Going Beyond Rote Auditory Learning: Neural Patterns of Generalized Auditory Learning

The ability to generalize across specific experiences is vital for the recognition of new patterns, especially in speech perception considering acoustic–phonetic pattern variability. Indeed, behavioral research has demonstrated that listeners are able via a process of generalized learning to leverage their experiences of past words said by difficult-to-understand talker to improve their understanding for new words said by that talker. Here, we examine differences in neural responses to generalized versus rote learning in auditory cortical processing by training listeners to understand a novel synthetic talker. Using a pretest–posttest design with EEG, participants were trained using either (1) a large inventory of words where no words were repeated across the experiment (generalized learning) or (2) a small inventory of words where words were repeated (rote learning). Analysis of long-latency auditory evoked potentials at pretest and posttest revealed that rote and generalized learning both produced rapid changes in auditory processing, yet the nature of these changes differed. Generalized learning was marked by an amplitude reduction in the N1–P2 complex and by the presence of a late negativity wave in the auditory evoked potential following training; rote learning was marked only by temporally later scalp topography differences. The early N1–P2 change, found only for generalized learning, is consistent with an active processing account of speech perception, which proposes that the ability to rapidly adjust to the specific vocal characteristics of a new talker (for which rote learning is rare) relies on attentional mechanisms to selectively modify early auditory processing sensitivity

Quantifying similarity in animal vocal sequences: Which metric performs best?

1. Many animals communicate using sequences of discrete acoustic elements which can be complex, vary in their degree of stereotypy, and are potentially open-ended. Variation in sequences can provide important ecological, behavioural, or evolutionary information about the structure and connectivity of populations, mechanisms for vocal cultural evolution, and the underlying drivers responsible for these processes. Various mathematical techniques have been used to form a realistic approximation of sequence similarity for such tasks. 2. Here, we use both simulated and empirical datasets from animal vocal sequences (rock hyrax, Procavia capensis; humpback whale, Megaptera novaeangliae; bottlenose dolphin, Tursiops truncatus; and Carolina chickadee, Poecile carolinensis) to test which of eight sequence analysis metrics are more likely to reconstruct the information encoded in the sequences, and to test the fidelity of estimation of model parameters, when the sequences are assumed to conform to particular statistical models. 3. Results from the simulated data indicated that multiple metrics were equally successful in reconstructing the information encoded in the sequences of simulated individuals (Markov chains, n-gram models, repeat distribution, and edit distance), and data generated by different stochastic processes (entropy rate and n-grams). However, the string edit (Levenshtein) distance performed consistently and significantly better than all other tested metrics (including entropy, Markov chains, n-grams, mutual information) for all empirical datasets, despite being less commonly used in the field of animal acoustic communication. 4. The Levenshtein distance metric provides a robust analytical approach that should be considered in the comparison of animal acoustic sequences in preference to other commonly employed techniques (such as Markov chains, hidden Markov models, or Shannon entropy). The recent discovery that non-Markovian vocal sequences may be more common in animal communication than previously thought, provides a rich area for future research that requires non-Markovian based analysis techniques to investigate animal grammars and potentially the origin of human language.We thank Melinda Rekdahl, Todd Freeberg and his graduate students, Amiyaal Ilany, Elizabeth Hobson, and Jessica Crance for providing comments of on a previous version of this manuscript. We thank Mike Noad, Melinda Rekdahl, and Claire Garrigue for assistance with humpback whale song collection and initial categorisation of the song, Vincent Janik and Laela Sayigh for assistance with signature whistle collection, Todd Freeberg with chickadee recordings, and Eli Geffen and Amiyaal Ilany for assistance with hyrax song collection and analysis. E.C.G is supported by a Newton International Fellowship. Part of this work was conducted while E.C.G. was supported by a National Research Council (National Academy of Sciences) Postdoctoral Fellowship at the National Marine Mammal Laboratory, AFSC, NMFS, NOAA. The findings and conclusions in this paper are those of the authors and do not necessarily represent the views of the National Marine Fisheries Service. We would also like to thank Randall Wells and the Sarasota Dolphin Research Program for the opportunity to record the Sarasota dolphins, where data were collected under a series of National Marine Fisheries Service Scientific Research Permits issued to Randall Wells. A.K. is supported by the Herchel Smith Postdoctoral Fellowship Fund. Part of this work was conducted while A.K. was a Postdoctoral Fellow at the National Institute for Mathematical and Biological Synthesis, an Institute sponsored by the National Science Foundation through NSF Award #DBI-1300426, with additional support from The University of Tennessee, Knoxville.This is the author accepted manuscript. The final version is available from Wiley via http://dx.doi.org/10.1111/2041-210X.1243

Behavioral responses associated with a human-mediated predator shelter

Human activities in protected areas can affect wildlife populations in a similar manner to predation risk, causing increases in movement and vigilance, shifts in habitat use and changes in group size. Nevertheless, recent evidence indicates that in certain situations ungulate species may actually utilize areas associated with higher levels of human presence as a potential refuge from disturbance-sensitive predators. We now use four-years of behavioral activity budget data collected from pronghorn (Antilocapra americana) and elk (Cervus elephus) in Grand Teton National Park, USA to test whether predictable patterns of human presence can provide a shelter from predatory risk. Daily behavioral scans were conducted along two parallel sections of road that differed in traffic volume - with the main Teton Park Road experiencing vehicle use that was approximately thirty-fold greater than the River Road. At the busier Teton Park Road, both species of ungulate engaged in higher levels of feeding (27% increase in the proportion of pronghorn feeding and 21% increase for elk), lower levels of alert behavior (18% decrease for pronghorn and 9% decrease for elk) and formed smaller groups. These responses are commonly associated with reduced predatory threat. Pronghorn also exhibited a 30% increase in the proportion of individuals moving at the River Road as would be expected under greater exposure to predation risk. Our findings concur with the �predator shelter hypothesis�, suggesting that ungulates in GTNP use human presence as a potential refuge from predation risk, adjusting their behavior accordingly. Human activity has the potential to alter predator-prey interactions and drive trophic-mediated effects that could ultimately impact ecosystem function and biodiversity

Magnetoelectric ordering of BiFeO3 from the perspective of crystal chemistry

In this paper we examine the role of crystal chemistry factors in creating conditions for formation of magnetoelectric ordering in BiFeO3. It is generally accepted that the main reason of the ferroelectric distortion in BiFeO3 is concerned with a stereochemical activity of the Bi lone pair. However, the lone pair is stereochemically active in the paraelectric orthorhombic beta-phase as well. We demonstrate that a crucial role in emerging of phase transitions of the metal-insulator, paraelectric-ferroelectric and magnetic disorder-order types belongs to the change of the degree of the lone pair stereochemical activity - its consecutive increase with the temperature decrease. Using the structural data, we calculated the sign and strength of magnetic couplings in BiFeO3 in the range from 945 C down to 25 C and found the couplings, which undergo the antiferromagnetic-ferromagnetic transition with the temperature decrease and give rise to the antiferromagnetic ordering and its delay in regard to temperature, as compared to the ferroelectric ordering. We discuss the reasons of emerging of the spatially modulated spin structure and its suppression by doping with La3+.Comment: 18 pages, 5 figures, 3 table

An Integrated TCGA Pan-Cancer Clinical Data Resource to Drive High-Quality Survival Outcome Analytics

For a decade, The Cancer Genome Atlas (TCGA) program collected clinicopathologic annotation data along with multi-platform molecular profiles of more than 11,000 human tumors across 33 different cancer types. TCGA clinical data contain key features representing the democratized nature of the data collection process. To ensure proper use of this large clinical dataset associated with genomic features, we developed a standardized dataset named the TCGA Pan-Cancer Clinical Data Resource (TCGA-CDR), which includes four major clinical outcome endpoints. In addition to detailing major challenges and statistical limitations encountered during the effort of integrating the acquired clinical data, we present a summary that includes endpoint usage recommendations for each cancer type. These TCGA-CDR findings appear to be consistent with cancer genomics studies independent of the TCGA effort and provide opportunities for investigating cancer biology using clinical correlates at an unprecedented scale. Analysis of clinicopathologic annotations for over 11,000 cancer patients in the TCGA program leads to the generation of TCGA Clinical Data Resource, which provides recommendations of clinical outcome endpoint usage for 33 cancer types