Cognitive agency in sociocultural evolution

Models of sociocultural evolution generally study the population dynamics of cultural traits given known biases in social learning. Cognitive agency, understood as the dynamics underlying a specific agent’s adoption of a given trait, is essentially irrelevant in this framework. This article argues that although implementing and instrumenting agency in computational models is fundamentally challenging, it is ultimately possible and would help us overcome major limitations in our understanding of sociocultural dynamics.Indeed, the behaviour of humans is not causally generated by a set of predefined behavioural laws, but by the situated activity of their cognitive architecture. Idealised models of biased transmission certainly help us understand specific features of population dynamics. However, they distract us from the deep intrication of the cognitive and ecological processes underlying sociocultural evolution, and erase their embodied, subjective nature.In line with the earlier “Thinking Through Other Minds” account of sociocultural evolution, this article highlights how the Active Inference framework can help us implement and instrument computational models that address these limitations. Such models would not only help ground our understanding of sociocultural evolution in the underlying cognitive dynamics, but also help solve (or frame) open questions in the study of ritual, relation between cultural transmission and innovation, as well as scales of cultural evolution.

Impact of decision-making system in social navigation

Facing human activity-aware navigation with a cognitive architecture raises several difficulties integrating the components and orchestrating behaviors and skills to perform social tasks. In a real-world scenario, the navigation system should not only consider individuals like obstacles. It is necessary to offer particular and dynamic people representation to enhance the HRI experience. The robot’s behaviors must be modified by humans, directly or indirectly. In this paper, we integrate our human representation framework in a cognitive architecture to allow that people who interact with the robot could modify its behavior, not only with the interaction but also with their culture or the social context. The human representation framework represents and distributes the proxemic zones’ information in a standard way, through a cost map. We have evaluated the influence of the decision-making system in human-aware navigation and how a local planner may be decisive in this navigation. The material developed during this research can be found in a public repository (https://github.com/IntelligentRoboticsLabs/social_navigation2_WAF) and instructions to facilitate the reproducibility of the results.

Remarks on Multimodality: Grammatical Interactions in the Parallel Architecture

Language is typically embedded in multimodal communication, yet models of linguistic competence do not often incorporate this complexity. Meanwhile, speech, gesture, and/or pictures are each considered as indivisible components of multimodal messages. Here, we argue that multimodality should not be characterized by whole interacting behaviors, but by interactions of similar substructures which permeate across expressive behaviors. These structures comprise a unified architecture and align within Jackendoff's Parallel Architecture: a modality, meaning, and grammar. Because this tripartite architecture persists across modalities, interactions can manifest within each of these substructures. Interactions between modalities alone create correspondences in time (ex. speech with gesture) or space (ex. writing with pictures) of the sensory signals, while multimodal meaning-making balances how modalities carry “semantic weight” for the gist of the whole expression. Here we focus primarily on interactions between grammars, which contrast across two variables: symmetry, related to the complexity of the grammars, and allocation, related to the relative independence of interacting grammars. While independent allocations keep grammars separate, substitutive allocation inserts expressions from one grammar into those of another. We show that substitution operates in interactions between all three natural modalities (vocal, bodily, graphic), and also in unimodal contexts within and between languages, as in codeswitching. Altogether, we argue that unimodal and multimodal expressions arise as emergent interactive states from a unified cognitive architecture, heralding a reconsideration of the “language faculty” itself.

Fostering Transversal Skills in Game-Based Learning

The current chapter examines the relationship between facets of cognitive abilities and relevant learning activities by drawing on literature pertaining to higher- and lower-order critical thinking. Specific discussions were made on cognitive architecture and deep learning, modality and information process, and cognitive abilities and levels of process in learning activities. The cognitive ability-learning activity matrix was proposed to (1) raise attention to the relationship between cognitive abilities and relevant learning activities in transversal critical thinking in game-based learning and (2) guide educators, teachers, and professional trainers to facilitate effective transversal of critical thinking skills across domains, disciplines, and learning communities. Discussions of the theoretical and practical significance of the proposed matrix were made. Recommendations for future research were proposed to guide the direction and practice in fostering transversal skills in game-based learning.

Shame

Reliance on mutual aid is a distinctive characteristic of human biology. Consequently, a central adaptive problem for our ancestors was the potential or actual spread of reputationally damaging information about the self – information that would decrease the inclination of other group members to render assistance. The emotion of shame appears to be the solution engineered by natural selection to defend against this threat. The existing evidence suggests that shame is a neurocomputational program that orchestrates various elements of the cognitive architecture in the service of (i) deterring the individual from making choices wherein the personal benefits are exceeded by the prospective costs of being devalued by others, (ii) preventing negative information about the self from reaching others, and (iii) minimizing the adverse effects of social devaluation when it occurs. The flow of costs (e.g., punishment) and benefits (e.g., income, aid during times of hardship) in human societies is regulated to an important extent by this interlinked psychology of social evaluation and shame (as well as other social emotions). For example, the intensity of shame that laypeople express at the prospect of committing each of various offenses closely matches the intensity of the actual offense-specific punishments called for by criminal laws, including modern laws and ancient laws that are millennia old. Because shame, like pain, causes personal suffering and sometimes leads to hostile behavior, shame has been termed a “maladaptive” and “ugly” emotion. However, an evolutionary psychological analysis suggests that the shame system is elegantly designed to deter injurious choices and make the best of a bad situation.

A Storytelling Robot Managing Persuasive and Ethical Stances via ACT-R: An Exploratory Study

We present a storytelling robot, controlled via the ACT-R cognitive architecture, able to adopt different persuasive techniques and ethical stances while conversing about some topics concerning COVID-19. The main contribution of the paper consists in the proposal of a needs-driven model that guides and evaluates, during the dialogue, the use (if any) of persuasive techniques available in the agent procedural memory. The portfolio of persuasive techniques tested in such a model ranges from the use of storytelling to framing techniques and rhetorical-based arguments. To the best of our knowledge, this represents the first attempt of building a persuasive agent able to integrate a mix of explicitly grounded cognitive assumptions about dialogue management, storytelling and persuasive techniques as well as ethical attitudes. The paper presents the results of an exploratory evaluation of the system on 63 participants.

Development of interactive gadgets: pedagogical, methodological and ergonomic aspects for industrial design

The Industry 4.0 is a consequence of the evolution in technological advances, which has allowed and the use of new tools for simulation, digital integration, fabrication flexibility, and personalization to achieve new product design solutions. The importance and actuality of this revolution have had a great impact on the engineering and design education system, and this is the case of the Faculty of Engineering and Technology Sciences (FCITEC), from the Autonomous University of Baja California (UABC), where the implementation of gadget prototyping has been encouraged. This ongoing work is intended to delineate the methodological, pedagogical, and ergonomic aspects of gadget prototyping with platforms such as Arduino and NodeMCU, and its benefits to the Industrial Design (ID) Discipline. It is a project that started in 2018 with the scope of understanding interactivity, usability, and multidisciplinary collaboration, which are key for a designer’s profile. In this sense, User-Centered Design methodology is used as a framework for usable product development, with the aid of task, interface, and housing design. Specific tools of particular interest are persona design, interface analysis, and cognitive architecture outline. Important results so far include 1) student-made prototypes, 2) usability workshops in international congresses, 3) intellectual property registration, and 4) academic course designs.

An Affective Computing Multilayer Cognitive Architecture for Evolutionary Cognitive Robotics

<p>Robots are entering our daily lives from self-driving cars to health-care robots. Historically, pre-programmed robots were vulnerable to changing conditions in daily life, primarily because of a lack of ability to generate novel, non-preset flexible solutions. Thus there is a need for robotics to incorporate adaptation, which is a trait of higher order natural species. This adaptation allows higher-order natural species to change their behaviours and internal mechanisms based on experience with often dynamic environment. The ability to adapt emerged through evolutionary processes. Evolutionary Robotics is an approach to create autonomous robots that are capable of automatically generating artificial behaviors and morphologies to achieve adaptation. Evolutionary robotics has the potential to automatically synthesize controllers for real autonomous robots and generate solutions to complete tasks in the uncertain real-world. Compared to the inflexibility of pre-programmed robots, evolutionary robots are able to learn flexible solutions to given tasks through evolutionary methods. Cognitive robotics, a branch of artificial cognitive systems research, is such an attempt to create autonomous robots by applying bio-inspired methods. As the robot interacts with environment, an underlying cognitive system can learn its own solutions toward task completion. This learning-solution-from-interaction approach, also termed as a Reinforcement Learning (RL) approach, is widely applied in cognitive robotics to learn the solutions automatically. Ideally, the solutions can emerge in the cognitive system through the trial-and-error process of the RL approach without introducing human bias. This thesis aims to develop an evolutionary cognitive architecture (system) for a robot that can learn adaptive solutions to complete tasks. Inspired by emotion theories, this work proposes Affective Computing Multilayer Cognitive Architecture (ACMCA), a universal cognitive architecture, which is able to learn diverse solutions. Extending from previous work, ACMCA has a five-layer structure, where each layer aims to achieve different components of the solutions. The position of this thesis is that introducing a novel emotion inspired multilayer architecture that produces task solutions through subsumption operations and underlying appropriate machine learning algorithms will allow a robot to complete admissible tasks. ACMCA’s five layers are: primary reinforcer layer, secondary reinforcer layer, core affect state layer, strategy layer, and behaviour layer. This five-layer decomposition also meets the traditional decomposition of a mobile control system into functional modules (e.g. perception, modelling, planning, task execution, and motor control). Each layer contains computing nodes as functional modules that process various Stimuli, Actions, and their consequential Outcomes of the cognitive system. In this work, 17 computing nodes and their connections in ACMCA represent the solutions that a mobile robot has learned to complete navigation tasks in complex scenarios. Inspired by the Constructive Theory 1 and the robotic subsumption system, this work proposes a contingency-based subsumption approach to construct ACMCA. This contingency is termed Stimuli-Action-Outcome Contingency (SAOC), which is extended from the Action-Outcome (AO) contingency of Construction Theory. SAOCs are represented by “if-then” rules, termed SAOC rules, which encapsulate Stimuli, Actions, and their consequential Outcomes, providing clear symbolic interpretations. That is, the symbolic meaning of a SAOC rule can be interpreted as: if the input stimulus is perceived, the output action will be advocated as a cognitive response, expecting the outcome of the action with an estimation of relevance. As low-level computing nodes encapsulate Stimulus, Actions, and Outcomes, high-level computing nodes can subsume these low-level ones through the form of SAOC rules. Therefore, the proposed ACMCA can be constructed by subsumption layers of Stimuli-Action-Outcome Contingency (SAOC) rules. This work applies machine learning techniques to facilitate ACMCA’s real-world robotic implementation. This work selects Accuracy-based Learning Classifier Systems (XCS) algorithms as the underlying machine learning techniques that are deployed at computing nodes for the contingency-based subsumption operations. The mitosis approach of XCS and the XCS with a Combined Reward method (XCSCR) are two novel variants of XCS algorithm. They are proposed to amend two challenges that occur when the standard XCS approaches are applied for robotic applications. The mitosis approach introduces an accuracy pressure into the algorithm’s evolutionary process, improving the algorithms’ performance in robotic applications where noisy interferences exist. The XCSCR enables the policy to emerge earlier and more frequently than the existing benchmark approaches in multistep problems. Therefore, a robot with the XCSCR can handle a multistep scenario more effectively than those with the benchmarked algorithms. This work conducts five experiments to test the capability of ACMCA and its underlying algorithms in learning solutions for robotic navigation tasks. The five experiments are conducted as follows: reflex-learning, IR-tuning, deliberation-establishing, emotion model, and combined reward assignment. As the results of the experiments, three different affective patterns have emerged in the first three experiments, an emotion model has emerged in the fourth experiments, and the fifth experiment explores ACMCA’s potential implementation in the life-long learning scenario. These results demonstrate that ACMCA, a novel emotion inspired multilayer architecture, can produce task solutions through contingency-based subsumption operations and underlying appropriate machine learning algorithms, allowing a robot to complete admissible tasks through evolutionary processes. The contingency-based subsumption operations can establish three contingencies and one emotion model between the subsumed components by multiple RL agents which deploy the proposed mitosis approach of XCS algorithms. These three emotion patterns and emotion model can consistently improve the robot’s navigation performance with interpretable explanations. These two variants of XCS algorithms can amend shortfalls of the standard XCS approach in real-world robotic implementations. It has been demonstrated that the diverse solutions learned by ACMCA improve the navigation performance of the robot in terms of higher flexibility, reduction in continuous collisions and shorter navigation time consumption.</p>