ACT-RΦ: A cognitive architecture with physiology and affect

Dancy, Christopher L.

doi:10.1016/j.bica.2013.07.008

Cited by 27 publications

(14 citation statements)

References 16 publications

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“…Another potential reason for this failure is our highly simplified representation of moods. Previous studies have represented mood in terms of physiology (Dancy, ) or in terms of expectations and desirability of the state of the world (Marsella & Gratch, ). Theories focused on physiology will be most suitable for making predictions for how emotions affect physiological parameters such as cortisol.…”

Section: Discussionmentioning

confidence: 99%

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How Does Rumination Impact Cognition? A First Mechanistic Model

Vugt

Velde

2018

Topics in Cognitive Science

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Rumination is a process of uncontrolled, narrowly focused negative thinking that is often self-referential, and that is a hallmark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we developed on the basis of our existing model of mindwandering. The rumination model implements the hypothesis that rumination is caused by maladaptive habits of thought. These habits of thought are modeled by adjusting the number of memory chunks and their associative structure, which changes the sequence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memories is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sampling study in healthy and depressed participants. On the basis of this empirically derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.patients. This study demonstrates how we can use cognitive models to better understand the cognitive mechanisms underlying rumination and depression.

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Section: Discussionmentioning

confidence: 99%

“…The specific moods in that study were chosen because they maximally differentiated between control, depressed, and psychotic patients. We decided to keep these moods merely as symbolic attributes instead of modeling them physiologically (Dancy, ; Dancy, Ritter, Berry, & Klein, ) to keep the model as simple as possible.…”

Section: Methodsmentioning

confidence: 99%

How Does Rumination Impact Cognition? A First Mechanistic Model

Vugt

Velde

2018

Topics in Cognitive Science

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“…Among other cognitive paradigms, cognitivism has undoubtedly been predominant to date [2]. Within the cognitivism paradigm, several cognitive architectures were developed, including Soar [6], ACT-R [3] (and its extensions ACT-R/E [4], ACT-RΦ [5], etc), C4 [7], and architectures for robotics [8], which are relatively independent of applications [37]. Because architectures represent the mechanism for cognition but lack the relevant information for using that mechanism, they cannot accomplish anything in their own right and need to be provided with knowledge to conduct a specific task.…”

Section: Artificial Cognitive Modelingmentioning

confidence: 99%

“…Among other mechanisms, cognitivism is a most widely used cognitive paradigm [2]. Several cognitive architectures were developed within this paradigm, including ACT-R [3] (and its extensions ACT-R/E [4], ACT-RΦ [5], etc), Soar [6], C4 [7], and architectures for robotics [8]. Because an architecture represents the connection and interaction of different cognitive components, it cannot accomplish a specific task on its own without specifying each component that can provide knowledge to the cognitive architecture.…”

Section: Introductionmentioning

confidence: 99%

SRAC: Self-Reflective Risk-Aware Artificial Cognitive models for robot response to human activities

Zhang

Reardon

Han

et al. 2016

2016 IEEE International Conference on Robotics and Automation (ICRA)

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In human-robot teaming, interpretation of human actions, recognition of new situations, and appropriate decision making are crucial abilities for cooperative robots ("co-robots") to interact intelligently with humans. Given an observation, it is important that human activities are interpreted the same way by co-robots as human peers so that robot actions can be appropriate to the activity at hand. A novel interpretability indicator is introduced to address this issue. When a robot encounters a new scenario, the pretrained activity recognition model, no matter how accurate in a known situation, may not produce the correct information necessary to act appropriately and safely in new situations. To effectively and safely interact with people, we introduce a new generalizability indicator that allows a co-robot to self-reflect and reason about when an observation falls outside the co-robot's learned model. Based on topic modeling and the two novel indicators, we propose a new Self-reflective Risk-aware Artificial Cognitive (SRAC) model, which allows a robot to make better decisions by incorporating robot action risks and identifying new situations. Experiments both using real-world datasets and on physical robots suggest that our SRAC model significantly outperforms the traditional methodology and enables better decision making in response to human behaviors.

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“…There have been numerous attempts to consider the emotional component in modeling the cognitive process (see, e.g., Dancy, 2013;Hudlicka, 2014;Izhikevich & Edelman, 2008;Larue, Poirier, & Nkambou, 2013;Shamis, 2006;Yakhno, 1995;Zhdanov, 2009). The majority of researches refers to the active agent concept (Laird, 2012;Samsonovich, 2007Samsonovich, , 2013Sellers, 2013) and suggests various principles of organization of the ''emotional space'' that affect the cognitive process.…”

Section: Introductionmentioning

confidence: 99%