Matching Items (3)
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- All Subjects: Categorization (Psychology)
- Genre: Doctoral Dissertation
- Creators: Glenberg, Arthur
- Member of: Theses and Dissertations
- Status: Published
Description
Most people are experts in some area of information; however, they may not be knowledgeable about other closely related areas. How knowledge is generalized to hierarchically related categories was explored. Past work has found little to no generalization to categories closely related to learned categories. These results do not fit well with other work focusing on attention during and after category learning. The current work attempted to merge these two areas of by creating a category structure with the best chance to detect generalization. Participants learned order level bird categories and family level wading bird categories. Then participants completed multiple measures to test generalization to old wading bird categories, new wading bird categories, owl and raptor categories, and lizard categories. As expected, the generalization measures converged on a single overall pattern of generalization. No generalization was found, except for already learned categories. This pattern fits well with past work on generalization within a hierarchy, but do not fit well with theories of dimensional attention. Reasons why these findings do not match are discussed, as well as directions for future research.
ContributorsLancaster, Matthew E (Author) / Homa, Donald (Thesis advisor) / Glenberg, Arthur (Committee member) / Chi, Michelene (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2013
Description
Categories are often defined by rules regarding their features. These rules may be intensely complex yet, despite the complexity of these rules, we are often able to learn them with sufficient practice. A possible explanation for how we arrive at consistent category judgments despite these difficulties would be that we may define these complex categories such as chairs, tables, or stairs by understanding the simpler rules defined by potential interactions with these objects. This concept, called grounding, allows for the learning and transfer of complex categorization rules if said rules are capable of being expressed in a more simple fashion by virtue of meaningful physical interactions. The present experiment tested this hypothesis by having participants engage in either a Rule Based (RB) or Information Integration (II) categorization task with instructions to engage with the stimuli in either a non-interactive or interactive fashion. If participants were capable of grounding the categories, which were defined in the II task with a complex visual rule, to a simpler interactive rule, then participants with interactive instructions should outperform participants with non-interactive instructions. Results indicated that physical interaction with stimuli had a marginally beneficial effect on category learning, but this effect seemed most prevalent in participants were engaged in an II task.
ContributorsCrawford, Thomas (Author) / Homa, Donald (Thesis advisor) / Glenberg, Arthur (Committee member) / McBeath, Michael (Committee member) / Brewer, Gene (Committee member) / Arizona State University (Publisher)
Created2014
Description
A converging operations approach using response time distribution modeling was adopted to better characterize the cognitive control dynamics underlying ongoing task cost and cue detection in event based prospective memory (PM). In Experiment 1, individual differences analyses revealed that working memory capacity uniquely predicted nonfocal cue detection, while proactive control and inhibition predicted variation in ongoing task cost of the ex-Gaussian parameter associated with continuous monitoring strategies (mu). In Experiments 2A and 2B, quasi-experimental techniques aimed at identifying the role of proactive control abilities in PM monitoring and cue detection suggested that low ability participants may have PM deficits during demanding tasks due to inefficient monitoring strategies, but that emphasizing importance of the intention can increase reliance on more efficacious monitoring strategies that boosts performance (Experiment 2A). Furthermore, high proactive control ability participants are able to efficiently regulate their monitoring strategies under scenarios that do not require costly monitoring for successful cue detection (Experiment 2B). In Experiments 3A and 3B, it was found that proactive control benefited cue detection in interference-rich environments, but the neural correlates of cue detection or intention execution did not differ when engaged in proactive versus reactive control. The results from the current set of studies highlight the importance of response time distribution modeling in understanding PM cost. Additionally, these results have important implications for extant theories of PM and have considerable applied ramifications concerning the cognitive control processes that should be targeted to improve PM abilities.
ContributorsBall, Brett Hunter (Author) / Brewer, Gene A. (Thesis advisor) / Goldinger, Stephen (Committee member) / Glenberg, Arthur (Committee member) / Amazeen, Eric (Committee member) / Arizona State University (Publisher)
Created2015