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In task switching paradigms, the participant is not asked to implement one task goal, but two (or more), as illustrated in Figure 1 (for a list of key term definitions, see Appendix A). One tool commonly used in the study of our ability to switch between goals are task switching paradigms ( Jersild, 1927 for reviews, see Kiesel et al., 2010 Koch, Poljac, Müller, & Kiesel, 2018 Monsell, 2003 Vandierendonck, Liefooghe, & Verbruggen, 2010). Understanding how we maintain multiple goals and shift between them, and what costs or benefits might arise from this multitasking, are therefore vital questions that much research has focused on understanding. While at work, we might repeatedly shift our attention between checking emails, writing a manuscript, and checking on the Java code running in the background. In our everyday interactions with the world, we often balance multiple goals concurrently, flexibly switching between them as needed. Keywords: computational modelling, neural networks, episodic memory, binding, switch costs, feature integration, task-rule congruency, instruction implementation, goals DOI: Given the parsimony and unique explanatory power of the episodic account presented here, our results suggest that feature-integration biases have a far greater power in explaining task-switching performance than previously assumed. We demonstrate that the PEP model fits the participant data well, that the model does not possess the flexibility to match any pattern of results, and that a number of competing task switching models fail to account for key observations that the PEP model produces naturally.
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These include the switch cost, task-rule congruency effects, response repetition asymmetries, cue repetition benefits, and the full pattern of means from a recent feature integration decomposition of cued task switching (Schmidt & Liefooghe, 2016). The extended PEP model is then used to simulate a number of key findings from the task switching domain. In this paper, we show how the PEP model can be designed to follow instructions (e.g., task rules and goals). It learns with an exemplar-based memory store and it is capable of modelling findings from various subdomains of cognition. The Parallel Episodic Processing (PEP) model is a neural network for simulating human performance
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Windows XP/ Vista/ 7/ 8/ 8.An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory Review Article An Episodic Model of Task Switching Effects: Erasing the Homunculus from Memory Authors: Abstract
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