The Role of Familiarity, Priming and Perception in Similarity Judgments

The Role of Familiarity, Priming and Perception in Similarity Judgments Laura M. Hiatt and J. Gregory Trafton U.S. Naval Research Laboratory Washington, DC 20375 USA {laura.hiatt, greg.trafton}@nrl.navy. mil Abstract no clear prototype). The experiment showed a striking asym- metry in similarity judgments between the different colors when they were presented with different frequencies during an irrelevant training task: colors which had been trained on less often were considered more similar to colors which had been trained on more often than the other way around. To account for these low-level results, Polk et al. (2002) imple- mented a neural network which simulated the asymmetry by measuring the ease with which the network switches between different activation patterns; those that are more stable (e.g., high-frequency patterns) were easier to assimilate to. In our approach, we match the human subject data from Polk et al. (2002) while attempting to address three additional points. First, there was a second significant effect, that the ratings in general increased over time, that the above models do not address. Second, we wanted our approach to provide explicit cognitive processes for similarity ratings. Third, we believe that inherent perceptual similarity also plays a part in these types of similarity judgments (e.g., purple is inherently more similar to blue than to orange) (Smith & Heise, 1992). To this end, we begin our approach with the cognitive ar- chitecture ACT-R (Adaptive Characterization of Thought – Rational) (Anderson, 2007). Using ACT-R, we account for similarity judgments by considering three values provided a priori by the architecture. The first, familiarity, is represented as a base-level activation value of a concept, which represents its frequency and recency of use. The second, priming, is based on spreading activation, which disperses activation be- tween different, associated concepts in declarative memory (Anderson, 1983; Harrison & Trafton, 2010). In addition, we utilize an extension to ACT-R which provides it with a cal- culation for measuring color similarity (Breslow, Ratwani, & Trafton, 2009; Breslow, Trafton, & Ratwani, 2009). Our model starts without any pre-existing declarative knowledge or network structures; all knowledge is created during the experiment. Over time, our cognitive model builds a network of concepts (e.g., color blocks) by learning associ- ations between them in the form of subsymbolic connections between their representations in declarative knowledge. Dur- ing each similarity judgment, the model combines its mea- sure of perceptual similarity with base-level and spreading activation to determine its response. On the first trial of an experiment, there is no spreading activation since there is no declarative knowledge and so the judgment is based purely on base-level activation and perceptual similarity; however, over time the model builds up declarative memories that may contribute to spreading activation in later trials. This explains the two main effects found in Polk et al.’s experiment. Dur- We present a novel way of accounting for similarity judgments. Our approach posits that similarity ratings stem from three main sources: familiarity, priming, and inherent perceptual similarity. We present a process model of our approach in the cognitive architecture ACT-R, and match our model’s predic- tions to data collected from a human subject experiment which involved simple perceptual stimuli. Familiarity accounts for rising ratings over time; priming accounts for asymmetric ef- fects that arise when the stimuli are shown with different fre- quencies. Pure perceptual similarity also predicts trends in the results. Overall, our model matched the data with R 2 of 0.99. Introduction Similarity is a critical and pervasive part of human cogni- tion (Medin, Goldstone, & Gentner, 1993). Similarity mea- sures are integral to object categorization and classification (Nosofsky, 1992). Similarity is also pervasive in problem solving (Novick, 1990), decision-making (Medin, Goldstone, & Markman, 1995), and memory (Roediger, 1990). As with many aspects of human cognition, however, the mechanisms that determine similarity are not yet fully understood. Var- ious theories abound, with none yet able to capture enough different types of situations to be called the winner (Rorissa, One interesting result in this field is asymmetries that have been shown to arise when making similarity judgments, even of very simple perceptual stimuli (Tversky, 1977; Rosch, 1975). Rosch (1975) argued that such similarity is based on mapping stimuli onto one another and, intuitively, non- prototypical stimuli map more easily onto prototypical stim- uli than vice versa. Tversky (1977) argued that is due to weighted feature matching, where the salience of features in the current context determines their weight; others agree with this thought in general (Medin et al., 1993; Glucksberg & Keysar, 1990). These two explanations, however, assume that either there is a clear prototype inherently present in the experiment (such as the more perceptually complex stimulus), or that stim- uli have various features which have a clear inherent or- der of cognitive preference and saliency (such as symmetry). They do not, however, provide any concrete explanations for why complexity or symmetry may lead to prototypicality or saliency. Polk, Behensky, Gonzalez, and Smith (2002) shed light on the situation by presenting an experiment that avoids the question entirely by using perceptual stimuli where the only feature was color (so there are no features to comparatively weigh), and where the color hues are fairly similar (so there is