The credibility of simulation-based environments : user judgments of verisimilitude

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

At 6-9 months, human infants know the meanings of many common nouns.

It is widely accepted that infants begin learning their native language not by learning words, but by discovering features of the speech signal: consonants, vowels, and combinations of these sounds. Learning to understand words, as opposed to just perceiving their sounds, is said to come later, between 9 and 15 mo of age, when infants develop a capacity for interpreting others' goals and intentions. Here, we demonstrate that this consensus about the developmental sequence of human language learning is flawed: in fact, infants already know the meanings of several common words from the age of 6 mo onward. We presented 6- to 9-mo-old infants with sets of pictures to view while their parent named a picture in each set. Over this entire age range, infants directed their gaze to the named pictures, indicating their understanding of spoken words. Because the words were not trained in the laboratory, the results show that even young infants learn ordinary words through daily experience with language. This surprising accomplishment indicates that, contrary to prevailing beliefs, either infants can already grasp the referential intentions of adults at 6 mo or infants can learn words before this ability emerges. The precocious discovery of word meanings suggests a perspective in which learning vocabulary and learning the sound structure of spoken language go hand in hand as language acquisition begins.

The credibility of simulation-based environments : user judgments of verisimilitude

Mémoire numérisé par la Direction des bibliothèques de l'Université de Montréal.

Improving innovation and performance of Social Enterprises: Knowing what they know

Social Enterprises (SEs) are normally micro and small businesses that trade to tackle social problems, and to improve communities, people’s life chances, and the environment. Thus, their importance to society and economies is increasing. However, there is still a need for more understanding of how these organisations operate, perform, innovate and scale-up. This knowledge is crucial to design and provide accurate strategies to enhance the sector and increase its impact and coverage. Obtaining this understanding is the main driver of this paper, which follows the theoretical lens of the Knowledge-based View (KBV) theory to develop and assess empirically a novel model for knowledge management capabilities (KMCs) development that improves performance of SEs. The empirical assessment consisted of a quantitative study with 432 owners and senior members of SEs in UK, underpinned by 21 interviews. The findings demonstrate how particular organisational characteristics of SEs, the external conditions in which they operate, and informal knowledge management activities, have created overall improvements in their performance of up to 20%, based on a year-to-year comparison, including innovation and creation of social and environmental value. These findings elucidate new perspectives that can contribute not only to SEs and SE supporters, but also to other firms.

Applied behavior analysis: What do teachers of students with autism spectrum disorder know?

With rising numbers of school-aged children with autism educated in mainstream classroomsand applied behavior analysis (ABA) considered the basis of best practice, teachers’ knowledgein this field has become a key concern for inclusion. Self-reported knowledge of ABA of specialneeds teachers (n=165) was measured and compared to their actual knowledge of ABAdemonstrated in accurate responses to a multiple-choice test. Findings reported here show thatteachers’ self-perceived knowledge exceeded actual knowledge and that actual knowledge ofABA was not

The influence of genotype information on psychiatrists' treatment recommendations: more experienced clinicians know better what to ignore

Objectives: To assess if psychiatrists were influenced by a patient’s genetic information, even when the patient’s response to treatment was already known to them. Methods: Sixty-seven psychiatrists were presented with patients' pre and post-treatment scores on the PANSS for two hypothetical treatments for schizophrenia. Psychiatrists were also informed whether the patient possessed a genotype linked to hyper-responsiveness to one of the treatments, and were asked to recommend one of these two treatments. Attribute non-attendance assessed whether the information on genotype influenced psychiatrists' treatment recommendations. Results: Years of experience predicted whether psychiatrists were influenced by the genetic information. Psychiatrists with one year or less of experience had a 46% probability of considering genetic information, while psychiatrists with at least 15 years of experience had a lower probability (7%). Conclusions: Psychiatrists and other clinicians should be cautious about allowing a patient's genetic information to carry unnecessary weight in their clinical decision making.

“I know, therefore I adapt?” Complexities of individual adaptation to climate-induced forest dieback in Alaska

Individual actions to avoid, benefit from, or cope with climate change impacts partly shape adaptation; much research on adaptation has focused at the systems level, overlooking drivers of individual responses. Theoretical frameworks and empirical studies of environmental behavior identify a complex web of cognitive, affective, and evaluative factors that motivate stewardship. We explore the relationship between knowledge of, and adaptation to, widespread, climate-induced tree mortality to understand the cognitive (i.e., knowledge and learning), affective (i.e., attitudes and place attachment), and evaluative (i.e., use values) factors that influence how individuals respond to climate-change impacts. From 43 semistructured interviews with forest managers and users in a temperate forest, we identified distinct responses to local, climate-induced environmental changes that we then categorized as either behavioral or psychological adaptations. Interviewees developed a depth of knowledge about the dieback through a combination of direct, place-based experiences and indirect, mediated learning through social interactions. Knowing that the dieback was associated with climate change led to different adaptive responses among the interviewees, although knowledge alone did not explain this variation. Forest users reported psychological adaptations to process negative attitudes; these adaptations were spurred by knowledge of the causes, losses of intangible values, and impacts to a species to which they held attachment. Behavioral adaptations exclusive to a high level of knowledge included actions such as using the forests to educate others or changing transportation behaviors to reduce personal energy consumption. Managers integrated awareness of the dieback and its dynamics across spatial scales into current management objectives. Our findings suggest that adaptive management may occur from the bottom up, as individual managers implement new practices in advance of policies. As knowledge of climate-change impacts in local environments increases, resource users may benefit from programs and educational interventions that facilitate coping strategies.

‘We do not know exactly what we are going to say until we have said it’: interview data on how poems are made

Know Your Rights, June 24, 2016

In Iowa, the Managed Care Ombudsman Program was established to advocate for the rights and wishes of Medicaid managed care members who receive care in a health care facility, assisted living program or elder group home, as well as members enrolled in one of the following seven home and community-based services (HCBS) waiver programs.

Microblogging Temporal Summarization: Filtering Important Twitter Updates for Breaking News

While news stories are an important traditional medium to broadcast and consume news, microblogging has recently emerged as a place where people can dis- cuss, disseminate, collect or report information about news. However, the massive information in the microblogosphere makes it hard for readers to keep up with these real-time updates. This is especially a problem when it comes to breaking news, where people are more eager to know “what is happening”. Therefore, this dis- sertation is intended as an exploratory effort to investigate computational methods to augment human effort when monitoring the development of breaking news on a given topic from a microblog stream by extractively summarizing the updates in a timely manner. More specifically, given an interest in a topic, either entered as a query or presented as an initial news report, a microblog temporal summarization system is proposed to filter microblog posts from a stream with three primary concerns: topical relevance, novelty, and salience. Considering the relatively high arrival rate of microblog streams, a cascade framework consisting of three stages is proposed to progressively reduce quantity of posts. For each step in the cascade, this dissertation studies methods that improve over current baselines. In the relevance filtering stage, query and document expansion techniques are applied to mitigate sparsity and vocabulary mismatch issues. The use of word embedding as a basis for filtering is also explored, using unsupervised and supervised modeling to characterize lexical and semantic similarity. In the novelty filtering stage, several statistical ways of characterizing novelty are investigated and ensemble learning techniques are used to integrate results from these diverse techniques. These results are compared with a baseline clustering approach using both standard and delay-discounted measures. In the salience filtering stage, because of the real-time prediction requirement a method of learning verb phrase usage from past relevant news reports is used in conjunction with some standard measures for characterizing writing quality. Following a Cranfield-like evaluation paradigm, this dissertation includes a se- ries of experiments to evaluate the proposed methods for each step, and for the end- to-end system. New microblog novelty and salience judgments are created, building on existing relevance judgments from the TREC Microblog track. The results point to future research directions at the intersection of social media, computational jour- nalism, information retrieval, automatic summarization, and machine learning.