The prototype-distance model (Posner, 1969) predicts that when a series of similar visual stimuli are experienced, a prototype is abstracted at the point in the multidimensional similarity structure which represents the greatest similarity to all stimuli, whether the elements of the prototype have actually been experienced or not. The attribute-frequency model (Neumann, 1974) predicts the prototype as a pattern composed of the most frequently experienced elements on each dimension of variability. In three experiments, it was determined that: (1) Under some conditions, a prototype is formed of unexperienced values, and, under other conditions, the best recognized stimuli are those incorporating the most frequent values; (2) the present form of the prototype-distance model cannot account for best recognized stimuli being other than the central tendency; and, (3) the attribute-frequency model can, in principle, account for either finding by incorporating additional assumptions about the specificity with which values on dimensions of variability are encoded.

An attribute frequency model for the abstraction of prototypes is proposed as an alternative to the prototype-plus-transforrnation model. A specific model is tested in a Franks and Bransford visual pattern paradigm under conditions in which the two models generate contrasting predictions. The results support the attribute frequency model. Application of the model to reported data obtained in other paradigms is illustrated and discussed.Studies of concept formation have traditionally used concepts defmed by a listing of relevant attributes (Bourne, 1966). Recently, Franks and Bransford (1971) have proposed that concepts might more generally be defined in terms of a base pattern, or prototype, plus an allowable set of transformations. In their experiments, the base pattern consisted of a particular arrangement of four discrete, discriminable visual attributes. A set of formal transformation rules was defined, and distortions of the prototype were produced by varying the prototypical pattern in accordance with various combinations of these rules. An acquisition set was assembled with the restriction that each type of distortion defined by the transformation rules was represented and that the prototype remain the "central tendency" of the acquisition set. The central tendency of the prototype is ensured by determining that the total number of individual transformations required to produce representations of all transformations defined by the set of rules is least when the prototype is used as the base pattern.Following exposure to the acquisition set, Ss were exposed to a recognition set and told to decide whether they had seen each pattern in the acquisition set and to rate their confidence on a 5-point scale. The results indicate that the prototype (generally not included in the acquisition set) received the highest positive (recognized) rating, followed by patterns of increasing transformational distance. Patterns that could not be produced by the formal transformation rules tended to receive negative (not recognized) ratings. These results were interpreted as supportive of a schematic memory structure for concepts consisting of a prototype and a set of transformation rules that produce instances of the concept. The nature of the experimental paradigm used by Franks and Bransford would seem to make these results equally (or more) amenable to the predictions of an attribute frequency model. The general model proposed here associates each attribute of the stimulus with a set of frequency counts, one for each discriminable state of each dimension upon which the attribute varies. In addition, when specific cues are present which define relationships among attributes, an additional frequency count is associated with each discriminable relationship between attributes consistent with these cues.Frequency counts are compiled from the acquisition stimuli to be shown Ss. The number of appearances of each value of each dimension upon which the attribute varies is counted. For example, if the attribute triangZe varies on t...

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The application and development of reusable components (Intellectual Property, IP) has become a regular part of modern design practices. The IP provider on the one hand side and the IP integrator (user) on the other side may be in the same company or separate participants in the microelectronic design market. In both cases, the transfer of IP remains a complex and time-consuming task. The qualification of IP gains a significant relevance for successful application and transfer of IP. This paper proposes an IP qualification methodology for an automated quality check that also incorporates current standards. Through embedding of the new concept into the regular design flow, IP transfer comes closer to an easy mix and match of virtual components. The presented approach has been validated during an industrial case study.