Biomedical subjects

T J Palmeri

Publications and source records attributed to T J Palmeri.

7 recordsLinked to original sources

PubMed1997

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.

Adult↗

PubMed1997

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.

Concept Formation↗

PubMed1997

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.

Decision Making↗

PubMed1995

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.

Humans↗

PubMed1994

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.

Adult↗

PubMed1994

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.

Adult↗

PubMed1993

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.

Adult↗