{"id":{"repo_id":"toronto-retro","oai_identifier":"oai:utoronto.scholaris.ca:1807/108693"},"canonical_url":"https://search.dev.ndltd.org/etd/toronto-retro/oai:utoronto.scholaris.ca:1807/108693","repository":{"repo_id":"toronto-retro","name":"University of Toronto","base_url":"https://utoronto.scholaris.ca/server/oai/request"},"display":{"title":"Aging and Referential Communication: Insights from Interactions with Artificial Agents","abstract":"The global aging population has led to an increase in research on patterns of change across the lifespan. The evidence to-date suggests that older adults experience declines in sensory and cognitive abilities, however, less is known about changes in the language domain. Language is a fundamental component of everyday communication, not only in the context of interactions with humans but also with artificial agents. There are now increasing multidisciplinary efforts to develop technologies that provide assistance and/or companionship to older adults through spoken language interfaces (e.g., smart homes, social robots). Yet, there is little research on how effectively older adults communicate with artificial agents. Given that a key aspect of everyday communication with humans and artificial agents involves reference to objects in the here-and-now, this dissertation explores age-related differences in referential abilities. The goal is to advance our understanding of patterns of change in referential communication by drawing on insights from interactions with artificial agents. The first study, which explored how speakers design descriptions for different addressees (younger adult, older adult, computer), revealed that although older speakers produced more redundant information than younger speakers, they were similar in terms of performance measures (speech onset latency, speech rate, fluency). Intriguingly, effects were similar regardless of addressee type. The next study examined potential age-related differences in pragmatic inferencing during comprehension. Like younger adults, older listeners generated inferences based on relevant visual information, but had more difficulty suppressing these inferences when warranted (i.e., when a robot speaker had limited perceptual abilities). The final study, which explored the effect of redundant information in descriptions produced by a robot, revealed no age-related differences in real-time processing. Paralleling human-human studies, redundant information that helped to narrow listeners' visual attention facilitated comprehension. Together, the results show that patterns of referential communication with artificial agents are quite similar to previously-observed patterns with humans. Further, although meaningful age-related differences were sometimes found, many aspects of referential communication seem to be preserved in aging. These findings enhance our understanding of referential behavior in aging in the context of interacting with artificial agents and also inform the design of future technologies.","abstract_html":"The global aging population has led to an increase in research on patterns of change across the lifespan. The evidence to-date suggests that older adults experience declines in sensory and cognitive abilities, however, less is known about changes in the language domain. Language is a fundamental component of everyday communication, not only in the context of interactions with humans but also with artificial agents. There are now increasing multidisciplinary efforts to develop technologies that provide assistance and/or companionship to older adults through spoken language interfaces (e.g., smart homes, social robots). Yet, there is little research on how effectively older adults communicate with artificial agents. Given that a key aspect of everyday communication with humans and artificial agents involves reference to objects in the here-and-now, this dissertation explores age-related differences in referential abilities. The goal is to advance our understanding of patterns of change in referential communication by drawing on insights from interactions with artificial agents. The first study, which explored how speakers design descriptions for different addressees (younger adult, older adult, computer), revealed that although older speakers produced more redundant information than younger speakers, they were similar in terms of performance measures (speech onset latency, speech rate, fluency). Intriguingly, effects were similar regardless of addressee type. The next study examined potential age-related differences in pragmatic inferencing during comprehension. Like younger adults, older listeners generated inferences based on relevant visual information, but had more difficulty suppressing these inferences when warranted (i.e., when a robot speaker had limited perceptual abilities). The final study, which explored the effect of redundant information in descriptions produced by a robot, revealed no age-related differences in real-time processing. Paralleling human-human studies, redundant information that helped to narrow listeners' visual attention facilitated comprehension. Together, the results show that patterns of referential communication with artificial agents are quite similar to previously-observed patterns with humans. Further, although meaningful age-related differences were sometimes found, many aspects of referential communication seem to be preserved in aging. These findings enhance our understanding of referential behavior in aging in the context of interacting with artificial agents and also inform the design of future technologies.","abstract_has_math":false,"creators":["Haji Gholam Saryazdi, Raheleh"],"institution":null,"degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":"Psychology","school":null,"contributors":[],"advisors":["Chambers, Craig G"],"committee_chairs":[],"committee_members":[],"year":2021,"date_issued":"2021-11","date_published":"2021-11","updated_at":"2026-07-27T21:28:16Z","subjects":["Aging","Artificial agents","Pragmatic inference","Real-time processing","Referential communication","Social robots"],"languages":[],"rights":["Attribution-NonCommercial-NoDerivatives 4.0 International"],"rights_urls":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/1807/108693","outbound_label":"Handle","outbound_source":"dc:identifier.uri"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor.advisor","label":"Advisor","values":["Chambers, Craig G"]},{"key":"dc:contributor.department","label":"Department","values":["Psychology"]},{"key":"dc:creator","label":"Author","values":["Haji Gholam Saryazdi, Raheleh"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2021-11"]},{"key":"dc:date.accessioned","label":"Dc Date Accessioned","values":["2021-11-30T16:40:49Z"]},{"key":"dc:date.available","label":"Dc Date Available","values":["2021-11-30T16:40:49Z"]},{"key":"dc:date.issued","label":"Date","values":["2021-11"]},{"key":"dc:type","label":"Dc Type","values":["Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["Aging","Artificial agents","Pragmatic inference","Real-time processing","Referential communication","Social robots"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:rights","label":"Dc Rights","values":["Attribution-NonCommercial-NoDerivatives 4.0 International"]},{"key":"dc:rights.uri","label":"Rights URI","values":["http://creativecommons.org/licenses/by-nc-nd/4.0/"]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier.uri","label":"Identifier URI","values":["http://hdl.handle.net/1807/108693"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description.abstract","label":"Abstract","values":["The global aging population has led to an increase in research on patterns of change across the lifespan. The evidence to-date suggests that older adults experience declines in sensory and cognitive abilities, however, less is known about changes in the language domain. Language is a fundamental component of everyday communication, not only in the context of interactions with humans but also with artificial agents. There are now increasing multidisciplinary efforts to develop technologies that provide assistance and/or companionship to older adults through spoken language interfaces (e.g., smart homes, social robots). Yet, there is little research on how effectively older adults communicate with artificial agents. Given that a key aspect of everyday communication with humans and artificial agents involves reference to objects in the here-and-now, this dissertation explores age-related differences in referential abilities. The goal is to advance our understanding of patterns of change in referential communication by drawing on insights from interactions with artificial agents. The first study, which explored how speakers design descriptions for different addressees (younger adult, older adult, computer), revealed that although older speakers produced more redundant information than younger speakers, they were similar in terms of performance measures (speech onset latency, speech rate, fluency). Intriguingly, effects were similar regardless of addressee type. The next study examined potential age-related differences in pragmatic inferencing during comprehension. Like younger adults, older listeners generated inferences based on relevant visual information, but had more difficulty suppressing these inferences when warranted (i.e., when a robot speaker had limited perceptual abilities). The final study, which explored the effect of redundant information in descriptions produced by a robot, revealed no age-related differences in real-time processing. Paralleling human-human studies, redundant information that helped to narrow listeners' visual attention facilitated comprehension. Together, the results show that patterns of referential communication with artificial agents are quite similar to previously-observed patterns with humans. Further, although meaningful age-related differences were sometimes found, many aspects of referential communication seem to be preserved in aging. These findings enhance our understanding of referential behavior in aging in the context of interacting with artificial agents and also inform the design of future technologies."]},{"key":"dc:description.degree","label":"Dc Description Degree","values":["Ph.D."]},{"key":"dc:title","label":"Title","values":["Aging and Referential Communication: Insights from Interactions with Artificial Agents"]}]}],"canonical_facts":{"dc:contributor.advisor":["Chambers, Craig G"],"dc:contributor.department":["Psychology"],"dc:creator":["Haji Gholam Saryazdi, Raheleh"],"dc:date":["2021-11"],"dc:date.accessioned":["2021-11-30T16:40:49Z"],"dc:date.available":["2021-11-30T16:40:49Z"],"dc:date.issued":["2021-11"],"dc:description.abstract":["The global aging population has led to an increase in research on patterns of change across the lifespan. The evidence to-date suggests that older adults experience declines in sensory and cognitive abilities, however, less is known about changes in the language domain. Language is a fundamental component of everyday communication, not only in the context of interactions with humans but also with artificial agents. There are now increasing multidisciplinary efforts to develop technologies that provide assistance and/or companionship to older adults through spoken language interfaces (e.g., smart homes, social robots). Yet, there is little research on how effectively older adults communicate with artificial agents. Given that a key aspect of everyday communication with humans and artificial agents involves reference to objects in the here-and-now, this dissertation explores age-related differences in referential abilities. The goal is to advance our understanding of patterns of change in referential communication by drawing on insights from interactions with artificial agents. The first study, which explored how speakers design descriptions for different addressees (younger adult, older adult, computer), revealed that although older speakers produced more redundant information than younger speakers, they were similar in terms of performance measures (speech onset latency, speech rate, fluency). Intriguingly, effects were similar regardless of addressee type. The next study examined potential age-related differences in pragmatic inferencing during comprehension. Like younger adults, older listeners generated inferences based on relevant visual information, but had more difficulty suppressing these inferences when warranted (i.e., when a robot speaker had limited perceptual abilities). The final study, which explored the effect of redundant information in descriptions produced by a robot, revealed no age-related differences in real-time processing. Paralleling human-human studies, redundant information that helped to narrow listeners' visual attention facilitated comprehension. Together, the results show that patterns of referential communication with artificial agents are quite similar to previously-observed patterns with humans. Further, although meaningful age-related differences were sometimes found, many aspects of referential communication seem to be preserved in aging. These findings enhance our understanding of referential behavior in aging in the context of interacting with artificial agents and also inform the design of future technologies."],"dc:description.degree":["Ph.D."],"dc:identifier.uri":["http://hdl.handle.net/1807/108693"],"dc:rights":["Attribution-NonCommercial-NoDerivatives 4.0 International"],"dc:rights.uri":["http://creativecommons.org/licenses/by-nc-nd/4.0/"],"dc:subject":["Aging","Artificial agents","Pragmatic inference","Real-time processing","Referential communication","Social robots"],"dc:title":["Aging and Referential Communication: Insights from Interactions with Artificial Agents"],"dc:type":["Thesis"]},"updated_at":"2026-07-27T21:28:16Z"}