scholarly journals Implicit statistical learning in naturalistic and instructed morphosyntactic attainment: An aptitude-treatment interaction design

2021 ◽  
Author(s):  
Cylcia Bolibaugh ◽  
Pauline Foster
Keyword(s):  
Implicit Learning ◽  
Statistical Learning ◽  
Interaction Design ◽  
Short Term Memory ◽  
Age Of Onset ◽  
Learning Mechanisms ◽  
Short Term ◽  
Learning Abilities ◽  
Term Memory ◽  
Wide Range

We investigated the potential influence of implicit learning mechanisms on L2 morphosyntactic attainment by examining the relationship between age of onset (AoO), two cognitive abilities hypothesised to underlie implicit learning (phonological short-term memory and implicit statistical learning), and performance on an auditory grammatically judgement test (GJT). Participants were 71 Polish - English long term bilinguals with a wide range of AoO (1–35), who differed in their context of learning and use (immersed vs instructed). In immersed learners, we observed a growing dissociation between performance on grammatical and ungrammatical sentences as AoO was delayed. This effect was attenuated in those with better phonological short-term memory and statistical learning abilities, and is consistent with a decline in the ability to learn from implicit negative evidence. In instructed learners, GJT performance was subject to additive effects of AoO and grammaticality, and was not associated with either cognitive predictor, suggesting that implicit learning mechanisms were not involved.

scholarly journals Dissociating sensory and cognitive theories of harmony perception through computational modeling

2018 ◽  
Author(s):  
Peter Harrison ◽  
Marcus Thomas Pearce
Keyword(s):  
Statistical Learning ◽  
Short Term Memory ◽  
Syntactic Structure ◽  
Explanatory Power ◽  
Memory Models ◽  
Acoustic Properties ◽  
Probabilistic Prediction ◽  
Short Term ◽  
Cognitive Approach ◽  
Term Memory

Two approaches exist for explaining harmonic expectation. The sensory approach claims that harmonic expectation is a low-level process driven by sensory responses to acoustic properties of musical sounds. Conversely, the cognitive approach describes harmonic expectation as a high-level cognitive process driven by the recognition of syntactic structure learned through experience. Many previous studies have sought to distinguish these two hypotheses, largely yielding support for the cognitive hypothesis. However, subsequent re-analysis has shown that most of these results can parsimoniously be explained by a computational model from the sensory tradition, namely Leman’s (2000) model of auditory short- term memory (Bigand, Delbé, Poulin-Charronnat, Leman, & Tillmann, 2014). In this research we re-examine the explanatory power of auditory short-term memory models, and compare them to a new model in the Information Dynamics Of Music (IDyOM) tradition, which simulates a cognitive theory of harmony perception based on statistical learning and probabilistic prediction. We test the ability of these models to predict the surprisingness of chords within chord sequences (N = 300), as reported by a sample group of university undergraduates (N = 50). In contrast to previous studies, which typically use artificial stimuli composed in a classical idiom, we use naturalistic chord sequences sampled from a large dataset of popular music. Our results show that the auditory short-term memory models have remarkably low explanatory power in this context. In contrast, the new statistical learning model predicts surprisingness ratings relatively effectively. We conclude that auditory short-term memory is insufficient to explain harmonic expectation, and that cognitive processes of statistical learning and probabilistic prediction provide a viable alternative.

scholarly journals A Diverse Range of Factors Affect the Nature of Neural Representations Underlying Short-Term Memory

2018 ◽  
Author(s):  
A. Emin Orhan ◽  
Wei Ji Ma
Keyword(s):  
Neural Circuits ◽  
Short Term Memory ◽  
Persistent Activity ◽  
Short Term ◽  
Diverse Range ◽  
Term Memory ◽  
Temporal Complexity ◽  
Wide Range ◽  
Activity Models ◽  
The Brain

AbstractSequential and persistent activity models are two prominent models of short-term memory in neural circuits. In persistent activity models, memories are represented in persistent or nearly persistent activity patterns across a population of neurons, whereas in sequential models, memories are represented dynamically by a sequential pattern of activity across the population. Experimental evidence for both types of model in the brain has been reported previously. However, it has been unclear under what conditions these two qualitatively different types of solutions emerge in neural circuits. Here, we address this question by training recurrent neural networks on several short-term memory tasks under a wide range of circuit and task manipulations. We show that sequential and nearly persistent solutions are both part of a spectrum that emerges naturally in trained networks under different conditions. Fixed delay durations, tasks with higher temporal complexity, strong network coupling, motion-related dynamic inputs and prior training in a different task favor more sequential solutions, whereas variable delay durations, tasks with low temporal complexity, weak network coupling and symmetric Hebbian short-term synaptic plasticity favor more persistent solutions. Our results help clarify some seemingly contradictory experimental results on the existence of sequential vs. persistent activity based memory mechanisms in the brain.

scholarly journals A Novel User Emotional Interaction Design Model Using Long and Short-Term Memory Networks and Deep Learning

2021 ◽  
Vol 12 ◽  
Author(s):  
Xiang Chen ◽  
Rubing Huang ◽  
Xin Li ◽  
Lei Xiao ◽  
Ming Zhou ◽  
...  
Keyword(s):  
Deep Learning ◽  
Emotion Recognition ◽  
User Experience ◽  
Interaction Design ◽  
Speech Signal ◽  
Short Term Memory ◽  
Emotional Design ◽  
Short Term ◽  
Term Memory ◽  
Emotional Interaction

Emotional design is an important development trend of interaction design. Emotional design in products plays a key role in enhancing user experience and inducing user emotional resonance. In recent years, based on the user's emotional experience, the design concept of strengthening product emotional design has become a new direction for most designers to improve their design thinking. In the emotional interaction design, the machine needs to capture the user's key information in real time, recognize the user's emotional state, and use a variety of clues to finally determine the appropriate user model. Based on this background, this research uses a deep learning mechanism for more accurate and effective emotion recognition, thereby optimizing the design of the interactive system and improving the user experience. First of all, this research discusses how to use user characteristics such as speech, facial expression, video, heartbeat, etc., to make machines more accurately recognize human emotions. Through the analysis of various characteristics, the speech is selected as the experimental material. Second, a speech-based emotion recognition method is proposed. The mel-Frequency cepstral coefficient (MFCC) of the speech signal is used as the input of the improved long and short-term memory network (ILSTM). To ensure the integrity of the information and the accuracy of the output at the next moment, ILSTM makes peephole connections in the forget gate and input gate of LSTM, and adds the unit state as input data to the threshold layer. The emotional features obtained by ILSTM are input into the attention layer, and the self-attention mechanism is used to calculate the weight of each frame of speech signal. The speech features with higher weights are used to distinguish different emotions and complete the emotion recognition of the speech signal. Experiments on the EMO-DB and CASIA datasets verify the effectiveness of the model for emotion recognition. Finally, the feasibility of emotional interaction system design is discussed.

Do serial order short-term memory and long-term learning abilities predict spelling skills in school-age children?

Cognition ◽  
2021 ◽  
Vol 206 ◽  
pp. 104479
Author(s):  
Laura Ordonez Magro ◽  
Steve Majerus ◽  
Lucie Attout ◽  
Martine Poncelet ◽  
Eleonore H.M. Smalle ◽  
...  
Keyword(s):  
Short Term Memory ◽  
Serial Order ◽  
School Age Children ◽  
School Age ◽  
Short Term ◽  
Learning Abilities ◽  
Term Memory

Effects of bilingualism on statistical learning in preschoolers

2020 ◽  
Author(s):  
Josje Verhagen ◽  
Elise de Bree
Keyword(s):  
Statistical Learning ◽  
Short Term Memory ◽  
Reaction Times ◽  
Reaction Time Task ◽  
Nonword Repetition ◽  
Short Term ◽  
Mixed Effect ◽  
Term Memory ◽  
Memory Skill ◽  
Post Hoc

Abstract Earlier work indicates that bilingualism may positively affect statistical learning, but leaves open whether a bilingual benefit is (1) found during learning rather than in a post-hoc test following a learning phase and (2) explained by enhanced verbal short-term memory skill in the bilinguals. Forty-one bilingual and 56 monolingual preschoolers completed a serial reaction time task and a nonword repetition task (NWR). Linear mixed-effect regressions indicated that the bilinguals showed a stronger decrease in reaction times over the regular blocks of the task than the monolinguals. No group differences in accuracy-based measures were found. NWR performance, which did not differ between the groups, did not account for the attested effect of bilingualism. These results provide partial support for effects of bilingualism on statistical learning, which appear during learning and are not due to enhanced verbal short-term memory. Taken together, these findings add to a growing body of research on effects of bilingualism on statistical learning, and constitute a first step towards investigating the factors which may underlie such effects.

scholarly journals Factors associated with school achievement of children aged 8–10 years in rural Bangladesh: Findings from a post hoc analysis of a community-based study

PLoS ONE ◽  
2021 ◽  
Vol 16 (7) ◽  
pp. e0254693
Author(s):  
Sheikh Jamal Hossain ◽  
Fahmida Tofail ◽  
Hasan Mahmud Sujan ◽  
Shams El Arifeen ◽  
Jena Hamadani
Keyword(s):  
Short Term Memory ◽  
School Achievement ◽  
Linear Regression Analysis ◽  
Early Age ◽  
Short Term ◽  
Term Memory ◽  
Rural Bangladesh ◽  
Factors Associated ◽  
Wide Range ◽  
Years Of Schooling

Background Education is one of the most important human capitals. Investment in education at early age returns best. A lot of factors influence children’s educational achievement. Studies in developed countries well established the relation of school achievement with its associated variables. But information is lack on what factors play important role for school achievement at early age in low resource settings like Bangladesh. We aimed to find factors associated with school achievement in rural Bangladesh. Method The data were acquired from a long-term follow up study, conducted in 8–10 years old children (n = 372). We used a locally developed school achievement tool based on Wide Range Achievement Test-4 to measure reading, spelling and math computation, Wechsler abbreviated scale of intelligence to measure intelligence Quotient (IQ), Digit span forward and backward for short term memory, and locally available Strength and Difficulties Questionnaire to measure behaviour. Socioeconomic and anthropometric information of the mothers and children were also collected. Multicollinearity of the data was checked. Unadjusted and adjusted multiple linear regression analysis was performed. Findings Years of schooling and short-term memory were positively related to reading, spelling and math computation. For years of schooling it was-reading B = 8.09 (CI 5.84, 10.31), spelling 4.43 (4.33, 8.53) and math computation 5.23 (3.60, 6.87) and for short term memory- reading 3.56 (2.01,5.05), spelling 4.01 (2.56, 5.46) and math computation 2.49 (1.37, 3.62). Older children had lower scores of reading -0.48 (-0.94, -0.02), spelling -0.41 (-0.88, -0.02) and math computation -0.47 (-0.80, -0.14). Children’s IQ predicted reading 0.48 (0.14, 0.81) and spelling 0.50 (0.18, 0.82) skills. Mother and father’s education predicted Spelling 0.82 (0.16, 1.48) and reading 0.68 (0.06, 1.30) capacity respectively. Children enrolled in private schools had higher reading 10.28 (5.05, 15.51) and spelling 6.22 (1.31, 11.13) than those in the government schools. Children with more difficult behaviour tended to have lower scores in reading -0.51 (-0.96, -0.05). Conclusion Children’s school achievement is influenced by their IQ, years of schooling, type of school and parents’ education. Therefore, intervention should be made to focus specifically on these variables and establish the effect of this intervention through robust research design.

scholarly journals The Role of Statistical Learning and Verbal Short-Term Memory in Impaired and Typical Lexical Development

2021 ◽  
Vol 6 ◽  
Author(s):  
Ferenc Kemény ◽  
Ágnes Lukács
Keyword(s):  
Statistical Learning ◽  
Short Term Memory ◽  
Language Disorder ◽  
Developmental Language Disorder ◽  
Short Term ◽  
Vocabulary Size ◽  
Learning Skills ◽  
Lexical Development ◽  
Term Memory ◽  
Independent Contribution

Purpose: Studies on the interface between statistical learning and language are dominated by its role in word segmentation and association with grammar skills, while research on its role in lexical development is scarce. The current study is aimed at exploring whether and how statistical learning and verbal short-term memory are associated with lexical skills in typically developing German-speaker primary school children (Experiment 1) and Hungarian-speaking children with developmental language disorder (DLD, Experiment 2).Methods: We used the language-relevant Peabody Picture Vocabulary Tests to measure individual differences in vocabulary. Statistical learning skills were assessed with the Weather Prediction task, in which participants learn probabilistic cue-outcome associations based on item-based feedback. Verbal short-term memory span was assessed with the Forward digit span task.Results: Hierarchical linear regression modelling was used to test the contribution of different functions to vocabulary size. In TD children, statistical learning skills had an independent contribution to vocabulary size over and above age, receptive grammatical abilities and short-term memory, whereas working memory did not have an independent contribution. The pattern was reverse in SLI: Vocabulary size was predicted by short-term memory skills over and above age, receptive grammar and statistical learning, whereas statistical learning had no independent contribution.Conclusion: Our results suggest that lexical development rely on different underlying memory processes in typical development and in developmental language disorder to different degrees. This qualitative difference is discussed in the light of different stages of lexical development, as well as the contribution of the different human memory systems to vocabulary acquisition.

scholarly journals Prediction of gait trajectories based on the Long Short Term Memory neural networks

PLoS ONE ◽  
2021 ◽  
Vol 16 (8) ◽  
pp. e0255597
Author(s):  
Abdelrahman Zaroug ◽  
Alessandro Garofolini ◽  
Daniel T. H. Lai ◽  
Kurt Mudie ◽  
Rezaul Begg
Keyword(s):  
Lower Limb ◽  
Short Term Memory ◽  
Series Data ◽  
Short Term ◽  
Term Memory ◽  
Wide Range ◽  
Limb Kinematics ◽  
Long Short Term Memory ◽  
Balance Loss ◽  
Lower Limb Kinematics

The forecasting of lower limb trajectories can improve the operation of assistive devices and minimise the risk of tripping and balance loss. The aim of this work was to examine four Long Short Term Memory (LSTM) neural network architectures (Vanilla, Stacked, Bidirectional and Autoencoders) in predicting the future trajectories of lower limb kinematics, i.e. Angular Velocity (AV) and Linear Acceleration (LA). Kinematics data of foot, shank and thigh (LA and AV) were collected from 13 male and 3 female participants (28 ± 4 years old, 1.72 ± 0.07 m in height, 66 ± 10 kg in mass) who walked for 10 minutes at preferred walking speed (4.34 ± 0.43 km.h-1) and at an imposed speed (5km.h-1, 15.4% ± 7.6% faster) on a 0% gradient treadmill. The sliding window technique was adopted for training and testing the LSTM models with total kinematics time-series data of 10,500 strides. Results based on leave-one-out cross validation, suggested that the LSTM autoencoders is the top predictor of the lower limb kinematics trajectories (i.e. up to 0.1s). The normalised mean squared error was evaluated on trajectory predictions at each time-step and it obtained 2.82–5.31% for the LSTM autoencoders. The ability to predict future lower limb motions may have a wide range of applications including the design and control of bionics allowing improved human-machine interface and mitigating the risk of falls and balance loss.

scholarly journals Bottleneck Based Gridlock Prediction in an Urban Road Network Using Long Short-Term Memory

Electronics ◽  
2020 ◽  
Vol 9 (9) ◽  
pp. 1412
Author(s):  
Ei Ei Mon ◽  
Hideya Ochiai ◽  
Chaiyachet Saivichit ◽  
Chaodit Aswakul
Keyword(s):  
Road Network ◽  
Short Term Memory ◽  
Road Networks ◽  
Sample Sizes ◽  
Short Term ◽  
Urban Road Network ◽  
Term Memory ◽  
Urban Road ◽  
Wide Range ◽  
Long Short Term Memory

The traffic bottlenecks in urban road networks are more challenging to investigate and discover than in freeways or simple arterial networks. A bottleneck indicates the congestion evolution and queue formation, which consequently disturb travel delay and degrade the urban traffic environment and safety. For urban road networks, sensors are needed to cover a wide range of areas, especially for bottleneck and gridlock analysis, requiring high installation and maintenance costs. The emerging widespread availability of GPS vehicles significantly helps to overcome the geographic coverage and spacing limitations of traditional fixed-location detector data. Therefore, this study investigated GPS vehicles that have passed through the links in the simulated gridlock-looped intersection area. The sample size estimation is fundamental to any traffic engineering analysis. Therefore, this study tried a different number of sample sizes to analyze the severe congestion state of gridlock. Traffic condition prediction is one of the primary components of intelligent transportation systems. In this study, the Long Short-Term Memory (LSTM) neural network was applied to predict gridlock based on bottleneck states of intersections in the simulated urban road network. This study chose to work on the Chula-Sathorn SUMO Simulator (Chula-SSS) dataset. It was calibrated with the past actual traffic data collection by using the Simulation of Urban MObility (SUMO) software. The experiments show that LSTM provides satisfactory results for gridlock prediction with temporal dependencies. The reported prediction error is based on long-range time dependencies on the respective sample sizes using the calibrated Chula-SSS dataset. On the other hand, the low sampling rate of GPS trajectories gives high RMSE and MAE error, but with reduced computation time. Analyzing the percentage of simulated GPS data with different random seed numbers suggests the possibility of gridlock identification and reports satisfying prediction errors.

scholarly journals Time Series Segmentation Based on Stationarity Analysis to Improve New Samples Prediction

Sensors ◽  
2021 ◽  
Vol 21 (21) ◽  
pp. 7333
Author(s):  
Ricardo Petri Silva ◽  
Bruno Bogaz Zarpelão ◽  
Alberto Cano ◽  
Sylvio Barbon Junior
Keyword(s):  
Time Series ◽  
Short Term Memory ◽  
Machine Learning Algorithms ◽  
Detection Methods ◽  
Sequential Data ◽  
Short Term ◽  
Term Memory ◽  
Wide Range ◽  
Long Short Term Memory ◽  
Adf Test

A wide range of applications based on sequential data, named time series, have become increasingly popular in recent years, mainly those based on the Internet of Things (IoT). Several different machine learning algorithms exploit the patterns extracted from sequential data to support multiple tasks. However, this data can suffer from unreliable readings that can lead to low accuracy models due to the low-quality training sets available. Detecting the change point between high representative segments is an important ally to find and thread biased subsequences. By constructing a framework based on the Augmented Dickey-Fuller (ADF) test for data stationarity, two proposals to automatically segment subsequences in a time series were developed. The former proposal, called Change Detector segmentation, relies on change detection methods of data stream mining. The latter, called ADF-based segmentation, is constructed on a new change detector derived from the ADF test only. Experiments over real-file IoT databases and benchmarks showed the improvement provided by our proposals for prediction tasks with traditional Autoregressive integrated moving average (ARIMA) and Deep Learning (Long short-term memory and Temporal Convolutional Networks) methods. Results obtained by the Long short-term memory predictive model reduced the relative prediction error from 1 to 0.67, compared to time series without segmentation.

Sign in / Sign up

Export Citation Format

Share Document