What Are The Key Regulations In Place To Protect Animals Used In Cognitive Neuroscience Research?

Introduction

Reproducibility is an essential feature in any field of experimental sciences, this feature provides reliability to the experimentally obtained findings (for details, see Glossary). The currently available empirical estimates on the topic propose that less than half (ranging from 49% down to 11%) of scientific results are reproducible (Prinz et al., 2011; Begley and Ellis, 2012; Freedman et al., 2015, 2017). While it can be argued that the accurateness of these estimations needs confirmation, we (as a scientific community) have to recognize that poor reproducibility is a major problem in the life sciences.

The perception of an undergoing "reproducibility crunch" has led to the establishment of crowdsourced initiatives effectually the earth addressing reproducibility issues in sciences, such as behavioral neuroscience (Open Science, 2015; Freedman et al., 2017; Reproducibility Project and Cancer Biology, 2017; Amaral et al., 2019). Amongst the explanations for poor quality in published inquiry, in that location is the prevalent civilisation of "reporting positive results" (publication bias) and the high incidence of various types of experimental bias, such as lack of transparency and poor description of methods, lack of predefined inclusion and exclusion criteria resulting in unlimited flexibility for deciding which experiments will be reported, insufficient knowledge of the scientific method and statistical tools when designing and analyzing experiments (Ioannidis, 2005; Cumming, 2008; Sena et al., 2010; Freedman et al., 2017; Vsevolozhskaya et al., 2017; Ramos-Hryb et al., 2018; Catillon, 2019; Neves and Amaral, 2020; Neves et al., 2020). Further discussions on the causes, consequences, and actions to overcome poor research practices and reproducibility in sciences are many (Altman, 1994; Macleod et al., 2014; Strech et al., 2020) and beyond the scope of this text. Here, we focus on the aspects relevant to the field of behavioral neuroscience, whereby poor research functioning may touch not simply the economical and translational aspects of science simply also implies ethical issues in one case it involves necessarily living subjects, mostly laboratory animals (Prinz et al., 2011; Begley and Ellis, 2012; Festing, 2014; Freedman et al., 2015; Voelkl and Wurbel, 2021).

In our opinion, combining principles of fauna welfare with experimental rigor may atomic number 82 to improvement in the quality of studies in behavioral neuroscience. Hence, we will briefly discuss how adherence to legislations, guidelines, and ethical principles in animal research may guide more than rigorous behavioral studies. Thereafter, we condense discussions on how (i) the amend understanding of the conceptualization, validation, and limitations of the animal models; (ii) the use of suitable statistical methods for study pattern and data assay; and (3) the employ of environmental enrichment in inquiry facilities to favor welfare of animals may meliorate quality of studies in behavioral neuroscience (some practical tips in Table 1) and, hopefully, the reproducibility in the field.

Table 1. Practical tips combining animal welfare and experimental rigor to improve reproducibility in behavioral neuroscience*.

Advantages of the Adherence to the Regulations to the Quality of Behavioral Neuroscience

Behavioral studies in laboratory animals are performed worldwide under specific guidelines conciliating the needs of science, scientists, and animal welfare (Smith et al., 2018). Regulations constitute obligations and responsibilities for institutional actors involved in creature experimentation, from students to deans (please consult i's own institution virtually regulations applied to a project). Here, we claim that, besides being ethical, adherence to the regulations is advantageous to the quality of behavioral studies. Why? Because, regulations in brute research consider, amidst other things, the 3Rs principle (replace, reduce, and refine), which are the useful frameworks to ready good quality experiments taking creature welfare into account, equally discussed by previous authors (e.g., Franco and Olsson, 2014; Bayne et al., 2015; Aske and Waugh, 2017; Strech and Dirnagl, 2019) and in the further sections. "Supplant" prompts scientists to consider alternatives to behavioral studies in laboratory animals for reaching a giving aim, in the kickoff place. Once a behavioral study in laboratory animals is considered necessary, "reduce" may guide designs using well-established rules for rigorous experimentation to extract the maximum data of a study with a minimum number of subjects. The principle "refine" assists scientists to devise meliorate strategies guaranteeing animal welfare according to species-sex-age-specific needs. At that place is evidence that "happy animals brand amend scientific discipline" (Poole, 1997; Grimm, 2018). Besides, poor welfare in laboratory settings affects the laboratory animals in unpredictable, and frequently deleterious ways, compromising behavioral outcomes in the experiments (e.thou., Emmer et al., 2018), and increasing the number of experimental animals unnecessarily. Therefore, personnel handling animals (experimenters, technicians, and caregivers) may contribute to the efforts to minimize the gamble of creature suffering during procedures improving inquiry quality. There are many free resource for grooming staff in the 3Rs principle made bachelor by international organizations, such as NC3Rs¹ or Brute Research Tomorrow,² which could be easily implemented in behavioral studies.

Suitable Creature Models and Behavioral Tests Should Amend Studies in Behavioral Neuroscience

The option of an adequate animal model is a pivotal step in behavioral studies. Physical models (Godfrey-Smith, 2009) are central tools in neuroscientific research. Neuroscientists unremarkably employ in vivo animate being models, aimed to simulate physiological, genetic, or anatomical features observed in humans (equally is the case with studies of disease) or replicate natural situations nether controlled laboratory conditions (van der Staay, 2006; Maximino and van der Staay, 2019). By definition, a model is a construct of a real physical component or property observed in nature. Therefore, a model is ever imperfect and does not contemplate the full complication of the existent arrangement that is being modeled (Garner et al., 2017). Much has been discussed about the validity and translational potential of brute models (Nestler and Hyman, 2010). Here, our aim is to consider how the misuse of animal models may affect the reproducibility and reliability of neurobiological enquiry results. Firstly, at that place appears to exist defoliation about the definition of animal models and behavioral tests (Willner, 1986) that ultimately causes the misinterpretation of results. Animal models deliberately prompt changes in biological variables (such as behavior), while behavioral tests are paradigms in which animal models are subjected to having their behavior assessed. Past this definition, a behavioral bioassay (an intact animal plus an apparatus) is not a model in a strict sense (van der Staay, 2006; Maximino and van der Staay, 2019), although useful to study normal creature beliefs (east.k., exploration of a maze and immobility in forced swim test) and its underlying mechanisms (Maximino and van der Staay, 2019; de Kloet and Molendijk, 2021). Secondly, information technology is of import to be aware of the conditions validated for the exam considering modifying some of them (east.chiliad., calorie-free intensity or animal species/strain) may yield dissimilar results than those observed in the standardizations for the exam (Griebel et al., 1993; Holmes et al., 2000; Garcia et al., 2005). For example, the dichotomic behavioral effect (mobility or immobility) of mice is often registered in the tail suspension test. However, some mice (due east.k., C57BL/half dozen strain) besides present climbing beliefs which may exist mistaken by immobility (Mayorga and Lucki, 2001; Tin can et al., 2012). Third, we have to avoid the extrapolation of uncomplicated behavioral measures (those variables that nosotros actually measure in a task) to complex multidimensional abstract behaviors (e.g., anxiety, retentiveness, locomotor, and exploratory activities). For example, measuring only distance traveled (or the number of crossings) in an open field loonshit is not sufficient to fully capture the complexity of locomotor behavior (Paulus et al., 1999; Loss et al., 2014, 2015). Therefore, it alone does not provide enough information to make conclusions about locomotor activity, a multidimensional behavior that encompasses not just how much an animal moves (distance traveled and locomoting fourth dimension) just too how it moves (average speed, number of stops fabricated, among others) (Eilam et al., 2003; Loss et al., 2014, 2015). This extrapolation becomes even greater when we recall nearly exploratory activity, which encompasses locomotor activity and other behaviors (such as time and frequency of rearing) (Loss et al., 2014, 2015). Similarly, Rubinstein et al. (1997) observed that mutant mice lacking D4 dopamine receptors moved less in the open field arena but outperformed their wild-type littermates in the rotarod test, which highlights that nosotros cannot conclude much about motor function by measuring just the altitude traveled (even if the amount of movement registered is similar between the groups). Finally, it is imperative to know whether the beast model we intend to examination meets the assumptions of the behavioral paradigm (or our report hypothesis) that information technology will exist tested. For example, animals with compromised mobility (e.thou., models for spinal cord injury) will not provide meaningful results in tests that rely on preserved motor role (e.1000., forced swim test, elevated plus maze). Similarly, subjecting a pigeon to the Morris water maze may lead one to conclude that pigeons have poor spatial memory. But, pigeons do not swim in the first place making the last experimental proposal non only inappropriate but cool. Hence, knowledge near the biology of laboratory animals seems fundamental to the selection of a suitable arroyo for an intended behavioral study.

Rigorous Design of Studies and Assay of Data Should Amend the Quality of Behavioral Neuroscience

Limited knowledge of the scientific method and statistics are among the reasons for the high levels of experimental bias and irreproducibility (Ioannidis, 2005; Lazic, 2018; Lazic et al., 2018) leading ones to suggest that we are actually facing an "epistemological crisis" (Park, 2020). Several guidelines for experimental blueprint, assay, and reporting are available (come across Festing and Altman, 2002; Lazic, 2016; Percie du Sert et al., 2020), describing rigorous methods that should be adopted to avoid bias achieving high-quality data production. However, it seems that some of the most basic expert practices described in these guidelines take been neglected or ignored (Goodman, 2008; Festing, 2014; Hair et al., 2019). Some frequent sources of biases are pseudoreplication (Freeberg and Lucas, 2009; Lazic, 2010; Lazic et al., 2020; Eisner, 2021; Zimmerman et al., 2021) and violations of rules for experimental design, such as a priori calculating the sample size, unbiased allocation of samples to groups (randomization), blinded assessment of outcomes, consummate reporting of results, and choosing the method for data analysis beforehand (Macleod et al., 2015). The lack of a rigorous plan results in the massive production of underpowered exploratory studies (Maxwell, 2004; Button et al., 2013; Lazic, 2018), with the aggravating gene that they are often misinterpreted every bit confirmatory studies ones (Wagenmakers et al., 2012; Nosek et al., 2018). It is not unusual to find discussions about the so-called "statistical trend" in studies in which both biological effect sizes and sample sizes are assumed post hoc. In improver, the extensive do of exclusively using linear models (such as Student'south t-exam or ANOVA) to analyze the information, assuming that all variables present Gaussian distribution, contribute to the misinterpretation of results (Lazic, 2015; Eisner, 2021). Currently, in that location are culling methods that we strongly suggest to exist incorporated in research projects by the whole neuroscientific customs. For example, Generalized Linear (Mixed) Models and Generalizing Estimating Equations (GLM, GLMM, and GEE, respectively) fit distinct types of distribution (such as the Gaussian distribution) and correct for confounding factors (Shkedy et al., 2005a,b; Lazic and Essioux, 2013; Lazic, 2015, 2018; Bono et al., 2021; Eisner, 2021; Zimmerman et al., 2021). Adopting randomized block experimental designs (that are more than powerful, have higher external validity, and are less subject to bias than the completely randomized designs typically used in behavioral research) is besides necessary for controlling confounding factor-related variability and producing more reproducible results (Festing, 2014). Considering the use of multivariate statistical tools (instead of the widely used univariate approach) is an culling to achieve more accurate outcomes from experiments with big data, specially in behavioral studies (Sanguansat, 2012; Loss et al., 2014, 2015; Quadros et al., 2016). Amongst the advantages of using these culling approaches is the increased accurateness in parameter estimation (thus avoiding making incommunicable predictions), resulting in reduced probability of making Type I Error (due to invalid interpretation of p-values, for example) and Type II Error (due to lack of statistical power). Rigorous design of studies and assay of information should help to excerpt the maximum data of a study with the acceptable calculated number of subjects and foreclose waste product of scientific efforts in behavioral neuroscience. In addition, rigorous and systematic reporting of methods (with plenty details to allow replication) and results (with consummate description of upshot sizes and their confidence intervals rather than uninformative p-values) are also necessary to increase transparency and, consequently, the quality of the studies (Halsey et al., 2015; Halsey, 2019; Percie du Sert et al., 2020).

Ecology Enrichment in Inquiry Facilities May Favor Translational Neuroscience

Equally mentioned, "Happy animals make better science" (Poole, 1997; Grimm, 2018). It is a worldwide acknowledgment that environmental stimulus is necessary to improve the quality of life and welfare of captive animals, such as inquiry animals. It has been more than a decade since the Directive 2010/63/EU was established (EC, 2010). Withal, this and other directives are far from being effectively complied with by the entire scientific community. A mutual not-tested argument to raise research animals in impoverished standard weather is that the data variability among laboratories, or even within them, would increase by raising the animals in enriched not-standard atmospheric condition (Voelkl et al., 2020). This last claim has been criticized over the by 2 decades and suggested to be a fallacy (Wolfer et al., 2004; Kentner et al., 2021; Voelkl et al., 2021). For example, Wolfer et al. (2004) and Bailoo et al. (2018) observed that data variability did not increase after raising the animals in enriched environments when compared with raising them in standard laboratory environments. Furthermore, Richter et al. (2011) constitute that rearing animals in enriched environments decreased variation between experiments, strain-by-laboratory interaction on data variability. In other words, heterogenized housing designs appear to have improved information reproducibility. Therefore, it was claimed (and we agree) that we should embrace environmental variability (instead of static environmental standardization) because environmental heterogeneity better represents the wide variation (richness and complexity) of mental and physical stimulations in both human and non-human animals (Nithianantharajah and Hannan, 2006; Richter, 2017). In fact, drug development and discovery may be affected by the culture of raising animals in impoverished (extremely artificial) environments. There are studies showing that some drugs present biological effects when tested in animals raised in impoverished environments only not in animals raised in enriched environments (which is more similar to real-life conditions) (Akkerman et al., 2014; Possamai et al., 2015). Furthermore, nosotros cannot condone that more pronounced effects could be found whether drugs were tested in animals raised in enriched when compared to impoverished environments (Gurwitz, 2001). While one can debate that in that location are non plenty studies strengthening this exclamation, the low quality of life of captive animals, the depression reproducibility of studies, and the poor translational rate of preclinical research reinforce the necessity of a epitome shift related to the welfare of animals (Akkerman et al., 2014; Voelkl et al., 2020). This debate should not exist restricted to rodents and shall include avians (Melleu et al., 2016; Campbell et al., 2018), reptiles (Burghardt et al., 1996), fishes (Turschwell and White, 2016; Fong et al., 2019; Masud et al., 2020), and even invertebrate animals (Ayub et al., 2011; Mallory et al., 2016; Bertapelle et al., 2017; Wang et al., 2018; Guisnet et al., 2021). Nosotros bring 2 applied examples (or recommendations) of improvements that we (the neuroscientific community) could do: (i) when using animal models nosotros should implement environmental enrichment equally the standard in the beast facilities (especially for those animal models that endeavor to simulate central nervous arrangement disorders), as raising animals in impoverished environments provides suboptimal sensory, cognitive and motor stimulation, making them too reactive to any kind of intervention (i.e., "dissonance amplifiers") (Nithianantharajah and Hannan, 2006); (2) when proposing alternative organisms to study behavior (east.g., zebrafish), we should learn from by and present mistakes (more often than not in rodents), keeping in mind the ethological and natural needs of the species (Branchi and Ricceri, 2004; Lee et al., 2019; Stevens et al., 2021). Chiefly, when making these improvements nosotros should advisedly respect the species-specific characteristics. For example, rats and mice share some characteristics, such every bit nocturnal habits (which means that both species need places to hide during the calorie-free period, to provide a sense of security) (Loss et al., 2015). However, they also accept some distinct characteristics, such as the need for running (which is higher in mice) (Meijer and Robbers, 2014). This means that providing running wheels for mice is really necessary, while for rats, (that run less but are more social than mice) (Kondrakiewicz et al., 2019) the space dedicated to some of the running wheels could be better used by increasing (carefully not to compromise the population density) the number of individuals in the home cage. On the other hand, zebrafish needs aquatic plants and several substrates in their environment, such as mud, gravel or sand, to stand for their own eco-ethological expansions of behavior (Engeszer et al., 2007; Spence et al., 2008; Arunachalam et al., 2013; Parichy, 2015; Stevens et al., 2021). The substrates might provide some camouflage for zebrafish against the predator, which may contribute to feelings of security and improved welfare (Schroeder et al., 2014). Taking all these together, in our opinion, the scientific community must think over the long-term costs (economical and ethical ones) of keeping the culture of raising animals in impoverished environments, a status that potentially disrupt the translation of behavioral neuroscience results into applicative benefits (Akkerman et al., 2014).

Future Directions

Equally previously stated, a "reproducibility crisis" is non an issue express to the field of behavioral neuroscience, and several crowdsourced initiatives were established around the world addressing reproducibility (Open Science, 2015; Freedman et al., 2017; Reproducibility Projection and Cancer Biological science, 2017; Amaral et al., 2019). An essential step to face this outcome is to kickoff recognize that in that location is a crisis and that it is a major problem. Secondly, the scientific communities have been developing and disseminating guidelines for skilful experimental practices to be implemented by themselves (more than information can be found in http://www.consort-statement.org/ and besides in https://www.equator-network.org/). In addition, encouraging the preregistration of the projects and experimental protocols (a practise that is essential for carrying out confirmatory studies) (Wagenmakers et al., 2012; Nosek et al., 2018) and the embracement of open enquiry practices (open up data sharing) (Ferguson et al., 2014; Steckler et al., 2015; Gilmore et al., 2017) are as well alternatives to ameliorate reproducibility. Interestingly, it seems that simply encouraging good inquiry practices is not plenty to assure compliance with the proposed guidelines (Bakery et al., 2014; Pilus et al., 2019). This suggests that the participation of inquiry funding agencies is necessary likewise as of peer reviewers and journal editors in enervating adherence to these directives (Kilkenny et al., 2009; Bakery et al., 2014; Han et al., 2017; Hair et al., 2019).

In determination, paraphrasing Lazic et al. (2018), "There are few means to conduct an experiment well, simply many ways to comport information technology poorly." In our opinion, we, equally a scientific community, have to exist worried about the rigor of the experiments we are conducting and the quality of the studies we are producing. Publishing not-reproducible results (or reproducible noise) can lead to upstanding, economic, and technological consequences leading to scientific discredit. Furthermore, poor reproducibility delays discovery and development and hinders the progress of scientific knowledge. Wide adherence and avant-garde training to principles of creature welfare and good experimental practices may drag the standards of behavioral neuroscience. Finally, perhaps we, as the scientific community, should strive to refine our current brute models and focus our efforts in the development of new, more than robust, ethologically relevant models that could potentially improve both the description of our reality and the translational potential of our bones research.

Author Contributions

CML was responsible for the conceptualization of the opinion article. All authors were responsible for writing and revising the manuscript and read and approved the concluding manuscript.

Funding

Grants of Alexander von Humboldt Foundation (Germany) to CLi. CML was recipient of Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES) research fellowship through the Instituto Nacional de Ciência due east Tecnologia Translacional em Medicina (INCT-TM), Brazil. FM was supported by Post-doctoral fellowship grant #2018/25857-5, São Paulo Research Foundation (FAPESP), Brazil. KD was supported past Young man BIPD/FCT Proj2020/i3S/26040705/2021, Fundação para a Ciência e Tecnologia, Portugal. This study was financed in part by the Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES) – Finance Lawmaking 001.

Conflict of Involvement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed equally a potential conflict of involvement.

Publisher's Annotation

All claims expressed in this commodity are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may exist evaluated in this article, or claim that may be fabricated by its manufacturer, is non guaranteed or endorsed by the publisher.

Acknowledgments

We are grateful to the Alexander von Humboldt Foundation (Germany) and the Brazilian funding agencies for the financial back up and fellowships granted. We are also grateful to Ann Colette Ferry (in memoriam) for providing language assist.

Footnotes

1. ^ https://www.nc3rs.org.uk
2. ^ https://animalresearchtomorrow.org

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Primal concepts (Glossary)

• Reproducibility: Obtaining the same results (similar effect sizes) equally the original report by carrying out independent experiments (in unlike locations, laboratories, and enquiry groups) in which the experimental procedures were as shut every bit possible to the original study. Importantly, there is no need for the reproduction study to have exactly the same experimental pattern as the original report, for its result to be considered a reproduction. Also, every bit stated in Reproducibility Projection and Cancer Biology, 2017, "if a replication reproduces some of the primal experiments in the original study and sees effects that are like to those seen in the original in other experiments, we demand to conclude that it has substantially reproduced the original study."

• Environmental enrichment: Information technology consists in modifying the surround of animals past increasing perceptual, cerebral, concrete, and social stimulation. In convict animals, it promotes improvements in the quality of life and beast welfare. Environmental enrichment represents an opportunity for the animals to evocate their ethological behaviors. For example, nocturnal animals usually escape bright environments by entering into shelters. In a time to come approach, it may correspond a controlled naturalistic environment, such every bit a forest (equally described in Landers et al., 2011).

• Supplant: Co-ordinate to NC3Rs, information technology is "accelerating the development and utilize of models and tools, based on the latest science and technologies, to accost of import scientific questions without the use of animals."

• Reduce: According to NC3Rs, it is "accordingly designing and analyzing beast experiments that are robust and reproducible and truly add together to the cognition base."

• Refine: According to NC3Rs, it is "advancing fauna welfare by exploiting the latest in vivo technologies and by improving understanding of the touch on of welfare on scientific outcomes."

• Physical models: According to Godfrey-Smith (2009), they are real systems purposely built to understand another real system.

• Brute models: According to Willner (1986), they are beast manipulations designed to model sure aspects (specific symptoms, for case) of a known disease.

• Behavioral tests: Paradigms designed to appraise animate being behavior. Ordinarily, they are used to evaluate the behavior of animals that were previously subjected to genetic, pharmacological, or environmental manipulations. In add-on, they can also be used to investigation of the natural behavior of "naïve" animals.

• Pseudoreplication: Information technology occurs when the researcher artificially inflates the number of experimental units by using samples that are heavily dependent on each other without correcting for it. Example ane) measuring multiple animals in a litter (afterward allocating all them to the same group) and treating them as contained samples (i.e., "N" equals the multiple measurements). Instance 2) measuring two experimental animals that interacted with each other in a social interaction prototype (i.eastward., the way that an animal behaves is influenced by the mode the other 1 behaves, and vice-versa) and treating them as contained samples (i.e., "Due north" equals 2).

• Experimental unit: It is the smallest entity that can be randomly and independently assigned to a treatment condition. For experimental units to exist considered as genuine replications (i.e., the existent "N") they must not influence each other and must undergo experimental treatment independently. Its biological definition tin can change from one experiment to another (i.eastward., "N equals one" can be a single animal in an experiment and a pair of animals or even a whole litter in others).

• Exploratory studies: The ones that nowadays more flexible experimental methods and designs. Their aim is not to accomplish statistical conclusions, but to assemble information to the postulation of experimental hypotheses that must be tested and replicated through confirmatory studies before being causeless as strong evidence.

• Confirmatory studies: The ones that present articulate predefined hypotheses to be tested and rigid methods to doing so (e.one thousand., impartial assignment of experimental units to experimental groups, blinding during data collection and analysis, complete reporting of methods and results). Experimental blueprint cannot be inverse later on the experiments are running. Must be presented in advance with well-defined biological outcome sizes and statistical power, in add-on to the a priori adding of sample sizes. A clear example of confirmatory written report is the Stage Iii of clinical trials in the procedure of vaccine development.

• Biological effect size: The calculated minimum consequence size that is considered to be biologically relevant by the researcher.

• Confounding factors: Variables that can affect the outcomes that the researcher is measuring. Normally, they are not in the interest of the researcher and may presume chiselled (due east.g., litter, experimental blocks, and repeated measurements) or continual nature (e.m., age and body weight). Example 1) measuring siblings (after correctly allocating each 1 to a distinct experimental group) and analyzing their data as if they were not relatives. If the between-litter variation is higher than inside-litter variation (i.e., the departure betwixt families is college than differences betwixt siblings and, in this case, between experimental groups) the high information variability between litters could mask the outcome of treatments. Case 2) Measuring drug-seeking beliefs in a self-administration paradigm and analyzing the data without considering the basal motivation to cocky-administrating the drug (fifty-fifty when its variability was well controlled by randomization). If the basal motivation affects self-assistants behavior the high within-grouping data variability (as a consequence of basal motivation variability) could mask the effect of treatments.

• Impossible predictions: Incorrectly estimating of values that are impossible to be observed for some types of data. It can occur when using linear models for analyzing count data (e.grand., number of visible marbles, grooming, rearing, and pressures in a lever), where negative values are impossible to be observed just they can exist ofttimes estimated by the analysis when the observed mean is depression and/or the standard deviations are high.

• Directive 2010/63/Eu: European Union Directive about animal welfare that established, amid others, that "…all animals shall be provided with space of sufficient complication to allow expression of a wide range of normal behavior. They shall be given a degree of control and choice over their environs to reduce stress-induced behavior."

• Impoverished standard weather condition: The conditions under which laboratory animals are bred past default in research facilities around the world. In general, the cages are too limited in space and incorporate only bedding (e.chiliad., sawdust) plus water and food advertizing libitum. Improvements were fabricated after some directives were established, but the "new standard" remains impoverished.

• Paradigm shift: According to Kuhn (1962), information technology is a fundamental change of concepts and experimental practices in scientific discipline. Here, nosotros adopted a more than restricted use for this term. It represents a change in the experimental practices specifically for the environmental atmospheric condition of laboratory animals.

• Ethology: According to Merriam-Webster (https://www.merriam-webster.com/dictionary/ethology), it is the scientific study of animal behavior, usually with a focus on creature behavior under natural conditions. Viewing animal behavior as an evolutionarily adaptive trait.

• Ethological needs of the species: The basic natural needs (and likewise behavioral phenotypes) are distinct betwixt each species. Based on the ethology concept, the environments where laboratory animals are kept or behaviorally tested must encounter the intrinsic features of each species. Even though rats and mice are both rodents, they are different species and their characteristics and basic needs are not the aforementioned. This concept should be applied to all laboratory animals. For instance, for ethical reasons, researchers do non submit rats to the tail pause test. However, they practise submit mice to the forced swim exam (even though mice do not swim in nature).

Source: https://www.frontiersin.org/articles/10.3389/fnbeh.2021.763428/full

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