Pairwise partitioning is a nonmetric, divisive algorithm, for identifying feature structures based on pairwise similarities. For errorless data, this algorithm is shown to identify only (and sometimes all) valid features for certain hierarchical and multidimensional feature structures. Unfortunately, the algorithm is also extremely sensitive to error in the data. Fortunately, several modifications of the algorithm are shown to compensate significantly for this deficiency. The algorithm is illustrated with simulations and analyses of three sets of similarity data. The results suggest that this algorithm will be most useful (a) as an exploratory tool for generating a relatively large set of potential features that can be reduced using other criteria (either statistical or substantive) and (b) as a source of confirmatory or disconfirmatory evidence.

By reference to nominated attributes, a genus, being a population of objects of one specified kind, may be partitioned into species, being subpopulations of different kinds. A prototype is an object representative of its species within the genus. Using this framework, the paper describes how objects can be relatively differentiated with respect to attributes, and how attributes can be relatively differentiating with respect to objects. Methods and rationale for such differential ordering of objects and attributes are presented by example, formal development, and application.

For a genus Ω comprising n species of object there is a subset P ofn distinct prototypes. With respect to m nominated attributes, each object in Ω has an m-element characterization. Together these determine an n × m objects × attributes matrix, the rows of which are the characterizations of the prototypical objects. Over then species in Ω, an associated relative frequency vector gives the distribution of objects (and of their characterizations). The matrix and vector associate the objects in Ω with points in a metric space (P, δ); and it is with respect to various sums of distances in this attribute space that one can differentially order objects and attributes.

The definition of the distance function δ is generalized across kinds of difference, types of characterization, scale-types of measurement, Minkowski index ≧ 1, and any form of distribution of objects over species. Explanatory and taxonomic applications in psychology and other fields are discussed, with focus on classification, identification, recognition, and search. The Braille code and the identification of its characters provide illustration.

Various recent works have developed “feature” or “aspect” models of similarity and preference. These models are more concerned with the fine detail of the judgment process than were prior models, but nevertheless they have not in general developed an underlying stochastic process compatible with the assumed structure. In this paper, we show that a particular class of multivariate stochastic processes, namely those associated with the Marshall-Olkin multivariate exponential distribution, generates several of these models. In particular, such stochastic processes (appropriately interpreted) yield Tversky's elimination by aspects model, Edgell and Geisler's (normal) additive random aspects model, and Shepard and Arabie's additive cluster model.

Probabilistic models of same-different and identification judgments are compared (within each paradigm) with regard to their sensitivity to perceptual dependence or the degree to which the underlying psychological dimensions are correlated. Three same-different judgment models are compared. One is a step function or decision bound model and the other two are probabilistic variants of a similarity model proposed by Shepard. Three types of identification models are compared: decision bound models, a probabilistic multidimensional scaling model, and probabilistic models based on the Shepard-Luce choice rule. The decision bound models were found to be most sensitive to perceptual dependence, especially when there is considerable distributional overlap. The same-different model based on the city-block metric and an exponential decay similarity function, and the corresponding identification model were found to be particularly insensitive to perceptual dependence. These results suggest that if a Shepard-type similarity function accurately describes behavior, then under typical experimental conditions it should be difficult to see the effects of perceptual dependence. This result provides strong support for a perceptual independence assumption when using these models. These theoretical results may also play an important role in studying different decision rules employed at different stages of identification training.

Decision makers typically possess limited knowledge on states of the world so that use of information from past similar experiences is reasonable. This analogical thinking is formalised by case-based decision theory (CBDT). We created a novel experimental setting to validate the predictive power of CBDT versus Bayesian reasoning. Participants encountered a salient but irrelevant cue which a Bayesian decision maker is likely to ignore but a case-based decision maker may use in assessing similarity. We find that although the irrelevant similarity cue was used, the pattern in participants’ decisions is neither case-based nor Bayesian. The results suggest that CBDT does not apply in simple decision settings where similarity cues are uninformative.

A simple property of networks is used as the basis for a scaling algorithm that represents nonsymmetric proximities as network distances. The algorithm determines which vertices are directly connected by an arc and estimates the length of each arc. Network distance, defined as the minimum pathlength between vertices, is assumed to be a generalized power function of the data. The derived network structure, however, is invariant across monotonic transformations of the data. A Monte Carlo simulation and applications to eight sets of proximity data support the practical utility of the algorithm.

Many-one mappings between stimulus properties and pairwise generated similarities are intrinsic to definitions of similarity. This of itself is not sufficient as a basis for predicting the variance associated with any single similarity judgment. An extension to cover this has to be made either by making ancillary assumptions about noise, or by using nonlinear models. The derivation of the variance of similarity judgments is made from the 3Γ process in nonlinear psychophysics. The idea of separability of dimensions in metric space theories of similarity is replaced by one parameter which represents the degree of a form of interdimensional crosscoupling

Human creativity originates from brain cortical networks that are specialized in idea generation, processing, and evaluation. The concurrent verbalization of our inner thoughts during the execution of a design task enables the use of dynamic semantic networks as a tool for investigating, evaluating, and monitoring creative thought. The primary advantage of using lexical databases such as WordNet for reproducible information-theoretic quantification of convergence or divergence of design ideas in creative problem solving is the simultaneous handling of both words and meanings, which enables interpretation of the constructed dynamic semantic networks in terms of underlying functionally active brain cortical regions involved in concept comprehension and production. In this study, the quantitative dynamics of semantic measures computed with a moving time window is investigated empirically in the DTRS10 dataset with design review conversations and detected divergent thinking is shown to predict success of design ideas. Thus, dynamic semantic networks present an opportunity for real-time computer-assisted detection of critical events during creative problem solving, with the goal of employing this knowledge to artificially augment human creativity.

The question of whether extraterrestrials exist has driven both the search for extraterrestrial intelligence (SETI) and some attempts of messaging to extraterrestrial intelligence (METI). Nevertheless, no data-driven or theory-based behavioural policy has been suggested. Here we simulate a comprehensive set of human–extraterrestrial strategic interactions, modelled as two-by-two game-theoretic matrices. We examine a sample of possible outcomes by relying on the theory of subjective expected relative similarity (SERS), which takes into account both the expected payoffs and the extent of strategic similarity – the prospects of the opponent making identical choices. Simulation results suggest: focusing messaging efforts on signalling of complete strategic similarity, monitoring potential alien communications for similarity-indicating signals, and using risk-averse decision rules for policy planning and decision-making. The discussion puts forward three guidelines for METI initiatives and addresses the relevance of the findings to human conflict management.

Inclusion of a decoy alternative dominated by a target option, but not its competitor, typically leads to increased choice for the target over the competitor, known as the attraction effect. However, the reverse sometimes occurs, known as the repulsion effect. This research tested factors that moderate the repulsion effect in preferential choice scenarios with numerical attributes. Experiment 1 used a between-subjects design with a small set of consumer products and demonstrated robust repulsion effects that did not depend on the relative similarity of the decoy and target. Experiments 2 and 4 used a more powerful within-subjects design along with an expanded set of products and showed that repulsion effects were generally enhanced when the decoy and target had more similar attributes; however, the moderating effect of decoy–target similarity appeared to be fragile and sensitive to stimulus presentation factors. These findings provided mixed support for the hypothesis that the target is tainted by its proximity to the decoy. Experiments 3 and 5 tested whether the extremity of values on the attribute favoring the target moderates the repulsion effect. The results demonstrated that repulsion is more likely when all the alternatives have extremely high values on the target’s better attribute. Extremity of attribute values on the dimension favoring the target may result in a categorical assessment along that dimension and shift focus to the attribute favoring the competitor as one way to foster the repulsion effect.

The retrieval of past instances stored in memory can guide inferential choices and judgments. Yet, little process-level evidence exists that would allow a similar conclusion for preferential judgments. Recent research suggests that eye movements can trace information search in memory. During retrieval, people gaze at spatial locations associated with relevant information, even if the information is no longer present (the so-called ‘looking-at-nothing’ behavior). We examined eye movements based on the looking-at-nothing behavior to explore memory retrieval in inferential and preferential judgments. In Experiment 1, participants assessed their preference for smoothies with different ingredients, while the other half gauged another person’s preference. In Experiment 2, all participants made preferential judgments with or without instructions to respond as consistently as possible. People looked at exemplar locations in both inferential and preferential judgments, and both with and without consistency instructions. Eye movements to similar training exemplars predicted test judgments but not eye movements to dissimilar exemplars. These results suggest that people retrieve exemplar information in preferential judgments but that retrieval processes are not the sole determinant of judgments.