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Friday Noon Posters 2035–2040 ity judgment. To avoid some of the confounds in such designs, we compared a yes/no comprehension question-answering task (Experiment 1) with an overt acceptability judgment task (Experiment 2), measuring BOLD signal change in event-related fMRI. Both experiments used the same stimuli, consisting of sentence triples in which the matrix verb’s direct object varied in separately rated plausibility, as in “Vanessa threw the javelin/feather/situation but did not win the competition.” Comparing across three plausibility levels permitted isolation of a single left frontal cortical region sensitive to differences among all conditions, and this region’s response interacted with task. Additional correlational analyses revealed differential plausibility sensitivity of this region within different plausibility levels. (2035) Frequency and Context Cues in Ambiguity Resolution As a Function of Working Memory Capacity. LOUISE A. STANCZAK & GLORIA S. WATERS, Boston University, NEAL J. PEARLMUTTER, Northeastern University, & DAVID N. CAPLAN, Massachusetts General Hospital (sponsored by Gloria S. Waters)—Previous studies have investigated the contributions of frequency and context information during sentence comprehension to resolve ambiguities. The interpretation of a homograph (bank) can be affected by the frequency of the meaning and the preceding sentential context. Parsing the direct object (DO)/sentential complement (SC) ambiguity (She acknowledged the shirt was dirty) can be affected by the frequency with which a DO/SC follows a particular verb and the plausibility of the noun following the verb. Some working memory theories postulate that highspan participants resolve lexical/syntactic ambiguities more efficiently than do low-span participants, while other theories do not. Differences may lie within frequency and/or context cues during offline and/or online processing. We found no offline differences in sensitivity to frequency cues (homograph and verb bias) or context cues (preceding context and plausibility ratings) as a function of working memory. (2036) Rules, Heuristics, and Language Working Memory During Sentence Processing. MANUEL MARTIN-LOECHES, Center UCM-ISCIII for Human Evolution and Behavior, RASHA ABDEL-RAHMAN, Humboldt University, Berlin, PILAR CASADO & ANNETTE HOHLFELD, Center UCM-ISCIII for Human Evolution and Behavior, & ANNEKATHRIN SCHACHT & WERNER SOMMER, Humboldt University, Berlin—How information contained in a second task affects semantic and syntactic processing during sentence comprehension in a primary task was studied using event-related potentials in 32 participants. In one condition, material in the second task could be semantically congruent or incongruent relative to the adjective in the sentence, the latter being either semantically correct or incorrect relative to the sentence. Homologous syntactic (gender) manipulations were performed in another condition. Whereas syntactic processing appeared blind to the syntactic content of the second task, semantically incongruous material of the second task induced fluctuations typically associated with the detection of within-sentence semantic anomalies (N400) even in semantically correct sentence adjectives. Semantically incongruous material also influenced later stages of the processing of incorrect adjectives. The data add to recent discussions on an algorithmic semantic subsystem relevant for the syntactic structure, and support independent working memory systems for semantic and syntactic information separately. (2037) The Effect of Distractive Elaboration on the Long-Term Memory of Predictive Inferences. SYREETA A. JONES & QUN GUAN, Florida State University (sponsored by Wanjin Meng)—The study is supported by the grant of the second author. The research question was: What are the effects of text elaboration and text predictability on the online activation and long-term memory of the predictive inference (PI)? The text elaboration is distractive in nature, suppressing the activation of 74 the primary PI. Participants include 39 English-native-speaking college students. A 2 (high- vs. low-elaboration) � 2 (inference vs. control) repeated measure design was conducted. The online PI activation was assessed by naming task. Long-term PI was assessed by cued recall task. The results showed that PIs represented by quicker (within 500 msec) naming latencies were recalled better than those represented by longer (beyond 500 msec) naming latencies. The higher the level of distractive elaboration in the text, the lower the level of online activation of PI, but the higher the probability of the offline longterm recall of PI. The results were interpreted in terms of the constructivist view of text comprehension. (2038) Representing Political Concepts in High-Dimensional Semantic Space. CURT BURGESS, CHAD MURPHY, MARTIN JOHNSON, & SHAUN BOWLER, University of California, Riverside, & CATHER- INE H. DECKER, Chaffey College—Political science and cognitive science share a long history and interest in the empirical study of meaning. The field of cognitive science has seen a dramatic increase in the ability to computationally model meaning in memory and language over the last 15 years. A substantial difference between the concepts of interest between the two fields is that political semantics tend to be more abstract and related to cultural and social influences. We describe a research program that applies the HAL model to political relationships related to persuasion, characteristics of campaigns, ambiguity in language, and ideological and cultural differences. It is argued that the high-dimensional theoretical approach to modeling meaning integrates political, cognitive, and social phenomena into a unifying framework. • CATEGORY LEARNING • (2039) Prior Knowledge and Nonminimal Category Learning: Experimental and Modeling Results. HARLAN D. HARRIS, New York University, AARON B. HOFFMAN, University of Texas, Austin, & GREGORY L. MURPHY, New York University—Minimal-learning models of category learning predict that subjects learning categories with many stimulus dimensions should either learn a few dimensions well or all dimensions moderately. We performed a study of the combined influence of dimensionality and prior knowledge on category learning. Subjects learned structures with varying numbers of redundant dimensions, and with either knowledge-related or knowledgeunrelated features. Despite similar learning rates and reaction times, subjects learned more dimensions when more were present, and prior knowledge further increased what was learned. A second experiment confirmed that the participants did in fact learn the additional features rather than simply inferring the correct answer at test. Computational modeling showed how top-down resonance of activation in the KRES model of category learning (Rehder & Murphy, 2003) can account for the results, whereas models that assume independent contributions from each dimension cannot. We conclude that both high dimensionality and prior knowledge yield nonminimal learning. (2040) Similarity Detection in Incidental Category Learning. JOHN P. CLAPPER, California State University, San Bernardino—A major limitation of failure-driven or contrast-based models of incidental category learning is that they can only discover new categories in response to stimuli that fall outside the boundaries of existing categories. Thus, they cannot identify clusters of similar objects within an existing category and use them to create new subcategories. Here, members of a category with several overlapping properties were presented in an immediate-memory task with an equal number of stimuli composed of random feature combinations. In the diagnostic-label condition, members shared a common label; in the nondiagnostic condition, they did not. Participants were more likely to discover the category and learn its consistent features in the diagnostic condition. Im-
Posters 2041–2047 Friday Noon portantly, this facilitation could only have occurred if people noticed the shared label, an example of category discovery via “similarity detection” as opposed to contrast or “difference detection.” This ability might compensate for the limitations of a purely failure-driven approach. (2041) Motivational Focus Interacts With Reward Structure in Category Learning. RUBY T. NADLER, JOHN PAUL MINDA, & LILY PEI- SHIUAN LIN, University of Western Ontario—Recent research suggests that categories are learned by multiple brain systems, and that situations that allow for greater cognitive flexibility will lead to better performance on rule-described categories that depend on an explicit system. We examined the effects of motivational focus (e.g., promotion or prevention) and task reward structure (e.g., gain or loss) on participants’ ability to learn suboptimal or optimal rules. Two conditions featured a motivational focus that matched the reward structure of the task (promotion/gain and prevention/loss). Two conditions featured a motivation structure that did not match the reward structure of the task (promotion/loss and prevention/gain). We found that performance was generally better in the matching conditions than in the mismatching conditions and that participants in the matching conditions were more likely to learn complex, optimal rules. These results suggest that the matching conditions allow for greater cognitive flexibility and support a multiple-systems account of category learning. (2042) The Incidental Learning of Within-Category Attribute Correlations in a One-Attribute Rule Visual Search Classification Paradigm. GUY L. LACROIX, Carleton University, GYSLAIN GIGUÈRE, University of Quebec, Montreal, GLEN HOWELL, Carleton University, & SERGE LAROCHELLE, University of Montreal—Giguère, Lacroix, and Larochelle (2007) showed that participants could incidentally learn within-category attribute correlations in a one-attribute rule classification task. Nevertheless, learning was limited. In this study, participants categorized stimuli in a one-attribute rule visual search classification paradigm. We hypothesized that the search component would increase learning. Two experiments were conducted. Both involved a classification training phase of 640 trials. The stimuli were six-shape displays that included a rule attribute and five diagnostic attributes. In Experiment 1, the rule attribute (and up to two other attributes) were removed at transfer. The results showed that several attributes (color, texture, and size) of varying diagnosticity were used to correctly classify the stimuli. In Experiment 2, attribute values were changed at transfer. Slower RTs were obtained when attribute values from conflicting categories were used. These experiments provide evidence that within-category attribute correlations can be learned in a classification task without inference learning instructions. (2043) New Category Representations Are Biased by Implicit Perceptions. MICHAEL E. ROBERTS & ROBERT L. GOLDSTONE, Indiana University—Our study examines the effects of implicit perceptual representations when a participant first learns a category. Participants sequentially viewed pictures of two category members for familiar categories (e.g., dog, cow, airplane, etc.) and novel categories (artificial stimuli), and the second, primed member was followed by either a relevant or irrelevant story. After training on all categories in a set, participants completed a delayed matching test that showed a category label and then a picture of a category member or a member from another category. Participants responded significantly faster and more accurately for trials involving familiar categories, and we attribute this enhancement to well-learned category representations. However, for the novel category set, participants responded significantly faster to primed members that had been followed by relevant stories. This result suggests that participants perceptually invoked the primed member representation while reading the story, and that these automatic invocations biased the new category representation. 75 (2044) Contribution of the Declarative Memory System to Probabilistic Category Learning. JULIE SPICER, Columbia University, MURRAY GROSSMAN, University of Pennsylvania, & EDWARD E. SMITH, Columbia University—The multiple memory systems approach posits that different forms of knowledge are acquired and stored in qualitatively different ways. Evidence has suggested that probabilistic category learning, as assessed with the weather prediction (WP) task, can be supported by two systems: a system that acquires nondeclarative habit knowledge and a system that acquires declarative flexible knowledge. One focus of recent research has been to characterize the mechanisms through which each system contributes to behavior as information is acquired in parallel. In the present study, we have further characterized the contribution of the declarative system to WP learning. We show that individual differences in working memory relate positively to categorization accuracy and that patients with Alzheimer’s Disease do not reach an above-chance level of categorization accuracy, supporting a strong role for declarative learning in the WP task. Implications for the role of nondeclarative learning are also discussed. (2045) Category Learning, Binding, and the Medial Temporal Lobe: Evidence From Early Alzheimer’s Patients. JARED X. VAN SNELLEN- BERG & JANET METCALFE, Columbia University, MURRAY GROSSMAN, University of Pennsylvania, & EDWARD E. SMITH, Columbia University (sponsored by Edward E. Smith)—Despite considerable impairments in explicit memory, patients with medial temporal lobe (MTL) lesions and patients with Alzheimer’s disease (AD) have been shown to have intact learning in a number of implicit learning paradigms, including category learning. Some recent evidence, however, suggests that “explicitness” per se may not determine the integrity of learning in individuals with a compromised MTL. One alternative is that a “binding” process, in which distinct elements of a stimulus or event become associated in memory, is critically mediated by MTL. In a test of this hypothesis, we showed that AD patients are at chance performance, and significantly worse than control participants, on an implicit two-category learning task that requires binding for successful performance. (2046) Modeling Process Differences in Implicit and Explicit Category Learning: A Symbolic-Connectionist Approach. LEONIDAS A. A. DOUMAS, Indiana University, & ROBERT G. MORRISON, Northwestern University—Much experimental evidence suggests both implicit (i.e., feature-based) and explicit (i.e., rule-based) mechanisms for learning categories, and these systems may compete and/or interact in different circumstances. However, most computational attempts to capture these processes typically rely on very different representational assumptions. For instance, models of implicit category learning are typically based on connectionist architectures consisting of networks of distributed units. In contrast, models of explicit category learning typically employ some type of symbolic architecture allowing for the flexibility characteristic of categories learned via rules. We present a model of category learning, DORA, that uses different learning mechanisms on a single distributed representation, allowing us to account for both implicit and explicit learning from a visual category learning task (Maddox, Ashby, & Bohil, 2003). We also successfully simulate differential working memory effects on implicit and explicit versions of this task as reported by Zeithamova and Maddox (2006). • ANIMAL COGNITION • (2047) Rats’ Memory for Event Duration: Effect of Postsample Cues to Remember and Forget. ANGELO SANTI, NEIL MCMILLAN, & PATRICK VAN ROOYEN, Wilfrid Laurier University—Rats were trained to discriminate 2 sec versus 8 sec of magazine light illumination by responding to either a stationary lever or a moving lever. Dur-
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