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Research has demonstrated that consumers are commonly insensitive to missing information and that this insensitivity can lead them to form strong beliefs and evaluations on the basis of weak evidence. A growing body of research has shown that sensitivity to omissions can be heightened and that this increased sensitivity results in more appropriate evaluations. Expanding on this, the current research finds that the level of abstraction by which a situation is construed can influence the likelihood of omission detection and the resulting evaluative judgments. A series of studies reveal that people are more likely to spontaneously detect omissions in near vs. distant judgments, in concrete vs. abstract mindsets, and when they are inherently more likely to interpret actions in concrete vs. abstract terms. Further, although prior findings suggest that people may have differential sensitivity to primary and secondary missing features at different levels of construal, the current research finds no such difference. The results of this study indicate that people are more sensitive to all types of missing information when construal levels are low, and that this sensitivity leads to more moderate and appropriate judgments.

The use of special fonts in marketing communications may have more complex effects than expected. This study examines multiple effects of special fonts and proposes boundary conditions for the effects. Special fonts are perceived as more unique and difficult to read than regular fonts. Five experimental studies show that whereas the perception of uniqueness decreases the awareness of missing information, leading to more favorable initial judgments but a higher likelihood of regret later, the perception of difficulty has the opposite effects. These competing effects are moderated by contextual cues that vary the salience of uniqueness versus difficulty associated with special fonts. Specifically, consumers are more influenced by the uniqueness of special fonts when they rate the degree of uniqueness before the degree of difficulty or when they evaluate a product category (e.g., a handmade item or a décor) that is generally expected to be unique. On the contrary, they are more influenced by difficulty when they rate difficulty first or when they evaluate a product category (e.g., "a tax preparation service") that is unexpected to be unique. Implications of the results for understanding the effects of fonts on information processing and consumer inference are discussed.

This semiotic analysis demonstrates how pharmaceutical companies strategically frame depression within the hotly contested terrain of direct-to-consumer (DTC) advertising. The study tracks regulation of the pharmaceutical industry, relative to DTC advertising, including recent industry codes of conduct. Focusing on the antidepressant category, and its three major brands—Paxil (GlaxoSmithKline), Prozac (Eli Lilly), and Zoloft (Pfizer)—this comparative study analyzes 7 years of print advertising following deregulation in 1997. The authors glean themes from within the advertising texts, across the drug category and within individual-brand campaigns. The findings indicate that DTC advertising of antidepressants frames depression within the biochemical model of causation, privileges benefits over risks, fails to adequately educate consumers, and frames depression as a female condition. The authors close with commentary on the potential implications, with particular focus on the new codes of conduct, and offer suggestions for future research.

Frame Identification (FI) is a fundamental and challenging task in frame semantic parsing. The task aims to find the exact frame evoked by a target word in a given sentence. It is generally regarded as a classification task in existing work, where frames are treated as discrete labels or represented using one-hot embeddings. However, the valuable knowledge about frames is neglected. In this paper, we propose a Knowledge-Guided Frame Identification framework (KGFI) that integrates three types frame knowledge, including frame definitions, frame elements and frameto frame relations, to learn better frame representation, which guides the KGFI to jointly map target words and frames into the same embedding space and subsequently identify the best frame by calculating the dot-product similarity scores between the target word embedding and all of the frame embeddings. The extensive experimental results demonstrate KG-FI significantly outperforms the state-of-theart methods on two benchmark datasets.