ERP to chess stimuli reveal expert-novice differences in the amplitudes of N2 and P3 components

This is the author's accepted manuscript. The final published article is available from the link below. Copyright @ 2013 Society for Psychophysiological Research.ERP experiments were conducted to analyze the underlying neural events when chess players make simple judgments of a board position. Fourteen expert players and 14 age-matched novices viewed, for each of four tasks, 128 unique positions on a mini (4 × 4) chess board each presented for 0.5 s. The tasks were to respond: (a) if white king was in check, (b) if black knight was present, (c) if white king was not in check, and (d) if no black knight was present. Experts showed an enhanced N2 with check targets and a larger P3 with knight targets, relative to novices. Expert-novice differences in posterior N2 began as early as 240 ms on check-related searches. Results were consistent with the view that prolonged N2 components reflect matching of current perceptual input to memory, and thus are sensitive to experts' superior pattern recognition and memory retrieval of chunks

Usage Bibliometrics

Scholarly usage data provides unique opportunities to address the known shortcomings of citation analysis. However, the collection, processing and analysis of usage data remains an area of active research. This article provides a review of the state-of-the-art in usage-based informetric, i.e. the use of usage data to study the scholarly process.Comment: Publisher's PDF (by permission). Publisher web site: books.infotoday.com/asist/arist44.shtm

Active Object Localization in Visual Situations

We describe a method for performing active localization of objects in instances of visual situations. A visual situation is an abstract concept---e.g., "a boxing match", "a birthday party", "walking the dog", "waiting for a bus"---whose image instantiations are linked more by their common spatial and semantic structure than by low-level visual similarity. Our system combines given and learned knowledge of the structure of a particular situation, and adapts that knowledge to a new situation instance as it actively searches for objects. More specifically, the system learns a set of probability distributions describing spatial and other relationships among relevant objects. The system uses those distributions to iteratively sample object proposals on a test image, but also continually uses information from those object proposals to adaptively modify the distributions based on what the system has detected. We test our approach's ability to efficiently localize objects, using a situation-specific image dataset created by our group. We compare the results with several baselines and variations on our method, and demonstrate the strong benefit of using situation knowledge and active context-driven localization. Finally, we contrast our method with several other approaches that use context as well as active search for object localization in images.Comment: 14 page

Climate change promotes hybridisation between deeply divergent species

Rare hybridisations between deeply divergent animal species have been reported for decades in a wide range of taxa, but have often remained unexplained, mainly considered chance events and reported as anecdotal. Here, we combine field observations with long-term data concerning natural hybridisations, climate, land-use, and field-validated species distribution models for two deeply divergent and naturally sympatric toad species in Europe (Bufo bufo and Bufotes viridis species groups). We show that climate warming and seasonal extreme temperatures are conspiring to set the scene for these maladaptive hybridisations, by differentially affecting life-history traits of both species. Our results identify and provide evidence of an ultimate cause for such events, and reveal that the potential influence of climate change on interspecific hybridisations goes far beyond closely related species. Furthermore, climate projections suggest that the chances for these events will steadily increase in the near future