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The study of attitudes in the social psychology literature displays a dearth of computational modeling efforts. The principal modeling approach has been artificial neural networks, typically in the form of simple recurrent networks. The most recent and influential work in this vein relies on Ising-like or Hopfield-like models, with a focus on network properties and parameters such as system temperature and their effects on the dynamics of attitude formation. This work, however, is seldom informed by or integrated with contemporary cognitive modeling. This affects (i) the broader validity of the social psychology approach, (ii) its ability to account for learning in a principled way, and (iii) an understanding of the dynamics of attitude retrieval. We describe two studies that provide a simple but direct comparison between the social psychology approach and cognitive modeling, focusing on characterizing the performance differences between the two modeling paradigms.

The basal ganglia (BG) performs an important functional role in cognition, but models disagree about the nature of the relationship between BG activity and activity in other cortical areas. Previous computational models can be categorized as implementing the effects of the BG on prefrontal cortex as either local and direct, or involving other regions and, therefore, modulatory. To test which of these two effects best represents the role of the BG, a large fMRI dataset of 200 participants performing six, representative cognitive tasks was analyzed through Dynamic Causal Modeling (DCM). To ensure that DCM models were realistic and representative of a general brain architecture, the models were implemented within the putative neural underpinnings of the Common Model of Cognition, an abstract blueprint for cognition. The comparison showed that Mixed model, including both Direct and Modulatory connectivity, consistently outperformed models that included only direct or modulatory connections. It was also found that the relative rankings of the Direct and Modulatory models depended on the specific task, suggesting that the BG is a flexible system that adapts to task demands.

Some of the required characteristics for a true machine theory of mind (MToM) include the ability to (1) reproduce the full diversity of human thought and behavior, (2) develop a personalized model of an individual with very limited data, and (3) provide an explanation for behavioral predictions grounded in the cognitive processes of the individual. We propose that a certain class of cognitive models provide an approach that is well suited to meeting those requirements. Being grounded in a mechanistic framework like a cognitive architecture such as ACT‐R naturally fulfills the third requirement by mapping behavior to cognitive mechanisms. Exploiting a modeling paradigm such as instance‐based learning accounts for the first requirement by reflecting variations in individual experience into a diversity of behavior. Mechanisms such as knowledge tracing and model tracing allow a specific run of the cognitive model to be aligned with a given individual behavior trace, fulfilling the second requirement. We illustrate these principles with a cognitive model of decision‐making in a search and rescue task in the Minecraft simulation environment. We demonstrate that cognitive models personalized to individual human players can provide the MToM capability to optimize artificial intelligence agents by diagnosing the underlying causes of observed human behavior, projecting the future effects of potential interventions, and managing the adaptive process of shaping human behavior. Examples of the inputs provided by such analytic cognitive agents include predictions of cognitive load, probability of error, estimates of player self‐efficacy, and trust calibration. Finally, we discuss implications for future research and applications to collective human–machine intelligence. [ABSTRACT FROM AUTHOR]

We describe a method for determining feature salience of action decisions in intelligent agents based on cognitively-inspired salience. Salience is defined as the degree of influence that a factor has on a given decision. This is generatedby having a cognitive model using instance-based learning theory to mirror the actions of an intelligent agent, and thendetermining which features most uniquely contributed to the actions of the agent. We present three examples of thissalience techniques, including reinforcement learning agents based in the StarCraft II and autonomous drone domains, aswell as part of a risk assessment model. A benefit of our method is that it does not rely on a specific implementation ofan agent, it only requires the underlying decision feature-space. It is also capable of utilizing features at different levels ofabstraction

Cognitive models that represent individuals provide many benefits for understanding the full range of human behavior. One way in which individual differences emerge is through differences in knowledge. In dynamic situations, where decisions are made from experience, models built upon a theory of experiential choice (instance‐based learning theory; IBLT) can provide accurate predictions of individual human learning and adaptivity to changing environments. Here, we demonstrate how an instance‐based learning (IBL) cognitive model, implemented in a cognitive architecture (Adaptive Control of Thought–Rational), can be used to model an individual's decisions in a cybersecurity defense task, accounting for both population average and individual variances. The same IBL model structure with identical architectural parameters generates the full range of human behavior through stochastic memory retrieval processes operating over and contributing to unique experiences. Recurrence quantification analyses allow us to look beyond average behavior between and within individuals to sequential patterns of trial‐to‐trial behavior. We show how model‐tracing and knowledge‐tracing techniques can be used to align the model to an individual in real time to drive adaptive and personalized signaling algorithms for a cybersecurity defense system. We also present a method for introspecting into the cognitive model to gain further insight into the cognitive salience of features factored into individual decisions. The combination of techniques provides a blueprint for personalized modeling of individuals. We discuss the results and implications of this adaptive and personalized method for cybersecurity defense and more generally for intelligent artifacts tailored to individual differences in domains such as human–machine teaming. [ABSTRACT FROM AUTHOR]