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360° salient object detection (SOD) is crucial for analyzing and understanding panoramic scenes. However, current 360° SOD methods face a significant limitation: models are often optimized for individual datasets, leading to dataset-specific overfitting and poor generalization in downstream applications. To overcome this challenge, we propose a data-efficient cross-dataset collaborative training strategy. By integrating four 360° SOD datasets and addressing redundancy and label conflicts, we construct a refined training corpus that retains 66.7% of the original data volume. To fully leverage multi-source data while minimizing spherical distortion artifacts, we introduce 360Mamba—the first Mamba-based network designed specifically for 360° scenes. Experiments show that our method outperforms state-of-the-art models on two benchmark datasets with 33% less training data, demonstrating the benefits of multi-dataset collaborative training and the Mamba architecture.

This article revisits and further develops Mitchell et al.'s (Acad Manag Rev 22(4): 853-886, 1997) theory of stakeholder identification and salience. Stakeholder salience holds considerable unrealized potential for understanding how organizations may best manage multiple stakeholder relationships. While the salience framework has been cited numerous times, attempts to develop it further have been relatively limited. We begin by reviewing the key contributions of other researchers. We then identify and seek to resolve three residual weaknesses in Mitchell et al.'s (1997) framework, thereby strengthening its foundations for further development. We argue, first, that urgency is not relevant for identifying stakeholders; second, that it is primarily the moral legitimacy of the stakeholder's claim that applies to stakeholder salience; and last, that the salience of stakeholders will vary as the degrees of the attributes vary. These insights inform revised definitions of stakeholder salience and legitimacy, and necessitate a new theoretical underpinning for the role of legitimacy. Finally, we present an extensive agenda for future research with the objective of refueling research in stakeholder salience.

This article shows that accounting for variation in mistakes can be crucial for welfare analysis. Focusing on consumer under-reaction to not-fully-salient sales taxes, we show theoretically that the efficiency costs of taxation are amplified by differences in under-reaction across individuals and across tax rates. To empirically assess the importance of these issues, we implement an online shopping experiment in which 2,998 consumers purchase common household products, facing tax rates that vary in size and salience. We replicate prior findings that, on average, consumers under-react to non-salient sales taxes—consumers in our study react to existing sales taxes as if they were only 25% of their size. However, we find significant individual differences in this under-reaction, and accounting for this heterogeneity increases the efficiency cost of taxation estimates by at least 200%. Tripling existing sales tax rates nearly doubles consumers’ attention to taxes, and accounting for this endogeneity increases efficiency cost estimates by 336%. Our results provide new insights into the mechanisms and determinants of boundedly rational processing of not-fully-salient incentives, and our general approach provides a framework for robust behavioural welfare analysis.

Previous research suggests that observers can suppress salient-but-irrelevant stimuli in a top-down manner. However, one question left unresolved is whether such suppression is, in fact, solely due to distractor-feature suppression or whether it instead also reflects some degree of target-feature enhancement. The present study (N = 60) addressed this issue. On search trials (70% of trials), participants searched for a shape target when an irrelevant color singleton was either present or absent; performance was better when a color singleton was present. On interleaved probe trials (30% of trials), participants searched for a letter target. Responses were faster for the letter on a target-colored item than on a neutral-colored item, whereas responses were slower for the letter on a distractor-colored item than on a neutral-colored item. The results demonstrate that target-feature enhancement and distractor-feature suppression contribute to attentional guidance independently; enhancement and suppression flexibly guide attention as the occasion demands.

Most Convolutional Neural Network (CNN) interpretation techniques visualize only the dominant cues that the model relies on, but there is no guarantee that these represent all the evidence the model uses for classification. This limitation becomes critical when hidden secondary cues—potentially more meaningful than the visualized ones—remain undiscovered. This study introduces CasCAM (Cascaded Class Activation Mapping) to address this fundamental limitation through counterfactual reasoning. By asking “if this dominant cue were absent, what other evidence would the model use?”, CasCAM progressively masks the most salient features and systematically uncovers the hierarchy of classification evidence hidden beneath them. Experimental results demonstrate that CasCAM effectively discovers the full spectrum of reasoning evidence and can be universally applied with nine existing interpretation methods.

What determines where we look? Theories of attentional guidance hold that image features and task demands govern fixation behavior, while differences between observers are interpreted as a “noise-ceiling” that strictly limits predictability of fixations. However, recent twin studies suggest a genetic basis of gaze-trace similarity for a given stimulus. This leads to the question of how individuals differ in their gaze behavior and what may explain these differences. Here, we investigated the fixations of >100 human adults freely viewing a large set of complex scenes containing thousands of semantically annotated objects. We found systematic individual differences in fixation frequencies along six semantic stimulus dimensions. These differences were large (>twofold) and highly stable across images and time. Surprisingly, they also held for first fixations directed toward each image, commonly interpreted as “bottom-up” visual salience. Their perceptual relevance was documented by a correlation between individual face salience and face recognition skills. The set of reliable individual salience dimensions and their covariance pattern replicated across samples from three different countries, suggesting they reflect fundamental biological mechanisms of attention. Our findings show stable individual differences in salience along a set of fundamental semantic dimensions and that these differences have meaningful perceptual implications. Visual salience reflects features of the observer as well as the image.

Examining a shock to the salience of the sustainability of the U.S. mutual fund market, we present causal evidence that investors marketwide value sustainability: being categorized as low sustainability resulted in net outflows of more than $12 billion while being categorized as high sustainability led to net inflows of more than $24 billion. Experimental evidence suggests that sustainability is viewed as positively predicting future performance, but we do not find evidence that high-sustainability funds outperform low-sustainability funds. The evidence is consistent with positive affect influencing expectations of sustainable fund performance and nonpecuniary motives influencing investment decisions.

This paper advances stakeholder salience theory from the viewpoint of small businesses. It is argued that the stakeholder salience process for small businesses is influenced by their local embeddedness, captured by the idea of social proximity, and characterised by multiple relationships that the owner-manager and stakeholders share beyond the business context. It is further stated that the ethics of care is a valuable ethical lens through which to understand social proximity in small businesses. The contribution of the study conceptualises how the perceived social proximity between local stakeholders and small business owner-managers influences managerial considerations of the legitimacy, power and urgency of stakeholders and their claims. Specifically, the paradoxical nature of close relationships in the salience process is acknowledged and-discussed.

How does the salience of environmental issues influence climate policy adoption in the American states? This article considers how two aspects of public salience, issue problem status and issue attention, work with environmental interest group membership to influence climate policy adoption in the American states. We contribute to the theoretical development of issue salience and offer alternative measures that capture differences in salience across subnational units. We find evidence that states where climate change is perceived to be a problem, and where attention to environmental issues is high, are more likely to adopt relevant policies. Furthermore, states with Republican majorities in either legislative chamber are less likely to adopt climate policies. Our findings have implications for the impact of salience on the policy process.