In 6 experiments, the authors examined the use of prior knowledge in category learning. Previous studies of the effects of knowledge on category learning have used categories in which knowledge was related to all of the category's features. However, people's knowledge of real-world categories often consists of many "rote" features that are not related to their prior knowledge. Five experiments found that even minimal prior knowledge (1 knowledge-relevant feature and 5 rote features per exemplar) can facilitate category learning. Posttests revealed that although the knowledge aided learning, subjects also acquired the rote features that were not related to knowledge, contradicting predictions of an attentional explanation of the knowledge effect. The results of Experiment 6 suggested that subjects attempt to link even rote features to their knowledge. When people learn a new category, they are influenced by both the properties of the observed category exemplars and their knowledge about the category's domain. Learning is easier when prior knowledge is consistent with a category that is to be learned than when knowledge is inconsistent or simply absent (Murphy & Allopenna, 1994; Pazzani, 1991; Wattenmaker, Dewey, T. Murphy, & Medin, 1986). By prior knowledge we mean knowledge about a broad domain, such as animals, birds, vehicles, or computers, that exists before learning a new category. This knowledge connects features of a new category, perhaps through causal relations, and provides some sort of explanation of why the category has the properties it does (see Carey, 1985; Keil, 1989; Murphy, 1993, in press; Murphy & Medin, 1985). To give a very simple example: People have some idea about how animals fly that provides a possible explanation for why something that has wings can live high on a cliff-the wings explain flying, which in turn explains why this habitat is possible. Such prior knowledge contrasts with category structure: empirical or statistical information about a category of the sort "most of the category members have wings" or "one of the exemplars has a red back, wings, and three legs." Both sources of information are used in much category learning, as one cannot rely solely on prior knowledge in learning something new, and yet such knowledge can greatly speed learning when it is correct. One of the main issues facing the psychology of concepts is to understand how prior knowledge influences category learning. Although it seems intuitively obvious that such knowledge can aid

Four experiments examined the extent to which prior knowledge influences the acquisition of category structure in unsupervised learning conditions. Prior knowledge is general knowledge about a broad domain that explains why an object has the features it does. Category structure refers to the statistical regularities of features within and across categories. Subjects viewed items and then divided them up into the categories that seemed most natural. Each item had one feature that was related to prior knowledge and five features that were not. The results showed that even this small amount of prior knowledge helped subjects to discover the category structure. In addition, prior knowledge enhanced the learning of many of the category's features, and not just the features that were directly relevant to the knowledge. The results suggest that prior knowledge may help to integrate the features of a category, thereby improving the acquisition of category structure.

Medin, Goldstone, and Markman (1995) recently described a series of parallel effects in similarity and choice. They suggested that similarity and choice are related in a nontrivial way such that choice may entail a similarity judgment to an explicit or constructed ideal. In this paper, the correspondences between similarity and choice were investigated with respect to a phenomenon in similarity known as the coincidence effect. In coincidence (pronounced "coincide-ence"), two items that match on one dimension but have a large difference on another dimension receive a higher similarity rating than do two items that have only modest differences on both dimensions. We conducted five experiments in order to examine commonalities between similarity and choice processes with respect to coincidence. Four types of tasks were given: similarity ratings, desirability ratings, forced choice similarities (which of two items is most similar to a target), and forced choice preferences (which of two items one would prefer, given a target). We found a main effect for ratings as opposed to forced choices, with ratings showing greater coincidence effects than did choices. Similarity measures tended to produce more coincidence than did preference measures. The overall pattern of results suggests the presence of dimensional weighting processes sensitive to task characteristics and operating somewhat differently for similarity and decision making.

Three experiments examined the interactions of category structure and prior knowledge in category learning. Experiment 1 examined the distribution of atypical or "crossover" features in category learning. In real categories, crossover features may be unevenly distributed--found primarily in very unusual examples of a category (like whales or ostriches). In contrast, in many psychology experiments, each item has exactly one crossover feature. Even versus uneven distribution of crossover features did not affect category learning when the features were neutral. However, when the features were connected by prior knowledge, it was much harder for subjects to learn the structure with the uneven distribution of crossover features. Experiments 2 and 3 found similar results with a slightly less uneven condition. We conclude that learning is a function of the interaction of category structure and prior knowledge rather than either one alone. Furthermore, knowledge benefits learning even when the category contains contradictions of the knowledge, so long as the contradictions are not very salient.

Communications self-assembled thin films were fabricated by an electrostatic deposition technique similar to that described by Decher[2]; Rubner [3], and others [9]. The polymer films consist of alternating layers of a cationic poly(p-pyridiniuin vinylene) (PHPyV or Bu-PHPyV) and anionic sulfonated polyaniline (SPANI) or poly(4-styrene sulfonate) (PSS). Films are grown by first initiating ITO-coated glass substrates with positive charges. This initiation is carried out by treating the ITO-coated glass with (3-aminopropyl)triethoxysilane[7] or by depositing a thin film of polypyrrole or polyaniline directly on the substrate[3b]. In the latter case, the thickiicss of the conducting polymer film was limited to keep the total optical density of the resulting substrate below 0.1 in the visible part of the spectrum. The assembly ofthe multilayer polymer film on the cationicsubstrate was carried out by sequentially dipping the substrate into each polymer solution (alternating between anionic and cationic). Dipping time in each solution was 30 minutes. After each dip, the film was rinsed with water (for PSS or SPANI) or THF-acetone (for Bu-PHPyV) or m-cresol-acetonitrile (for PHPyV) and dried under nitrogen flow before the next deposition.The LEDs were fiiiishcd by vacuum depositing an aluminum electrode on top of the organic film. The base pressure of the chamber was mbar and the evaporating pressure was 10-' mbar The thickness of the electrode was ca.1000 a and the area of the electrode was 1 mm2. The I-Vcharacteristics of the devices were measured on an H P 414BB analyzer and the electroluminescent spectra were collected using ail EG & G nionochromator and Electriin 1000 TE CCD camera. The light intensity was detected by a calibrated broad-area silicon photodiode. ahose photocurrent was measured by H P picoampere meters.The electronic absorption spectra weremeasured on an AVIV Spectrophotometer model 14DS (modified Cary-14) and the photoluminescence spectra were taken with a Perkin-Elmer LS-50 Luminescence Spectrometer. The excitation wavelengths used were thc maximum absorption wavelengths in the electronic absorption spectra. PSS is tiansparent at both the excitation and emission wavelengths, while polypyrrole, polyaniline and SPANI have very low optical densities at these wavelengths in the samples prepared here. Photoluminescence quantum yields of Bu-PPyV and Bu-PHPyV were measured in degassed T H F solutions. typically M (based on the monomer repeat unit). The Bu-PH-PyV was made by adding stoichiometric amount methane sulfonic acid to the Bu-PPyV solution. Rhodamine B in ethanol was used as a reference (u, = 1) [11].The excitation wavelengths for the polymer solutions and Rhodamine B were 420 nm and 530 nm. respectively. The photoluminescent quantum yield of Bu-PPyV is greater than 0.9, and the quantum yield of Bu-PHPyV is ca. 0.8. M R 9357415. The authors wish to thank Victor Gruol for assistmce with the XPS measurements, and Andrew Gewirth (University of Illinois) for generous donation of the HOPG substrate. ...