A hybrid cognitive architecture with primal affect and physiology

Though computational cognitive architectures have been used to study several processes associated with human behavior, the study of integration of affect and emotion in these processes has been relatively sparse. Theory from affective science and affective neuroscience can be used to systematically integrate affect into cognitive architectures, particularly in areas where cognitive system behavior is known to be associated with physiological structure and behavior. I introduce a unified theory and model of human behavior that integrates physiology and primal affect with cognitive processes in a cognitive architecture. This new architecture gives a more tractable, mechanistic way to simulate affect-cognition interactions to provide specific, quantitative predictions. It considers affect as a lower-level, functional process that interacts with cognitive processes (e.g., declarative memory) to result in emotional behavior. This formulation makes it more straightforward to connect these affective representations with other related moderating processes that may not specifically be considered as emotional (e.g., thirst or stress). An improved understanding of the architecture that constrains our behavior gives us a better opportunity to comprehend why we behave the way we do and how we can use this knowledge to recognize and construct a more ideal internal and external environment

Using a Cognitive Architecture to consider antiblackness in design and development of AI systems

How might we use cognitive modeling to consider the ways in which antiblackness, and racism more broadly, impact the design and development of AI systems? We provide a discussion and an example towards an answer to this question. We use the ACT-R/{\Phi} cognitive architecture and an existing knowledge graph system, ConceptNet, to consider this question not only from a cognitive and sociocultural perspective, but also from a physiological perspective. In addition to using a cognitive modeling as a means to explore how antiblackness may manifest in the design and development of AI systems (particularly from a software engineering perspective), we also introduce connections between antiblackness, the Human, and computational cognitive modeling. We argue that the typical eschewing of sociocultural processes and knowledge structures in cognitive architectures and cognitive modeling implicitly furthers a colorblind approach to cognitive modeling and hides sociocultural context that is always present in human behavior and affects cognitive processes.Comment: To be published in ICCM Conference proceedings. 8 Pages, 1 figur

Towards a physio-cognitive model of the exploration exploitation trade-off.

Managing the exploration vs exploitation trade-off is an important part of our everyday lives. It occurs in minor decisions such as choosing what music to listen to as well as major decisions, such as picking a research direction to pursue. The dilemma is the same despite the context: does one exploit the environment, using current knowledge to acquire a satisfactory solution, or explore other options and potentially find a better answer. An accurate cognitive model must be able to handle this trade-off because of the importance it plays in our lives. We are developing physio-cognitive models to better understand how physiological and cognitive processes interact to mediate decisions to explore or exploit. To accomplish this, we utilize the ACT-R/Φ hybrid architecture (Dancy, 2013; Dancy et al., 2015) and the Project Malmo AI platform (Johnson et al., 2016)

Simulating Human-AI Collaboration with ACT-R and Project Malmo

We use the ACT-R cognitive architecture (Anderson, 2007) to explore human-AI collaboration. Computational models of human and AI behavior, and their interaction, allow for more effective development of collaborative artificial intelligent agents. With these computational models and simulations, one may be better equipped to predict the situations in which certain classes of intelligent agents may be more suited to collaborate with people. One can more tractably understand and predict how different AI agents affect task behavior in these situations. To simulate human-AI collaboration, we are developing ACT-R models that work with more traditional AI agents to solve a task in Project Malmo (Johnson et al., 2016). We use existing AI agents that were originally developed as the AI portion of the Human-AI collaboration. In addition, creating a model in ACT-R to simulate human behavior gives us the opportunity to play out these interactions much faster than would be possible in real time

Examining the Effects of Race on Human-AI Cooperation

Recent literature has shown that racism and implicit racial biases can affect one’s actions in major ways, from the time it takes police to decide whether they shoot an armed suspect, to a decision on whether to trust a stranger. Given that race is a social/power construct, artifacts can also be racialized, and these racialized agents have also been found to be treated differently based on their perceived race. We explored whether people’s decision to cooperate with an AI agent during a task (a modified version of the Stag hunt task) is affected by the knowledge that the AI agent was trained on a population of a particular race (Black, White, or a non-racialized control condition). These data show that White participants performed the best when the agent was racialized as White and not racialized at all, while Black participants achieved the highest score when the agent was racialized as Black. Qualitative data indicated that White participants were less likely to report that they believed that the AI agent was attempting to cooperate during the task and were more likely to report that they doubted the intelligence of the AI agent. This work suggests that racialization of AI agents, even if superficial and not explicitly related to the behavior of that agent, may result in different cooperation behavior with that agent, showing potentially insidious and pervasive effects of racism on the way people interact with AI agents

Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition

Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition

On Integrating Generative Models into Cognitive Architectures for Improved Computational Sociocultural Representations

What might the integration of cognitive architectures and generative models mean for sociocultural representations within both systems? Beyond just integration, we see this question as paramount to understanding the potential wider impact of integrations between these two types of computational systems. Generative models, though an imperfect representation of the world and various con-texts, nonetheless may be useful as general world knowledge with careful considerations of sociocultural representations provided therein, including the represented sociocultural systems or, as we explain, genres of the Human. Thus, such an integration gives an opportunity to develop cognitive models that represent from the physiological/biological time scale to the social timescale and that more accurately represent the effects of ongoing sociocultural systems and structures on behavior. In addition, integrating these systems should prove useful to audit and test many generative models under more realistic cognitive uses and conditions. That is, we can ask what it means that people will likely be using knowledge from such models as knowledge for their own behavior and actions. We further discuss these perspectives and focus these perspectives using ongoing and potential work with (primarily) the ACT-R cognitive architecture. We also discuss issues with using generative models as a system for integration

Emotion in the Common Model of Cognition

Emotions play an important role in human cognition and therefore need to be present in the Common Model of Cognition. In this paper, the emotion working group focuses on functional aspects of emotions and describes what we believe are the points of interactions with the Common Model of Cognition. The present paper should not be viewed as a consensus of the group but rather as a first attempt to extract common and divergent aspects of different models of emotions and how they relate to the Common Model of Cognition