Overview: Lab philosophyResearch in our lab seeks to understand the basic mechanisms of cognitive and perceptual processing, particularly in the domain of spoken language. We focus on spoken language because it lies at the intersection of basic perceptual and higher-level cognitive processing – drawing on both and elucidating the core principles that span all of cognitive function. Our research follows three core tenets: 1. Show me the model. A verbal account can be taken to predict (or not predict) just about any observed pattern, especially when the account posits complex interactions between multiple processes. We use computational models to concretely instantiate our verbal theories so they can be tested and used to make new predictions. 2. The same and not the same. No two people have exactly the same cognitive system. How individuals differ provides unique insights into cognitive processes. We study individual differences in their many and varied forms: differences among typical college students, differences due to normal development and aging, differences induced by neurological damage such as stroke, and atypical development such as autism spectrum disorders. (This tenet’s title is borrowed from Dr. Roald Hoffmann’s wonderful book, we hope he doesn’t mind.) 3. Methods for the madness. When the only tool you have is a hammer, every problem looks like a nail. We try to improve and expand the set of tools available to cognitive neuroscientists and neuropsychologists. This includes statistical methods for analysis of time series data (GCA), a large web-based database to facilitate the use of cognitive neuropsychological methods in the study of language and cognition (MAPPD), and many tutorials.Current projects apply these tenets to several inter-related research projects: Processing and representation of semantic knowledgeThere are two kinds of things we know about (object) concepts: their features, which tell us taxonomic information about the object (that an apple is a fruit, that it is the same kind of thing as a pear or a peach, that it is round and edible, etc.), and the events or situations in which they participate, which tell us thematic information about the object (that apples grow on trees and ripen in the fall, that they can be baked into pies or made into cider, that they sometimes have worms inside, etc.). Our previous studies have shown that feature-based similarity predicts the degree of activation during word recognition (Mirman & Magnuson, 2009b) and that distinctive semantic features (ones that are relatively unusual) play a particularly important role in processing of word meanings (Mirman & Magnuson, 2009a). More recently, we have found that taxonomic and thematic semantic knowledge are functionally (Mirman & Graziano, in press; Kalenine et al., under review) and neuroanatomically distinct, with the anterior temporal lobe playing a particularly important role in taxonomic semantics and the temporo-parietal cortex playing a particularly important role in thematic semantics (Schwartz et al., 2011; Mirman & Graziano, in preparation). We are delving deeper into this distinction to try to understand its functional basis and the reason for the apparent neuroanatomic specialization. Competition and cooperation among co-activated representations
One of the most widely-held basic principles of cognitive processing is that during a task (recognition, categorization, memory retrieval, etc.) multiple related or similar representations are activated in parallel. But, once activated, do these representations compete or cooperate? Both facilitative and inhibitory effects have been shown, but researchers have tended to focus on just one kind of effect at a time. We showed that near semantic neighbors (words that are highly similar in meaning) have inhibitory effects and distant semantic neighbors (words that are moderately similar in meaning) have facilitative effects on word recognition (Mirman & Magnuson, 2008) and word production (Mirman, 2011). Then, in a series of simulations using a simple interactive activation and competition framework, we showed that the complex pattern of facilitative and inhibitory neighbor effects across orthographic, phonological, and semantic domains in word recognition and word production arises from a simple computational principle: strongly active neighbors have a net inhibitory effect and weakly active neighbors have a net facilitative effect (Chen & Mirman, in press). We are currently pursuing novel predictions from this simple model.
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