The Resonant Dynamics of Speech Perception: Interword Integration and Duration-Dependent Backward Effects

How do listeners integrate temporally distributed phonemic information into coherent representations of syllables and words? During fluent speech perception, variations in the durations of speech sounds and silent pauses can produce different pereeived groupings. For exarnple, increasing the silence interval between the words "gray chip" may result in the percept "great chip", whereas increasing the duration of fricative noise in "chip" may alter the percept to "great ship" (Repp et al., 1978). The ARTWORD neural model quantitatively simulates such context-sensitive speech data. In AHTWORD, sequential activation and storage of phonemic items in working memory provides bottom-up input to unitized representations, or list chunks, that group together sequences of items of variable length. The list chunks compete with each other as they dynamically integrate this bottom-up information. The winning groupings feed back to provide top-down supportto their phonemic items. Feedback establishes a resonance which temporarily boosts the activation levels of selected items and chunks, thereby creating an emergent conscious percept. Because the resonance evolves more slowly than wotking memory activation, it can be influenced by information presented after relatively long intervening silence intervals. The same phonemic input can hereby yield different groupings depending on its arrival time. Processes of resonant transfer and competitive teaming help determine which groupings win the competition. Habituating levels of neurotransmitter along the pathways that sustain the resonant feedback lead to a resonant collapsee that permits the formation of subsequent. resonances.Air Force Office of Scientific Research (F49620-92-J-0225); Defense Advanced Research projects Agency and Office of Naval Research (N00014-95-1-0409); National Science Foundation (IRI-97-20333); Office of Naval Research (N00014-92-J-1309, NOOO14-95-1-0657

Multiscale metabolic modeling of C4 plants: connecting nonlinear genome-scale models to leaf-scale metabolism in developing maize leaves

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, here the predicted fluxes achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. We suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data.Comment: 57 pages, 14 figures; submitted to PLoS Computational Biology; source code available at http://github.com/ebogart/fluxtools and http://github.com/ebogart/multiscale_c4_sourc

Strategy Constancy Amidst Implementation Differences: Interaction-Intensive Versus Memory-Intensive Adaptations To Information Access In Decision-Making

Over the last two decades attempts to quantify decision-making have established that, under a wide range of conditions, people trade-off effectiveness for efficiency in the strategies they adopt. However, as interesting, significant, and influential as this research has been, its scope is limited by three factors; the coarseness of how effort was measured, the confounding of the costs of steps in the decision-making algorithm with the costs of steps in a given task environment, and the static nature of the decision tasks studied. In the current study, we embedded a decision-making task in a dynamic task environment and varied the cost required for the information access step. Across three conditions, small changes in the cost of interactive behavior led to changes in the strategy adopted for decision-making as well as to differences in how a step in the same strategy was implemented