14th Denman Undergraduate Research Forum (2009)

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    Motivational Effects of Need to Belong on Intergroup Memory in Minimal Groups
    (2009-05-13) O'Connor, Rachel; Van Bavel, Jay; Cunningham, William; Cunningham, William
    It has been repeatedly demonstrated that there is an in-group bias for face recognition such that people show greater memory for in-group compared to out-group members. Social categorization models have proposed that this bias is due to the differential way in which people are categorized. In-groups may initiate a more elaborate encoding process involving more individuation and leading to better memory. The current research proposes a motivational mechanism for the differential encoding of in-group members. In two studies, we tested whether people with greater need to belong, an important social motivator, would show increased in-group recognition bias. In both studies, participants were members of one of two teams. They completed a learning task in which they learned the faces of both teams and then completed a memory task in which previously learned faces and novel faces were presented and participants indicated which faces they had previously seen in the learning task. Results from study 1 indicated that people who were higher in trait-level need to belong showed even greater in-group face memory bias and this had a greater impact at the encoding rather than retrieval stage of memory recall. Study 2 experimentally manipulated need to belong and indicated that participants who had been socially rejected, and presumably had greater belonging needs, compared to non-socially rejected participants showed greater in-group face memory bias. Together these studies show that need to belong has differential motivational impacts on in-group bias in face recognition.
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    Dichotic Word Recognition for Young Adults with Normal Hearing
    (2009-05-13) Bournique, Jennifer; Roup, Christina
    The purpose of this study was to measure dichotic word recognition at various intensity levels in order to produce a performance-intensity (PI) function that modeled the data. For any test that requires a behavioral response from a listener, such as a dichotic listening task, it is important to understand the relationship between the stimulus and the responses given. One way of characterizing this relationship is by generating a psychometric function, or in the case of speech recognition, a PI function. These functions display percentage of correct responses as a function of intensity in dB HL. The purpose of this study was to create a PI function based on the dichotic listening results from normal hearing young adults. The present study attempted to determine if the slope and threshold characteristics of the PI functions for dichotic listening differed between right and left ears as well as from that of monaural listening PI functions. Ten young adults with normal hearing were recruited to participate in the present study. Dichotic word recognition performance was measured at six different intensity levels. The results indicated a significant difference in the slope of the PI function between the right and left ears, whereas no significant difference in threshold was observed between the right and left ears. The overall shape of the dichotic PI functions is similar to that of monaural PI functions. In contrast, both slope and threshold characteristics of dichotic PI functions differed from monaural PI functions. Specifically, dichotic PI function slopes were shallower and thresholds were higher when compared to monaural functions.
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    File Harvest: Targeted, Legal Crawling and Downloading of Online Media (Poster)
    (2009-05-13) Sowald, Chad; Sivilotti, Paul
    Today's Internet user has a limited amount of time to manually mine the Internet for content such as videos, images, and documents that they want to view. Much of the user's time is wasted overhead: clicking hyperlinks, waiting for pages to load, and actually downloading the content for offline viewing. Therefore, many users would benefit from an application that could automatically crawl and download a large amount of content from the Internet, so that users could browse and further filter the content offline at a much faster speed and without the unnecessary overhead. I have developed a web crawling and downloading program, File Harvest - written in C# and using the .NET framework - that allows the user to quickly configure the web crawling mechanism before starting it. The web crawler functions by following hyperlinks and examining each page it encounters along the way. The user specifies what web pages to crawl, how many levels of hyperlinks to crawl, and what types of content to download. The primary insight of the work is the value of combining crawling and downloading in a single program – something that related efforts have yet to do. The program uses various web page analysis techniques such as HTTP traffic proxying and static analysis of the page HTML to help the user find as much relevant content as possible to download. There are some limitations as to what can be found through crawling, and these limitations are the primary focus of the research going forward. In general, File Harvest can greatly expedite the discovery and downloading of media for users.