Traditional human reliability analysis (HRA) methods, such as IDHEAS-ECA, rely on expert judgment and empirical rules that often overlook the cognitive underpinnings of human error. Moreover, conducting human-in-the-loop experiments for advanced nuclear power plants is increasingly impractical due to novel interfaces and limited operational data. This study proposes a cognitive-mechanistic framework (COGMIF) that enhances the IDHEAS-ECA methodology by integrating an ACT-R-based human digital twin (HDT) with TimeGAN-augmented simulation. The ACT-R model simulates operator cognition, including memory retrieval, goal-directed procedural reasoning, and perceptual-motor execution--under high-fidelity scenarios derived from a high-temperature gas-cooled reactor (HTGR) simulator. To overcome the resource constraints of large-scale cognitive modeling, TimeGAN is trained on ACT-R-generated time-series data to produce high-fidelity synthetic operator behavior datasets. These simulations are then used to drive IDHEAS-ECA assessments, enabling scalable, mechanism-informed estimation of human error probabilities (HEPs). Comparative analyses with SPAR-H and sensitivity assessments demonstrate the robustness and practical advantages of the proposed COGMIF. This work offers a credible and computationally efficient pathway to integrate cognitive theory into industrial HRA practices. Keywords: Human Reliability, Human Digital Twins, IDHEAS-ECA, TimeGAN, Bayesian 1 Introduction Human reliability analysis (HRA) plays a pivotal role in the safety assessment of complex socio-technical systems, particularly in high-risk domains such as nuclear power generation [1]. As a fundamental component of probabilistic risk assessment (PRA), HRA aims to estimate the likelihood of human error under specific operational contexts, thereby supporting risk-informed decision-making and the design of resilient safety systems. Over the past decades, a range of structured methodologies, such as the standardized plant analysis risk-human reliability analysis (SPAR-H) [2], the technique for human error rate prediction (THERP) [3], and more recently, the integrated human event analysis system for event and condition assessment (IDHEAS-ECA) [4], have been developed to quantify human error probabilities (HEPs). While these frameworks offer operational utility, they are primarily grounded in expert judgment, predefined performance shaping factors (PSFs), and empirically derived databases, often lacking a mechanistic understanding of the cognitive processes that drive operator actions and errors. Furthermore, traditional HRA approaches are highly dependent on two major data sources: (1) retrospective analysis of operational events, and (2) human-in-the-loop (HITL) simulation experiments conducted in controlled environments.

Immersion in a task is a prerequisite for creativity. However, excessive arousal in a single task has drawbacks, such as overlooking events outside of the task. To examine such a negative aspect, this study constructs a computational model of arousal dynamics where the excessively increased arousal makes the task transition difficult. The model was developed using functions integrated into the cognitive architecture Adaptive Control of Thought-Rational (ACT-R). Under the framework, arousal is treated as a coefficient affecting the overall activation level in the model. In our simulations, we set up two conditions demanding low and high arousal, trying to replicate corresponding human experiments. In each simulation condition, two sets of ACT-R parameters were assumed from the different interpretations of the human experimental settings. The results showed consistency of behavior between humans and models both in the two different simulation settings. This result suggests the validity of our assumptions and has implications of controlling arousal in our daily life.

Music streaming services often leverage sequential recommender systems to predict the best music to showcase to users based on past sequences of listening sessions. Nonetheless, most sequential recommendation methods ignore or insufficiently account for repetitive behaviors. This is a crucial limitation for music recommendation, as repeatedly listening to the same song over time is a common phenomenon that can even change the way users perceive this song. In this paper, we introduce PISA (Psychology-Informed Session embedding using ACT-R), a session-level sequential recommender system that overcomes this limitation. PISA employs a Transformer architecture learning embedding representations of listening sessions and users using attention mechanisms inspired by Anderson's ACT-R (Adaptive Control of Thought-Rational), a cognitive architecture modeling human information access and memory dynamics. This approach enables us to capture dynamic and repetitive patterns from user behaviors, allowing us to effectively predict the songs they will listen to in subsequent sessions, whether they are repeated or new ones. We demonstrate the empirical relevance of PISA using both publicly available listening data from Last.fm and proprietary data from Deezer, a global music streaming service, confirming the critical importance of repetition modeling for sequential listening session recommendation. Along with this paper, we publicly release our proprietary dataset to foster future research in this field, as well as the source code of PISA to facilitate its future use.

This document presents endeavors to represent emotion in a computational cognitive architecture. The first part introduces research organizing with two axes of emotional affect: pleasantness and arousal. Following this basic of emotional components, the document discusses an aspect of emergent properties of emotion, showing interaction studies with human users. With these past author's studies, the document concludes that the advantage of the cognitive human-agent interaction approach is in representing human internal states and processes.

Reminiscence therapy is mental health care based on the recollection of memories. However, the effectiveness of this method varies amongst individuals. To solve this problem, it is necessary to provide more personalized support; therefore, this study utilized a computational model of personal memory recollection based on a cognitive architecture adaptive control of thought-rational (ACT-R). An ACT-R memory model reflecting the state of users is expected to facilitate personal recollection. In this study, we proposed a method for estimating the internal states of users through repeated interactions with the memory model. The model, which contains the lifelog of the user, presents a memory item (stimulus) to the user, and receives the response of the user to the stimulus, based on which it adjusts the internal parameters of the model. Through the repetition of these processes, the parameters of the model will reflect the internal states of the user. To confirm the feasibility of the proposed method, we analyzed utterances of users when using a system that incorporates this model. The results confirmed the ability of the method to estimate the memory retrieval parameters of the model from the utterances of the user. In addition, the ability of the method to estimate changes in the mood of the user caused by using the system was confirmed. These results support the feasibility of the interactive method for estimating human internal states, which will eventually contribute to the ability to induce memory recall and emotions for our well-being.

We explore replacing the declarative memory system of the ACT-R cognitive architecture with a distributional semantics model. ACT-R is a widely used cognitive architecture, but scales poorly to big data applications and lacks a robust model for learning association strengths between stimuli. Distributional semantics models can process millions of data points to infer semantic similarities from language data or to infer product recommendations from patterns of user preferences. We demonstrate that a distributional semantics model can account for the primacy and recency effects in free recall, the fan effect in recognition, and human performance on iterated decisions with initially unknown payoffs. The model we propose provides a flexible, scalable alternative to ACT-R's declarative memory at a level of description that bridges symbolic, quantum, and neural models of cognition. Our intent is to advance toward a cognitive architecture capable of modeling human performance at all scales of learning.

People feared that one day, machines would overtake humans and seize control of everything in the past. However, irrespective of the fear, there has been ground-breaking research in Artificial General Intelligence (AGI), which makes artificial intelligence more human-like. The human brain comprises large sets of elements, and it self-organises the dynamical structures to respond with our bodies. Therefore, it has been a natural way to work on AGI to make it adaptive self-organisation. Another way that is used to build AGI models is computational neuroscience.

Activation-based conditional inference applies conditional reasoning to ACT-R, a cognitive architecture developed to formalize human reasoning. The idea of activation-based conditional inference is to determine a reasonable subset of a conditional belief base in order to draw inductive inferences in time. Central to activation-based conditional inference is the activation function which assigns to the conditionals in the belief base a degree of activation mainly based on the conditional's relevance for the current query and its usage history.

In the last decade, the field of Human-Computer Interaction (HCI) has started to focus its attention on the design and implementation of artificial systems "orienting" attitudes and/or behaviours of a user according to a predefined direction. This growing sub-field, studying the so-called Persuasive Technologies, concerns a variety of system typologies that can adopt different strategies to pursue their goals. Building persuasive robots able to interact with human beings on a specific topic (or in a multi-domain setting) in a realistic and persuasive way, represents an open problem and research challenge in Social Robotics. To this aim, a strategy often used in human-human communication to make people reconsider their behaviour and beliefs, and similarly proposed in human-robot interaction, is to exploit storytelling to let people identify themselves with the characters or roles in a story in order to understand different perspectives and needs. In the design of a persuasive system, in addition, it is also important to not ignore the ethical dimension: i.e. an intelligent artificial system should be able to make decision and act in an ethical way, taking into account norms of social practices and needs of other individuals.

The proposed standard model began as an initial consensus at the 2013 AAAI Fall Symposium on Integrated Cognition, but is extended here through a synthesis across three existing cognitive architectures: ACT-R, Sigma, and Soar. The resulting standard model spans key aspects of structure and processing, memory and content, learning, and perception and motor, and highlights loci of architectural agreement as well as disagreement with the consensus while identifying potential areas of remaining incompleteness. The hope is that this work will provide an important step toward engaging the broader community in further development of the standard model of the mind.