Cognitive Models for Machine Theory of Mind.

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.

Personalized Model-Driven Interventions for Decisions From Experience.

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.

A 10-year prospectus for mathematical epidemiology.

There is little significant work at the intersection of mathematical and computational epidemiology and detailed psychological processes, representations, and mechanisms. This is true despite general agreement in the scientific community and the general public that human behavior in its seemingly infinite variation and heterogeneity, susceptibility to bias, context, and habit is an integral if not fundamental component of what drives the dynamics of infectious disease. The COVID-19 pandemic serves as a close and poignant reminder.

A computational cognitive model of behaviors and decisions that modulate pandemic transmission: Expectancy-value, attitudes, self-efficacy, and motivational intensity.

We present a computational cognitive model that incorporates and formalizes aspects of theories of individual-level behavior change and present simulations of COVID-19 behavioral response that modulates transmission rates. This formalization includes addressing the psychological constructs of attitudes, self-efficacy, and motivational intensity. The model yields signature phenomena that appear in the oscillating dynamics of mask wearing and the effective reproduction number, as well as the overall increase of rates of mask-wearing in response to awareness of an ongoing pandemic.

Toward a Psychology of Deep Reinforcement Learning Agents Using a Cognitive Architecture.

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision-making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms.

Discovering skill.

This paper shows how identical skills can emerge either from instruction or discovery when both result in an understanding of the causal structure of the task domain. The paper focuses on the discovery process, extending the skill acquisition model of Anderson et al. (2019) to address learning by discovery.

Adaptive Cognitive Mechanisms to Maintain Calibrated Trust and Reliance in Automation.

Trust calibration for a human-machine team is the process by which a human adjusts their expectations of the automation's reliability and trustworthiness; adaptive support for trust calibration is needed to engender appropriate reliance on automation. Herein, we leverage an instance-based learning ACT-R cognitive model of decisions to obtain and rely on an automated assistant for visual search in a UAV interface. This cognitive model matches well with the human predictive power statistics measuring reliance decisions; we obtain from the model an internal estimate of automation reliability that mirrors human subjective ratings.

Analysis of the human connectome data supports the notion of a "Common Model of Cognition" for human and human-like intelligence across domains.

The Common Model of Cognition (CMC) is a recently proposed, consensus architecture intended to capture decades of progress in cognitive science on modeling human and human-like intelligence. Because of the broad agreement around it and preliminary mappings of its components to specific brain areas, we hypothesized that the CMC could be a candidate model of the large-scale functional architecture of the human brain. To test this hypothesis, we analyzed functional MRI data from 200 participants and seven different tasks that cover a broad range of cognitive domains.

Toward Personalized Deceptive Signaling for Cyber Defense Using Cognitive Models.

Recent research in cybersecurity has begun to develop active defense strategies using game-theoretic optimization of the allocation of limited defenses combined with deceptive signaling. These algorithms assume rational human behavior. However, human behavior in an online game designed to simulate an insider attack scenario shows that humans, playing the role of attackers, attack far more often than predicted under perfect rationality.

Cognitive Models in Cybersecurity: Learning From Expert Analysts and Predicting Attacker Behavior.

Cybersecurity stands to benefit greatly from models able to generate predictions of attacker and defender behavior. On the defender side, there is promising research suggesting that Symbolic Deep Learning (SDL) may be employed to automatically construct cognitive models of expert behavior based on small samples of expert decisions. Such models could then be employed to provide decision support for non-expert users in the form of explainable expert-based suggestions.

Learning rapid and precise skills.

A theory is presented about how instruction and experience combine to produce human fluency in a complex skill. The theory depends critically on 4 aspects of the ACT-R architecture. The first is the timing of various modules, particularly motor timing, which results in behavior that closely matches human behavior.

Mission Command in the Age of Network-Enabled Operations: Social Network Analysis of Information Sharing and Situation Awareness.

A common assumption in organizations is that information sharing improves situation awareness and ultimately organizational effectiveness. The sheer volume and rapid pace of information and communications received and readily accessible through computer networks, however, can overwhelm individuals, resulting in data overload from a combination of diverse data sources, multiple data formats, and large data volumes. The current conceptual framework of network enabled operations (NEO) posits that robust networking and information sharing act as a positive feedback loop resulting in greater situation awareness and mission effectiveness in military operations (Alberts and Garstka, 2004).

Inhibitory synapses between striatal projection neurons support efficient enhancement of cortical signals: a computational model.

The function of lateral inhibitory synapses between striatal projection neurons is currently poorly understood. This paper puts forward a model suggesting that inhibitory collaterals can be used to enhance the incoming cortical signals. In particular, we propose that lateral inhibition between projection neurons performs a signal-enhancing process that resembles the image processing technique of "unsharp masking", where a blurred copy is used to enhance and sharpen an input image.

A functional model of sensemaking in a neurocognitive architecture.

Sensemaking is the active process of constructing a meaningful representation (i.e., making sense) of some complex aspect of the world.

Cognitive architectures combine formal and heuristic approaches.

Quantum probability (QP) theory provides an alternative account of empirical phenomena in decision making that classical probability (CP) theory cannot explain. Cognitive architectures combine probabilistic mechanisms with symbolic knowledge-based representations (e.g.

Distinct contributions of the caudate nucleus, rostral prefrontal cortex, and parietal cortex to the execution of instructed tasks.

When we behave according to rules and instructions, our brains interpret abstract representations of what to do and transform them into actual behavior. In order to investigate the neural mechanisms behind this process, we devised an fMRI experiment that explicitly isolated rule interpretation from rule encoding and execution. Our results showed that a specific network of regions (including the left rostral prefrontal cortex, the caudate nucleus, and the bilateral posterior parietal cortices) is responsible for translating rules into executable form.

The effects of individual and context on aggression in repeated social interaction.

In two studies using variations of the Prisoner's Dilemma game, we explore the impact of individual traits and social context on aggressive behavior. In the first study, we compared defection rates in the Iterated Prisoner's Dilemma when participants were presented with a payoff matrix (Description condition) or learned payoffs through experience (Experience condition). Interpersonal trust and maximizing tendency led to relatively less defection in the Description condition than in the Experience condition, demonstrating that individual characteristics manifest differently depending on the information available to decision-makers.

Memory activation and the availability of explanations in sequential diagnostic reasoning.

In the field of diagnostic reasoning, it has been argued that memory activation can provide the reasoner with a subset of possible explanations from memory that are highly adaptive for the task at hand. However, few studies have experimentally tested this assumption. Even less empirical and theoretical work has investigated how newly incoming observations affect the availability of explanations in memory over time.

Conditional routing of information to the cortex: a model of the basal ganglia's role in cognitive coordination.

The basal ganglia play a central role in cognition and are involved in such general functions as action selection and reinforcement learning. Here, we present a model exploring the hypothesis that the basal ganglia implement a conditional information-routing system. The system directs the transmission of cortical signals between pairs of regions by manipulating separately the selection of sources and destinations of information transfers.

An integrated theory of the mind.

Adaptive control of thought-rational (ACT-R; J. R. Anderson & C.

The Newell Test for a theory of cognition.

Newell (1980; 1990) proposed that cognitive theories be developed in an effort to satisfy multiple criteria and to avoid theoretical myopia. He provided two overlapping lists of 13 criteria that the human cognitive architecture would have to satisfy in order to be functional. We have distilled these into 12 criteria: flexible behavior, real-time performance, adaptive behavior, vast knowledge base, dynamic behavior, knowledge integration, natural language, learning, development, evolution, and brain realization.