This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence (AI) has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, (...) which can be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools. (shrink)

The goal of cognitive modeling is to build faithful simulations of human cognition. One of the challenges is that multiple models can often explain the same phenomena. Another challenge is that models are often very hard to understand, explore, and reuse by others. We discuss some of the solutions that were discussed during the 2015 International Conference on Cognitive Modeling.

The goal of cognitive modeling is to build faithful simulations of human cognition. One of the challenges is that multiple models can often explain the same phenomena. Another challenge is that models are often very hard to understand, explore, and reuse by others. We discuss some of the solutions that were discussed during the 2015 International Conference on Cognitive Modeling.

Computational cognitive models hypothesize internal mental processes of human cognitive activities and express such activities by computer programs Such computational models often consist of many components and aspects Claims are often made that certain aspects of the models play a key role in modeling but such claims are sometimes not well justi ed or explored In this paper we rst review some fundamental distinctions and issues in computational modeling We then discuss in principle systematic ways of (...) identifying the source of power in the models.. (shrink)

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. (shrink)

This paper describes the processes of cognitive modeling and representation of human expertise for developing an ontology and knowledge base of an expert system. An ontology is an organization and classification of knowledge. Ontological engineering in artificial intelligence has the practical goal of constructing frameworks for knowledge that allow computational systems to tackle knowledge-intensive problems and supports knowledge sharing and reuse. Ontological engineering is also a process that facilitates construction of the knowledge base of an intelligent system, which (...) can be defined as a computer program that can duplicate problem-solving capabilities of human experts in specific areas. This paper presents the processes of knowledge acquisition, analysis, and representation, which laid the basis for ontology construction. In this case, the processes are applied in ontological engineering for construction of an expert system in the domain of monitoring of a petroleum production and separation facility. The acquired knowledge was also formally represented in two knowledge acquisition tools. (shrink)

Ecological-enactive approaches to cognition aim to explain cognition in terms of the dynamic coupling between agent and environment. Accordingly, cognition of one’s immediate environment depends on enaction and the picking up of affordances. However, ecological-enactive views supposedly fail to account for what is sometimes called “higher” cognition, i.e., cognition about potentially absent targets, which therefore can only be explained by postulating representational content. This challenge levelled against ecological-enactive approaches highlights a putative explanatory gap between basic and higher cognition. In this (...) paper, we examine scientific cognition—a paradigmatic case of higher cognition—and argue that it shares fundamental features with basic cognition, for enaction and affordance selection are central to the scientific enterprise. Our argument focuses on modeling, and on how models promote scientific understanding. We base our argument on a non-representational account of scientific understanding and on the material engagement theory, for models are hereby conceived as material objects designed for scientific engagements. Having done so, we conclude that the explanatory gap is significantly less threatening to the ecological-enactive approach than it might appear. (shrink)

Ecological-enactive approaches to cognition aim to explain cognition in terms of the dynamic coupling between agent and environment. Accordingly, cognition of one’s immediate environment (which is sometimes labeled “basic” cognition) depends on enaction and the picking up of affordances. However, ecological-enactive views supposedly fail to account for what is sometimes called “higher” cognition, i.e., cognition about potentially absent targets, which therefore can only be explained by postulating representational content. This challenge levelled against ecological-enactive approaches highlights a putative explanatory gap between (...) basic and higher cognition. In this paper, we examine scientific cognition—a paradigmatic case of higher cognition—and argue that it shares fundamental features with basic cognition, for enaction and affordance selection are central to the scientific enterprise. Our argument focuses on modeling, and on how models promote scientific understanding. We base our argument on a non-representational account of scientific understanding and on the material engagement theory, for models are hereby conceived as material objects designed for scientific engagements. Having done so, we conclude that the explanatory gap is significantly less threatening to the ecological-enactive approach than it might appear. (shrink)

The article first addresses the importance of cognitive modeling, in terms of its value to cognitive science (as well as other social and behavioral sciences). In particular, it emphasizes the use of cognitive architectures in this undertaking. Based on this approach, the article addresses, in detail, the idea of a multi-level approach that ranges from social to neural levels. In physical sciences, a rigorous set of theories is a hierarchy of descriptions/explanations, in which causal relationships among (...) entities at a high level can be reduced to causal relationships among simpler entities at a more detailed level. We argue that a similar hierarchy makes possible an equally productive approach toward cognitive modeling. The levels of models that we conceive in relation to cognition include, at the highest level, sociological/anthropological models of collective human behavior, behavioral models of individual performance, cognitive models involving detailed mechanisms, representations, and processes, as well as biological/physiological models of neural circuits, brain regions, and other detailed biological processes. (shrink)

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. In this special issue of topiCS, we present the best papers from the ICCM meeting. These best papers represent advances in the state of the art in cognitive (...) class='Hi'>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. (shrink)

Parametric cognitive models are increasingly popular tools for analyzing data obtained from psychological experiments. One of the main goals of such models is to formalize psychological theories using parameters that represent distinct psychological processes. We argue that systematic quantitative reviews of parameter estimates can make an important contribution to robust and cumulative cognitive modeling. Parameter reviews can benefit model development and model assessment by providing valuable information about the expected parameter space, and can facilitate the more efficient (...) design of experiments. Importantly, parameter reviews provide crucial—if not indispensable—information for the specification of informative prior distributions in Bayesian cognitive modeling. From the Bayesian perspective, prior distributions are an integral part of a model, reflecting cumulative theoretical knowledge about plausible values of the model's parameters (Lee,2018). In this paper we illustrate how systematic parameter reviews can be implemented to generate informed prior distributions for the Diffusion Decision Model (DDM; Ratcliff and McKoon,2008), the most widely used model of speeded decision making. We surveyed the published literature on empirical applications of the DDM, extracted the reported parameter estimates, and synthesized this information in the form of prior distributions. Our parameter review establishes a comprehensive reference resource for plausible DDM parameter values in various experimental paradigms that can guide future applications of the model. Based on the challenges we faced during the parameter review, we formulate a set of general and DDM-specific suggestions aiming to increase reproducibility and the information gained from the review process. (shrink)

In this issue of topiCS, we present the best papers from the ICCM meeting. 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.

This article explores the view that computational models of cognition may constitute valid theories of cognition, often in the full sense of the term ‘‘theory”. In this discussion, this article examines various (existent or possible) positions on this issue and argues in favor of the view above. It also connects this issue with a number of other relevant issues, such as the general relationship between theory and data, the validation of models, and the practical benefits of computational modeling. All (...) the discussions point to the position that computational cognitive models can be true theories of cognition. Ó 2008 Elsevier B.V. All rights reserved. (shrink)

In this chapter, the author examines how the simplicity heuristic adversely affects a relatively new tool in experimental comparative cognition: cognitive models. It does so, she argues, by directing intellectual resources into the development and refinement of putatively simple cognitive models at the expense of putatively more complex ones, which in turn directs experimenters to develop tests to rule out these simple models.

Classical (Bayesian) probability (CP) theory has led to an influential research tradition for modeling cognitive processes. Cognitive scientists have been trained to work with CP principles for so long that it is hard even to imagine alternative ways to formalize probabilities. However, in physics, quantum probability (QP) theory has been the dominant probabilistic approach for nearly 100 years. Could QP theory provide us with any advantages in cognitive modeling as well? Note first that both CP (...) and QP theory share the fundamental assumption that it is possible to model cognition on the basis of formal, probabilistic principles. But why consider a QP approach? The answers are that (1) there are many well-established empirical findings (e.g., from the influential Tversky, Kahneman research tradition) that are hard to reconcile with CP principles; and (2) these same findings have natural and straightforward explanations with quantum principles. In QP theory, probabilistic assessment is often strongly context- and order-dependent, individual states can be superposition states (that are impossible to associate with specific values), and composite systems can be entangled (they cannot be decomposed into their subsystems). All these characteristics appear perplexing from a classical perspective. However, our thesis is that they provide a more accurate and powerful account of certain cognitive processes. We first introduce QP theory and illustrate its application with psychological examples. We then review empirical findings that motivate the use of quantum theory in cognitive theory, but also discuss ways in which QP and CP theories converge. Finally, we consider the implications of a QP theory approach to cognition for human rationality. (shrink)

Much of cognitive psychology focuses on effects measured in tens of milliseconds while significant educational outcomes take tens of hours to achieve. The task of bridging this gap is analyzed in terms of Newell's (1990) bands of cognition—the Biological, Cognitive, Rational, and Social Bands. The 10 millisecond effects reside in his Biological Band while the significant learning outcomes reside in his Social Band. The paper assesses three theses: The Decomposition Thesis claims that learning occurring at the Social Band (...) can be reduced to learning occurring at lower bands. The Relevance Thesis claims that instructional outcomes at the Social Band can be improved by paying attention to cognition at the lower bands. The Modeling Thesis claims that cognitive modeling provides a basis for bridging between events on the small scale and desired outcomes on the large scale. The unit-task level, at the boundary of the Cognitive and Rational Bands, is useful for assessing these theses. There is good evidence for all three theses in efforts that bridge from the unit-task level to educational applications. While there is evidence for the Decomposition Thesis all the way down to the 10 millisecond level, more work needs to be done to establish the Relevance Thesis and particularly the Modeling Thesis at the lower levels. (shrink)

In this paper we review some problems with traditional approaches for acquiring and representing knowledge in the context of developing user interfaces. Methodological implications for knowledge engineering and for human-computer interaction are studied. It turns out that in order to achieve the goal of developing human-oriented (in contrast to technology-oriented) human-computer interfaces developers have to develop sound knowledge of the structure and the representational dynamics of the cognitive system which is interacting with the computer.We show that in a first (...) step it is necessary to study and investigate the different levels and forms of representation that are involved in the interaction processes between computers and human cognitive systems. Only if designers have achieved some understanding about these representational mechanisms, user interfaces enabling individual experiences and skill development can be designed. In this paper we review mechanisms and processes for knowledge representation on a conceptual, epistemological, and methodologieal level, and sketch some ways out of the identified dilemmas for cognitive modeling in the domain of human-computer interaction. (shrink)

Shanker & King's (S&K's) proposal is consistent with a Vygotskian model of development which assumes that cognition is first social and visible, and only later internalized and invisible. Rather than slipping into positing “epistemic operators” like understand or intend as generative of behavior during language learning or theory of mind tasks, this approach profits from keeping its focus on charting the ontogeny of embodied interactions.

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.

Analogy and similarity are central phenomena in human cognition, involved in processes ranging from visual perception to conceptual change. To capture this centrality requires that a model of comparison must be able to integrate with other processes and handle the size and complexity of the representations required by the tasks being modeled. This paper describes extensions to Structure-Mapping Engine since its inception in 1986 that have increased its scope of operation. We first review the basic SME algorithm, describe psychological evidence (...) for SME as a process model, and summarize its role in simulating similarity-based retrieval and generalization. Then we describe five techniques now incorporated into the SME that have enabled it to tackle large-scale modeling tasks: Greedy merging rapidly constructs one or more best interpretations of a match in polynomial time: O); Incremental operation enables mappings to be extended as new information is retrieved or derived about the base or target, to model situations where information in a task is updated over time; Ubiquitous predicates model the varying degrees to which items may suggest alignment; Structural evaluation of analogical inferences models aspects of plausibility judgments; Match filters enable large-scale task models to communicate constraints to SME to influence the mapping process. We illustrate via examples from published studies how these enable it to capture a broader range of psychological phenomena than before. (shrink)

The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the five best representative papers on this work from our 19th meeting, ICCM 2021, which was held virtually from July 3 to July 9, 2021. Three of these papers provide new techniques for refining computational models, giving better methods for taking empirical data and producing (...) accurate computational models of the cognitive systems that produce them. The other two papers focus on explanation: using models to elucidate the underlying processes affecting cognition in such diverse domains as logical reasoning and the effects of caffeine. (shrink)

The International Conference on Cognitive Modeling brings together researchers from around the world whose main goal is to build computational systems that reflect the internal processes of the mind. In this issue, we present the five best representative papers on this work from our 19th meeting, ICCM 2021, which was held virtually from July 3 to July 9, 2021. Three of these papers provide new techniques for refining computational models, giving better methods for taking empirical data and producing (...) accurate computational models of the cognitive systems that produce them. The other two papers focus on explanation: using models to elucidate the underlying processes affecting cognition in such diverse domains as logical reasoning and the effects of caffeine. (shrink)

Quantum probability theory offers a viable alternative to classical probability, although there are some ambiguities inherent in transferring the quantum formalism to a less determined realm. A number of physicists are now looking at the applicability of quantum ideas to the assessment of physics learning, an area particularly suited to quantum probability ideas.

A long history of research has revealed many neurophysiological changes and concomitant behavioral impacts of sleep deprivation, sleep restriction, and circadian rhythms. Little research, however, has been conducted in the area of computational cognitive modeling to understand the information processing mechanisms through which neurobehavioral factors operate to produce degradations in human performance. Our approach to understanding this relationship is to link predictions of overall cognitive functioning, or alertness, from existing biomathematical models to information processing parameters in a (...) cognitive architecture, leveraging the strengths from each to develop a more comprehensive explanation. The integration of these methodologies is used to account for changes in human performance on a sustained attention task across 88 h of total sleep deprivation. The integrated model captures changes due to time awake and circadian rhythms, and it also provides an account for underlying changes in the cognitive processes that give rise to those effects. The results show the potential for developing mechanistic accounts of how fatigue impacts cognition, and they illustrate the increased explanatory power that is possible by combining theoretical insights from multiple methodologies. (shrink)

Reduced forms such as the pronoun he provide little information about their intended meaning compared to more elaborate descriptions such as the lead singer of Coldplay. Listeners must therefore use contextual information to recover their meaning. Across languages, there appears to be a trade‐off between the informativity of a form and the prominence of its referent. For example, Italian adults generally interpret informationally empty null pronouns as in the sentence Corre (meaning “He/She/It runs”) as referring to the most prominent referent (...) in the discourse, and more informative overt pronouns (e.g., lui in Lui corre, “He runs”) as referring to less prominent referents. Although children acquiring Italian are known to experience difficulties interpreting pronouns, it is unclear how they acquire this division of pragmatic labor between null and overt subject pronouns, and how this relates to the development of their cognitive capacities. Here we show that cognitive development can account for the general interpretation patterns displayed by Italian‐speaking children and adults. Using experimental studies and computational simulations in a framework modeling bounded‐rational behavior, we argue that null pronoun interpretation is influenced by working memory capacity and thus appears to depend on discourse context, whereas overt pronoun interpretation is influenced by processing speed, suggesting that listeners must reason about the speaker's choices. Our results demonstrate that cognitive capacities may constrain the acquisition of linguistic forms and their meanings in various ways. The novel predictions generated by the computational simulations point out several directions for future research. (shrink)

The 18th International Conference on Cognitive Modelling (ICCM 2020) brought together researchers whose goal is to develop computational simulations of the mind, and to use those simulations to test theories about how the mind works. In this special issue, we present four top papers from ICCM 2020. Two of these address the challenge of scaling up to more complex tasks, and the other two address the challenge of scaling down to connect these computational models to neuroscience.

Frost's critique reveals the limitations of the reverse-engineering approach to cognitive modeling – the style of psychological explanation in which a stipulated internal organization explains a relatively narrow set of phenomena. An alternative is to view organization as both the explanation for some phenomena and a phenomenon to be explained. This move poses new and interesting theoretical challenges for theories of word reading.

Colin Allen prescribes cognitive modeling as “the right kind of theory” to use in comparative animal cognition and predicts that unless researchers shift from using conceptual framework hypotheses (“the wrong kind of theory”) to cognitive models, the field will fail to be sustained or develop further. I argue, on the contrary, that the robust development of the field over the past 35 years actually belies Allen's dire prediction. What is more, there is reason to be concerned that (...) if Allen's prescription were wholeheartedly adopted by researchers, it would undermine one of the principal aims of the field: the discovery of the reasons for which animals act. Although cognitive modeling may be a useful tool to use to verify predictions made by conceptual framework hypotheses, I see no reason to think that comparative animal cognition would be, or needs to be, saved by researchers coming to taking cognitive modeling as the right kind of theory to use in the field. (shrink)

Computational modeling has long been one of the traditional pillars of cognitive science. Unfortunately, the computer models of cognition being developed today have not kept up with the enormous changes that have taken place in computer technology and, especially, in human-computer interfaces. For all intents and purposes, modeling is still done today as it was 25, or even 35, years ago. Everyone still programs in his or her own favorite programming language, source code is rarely made available, (...) accessibility of models to non-programming researchers is essentially non-existent, and even for other modelers, the profusion of source code in a multitude of programming languages, written without programming guidelines, makes it almost impossible to access, check, explore, re-use, or continue to develop. It is high time to change this situation, especially since the tools are now readily available to do so. We propose that the modeling community adopt three simple guidelines that would ensure that computational models would be accessible to the broad range of researchers in cognitive science. We further emphasize the pivotal role that journal editors must play in making computational models accessible to readers of their journals. (shrink)

The Role of Dorsal Premotor Cortex in Resolving Abstract Motor Rules provides alternative hypotheses about the cognitive functions affected by the application of repetitive transcranial magnetic stimulation. Their model simulated the effect of stimulation of the left dorsal premotor cortex right as participants provide a Models were used to demonstrate that the increased variability in observed response times can result from interference in replanning during the process of responding to the uninstructed stimulus.