The bow and sequential effects in absolute identification

The bow and sequential effects in absolute identification are investigated in this paper by following two strategies: (1) Experiments are performed in which sequential dependencies in signal presentations are manipulated, and 12) analyses are conducted (some of which are largely free of model-specific assumptions) which bear directly on the question of the origin of the sequential effects. The main result of the study is that absolute identification performance is greatly improved in a design in which each signal lies close to the preceding signal presented, even though the entire range of signals used is the same as in a random presentation design. This finding is consistent with the attention-band model of Luce, Green, and Weber (1976) and rejects hypotheses that suggest that the variability in the signal representation in absolute identification is a function solely of the range of signals being used. However, nonparametric analyses of sequential response errors show that a plausible assumption concerning the trial by-trial movement of the attention band provides an incomplete explanation of Seluential effects in absolute identification. These results are far better explained in terms of systematic shifts of category boundaries in a Thurstonian model, as suggested by Purks, Callahan, Braida, and Durlach (1980). Experiments are also performed which suggest that memory decay is not the major factor accounting for the bow effect in absolute identification.     

Information integration and the identification of stimulus noise and criterial noise in absolute judgment

Two main classes of theories have been proposed regarding range effects in unidimensional absolute-identification tasks. One class posits that as range is increased, criterial noise increases but stimulus noise remains constant. Another class posits increasing stimulus noise but constant criterial noise. In this study, an effort is made to help decide this issue. Multiple observations are used in several absolute-identification tasks of varying range. A stimulus integration model is proposed in which averaging takes place over stimulus internal representations, thereby reducing stimulus variance; on the other hand, it is assumed that criterial variance is unaffected by the number of observations. The model allows one to identify the relative amounts of stimulus noise and criterial noise inherent in observers' recognition judgments. The model yields good fits to data in several experiments, and it is concluded that both stimulus noise and criterial noise increase as range in the absolute-identification task is increased.     

Typicality in logically defined categories: Exemplar-similarity versus rule instantiation

A rule-instantiation model and a similarity-to-exemplars model were contrasted in terms of their predictions of typicality judgments and speeded classifications for members of logically defined categories. In Experiment 1, subjects learned a unidimensional rule based on the size of objects. It was assumed that items that maximally instantiated the rule were those farthest from the category boundary that separated small and large stimuli. In Experiment 2, subjects learned a disjunctive rule of the form "x or y or both". It was assumed that items that maximally instantiated the rule were those with both positive values (x and y). In both experiments, the frequency with which different exemplars were presented during classification learning was manipulated across conditions. These frequency manipulations exerted a major impact on subjects' postacquisition goodness-of-example judgments, and they also influenced reaction times in a speeded classification task. The results could not be predicted solely on the basis of the degree to which the rules were instantiated. The goodness judgments were predicted fairly well by a mixed exemplar model involving both relative-similarity and absolute-similarity components. It was concluded that even for logically defined concepts, stored exemplars may form a major component of the category representation.     

Attention and learning processes in the identification and categorization of integral stimuli

The relationship between subjects' identification and categorization learning of integral-dimension stimuli was studied within the framework of an exemplar-based generalization model. The model was used to predict subjects' learning in six different categorization conditions on the basis of data obtained in a single identification learning condition. A crucial assumption in the model is that because of selective attention to component dimensions, similarity relations may change in systematic ways across different experimental contexts. The theoretical analysis provided evidence that, at least under unspeeded conditions, selective attention may play a critical role in determining the identification-categorization relationship for integral stimuli. Evidence was also provided that similarity among exemplars decreased as a function of identification learning. Various alternative classification models, including prototype, multiple-prototype, average distance, and "value-on-dimensions" models, were unable to account for the results.     

Recognizing distinctive faces: A hybrid-similarity exemplar model account

In recognition memory experiments, Nosofsky and Zaki (2003) found that adding discrete distinctive features to continuous-dimension color stimuli helped participants to identify old items as old (the old-item distinctiveness effect), as well as to identify new items as new. The present study tests the extent to which these results generalize to the domain of face recognition. Two experiments were conducted, one using artificial faces and one using natural faces. Artificial faces were used to test memory for faces with discrete distinctive features while controlling the similarity of the faces themselves on more continuous dimensions. The natural-face experiment used the faces of 40 bald men categorized into three groups (typical, isolated, and distinctive) based on experimental ratings of distinctiveness. In both experiments, there were strong effects of the distinctive features on recognition performance. The data were accounted for reasonably well by a hybrid-similarity version of an exemplar recognition model (Nosofsky and Zaki, 2003), which includes a feature-matching mechanism that can provide boosts to an item's self-similarity.     

Speeded classification in a probabilistic category structure: contrasting exemplar-retrieval, decision-boundary, and prototype models

Speeded perceptual classification experiments were conducted to distinguish among the predictions of exemplar-retrieval, decision-boundary, and prototype models. The key manipulation was that across conditions, individual stimuli received either probabilistic or deterministic category feedback. Regardless of the probabilistic feedback, however, an ideal observer would always classify the stimuli by using an identical linear decision boundary. Subjects classified the probabilistic stimuli with lower accuracy and longer response times than they classified the deterministic stimuli. These results are in accord with the predictions of the exemplar model and challenge the predictions of the prototype and decision-boundary models.     

The structure of short-term memory scanning: an investigation using response time distribution models

A classic question in cognitive psychology concerns the nature of memory search in short-term recognition. Despite its long history of investigation, however, there is still no consensus on whether memory search takes place serially or in parallel or is based on global access. In the present investigation, we formalize a variety of models designed to account for detailed response time distribution data in the classic Sternberg (Science 153: 652-654, 1966) memory-scanning task. The models vary in their mental architectures (serial exhaustive, parallel self-terminating, and global access). Furthermore, the component processes within the architectures that make match/mismatch decisions are formalized as linear ballistic accumulators (LBAs). In fast presentation rate conditions, the parallel and global access models provide far better accounts of the data than does the serial model. LBA drift rates are found to depend almost solely on the lag between study items and test probes, whereas response thresholds change with memory set size. Under slow presentation rate conditions, even simple versions of the serial-exhaustive model provide accounts of the data that are as good as those of the parallel and global access models. We provide alternative interpretations of the results in our General Discussion.     

A power-law model of psychological memory strength in short- and long-term recognition

A classic law of cognition is that forgetting curves are closely approximated by power functions. This law describes relations between different empirical dependent variables and the retention interval, and the precise form of the functional relation depends on the scale used to measure each variable. In the research reported here, we conducted a recognition task involving both short- and long-term probes. We discovered that formal memory-strength parameters from an exemplar-recognition model closely followed a power function of the lag between studied items and a test probe. The model accounted for rich sets of response time (RT) data at both individual-subject and individual-lag levels. Because memory strengths were derived from model fits to choices and RTs from individual trials, the psychological power law was independent of the scale used to summarize the forgetting functions. Alternative models that assumed different functional relations or posited a separate fixed-strength working memory store fared considerably worse than the power-law model did in predicting the data.     

Landscaping analyses of the ROC predictions of discrete-slots and signal-detection models of visual working memory

A fundamental issue concerning visual working memory is whether its capacity limits are better characterized in terms of a limited number of discrete slots (DSs) or a limited amount of a shared continuous resource. Rouder et al. (2008) found that a mixed-attention, fixed-capacity, DS model provided the best explanation of behavior in a change detection task, outperforming alternative continuous signal detection theory (SDT) models. Here, we extend their analysis in two ways: first, with experiments aimed at better distinguishing between the predictions of the DS and SDT models, and second, using a model-based analysis technique called landscaping, in which the functional-form complexity of the models is taken into account. We find that the balance of evidence supports a DS account of behavior in change detection tasks but that the SDT model is best when the visual displays always consist of the same number of items. In our General Discussion section, we outline, but ultimately reject, a number of potential explanations for the observed pattern of results. We finish by describing future research that is needed to pinpoint the basis for this observed pattern of results.     

Exemplar and prototype models revisited: response strategies,selective attention,and stimulus generalization

J. D. Smith and colleagues (J. P. Minda & J. D. Smith, 2001; J. D. Smith & J. P. Minda, 1998,2000; J. D. Smith, M. J. Murray, & J. P. Minda, 1997) presented evidence that they claimed challenged the predictions of exemplar models and that supported prototype models. In the authors' view, this evidence confounded the issue of the nature of the category representation with the type of response rule (probabilistic vs. deterministic) that was used. Also, their designs did not test whether the prototype models correctly predicted generalization performance. The present work demonstrates that an exemplar model that includes a response-scaling mechanism provides a natural account of all of Smith et al.'s experimental results. Furthermore, the exemplar model predicts classification performance better than the prototype models when novel transfer stimuli are included in the experimental designs.