scholarly journals Audiovisual Learning in Dyslexic and Typical Adults: Modulating Influences of Location and Context Consistency

2021 ◽  
Vol 12 ◽  
Author(s):  
Simone L. Calabrich ◽  
Gary M. Oppenheim ◽  
Manon W. Jones
Keyword(s):  
Episodic Memory ◽  
Learning Process ◽  
Contextual Information ◽  
Stimulus Frequency ◽  
Error Rates ◽  
Auditory Information ◽  
The Novel ◽  
Adult Readers ◽  
Multiple Exposures ◽  
Letter Sound

Learning to read involves efficient binding of visual to auditory information. Aberrant cross-modal binding skill has been observed in both children and adults with developmental dyslexia. Here, we examine the contribution of episodic memory to acquisition of novel cross-modal bindings in typical and dyslexic adult readers. Participants gradually learned arbitrary associations between unfamiliar Mandarin Chinese characters and English-like pseudowords over multiple exposures, simulating the early stages of letter-to-letter sound mapping. The novel cross-modal bindings were presented in consistent or varied locations (i.e., screen positions), and within consistent or varied contexts (i.e., co-occurring distractor items). Our goal was to examine the contribution, if any, of these episodic memory cues (i.e., the contextual and spatial properties of the stimuli) to binding acquisition, and investigate the extent to which readers with and without dyslexia would differ in their reliance on episodic memory during the learning process. Participants were tested on their ability to recognize and recall the bindings both during training and then in post-training tasks. We tracked participants’ eye movements remotely with their personal webcams to assess whether they would re-fixate relevant empty screen locations upon hearing an auditory cue—indicative of episodic memory retrieval—and the extent to which the so-called “looking-at-nothing behavior” would modulate recognition of the novel bindings. Readers with dyslexia both recognized and recalled significantly fewer bindings than typical readers, providing further evidence of their persistent difficulties with cross-modal binding. Looking-at-nothing behavior was generally associated with higher recognition error rates for both groups, a pattern that was particularly more evident in later blocks for bindings encoded in the inconsistent location condition. Our findings also show that whilst readers with and without dyslexia are capable of using stimulus consistencies in the input—both location and context—to assist in audiovisual learning, readers with dyslexia appear particularly reliant on consistent contextual information. Taken together, our results suggest that whilst readers with dyslexia fail to efficiently learn audiovisual binding as a function of stimulus frequency, they are able to use stimulus consistency—aided by episodic recall—to assist in the learning process.

scholarly journals Embedding Computer Vision in Citizen Science

2019 ◽  
Vol 3 ◽  
Author(s):  
Tom August ◽  
J Terry ◽  
David Roy
Keyword(s):  
Decision Making ◽  
Computer Vision ◽  
Data Quality ◽  
Citizen Science ◽  
Learning Process ◽  
Contextual Information ◽  
Limited Resource ◽  
Machine Learning Techniques ◽  
Ecological Knowledge ◽  
Time Of Year

The rapid rise of Artificial Intelligence (AI) methods has presented new opportunities for those who work with biodiversity data. Computer vision, in particular where computers can be trained to identify species in digital photographs, has significant potential to address a number of existing challenges in citizen science. The Biological Records Centre (www.brc.ac.uk) has been a central focus for terrestrial and freshwater citizen science in the United Kingdom for over 50 years. We will present our research on how computer vision can be embedded in citizen science, addressing three important questions. How can contextual information, such as time of year, be included in computer vision? A naturalist will use a wealth of ecological knowledge about species in combination with information about where and when the image was taken to augment their decision making; we should emulate this in our AI. How can citizen scientists be best supported by computer vision? Our ambition is not to replace identification skills with AI but to use AI to support the learning process. How can computer vision support our limited resource of expert verifiers as data volumes increase? We receive more and more data each year, which puts a greater demand on our expert verifiers, who review all records to ensure data quality. We have been exploring how computer vision can lighten this workload. How can contextual information, such as time of year, be included in computer vision? A naturalist will use a wealth of ecological knowledge about species in combination with information about where and when the image was taken to augment their decision making; we should emulate this in our AI. How can citizen scientists be best supported by computer vision? Our ambition is not to replace identification skills with AI but to use AI to support the learning process. How can computer vision support our limited resource of expert verifiers as data volumes increase? We receive more and more data each year, which puts a greater demand on our expert verifiers, who review all records to ensure data quality. We have been exploring how computer vision can lighten this workload. We will present work that addresses these questions including: developing machine learning techniques that incorporate ecological information as well as images to arrive at a species classification; co-designing an identification tool to help farmers identify flowers beneficial to wildlife; and assessing the optimal combination of computer vision and expert verification to improve our verification systems.

scholarly journals Design of a multimodal hearing system

2013 ◽  
Vol 10 (1) ◽  
pp. 483-501 ◽  
Author(s):  
Bernd Tessendorf ◽  
Matjaz Debevc ◽  
Peter Derleth ◽  
Manuela Feilner ◽  
Franz Gravenhorst ◽  
...  
Keyword(s):  
Contextual Information ◽  
Body Movement ◽  
Audio Signal ◽  
The Novel ◽  
Missing Information ◽  
Sound Processing ◽  
Acoustic Environment ◽  
Novel Approach ◽  
Hearing Instruments ◽  
Hearing System

Hearing instruments (HIs) have become context-aware devices that analyze the acoustic environment in order to automatically adapt sound processing to the user?s current hearing wish. However, in the same acoustic environment an HI user can have different hearing wishes requiring different behaviors from the hearing instrument. In these cases, the audio signal alone contains too little contextual information to determine the user?s hearing wish. Additional modalities to sound can provide the missing information to improve the adaption. In this work, we review additional modalities to sound in HIs and present a prototype of a newly developed wireless multimodal hearing system. The platform takes into account additional sensor modalities such as the user?s body movement and location. We characterize the system regarding runtime, latency and reliability of the wireless connection, and point out possibilities arising from the novel approach.

Relationships in the Learning Process of Reading Comprehension in Spanish and English

2020 ◽  
pp. 281-301
Author(s):  
Raúl Gutiérrez-Fresneda
Keyword(s):  
Second Language ◽  
Experimental Design ◽  
Learning Process ◽  
Mother Tongue ◽  
Contextual Information ◽  
Complex Task ◽  
Written Information ◽  
Number Of Factors ◽  
Post Test ◽  
Quasi Experimental

English is one of the most studied and used languages worldwide. The process of acquiring reading is a complex task that involves mastering a set of strategies aimed at assimilating written information by the reader. Different studies have shown that the process of understanding reading in the mother tongue has certain similarities with this same learning in English because in both situations semantic and contextual information is used, but there are also several authors who point out that there are distinctions between reading models in a first and second language. This chapter delves into these relationships, which focus on analysing the variables that most influence the learning of comprehensive capacity in Spanish and English. A quasi-experimental design of comparison between groups with pre-test and post-test measurements was used. The study involved 120 students aged between 8 and 9. The results indicate that there are a number of factors that are related in learning to read in Spanish and English.

scholarly journals A Novel Mutual Information Based Feature Set for Drivers’ Mental Workload Evaluation Using Machine Learning

2020 ◽  
Vol 10 (8) ◽  
pp. 551
Author(s):  
Mir Riyanul Islam ◽  
Shaibal Barua ◽  
Mobyen Uddin Ahmed ◽  
Shahina Begum ◽  
Pietro Aricò ◽  
...  
Keyword(s):  
Machine Learning ◽  
Mutual Information ◽  
Mental Workload ◽  
Contextual Information ◽  
Absolute Error ◽  
The Novel ◽  
Promising Technique ◽  
Extensive Evaluation ◽  
Target Values ◽  
Classification Tasks

Analysis of physiological signals, electroencephalography more specifically, is considered a very promising technique to obtain objective measures for mental workload evaluation, however, it requires a complex apparatus to record, and thus, with poor usability in monitoring in-vehicle drivers’ mental workload. This study proposes a methodology of constructing a novel mutual information-based feature set from the fusion of electroencephalography and vehicular signals acquired through a real driving experiment and deployed in evaluating drivers’ mental workload. Mutual information of electroencephalography and vehicular signals were used as the prime factor for the fusion of features. In order to assess the reliability of the developed feature set mental workload score prediction, classification and event classification tasks were performed using different machine learning models. Moreover, features extracted from electroencephalography were used to compare the performance. In the prediction of mental workload score, expert-defined scores were used as the target values. For classification tasks, true labels were set from contextual information of the experiment. An extensive evaluation of every prediction tasks was carried out using different validation methods. In predicting the mental workload score from the proposed feature set lowest mean absolute error was 0.09 and for classifying mental workload highest accuracy was 94%. According to the outcome of the study, it can be stated that the novel mutual information based features developed through the proposed approach can be employed to classify and monitor in-vehicle drivers’ mental workload.

Teaching the Novel in the Audio Age

PMLA ◽  
2020 ◽  
Vol 135 (2) ◽  
pp. 419-426
Author(s):  
James F. English
Keyword(s):  
United States ◽  
New York ◽  
Publishing Industry ◽  
The United States ◽  
First Century ◽  
The Novel ◽  
Adult Readers ◽  
Customer Base ◽  
The United Kingdom ◽  
Steep Decline

According to Practically Every Metric of the Publishing Industry, Audiobooks are Winning the Format Wars. The Codex continues its twenty-first-century struggle to maintain market share, and the e-book has plunged into a steep decline, but the audiobook goes from strength to strength. Sales in the United States are up threefold in the last decade and more than fifty percent just in the last two years (“New Survey”; Watson). In the United Kingdom, unit sales have doubled and revenues tripled since 2014 (“Michelle Obama”). Roughly a quarter of adults in the United States, and half of all adult readers, now listen to at least one audiobook a year. To service this swelling customer base, the industry is producing over five thousand new full-length recordings every month, ten times as many as a decade ago. Audible's studio division has become the largest employer of actors in the New York City area (Kozlowski).

scholarly journals Traces of semantization - from episodic to semantic memory in a spiking cortical network model

2021 ◽  
Author(s):  
Nikolaos Chrysanthidis ◽  
Florian Fiebig ◽  
Anders Lansner ◽  
Pawel Herman
Keyword(s):  
Neural Network ◽  
Episodic Memory ◽  
Semantic Memory ◽  
Network Model ◽  
Hebbian Learning ◽  
Contextual Information ◽  
Memory Task ◽  
Learning Rule ◽  
Mechanistic Explanation ◽  
Spike Timing

Episodic memory is the recollection of past personal experiences associated with particular times and places. This kind of memory is commonly subject to loss of contextual information or "semantization", which gradually decouples the encoded memory items from their associated contexts while transforming them into semantic or gist-like representations. Novel extensions to the classical Remember/Know behavioral paradigm attribute the loss of episodicity to multiple exposures of an item in different contexts. Despite recent advancements explaining semantization at a behavioral level, the underlying neural mechanisms remain poorly understood. In this study, we suggest and evaluate a novel hypothesis proposing that Bayesian-Hebbian synaptic plasticity mechanisms might cause semantization of episodic memory. We implement a cortical spiking neural network model with a Bayesian-Hebbian learning rule called Bayesian Confidence Propagation Neural Network (BCPNN), which captures the semantization phenomenon and offers a mechanistic explanation for it. Encoding items across multiple contexts leads to item-context decoupling akin to semantization. We compare BCPNN plasticity with the more commonly used spike-timing dependent plasticity (STDP) learning rule in the same episodic memory task. Unlike BCPNN, STDP does not explain the decontextualization process. We also examine how selective plasticity modulation of isolated salient events may enhance preferential retention and resistance to semantization. Our model reproduces important features of episodicity on behavioral timescales under various biological constraints whilst also offering a novel neural and synaptic explanation for semantization, thereby casting new light on the interplay between episodic and semantic memory processes.

scholarly journals Deep Learning-Based Self-Driving Car: JetBot with NVIDIA AI Board to Deliver Items at Agricultural Workplace with Object-Finding and Avoidance Functions

2020 ◽  
Vol 2 (3) ◽  
Author(s):  
Shinji Kawakura ◽  
Ryosuke Shibasaki
Keyword(s):  
Deep Learning ◽  
Structural Features ◽  
Error Rates ◽  
Stress Disorders ◽  
The Novel ◽  
Pet Bottles ◽  
System 2 ◽  
Heat Stress Disorders ◽  
Experimental Trials ◽  
Working Situation

In this study, we attempt to develop a deep learning-based self-driving car system to deliver items (e.g., harvested onions, agri-tools, PET bottles) to agricultural (agri-) workers at an agri-workplace. The system is based around a car-shaped robot, JetBot, with an NVIDIA artificial intelligence (AI) oriented board. JetBot can find diverse objects and avoid them. We implemented experimental trials at a real warehouse where various items (glove, boot, sickle (falx), scissors, and hoe), called obstacles, were scattered. The assumed agri-worker was a man suspending dried onions on a beam. Specifically, we developed a system focusing on the function of precisely detecting obstacles with deep learning-based techniques (techs), self-avoidance, and automatic delivery of small items for manual agri-workers and managers. Both the car-shaped figure and the deep learning-based obstacles-avoidance function differ from existing mobile agri-machine techs and products with respect to their main aims and structural features. Their advantages are their low costs in comparison with past similar mechanical systems found in the literature and similar commercial goods. The robot is extremely agile and easily identifies and learns obstacles. Additionally, the JetBot kit is a minimal product and includes a feature allowing users to arbitrarily expand and change functions and mechanical settings. This study consists of six phases: (1) designing and confirming the validity of the entire system, (2) constructing and tuning various minor system settings (e.g., programs and JetBot specifications), (3) accumulating obstacle picture data, (4) executing deep learning, (5) conducting experiments in an indoor warehouse to simulate a real agri-working situation, and (6) assessing and discussing the trial data quantitatively (presenting the success and error rates of the trials) and qualitatively. We consider that from the limited trials, the system can be judged as valid to some extent in certain situations. However, we were unable to perform more broad or generalizable experiments (e.g., execution at mud farmlands and running JetBot on non-flat floor). We present experimental ranges for the success ratio of these trials, particularly noting crashed obstacle types and other error types. We were also able to observe features of the system’s practical operations. The novel achievements of this study lie in the fusion of recent deep learning-based agricultural informatics. In the future, agri-workers and their managers could use the proposed system in real agri-places as a common automatic delivering system. Furthermore, we believe, by combining this application with other existing systems, future agri-fields and other workplaces could become more comfortable and secure (e.g., delivering water bottles could avoid heat (stress) disorders).

scholarly journals Yaqui Delgado Wants to Kick Your Ass by M. Medina

2015 ◽  
Vol 4 (4) ◽  
Author(s):  
Rachel Wild
Keyword(s):  
High School ◽  
Young Adult ◽  
School Library ◽  
The Novel ◽  
Latino Culture ◽  
Adult Readers ◽  
Self Image ◽  
The Everyday ◽  
The Face ◽  
High School Library

Medina, Meg. Yaqui Delgado Wants to Kick Your Ass. Massachusetts: Candlewick Press, 2014. Print.Piddy Sanchez has only been at Daniel Jones High School for five weeks when a classmate tells her “Yaqui Delgado wants to kick your ass”. Unknowingly and inexplicably, Piddy becomes the target of the fierce Yaqui Delgado and her gang.A winner of the 2014 Pura Belpré Author Award, Meg Medina creates an unflinching portrayal of Piddy as she struggles to maintain her identity and dignity in the face of extreme bullying. Medina’s depiction of Piddy is honest and readers will readily identify with the everyday adolescent problems she deals with; self-image, school, family and relationships. Medina addresses the topic of bullying in a manner that is realistic and does not provide easy solutions for Piddy or the reader.A  2014 Top Ten Quick Picks for Reluctant Young Adult Readers, this book is a well-paced read, divided into short chapters making it highly accessible to a variety of readers. The book is rich in dialogue and Medina does an excellent job of creating fully-developed characters who struggle with all-too-human flaws and foibles.Some students may struggle with specific references to Latino culture, but the themes and topics in the novel are universal issues that the majority of adolescents will connect with.  Because it addresses difficult topics such as bullying and adolescent sexuality this book may not appeal to all readers and should be considered a mature read.This book should be considered an excellent addition to any high school library or classroom, particularly for students or educators who are searching for a book that depicts the issue of bullying in a manner that is honest and realistic.Highly recommended: 4 out of 4 starsReviewer: Rachel WildRachel Wild is an English teacher at Parkland Composite High School in Edson, Alberta. She is currently enrolled the Teacher Librarianship Masters degree program through distance education. Reading and reviewing a plethora of young adult novels has renewed her interest in and passion for this genre.

scholarly journals Integration of LabVIEW for Novel Interactive Learning Courseware on Digital Electronics

2014 ◽  
Vol 2 (11) ◽  
pp. 156-163
Author(s):  
Yap Wing Fen ◽  
Luqman Al-Hakim Mohd Sabri
Keyword(s):  
Learning Process ◽  
Interactive Learning ◽  
Higher Learning ◽  
The Novel ◽  
Digital Electronics ◽  
Virtual Laboratories ◽  
Hands On ◽  
Important Field ◽  
Almost All ◽  
Learning Institutions

Digital electronics involves communication between systems or instruments in digital form. Digital electronics is an important field in physics and engineering, and included in the syllabus in almost all higher learning institutions. The main objective of this study is to develop an interactive learning courseware for Digital Electronics with the integration of LabVIEW applications in order to facilitate the learning process of Digital Electronics. The novel developed courseware mainly covers the basics of digital electronics. The integration of LabVIEW enables students to get hands on real time experiences as in a real laboratory. These virtual laboratories can be accessible anytime and anywhere. Students can interact with the courseware which makes the learning process more dynamic.

scholarly journals Practice induces a qualitative change in the memory representation for visuomotor learning

2017 ◽  
Author(s):  
David M. Huberdeau ◽  
John W. Krakauer ◽  
Adrian M. Haith
Keyword(s):  
Learning Process ◽  
Prior Experience ◽  
Qualitative Change ◽  
Skill Learning ◽  
Explicit Learning ◽  
Memory Representation ◽  
Interference Effects ◽  
Visuomotor Learning ◽  
Multiple Exposures ◽  
Selection Of

AbstractAdaptation of our movements to changes in the environment is known to be supported by multiple learning processes which act in parallel. An implicit process recalibrates motor output to maintain alignment between intended and observed movement outcomes (“implicit recalibration”). In parallel, an explicit learning process drives more strategic adjustments of behavior, often by deliberately aiming movements away from an intended target (“deliberate re-aiming”). It has long been established that people form a memory for prior experience adapting to a perturbation through the fact that they become able to more rapidly adapt to familiar perturbations (a phenomenon known as “savings”). Repeated exposures to the same perturbation can further strengthen savings. It remains unclear, however, which underlying learning process is responsible for this practice-related improvement in savings. We measured the relative contributions of implicit recalibration and deliberate re-aiming to adaptation during multiple exposures to an alternating sequence of perturbations over two days. We found that the implicit recalibration followed an invariant learning curve despite prolonged practice, and thus exhibited no memory of prior experience. Instead, practice led to a qualitative change in re-aiming which, in addition to supporting savings, became able to be expressed rapidly and automatically. This qualitative change appeared to enable participants to form memories for two opposing perturbations, overcoming interference effects that typically prohibit savings when learning multiple, opposing perturbations. Our results are consistent with longstanding theories that frame skill learning as a transition from deliberate to automatic selection of actions.

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