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Savvy consumers attribute a product's market performance to its intrinsic quality as well as the seller's marketing push. The authors study how sellers should optimize their marketing decisions in response. They find that a seller can benefit from \"demarketing\" its product, meaning visibly toning down its marketing efforts. Demarketing lowers expected sales ex ante but improves product quality image ex post, as consumers attribute good sales to superior quality and lackluster sales to insufficient marketing. The authors derive conditions under which demarketing can be a recommendable business strategy. A series of experiments confirm these predictions.

•We compared observational learning of fear from friends and strangers.•Familiarity does not enhance social learning of fear in humans.•Bayesian statistics confirm absence of differences between friends and strangers.•Observational fear learning activates social and fear networks including amygdala.•Amygdala activations are absent when learned fear is recalled. Humans often benefit from social cues when learning about the world. For instance, learning about threats from others can save the individual from dangerous first-hand experiences. Familiarity is believed to increase the effectiveness of social learning, but it is not clear whether it plays a role in learning about threats. Using functional magnetic resonance imaging, we undertook a naturalistic approach and investigated whether there was a difference between observational fear learning from friends and strangers. Participants (observers) witnessed either their friends or strangers (demonstrators) receiving aversive (shock) stimuli paired with colored squares (observational learning stage). Subsequently, participants watched the same squares, but without receiving any shocks (direct-expression stage). We observed a similar pattern of brain activity in both groups of observers. Regions related to threat responses (amygdala, anterior insula, anterior cingulate cortex) and social perception (fusiform gyrus, posterior superior temporal sulcus) were activated during the observational phase, possibly reflecting the emotional contagion process. The anterior insula and anterior cingulate cortex were also activated during the subsequent stage, indicating the expression of learned threat. Because there were no differences between participants observing friends and strangers, we argue that social threat learning is independent of the level of familiarity with the demonstrator.

•This study is the first to explore how trait anxiety affects observational fear learning through behavioral, physiological, and brain activation measures.•Individuals with high trait anxiety (HTA) exhibited elevated fear responses and medial prefrontal cortex activation to both threatening and non-threatening stimuli, compared to those with low trait anxiety.•Even in safe settings, individuals with HTA displayed stronger skin conductance responses to vicarious threats.•Excessive observational fear learning in individuals with HTA may pose a risk for developing anxiety-related disorders. Observational fear learning delineates the process by which individuals learn about potential threats through observing others’ reactions. Prior research indicates that individuals with high trait anxiety (HTA) manifest pronounced fear responses in direct fear learning scenarios. However, the specific influence of trait anxiety on observational fear learning remains insufficiently explored. This study aimed to fill this gap by examining 64 university students, divided equally between those with HTA and low trait anxiety (LTA), selected from an initial pool of 483 participants. Participants were subjected to observational fear learning tasks, and their behavioral responses, physiological reactions, and brain activations were recorded. Results demonstrated that HTA participants exhibited differentiated skin conductance responses to threat and safety stimuli during the observational fear acquisition phase, notwithstanding prior assurances against shock delivery. Furthermore, during the direct test phase, HTA participants reported significantly elevated fear and shock expectancy ratings for both types of stimuli, in contrast to their LTA counterparts. Neuroimaging data, derived via functional near-infrared spectroscopy (fNIRS) revealed heightened medial prefrontal cortex activation in HTA participants when directly facing threats. This study systematically explores the influence of high trait anxiety on observational fear learning, uncovering that HTA individuals exhibit excessive fear responses. These findings highlight the critical role of trait anxiety as a significant risk factor in the development of anxiety disorders.

Prior experience of social hierarchy is known to modulate emotional contagion, a basic form of affective empathy. However, it is not known whether this behavioral effect occurs through changes in an individual's traits due to their experience of social hierarchy or specific social interrelationships between the individuals. Groups of four mice with an established in‐group hierarchy were used to address this in conjunction with a tube test. The rank‐1 and rank‐4 mice were designated as the dominant or subordinate groups, respectively. The two individuals in between were designated as the intermediate groups, which were then used as the observers in observational fear learning (OFL) experiments, an assay for emotional contagion. The intermediate observers showed greater OFL responses to the dominant demonstrator than the subordinate demonstrators recruited from the same home‐cage. When the demonstrators were strangers from different cages, the intermediate observers did not distinguish between dominant and subordinate, displaying the same level of OFL. In a reverse setting in which the intermediate group was used as the demonstrator, the subordinate observers showed higher OFL responses than the dominant observers, and this occurred only when the demonstrators were cagemates of the observers. Furthermore, the bigger the rank difference between a pair, the higher the OFL level that the observer displayed. Altogether, these results demonstrate that the hierarchical interrelationship established between a given pair of animals is critical for expressing emotional contagion between them rather than any potential changes in intrinsic traits due to the experience of dominant/subordinate hierarchy. Practitioner points Subordinate observer or dominant demonstrator resulted in higher affective empathic response in familiar pairs but not unfamiliar pairs. The relative social rank of the observer with respect to the demonstrator had a negative linear correlation with the affective empathic response of the observer in familiar pairs but not unfamiliar pairs. The effect of social rank on affective empathy is attributed to the prior social hierarchical interrelationship between them and is not due to intrinsic attributes of an individual based on one's dominance rank. Subordinate observer or dominant demonstrator resulted in higher empathic response in familiar pair. The hierarchical effect on empathy was not observed toward an unfamiliar partner. The relative hierarchy is sufficient to distinguish the difference in empathy.

Excessive avoidance behavior, in which an instrumental action prevents an upcoming aversive event, is a defining feature of anxiety disorders. Left unchecked, both fear and avoidance of potentially threatening stimuli may generalize to perceptually related stimuli and situations. The behavioral consequences of generalization mean that aversive learning experiences with specific threats may lead to the inference that classes of related stimuli are threatening, potentially dangerous, and need to be avoided, despite differences in physical form. Little is known however about avoidance generalization in humans and the learning pathways by which it may be transmitted. In the present study, we compared two pathways to avoidance-instructions and social observation-on subsequent generalization of avoidance behavior, fear expectancy and physiological arousal. Participants first learned that one cue was a danger cue (conditioned stimulus, CS+) and another was a safety cue (CS-). Groups were then either instructed that a simple avoidance response in the presence of the CS+ cancelled upcoming shock (instructed-learning group) or observed a short movie showing a demonstrator performing the avoidance response to prevent shock (observational-learning group). During generalization testing, danger and safety cues were presented along with generalization stimuli that parametrically varied in perceptual similarity to the CS+. Reinstatement of fear and avoidance was also tested. Findings demonstrate, for the first time, generalization of socially transmitted and instructed avoidance: both groups showed comparable generalization gradients in fear expectancy, avoidance behavior and arousal. Return of fear was evident, suggesting that generalized avoidance remains persistent following extinction testing. The utility of the present paradigm for research on avoidance generalization is discussed.

Histopathology image evaluation is indispensable for cancer diagnoses and subtype classification. Standard artificial intelligence methods for histopathology image analyses have focused on optimizing specialized models for each diagnostic task 1 , 2 . Although such methods have achieved some success, they often have limited generalizability to images generated by different digitization protocols or samples collected from different populations 3 . Here, to address this challenge, we devised the Clinical Histopathology Imaging Evaluation Foundation (CHIEF) model, a general-purpose weakly supervised machine learning framework to extract pathology imaging features for systematic cancer evaluation. CHIEF leverages two complementary pretraining methods to extract diverse pathology representations: unsupervised pretraining for tile-level feature identification and weakly supervised pretraining for whole-slide pattern recognition. We developed CHIEF using 60,530 whole-slide images spanning 19 anatomical sites. Through pretraining on 44 terabytes of high-resolution pathology imaging datasets, CHIEF extracted microscopic representations useful for cancer cell detection, tumour origin identification, molecular profile characterization and prognostic prediction. We successfully validated CHIEF using 19,491 whole-slide images from 32 independent slide sets collected from 24 hospitals and cohorts internationally. Overall, CHIEF outperformed the state-of-the-art deep learning methods by up to 36.1%, showing its ability to address domain shifts observed in samples from diverse populations and processed by different slide preparation methods. CHIEF provides a generalizable foundation for efficient digital pathology evaluation for patients with cancer. A study describes the development of a generalizable foundation machine learning framework to extract pathology imaging features for cancer diagnosis and prognosis prediction.

Rats can acquire fear by observing conspecifics that express fear in the presence of conditioned fear stimuli. This process is called observational fear learning and is based on the social transmission of the demonstrator rat’s emotion and the induction of an empathy-like or anxiety state in the observer. The aim of the present study was to investigate the role of trait anxiety and ultrasonic vocalization in observational fear learning. Two experiments with male Wistar rats were performed. In the first experiment, trait anxiety was assessed in a light–dark box test before the rats were submitted to the observational fear learning procedure. In the second experiment, ultrasonic vocalization was recorded throughout the whole observational fear learning procedure, and 22 kHz and 50 kHz calls were analyzed. The results of our study show that trait anxiety differently affects direct fear learning and observational fear learning. Direct fear learning was more pronounced with higher trait anxiety, while observational fear learning was the best with a medium-level of trait anxiety. There were no indications in the present study that ultrasonic vocalization, especially emission of 22 kHz calls, but also 50 kHz calls, are critical for observational fear learning.

Soil carbon sequestration is seen as an effective means to draw down atmospheric CO 2 , but at the same time warming may accelerate the loss of extant soil carbon, so an accurate estimation of soil carbon stocks and their vulnerability to climate change is required. Here we demonstrate how separating soil carbon into particulate and mineral-associated organic matter (POM and MAOM, respectively) aids in the understanding of its vulnerability to climate change and identification of carbon sequestration strategies. By coupling European-wide databases with soil organic matter physical fractionation, we assessed the current geographical distribution of mineral topsoil carbon in POM and MAOM by land cover using a machine-learning approach. Further, using observed climate relationships, we projected the vulnerability of carbon in POM and MAOM to future climate change. Arable and coniferous forest soils contain the largest and most vulnerable carbon stocks when cumulated at the European scale. Although we show a lower carbon loss from mineral topsoils with climate change (2.5 ± 1.2 PgC by 2080) than those in some previous predictions, we urge the implementation of coniferous forest management practices that increase plant inputs to soils to offset POM losses, and the adoption of best management practices to avert the loss of and to build up both POM and MAOM in arable soils. Particulate and mineral-associated soil organic carbon have different climate sensitivity and distributions in Europe, according to analyses of measurements of soil carbon fractions from 352 topsoils.

Global vegetation and associated ecosystem services critically depend on soil moisture availability which has decreased in many regions during the last three decades. While spatial patterns of vegetation sensitivity to global soil water have been recently investigated, long-term changes in vegetation sensitivity to soil water availability are still unclear. Here we assess global vegetation sensitivity to soil moisture during 1982-2017 by applying explainable machine learning with observation-based leaf area index (LAI) and hydro-climate anomaly data. We show that LAI sensitivity to soil moisture significantly increases in many semi-arid and arid regions. LAI sensitivity trends are associated with multiple hydro-climate and ecological variables, and strongest increasing trends occur in the most water-sensitive regions which additionally experience declining precipitation. State-of-the-art land surface models do not reproduce this increasing sensitivity as they misrepresent water-sensitive regions and sensitivity strength. Our sensitivity results imply an increasing ecosystem vulnerability to water availability which can lead to exacerbated reductions in vegetation carbon uptake under future intensified drought, consequently amplifying climate change. Water availability is a major control of vegetation dynamics and terrestrial carbon cycling. Here, the authors show that vegetation sensitivity to soil moisture has been increasing in the last 36 years, especially in (semi)arid areas, and that state-of-the-art land surface models fail to capture this trend.

We propose a new architecture based on a fully connected feed-forward Artificial Neural Network (ANN) model to estimate surface soil moisture from satellite images on a large alluvial fan of the Kosi River in the Himalayan Foreland. We have extracted nine different features from Sentinel-1 (dual-polarised radar backscatter), Sentinel-2 (red and near-infrared bands), and Shuttle Radar Topographic Mission (digital elevation model) satellite products by leveraging the linear data fusion and graphical indicators. We performed a feature importance analysis by using the regression ensemble tree approach and also feature sensitivity to evaluate the impact of each feature on the response variable. For training and assessing the model performance, we conducted two field campaigns on the Kosi Fan in December 11–19, 2019 and March 01–06, 2022. We used a calibrated TDR probe to measure surface soil moisture at 224 different locations distributed throughout the fan surface. We used input features to train, validate, and test the performance of the feed-forward ANN model in a 60:10:30 ratio, respectively. We compared the performance of ANN model with ten different machine learning algorithms [i.e., Generalised Regression Neural Network (GRNN), Radial Basis Network (RBN), Exact RBN (ERBN), Gaussian Process Regression (GPR), Support Vector Regression (SVR), Random Forest (RF), Boosting Ensemble Learning (Boosting EL), Recurrent Neural Network (RNN), Binary Decision Tree (BDT), and Automated Machine Learning (AutoML)]. We observed that the ANN model accurately predicts the soil moisture and outperforms all the benchmark algorithms with correlation coefficient (R = 0.80), Root Mean Square Error (RMSE = 0.040 m 3 / m 3 ), and bias = 0.004 m 3 / m 3 . Finally, for an unbiased and robust conclusion, we performed spatial distribution analysis by creating thirty different sets of training-validation-testing datasets. We observed that the performance remains consistent in all thirty scenarios. The outcomes of this study will foster new and existing applications of soil moisture.