scholarly journals Reassessing Syntax-Related ERP Components Using Popular Music Chord Sequences

2021 ◽  
Vol 39 (2) ◽  
pp. 118-144
Author(s):  
Andrew Goldman ◽  
Peter M. C. Harrison ◽  
Tyreek Jackson ◽  
Marcus T. Pearce
Keyword(s):  
Recent Work ◽  
Short Term Memory ◽  
Cognitive Model ◽  
Memory Models ◽  
Local Context ◽  
Large Set ◽  
Musical Structure ◽  
Acoustic Features ◽  
Short Term ◽  
Term Memory

Electroencephalographic responses to unexpected musical events allow researchers to test listeners’ internal models of syntax. One major challenge is dissociating cognitive syntactic violations—based on the abstract identity of a particular musical structure—from unexpected acoustic features. Despite careful controls in past studies, recent work by Bigand, Delbe, Poulin-Carronnat, Leman, and Tillmann (2014) has argued that ERP findings attributed to cognitive surprisal cannot be unequivocally separated from sensory surprisal. Here we report a novel EEG paradigm that uses three auditory short-term memory models and one cognitive model to predict surprisal as indexed by several ERP components (ERAN, N5, P600, and P3a), directly comparing sensory and cognitive contributions. Our paradigm parameterizes a large set of stimuli rather than using categorically “high” and “low” surprisal conditions, addressing issues with past work in which participants may learn where to expect violations and may be biased by local context. The cognitive model (Harrison & Pearce, 2018) predicted higher P3a amplitudes, as did Leman’s (2000) model, indicating both sensory and cognitive contributions to expectation violation. However, no model predicted ERAN, N5, or P600 amplitudes, raising questions about whether traditional interpretations of these ERP components generalize to broader collections of stimuli or rather are limited to less naturalistic stimuli.

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 Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory

Energies ◽  
2020 ◽  
Vol 13 (15) ◽  
pp. 4017 ◽  
Author(s):  
Dukhwan Yu ◽  
Wonik Choi ◽  
Myoungsoo Kim ◽  
Ling Liu
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Prediction Accuracy ◽  
Short Term Memory ◽  
Local Context ◽  
Short Term ◽  
Term Memory ◽  
Proposed Model ◽  
Long Short Term Memory ◽  
Pv Power Generation

The problem of Photovoltaic (PV) power generation forecasting is becoming crucial as the penetration level of Distributed Energy Resources (DERs) increases in microgrids and Virtual Power Plants (VPPs). In order to improve the stability of power systems, a fair amount of research has been proposed for increasing prediction performance in practical environments through statistical, machine learning, deep learning, and hybrid approaches. Despite these efforts, the problem of forecasting PV power generation remains to be challenging in power system operations since existing methods show limited accuracy and thus are not sufficiently practical enough to be widely deployed. Many existing methods using long historical data suffer from the long-term dependency problem and are not able to produce high prediction accuracy due to their failure to fully utilize all features of long sequence inputs. To address this problem, we propose a deep learning-based PV power generation forecasting model called Convolutional Self-Attention based Long Short-Term Memory (LSTM). By using the convolutional self-attention mechanism, we can significantly improve prediction accuracy by capturing the local context of the data and generating keys and queries that fit the local context. To validate the applicability of the proposed model, we conduct extensive experiments on both PV power generation forecasting using a real world dataset and power consumption forecasting. The experimental results of power generation forecasting using the real world datasets show that the MAPEs of the proposed model are much lower, in fact by 7.7%, 6%, 3.9% compared to the Deep Neural Network (DNN), LSTM and LSTM with the canonical self-attention, respectively. As for power consumption forecasting, the proposed model exhibits 32%, 17% and 44% lower Mean Absolute Percentage Error (MAPE) than the DNN, LSTM and LSTM with the canonical self-attention, respectively.

Short-term memory models for choice behavior

1980 ◽  
Vol 21 (1) ◽  
pp. 30-52 ◽  
Author(s):  
Byron J.T Morgan ◽  
Chris Robertson
Keyword(s):  
Choice Behavior ◽  
Short Term Memory ◽  
Memory Models ◽  
Short Term ◽  
Term Memory

Dual oscillations as the physiological basis for capacity limits

2001 ◽  
Vol 24 (1) ◽  
pp. 126-126 ◽  
Author(s):  
Ole Jensen ◽  
John E. Lisman
Keyword(s):  
Short Term Memory ◽  
Physiological Model ◽  
Memory Models ◽  
Theta Oscillations ◽  
Short Term ◽  
Physiological Basis ◽  
Term Memory ◽  
Capacity Limits ◽  
Sternberg Task

A physiological model for short-term memory (STM) based on dual theta (5–10 Hz) and gamma (20–60 Hz) oscillation was proposed by Lisman and Idiart (1995). In this model a memory is represented by groups of neurons that fire in the same gamma cycle. According to this model, capacity is determined by the number of gamma cycles that occur within the slower theta cycle. We will discuss here the implications of recent reports on theta oscillations recorded in humans performing the Sternberg task. Assuming that the oscillatory memory models are correct, these findings can help determine STM capacity.

scholarly journals Convolutional Bidirectional Long Short-Term Memory for Deception Detection With Acoustic Features

IEEE Access ◽  
2018 ◽  
Vol 6 ◽  
pp. 76527-76534 ◽  
Author(s):  
Yue Xie ◽  
Ruiyu Liang ◽  
Huawei Tao ◽  
Yue Zhu ◽  
Li Zhao
Keyword(s):  
Short Term Memory ◽  
Deception Detection ◽  
Acoustic Features ◽  
Short Term ◽  
Term Memory ◽  
Long Short Term Memory

Electroconvulsive Treatment and Short Term Memory

1973 ◽  
Vol 122 (570) ◽  
pp. 591-594 ◽  
Author(s):  
M. J. Stones
Keyword(s):  
Retrograde Amnesia ◽  
Recent Work ◽  
Electroconvulsive Shock ◽  
Inverse Relationship ◽  
Short Term Memory ◽  
Short Term ◽  
Electroconvulsive Treatment ◽  
Term Memory

Much of the recent work on electroconvulsive shock has been concerned with retrograde amnesia. These studies have generally produced results consistent with Ribot's Law (1885), which states that the susceptibility of a memory to impairment in retrograde amnesia bears an inverse relationship to the age of the trace (Chorover and Schiller, 1965; Kesner and D'Andrae, 1971).

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