Cue utilisation reduces the impact of response bias in histopathology.

Histopathologists make diagnostic decisions that are thought to be based on pattern recognition, likely informed by cue-based associations formed in memory, a process known as cue utilisation. Typically, the cases presented to the histopathologist have already been classified as 'abnormal' by clinical examination and/or other diagnostic tests. This results in a high disease prevalence, the potential for 'abnormality priming', and a response bias leading to false positives on normal cases. This study investigated whether higher cue utilisation is associated with a reduction in positive response bias in the diagnostic decisions of histopathologists. Data were collected from eighty-two histopathologists who completed a series of demographic and experience-related questions and the histopathology edition of the Expert Intensive Skills Evaluation 2.0 (EXPERTise 2.0) to establish behavioural indicators of context-related cue utilisation. They also completed a separate, diagnostic task comprising breast histopathology images where the frequency of abnormality was manipulated to create a high disease prevalence context for diagnostic decisions relating to normal tissue. Participants were assigned to higher or lower cue utilisation groups based on their performance on EXPERTise 2.0. When the effects of experience were controlled, higher cue utilisation was specifically associated with a greater accuracy classifying normal images, recording a lower positive response bias. This study suggests that cue utilisation may play a protective role against response biases in histopathology settings.

Dynamics of saccade target selection: race model analysis of double step and search step saccade production in human and macaque.

We investigated how saccade target selection by humans and macaque monkeys reacts to unexpected changes of the image. This was explored using double step and search step tasks in which a target, presented alone or as a singleton in a visual search array, steps to a different location on infrequent, random trials. We report that human and macaque monkey performance are qualitatively indistinguishable. Performance is stochastic with the probability of producing a compensated saccade to the final target location decreasing with the delay of the step. Compensated saccades to the final target location are produced with latencies relative to the step that are comparable to or less than the average latency of saccades on trials with no target step. Noncompensated errors to the initial target location are produced with latencies less than the average latency of saccades on trials with no target step. Noncompensated saccades to the initial target location are followed by corrective saccades to the final target location following an intersaccade interval that decreases with the interval between the target step and the initiation of the noncompensated saccade. We show that this pattern of results cannot be accounted for by a race between two stochastically independent processes producing the saccade to the initial target location and another process producing the saccade to the final target location. However, performance can be accounted for by a race between three stochastically independent processes--a GO process producing the saccade to the initial target location, a STOP process interrupting that GO process, and another GO process producing the saccade to the final target location. Furthermore, if the STOP process and second GO process start at the same time, then the model can account for the incidence and latency of mid-flight corrections and rapid corrective saccades. This model provides a computational account of saccade production when the image changes unexpectedly.

Central tendencies, extreme points, and prototype enhancement effects in ill-defined perceptual categorization.

In three perceptual classification experiments involving ill-defined category structures, extreme prototype enhancement effects were observed in which prototypes were classified more accurately than other category instances. Such empirical findings can prove theoretically challenging to exemplar-based models of categorization if prototypes are psychological central tendencies of category instances. We found instead that category prototypes were sometimes better characterized as psychological extreme points relative to contrast categories. Extending a classic and widely cited study (Posner & Keele, 1968), participants learned categories created from distortions of dot patterns arranged in familiar shapes. Participants then made pairwise similarity judgments of the patterns. Multidimensional scaling (MDS) analyses of the similarity data revealed the prototypes to be psychological extreme points, not central tendencies. Evidence for extreme point representations was also found for novel prototype patterns displaying a symmetry structure and for prototypes of grid patterns used in recent studies by McLaren and colleagues (McLaren, Bennet, Guttman-Nahir, Kim, & Mackintosh, 1995). When used in combination with the derived MDS solutions, an exemplar-based model of categorization, the Generalized Context Model (Nosofsky, 1986), provided good fits to the observed categorization data in all three experiments.

The role of background knowledge in speeded perceptual categorization.

We examined the time-course of the influence of background knowledge on perceptual categorization by manipulating the meaningfulness of labels associated with categories and by manipulating the amount of time provided to subjects for making a categorization decision. Extending a paradigm originally reported by Wisniewski and Medin (1994) (Cog. Sci. 18 (1994) 221), subjects learned two categories of children's drawings that were given either meaningless labels (drawings by children from 'group 1' or 'group 2') or meaningful labels (drawings by 'creative' or 'non-creative' children); the meaningfulness of the label had a significant effect on how new drawings were classified. In addition, half of the subjects were provided unlimited time to respond, while the other half of the subjects were forced to respond quickly; speeded response conditions had a relatively large effect on categorization decisions by subjects given the meaningless labels but had relatively little effect on categorization decisions by subjects given the meaningful labels. These results suggest that some forms of background knowledge can show an influence at relatively early stages in the time-course of a categorization decision.

Learning categories at different hierarchical levels: a comparison of category learning models.

Three formal models of category learning, the rational model (Anderson, 1990), the configural-cue model (Gluck & Bower, 1988a), and ALCOVE (Kruschke, 1992), were evaluated on their ability to account for differential learning of hierarchically structured categories. An experiment using a theoretically challenging category structure developed by Lassaline, Wisniewski, and Medin (1992) is reported. Subjects learned one of two different category structures. For one structure, diagnostic information was present along a single dimension (1-D). For the other structure, diagnostic information was distributed across four dimensions (4-D). Subjects learned these categories at a general or at a specific level of abstraction. For the 1-D structure, specific-level categories were learned more rapidly than general-level categories. For the 4-D structure, the opposite result was observed. These results proved highly diagnostic for evaluating the models--although ALCOVE provided a good account of the observed results, the rational model and the configural-cue model did not.

Comparing exemplar-retrieval and decision-bound models of speeded perceptual classification.

The authors compared the exemplar-based random-walk (EBRW) model of Nosofsky and Palmeri (1997) and the decision-bound model (DBM) of Ashby and Maddox (1994; Maddox & Ashby, 1996) on their ability to predict performance in Garner's (1974) speeded classification tasks. A key question was the extent to which the models could predict facilitation in the correlated task and interference in the filtering task, in situations involving integral-dimension stimuli. To obtain rigorous constraints for model evaluation, the goal was to fit the detailed structure of the response time (RT) distribution data associated with each individual stimulus in each task. Both models yielded reasonably good global quantitative fits to the RT distribution and accuracy data. However, the DBM failed to properly characterize the interference effects in the filtering task. Apparently, a fundamental limitation of the DBM is that it predicts that the fastest RTs in the filtering task should be faster than the fastest RTs in the control task, whereas the opposite pattern was observed in our data.

An exemplar-based random walk model of speeded classification.

The authors propose and test an exemplar-based random walk model for predicting response times in tasks of speeded, multidimensional perceptual classification. The model combines elements of R. M. Nosofsky's (1986) generalized context model of categorization and G. D. Logan's (1988) instance-based model of automaticity. In the model, exemplars race among one another to be retrieved from memory, with rates determined by their similarity to test items. The retrieved exemplars provide incremental information that enters into a random walk process for making classification decisions. The model predicts correctly effects of within- and between-categories similarity, individual-object familiarity, and extended practice on classification response times. It also builds bridges between the domains of categorization and automaticity.

Exemplar similarity and the development of automaticity.

Effects of exemplar similarity on the development of automaticity were investigated with a task in which participants judged the numerosity of random patterns of between 6 and 11 dots. After several days of training, response times were the same at all levels of numerosity, signaling the development of automaticity. In Experiment 1, response times to new patterns were a function of their similarity to old patterns. In Experiment 2, responses to patterns with high within-category similarity became automatized more quickly than responses to patterns with low within-category similarity. In Experiment 3, responses to patterns with high between-category similarity became automatized more slowly than responses to patterns with low between-category similarity. A new theory, the exemplar-based random walk (EBRW) model, was used to explain the results. Combining elements of G. D. Logan's (1988) instance theory of automaticity and R. M. Nosofsky's (1986) generalized context model of categorization, the theory embeds a dynamic similarity-based memory retrieval mechanism within a competitive random walk decision process.

Learning to classify integral-dimension stimuli.

The authors tested 288 participants in the classic category-learning tasks introduced by Shepard, Hovland, and Jenkins (1961). However, separable-dimension stimuli were used in previous tests, whereas integral-dimension stimuli were used in the present study. In contrast to previous results, which showed a superiority for Problem Type II over Problem Types III, IV, and V, the reverse pattern was observed in the present research. This result confirms a fundamental prediction made by modern exemplar-based models of classification learning. The results are interpreted in terms of the extent to which selective-attention learning mechanisms operate when separable-dimension versus integral-dimension stimuli are used.

Recognition memory for exceptions to the category rule.

Experiments were conducted to demonstrate the utility of a rule-plus-exception model for extending current exemplar-based views of categorization and recognition memory. According to the model, exemplars that are exceptions to category rules have a special status in memory relative to other old items. In each of 4 experiments, participants first learned to categorize items organized into 2 ill-defined categories and then made old-new recognition judgments. Although the categories afforded no perfect rules, a variety of imperfect rules could be formed combined with memorization of exceptions to those rules. In each experiment, superior recognition of exceptions to imperfect logical rules was found. In addition, participants demonstrated better memory for old exemplars than new ones. A mixed model, which assumed a combination of rule-plus-exception processing and residual exemplar storage, provided good quantitative accounts of the data.

Comparing models of rule-based classification learning: a replication and extension of Shepard, Hovland, and Jenkins (1961).

We partially replicate and extend Shepard, Hovland, and Jenkins's (1961) classic study of task difficulty for learning six fundamental types of rule-based categorization problems. Our main results mirrored those of Shepard et al., with the ordering of task difficulty being the same as in the original study. A much richer data set was collected, however, which enabled the generation of block-by-block learning curves suitable for quantitative fitting. Four current computational models of classification learning were fitted to the learning data: ALCOVE (Kruschke, 1992), the rational model (Anderson, 1991), the configural-cue model (Gluck & Bower, 1988b), and an extended version of the configural-cue model with dimensionalized, adaptive learning rate mechanisms. Although all of the models captured important qualitative aspects of the learning data, ALCOVE provided the best overall quantitative fit. The results suggest the need to incorporate some form of selective attention to dimensions in category-learning models based on stimulus generalization and cue conditioning.

Rule-plus-exception model of classification learning.

The authors propose a rule-plus-exception model (RULEX) of classification learning. According to RULEX, people learn to classify objects by forming simple logical rules and remembering occasional exceptions to those rules. Because the learning process in RULEX is stochastic, the model predicts that individual Ss will vary greatly in the particular rules that are formed and the exceptions that are stored. Averaged classification data are presumed to represent mixtures of these highly idiosyncratic rules and exceptions. RULEX accounts for numerous fundamental classification phenomena, including prototype and specific exemplar effects, sensitivity to correlational information, difficulty of learning linearly separable versus nonlinearly separable categories, selective attention effects, and difficulty of learning concepts with rules of differing complexity. RULEX also predicts distributions of generalization patterns observed at the individual subject level.

Episodic encoding of voice attributes and recognition memory for spoken words.

Recognition memory for spoken words was investigated with a continuous recognition memory task. Independent variables were number of intervening words (lag) between initial and subsequent presentations of a word, total number of talkers in the stimulus set, and whether words were repeated in the same voice or a different voice. In Experiment 1, recognition judgements were based on word identity alone. Same-voice repetitions were recognized more quickly and accurately than different-voice repetitions at all values of lag and at all levels of talker variability. In Experiment 2, recognition judgments were based on both word identity and voice identity. Subjects recognized repeated voices quite accurately. Gender of the talker affected voice recognition but not item recognition. These results suggest that detailed information about a talker's voice is retained in long-term episodic memory representations of spoken words.