Virtual ICCM II

Model convergence is an alternative approach for evaluating computational models of cognition. Convergence occurs when multiple models provide similar explanations for a phenomenon. In contrast to competitive comparisons which focus on model differences, identifying areas of convergence can provide evidence for overarching theoretical ideas. We proposed criteria for convergence which require models to be high in predictive and cognitive similarity. We then used a cross fitting method to explore the extent to which models from distinct computational frameworks---quantum cognition and the cognitive architecture ACT-R---converge on explanations of the interference effect. Our analysis revealed the models to be moderately high in predictive similarity but mixed for cognitive similarity. Though convergence was limited, the analysis suggests that interference effects emerge from interactions between uncertainty and the degree to which an individual relies on typical cases to make decisions. This result demonstrates the utility of convergence analysis as a method for integrating insights from multiple models.

We have developed an analysis stream for integrating a cognitive model with EEG data to reconstruct the cognition of individual subjects. A critical component of this method is the Sketch level that combines cognitive modeling and classification of EEG data using an HSMM to identify and place critical events over the timeline of a task. Multiple factors can influence sketch accuracy. In this study, we investigated the effect of game play elements on sketch accuracy across two EEG experiments where subjects interacted with the Space Fortress video game. Experiment 1 consisted of elaborate interface elements that accompanied game events (multiple sound effects, visual explosions). Subjects in Experiment 2 performed the same task, but audio and visual feedback elements were greatly reduced. We find that sketch accuracy while still much better than chance in Experiment 2, was significantly worse than in Experiment 1.

While evidence shows that cyber attackers are good at coordinating and collaborating in their attacks, network defenders are notoriously poor at sharing information and collaborating among themselves. To help promote cooperation among defenders, one requires models that can explain and make predictions of emergent cooperation decisions of each defender in a cyber security scenario. We propose a Multi-Agent Instance-Based Learning (MAIBL-PD) cognitive model based on Instance-based Learning (IBL) theory, and founded on the Prisoner's Dilemma (PD) of cooperation. MAIBL-PD aims at explaining how collaborations emerge to share information with other defenders in a group. MAIBL-PD was created to interact in a Multi-Defender-Game (MDG) that was used in an experimental study with human participants, intended to determine the effect of different levels of information sharing on collaboration. MAIBL-PD uses an extension of the utility function in IBL theory to capture the emergence of cooperation with higher levels of social information. Through simulations with MAIBL-PD we collect synthetic data to compare to the data set collected in human studies. Our results help explain the emergence of cooperation at increasing levels of information regarding others' actions. We demonstrate the ability of MAIBL-PD to predict human cooperation decisions in the MDG in situations in which players have only their own information and in situations in which they have information about the sharing behavior of the other players.

The difficulties encountered by children during language development varies among individuals. In particular, immaturity in phonological awareness, which supports speech perception, results in various speech defects. Accordingly, it is important to estimate the individual mechanism behind these problems to ensure proper support. In this study, we propose a method for estimating individual defects in the phonological process using cognitive models. As a preliminary step to targeting phonological processing difficulties in real world, we conducted an experiment with native adult speakers. Audio filters were applied to the output of the system to simulate phonological difficulties. This initial feasibility study revealed consistency in model preferences among participants when a particular audio filter was used. We consider that this study provides an important step toward the realizations of individualized cognitive modeling for mitigating various difficulties in language acquisition.