Abstract
The Parallel Episodic Processing (PEP) model is a neural network for simulating human performance in speeded response time tasks. It learns with an exemplar-based memory store and it is capable of modelling findings from various subdomains of cognition. In this paper, we show how the PEP model can be designed to follow instructions (e.g., task rules and goals). The extended PEP model is then used to simulate a number of key findings from the task switching domain. 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). 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. 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.
Highlights
In our everyday interactions with the world, we often balance multiple goals concurrently, flexibly switching between them as needed
We considered several potential approaches to testing our subjective notion that the Parallel Episodic Processing (PEP) model has relatively fixed qualitative predictions and settled on an approach that seemed feasible suggested by Roberts and Pashler (2000): see whether the model is capable of producing reversed effects
In Simulation 1, we observed that feature integration biases systematically inflated the switch cost, as in the participant data (Schmidt & Liefooghe, 2016)
Summary
In our everyday interactions with the world, we often balance multiple goals concurrently, flexibly switching between them as needed. Understanding how we maintain multiple goals and shift between them, and what costs or benefits might arise from this multitasking, are vital questions that much research has focused on understanding. 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). In the cued task switching procedure, participants are presented with a cue (e.g., a coloured rectangle) on each trial, which informs them which of two tasks must be performed. The switch cost is the observation that performance is substantially hindered when the task on the current trial (e.g., parity) is different from the task on the previous trial (e.g., magnitude), termed a task alternation (or task switch), relative to when the task is the same, termed a task repetition
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
