The study of complex behaviors is often challenging when using manual annotation due to the absence of quantifiable behavioral definitions and the subjective nature of behavioral annotation. Integration of supervised machine learning approaches mitigates some of these issues through the inclusion of accessible and explainable model interpretation. To decrease barriers to access, and with an emphasis on accessible model explainability, we developed the open-source Simple Behavioral Analysis (SimBA) platform for behavioral neuroscientists. SimBA introduces several machine learning interpretability tools, including SHapley Additive exPlanation (SHAP) scores, that aid in creating explainable and transparent behavioral classifiers. Here we show how the addition of explainability metrics allows for quantifiable comparisons of aggressive social behavior across research groups and species, reconceptualizing behavior as a sharable reagent and providing an open-source framework. We provide an open-source, graphical user interface (GUI)-driven, well-documented package to facilitate the movement toward improved automation and sharing of behavioral classification tools across laboratories. This paper presents Simple Behavioral Analysis (SimBA), an open-source platform for automated, explainable machine learning analysis of behavior. SimBA comes with extensive documentation, a graphical interface and an active community and works with any organism tracked by pose estimation.

Online impulse shopping is a growing industry. This paper uses the Stimulus-Organism-Response framework to model online impulse purchase behavior using a novel combination of stimuli and organism characteristics. The stimuli: social proof, limited-quantity scarcity, and high-demand, are three commonly used website features known as dark patterns. The organism characteristic personality is measured by the big 5 personality traits and persona generated through latent profile analysis. Using the machine learning algorithm XGBoost, impulse purchasing response was predicted separately for each dark pattern stimuli. Results show personality characteristics are important features when predicting consumer impulse purchasing in response to dark pattern messages. Moreover, the personality traits (and personas) most predictive of impulse shopping behavior varied by type of dark pattern. Findings suggest personality influences susceptibility to different dark patterns, indicating a need for tailored interventions to mitigate individual consumer vulnerabilities to impulse shopping.

ABSTRACT The zebrafish lateral line is an established model for hair cell organ damage, yet few studies link mechanistic disruptions to changes in biologically relevant behavior. We used larval zebrafish to determine how damage via ototoxic compounds impact rheotaxis. Larvae were treated with CuSO4 or neomycin to disrupt lateral line function then exposed to water flow stimuli. Their swimming behavior was recorded on video then DeepLabCut and SimBA software were used to track movements and classify rheotaxis behavior, respectively. Lateral line-disrupted fish performed rheotaxis, but they swam greater distances, for shorter durations, and with greater angular variance than controls. Furthermore, spectral decomposition analyses confirmed that lesioned fish exhibited ototoxic compound-specific behavioral profiles with distinct changes in the magnitude, frequency, and cross-correlation between fluctuations in linear and angular movements. Our observations demonstrate that lateral line input is needed for fish to hold their station in flow efficiently and reveals that commonly used lesion methods have unique effects on rheotaxis behavior.

Dark patterns – design interfaces or features that subtly manipulate people in making suboptimal decisions – are ubiquitous especially in e-commerce websites. Yet, there is little research on the effectiveness of dark patterns, and even lesser studies on testing interventions that can help mitigate their influence on consumers. To that end, we conducted two experiments. The first experiment tests the effectiveness of different dark patterns within a hypothetical single product online shopping context. Results show that, indeed, dark patterns increase the purchase impulsivity across all dark patterns, relative to the control. The second experiment tests the effectiveness of three behaviorally informed interventions on four different dark patterns also in a hypothetical online shopping scenario, but this time offering multiple products instead of a single product. Between-subject analysis shows that not all interventions are equally effective, with uneven impact across dark patterns. However, within-subject results indicate that all interventions significantly reduce purchase impulsivity pre- versus post-intervention, indicating that any intervention is better than none when it comes to combating dark patterns. We then end by discussing the policy implications of our results.

The use of rigorous ethological observation via machine learning techniques to understand brain function (computational neuroethology) is a rapidly growing approach that is poised to significantly change how behavioral neuroscience is commonly performed. With the development of open-source platforms for automated tracking and behavioral recognition, these approaches are now accessible to a wide array of neuroscientists despite variations in budget and computational experience. Importantly, this adoption has moved the field toward a common understanding of behavior and brain function through the removal of manual bias and the identification of previously unknown behavioral repertoires. Although less apparent, another consequence of this movement is the introduction of analytical tools that increase the explainabilty, transparency, and universality of the machine-based behavioral classifications both within and between research groups. Here, we focus on three main applications of such machine model explainabilty tools and metrics in the drive toward behavioral (i) standardization, (ii) specialization, and (iii) explainability. We provide a perspective on the use of explainability tools in computational neuroethology, and detail why this is a necessary next step in the expansion of the field. Specifically, as a possible solution in behavioral neuroscience, we propose the use of Shapley values via Shapley Additive Explanations (SHAP) as a diagnostic resource toward explainability of human annotation, as well as supervised and unsupervised behavioral machine learning analysis.

All mammalian mothers form some sort of caring bond with their infants that is crucial to the development of their offspring. The Pup Retrieval Test (PRT) is the leading procedure to assess pup-directed maternal care in laboratory rodents, used in a wide range of basic and preclinical research applications. Most PRT protocols require manual scoring, which is prone to bias and spatial and temporal inaccuracies. This study proposes a novel procedure using machine learning algorithms to enable reliable assessment of PRT performance. Automated tracking of a dam and one pup was established in DeepLabCut and was combined with automated behavioral classification of "maternal approach", "carrying" and "digging" in Simple Behavioral Analysis (SimBA). Our automated procedure estimated retrieval success with an accuracy of 86.7%, whereas accuracies of "approach", "carry" and "digging" were estimated at respectively 99.3%, 98.6% and 85.0%. We provide an open-source, step-by-step protocol for automated PRT assessment, which aims to increase reproducibility and reliability, and can be easily shared and distributed.

Background Short-term trials conducted in adults with type 2 diabetes mellitus (T2DM) showed that reducing sedentary behaviour by performing regular short bouts of light-intensity physical activity enhances health. Moreover, support for reducing sedentary behaviour may be provided at a low cost via mobile health technology (mHealth). There are a wide range of mHealth solutions available including SMS text message reminders and activity trackers that monitor the physical activity level and notify the user of prolonged sitting periods. The aim of this study is to evaluate the effects of a mHealth intervention on sedentary behaviour and physical activity and the associated changes in health in adults with T2DM. Methods A dual-arm, 12-month, randomized controlled trial (RCT) will be conducted within a nationwide Swedish collaboration for diabetes research in primary health care. Individuals with T2DM ( n = 142) and mainly sedentary work will be recruited across primary health care centres in five regions in Sweden. Participants will be randomized (1:1) into two groups. A mHealth intervention group who will receive an activity tracker wristband (Garmin Vivofit4), regular SMS text message reminders, and counselling with a diabetes specialist nurse, or a comparator group who will receive counselling with a diabetes specialist nurse only. The primary outcomes are device-measured total sitting time and total number of steps (activPAL3). The secondary outcomes are fatigue, health-related quality of life and musculoskeletal problems (self-reported questionnaires), number of sick leave days (diaries), diabetes medications (clinical record review) and cardiometabolic biomarkers including waist circumference, mean blood pressure, HbA1c, HDL-cholesterol and triglycerides. Discussion Successful interventions to increase physical activity among those with T2DM have been costly and long-term effectiveness remains uncertain. The use of mHealth technologies such as activity trackers and SMS text reminders may increase awareness of prolonged sedentary behaviour and encourage increase in regular physical activity. mHealth may, therefore, provide a valuable and novel tool to improve health outcomes and clinical management in those with T2DM. This 12-month RCT will evaluate longer-term effects of a mHealth intervention suitable for real-world primary health care settings. Trial registration ClinicalTrials.gov NCT04219800 . Registered on 7 January 2020.

Rationale Aggression, comorbid with neuropsychiatric disorders, exhibits with diverse clinical presentations and places a significant burden on patients, caregivers, and society. This diversity is observed because aggression is a complex behavior that can be ethologically demarcated as either appetitive (rewarding) or reactive (defensive), each with its own behavioral characteristics, functionality, and neural basis that may transition from adaptive to maladaptive depending on genetic and environmental factors. There has been a recent surge in the development of preclinical animal models for studying appetitive aggression-related behaviors and identifying the neural mechanisms guiding their progression and expression. However, adoption of these procedures is often impeded by the arduous task of manually scoring complex social interactions. Manual observations are generally susceptible to observer drift, long analysis times, and poor inter-rater reliability, and are further incompatible with the sampling frequencies required of modern neuroscience methods. Objectives In this review, we discuss recent advances in the preclinical study of appetitive aggression in mice, paired with our perspective on the potential for machine learning techniques in producing automated, robust scoring of aggressive social behavior. We discuss critical considerations for implementing valid computer classifications within behavioral pharmacological studies. Key results Open-source automated classification platforms can match or exceed the performance of human observers while removing the confounds of observer drift, bias, and inter-rater reliability. Furthermore, unsupervised approaches can identify previously uncharacterized aggression-related behavioral repertoires in model species. Discussion and conclusions Advances in open-source computational approaches hold promise for overcoming current manual annotation caveats while also introducing and generalizing computational neuroethology to the greater behavioral neuroscience community. We propose that currently available open-source approaches are sufficient for overcoming the main limitations preventing wide adoption of machine learning within the context of preclinical aggression behavioral research.

Impaired cognitive flexibility in visual reversal-learning tasks has been observed in a wide range of neurological and neuropsychiatric disorders. Although both human and animal studies have implicated striatal D2-like and D1-like receptors (D2R; D1R) in this form of flexibility, less is known about the contribution they make within distinct sub-regions of the striatum and the different phases of visual reversal learning. The present study investigated the involvement of D2R and D1R during the early (perseverative) phase of reversal learning as well as in the intermediate and late stages (new learning) after microinfusions of D2R and D1R antagonists into the nucleus accumbens core and shell (NAcC; NAcS), the anterior and posterior dorsomedial striatum (DMS) and the dorsolateral striatum (DLS) on a touchscreen visual serial reversal-learning task. Reversal learning was improved after dopamine receptor blockade in the nucleus accumbens; the D1R antagonist, SCH23390, in the NAcS and the D2R antagonist, raclopride, in the NAcC selectively reduced early, perseverative errors. In contrast, reversal learning was impaired by D2R antagonism, but not D1R antagonism, in the dorsal striatum: raclopride increased errors in the intermediate phase after DMS infusions, and increased errors across phases after DLS infusions. These findings indicate that D1R and D2R modulate different stages of reversal learning through effects localised to different sub-regions of the striatum. Thus, deficits in behavioral flexibility observed in disorders linked to dopamine perturbations may be attributable to specific D1R and D2R dysfunction in distinct striatal sub-regions.

Aberrant social behavior is a core feature of many neuropsychiatric disorders, yet the study of complex social behavior in freely moving rodents is relatively infrequently incorporated into preclinical models. This likely contributes to limited translational impact. A major bottleneck for the adoption of socially complex, ethology-rich, preclinical procedures are the technical limitations for consistently annotating detailed behavioral repertoires of rodent social behavior. Manual annotation is subjective, prone to observer drift, and extremely time-intensive. Commercial approaches are expensive and inferior to manual annotation. Open-source alternatives often require significant investments in specialized hardware and significant computational and programming knowledge. By combining recent computational advances in convolutional neural networks and pose-estimation with further machine learning analysis, complex rodent social behavior is primed for inclusion under the umbrella of computational neuroethology. Here we present an open-source package with graphical interface and workflow (Simple Behavioral Analysis, SimBA) that uses pose-estimation to create supervised machine learning predictive classifiers of rodent social behavior, with millisecond resolution and accuracies that can out-perform human observers. SimBA does not require specialized video acquisition hardware nor extensive computational background. Standard descriptive statistical analysis, along with graphical region of interest annotation, are provided in addition to predictive classifier generation. To increase ease-of-use for behavioural neuroscientists, we designed SimBA with accessible menus for pre-processing videos, annotating behavioural training datasets, selecting advanced machine learning options, robust classifier validation functions and flexible visualizations tools. This allows for predictive classifier transparency, explainability and tunability prior to, and during, experimental use. We demonstrate that this approach is flexible and robust in both mice and rats by classifying social behaviors that are commonly central to the study of brain function and social motivation. Finally, we provide a library of poseestimation weights and behavioral predictive classifiers for resident-intruder behaviors in mice and rats. All code and data, together with detailed tutorials and documentation, are available on the SimBA GitHub repository. Graphical abstract SimBA graphical interface (GUI) for creating supervised machine learning classifiers of rodent social behavior. (a) Pre-process videos . SimBA supports common video pre-processing functions (e.g., cropping, clipping, sampling, format conversion, etc.) that can be performed either on single videos, or as a batch. (b) Managing poseestimation data and creating classification projects . Pose-estimation tracking projects in DeepLabCut and DeepPoseKit can be either imported or created and managed within the SimBA graphical user interface, and the tracking results are imported into SimBA classification projects. SimBA also supports userdrawn region-of-interests (ROIs) for descriptive statistics of animal movements, or as features in machine learning classification projects. (c) Create classifiers, perform classifications, and analyze classification data . SimBA has graphical tools for correcting poseestimation tracking inaccuracies when multiple subjects are within a single frame, annotating behavioral events from videos, and optimizing machine learning hyperparameters and discrimination thresholds. A number of validation checkpoints and logs are included for increased classifier explainability and tunability prior to, and during, experimental use. Both detailed and summary data are provided at the end of classifier analysis. SimBA accepts behavioral annotations generated elsewhere (such as through JWatcher) that can be imported into SimBA classification projects. (d) Visualize classification results . SimBA has several options for visualizing machine learning classifications, animal movements and ROI data, and analyzing the durations and frequencies of classified behaviors. See the SimBA GitHub repository for a comprehensive documentation and user tutorials. ![Figure][1] ### Competing Interest Statement The authors have declared no competing interest. [1]: pending:yes

Serotonin (5-hydroxytryptamine, or 5-HT) and 5-HT receptors are involved in a heterogeneous and interconnected set of neuropsychological processes that support aspects of cognition. One such aspect is cognitive flexibility that can be tested in experimental settings across species. Impaired cognitive flexibility is a clinically relevant hallmark of many neuropsychiatric disorders and appears to be related to imbalances in serotonergic neurotransmission. Translational endeavors have provided opportunities to understand etiologies and refine treatments for disorders characterized by cognitive inflexibility; such findings also present significant opportunities to demonstrate performance enhancements in healthy individuals. In this chapter we focus on pharmacological studies that indicate the relevance of 5-HT in cognitive flexibility in both human participants and experimental animals. This includes 5-HT depletion studies, which can show cross-species reproducibility in impairing cognitive flexibility. We discuss drug studies that target serotonergic transmission and show remarkable efficacy for improving cognitive flexibility in animals. Yet the effect on cognitive flexibility by methods aimed to reduce 5-HT availability has at times been equivocal, and we highlight potentially important caveats that may contribute to translational difficulties of the positive pharmacological effects in experimental animals. Future directions for experiments that may address such issues are discussed.

Rationale Dopamine D2-like receptors (D2R) are important drug targets in schizophrenia and Parkinson's disease, but D2R ligands also cause cognitive inflexibility such as poor reversal learning. The specific role of D2R in reversal learning remains unclear. Objectives We tested the hypotheses that D2R agonism impairs reversal learning by blocking negative feedback and that antagonism of D1-like receptors (D1R) impairs learning from positive feedback. Methods Male Lister Hooded rats were trained on a novel visual reversal learning task. Performance on "probe trials", during which the correct or incorrect stimulus was presented with a third, probabilistically rewarded (50% of trials) and therefore intermediate stimulus, revealed individual learning curves for the processes of positive and negative feedback. The effects of D2R and D1R agonists and antagonists were evaluated. A separate cohort was tested on a spatial probabilistic reversal learning (PRL) task after D2R agonism. Computational reinforcement learning modelling was applied to choice data from the PRL task to evaluate the contribution of latent factors. Results D2R agonism with quinpirole dose-dependently impaired both visual reversal and PRL. Analysis of the probe trials on the visual task revealed a complete blockade of learning from negative feedback at the 0.25 mg/kg dose, while learning from positive feedback was intact. Estimated parameters from the model that best described the PRL choice data revealed a steep and selective decrease in learning rate from losses. D1R antagonism had a transient effect on the positive probe trials. Conclusions D2R stimulation impairs reversal learning by blocking the impact of negative feedback.

Background: Important tools in the study of prefrontal cortical -dependent executive functions are cross-species behavioural tasks with translational validity. A widely used test of executive function and attention in humans is the continuous performance task. Optimal performance in variations of this task is associated with activity along the medial wall of the prefrontal cortex, including the anterior cingulate cortex, for its essential components such as response control, target detection and processing of false alarm errors. Methods: We assess the validity of a recently developed rodent touchscreen continuous performance task that is analogous to typical human continuous performance task procedures. Here, we evaluate the performance of mice with quinolinic acid -induced lesions centred on the anterior cingulate cortex in the rodent touchscreen continuous performance task following a range of task parameter manipulations designed to challenge attention and impulse control. Results: Lesioned mice showed a disinhibited response profile expressed as a decreased response criterion and increased false alarm rates. Anterior cingulate cortex lesions also resulted in a milder increase in inter-trial interval responses and hit rate. Lesions did not affect discriminative sensitivity d′. The disinhibited behaviour of anterior cingulate cortex -lesioned animals was stable and not affected by the manipulation of variable task parameter manipulations designed to increase task difficulty. The results are in general agreement with human studies implicating the anterior cingulate cortex in the processing of inappropriate responses. Conclusion: We conclude that the rodent touchscreen continuous performance task may be useful for studying prefrontal cortex function in mice and has the capability of providing meaningful links between animal and human cognitive tasks.

The 22q11.2 deletion syndrome (22q11.2DS) confers high risk of neurodevelopmental disorders such as schizophrenia and attention-deficit hyperactivity disorder. These disorders are associated with attentional impairment, the remediation of which is important for successful therapeutic intervention. We assessed a 22q11.2DS mouse model (Df(h22q11)/+) on a touchscreen rodent continuous performance test (rCPT) of attention and executive function that is analogous to human CPT procedures. Relative to wild-type littermates, Df(h22q11)/+ male mice showed impaired attentional performance as shown by decreased correct response ratio (hit rate) and a reduced ability to discriminate target stimuli from non-target stimuli (discrimination sensitivity, or d’). The Df(h22q11)/+ model exhibited decreased prefrontal cortical-hippocampal oscillatory synchrony within multiple frequency ranges during quiet wakefulness, which may represent a biomarker of cognitive dysfunction. The stimulant amphetamine (0–1.0 mg/kg, i.p.) dose-dependently improved d’ in Df(h22q11)/+ mice whereas the highest dose of modafinil (40 mg/kg, i.p.) exacerbated their d’ impairment. This is the first report to directly implicate attentional impairment in a 22q11.2DS mouse model, mirroring a key endophenotype of the human disorder. The capacity of the rCPT to detect performance impairments in the 22q11.2DS mouse model, and improvement following psychostimulant-treatment, highlights the utility and translational potential of the Df(h22q11)/+ model and this automated behavioral procedure.

Dysregulation of the serotonin (5-HT) system is a pathophysiological component in major depressive disorder (MDD), a condition closely associated with abnormal emotional responsivity to positive and negative feedback. However, the precise mechanism through which 5-HT tone biases feedback responsivity remains unclear. 5-HT2C receptors (5-HT2CRs) are closely linked with aspects of depressive symptomatology, including abnormalities in reinforcement processes and response to stress. Thus, we aimed to determine the impact of 5-HT2CR function on response to feedback in biased reinforcement learning.We used two touchscreen assays designed to assess the impact of positive and negative feedback on probabilistic reinforcement in mice, including a novel valence-probe visual discrimination (VPVD) and a probabilistic reversal learning procedure (PRL). Systemic administration of a 5-HT2CR agonist and antagonist resulted in selective changes in the balance of feedback sensitivity bias on these tasks.Specifically, on VPVD, SB 242084, the 5-HT2CR antagonist, impaired acquisition of a discrimination dependent on appropriate integration of positive and negative feedback. On PRL, SB 242084 at 1 mg/kg resulted in changes in behaviour consistent with reduced sensitivity to positive feedback. In contrast, WAY 163909, the 5-HT2CR agonist, resulted in changes associated with increased sensitivity to positive feedback and decreased sensitivity to negative feedback.These results suggest that 5-HT2CRs tightly regulate feedback sensitivity bias in mice with consequent effects on learning and cognitive flexibility and specify a framework for the influence of 5-HT2CRs on sensitivity to reinforcement.

Background: The hemizygous 22q11.2 microdeletion is a common copy number variant in humans. The deletion confers high risk for neurodevelopmental disorders, including autism and schizophrenia. Up to 41% of deletion carriers experience psychotic symptoms. Methods: We present a new mouse model (Df(h22q11)/+) of the deletion syndrome (22q11.2DS) and report on, to our knowledge, the most comprehensive study undertaken to date in 22q11.2DS models. The study was conducted in male mice. Results: We found elevated postpubertal N-methyl-d-aspartate (NMDA) receptor antagonist–induced hyperlocomotion, age-independent prepulse inhibition (PPI) deficits and increased acoustic startle response (ASR). The PPI deficit and increased ASR were resistant to antipsychotic treatment. The PPI deficit was not a consequence of impaired hearing measured by auditory brain stem responses. The Df(h22q11)/+ mice also displayed increased amplitude of loudness-dependent auditory evoked potentials. Prefrontal cortex and dorsal striatal elevations of the dopamine metabolite DOPAC and increased dorsal striatal expression of the AMPA receptor subunit GluR1 was found. The Df(h22q11)/+ mice did not deviate from wild-type mice in a wide range of other behavioural and biochemical assays. Limitations: The 22q11.2 microdeletion has incomplete penetrance in humans, and the severity of disease depends on the complete genetic makeup in concert with environmental factors. In order to obtain more marked phenotypes reflecting the severe conditions related to 22q11.2DS it is suggested to expose the Df(h22q11)/+ mice to environmental stressors that may unmask latent psychopathology. Conclusion: The Df(h22q11)/+ model will be a valuable tool for increasing our understanding of the etiology of schizophrenia and other psychiatric disorders associated with the 22q11DS.

Impairments in attention and inhibitory control are endophenotypic markers of neuropsychiatric disorders such as schizophrenia and represent key targets for therapeutic management. Robust preclinical models and assays sensitive to clinically relevant treatments are crucial for improving cognitive enhancement strategies.

A chromosomal microdeletion at the 22q11.2 locus is associated with extensive cognitive impairments, schizophrenia and other psychopathology in humans. Previous reports indicate that mouse models of the 22q11.2 microdeletion syndrome (22q11.2DS) may model the genetic basis of cognitive deficits relevant for neuropsychiatric disorders such as schizophrenia. To assess the models usefulness for drug discovery, a novel mouse (Df(h22q11)/+) was assessed in an extensive battery of cognitive assays by partners within the NEWMEDS collaboration (Innovative Medicines Initiative Grant Agreement No. 115008). This battery included classic and touchscreen-based paradigms with recognized sensitivity and multiple attempts at reproducing previously published findings in 22q11.2DS mouse models. This work represents one of the most comprehensive reports of cognitive functioning in a transgenic animal model. In accordance with previous reports, there were non-significant trends or marginal impairment in some tasks. However, the Df(h22q11)/+ mouse did not show comprehensive deficits; no robust impairment was observed following more than 17 experiments and 14 behavioral paradigms. Thus - within the current protocols - the 22q11.2DS mouse model fails to mimic the cognitive alterations observed in human 22q11.2 deletion carriers. We suggest that the 22q11.2DS model may induce liability for cognitive dysfunction with additional "hits" being required for phenotypic expression.