340 research outputs found

    Of words and whistles: Statistical learning operates similarly for identical sounds perceived as speech and non-speechOf words and whistles: Statistical learning operates similarly for identical sounds perceived as speech and non-speech

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    Statistical learning is an ability that allows individuals to effortlessly extract patterns from the environment, such as sound patterns in speech. Some prior evidence suggests that statistical learning operates more robustly for speech compared to non-speech stimuli, supporting the idea that humans are predisposed to learn language. However, any apparent statistical learning advantage for speech could be driven by signal acoustics, rather than the subjective perception per se of sounds as speech. To resolve this issue, the current study assessed whether there is a statistical learning advantage for ambiguous sounds that are subjectively perceived as speech-like compared to the same sounds perceived as non-speech, thereby controlling for acoustic features. We first induced participants to perceive sine-wave speech (SWS)—a degraded form of speech not immediately perceptible as speech—as either speech or non-speech. After this induction phase, participants were exposed to a continuous stream of repeating trisyllabic nonsense words, composed of SWS syllables, and then completed an explicit familiarity rating task and an implicit target detection task to assess learning. Critically, participants showed robust and equivalent performance on both measures, regardless of their subjective speech perception. In contrast, participants who perceived the SWS syllables as more speech-like showed better detection of individual syllables embedded in speech streams. These results suggest that speech perception facilitates processing of individual sounds, but not the ability to extract patterns across sounds. Our findings suggest that statistical learning is not influenced by the perceived linguistic relevance of sounds, and that it may be conceptualized largely as an automatic, stimulus-driven mechanism

    Musical instrument familiarity affects statistical learning of tone sequences.

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    Most listeners have an implicit understanding of the rules that govern how music unfolds over time. This knowledge is acquired in part through statistical learning, a robust learning mechanism that allows individuals to extract regularities from the environment. However, it is presently unclear how this prior musical knowledge might facilitate or interfere with the learning of novel tone sequences that do not conform to familiar musical rules. In the present experiment, participants listened to novel, statistically structured tone sequences composed of pitch intervals not typically found in Western music. Between participants, the tone sequences either had the timbre of artificial, computerized instruments or familiar instruments (piano or violin). Knowledge of the statistical regularities was measured as by a two-alternative forced choice recognition task, requiring discrimination between novel sequences that followed versus violated the statistical structure, assessed at three time points (immediately post-training, as well as one day and one week post-training). Compared to artificial instruments, training on familiar instruments resulted in reduced accuracy. Moreover, sequences from familiar instruments - but not artificial instruments - were more likely to be judged as grammatical when they contained intervals that approximated those commonly used in Western music, even though this cue was non-informative. Overall, these results demonstrate that instrument familiarity can interfere with the learning of novel statistical regularities, presumably through biasing memory representations to be aligned with Western musical structures. These results demonstrate that real-world experience influences statistical learning in a non-linguistic domain, supporting the view that statistical learning involves the continuous updating of existing representations, rather than the establishment of entirely novel ones

    Designing Interactive Displays to Promote Effective use of Evidence

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    Interactive displays are increasing being used to convey information, and are a significant factor in promoting statistical literacy (and illiteracy). Durham University and the House of Commons Library are collaborating to create data visualisations (DV) which will be accessible to politicians, researchers and journalists. The focus of this paper is a DV designed to be useful in the run-up to the 2015 general election. The aim was to assemble a rich resource from multiple sources, and to make it easy for target groups to manipulate data and draw conclusions. We identify important changes to the DV as it evolved over 13 iterations, and draw conclusions about appropriate design processes and validation

    Is Hey Jude in the Right Key? Cognitive Components of Absolute Pitch Memory

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    Most individuals, regardless of formal musical training, have long-term absolute pitch memory (APM) for familiar musical recordings, though with varying levels of accuracy. The present study followed up on recent evidence suggesting an association between singing accuracy and APM (Halpern & Pfordresher, 2022, Attention, Perception, & Psychophysics, 84(1), 260–269), as well as tonal short-term memory (STM) and APM (Van Hedger et al., 2018, Quarterly Journal of Experimental Psychology, 71(4), 879–891). Participants from three research sites (n = 108) completed a battery of tasks including APM, tonal STM, singing accuracy, and self-reported auditory imagery. Both tonal STM and singing accuracy predicted APM, replicating prior results. Tonal STM also predicted singing accuracy, music training, and auditory imagery. Further tests suggested that the association between APM and singing accuracy was fully mediated by tonal STM. This pattern comports well with models of vocal pitch matching that include STM for pitch as a mechanism for sensorimotor translation

    Evaluating the spatial transferability and temporal repeatability of remote sensing-based lake water quality retrieval algorithms at the European scale:a meta-analysis approach

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    Many studies have shown the considerable potential for the application of remote-sensing-based methods for deriving estimates of lake water quality. However, the reliable application of these methods across time and space is complicated by the diversity of lake types, sensor configuration, and the multitude of different algorithms proposed. This study tested one operational and 46 empirical algorithms sourced from the peer-reviewed literature that have individually shown potential for estimating lake water quality properties in the form of chlorophyll-a (algal biomass) and Secchi disc depth (SDD) (water transparency) in independent studies. Nearly half (19) of the algorithms were unsuitable for use with the remote-sensing data available for this study. The remaining 28 were assessed using the Terra/Aqua satellite archive to identify the best performing algorithms in terms of accuracy and transferability within the period 2001–2004 in four test lakes, namely Vänern, Vättern, Geneva, and Balaton. These lakes represent the broad continuum of large European lake types, varying in terms of eco-region (latitude/longitude and altitude), morphology, mixing regime, and trophic status. All algorithms were tested for each lake separately and combined to assess the degree of their applicability in ecologically different sites. None of the algorithms assessed in this study exhibited promise when all four lakes were combined into a single data set and most algorithms performed poorly even for specific lake types. A chlorophyll-a retrieval algorithm originally developed for eutrophic lakes showed the most promising results (R2 = 0.59) in oligotrophic lakes. Two SDD retrieval algorithms, one originally developed for turbid lakes and the other for lakes with various characteristics, exhibited promising results in relatively less turbid lakes (R2 = 0.62 and 0.76, respectively). The results presented here highlight the complexity associated with remotely sensed lake water quality estimates and the high degree of uncertainty due to various limitations, including the lake water optical properties and the choice of methods
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