Wiley Interdisciplinary Reviews: Cognitive Science
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Online ISSN: 1939-5086
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- Representation of speech variability
Tessa Bent and Rachael F. Holt
Version of Record online: 3 FEB 2017 | DOI: 10.1002/wcs.1434
Highly detailed episodes of speech signals are represented in listeners’ cognitive systems. From these speech signals, listeners simultaneously extract linguistic information (top, green) with generally high accuracy and social-indexical information about the speaker (bottom, orange) with varying accuracy. Linguistic and social-indexical information (dashed arrows) interact during speech perception.
- You have free access to this contentThe role of sleep in cognitive processing: focusing on memory consolidation
Alexis M. Chambers
Version of Record online: 3 JAN 2017 | DOI: 10.1002/wcs.1433
Not only does sleep promote consolidation of declarative and nondeclarative memories, but it also facilitates the building of connections between information that gives rise to creative insights and other cognitive functions. New research suggests that presenting external cues during sleep may also enhance these processes.
- Neurogenesis and pattern separation: time for a divorce
Version of Record online: 27 DEC 2016 | DOI: 10.1002/wcs.1427
The generation of new neurons in the adult mammalian brain has led to numerous theories as to their functional significance. One of the most widely held views is that adult neurogenesis promotes pattern separation, a process by which overlapping patterns of neural activation are mapped to less overlapping, sparse representations. The schematic model of the hippocampus shown in (a) uses sparse coding to distinguish between similar patterns, whereas the model shown in (b) and (c) uses neurogenesis and sparse connectivity to overcome interference. The model input is a distributed pattern of activation across the entorhinal cortex pyramidal cells (PCs; activated cells shown in pink), generating a sparse pattern of activation across the mature dentate granule cells (GCs; active cells shown in blue). In (b) and (c), the model with neurogenesis also includes immature GCs (active cells shown in green), and this model is presented with two different input patterns and, in response, generates identical sparse activation patterns in mature GCs and overlapping but distinct distributed activation in immature GCs. While the two input patterns overlap by 40%, the two patterns of activation in the immature GC overlap by 50%, hence a decrease in pattern separation. In spite of this, the model with neurogenesis is able to generate distinct neural codes for the two similar inputs, in spite of a high plasticity in the immature cell population, due to the sparse connectivity of the immature GCs.