Search

The International Conference on Cognitive Modelling is dedicated to understanding how the complex processes of the mind can be explained in terms of detailed inner processing. In this issue, we present four representative papers of this field of research from our 20th meeting, ICCM 2022. This meeting was our first hybrid meeting, with a virtual version happening July 11–15, 2022, and an in‐person event from July 23–27, 2022, held in Toronto, Canada. The four papers presented here were the top‐ranked papers across both the virtual and in‐person events. Three of the papers develop novel computational theories about low‐level components within the mind and how those components result in high‐level phenomena such as motivation, anhedonia, and attention. The final paper demonstrates the use of cognitive modeling to develop novel explanations of a paired associate learning task, and uses those insights to develop and explain human performance in a more complex version of that task. [ABSTRACT FROM AUTHOR]

Cognitive modeling involves the creation of computer simulations that emulate the internal processes of the mind. This set of papers are the five best representatives of the papers presented at the 17th International Conference on Cognitive Modeling, ICCM 2019. While they represent a diversity of techniques and tasks, they all also share a striking similarity: They make strong statements about the importance of accounting for individual differences. [ABSTRACT FROM AUTHOR]

Newell (1973) argued that progress in psychology was slow because research focused on experiments trying to answer binary questions, such as serial versus parallel processing. In addition, not enough attention was paid to the strategies used by participants, and there was a lack of theories implemented as computer models offering sufficient precision for being tested rigorously. He proposed a three-headed research program: to develop computational models able to carry out the task they aimed to explain; to study one complex task in detail, such as chess; and to build computational models that can account for multiple tasks. This article assesses the extent to which the papers in this issue advance Newell's program. While half of the papers devote much attention to strategies, several papers still average across them, a capital sin according to Newell. The three courses of action he proposed were not popular in these papers: Only two papers used computational models, with no model being both able to carry out the task and to account for human data; there was no systematic analysis of a specific video game; and no paper proposed a computational model accounting for human data in several tasks. It is concluded that, while they use sophisticated methods of analysis and discuss interesting results, overall these papers contribute only little to Newell's program of research. In this respect, they reflect the current state of psychology and cognitive science. This is a shame, as Newell's ideas might help address the current crisis of lack of replication and fraud in psychology. [ABSTRACT FROM AUTHOR]

Abstract: Cognitive modeling is the effort to understand the mind by implementing theories of the mind in computer code, producing measures comparable to human behavior and mental activity. The community of cognitive modelers has traditionally met twice every 3 years at the International Conference on Cognitive Modeling (ICCM). In this special issue of topiCS, we present the best papers from the ICCM meeting. (The full proceedings are available on the ICCM website.) These best papers represent advances in the state of the art in cognitive modeling. Since ICCM was for the first time also held jointly with the Society for Mathematical Psychology, we use this preface to also reflect on the similarities and differences between mathematical psychology and cognitive modeling. [ABSTRACT FROM AUTHOR]

Buy-low-sell-high is one of the basic rules of thumb used by individuals for investment, although it is not considered to be a constructive strategy. In this paper, we show how the appropriate representation of a minute-by-minute trading time series through ordinal (i.e., permutation) patterns and the use of a simple decision heuristic may surprisingly result in significant benefits. We do not compare our proposed approach to sophisticated methods in trading but show how a mathematical model adhering to the idea of bounded rationality may result in significant benefits. [ABSTRACT FROM AUTHOR]

We present motivation and practical steps necessary to find parameter estimates of joint models of behavior and neural electrophysiological data. This tutorial is written for researchers wishing to build joint models of human behavior and scalp and intracranial electroencephalographic (EEG) or magnetoencephalographic (MEG) data, and more specifically those researchers who seek to understand human cognition. Although these techniques could easily be applied to animal models, the focus of this tutorial is on human participants. Joint modeling of M/EEG and behavior requires some knowledge of existing computational and cognitive theories, M/EEG artifact correction, M/EEG analysis techniques, cognitive modeling, and programming for statistical modeling implementation. This paper seeks to give an introduction to these techniques as they apply to estimating parameters from neurocognitive models of M/EEG and human behavior, and to evaluate model results and compare models. Due to our research and knowledge on the subject matter, our examples in this paper will focus on testing specific hypotheses in human decision-making theory. However, most of the motivation and discussion of this paper applies across many modeling procedures and applications. We provide Python (and linked R) code examples in the tutorial and appendix. Readers are encouraged to try the exercises at the end of the document.

In this paper, we develop a new theory termed Soft Logic. This theory addresses the need to combine real processes and cognitive ones in the same framework. At the same time, we develop a new concept of modeling and dealing with uncertainty: the uncertainty of time and space. We develop a language that can talk in two reference frames, and also suggest a way to combine them. This constitutes a fundamental new mathematics. In this paper, we present the theory from its foundation. In Soft Logic, we refer to situations of uncertainty occurring in the present. Ambiguity is created from the possible point of view of the observer of the phenomenon. Sometimes reality contains contradictory or opposite situations at the same time, which can be interpreted in different ways by the observer. Such is the case of the Necker cube or the Fechner color effect, in which a texture of black and white colors moving in a certain pattern creates a rainbow of colors. Another example is the Mobius strip, which from a local point of view seems to have two sides, but from a global point of view has only one. The mathematical solution for expressing uncertainty in Soft Logic is the blow-up of the number zero by distinguishing between multiples of this number. In this manner, we create a zero-axis and define a new type of number called Soft Numbers, which have the following form: a 0 ¯ + ̇ b 1 ¯. The zero-axis expresses the inner world of the observer and is the component of a subjective interpretation and a certain type of uncertainty that occurs in the present. Soft Numbers are similar to complex numbers, but the specific mathematical definitions are different and therefore Soft Numbers are a different world. This paper presents five axioms of Soft Logic, which form a new system of coordinates that differs from the Cartesian system and Gauss's complex plane. We define the algebra of Soft Numbers, show the connection to differential and integral calculus, expand analytical functions to soft functions, and describe continuous curves in the soft coordinate system. In conclusion, we suggest several avenues for theoretical and practical research. [ABSTRACT FROM AUTHOR]

Under the current geopolitical conditions and the economic sanctions imposed on Russia, there is an objective need to formulate a strategic development plan for the economy as a whole and for specific sectors of the economy. Various methods and tools can be used for strategic planning. One of the methods of strategic planning is the program-targeted method of planning, which has proved to be an effective method of foresight. It is possible to speak about failures of planning activities, but these failures were related not only to shortcomings and application of science-based planning methods but also to the efficiency of the managerial apparatus, which took decisions. It should be noted that it was in the period when science-based planning methods were applied that our country managed to form and develop industrial production in various sectors, and the issue of import substitution did not arise then, as all the stages of the product life cycle were represented at all the enterprises. Currently, the country is facing the problem of strategic development in the context of the imposed economic sanctions. The volume of sanctions is increasing day by day and one can only speculate on the future restrictions imposed. Therefore, there is a need to forecast activities at the level of the whole country, individual industries, and enterprises. One such method is cognitive modeling based on fuzzy logic. This approach involves the use of cognitive principles and methods to understand the behavior of individuals in the system, as well as the interactions and feedback loops between the various components. The purpose of this paper is to retrospectively analyze the application of the cognitive method to modeling. Information systems that have been developed in our country to implement the tasks of cognitive modeling are reviewed, and an assessment of existing software products is made. Also, theoretical materials on cognitive approach in modeling are presented in order to understand the application of this toolkit for modeling socioeconomic systems using elements of fuzzy logic. [ABSTRACT FROM AUTHOR]

Cognitive architectures (i.e., theorized blueprints on the structure of the mind) can be used to make predictions about the effect of multiregion brain activity on the systems level. Recent work has connected one high‐level cognitive architecture, known as the "Common Model of Cognition," to task‐based functional MRI data with great success. That approach, however, was limited in that it was intrinsically top‐down, and could thus only be compared with alternate architectures that the experimenter could contrive. In this paper, we propose a bottom‐up method to infer a cognitive architecture directly from brain imaging data itself, overcoming this limitation. Specifically, Granger causality modeling was applied to the same task‐based fMRI data to infer a network of causal connections between brain regions based on their functional connectivity. The resulting network shares many connections with those proposed by the Common Model of Cognition but also suggests important additions likely related to the role of episodic memory. This combined top‐down and bottom‐up modeling approach can be used to help formalize the computational instantiation of cognitive architectures and further refine a comprehensive theory of cognition. [ABSTRACT FROM AUTHOR]

The ability to anticipate team members' actions enables joint action towards a common goal. Task knowledge and mental simulation allow for anticipating other agents' actions and for making inferences about their underlying mental representations. In human–AI teams, providing AI agents with anticipatory mechanisms can facilitate collaboration and successful execution of joint action. This paper presents a computational cognitive model demonstrating mental simulation of operators' mental models of a situation and anticipation of their behavior. The work proposes two successive steps: (1) A hierarchical cluster algorithm is applied to recognize patterns of behavior among pilots. These behavioral clusters are used to derive commonalities in situation models from empirical data (N = 13 pilots). (2) An ACT‐R (adaptive control of thought ‐ rational) cognitive model is implemented to mentally simulate different possible outcomes of action decisions and timing of a pilot. model tracing of ACT‐R allows following up on operators' individual actions. Two models are implemented using the symbolic representations of ACT‐R: one simulating normative behavior and the other by simulating individual differences and using subsymbolic learning. Model performance is analyzed by a comparison of both models. Results indicate the improved performance of the individual differences over the normative model and are discussed regarding implications for cognitive assistance capable of anticipating operator behavior. "This paper illustrates how cognition‐inspired AI can help anticipate pilot actions in the cockpit. Unsupervised learning allowed for identification of pilot behavior patterns from empirical data. These patterns were then simulated with the help of an ACT‐R cognitive model to anticipate actions of individual pilots in new situations." [ABSTRACT FROM AUTHOR]

Despite lacking a generally accepted definition, Artificial General Intelligence (AGI) is commonly understood to refer to artificial agents possessing the capacity to build up a context-independent understanding of itself and the world and to generalize this knowledge across a multitude of contexts. In human agents, this capacity is, to a large degree, facilitated by processes of self-directed learning , during which agents voluntarily control the conditions under which episodes of learning and problem solving occur. Since self-directed learning depends on the degree of knowledge the agent has about various aspects of themselves (their bodily skills, their learning goal, etc.), an AGI implementation of this type of learning must build on a theory of how this self-knowledge is actualized and modified during the learning process. In this paper, we employ the pattern theory of self in order to characterize different aspects of an agent's self that are relevant for self-directed learning. Such aspects include agent-internal cognitive states such as thoughts, emotions, and intentions, but also relational states such as action possibilities in the environment. Combinations of these aspects form a characteristic pattern, which is unique to each individual agent, with no one aspect being necessary or sufficient for the individuation of that agent's self. Here, we focus on the interdependence of narrative and embodied aspects of the self-pattern, since they involve particularly salient challenges consisting in conceptualizing the interaction between propositional and motor representations. In our paper, we model the reciprocal interaction of these aspects of the self-pattern within an individual cognitive agent. We do so by extending an approach by Ryan, Agrawal, & Franklin (2020), who laid the groundwork for the implementation of the pattern theory of self in the LIDA (Learning Intelligent Decision Agent) model. We describe how embodied and narrative aspects of an agent's self-pattern are realized by patterns of interaction between different LIDA modules over time, and how interactions at multiple temporal scales allow the agent's self-pattern to be both dynamically variable and relatively stable. Finally, we investigate the implications this view has for the creation of artificial agents that can benefit from self-directed learning, both in the context of deliberate planning and adaptive motor execution. [ABSTRACT FROM AUTHOR]

According to L. A. Paul, the subjective value of an outcome is normally assessed by running a cognitive model of what it would be like if that outcome were to occur. However, cognitive models, along with the expectations in which they result, are unreliable for application to transformative experiences because we cannot know what it would be like for an outcome to occur if we have never experienced it before. This paper argues that despite their unreliability, expectations are still important in the case of chosen and unchosen transformative experiences because expectations about an outcome can systematically influence the very experience of that outcome. More precisely, empirical research shows that affective experiences tend to assimilate to affective expectations. In turn, more positive affective experiences lead, ceteris paribus, to higher subjective value. Therefore, rational agents confronting transformative outcomes should form or cultivate positive/optimistic affective expectations since, all else being equal, that maximizes subjective value. [ABSTRACT FROM AUTHOR]

Copyright of Brazilian Journal of Education, Technology & Society (BRAJETS) / Cadernos de Educação Tecnologia e Sociedade (CETS) is the property of Brazilian Journal of Education, Technology & Society - BRAJETS and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The paper examines the impact of innovations on the socioeconomic development of the oil and gas regions of Russia. It applies a cross-methodical approach, including cluster analysis, panel data analysis and cognitive modeling. Each method performs its function in the research process: cluster analysis—multifactorial systematics of regions, analysis of panel data—obtaining quantitative estimates of the impact of resource and innovation factors on GRP, and cognitive modeling—building scenario forecasts for the socioeconomic development of an oil and gas region. As a result, the features of the interaction between the oil and gas sector and the sphere of innovation activity were revealed, and the nature of their influence on the growth of the economy of the regions of the Russian Federation was determined. [ABSTRACT FROM AUTHOR]

This paper defines a range of linguistic provisions that (together with the concepts of the psychological theory of activity) can be integrated into the models of goal generation and into the technology of knowledge acquisition by intelligent systems. The evolution of linguistic interpretations of the "goal" concept is considered. We show that its logic consists in gradual removal of restrictions on introducing the prevalent knowledge on goal setting and psychological structure of human activity into linguistic research. Based on the material of the genre of instruction (which directly embodies the processes of goal setting and planning in a speech form) we propose the linguistic interpretation of the activity model of a sign. The linguistic means of expressing its semantic components are described as a prerequisite for formalizing the instructions. This paper reveals the heuristic value of including the ideas about the sign mediation of goal setting realized by intelligent system into the model of this process. [ABSTRACT FROM AUTHOR]

This study introduces a system design in a form of cognitive dialog game (DiaCog) to support pedagogical factors and student learning model ideas. The purpose of the study is to describe how such a design achieves tracking and adapting students' knowledge and mastery learning levels as a cognitive assistant. Also, this study shows alternative ways for supporting intelligent personal learning, tutoring systems, and MOOCS. This paper explains method called DiaCog that uses structure for students' thinking in an online dialog by tracking student's level of learning/knowledge status. The methodology of computing is the semantic that match between students' interactions in a dialog. By this way it informs DiaCog's learner model to inform the pedagogical model. Semantic fingerprint matching method of DiaCog allows making comparisons with expert knowledge to detect students' mastery levels in learning. The paper concludes with the DiaCog tool and methodologies that used for intelligent cognitive assistant design to implement pedagogical and learner model to track and adapt students' learning. Finally, this paper discusses future improvements and planned experimental set up to advance the techniques introduced in DiaCog design. [ABSTRACT FROM AUTHOR]

In this paper we start from the philosophical position in cognitive science known as enactivism. We formulate five basic enactivist tenets that we have carefully identified in the relevant literature as the main underlying principles of that philosophy. We then develop a mathematical framework to talk about cognitive systems (both artificial and natural) which complies with these enactivist tenets. In particular we pay attention that our mathematical modeling does not attribute contentful symbolic representations to the agents, and that the agent's nervous system or brain, body and environment are modeled in a way that makes them an inseparable part of a greater totality. The long-term purpose for which this article sets the stage is to create a mathematical foundation for cognition which is in line with enactivism. We see two main benefits of doing so: (1) It enables enactivist ideas to be more accessible for computer scientists, AI researchers, roboticists, cognitive scientists, and psychologists, and (2) it gives the philosophers a mathematical tool which can be used to clarify their notions and help with their debates. Our main notion is that of a sensorimotor system which is a special case of a well studied notion of a transition system. We also consider related notions such as labeled transition systems and deterministic automata. We analyze a notion called sufficiency and show that it is a very good candidate for a foundational notion in the "mathematics of cognition from an enactivist perspective." We demonstrate its importance by proving a uniqueness theorem about the minimal sufficient refinements (which correspond in some sense to an optimal attunement of an organism to its environment) and by showing that sufficiency corresponds to known notions such as sufficient history information spaces. In the end, we tie it all back to the enactivist tenets. [ABSTRACT FROM AUTHOR]

This paper presents two studies in which a peer‐assisted learning condition was compared to an individual learning condition. The first study used the paired‐associates learning task and the second study used an incrementally more complex task—the remote associate test. Participants in the peer‐assisted learning condition worked in groups of four. They had to solve a given problem individually and give a first answer before being able to request to see their peers' solutions; then, a second answer was issued. After six sessions of peer‐assisted practice, a final individual test was administered. Peer interaction was found to benefit learning in both studies but the benefit transferred to the final test only in the second study. Fine‐grained behavioral analyses and computational modeling suggested that the benefits of peer interaction were (partially) offset by its costs, particularly increased cognitive load and error exposure. Overall, the superiority of peer‐assisted learning over individual learning was more pronounced in the more complex task and for the more difficult problems in that task. A peer‐assisted learning condition was compared to an individual learning condition. The benefits of peer interaction were (partially) offset by its costs, particularly increased cognitive load and error exposure. The superiority of peer‐assisted learning over individual learning was more pronounced in the more complex task and for the more difficult problems in that task. [ABSTRACT FROM AUTHOR]

Goal‐directed behavior is a deeply important part of human psychology. People constantly set goals for themselves and pursue them in many domains of life. In this paper, we develop computational models that characterize how humans pursue goals in a complex dynamic environment and test how well they describe human behavior in an experiment. Our models are motivated by the principle of resource rationality and draw upon psychological insights about people's limited attention and planning capacities. We find that human goal pursuit is qualitatively different and substantially less efficient than optimal goal pursuit in our simulated environment. Models of goal pursuit based on the principle of resource rationality capture human behavior better than both a model of optimal goal pursuit and heuristics that are not resource‐rational. We conclude that the way humans pursue goals is shaped by the need to achieve goals effectively as well as cognitive costs and constraints on planning and attention. Our findings are an important step toward understanding humans' goal pursuit as cognitive limitations play a crucial role in shaping people's goal‐directed behavior. We apply resource‐rational analysis to human goal pursuit, developing computational models that account for limits on attention and planning. These models describe human behaviour in an experiment well, indicating that how people pursue their goals is shaped by their cognitive limitations. [ABSTRACT FROM AUTHOR]

Copyright of International Dialogue: East-West is the property of International Center for Slavic Education- Sveti Nikole and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Remote Associates Test (RAT) is a word association retrieval task that consists of a series of problems, each with three seemingly unrelated prompt words. The subject is asked to produce a single word that is related to all three prompt words. In this paper, we provide support for a theory in which the RAT assesses a person's ability to retrieve relevant word associations from long‐term memory. We present a computational model of humans solving the RAT and investigate how prior knowledge and memory retrieval mechanisms influence the model's ability to match human behavior. We expand prior modeling attempts by investigating multiple large knowledge bases and by creating a cognitive process model that uses long‐term memory spreading activation retrieval processes inspired by ACT‐R and implemented in Soar. We evaluate multiple model variants for their ability to model human problem difficulty, including the incorporation of noise and base‐level activation into memory retrieval. We conclude that the main factors affecting human difficulty are the existence of associations between prompt words and solutions, the relative strengths and directions of those associations compared to associations to other words, and the ability to perform multiple retrievals. [ABSTRACT FROM AUTHOR]