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n10:ext-h.bowman@kent.ac.uk
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n11:ext-6cd37a476c4a651d5173fe60c50f2f23 n11:ext-e69ffaf65adbe669a239fc71d288812e
dcterms:title
On the Possibility of Recalling Without Seeing: Evidence From State-Trace Analysis of the Experiential Blink
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2019-09
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n10:ext-wrj2@kent.ac.uk
bibo:abstract
In recent years, there has been much debate about how our conscious perception of the world relates to our ability to store and process information about it. Given the tight coupling between these two processes, it is not surprising that the nature of this relationship has proved difficult to establish with any degree of certainty. Recent findings however, have provided an opportunity to quantify evidence about how such a relationship might manifest. In this thesis, we follow up on one particular such set of findings: examining the relationship between participants' subjective experience of stimuli and their ability to encode them into working memory during the attentional blink. This problem is tackled in three progressive steps. Firstly, we attempt to establish with certainty that a difference does exists between these two cognitive processes. In order to quantify the distinctness of the two cognitive processes, we make use of state-trace analysis. Having established that the two cognitive processes are in some way distinct, we examine more closely what form their relationship takes; what kind of relationship of dependency exists between the two measures, is it possible to have one without the other? Finally, we attempt to provide a theory and computational model of the above results. Our findings provide evidence that working memory encoding and subjective experience are dissociated in some manner. Further examination yields evidence that it is possible that working memory encoding may exist as a necessary but insufficient condition for subjective experience. We develop a theory of this behaviour based on targets being encoded simultaneously, but only experienced in serial, and build a computational model of these results by integrating with an existing model - the Simultaneous Type/Serial Token model of attention. The predictions this model makes strongly match those observed in human participants.
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