Cognitive Systems Research最新文献

{"title":"Integrated model of cerebellal supervised learning and basal ganglia’s reinforcement learning for mobile robot behavioral decision-making","authors":"Zhiqiang Wu ,&nbsp;Dongshu Wang ,&nbsp;Lei Liu","doi":"10.1016/j.cogsys.2024.101302","DOIUrl":"10.1016/j.cogsys.2024.101302","url":null,"abstract":"<div><div>Behavioral decision-making in unknown environments of mobile robots is a crucial research topic in robotics. Inspired by the working mechanism of different brain regions in mammals, this paper designed a new hybrid model integrating the functions of cerebellum and basal ganglia by simulating the memory replay of hippocampus, so as to realize the autonomous behavioral decision-making of robot in unknown environments. A reinforcement learning module based on Actor-Critic framework and a developmental network module are used to simulate the functions of the basal ganglia and cerebellum, respectively. Considering the different functions of D1 and D2 dopamine receptors in basal ganglia, an Actor network module with separate learning of positive and negative rewards is designed for the basal ganglia to realize efficient exploration of the environments by the agent. According to the characteristics of biological memory, a physiological memory priority index is designed for hippocampus memory replay, which improves the offline learning efficiency of cerebellum. The integrated model enables dynamic switching between decisions made by cerebellum and basal ganglia based on the agent’s cognitive level with respect to the environment. Finally, the effectiveness of the proposed model is verified through experiments on agent navigation in both simulation and real environments, as well as through performance comparison experiments with other learning algorithms.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101302"},"PeriodicalIF":2.1,"publicationDate":"2024-10-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593526","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Cognitive biases in natural language: Automatically detecting, differentiating, and measuring bias in text","authors":"Kyrtin Atreides,&nbsp;David J. Kelley","doi":"10.1016/j.cogsys.2024.101304","DOIUrl":"10.1016/j.cogsys.2024.101304","url":null,"abstract":"<div><div>We examine preliminary results from the first automated system to detect the 188 cognitive biases included in the 2016 Cognitive Bias Codex, as applied to both human and AI-generated text, and compared to a human baseline of performance. The human baseline was constructed from the collective intelligence of a small but diverse group of volunteers independently submitting their detected cognitive biases for each sample in the task used for the first phase. This baseline was used as an approximation of the ground truth on this task, for lack of any prior established and relevant benchmark. Results showed the system’s performance to be above that of the average human, but below that of the top-performing human and the collective, with greater performance on a subset of 18 out of the 24 categories in the codex. This version of the system was also applied to analyzing responses to 150 open-ended questions put to each of the top 5 performing closed and open-source Large Language Models, as of the time of testing. Results from this second phase showed measurably higher rates of cognitive bias detection across roughly half of all categories than those observed when analyzing human-generated text. The level of model contamination was also considered for two types of contamination observed, where the models gave canned responses. Levels of cognitive bias detected in each model were compared both to one another and to data from the first phase.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101304"},"PeriodicalIF":2.1,"publicationDate":"2024-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142593527","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A new perspective on Misbeliefs: A computational model for perceived control","authors":"Haokui Xu ,&nbsp;Bohao Shi ,&nbsp;Yiming Zhu ,&nbsp;Jifan Zhou ,&nbsp;Mowei Shen","doi":"10.1016/j.cogsys.2024.101305","DOIUrl":"10.1016/j.cogsys.2024.101305","url":null,"abstract":"<div><div>The discovery of various cognitive biases and social illusions indicates that people routinely have misbeliefs. Focusing on the illusion of control (IOC), this article argues that when time and cognitive resources are limited, and information is imperfect, misbeliefs can be generated naturally in a normal belief formation system, and these misbeliefs might help people adapt better to the environment.<!--> <!-->In this study, we present a computational model—the informativeness-weighting model (IWM)—describing how beliefs are revised by observed evidence. To be precise, IOC is the result of distinct types of evidence being endowed with different weights according to its informativeness in a belief revision process. To evaluate the model, we also designed two behavioral experiments to compare people’s sense of control with that predicted by the model.<!--> <!-->In both experiments, our model outperformed two alternative models in predicting and explaining the misestimation of people’s perceived control. Thus, we suggest that our model reflects an adaptive strategy for information processing, which helps to explain why misbeliefs, like IOC, are prevalent in human cognition.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101305"},"PeriodicalIF":2.1,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526108","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"From human explanations to explainable AI: Insights from constrained optimization","authors":"Inga Ibs ,&nbsp;Claire Ott ,&nbsp;Frank Jäkel,&nbsp;Constantin A. Rothkopf","doi":"10.1016/j.cogsys.2024.101297","DOIUrl":"10.1016/j.cogsys.2024.101297","url":null,"abstract":"<div><div>Many complex decision-making scenarios encountered in the real-world, including energy systems and infrastructure planning, can be formulated as constrained optimization problems. Solutions for these problems are often obtained using white-box solvers based on linear program representations. Even though these algorithms are well understood and the optimality of the solution is guaranteed, explanations for the solutions are still necessary to build trust and ensure the implementation of policies. Solution algorithms represent the problem in a high-dimensional abstract space, which does not translate well to intuitive explanations for lay people. Here, we report three studies in which we pose constrained optimization problems in the form of a computer game to participants. In the game, called Furniture Factory, participants manage a company that produces furniture. In two qualitative studies, we first elicit representations and heuristics with concurrent explanations and validate their use in post-hoc explanations. We analyze the complexity of the explanations given by participants to gain a deeper understanding of how complex cognitively adequate explanations should be. Based on insights from the analysis of the two qualitative studies, we formalize strategies that in combination can act as descriptors for participants’ behavior and optimal solutions. We match the strategies to decisions in a large behavioral dataset (<span><math><mrow><mo>&gt;</mo><mn>150</mn></mrow></math></span> participants) gathered in a third study, and compare the complexity of strategy combinations to the complexity featured in participants’ explanations. Based on the analyses from these three studies, we discuss how these insights can inform the automatic generation of cognitively adequate explanations in future AI systems.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101297"},"PeriodicalIF":2.1,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142572379","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Technology roadmap toward the completion of whole-brain architecture with BRA-driven development","authors":"Hiroshi Yamakawa ,&nbsp;Yoshimasa Tawatsuji ,&nbsp;Yuta Ashihara ,&nbsp;Ayako Fukawa ,&nbsp;Naoya Arakawa ,&nbsp;Koichi Takahashi ,&nbsp;Yutaka Matsuo","doi":"10.1016/j.cogsys.2024.101300","DOIUrl":"10.1016/j.cogsys.2024.101300","url":null,"abstract":"<div><div>The development of brain-morphic software holds significant promise for creating artificial general intelligence that exhibits high affinity and interpretability for humans and also offers substantial benefits for medical applications. To facilitate this, creating Brain Reference Architecture (BRA) data, serving as a design specification for brain-morphic software is imperative. BRA-driven development, which utilizes Brain Information Flow (BIF) diagrams based on mesoscale brain anatomy and Hypothetical Component Diagrams (HCD) for corresponding computational functionalities, has been proposed to address this need. This methodology formalizes identifying possible functional structures by leveraging existing, albeit insufficient, neuroscientific knowledge. However, applying this methodology across the entire brain, thereby creating a Whole Brain Reference Architecture (WBRA), represents a significant research and development challenge due to its scale and complexity. Technology roadmaps have been introduced as a strategic tool to guide discussion, management, and distribution of resources within such expansive research and development activities. These roadmaps proposed a manual, anatomically based approach to incrementally construct BIF and HCD, thereby systematically expanding brain organ coverage toward achieving a complete WBRA. Large Language Model (LLM) technologies have introduced a paradigm shift, substantially automating the BRA-driven development process. This is largely due to the BRA data being structured around the brain’s anatomy and described in natural language, which aligns well with the capabilities of LLMs for supporting and automating the construction and verification processes. In this paper, we propose a novel technology roadmap to largely automate the creation of WBRA, leveraging neuroscientific insights. This roadmap includes 12 activities for automating BIF construction, notably extracting anatomical structures from scholarly articles. Furthermore, it details 11 activities aimed at enhancing the integration of Hypothetical Component Diagrams (HCD) into the WBRA, focusing on automating checks for functional consistency. This roadmap aims to establish a cost-effective and efficient design process for WBRA, ensuring the availability of brain-morphic software design specifications that are continually validated against the latest neuroscientific knowledge.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101300"},"PeriodicalIF":2.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142560689","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"To the problem of digital immortality","authors":"Olga Chernavskaya","doi":"10.1016/j.cogsys.2024.101303","DOIUrl":"10.1016/j.cogsys.2024.101303","url":null,"abstract":"<div><div>The idea of reproducing personality by means of digital (neural network) technologies (“digital immortality”), together with the concept of Digital Twin (DT), still attracts great attention. Recent advances in the DT industry permit to expect the production of perfect “mirror” DT in the near future. We argue that the “immortality in the memory of other people” could be approached quite closely due to creating an <em>analogue</em> of personal DT by simulating the personality. For this purpose, it is necessary to compose the “constructive portrait” of a chosen person (by extracting the key features and traits of personality) and try to reproduce it by means of a chosen model. We are developing an original model Natural Constructive Cognitive Architecture (NCCA) that inherently provides the interpretation of logical and intuitive thinking, subconscious, etc. This model should be adjusted to specific set of knowledge inherent in a particular person (books, films, photographs, etc.), with an emphasis on personal <em>lexicons</em> (verbal, emotion, behavioral). NCCA contains a large set of free model parameters, which enables us to reproduce a wide range of personality features, from thinking style to temperament. It is shown that popular Generative Pre-trained Transformers (GPTs) have much in common with NCCA and could be adapted and used as an analog of DT of a specific person. We argue that the proposed program would provide the possibility to create an analog of DT, which could give an impression (at least, an illusion) of communication with the desired specific person.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101303"},"PeriodicalIF":2.1,"publicationDate":"2024-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing resource allocation in home care services using MaxSAT","authors":"Irene Unceta ,&nbsp;Bernat Salbanya ,&nbsp;Jordi Coll ,&nbsp;Mateu Villaret ,&nbsp;Jordi Nin","doi":"10.1016/j.cogsys.2024.101291","DOIUrl":"10.1016/j.cogsys.2024.101291","url":null,"abstract":"<div><div>In large urban areas, enhancing the personal care and quality of life for elderly individuals poses a critical societal challenge. As the population ages and the amount of people requiring assistance grows, so does the demand for home care services. This will inevitably put tremendous pressure on a system that has historically struggled to provide high-quality assistance with limited resources, all while managing urgent, unforeseen additional demands. This scenario can be framed as a resource allocation problem, wherein caregivers must be efficiently matched with services based on availability, qualifications, and schedules. Given its scale and complexity, traditional computational approaches have struggled to address this problem effectively, leaving it largely unresolved. Currently, many European cities emphasize geographical and emotional proximity, offering a model for home care services based on reduced social urban sectors. This new paradigm provides opportunities for tackling the resource allocation problem while promoting desirable pairings between caregivers and elderly people. This paper presents a MaxSAT-based solution in this context. Our approach efficiently allocates services across various configurations, maximizing caregiver-user pairings’ similarity and consistency while minimizing costs. Moreover, we show that our method solves the resource allocation problem in a reasonable amount of time. Consequently, we can either provide an optimal allocation or highlight the limits of the available resources relative to the service demand.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101291"},"PeriodicalIF":2.1,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142525957","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"SimplifEx: Simplifying and Explaining Linear Programs","authors":"Claire Ott,&nbsp;Frank Jäkel","doi":"10.1016/j.cogsys.2024.101298","DOIUrl":"10.1016/j.cogsys.2024.101298","url":null,"abstract":"<div><div>Linear Programming is one of the most common methods for finding optimal solutions to complex problems. Despite its extensive use, solutions are not usually accompanied by explanations, especially explanations for non-experts. Our new tool SimplifEx combines well-known preprocessing techniques with cognitively adequate heuristics to simplify a given linear program, structure its variables, and explain the optimal solution that was found. SimplifEx is meant to improve intuitive understanding of linear programs. In addition, we introduce a generalization of the classical dominance relation in Linear Programming. The order of dominant and dominated variables in an optimal solution can give valuable insights into the structure of a problem and fits well with how humans approach linear programs. The resulting, automatically generated explanations include detailed step-wise listings of processing steps and graphs that provide an overview. The heuristics are based on historical and experimental observations of people solving linear programs by hand. We apply SimplifEx to Stigler’s diet problem as a running example.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101298"},"PeriodicalIF":2.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526106","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Supporting risk management through cyberspace: An adaptive network model simulating AI coach effects by inducing adherence to guidelines in neonatal medical protocols","authors":"Raghav Chawla ,&nbsp;Fakhra Jabeen ,&nbsp;Jan Treur ,&nbsp;H. Rob Taal ,&nbsp;Peter H.M.P. Roelofsma","doi":"10.1016/j.cogsys.2024.101290","DOIUrl":"10.1016/j.cogsys.2024.101290","url":null,"abstract":"<div><div>In this article, it is shown how second-order adaptive agent-based network models can be used to support a medical team in healthcare institutions to adhere to specific Neonatal Hypoglycemia and Neonatal Hyperbilirubinemia treatment guidelines through the integration of an Artificial Intelligence (AI) Virtual Coach. The proposed AI Coach is designed to provide timely interventions and correct deviations when lapses in the health care practitioner’s internal mental model occur. Through simulating three different scenarios, the internal dynamics of these mental models, adaptive changes of these mental models (learning and forgetting), and the interaction between health care practitioners and the world is shown when: (1) There is perfect adherence to guidelines, (2) There is imperfect adherence to guidelines and (3) There is both perfect and imperfect adherence to guidelines alongside interventions of the AI Coach in the latter case.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101290"},"PeriodicalIF":2.1,"publicationDate":"2024-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142433332","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deconstructing emotions in self-control through computational modeling","authors":"Andriani Nikodemou,&nbsp;Chris Christodoulou","doi":"10.1016/j.cogsys.2024.101294","DOIUrl":"10.1016/j.cogsys.2024.101294","url":null,"abstract":"<div><div>Positive and negative emotions have a determining role in self-control, a vital aspect of human decision-making, defined as the dilemma between a smaller sooner reward and a larger later reward. Self-control, as an internal conflict between the higher (pre-frontal cortex) and the lower (limbic system) parts of the brain, has already been simulated using the Iterated Prisoner’s Dilemma game with learning in a computational model. However, the concept of emotions, defined as states elicited by positive and negative reinforcers, is absent from the existing self-control model. By increasing and decreasing the values of the reinforcement signals in the Prisoner’s Dilemma payoff matrix in-between the rounds, we simulated the increment or decrement of positive or negative emotions’ intensity and thus the effects of the presence of emotions, rather than the emotions per se. Our results reflect the restorative role of positive emotions on self-control, the necessity of negative emotions for successful self-control and the impairment of self-control due to intense negative emotions. Furthermore, our results reveal the importance of parameters in self-regulation, such as the intensity of emotions and the frequency it changes. In conclusion, we incorporated the effect of emotions in a computational model of self-control, and with our results complying with cognitive science literature, we demonstrated the cognitive adequacy of our model. We anticipate in this way to provide novel approaches for comprehending self-control behaviour, and to contribute to the general attempt of modeling human behaviour.</div></div>","PeriodicalId":55242,"journal":{"name":"Cognitive Systems Research","volume":"88 ","pages":"Article 101294"},"PeriodicalIF":2.1,"publicationDate":"2024-10-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142526110","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"心理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}