Emotions are universally recognized from facial expressions—or so it has been claimed. To support that claim, research has been carried out in various modern cultures and in cultures relatively isolated from Western influence. A review of the methods used in that research raises questions of its ecological, convergent, and internal validity. Forced-choice response format, within-subject design, preselected photographs of posed facial expressions, and other features of method are each problematic. When they are altered, less supportive or nonsupportive results occur. When they are combined, these method factors may help to shape the results. Facial expressions and emotion labels are probably associated, but the association may vary with culture and is loose enough to be consistent with various alternative accounts, 8 of which are discussed. "Everyone knows that grief involves a gloomy and joy a cheerful countenance.... There are characteristic facial expressions which are observed to accompany anger, fear, erotic excitement, and all the other passions " (Aristotle, nd/1913, pp. 805, 808). Aristotle was not proposing a new idea but was cataloging what was known on the topic of physiognomy. The theory was that a person's physical appearance, especially in the face, reveals deeper characteristics: Poor proportions reveal a rogue, soft hair a coward, and a smile a happy person.' Today, few psychologists share Aristotle's belief about the meaning of poor proportions or soft hair, but many share his beliefs about facial expression and emotion. Oatley and Jenkins (1992) observed, "By far the most extensive body of data in the field of human emotions is that on facial expressions of emotion" (p. 67). Recent reviews of those data (see Table 1) agree that the face reveals emotion in a way that is universally understood: Happiness, surprise, fear, anger, contempt, disgust, and sadness—these seven emotions, plus or minus two, are recognized from facial expressions by all human beings, regardless of their cultural background.

Abstract: Cognitive categories in the geographic realm appear to manifest certain special features as contrasted with categories for objects at surveyable scales. We have argued that these features reflect specific ontological characteristics of geographic objects. This paper presents hypotheses as to the nature of the features mentioned, reviews previous empirical work on geographic categories, and presents the results of pilot experiments that used English-speaking subjects to test our hypotheses. Our experiments show geographic categories to be similar to their non-geographic counterparts in the ways in which they generate instances of different relative frequencies at different levels. Other tests, however, provide preliminary evidence for the existence of important differences in subjects ’ categorizations of geographic and non-geographic objects, and suggest further experimental work especially with regard to the role in cognitive categorization of different types of objectboundaries at different scales.

Emotion is a fundamental component of being human. Joy, hate, anger, and pride, among the plethora of other emotions, motivate action and add meaning and richness to virtually all human experience. Traditionally, human-computer interaction has been viewed as the “ultimate ” exception: Users must discard their emotional selves to work efficiently and rationality with computers, the quintessentially unemotional artifact. Emotion seemed at best marginally relevant to human-computer interaction―and at worst oxymoronic. Recent research in psychology and technology suggests a very different view of the relationship between humans, computers, and emotion. After a long period of dormancy and confusion, there has been an explosion of research on the psychology of emotion (Gross, 1999). Emotion is no longer seen as limited to the occasional outburst of fury when a computer crashes inexplicably, excitement when a videogame character leaps past an obstacle, or frustration at an incomprehensible error message. It is now understood that a wide range of emotions plays a critical role in every computer-related, goal-directed activity, from developing a 3-D CAD model and running calculations on a spreadsheet, to searching the Web and sending an email, to making an online purchase and playing solitaire. Indeed, many psychologists now argue that it is

Some documents provoke emotions in people viewing them. Will it be possible to describe emotions consistently and use this information in retrieval systems? We tested collective (statistically aggregated) emotion indexing using images as examples. Considering psychological results, basic emotions are anger, disgust, fear, happiness, and sadness. This study follows an approach developed by Lee and Neal (2007) for music emotion retrieval and applies scroll bars for tagging basic emotions and their intensities. A sample comprising 763 persons tagged emotions caused by images (retrieved from www.Flickr.com) applying scroll bars and (linguistic) tags. Using SPSS, we performed descriptive statistics and correlation analysis. For more than half of the images, the test persons have clear emotion favorites. There are prototypical images for given emotions. The document-specific consistency of tagging using a scroll bar is, for some images, very high. Most of the (most commonly used) linguistic tags are on the basic level (in the sense of Rosch’s basic level theory). The distributions of the linguistic tags in our examples follow an inverse power-law. Hence, it seems possible to apply collective image emotion tagging to image information systems and to present a new search option for basic emotions. This article is one of the first steps in the research area of emotional information retrieval (EmIR).