auto\",\"created_at\":\"2026-03-11 12:17:40\",\"extension\":\"png\",\"order_by\":11,\"title\":\"Figure 11\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":44069,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eMean time to succeed across groups in the inhibitory control task. Bars represent the mean time (in seconds) to complete the task for each group (Groups 1–8), and error bars indicate the standard error of the mean\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image11.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/4da1f7ba13ea4a256f0f83bc.png\"},{\"id\":104779435,\"identity\":\"9bb6be8e-2f3a-41e5-98a7-e98274e62b18\",\"added_by\":\"auto\",\"created_at\":\"2026-03-17 07:40:16\",\"extension\":\"png\",\"order_by\":12,\"title\":\"Figure 12\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":127845,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eViolin plot illustrating individual contact rate under environmental enrichment (EE) and bare (NE) conditions\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image12.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/2abd1f2d431fd517b95ddf4a.png\"},{\"id\":104088239,\"identity\":\"06374430-c995-4c1e-b055-19c250ae5feb\",\"added_by\":\"auto\",\"created_at\":\"2026-03-06 15:46:48\",\"extension\":\"png\",\"order_by\":13,\"title\":\"Figure 13\",\"display\":\"\",\"copyAsset\":false,\"role\":\"figure\",\"size\":55416,\"visible\":true,\"origin\":\"\",\"legend\":\"\\u003cp\\u003eBehavioural profiles of individuals across the training phase, grouped by PCA-based clusters. Each line represents the mean values of three behavioural variables: learning rate (LR), latency, and exploratory behaviour (EB) for a given cluster. Clusters capture distinct combinations of behavioural tendencies: Cluster 1 — moderate learners with quick responses and low-to-moderate activity; Cluster 2 — intermediate learners with moderate responsiveness and higher activity; Cluster 3 — slow learners with delayed responses and high exploratory tendencies; Cluster 4 — rapid learners with high exploratory activity. Values above the points indicate the mean for each variable\\u003c/p\\u003e\",\"description\":\"\",\"filename\":\"image13.png\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/7c172b4622d224e791d7f6cb.png\"},{\"id\":104783934,\"identity\":\"fc721f12-8c14-44ec-a510-ee0031edd5ad\",\"added_by\":\"auto\",\"created_at\":\"2026-03-17 08:04:07\",\"extension\":\"pdf\",\"order_by\":0,\"title\":\"\",\"display\":\"\",\"copyAsset\":false,\"role\":\"manuscript-pdf\",\"size\":1911290,\"visible\":true,\"origin\":\"\",\"legend\":\"\",\"description\":\"\",\"filename\":\"manuscript.pdf\",\"url\":\"https://assets-eu.researchsquare.com/files/rs-8960731/v1/ecba9f6b-d768-4090-948b-95d09d119199.pdf\"}],\"financialInterests\":\"No competing interests reported.\",\"formattedTitle\":\"Environmental enrichment selectively enhances learning, but not inhibitory control, in juvenile gilthead seabream (Sparus aurata)\",\"fulltext\":[{\"header\":\"1. Introduction\",\"content\":\"\\u003cp\\u003eHuman demand for seafood has become a major driver of ecological and environmental transformation worldwide. As global consumption increases, aquatic ecosystems are being reshaped through overexploitation, habitat modification, and the construction of human-engineered environments designed to sustain fish production (Coleman and Williams \\u003cspan citationid=\\\"CR22\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Clifford and Heffernan \\u003cspan citationid=\\\"CR21\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Dong et al. \\u003cspan citationid=\\\"CR26\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). These pressures alter the ecological, social, and sensory landscapes in which fish evolved, forcing many species to cope with rapidly changing conditions such as disrupted habitats, altered community structures, and increased interactions with humans and other species. While some fish display behavioural or cognitive flexibility that allows them to adapt to these disturbances, others struggle to function in environments that no longer correspond to their evolved biological and behavioural capacities (Huntingford et al. \\u003cspan citationid=\\\"CR32\\\" class=\\\"CitationRef\\\"\\u003e2006\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWithin this broader context of human-driven change, aquaculture has become one of the fastest-growing food-producing sectors worldwide and now provides a substantial share of the seafood consumed globally (Barreto et al. \\u003cspan citationid=\\\"CR7\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e; FAO \\u003cspan citationid=\\\"CR28\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e; Bj\\u0026oslash;rndal et al. \\u003cspan citationid=\\\"CR9\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). Unlike wild systems, aquaculture environments impose a distinct and more controlled set of challenges whose physical and social properties can differ sharply from natural habitats. Farmed fish must cope with ethological constraints (e.g., limited space, altered social dynamics, disrupted reproductive opportunities), environmental and physiological stressors, and a range of human-induced procedures that vary in intensity across species and production systems (Saraiva et al. \\u003cspan citationid=\\\"CR63\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). These conditions shape not only growth and physiology but also behaviour, cognition, and welfare (Conte \\u003cspan citationid=\\\"CR23\\\" class=\\\"CitationRef\\\"\\u003e2004\\u003c/span\\u003e; Ashley \\u003cspan citationid=\\\"CR4\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Arechavala-Lopez et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Concerns about stress, restricted environments, and artificial farming conditions have intensified, especially as research continues to demonstrate that fish experience affective states, have complex cognitive abilities, and are capable of behavioural sophistication comparable in many aspects to other vertebrates (Brown and Dorey 2019; Saraiva et al. \\u003cspan citationid=\\\"CR62\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Sneddon and Brown \\u003cspan citationid=\\\"CR69\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Lambert et al. \\u003cspan citationid=\\\"CR38\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eOne way to improve captive conditions is through environmental enrichment (EE), broadly defined as the addition of structural or sensory complexity to the environment (N\\u0026auml;slund and Johnsson \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e; Arechavala-Lopez et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Enriched environments promote exploration, enhance neural plasticity, and improve learning and memory across vertebrates (Braithwaite and Salvanes \\u003cspan citationid=\\\"CR10\\\" class=\\\"CitationRef\\\"\\u003e2005\\u003c/span\\u003e; Barcellos et al. \\u003cspan citationid=\\\"CR6\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). In fish, EE has been shown to reduce aggression, decrease stress responses, and improve spatial learning, behavioural flexibility, and overall welfare (Salvanes et al. \\u003cspan citationid=\\\"CR59\\\" class=\\\"CitationRef\\\"\\u003e2007\\u003c/span\\u003e; Brydges and Braithwaite \\u003cspan citationid=\\\"CR15\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Arechavala-Lopez et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Zhang et al. \\u003cspan citationid=\\\"CR76\\\" class=\\\"CitationRef\\\"\\u003e2021\\u003c/span\\u003e). These findings indicate that environmental complexity can play a key role in shaping how fish perceive, learn, and interact with their surroundings.\\u003c/p\\u003e \\u003cp\\u003eCognition in fish has historically been underestimated, but research over the past two decades demonstrates that teleosts exhibit impressive long-term memory, social learning, numerical discrimination, spatial mapping, and problem-solving abilities, sometimes comparable to those of non-human primates (Bshary et al. \\u003cspan citationid=\\\"CR17\\\" class=\\\"CitationRef\\\"\\u003e2002\\u003c/span\\u003e; Laland and Hoppitt \\u003cspan citationid=\\\"CR37\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Griffiths \\u003cspan citationid=\\\"CR31\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Warburton \\u003cspan citationid=\\\"CR75\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Broglio et al. \\u003cspan citationid=\\\"CR12\\\" class=\\\"CitationRef\\\"\\u003e2003\\u003c/span\\u003e; Brown et al. 2011; Pr\\u0026eacute;t\\u0026ocirc;t et al. \\u003cspan citationid=\\\"CR57\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). These capacities are influenced by ecological factors, developmental conditions, and early-life experiences in teleost fish and other vertebrates (N\\u0026auml;slund et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Ebbesson and Braithwaite \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; S\\u0026oslash;rensen et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). For example, teleosts reared in structurally complex or variable environments often show enhanced learning performance, greater behavioural flexibility, and improved problem-solving performance compared to individuals reared in barren or socially stressful conditions (N\\u0026auml;slund et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Ebbesson and Braithwaite \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; S\\u0026oslash;rensen et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Environmental complexity, in particular, has been shown to promote neural proliferation and brain growth in fish, whereas chronic social stress or impoverished rearing conditions can impair neural development and learning performance (N\\u0026auml;slund et al. \\u003cspan citationid=\\\"CR55\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Ebbesson and Braithwaite \\u003cspan citationid=\\\"CR27\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; S\\u0026oslash;rensen et al. \\u003cspan citationid=\\\"CR70\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Within the expansive study of cognition, inhibitory control has attracted considerable interest. It is a core executive function that allows individuals to suppress impulsive responses in favour of more effective strategies (Chudasama \\u003cspan citationid=\\\"CR20\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Diamond \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Inhibitory control influences foraging, predator avoidance, and social interactions, and is widely used as a proxy for behavioural flexibility (Band and van Boxtel \\u003cspan citationid=\\\"CR5\\\" class=\\\"CitationRef\\\"\\u003e1999\\u003c/span\\u003e; Shamosh et al. \\u003cspan citationid=\\\"CR67\\\" class=\\\"CitationRef\\\"\\u003e2008\\u003c/span\\u003e). A standard method for assessing this ability is the detour paradigm, or Cylinder Task, in which animals must resist approaching a visible reward directly through a barrier and instead detour to the open sides (MacLean et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Kabadayi et al. \\u003cspan citationid=\\\"CR35\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). This task has been applied across mammals, birds, and more recently teleost fish, with successful demonstrations in guppies (\\u003cem\\u003ePoecilia reticulata\\u003c/em\\u003e), Nile tilapia (\\u003cem\\u003eOreochromis niloticus\\u003c/em\\u003e), and zebrafish (\\u003cem\\u003eDanio rerio\\u003c/em\\u003e) (Lucon-Xiccato et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Santac\\u0026agrave; et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Brand\\u0026atilde;o et al. \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). In guppies, individuals were able to learn the detour rule and showed improvement across trials, revealing both learning effects and consistent individual differences in inhibitory control (Lucon-Xiccato et al. \\u003cspan citationid=\\\"CR43\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Nile tilapia also successfully solved the task, with performance linked to behavioural traits such as boldness and exploratory tendency, suggesting, according to Brand\\u0026atilde;o et al., \\u003cspan citationid=\\\"CR11\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e, a relationship between inhibitory control and personality. In zebrafish, inhibitory control was demonstrated through consistent detour performance, with variation among individuals indicating that executive functions can be meaningfully assessed in this species (Santac\\u0026agrave; et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eGilthead seabream (\\u003cem\\u003eSparus aurata\\u003c/em\\u003e) is a particularly interesting species for this type of research. It is one of the most economically important marine fish in Mediterranean aquaculture, valued for its robustness, adaptability, and wide environmental tolerance (Jobling \\u003cspan citationid=\\\"CR33\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Mhalhel et al. \\u003cspan citationid=\\\"CR48\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e). Behavioural studies on seabream have examined personality traits, learning abilities, and responses to EE, showing enhanced exploratory behaviour, improved spatial orientation, and reduced aggression in enriched settings (Millot et al. \\u003cspan citationid=\\\"CR49\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Castanheira et al. \\u003cspan citationid=\\\"CR19\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Arechavala-Lopez et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR1\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Despite its relevance in aquaculture and behavioural research, fundamental executive functions such as inhibitory control have never been tested in this species.\\u003c/p\\u003e \\u003cp\\u003eIn this study, we investigated inhibitory control in juvenile gilthead seabream using the Cylinder Task, a detour paradigm in which individuals must inhibit a direct response toward a visible food reward and instead access it by detouring to an open side. Specifically, the objectives of this study were twofold: first, to determine whether gilthead seabream can successfully perform the detour task, providing insight into their inhibitory control and cognitive flexibility; and second, to assess whether previous exposure to the structural environment influences task performance and learning, thereby shedding light on how experience with complex or altered habitats may shape behavioural responses relevant to coping with human-induced environmental change.\\u003c/p\\u003e\"},{\"header\":\"2. Materials and Methods\",\"content\":\"\\u003cdiv id=\\\"Sec3\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.1. Animals and housing\\u003c/h2\\u003e \\u003cp\\u003eJuvenile immature gilthead seabream (\\u003cem\\u003eSparus aurata\\u003c/em\\u003e) (N\\u0026thinsp;=\\u0026thinsp;39; mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD body weight: 24.4\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;2.0 g; body length: 10.2\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;0.4 cm) were housed at an indoor laboratory at the University of Algarve (Portugal). Fish were distributed into four 200 L tanks, each divided into two compartments by a perforated opaque partition, forming eight groups of 5 fish per compartment. Two tanks contained EE, consisting of three 13.5 cm plastic plants per compartment, each composed of three green, leaf-bearing branches attached to a white stone, and two tanks were non-enriched (NE), serving as controls (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig1\\\" class=\\\"InternalRef\\\"\\u003e1\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eFish were maintained under controlled conditions (19\\u0026ndash;20\\u0026deg;C, 36 PSU, pH 7.6), with continuous aeration and a 12L:12D photoperiod. Forty individuals were initially tagged (8-mm Trovan Ltd., UK microtransponders) for identification. One fish died before the start of the experiment, and it was not replaced to prevent any further disturbance to the social dynamics of the group, resulting in the final sample size of N\\u0026thinsp;=\\u0026thinsp;39.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec4\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.2. Experimental set-up\\u003c/h2\\u003e \\u003cp\\u003eTraining and testing were conducted in a separate 320-L tank (200 \\u0026times; 40 \\u0026times; 40 cm) divided into three compartments: (1) a waiting compartment (100 \\u0026times; 40 \\u0026times; 40 cm) where the group of fish to be tested in that session would be resting, (2) a holding compartment (20 \\u0026times; 40 \\u0026times; 40 cm) where the subject fish would be isolated before the testing started, and (3) an experimental compartment (80 \\u0026times; 40 \\u0026times; 40 cm) where the training or testing phase would take place (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig2\\\" class=\\\"InternalRef\\\"\\u003e2\\u003c/span\\u003e). Opaque partitions visually isolated compartments, whereas transparent partitions allowed visual access to the apparatus when required. Depending on the phase, the experimental compartment contained a white feeding plate (10 \\u0026times; 10 cm), a transparent cylinder (22 cm length, 10 cm diameter), or both (apparatus).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec5\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.3. Experimental design\\u003c/h2\\u003e \\u003cp\\u003eThe cylinder task consisted of three training phases (Plate Training, Cylinder Familiarization, and Forced Cylinder) followed by the Inhibitory Control Test. When a reward was used, it consisted of a small piece of shrimp, adjusted to the size of the fish\\u0026rsquo;s mouth to ensure it could be consumed in a single bite. A group of five fish belonging to the same housing group were tested within five to seven days following the schematic represented in Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig3\\\" class=\\\"InternalRef\\\"\\u003e3\\u003c/span\\u003e, with testing alternating between two EE and two NE groups, and the whole experiment being conducted over the course of 19 weeks, with a break of 8 weeks in the middle.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cdiv id=\\\"Sec6\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.3.1. Phase 1 \\u0026ndash; Plate training\\u003c/h2\\u003e \\u003cp\\u003eEach trial commenced with the fish isolated in the holding compartment for 10-min (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig4\\\" class=\\\"InternalRef\\\"\\u003e4\\u003c/span\\u003e). The opaque and transparent partitions were removed sequentially, providing access to a white plate containing a food reward placed 40 cm from the partition. Trials lasted up to 5 min, and a fish progressed to the next phase after achieving five successes in six consecutive trials.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec7\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.3.2. Phase 2 \\u0026ndash; Cylinder familiarization\\u003c/h2\\u003e \\u003cp\\u003eA transparent cylinder was placed at the bottom of the housing tanks for 48 h to allow fish to become accustomed to the object. No behavioural data were collected during this phase.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec8\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.3.3. Phase 3 \\u0026ndash; Forced cylinder\\u003c/h2\\u003e \\u003cp\\u003eThe cylinder was attached to a partition containing an entry hole, requiring the fish to pass through the cylinder to access the food reward placed 18 cm beyond the cylinder (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig5\\\" class=\\\"InternalRef\\\"\\u003e5\\u003c/span\\u003e). A fish completed this phase after passing through the cylinder at least one time in a 5-min trial.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec9\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e2.3.4. Phase 4 \\u0026ndash; Inhibitory Control test\\u003c/h2\\u003e \\u003cp\\u003eThe cylinder contained the food reward at the bottom of its central section, and was placed centrally within the experimental compartment, oriented perpendicular to the fish\\u0026rsquo;s swimming direction. Fish were required to detour, enter through an open end without touching the cylinder, and eat the food reward within 5 min (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig6\\\" class=\\\"InternalRef\\\"\\u003e6\\u003c/span\\u003e). Each fish performed 10\\u0026ndash;30 trials with a 10-min inter-trial interval. Outcomes were scored as:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eSuccess (S): detour without touching the cylinder\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eFailure (F): contact with the cylinder\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eFailure followed by success (FS): contact with the cylinder followed by success within the same trial\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eNo interaction (NI): absence of contact or approach (trial subsequently repeated)\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003cp\\u003eFor analyses of first-touch outcomes, FS trials were coded as failures.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec10\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.4. Data processing\\u003c/h2\\u003e \\u003cp\\u003eVariables included treatment (EE, NE), trial number, morphometric measurements (initial and final size and weight), latency to eat the food (phase 1), outcome, and behavioural metrics such as: time to succeed or fail, post-fail (time to succeed after fail), and food bites (number of attempts to reach the food). Derived variables were defined as follows:\\u003c/p\\u003e \\u003cp\\u003e \\u003cul\\u003e \\u003cli\\u003e \\u003cp\\u003eLearning rate (LR): slope of response time across trials (linear regression for each fish).\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eLatency score: ordinal scale (0\\u0026ndash;3), where 3 corresponds to faster responses, and 0 represents the failure outcomes based on response-time thresholds.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eExploratory behaviour: ordinal score from Phase 3 based on time taken to pass the cylinder.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eContact rate: number of contacts with the cylinder per second during test trials.\\u003c/p\\u003e \\u003c/li\\u003e \\u003cli\\u003e \\u003cp\\u003eEfficiency: mean time for an individual to succeed in phase 4, classified based on mean response time as either fast (\\u0026lt;\\u0026thinsp;100s), moderate (100\\u0026ndash;199s), or slow (200\\u0026ndash;299s).\\u003c/p\\u003e \\u003c/li\\u003e \\u003c/ul\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec11\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.5. Statistical analysis\\u003c/h2\\u003e \\u003cp\\u003eAnalyses were performed in R (v4.3). Normality and homoscedasticity were evaluated before selecting parametric or non-parametric tests.\\u003c/p\\u003e \\u003cp\\u003eTraining performance was analysed using linear mixed-effects models (LMMs) to account for repeated measures, with trial and treatment included as fixed effects and fish identity included as a random intercept, and with repeated-measures ANOVA to assess overall changes across trials.\\u003c/p\\u003e \\u003cp\\u003eInhibitory control performance during the test phase was analysed using generalized linear models (GLMs) and generalized linear mixed-effects models (GLMMs) with a binomial error structure, modelling success (success/failure) as the response variable. Trial number and treatment were included as fixed effects, and fish identity was included as a random intercept when repeated observations were present.\\u003c/p\\u003e \\u003cp\\u003eTo further characterise performance during testing, additional analyses were conducted on the first 10 valid responses per individual using linear mixed-effects models with fish identity as a random effect.\\u003c/p\\u003e \\u003cp\\u003eBehavioural variables including learning rate, latency, exploratory behaviour, and contact rate were analysed using ANOVA and when the assumptions were violated, non-parametric alternatives were applied, including Wilcoxon rank-sum tests or Kruskal-Wallis tests followed by Dunn\\u0026rsquo;s post hoc comparisons. Variables expressed as proportions were additionally analysed using beta regression models.\\u003c/p\\u003e \\u003cp\\u003eTo characterise individual behavioural profiles, principal component analysis (PCA) was applied to standardised behavioural variables from the training phase. Scores from the principal components were subsequently used in clustering analyses to identify distinct behavioural profiles. Differences among behavioural clusters were tested using one-way ANOVA. Statistical significance was set at α\\u0026thinsp;=\\u0026thinsp;0.05.\\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec12\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e2.6. Use of AI\\u003c/h2\\u003e \\u003cp\\u003eWe employed AI-assisted tools (ChatGPT by OpenAI and DeepL Write by DeepL SE) to facilitate tasks, including grammar refinement, enhancing language clarity, and providing assistance in conducting statistical analysis.\\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"3. Results\",\"content\":\"\\u003cdiv id=\\\"Sec14\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.1. Training performance\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec15\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.1.1. Skill acquisition during plate training\\u003c/h2\\u003e \\u003cp\\u003eFish performance during the plate training phase (Phase 1) improved significantly across trials. Repeated measures ANOVA revealed a significant main effect of trial (F(4,152)\\u0026thinsp;=\\u0026thinsp;9.28, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.01, ges\\u0026thinsp;=\\u0026thinsp;0.048) with fish completing the task more rapidly in later trials. Post hoc comparisons revealed that fish took significantly more time to complete the task in Trial 1 than in Trials 3, 4, and 5 (all p\\u0026thinsp;\\u0026le;\\u0026thinsp;0.013), consistent with a learning or habituation effect (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig7\\\" class=\\\"InternalRef\\\"\\u003e7\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThere was no significant main effect of treatment (F(1,37)\\u0026thinsp;=\\u0026thinsp;0.12, p\\u0026thinsp;=\\u0026thinsp;0.732, ges\\u0026thinsp;=\\u0026thinsp;0.002), indicating that overall performance did not differ between treatments. However, the interaction between trial and treatment was significant following Greenhouse\\u0026ndash;Geisser correction (F(4,148)\\u0026thinsp;=\\u0026thinsp;2.69, p\\u0026thinsp;=\\u0026thinsp;0.033), suggesting that performance differed between treatments.\\u003c/p\\u003e \\u003cp\\u003eWhen analysed separately, EE fish showed a significant improvement across trials (F(4,72)\\u0026thinsp;=\\u0026thinsp;8.30, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001, ges\\u0026thinsp;=\\u0026thinsp;0.115), with Post hoc comparisons indicating that the completion times in Trial 1 were significantly longer than those in Trials 3, 4, and 5 (all p\\u0026thinsp;=\\u0026thinsp;0.013). In contrast, NE fish did not exhibit a significant change in performance across trials (F(4,76)\\u0026thinsp;=\\u0026thinsp;2.05, p\\u0026thinsp;=\\u0026thinsp;0.096, ges\\u0026thinsp;=\\u0026thinsp;0.013; Greenhouse\\u0026ndash;Geisser corrected p\\u0026thinsp;=\\u0026thinsp;0.133), indicating the absence of measurable improvement with repeated exposure.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec16\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.1.2. Individual variation in task success and efficiency\\u003c/h2\\u003e \\u003cp\\u003eConsiderable inter-individual variation was observed during plate training (Phase 1), completion times were positively skewed (mean\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD: 68.79\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;94.89 s; median\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;SD: 51.38\\u0026thinsp;\\u0026plusmn;\\u0026thinsp;99.48 s), reflecting a majority of fast-performing individuals and a subset of fish that frequently failed to complete the task within the allowed time.\\u003c/p\\u003e \\u003cp\\u003eCompletion time was strongly correlated with task outcome (Spearman\\u0026rsquo;s ρ\\u0026thinsp;=\\u0026thinsp;0.58, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), supporting the validity of the efficiency classification used to categorise individuals as fast, moderate, slow, or failing. Despite this variation, no significant differences in median or mean completion times were detected between treatments (all p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.20), or across housing groups (all p\\u0026thinsp;\\u0026gt;\\u0026thinsp;0.14).\\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec17\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.2. Inhibitory control test performance\\u003c/h2\\u003e \\u003cdiv id=\\\"Sec18\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.2.1. Overall task outcomes\\u003c/h2\\u003e \\u003cp\\u003eDuring the Inhibitory Control Test, success rates were generally low across individuals. Considering \\u0026ldquo;failure followed by success\\u0026rdquo; trials as failures, the mean proportion of successful trials was 3.6% (\\u0026plusmn;\\u0026thinsp;4.3%), while failures were predominant (81.2% \\u0026plusmn; 19.4%) with timeouts occurring at an intermediate frequency (15.2% \\u0026plusmn; 19.0%).\\u003c/p\\u003e \\u003cp\\u003eThe distribution of the outcomes differed significantly between treatments (X\\u003csup\\u003e2\\u003c/sup\\u003e(2)\\u0026thinsp;=\\u0026thinsp;27.66, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Specifically, fish from the EE tanks exhibited a higher proportion of direct failures, whereas fish from the NE tanks showed relatively higher frequencies of \\u0026ldquo;failure followed by success\\u0026rdquo; and no-interaction trials. However, the proportion of successful trials (S) remained low in both treatments (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig8\\\" class=\\\"InternalRef\\\"\\u003e8\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eLinear models initially indicated no significant main effects of treatment (p\\u0026thinsp;=\\u0026thinsp;0.197), total trial number (p\\u0026thinsp;=\\u0026thinsp;0.220), or their interaction (p\\u0026thinsp;=\\u0026thinsp;0.105) on success rate. However, when the trial number \\u0026times; treatment interaction was explicitly included in the model, significant effects emerged for treatment (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), trial number (p\\u0026thinsp;=\\u0026thinsp;0.016), and their interaction (p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Beta regression models similarly revealed significant effects of treatment, total trial number, and their interaction on success probability (all p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). In contrast, failure and timeout rates were primarily explained by trial number rather than treatment: failure rate decreased significantly with increasing trial number (p\\u0026thinsp;=\\u0026thinsp;0.006), while timeout rate increased with the number of trials (p\\u0026thinsp;=\\u0026thinsp;0.002) (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig9\\\" class=\\\"InternalRef\\\"\\u003e9\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec19\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.2.2. Response time across treatment and the first 10 valid trials\\u003c/h2\\u003e \\u003cp\\u003eNo significant effect of treatment on response time was detected (p\\u0026thinsp;=\\u0026thinsp;0.524), and response time remained unchanged across trials (p\\u0026thinsp;=\\u0026thinsp;0.841). In addition, the interaction between treatment and trial number was not significant (p\\u0026thinsp;=\\u0026thinsp;0.180), indicating that response-time trajectories across trials did not differ between treatments (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig10\\\" class=\\\"InternalRef\\\"\\u003e10\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec20\\\" class=\\\"Section3\\\"\\u003e \\u003ch2\\u003e3.2.3. Testing period effects and morphometric differences\\u003c/h2\\u003e \\u003cp\\u003ePerformance during the Inhibitory Control Test differed significantly along the testing period. Fish tested in the second half of the experiment (N\\u0026thinsp;=\\u0026thinsp;19) showed shorter latencies to success than those tested in the first half (N\\u0026thinsp;=\\u0026thinsp;20; Wilcoxon rank-sum test: W\\u0026thinsp;=\\u0026thinsp;89, p\\u0026thinsp;=\\u0026thinsp;0.012).\\u003c/p\\u003e \\u003cp\\u003eInitial body size did not differ between these two groups, with no significant differences in starting body weight (First half: mean\\u0026thinsp;=\\u0026thinsp;14.03 g; Second half: mean\\u0026thinsp;=\\u0026thinsp;13.39 g; p\\u0026thinsp;=\\u0026thinsp;0.588) or starting length (First half: mean\\u0026thinsp;=\\u0026thinsp;8.20 cm; Second half: mean\\u0026thinsp;=\\u0026thinsp;7.92 cm; p\\u0026thinsp;=\\u0026thinsp;0.349). In contrast, fish tested in the second half of the experiment were significantly larger at the time of testing, with higher final body weight (First half: mean\\u0026thinsp;=\\u0026thinsp;27.12 g; Second half: mean\\u0026thinsp;=\\u0026thinsp;42.91 g; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001) and greater final length (First half: mean\\u0026thinsp;=\\u0026thinsp;11.14 cm; Second half: mean\\u0026thinsp;=\\u0026thinsp;13.06 cm; p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001), reflecting differences in growth at the time of testing and a possible effect in cognitive performance (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig11\\\" class=\\\"InternalRef\\\"\\u003e11\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e \\u003c/div\\u003e \\u003cdiv id=\\\"Sec21\\\" class=\\\"Section2\\\"\\u003e \\u003ch2\\u003e3.3. Contact rate and individual behavioural differences\\u003c/h2\\u003e \\u003cp\\u003eFish housed in enriched environments exhibited significantly higher food contact rate during the test phase than fish housed in NE conditions (Kruskal\\u0026ndash;Wallis χ\\u0026sup2; = 15.30, df\\u0026thinsp;=\\u0026thinsp;1, p\\u0026thinsp;\\u0026lt;\\u0026thinsp;0.001). Median contact rate was greater in enriched tanks (median\\u0026thinsp;=\\u0026thinsp;42.5, IQR\\u0026thinsp;=\\u0026thinsp;36.0\\u0026ndash;48.0) than in NE tanks (median\\u0026thinsp;=\\u0026thinsp;29.0, IQR\\u0026thinsp;=\\u0026thinsp;24.0\\u0026ndash;34.5), indicating higher levels of behavioural engagement in enriched conditions (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig12\\\" class=\\\"InternalRef\\\"\\u003e12\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003cp\\u003eExploratory clustering analyses based on training-phase behaviour identified distinct behavioural profiles among individuals. Cluster 1 includes moderate learners with relatively fast responses and low-to-moderate exploratory behaviour; Cluster 2 includes intermediate learners with moderate responsiveness and higher activity levels; Cluster 3 consists of slow learners with delayed responses and high exploratory behaviour; and Cluster 4 includes rapid learners with high exploratory activity. These profiles differed significantly in subsequent contact rate during testing (one-way ANOVA: F(3,31)\\u0026thinsp;=\\u0026thinsp;3.32, p\\u0026thinsp;=\\u0026thinsp;0.032), with slower learners investing less contact rate than faster-responding individuals (Fig.\\u0026nbsp;\\u003cspan refid=\\\"Fig13\\\" class=\\\"InternalRef\\\"\\u003e13\\u003c/span\\u003e). Behavioural cluster membership was not significantly associated with the treatment (Fisher\\u0026rsquo;s exact test, p\\u0026thinsp;=\\u0026thinsp;0.199), although Cluster 4 was observed only among EE individuals.\\u003c/p\\u003e \\u003cp\\u003e \\u003c/p\\u003e \\u003c/div\\u003e\"},{\"header\":\"4. Discussion\",\"content\":\"\\u003cp\\u003eThis study investigated whether EE influences learning and inhibitory control performance in juvenile gilthead seabream. During the training phase fish showed clear differences between treatments, with individuals housed in enriched environments exhibiting a more pronounced reduction in completion time, indicating enhanced learning. However, this effect did not extend to the inhibitory control test. Overall success rates during testing were low, and no significant effect of EE was detected on success probability or response time across trials. Instead, inhibitory control performance appeared to be shaped primarily by trial progression, individual variability, and developmental factors rather than housing conditions. However, enriched fish showed higher levels of behavioural engagement, as reflected by increased food contact rates, suggesting that EE can be affecting motivation and activity while not inhibitory control itself. Together, these results suggest that while EE can support learning and engagement, it does not enhance inhibitory control in juvenile seabream, indicating a stronger role of intrinsic and developmental factors in shaping cognitive performance (Diamond \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eThe overall success rate observed in the inhibitory control test was very low in juvenile gilthead seabream, indicating that this species may face particular challenges when performing the cylinder task at least at this developmental stage. Low success in this task has been reported in some fish species, but contrasts with higher success rates documented in others, such as guppies and cleaner fish, which have shown more robust inhibitory control under comparable cylinder-task paradigms (MacLean et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Lucon-Xiccato and Bisazza \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Lucon-Xiccato and Bertolucci \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Montalbano et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Lucon-Xiccato \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). These differences suggest that inhibitory control performance may be strongly shaped by species-specific ecological and behavioural traits rather than reflecting a general cognitive limitation (Kabadayi et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Santac\\u0026agrave; et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). Nonetheless, interspecific variation does not operate in isolation from development. Indeed, inhibitory control is known to develop gradually across ontogeny in many taxa, and reduced performance during juvenile stages is widely reported in birds and mammals, including humans (Moffitt et al. \\u003cspan citationid=\\\"CR51\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Diamond \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Savaş\\u0026ccedil;ı et al. 2021; Vernouillet et al. \\u003cspan citationid=\\\"CR72\\\" class=\\\"CitationRef\\\"\\u003e2025\\u003c/span\\u003e). Accordingly, the low success observed here may largely reflect developmental constraints, as inhibitory control was assessed in juvenile individuals, whereas studies reporting higher performance typically tested adults (MacLean et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Minter et al. \\u003cspan citationid=\\\"CR50\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Lucon-Xiccato and Bisazza \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e), with some exceptions (e.g., Savaş\\u0026ccedil;ı et al. 2021). In our study, developmental factors also played a role, as fish tested later in the experimental period exhibited shorter response times and greater task efficiency. The better performance in older fish (with consequential increased size) may reflect more advanced neural, sensory, or motor development, facilitating faster decision-making and more effective interaction with the task (Brydges et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Bensky et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Similar relationships between body size, developmental stage, and cognitive performance have been reported in fish, where ontogenetic changes strongly influence learning ability, behavioural flexibility, and problem-solving performance (Brydges et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Bensky et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Together, these findings indicate that inhibitory control performance in juvenile seabream likely reflects ongoing developmental processes operating both across and within ontogenetic stages, underscoring the importance of accounting for growth-related variation when interpreting task outcomes and when assessing executive functions in young fish (Brydges et al. \\u003cspan citationid=\\\"CR16\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Diamond \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; Bensky et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Testing adult seabream in similar paradigms would be a crucial next step to fully characterise inhibitory control capacities in this species.\\u003c/p\\u003e \\u003cp\\u003eGilthead seabream are benthic, opportunistic foragers that rely heavily on substrate exploration and flexible feeding strategies, rather than rapid visuomotor inhibition in frontal approaches, which may reduce their suitability for tasks requiring direct motor suppression (Arechavala-Lopez et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In addition, seabream are a highly social species, particularly during juvenile stages, and isolation during individual testing may have negatively affected motivation, stress levels, or task engagement, potentially contributing to the low success observed (Montero et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Arechavala-Lopez et al. \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Social isolation has been shown to alter behaviour, stress physiology, and cognitive responses in fish, including changes in exploration, attention, and feeding motivation (Montero et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Saraiva et al. \\u003cspan citationid=\\\"CR64\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e). In the present study, the combination of individual testing, early stages of development, and a cognitively demanding task may therefore have contributed to the overall low success rates observed. On the other hand, our findings seem to suggest that inhibitory control tasks developed and validated in other fish species may not fully capture cognitive performance in juvenile seabream, highlighting the importance of considering species ecology, sociality, and developmental stage when interpreting task outcomes (Kabadayi et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Santac\\u0026agrave; et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eInhibitory control is a multidimensional construct that can be assessed using different paradigms, including detour tasks, go/no-go tasks, reversal learning, and delay-of-gratification assays, each of which engages partially distinct cognitive and motivational processes (Diamond \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; MacLean et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). The cylinder task specifically assesses the ability to inhibit a prepotent motor response toward a visible reward, a core component of motor inhibitory control (MacLean et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Gatto et al. \\u003cspan citationid=\\\"CR29\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Lucon-Xiccato and Bertolucci \\u003cspan citationid=\\\"CR40\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Lucon-Xiccato \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e). However, the cylinder task was originally developed for terrestrial animals, which typically move in a two-dimensional plane and interact with obstacles using frontal approaches, potentially simplifying the motor inhibition demands compared to aquatic species (MacLean et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Kabadayi et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; van Horik et al. \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Griebling et al. \\u003cspan citationid=\\\"CR30\\\" class=\\\"CitationRef\\\"\\u003e2026\\u003c/span\\u003e). Fish, by contrast, move freely in three dimensions and often rely on continuous exploration rather than discrete frontal approaches, which may increase the perceptual and motor complexity of the task (Domenici and Blake \\u003cspan citationid=\\\"CR25\\\" class=\\\"CitationRef\\\"\\u003e1997\\u003c/span\\u003e; MacLean et al. \\u003cspan citationid=\\\"CR47\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Santac\\u0026agrave; et al. \\u003cspan citationid=\\\"CR61\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e). This requirement may be particularly demanding for species that rely heavily on exploratory, benthic, or substrate-oriented foraging strategies rather than direct frontal approaches to food, such as the gilthead seabream (Arechavala-Lopez et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In contrast, tasks such as reversal learning or go/no-go paradigms, which require suppressing previously learned responses or selectively responding to cues, may better align with the ecological and behavioural traits of some fish species and have been successfully applied in teleosts (Bensky et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Lucon-Xiccato and Bisazza \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e). Adapting inhibitory control assays to species-specific sensory, ecological, and developmental contexts may therefore provide a more accurate assessment of executive function in fish (Lucon-Xiccato and Bisazza \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Kabadayi et al. \\u003cspan citationid=\\\"CR34\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Lucon-Xiccato \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eWhile species-specific ecological traits and social context may partly explain the low overall success observed in the inhibitory control test, the EE had a clear positive effect on learning during the training phase. Fish housed in enriched environments showed a significant reduction in completion time across trials, while fish housed in NE conditions did not exhibit a significant improvement. This finding is consistent with previous studies demonstrating that environmental complexity enhances learning performance in fish by promoting exploration, sensory stimulation, and neural plasticity (Salvanes et al. \\u003cspan citationid=\\\"CR60\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e; N\\u0026auml;slund and Johnsson \\u003cspan citationid=\\\"CR56\\\" class=\\\"CitationRef\\\"\\u003e2016\\u003c/span\\u003e). In contrast, EE did not increase success probability during the inhibitory control test, or affect response time across trials, indicating that the benefits of enrichment did not contribute to inhibitory control performance. This result suggests that learning efficiency and inhibitory control rely on partially distinct cognitive and motivational processes, with EE primarily enhancing engagement and motivation rather than executive control mechanisms (Diamond \\u003cspan citationid=\\\"CR24\\\" class=\\\"CitationRef\\\"\\u003e2013\\u003c/span\\u003e). Similar patterns have been reported in other fish species, where EE improves learning speed and behavioural flexibility but does not consistently enhance inhibitory control or detour task performance (Bensky et al. \\u003cspan citationid=\\\"CR8\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Lucon-Xiccato and Bisazza \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Lucon-Xiccato et al. \\u003cspan citationid=\\\"CR46\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR44\\\" class=\\\"CitationRef\\\"\\u003e2023\\u003c/span\\u003e; Montalbano et al. \\u003cspan citationid=\\\"CR53\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Together, these results indicate that while EE can facilitate learning and increase behavioural engagement, it may not be sufficient to improve inhibitory control in juvenile seabream, reinforcing the idea that this cognitive domain is more strongly influenced by intrinsic or developmental factors than by enrichment alone (Savaş\\u0026ccedil;ı et al. 2021; Lucon-Xiccato \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eConsistent with the limited effect of EE on inhibitory control performance, substantial inter-individual variation emerged as a key factor shaping behaviour during both the training phase and the inhibitory control test. During training, individuals differed markedly in completion time and efficiency, with some fish consistently performing faster while others frequently failed to complete the task within the allotted time. These differences were not explained by housing conditions, suggesting stable individual variation in learning and task engagement. Similarly, during testing, individuals varied widely in behavioural investment, as reflected by differences in feeding contact rate and overall engagement with the task. Exploratory clustering analyses revealed distinct behavioural profiles that were maintained across experimental phases, indicating consistency in behavioural tendencies across contexts. These findings suggest that stable individual differences in activity, motivation, and task engagement have a strong influence on cognitive performance in juvenile seabream (Carere and Locurto \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Rowe and Healy \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e). The importance of such individual variation has also been demonstrated in other fish species using the cylinder task. For example, Lucon-Xiccato et al. (\\u003cspan citationid=\\\"CR42\\\" class=\\\"CitationRef\\\"\\u003e2020a\\u003c/span\\u003e) and Montalbano et al. (\\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e) reported large, repeatable individual differences in inhibitory performance, and studies in guppies, sticklebacks (\\u003cem\\u003eGasterosteus aculeatus\\u003c/em\\u003e) and mosquitofish (\\u003cem\\u003eGambusia affinis\\u003c/em\\u003e) show that personality traits such as boldness and exploration are closely linked to inhibitory control measures (Keagy et al. \\u003cspan citationid=\\\"CR36\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e; Lucon-Xiccato et al. \\u003cspan citationid=\\\"CR45\\\" class=\\\"CitationRef\\\"\\u003e2020b\\u003c/span\\u003e; Wallace et al. \\u003cspan citationid=\\\"CR74\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Savaş\\u0026ccedil;ı et al. 2021; Vinogradov et al. \\u003cspan citationid=\\\"CR73\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). Lucon-Xiccato and Bisazza (\\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e) showed that personality traits such as boldness and exploratory tendency strongly affected detour task performance in guppies, with bolder individuals often outperforming shy conspecifics, independent of learning ability. This indicates that performance in inhibitory control tasks may reflect differences in motivation, risk-taking, or exploratory strategies rather than inhibitory control alone (Sih and Del Giudice \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Rowe and Healy \\u003cspan citationid=\\\"CR58\\\" class=\\\"CitationRef\\\"\\u003e2014\\u003c/span\\u003e; Lucon-Xiccato and Bisazza \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; van Horik et al. \\u003cspan citationid=\\\"CR71\\\" class=\\\"CitationRef\\\"\\u003e2018\\u003c/span\\u003e; Montalbano et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e). Together, these findings highlight the importance of accounting for personality traits when assessing cognitive abilities, particularly in species such as gilthead seabream that exhibit high behavioural variability linked to flexible foraging strategies and strong social tendencies (Montero et al. \\u003cspan citationid=\\\"CR54\\\" class=\\\"CitationRef\\\"\\u003e2009\\u003c/span\\u003e; Carere and Locurto \\u003cspan citationid=\\\"CR18\\\" class=\\\"CitationRef\\\"\\u003e2011\\u003c/span\\u003e; Sih and Del Giudice \\u003cspan citationid=\\\"CR68\\\" class=\\\"CitationRef\\\"\\u003e2012\\u003c/span\\u003e; Lucon-Xiccato and Bisazza \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Arechavala-Lopez et al. \\u003cspan citationid=\\\"CR3\\\" class=\\\"CitationRef\\\"\\u003e2019\\u003c/span\\u003e, \\u003cspan citationid=\\\"CR2\\\" class=\\\"CitationRef\\\"\\u003e2022\\u003c/span\\u003e). In juveniles, this variability may further limit the sensitivity of inhibitory control tasks, as performance can be strongly influenced by motivation, exploration, and developmental state rather than inhibitory control alone (Lucon-Xiccato and Bisazza \\u003cspan citationid=\\\"CR41\\\" class=\\\"CitationRef\\\"\\u003e2017\\u003c/span\\u003e; Montalbano et al. \\u003cspan citationid=\\\"CR52\\\" class=\\\"CitationRef\\\"\\u003e2020\\u003c/span\\u003e; Savaş\\u0026ccedil;ı et al. 2021; Lucon-Xiccato \\u003cspan citationid=\\\"CR39\\\" class=\\\"CitationRef\\\"\\u003e2024\\u003c/span\\u003e).\\u003c/p\\u003e \\u003cp\\u003eIn general, the present findings indicate that inhibitory control performance in juvenile seabream is shaped by a complex interaction between developmental stage, individual behavioural variation, and experimental context, rather than by EE alone. While EE clearly enhanced learning during training and promoted behavioural engagement, it did not affect inhibitory control during testing. These results emphasise the need to consider species-specific ecology and sociality when applying standard cognitive tasks to fish, and caution against interpreting low performance as evidence of limited cognitive capacity.\\u003c/p\\u003e \\u003cp\\u003eIn conclusion, this study demonstrates that EE enhances learning performance and engagement in juvenile gilthead seabream, but does not improve inhibitory control when assessed by the cylinder task. Instead, inhibitory control was strongly influenced by developmental stage and individual behavioural variation: older, larger and more engaged individuals demonstrated faster responses, independently of the housing condition (EE \\u003cem\\u003evs\\u003c/em\\u003e. NE). The overall low success rates observed suggest that inhibitory control tasks developed for other taxa may be particularly challenging for juvenile seabream and may reflect developmental and ecological constraints rather than limited cognitive capacity. Together, these findings highlight the importance of accounting for ontogeny, individual differences, and species-specific ecology when assessing executive functions in fish. Furthermore, they emphasise the necessity to adapt cognitive tasks to better align with the biological and developmental characteristics of the species under study.\\u003c/p\\u003e\"},{\"header\":\"Declarations\",\"content\":\"\\u003cp\\u003e \\u003ch2\\u003eConflict of Interest Statement\\u003c/h2\\u003e \\u003cp\\u003eThe authors have no relevant financial or non-financial interests to disclose.\\u003c/p\\u003e \\u003c/p\\u003e\\u003ch2\\u003eAuthor Contribution\\u003c/h2\\u003e\\u003cp\\u003eI. S. N.: Writing original draft, Writing \\u0026ndash; review \\u0026amp; editing, Visualization, Validation, Software, Methodology, Investigation, Formal analysis, Data curation.J. L. S.: Writing \\u0026ndash; review \\u0026amp; editing, Resources, Funding acquisition.M. J. C-A: Writing \\u0026ndash; review \\u0026amp; editing, Resources, Project administration, Methodology, Conceptualization, Supervision, Funding acquisition.\\u003c/p\\u003e\\u003ch2\\u003eAcknowledgement\\u003c/h2\\u003e\\u003cp\\u003eThis study was conducted as part of the ManyFishes project, a Big Team Science Initiative that aims to assess the cognitive abilities across fish species through collaborative, multi-laboratory research (https://themanyfishes.github.io/). We thank all the laboratories and researchers involved in the ManyFishes project for their collective efforts and contributions to this collaborative work. Additionally, we are grateful to Gon\\u0026ccedil;alo Oliveira and Nuno Jesus for their invaluable technical assistance and support during the animal husbandry and experimental procedures and Dr. Pedro Guerreiro for his support on tank design. We thank Dr. Jos\\u0026eacute; Ricardo Paula for insightful discussions and constructive suggestions that helped improve the study. We also thank Dr. Andrea Meloni and Esther Hoyo Alvarez for their assistance and advice on statistical analysis. We thank the Portuguese national funds FCT - Foundation for Science and Technology, for funding the CCMAR research center through projects UIDB/04326/2020 (DOI:10.54499/UIDB/04326/2020) and LA/P/0101/2020 (DOI:10.54499/LA/P/0101/2020).\\u003c/p\\u003e\\u003ch2\\u003eData Availability\\u003c/h2\\u003e\\u003cp\\u003eAll relevant data are available from the authors upon request.\\u003c/p\\u003e\"},{\"header\":\"References\",\"content\":\"\\u003col\\u003e\\n\\u003cli\\u003eArechavala-Lopez P, Caballero-Froil\\u0026aacute;n JC, Jim\\u0026eacute;nez-Garc\\u0026iacute;a M, et al (2020) Enriched environments enhance cognition, exploratory behaviour and brain physiological functions of Sparus aurata. Sci Rep 10:11252. https://doi.org/10.1038/s41598-020-68306-6\\u003c/li\\u003e\\n\\u003cli\\u003eArechavala-Lopez P, Cabrera-\\u0026Aacute;lvarez MJ, Maia CM, Saraiva JL (2022) Environmental enrichment in fish aquaculture: A review of fundamental and practical aspects. 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Curr Zool 66:187\\u0026ndash;195. https://doi.org/10.1093/cz/zoz039\\u003c/li\\u003e\\n\\u003cli\\u003eLucon-Xiccato T, Montalbano G, Reddon AR, Bertolucci C (2022) Social environment affects inhibitory control via developmental plasticity in a fish. Anim Behav 183:69\\u0026ndash;76. https://doi.org/10.1016/j.anbehav.2021.11.001\\u003c/li\\u003e\\n\\u003cli\\u003eMacLean EL, Hare B, Nunn CL, et al (2014) The evolution of self-control. Proc Natl Acad Sci U S A 111:E2140-2148. https://doi.org/10.1073/pnas.1323533111\\u003c/li\\u003e\\n\\u003cli\\u003eMhalhel K, Levanti M, Abbate F, et al (2023) Review on Gilthead Seabream (Sparus aurata) Aquaculture: Life Cycle, Growth, Aquaculture Practices and Challenges. J Mar Sci Eng 11:2008. https://doi.org/10.3390/jmse11102008\\u003c/li\\u003e\\n\\u003cli\\u003eMillot S, B\\u0026eacute;gout M-L, Chatain B (2009) Exploration behaviour and flight response toward a stimulus in three sea bass strains (Dicentrarchus labrax L.). 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Intelligence 82:101486. https://doi.org/10.1016/j.intell.2020.101486\\u003c/li\\u003e\\n\\u003cli\\u003eMontalbano G, Bertolucci C, Lucon-Xiccato T (2022) Cognitive Phenotypic Plasticity: Environmental Enrichment Affects Learning but Not Executive Functions in a Teleost Fish, Poecilia reticulata. Biology 11:. https://doi.org/10.3390/biology11010064\\u003c/li\\u003e\\n\\u003cli\\u003eMontero D, Lalumera G, Izquierdo MS, et al (2009) Establishment of dominance relationships in gilthead sea bream Sparus aurata juveniles during feeding: effects on feeding behaviour, feed utilization and fish health. J Fish Biol 74:790\\u0026ndash;805. https://doi.org/10.1111/j.1095-8649.2008.02161.x\\u003c/li\\u003e\\n\\u003cli\\u003eN\\u0026auml;slund J, Aarestrup K, Thomassen ST, Johnsson JI (2012) Early enrichment effects on brain development in hatchery-reared Atlantic salmon ( \\u003cem\\u003eSalmo salar\\u003c/em\\u003e ): no evidence for a critical period. 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Aquaculture 533:736088. https://doi.org/10.1016/j.aquaculture.2020.736088\\u003c/li\\u003e\\n\\u003c/ol\\u003e\"}],\"fulltextSource\":\"\",\"fullText\":\"\",\"funders\":[],\"hasAdminPriorityOnWorkflow\":false,\"hasManuscriptDocX\":true,\"hasOptedInToPreprint\":true,\"hasPassedJournalQc\":\"\",\"hasAnyPriority\":false,\"hideJournal\":false,\"highlight\":\"\",\"institution\":\"\",\"isAcceptedByJournal\":false,\"isAuthorSuppliedPdf\":false,\"isDeskRejected\":\"\",\"isHiddenFromSearch\":false,\"isInQc\":false,\"isInWorkflow\":false,\"isPdf\":false,\"isPdfUpToDate\":true,\"isWithdrawnOrRetracted\":false,\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"animal-cognition\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"anco\",\"sideBox\":\"Learn more about [Animal Cognition](http://link.springer.com/journal/10071)\",\"snPcode\":\"10071\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10071/3\",\"title\":\"Animal Cognition\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false},\"keywords\":\"Aquaculture, Fish cognition, Learning, Ontogeny, Response inhibition\",\"lastPublishedDoi\":\"10.21203/rs.3.rs-8960731/v1\",\"lastPublishedDoiUrl\":\"https://doi.org/10.21203/rs.3.rs-8960731/v1\",\"license\":{\"name\":\"CC BY 4.0\",\"url\":\"https://creativecommons.org/licenses/by/4.0/\"},\"manuscriptAbstract\":\"\\u003cp\\u003eHuman-driven environmental change and the expansion of aquaculture have altered the ecological, social, and sensory conditions experienced by fish, raising concerns about how captive environments affect behaviour, cognition, and welfare. Environmental enrichment is widely used to mitigate these impacts and may also enhance welfare and cognitive performance in captive animals. However, its effects on executive functions during early developmental stages remain poorly understood in fish. We investigated inhibitory control and learning in juvenile gilthead seabream (\\u003cem\\u003eSparus aurata\\u003c/em\\u003e) using the cylinder task, a detour paradigm requiring inhibition of a direct response towards a visible food reward. Fish were housed under enriched and non-enriched conditions and completed a training phase followed by an inhibitory control test. During training, enriched fish showed a significant reduction in the time until task completion across trials, while non-enriched fish did not, indicating differences in learning. In contrast, inhibitory control performance remained low in both treatments, with no effects of enrichment on success rate or response time. Performance variation was instead explained by trial progression, individual behavioural differences, and developmental factors. Fish tested later in the experiment exhibited shorter response times, consistent with ontogenetic changes. Enriched fish displayed higher feeding contact rates, reflecting increased motivation and task engagement rather than improved inhibitory control. Clustering analyses revealed stable individual behavioural profiles predicting task engagement, independently of housing treatment. Overall, environmental enrichment enhances learning and motivation in juvenile seabream but does not improve inhibitory control at least at this developmental stage, highlighting the importance of ontogeny, individual variation, and species-specific ecology when assessing executive functions in fish.\\u003c/p\\u003e\",\"manuscriptTitle\":\"Environmental enrichment selectively enhances learning, but not inhibitory control, in juvenile gilthead seabream (Sparus aurata)\",\"msid\":\"\",\"msnumber\":\"\",\"nonDraftVersions\":[{\"code\":1,\"date\":\"2026-03-06 15:46:41\",\"doi\":\"10.21203/rs.3.rs-8960731/v1\",\"editorialEvents\":[{\"type\":\"communityComments\",\"content\":0},{\"type\":\"editorInvitedReview\",\"content\":\"\",\"date\":\"2026-04-22T15:08:27+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"194742837251203325500373278572966090964\",\"date\":\"2026-03-31T11:21:47+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewerAgreed\",\"content\":\"150878643683464934068223982989770684745\",\"date\":\"2026-03-05T12:57:19+00:00\",\"index\":\"hide\",\"fulltext\":\"\"},{\"type\":\"reviewersInvited\",\"content\":\"\",\"date\":\"2026-03-03T04:30:24+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"editorAssigned\",\"content\":\"\",\"date\":\"2026-02-25T09:12:34+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"checksComplete\",\"content\":\"\",\"date\":\"2026-02-25T07:22:29+00:00\",\"index\":\"\",\"fulltext\":\"\"},{\"type\":\"submitted\",\"content\":\"Animal Cognition\",\"date\":\"2026-02-24T19:45:44+00:00\",\"index\":\"\",\"fulltext\":\"\"}],\"status\":\"published\",\"journal\":{\"display\":true,\"email\":\"info@researchsquare.com\",\"identity\":\"animal-cognition\",\"isNatureJournal\":false,\"hasQc\":true,\"allowDirectSubmit\":false,\"externalIdentity\":\"anco\",\"sideBox\":\"Learn more about [Animal Cognition](http://link.springer.com/journal/10071)\",\"snPcode\":\"10071\",\"submissionUrl\":\"https://submission.nature.com/new-submission/10071/3\",\"title\":\"Animal Cognition\",\"twitterHandle\":\"\",\"acdcEnabled\":true,\"dfaEnabled\":true,\"editorialSystem\":\"em\",\"reportingPortfolio\":\"Springer Hybrid\",\"inReviewEnabled\":true,\"inReviewRevisionsEnabled\":false}}],\"origin\":\"\",\"ownerIdentity\":\"4090af1f-d0d4-4135-a6f6-721f952e98b4\",\"owner\":[],\"postedDate\":\"March 6th, 2026\",\"published\":true,\"recentEditorialEvents\":[],\"rejectedJournal\":[],\"revision\":\"\",\"amendment\":\"\",\"status\":\"under-review\",\"subjectAreas\":[],\"tags\":[],\"updatedAt\":\"2026-03-06T15:46:41+00:00\",\"versionOfRecord\":[],\"versionCreatedAt\":\"2026-03-06 15:46:41\",\"video\":\"\",\"vorDoi\":\"\",\"vorDoiUrl\":\"\",\"workflowStages\":[]},\"version\":\"v1\",\"identity\":\"rs-8960731\",\"journalConfig\":\"researchsquare\"},\"__N_SSP\":true},\"page\":\"/article/[identity]/[[...version]]\",\"query\":{\"redirect\":\"/article/rs-8960731\",\"identity\":\"rs-8960731\",\"version\":[\"v1\"]},\"buildId\":\"XKTyCvWXoU3ODBz1xrDgd\",\"isFallback\":false,\"isExperimentalCompile\":false,\"dynamicIds\":[84888],\"gssp\":true,\"scriptLoader\":[]}","source_license":"CC-BY-4.0","license_restricted":false}