Achieving More With Less: Intuitive Correction in Selection

Choosing between candidates for a position can be tricky, especially when the selection test is affected by irrelevant characteristics (e.g., reading speed). One can correct for this irrelevant attribute by penalizing individuals who have unjustifiably benefited from it. Statistical models do so by including the irrelevant attribute as a suppressor variable, but can people do the same without the help of a model? In three experiments (total N = 357), participants had to choose between two candidates, one of whom had higher levels of an irrelevant attribute and thus enjoyed an unfair advantage. Participants showed a substantial preference for the candidate with high levels of the irrelevant attribute, thus choosing the less suitable candidate. This bias was attenuated when the irrelevant attribute was a situational factor, probably by making the correction process more intuitive. Understanding the intuitive judgment of suppressor variables can help candidates from underprivileged groups boost their chances to succeed.

Attributes’ Importance for Zero-Shot Pose-Classification Based on Wearable Sensors

This paper presents a simple yet effective method for improving the performance of zero-shot learning (ZSL). ZSL classifies instances of unseen classes, from which no training data is available, by utilizing the attributes of the classes. Conventional ZSL methods have equally dealt with all the available attributes, but this sometimes causes misclassification. This is because an attribute that is effective for classifying instances of one class is not always effective for another class. In this case, a metric of classifying the latter class can be undesirably influenced by the irrelevant attribute. This paper solves this problem by taking the importance of each attribute for each class into account when calculating the metric. In addition to the proposal of this new method, this paper also contributes by providing a dataset for pose classification based on wearable sensors, named HDPoseDS. It contains 22 classes of poses performed by 10 subjects with 31 IMU sensors across full body. To the best of our knowledge, it is the richest wearable-sensor dataset especially in terms of sensor density, and thus it is suitable for studying zero-shot pose/action recognition. The presented method was evaluated on HDPoseDS and outperformed relative improvement of 5.9% in comparison to the best baseline method.

Meaningful Brands from Meaningless Differentiation: The Dependence on Irrelevant Attributes

Conventional product differentiation strategies prescribe distinguishing a product or brand from competitors’ on the basis of an attribute that is relevant, meaningful, and valuable to consumers. However, brands also successfully differentiate on an attribute that appears to create a meaningful product difference but on closer examination is irrelevant to creating that benefit—“meaningless” differentiation. The authors examine how meaningless differentiation can produce a meaningfully differentiated brand. They argue that buyers may infer that a distinguishing but irrelevant attribute is in fact relevant and valuable under certain conditions, creating a meaningfully differentiated brand. They outline the consumer inference process and develop a set of hypotheses about when it will produce meaningful brands from meaningless differentiation. Experimental tests in three product categories support their analysis. They explore the implications of the results for product differentiation strategies, consumer preference formation, and the nature of competition.