Syftet med studien var att undersöka vilken effekt ljud har på korrekturläsning när texten harett typsnitt som ökar svårigheten på uppgiften (Haettenschweiler) och hur det skiljer sig fråneffekten när texten har ett typsnitt som är mer lättläst (Times New Roman). Experimentethade tre ljudbetingelser: tystnad, roterat tal och tal. En 2×3 variansanalys med upprepadmätning användes för att analysera resultatet. Två typer av fel tillämpades i texterna: stavfeloch kontextuellt felaktiga ord. Resultatet visade att man blir sämre på att hitta kontextuelltfelaktiga funktionsord i talbetingelsen, men bara i Times New Roman, inte iHaettenschweiler. Vidare visar resultatet att man missar färre felstavade ord i talbetingelsenoch att läshastigheten minskar i den betingelsen. Det verkar alltså som det svårlästa typsnittetskyddade försöksdeltagarna från effekten av irrelevant tal när de skulle hitta kontextuelltfelaktiga funktionsord.

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In this study, I examined the relation between various construct relevant and irrelevant variables and a math problem solving assessment. I used independent performance measures representing the variables of mathematics content knowledge, general ability, and reading fluency. Non-performance variables included gender, socioeconomic status, language proficiency and special education qualification. Using a sequential regression and commonality analysis, I determined the amount of variance explained by each performance measure on the Oregon state math assessment in third grade. All variables were independently predictive of math problem solving scores, and used together, they explained 58% score variance. The math content knowledge measure explained the most variance uniquely (12%), and the measures of math content and general ability explained the most variance commonly (16%). In the second analysis, I investigated whether additional variance was explained once student demographic characteristics were controlled and how this affected the unique variance explained by each independent performance measure. By controlling for demographics, the model explained slightly more than 1% additional variance in math scores. The unique variance explained by each independent measure decreased slightly. This study highlighted the influence of various construct relevant and irrelevant variables on math problem solving scores, including the extent to which a language-free measure of general ability might help to inform likely outcomes. The use of variance partitioning expanded understanding of the unique and common underlying constructs that affect math problem solving assessment. Finally, this study provided more information regarding the influence demographic information has on outcomes related to state math assessments.

This study looks at the effects of silence as compared to two different types of music and one type of irrelevant speech to analyze the effects on an assessment of 4 categories of mathematical questions. The hypothesis tested was that students would perform best when subject to no distraction (silence), followed closely by non-verbal music (dubstep), while verbal music (Rap) and irrelevant self-speech (repeating the word ‘za’) would result in a decrease in performance. The hypothesis was not found to be statistically significant, but a general trend supporting the hypothesis was present and found to be consistent with similar research. / text

An effective alarm system is a critical part of many different types of jobs. It is also important that the alarm signal can capture human attention and convey appropriate urgency. In the current study the effect of siren sounds with or without unexpected, deviant sounds represented by a change of speed (a temporal deviant) were tested to evaluate if such change could successfully capture attention. The results showed that distraction was more pronounced when the deviant within the sound was a change from fast to slow as compared with slow to fast. Therefore, an alarm signal using a temporal deviant – changing from fast to slow—can be effective in capturing human attention and might be factored into the design of alarm systems.

The irrelevant sound effect (ISE) describes the disruption of processes involved in maintaining information in working memory (WM) when irrelevant noise is present in the environment. While some posit that the ISE arises due to split obligation of attention to the irrelevant sound and the to-be-remembered information, others have argued that background noise corrupts the order of information within WM. Support for the latter position comes from research showing that the ISE appears to be most robust in tasks that emphasize ordered maintenance by a serial rehearsal strategy, and diminished when rehearsal is discouraged or precluded by task characteristics. Evidence supporting such a stance has been used to create a narrow narrative in which the ISE should only emerge on tasks with ordered output demands, when a serial rehearsal strategy is used, and in the presence of changing-state auditory distractor sequences. However, an ISE has been documented in many situations that do not match the scenario described above, thus raising questions as to what specific factors and combination of factors give rise to the ISE. The present study aims to disentangle each of the proposed contributing variables to the ISE by using eight working memory tasks that vary based on demands and features in the presence of multiple sound conditions. Further, strategy use is assessed on a task-by-task basis using an informed, multi-step process. The results reveal patterns of the ISE that do not match the claims made by rehearsal-disruption nor attentional accounts, and instead support a narrative in which poor cognitive control likely leads to the adoption of ineffective strategies for memory maintenance, and the combination of such factors increases one’s susceptibility to disruption by irrelevant sounds. / Psychology

<p>Convolutional Neural Networks (CNNs) are machine learning models that are commonly used for computer vision tasks like image classification and object detection. State-of-the-art CNNs achieve high accuracy by using many convolutional filters to extract features from the input images for correct predictions. This high accuracy is achieved at the cost of high computational intensity. Large, accurate CNNs typically require powerful Graphics Processing Units (GPUs) to train and deploy, while attempts at creating smaller, less computationally-intense CNNs lose accuracy. In fact, maintaining consistent accuracy is a challenge for even the state-of-the-art CNNs. This presents a problem: the vast energy expenditure demanded by CNN training raises concerns about environmental impact and sustainability, while the computational intensity of CNN inference makes it challenging for low-power devices (e.g. embedded, mobile, Internet-of-Things) to deploy the CNNs on their limited hardware. Further, when reliable network is limited or when extremely low latency is required, the cloud cannot be used to offload computing from the low-power device, forcing a need to research methods to deploy CNNs on the device itself: to improve energy efficiency and mitigate consistency and accuracy losses of CNNs.</p> <p>This dissertation investigates causes of CNN accuracy inconsistency and energy consumption. We further propose methods to improve both, enabling CNN deployment on low-power devices. Our methods do not require training to avoid the high energy costs associated with training.</p> <p>To address accuracy inconsistency, we first design a new metric to properly capture such behavior. We conduct a study of modern object detectors to find that they all exhibit inconsistent behavior. That is, when two images are similar, an object detector can sometimes produce completely different predictions. Malicious actors exploit this to cause CNNs to mispredict, while  image distortions caused by camera equipment and natural phenomena can also cause mispredictions. Regardless the cause of the misprediction, we find that modern accuracy metrics do not capture this behavior, and we create a new consistency metric to measure the behavior. Finally, we demonstrate the use of image processing techniques to improve CNN consistency on modern object detection datasets.</p> <p>To improve CNN energy efficiency and reduce inference latency, we design the focused convolution operation. We observe that in a given image, many pixels are often irrelevant to the computer vision task -- if the pixels are deleted, the CNN can still give the correct prediction. We design a method to use a depth mapping neural network to identify which pixels are irrelevant in modern computer vision datasets. Next, we design the focused convolution to automatically ignore any pixels marked irrelevant outside the Area of Interest (AoI). By replacing the standard convolutional operations in CNNs with our focused convolutions, we find that ignoring those irrelevant pixels can save up to 45% energy and inference latency. </p> <p>Finally, we improve the focused convolutions, allowing for (1) energy-efficient, automated AoI generation within the CNN itself and (2) improved memory alignment and better utilization of parallel processing hardware. The original focused convolution required AoI generation in advance, using a computationally-intense depth mapping method. Our AoI generation technique automatically filters the features from the early layers of a CNN using a threshold. The threshold is determined using an Accuracy vs Latency curve search method. The remaining layers will apply focused convolutions to the AoI to reduce energy use. This will allow focused convolutions to be deployed within any pretrained CNN for various observed use cases. No training is required.</p>

Contemporary learning theories derive much of their explanatory power from the assumption that all stimuli presented vie for associative strength, the assumption of Shared Weight Space (SWS). Theories based on this assumption have proven successful in explaining many of the observed conditioning phenomena in animals. However, work with humans has proven more complex due to outside knowledge, biases, and heuristics (see, e.g., Chapman, 1991; Msetfi et al., 2005; Perales et al., 2004; Tversky & Kahneman, 1974; Viken et al., 2005; Waldmann, 2000 & 2001). The present series of experiments sought to test the assumption of SWS in a task that is less susceptible to the influence of "top-down" factors. An information processing task (i.e., the correlated flankers task) was used so that human participants were completing a central task (i.e., responding to the target) and were unaware as to the importance of the contingencies in the designs, yet were still exposed to them via the irrelevant information (i.e., flankers). Four compound conditioning phenomena were studied in order to test the assumption of SWS. Evidence for the simple predictions coming from SWS theories was mixed. However, a slightly more complex version of these theories can explain the entire pattern of data quite elegantly.

Objectives.The general objectives of this dissertationwere to examine the effects of acute exposure to meaningfulirrelevant speech and road traffic noise on memory performance,and to explore annoyance responses to noise exposure in theschool environment for pupils and teachers in different agegroups. Methods. The thesis comprises seven papers, representingdifferent methodological approaches: experiments, surveystudies and interviews. In the experiments, reported in PapersI-V, 288 pupils and teachers participated in the age groups,13-14 years (n=96), 18-20 years (n=96), 35-45 years (n=48) and55-65 years (n=48). The subjects were randomly assigned to oneof three conditions: (a) meaningful irrelevant speech, (b) roadtraffic noise, and (c) silence. The equivalent sound level inthe noise conditions was set to 66 dB(A). A test batteryreflecting episodic and semantic memory were used. The surveystudies, reported in Paper VI and VII, included 207 pupils(M=13.5) and 166 teachers (M=45.9). Two separate questionnairesmainly comprising items about annoyance, noise sensitivity andstress symptoms were administered. Paper VI presents results offocus group interviews (n=16) treating the main topics:disturbing sounds, emotions, ongoing activity, and suggestionsconcerning future changes. Results. The overall findings showedthat both noise sources affected episodic and semantic memoryto the same degree for all age groups. The results indicatedthat the similarity of semantic content between noise and thetask at hand was not the only suitable explanation model, sincea non-speech noise impaired memory as much as speech. Resultsalso indicated that attention effects did notmediate the obtained noise effects and that the noise effectsdid not differ between age groups. Therefore, it seemedunlikely that different memory and attentional capacities stoodout as explanatory factors of the memory effects. Sinceperformances of both episodic and semantic memory tasks wereimpaired, the explanation based on level of access to long-termmemory was also ruled out. However, the episodic memory task,reading comprehension, stood out to be most impaired by noise,suggesting that complexity of the task to perform was ofimportance. For reading comprehension there was also adifferent noise pattern obtained. Participants performance wasin this task, more impaired by meaningful irrelevant speechthan by road traffic noise. This effect indicated thatmeaningful irrelevant speech might reduce the availablecognitive resources necessary for learning the text. Theannoyance models derived from the survey studies indicated thatsensitivity acted as a mediator between hearing status andannoyance, with stress symptoms as an outcome. Whetherannoyance arises or not was also determined by control andpredictability of the noise. In the interviews a differentannoyance pattern was found, in that stress symptoms appearedto be a determinant of annoyance. To be involved, respected,take own responsibility and respect others were suggestions onhow to change the environment to become more silent. Conclusions.For both pupils and teachers acute exposureto meaningful irrelevant speech and road traffic noiseinfluenced both the achieving and providing of knowledge. Acommon annoyance pattern was also found for pupils andteachers, where individual and situational factors were ofimportance. To achieve a more silent school environment in thefuture, the pupils pointed out that the interaction betweenthemselves and their teachers was of importance. Key words:Noise, meaningful irrelevant speech, roadtraffic noise, memory, age groups, school environment, pupils,teachers

Objectives.The general objectives of this dissertationwere to examine the effects of acute exposure to meaningfulirrelevant speech and road traffic noise on memory performance,and to explore annoyance responses to noise exposure in theschool environment for pupils and teachers in different agegroups. Methods. The thesis comprises seven papers, representingdifferent methodological approaches: experiments, surveystudies and interviews. In the experiments, reported in PapersI-V, 288 pupils and teachers participated in the age groups,13-14 years (n=96), 18-20 years (n=96), 35-45 years (n=48) and55-65 years (n=48). The subjects were randomly assigned to oneof three conditions: (a) meaningful irrelevant speech, (b) roadtraffic noise, and (c) silence. The equivalent sound level inthe noise conditions was set to 66 dB(A). A test batteryreflecting episodic and semantic memory were used. The surveystudies, reported in Paper VI and VII, included 207 pupils(M=13.5) and 166 teachers (M=45.9). Two separate questionnairesmainly comprising items about annoyance, noise sensitivity andstress symptoms were administered. Paper VI presents results offocus group interviews (n=16) treating the main topics:disturbing sounds, emotions, ongoing activity, and suggestionsconcerning future changes. Results. The overall findings showedthat both noise sources affected episodic and semantic memoryto the same degree for all age groups. The results indicatedthat the similarity of semantic content between noise and thetask at hand was not the only suitable explanation model, sincea non-speech noise impaired memory as much as speech. Resultsalso indicated that attention effects did notmediate the obtained noise effects and that the noise effectsdid not differ between age groups. Therefore, it seemedunlikely that different memory and attentional capacities stoodout as explanatory factors of the memory effects. Sinceperformances of both episodic and semantic memory tasks wereimpaired, the explanation based on level of access to long-termmemory was also ruled out. However, the episodic memory task,reading comprehension, stood out to be most impaired by noise,suggesting that complexity of the task to perform was ofimportance. For reading comprehension there was also adifferent noise pattern obtained. Participants performance wasin this task, more impaired by meaningful irrelevant speechthan by road traffic noise. This effect indicated thatmeaningful irrelevant speech might reduce the availablecognitive resources necessary for learning the text. Theannoyance models derived from the survey studies indicated thatsensitivity acted as a mediator between hearing status andannoyance, with stress symptoms as an outcome. Whetherannoyance arises or not was also determined by control andpredictability of the noise. In the interviews a differentannoyance pattern was found, in that stress symptoms appearedto be a determinant of annoyance. To be involved, respected,take own responsibility and respect others were suggestions onhow to change the environment to become more silent. Conclusions.For both pupils and teachers acute exposureto meaningful irrelevant speech and road traffic noiseinfluenced both the achieving and providing of knowledge. Acommon annoyance pattern was also found for pupils andteachers, where individual and situational factors were ofimportance. To achieve a more silent school environment in thefuture, the pupils pointed out that the interaction betweenthemselves and their teachers was of importance. Key words:Noise, meaningful irrelevant speech, roadtraffic noise, memory, age groups, school environment, pupils,teachers