Who is Afraid of Figure of Speech?

Aristotle's illustrations of the fallacy of Figure of Speech (or Form of Expression) are none too convincing. They are tied to Aristotle's theory of categories and to peculiarities of Greek grammar that fail to hold appeal for a contemporary readership. Yet, upon closer inspection, Figure of Speech shows many points of contact with views and problems that inhabit 20th-century analytical philosophy. In the paper, some Aristotelian examples will be analyzed to gain a better understanding of this fallacy. The case of the Third Man argument and some modern cases lend plausibility to the claim that Figure of Speech is of more interest as a type of fallacy than has generally been assumed. Finally, a case is made for the view that Figure of Speech, though listed among the fallacies dependent upon language, is not properly classified as a fallacy of ambiguity. More likely, it should be looked upon as a type of non sequitur. This has important consequences for the profile of dialogue associated with this fallacy.     

The place of human values in the language of science: Kuhn,Saussure, and structuralism

The current paradigm in medicine generally distinguishes between genetic and environmental causes of disease. Although the word paradigm has become a commonplace, the theories of Thomas Kuhn have not received much attention in the journals of medicine. Kuhn's structuralist method differs radically from the daily activities of the scientific method itself. Using linguistic theory, this essay offers a structuralist reading of Thomas Kuhn's The Structure of Scientific Revolutions. Our purpose is to highlight the similarities between these structuralist models of science and language. In part, we focus on the logic that enables Kuhn to assert the priority of perception over interpretation in the history of science. To illustrate some of these issues, we refer to the distinction between environmental and genetic causes of disease. While the activity of scientific research results in the revision of concepts in science, the production of significant differences that shape our knowledge is in part a social and linguistic process.     

Predicting face similarity judgements with a computational model of face space

Current models of face representation involve the notion of a high-dimensional face space. Computational models of face space based on principal components analysis (PCA) have been successfully used to predict human judgements of face sex or race. In this work the capability of PCA-based face spaces to predict human judgements of face similarity is examined. Three different paradigms were used. In Experiment 1 subjects learned face-name associations for 18 faces and identified these faces on tachistoscopic presentation. The number of confusions was used as a measure of face similarity. In Experiment 2 the same subjects subjectively rated the similarity of all 153 possible face pairs. In Experiment 3 reaction time to identify a face in an odd-man-out task was measured as an index of face similarity. These empirical measures were correlated with distance of the faces in PCA-based spaces of different dimensionalities. For Experiments 1 and 2 these correlations were highest for one-or two-dimensional face spaces (r=−0.27 vs. −0.28). For Experiment 3 the correlation was highest for a space consisting of 13 dimensions (r=−0.51). Thus PCA-based spaces seem capable to predict human similarity judgements to some extent. Possible reasons for the differences in predictability between paradigms are discussed.     

拟人化:从“它”到“他”

拟人化是将人类特征、动机、意向或心理状态赋予非人对象的心理过程或者个体差异。拟人化的产生受到激发主体知识、效能动机和社会动机的影响,现有研究包括对自然、超自然、动物、机器、品牌和产品等的拟人化。对自然的拟人化能够促进环境保护行为,对动物、机器、品牌和产品的拟人化则形式多样、后果复杂。未来研究的焦点可能在人-机器人交互以及拟人化与可爱的关系问题上。     

Attention in the predictive mind

It has recently become popular to suggest that cognition can be explained as a process of Bayesian prediction error minimization. Some advocates of this view propose that attention should be understood as the optimization of expected precisions in the prediction-error signal (Clark, 2013, 2016; Feldman & Friston, 2010; Hohwy, 2012, 2013). This proposal successfully accounts for several attention-related phenomena. We claim that it cannot account for all of them, since there are certain forms of voluntary attention that it cannot accommodate. We therefore suggest that, although the theory of Bayesian prediction error minimization introduces some powerful tools for the explanation of mental phenomena, its advocates have been wrong to claim that Bayesian prediction error minimization is ‘all the brain ever does’.