Virtual ICCM IV

We propose an ACT-R model of processing German personal and demonstrative pronouns. The model extends existing cue-based retrieval models of sentence processing (Lewis & Vasishth, 2005; Lewis et al. 2006) and pronoun resolution (Parker & Phillips, 2017; Patil & Lago, 2021) by adding prominence constraints as weighted retrieval cues. We model data from an antecedent selection task reported in Schumacher et al. (2016). The experiment varied word orders (canonical vs. non-canonical) and verb types (active accusative vs. dative experiencer) to test the effect of varying referential prominence on antecedent preferences for personal and demonstrative pronouns. The model with weighted prominence cues captures key effects across two word orders and verb types, and demonstrates that the contrastive antecedent preferences of personal and demonstrative pronouns can be captured using weighted retrieval cues reflecting prominence constraints.

Multitasking is a challenging cognitive task, and there are many factors driving which strategy participants use to complete tasks concurrently. We utilized a model comparison approach to evaluate how participants decide which task to switch to next using the Air Force Multiple Attribute Battery (AF-MATB). We used the cognitive architecture, Adaptive Control of Thought – Rational (ACT-R), to simulate multitasking in the AF-MATB. We varied how the model decided which task to attend to next by comparing a purely top-down strategy, a purely reactive, bottom-up selection strategy, and mixtures of the two. We compared simulations of the model to data from Bowers et al., (2014). The best combination involved a mixture of top-down and bottom-up selection. Neither the purely top-down nor bottom-up selection models performed well. These results suggest that participants use a complex mixture of strategies to multitasking. The use of a top-down strategy suggests participants could develop efficient strategies to multitask successfully, and that participants may be using a more effortful serial search for tasks, as indicated by the model's serial processing implementation.

Organizations typically use simulation campaigns to train employees to detect phishing emails but are non-personalized and fail to account for human experiential learning and adaptivity. We propose a method to improve the effectiveness of training by combining cognitive modeling with machine learning methods. We frame the problem as one of scheduling and use the restless multi-armed bandit (RMAB) framework to select which users to target for intervention at each trial, while using a cognitive model of phishing susceptibility to inform the parameters of the RMAB. We compare the effectiveness of the RMAB solution to two purely cognitive approaches in a series of simulation studies using the cognitive model as simulated participants. Both approaches show improvement compared to random selection and we highlight the pros and cons of each approach. We discuss the implications of these findings and future research that aims to combine the benefits of both methods for a more effective solution.