Neural response development during distributional learning.

We investigated online electrophysiological components of distributional learning, specifically of tones by listeners of a non-tonal language. German listeners were presented with a bimodal distribution of syllables with lexical tones from a synthesized continuum based on Cantonese level tones. Tones were presented in sets of four standards (within-category tokens) followed by a deviant (across-category token). Mismatch negativity (MMN) was measured. Earlier behavioral data showed that exposure to this bimodal distribution improved both categorical perception and perceptual acuity for level tones [1]. In the present study we present analyses of the electrophysiological response recorded during this exposure, i.e., the development of the MMN response during distributional learning. This development over time is analyzed using Generalized Additive Mixed Models and results showed that the MMN amplitude increased for both within- and across-category tokens, reflecting higher perceptual acuity accompanying category formation. This is evidence that learners zooming in on phonological categories undergo neural changes associated with more accurate phonetic perception.This research was also supported by a Research Networking grant (ESF) NetwordS No. 6609 to NB and a Leiden University AMT Individual Researcher Grant to JSN

Short-term exposure enhances perception of both between- and within-category acoustic information.

A critical question in speech research is how listeners use non-discrete acoustic cues for discrimination between discrete alternative messages (e.g. words). Previous studies have shown that distributional learning can improve listeners’ discrimination of non-native speech sounds. Less is known about effects of training on perception of within-category acoustic detail. The present research investigates adult listeners’ perception of and discrimination between lexical tones without training or after a brief training exposure. Native speakers of German (a language without lexical tone) heard a 13-step pitch continuum of the syllable /li:/. Two different tasks were used to assess sensitivity to acoustic differences on this continuum: a) pitch height estimation and b) AX discrimination. Participants performed these tasks either without exposure or after exposure to a bimodal distribution of the pitch continuum. The AX discrimination results show that exposure to a bimodal distribution enhanced discrimination at the category boundary (i.e. categorical perception) of high vs. low tones. Interestingly, the pitch estimation task results followed a categorisation (sigmoid) function without exposure, but a linear function after exposure, suggesting estimates became less categorical in this task. The results suggest that training exposure may enhance not only discrimination between contrastive speech sounds (consistent with previous studies), but also perception of withincategory acoustic differences. Different tasks may reveal different skills

Serum Metabolome and Lipidome Changes in Adult Patients with Primary Dengue Infection

10.1371/journal.pntd.0002373PLoS Neglected Tropical Diseases78

The United States COVID-19 Forecast Hub dataset

Academic researchers, government agencies, industry groups, and individuals have produced forecasts at an unprecedented scale during the COVID-19 pandemic. To leverage these forecasts, the United States Centers for Disease Control and Prevention (CDC) partnered with an academic research lab at the University of Massachusetts Amherst to create the US COVID-19 Forecast Hub. Launched in April 2020, the Forecast Hub is a dataset with point and probabilistic forecasts of incident cases, incident hospitalizations, incident deaths, and cumulative deaths due to COVID-19 at county, state, and national, levels in the United States. Included forecasts represent a variety of modeling approaches, data sources, and assumptions regarding the spread of COVID-19. The goal of this dataset is to establish a standardized and comparable set of short-term forecasts from modeling teams. These data can be used to develop ensemble models, communicate forecasts to the public, create visualizations, compare models, and inform policies regarding COVID-19 mitigation. These open-source data are available via download from GitHub, through an online API, and through R packages

Effective acoustic cue learning is not just statistical, it is discriminative

A growing statistical learning literature suggests that listeners extract statistical information from the linguistic environment. However, distributional frequency may be insufficient for important but relatively low-frequency cues. Acquisition of linguistic knowledge may rely not merely on co-occurrences but on predictive relationships between cues and their outcomes. The present study investigates effects of predictive temporal cue structure on acquisition of a non-native acoustic cue dimension. During training, native English speakers saw coloured shape objects and heard spoken Min Chinese words with six different lexical tones. Tones were the only reliable cue to identifying the associated object. Words also contained a salient cue that did not discriminate between objects. Three tones occurred with high-frequency and three with low-frequency in training. The critical manipulation was the presentation order: either words, containing complex cue structure, preceded object outcomes (discriminative order) or objects preceded words (non-discriminative order). Generalised linear mixed models showed accuracy was significantly higher in the discriminative order than the non-discriminative order. These results demonstrate that predictive cue structure can facilitate acquisition of a non-native cue dimension. Feedback from prediction error drives learners to ignore salient non-discriminative cues and effectively learn to use the target cue dimension

Acoustic cue variability affects eye movement behaviour during non-native speech perception

A fundamental question in speech research is how listeners use continuous (non-discrete) acoustic cues to discriminate between discrete alternative messages. An important factor is the statistical distribution of acoustic cues in speech. Previous research has shown that when native speakers listen to speech with high within-category variability in the discriminative cue dimension, perceptual uncertainty increases, resulting in increased looks to competitor objects. The present study investigated effects of within-category acoustic variability on eye movements during acquisition of a non-native acoustic dimension, namely English speakers acquisition of lexical tone. All participants heard a bimodal distribution of stimuli, with distribution peaks at the prototypical pitch values for Cantonese high and mid level tones; however, presentation frequency differed between conditions: high-variance vs. low-variance. Based on previous research, we expected lower uncertainty and better learning in the low-variance condition. GAMM models showed that towards the end of the experiment, fixations were closer to the target object in the low-variance, compared to the high-variance condition. This suggests that within-category acoustic variability not only increases uncertainty for native listeners, but may also initially hinder learning of acoustic cues during non-native language acquisition

Does error-driven learning occur in the absence of cues? Examination of the effects of updating connection weights to absent cues

The Rescorla-Wagner model has seen widespread success in modelling not only its original target of animal learning, but also several areas of human learning. However, despite its success, a number of studies with humans have found effects that are not predicted by the model, thus inspiring proposals for modifications to the model. One such proposal, by Van Hamme and Wasserman (1994, VHW), is that humans not only learn from present cues to all (present and absent) outcomes, as in the original model, but also learn from the absence of cues. They set out to test this hypothesis with a causal rating experiment. However, behaviour in learning studies may depend on the task. We propose that error-driven learning should be considered to be a form of implicit learning and that the results of VHW’s contingency judgement task might stem from explicit strategies involving logic and reasoning. The present study investigates this question by a) running simulations with both the original and modified versions of the model; b) replicating the VHW experiment (Experiment 1); and c) extending the experiment with new stimuli and by including unseen stimuli following the learning phases (Experiment 2). Simulations show that the VHW modified model predicts that cues learnt at the beginning will be unlearnt when absent over the following blocks, so that they become negative predictors over time. In contrast, the original RW predicts that the absent cues remain steady (positive) predictors over the blocks. Results showed no significant difference in cue assignment between training and test, in line with the original RW model. Moreover, predictive cues in the training phase showed significantly higher ratings than a new cue introduced in the test phase, at least in some cases, also partially supporting the original RW. We propose that in the development of human learning theory, attention should be paid to whether the behaviour (or other learning data) to be modelled results from implicit learning or involves higher level cognitive processes. We suggest that the RW may best capture implicit error-driven learning