Enzymes are remarkable molecules which make metabolism possible. Their processing powers are considerable for not only are they catalysts they also contribute to information processing, integration, coherence and memory in the cell. This complex of attributes suggests that a complementary perspective to enzyme nature and activity is needed related to what enzymes and verbs have in common. The value of this kind of thinking is that it shifts the focus from objects and mechanisms to processes and information. In order to (...) support this idea a number of features which enzymes and verbs share are discussed including, context-dependence, occurrence, cases, voice, mood and glue/integrative capacities. The paper concludes with some reflections on the utility of a view of enzymes as verbs. (shrink)

We investigate the hypothesis that our conceptual systems provide two formally distinct ways of representing categories by investigating the manner in which lexical nominals (e.g., tree, picnic table) and phrasal nominals (e.g., black bird, birds that like rice) are interpreted. Four experiments found that lexical nominals may be mapped onto kind representations, whereas phrasal nominals map onto class representations but not kind representations. Experiment 1 found that phrasal nominals, unlike lexical nominals, are mapped onto categories whose members need not be (...) of a single kind. Experiments 2 and 3 found that categories named by lexical nominals enter into both class inclusion and kind hierarchies and thus support both class inclusion (is a) and kind specification (kind of) relations, whereas phrasal nominals map onto class representations which support only class inclusion relations. Experiment 4 showed that the two types of nominals represent hierarchical relations in different ways. Phrasal nominals (e.g., white bear) are mapped onto classes that have criteria of membership in addition to those specified by the class picked out by the head noun of the phrase (e.g., bear). In contrast, lexical nominals (e.g., polar bear) specify one way to meet the criteria specified by the more general kind concept (e.g., bear). Implications for the language–conceptual system interface, representation of hierarchical relations, lexicalization, and theories of conceptual combination are discussed. (shrink)

The generality problem is commonly considered to be a critical difficulty for reliabilism. In this paper, we present a dynamical perspective on the problem in the spirit of naturalized epistemology. According to this outlook, it is worth investigating how token belief-forming processes instantiate specific types in the biological agent’s cognitive architecture and background experience, consisting in the process of attractor-guided neural activation. While our discussion of the generality problem assigns “scientific types” to token processes, it represents a unified account in (...) the sense that it incorporates contextual and common sense features emphasized by other authors. (shrink)

Metaphor influences the construction of biological models and theories and the analysis of its use can reveal important tools of thought. Some aspects of biological organisation are investigated through the analysis of metaphors associated with treating biosystems as a kind of text. In particular, the use of glue and verbs is considered. Some of the reasons why glue is important in the construction of hierarchies are pursued in the light of specific examples, and some of the conceptual links between glue (...) in biology and other domains is discussed. Verbs are shown to be important in the construction of networks. Some of the relations between glue, verb and text are considered and the text metaphor is placed within a much broader context of ideas associated with form, relation and system. The paper concludes with comments on the nature of biological information and the need for extending or better understanding the verbal vocabulary. (shrink)

Previous neural network models of concept learning were mainly implemented with supervised learning schemes. However, studies of human conceptual memory have shown that concepts may be learned without a teacher who provides the category name to associate with exemplars. A modular neural network architecture that realizes concept acquisition through two functionally distinct operations, categorizing and naming, is proposed as an alternative. An unsupervised algorithm realizes the categorizing module by constructing representations of categories compatible with prototype theory. The naming module associates (...) category names to the output of the categorizing module in a supervised mode. In such a modular architecture, the interface between the modules can be conceived of as an “information relay” that encodes, constrains, and propagates important information. Five experiments were conducted to analyze the relationships among internal conceptual codes and simple conceptual and lexical development. The first two experiments show a prototype effect and illustrate some basic characteristics of the system. The third experiment presents a bottom‐up model of the narrowing down of children's early lexical categories that honors mutual exclusivity. The fourth experiment introduces top‐down constraints on conceptual coding. The fifth experiment exhibits how hierarchical relationships between concepts are learned by the architecture, and also demonstrates how a spectrum of conceptual expertise may gradually emerge as a consequence of experiencing more with certain categories than with others. (shrink)