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theses

Visual working (WM) and long-term memory (LTM) are intricately intertwined. As such, current theories and models of VWM have been extended to characterize behavior in long-term memory. For example, a popular framework for investigating VWM is the remember-guess paradigm, which suggests that information is either recalled with some noise, or is no longer retrievable and individuals resort to random guessing (e.g. Brady et al., 2013). This framework has been extended to include an additional factor that contributes to memory, namely interference from non-target information (a.k.a. misassociations; Lew et al, 2015). In this way, individuals recall information with noise, missassociate memories to other task relevant information, or guess randomly. The compilation of these studies has identified the contribution of memory fidelity, misassociations, and random guesses to recall performance. Notably, the remember-guess framework stands in stark contrast to theoretical Bayesian models of memory, which suggests that prior knowledge and expectations for the statistical regularities of the environment influences recall from long-term memory (Hemmer & Steyvers, 2009b). The influence of prior knowledge is most prevalent when the stimuli in the memory tasks mirror the regularities of the natural world. In this dissertation, I seek to challenge current theories of memory regarding the contribution of fidelity, misassociations, and random guesses to LTM, by evaluating the simultaneous contribution of prior knowledge. The combination of results from these studies suggest that prior knowledge plays a crucial recall in reconstruction from long-term episodic memory, and when prior knowledge is brought to the task of remembering, it alters the contribution of misassociations and random guessing to recall performance.

ETD_8836

Ph.D.

Includes bibliographical references

by Kimele Persaud

doi:10.7282/T3QJ7MR3

ETD doctoral

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