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A fundamental question in word learning is how, given onlyevidence about what objects a word has previously referred to,children are able to generalize the total class (Smith & Medin,1981; Xu & Tenenbaum, 2007). E.g. how a child ends upknowing that ‘poodle’ only picks out a specific subset of dogsrather than the whole class and vice versa. The Na ̈ıve Gen-eralization Model (NGM) presented in this paper offers an ex-planation of word learning phenomena grounded in categoryformation (Smith & Medin, 1981) The NGM captures a rangeof relevant experimental findings (Xu & Tenenbaum, 2007;Spencer, Perone, Smith, & Samuelson, 2011), including thosewhich are in conflict with a Bayesian inference theory (Xu &Tenenbaum, 2007).

Error-driven learning algorithms, which iteratively adjust expectations based on prediction error, are the basis for a vast array of computational models in the brain and cognitive sciences that often differ widely in their precise form and application: they range from simple models in psychology and cybernetics to current complex deep learning models dominating discussions in machine learning and artificial intelligence. However, despite the ubiquity of this mechanism, detailed analyses of its basic workings uninfluenced by existing theories or specific research goals are rare in the literature. To address this, we present an exposition of error-driven learning – focusing on its simplest form for clarity – and relate this to the historical development of error-driven learning models in the cognitive sciences. Although historically error-driven models have been thought of as associative, such that learning is thought to combine preexisting elemental representations, our analysis will highlight the discriminative nature of learning in these models and the implications of this for the way how learning is conceptualized. We complement our theoretical introduction to error-driven learning with a practical guide to the application of simple error-driven learning models in which we discuss a number of example simulations, that are also presented in detail in an accompanying tutorial. [ABSTRACT FROM AUTHOR]

For decades, a significant number of models explaining human syllogistic inference processes were developed. There is profound work fitting the models' parameters and analyzing each model's ability to account for the data in order to support or reject the underlying theories. However, the model parameters are rarely used to extract explanations and hypotheses for phenomena that go beyond the original scope of the models. In this work, we apply three state‐of‐the‐art models, the probability heuristics model (PHM), mReasoner, and TransSet, to data from reasoning experiments where participants received feedback for their conclusions. We derived hypotheses based on the models' explanations for the feedback effect and put these to the test by conducting an experiment targeting the hypotheses. The work contributes to the field in three ways: (a) the feedback effect could be replicated and was shown to be a robust effect; (b) we demonstrate the use of the model parameters in order to derive new hypotheses; (c) we present possible explanations for the feedback effect based on existing theories. We apply three state‐of‐the‐art models for syllogistic reasoning to data from experiments where participants received feedback for their conclusions in order to demonstrate the use of model parameters to derive new hypotheses and present possible explanations for the feedback effect. [ABSTRACT FROM AUTHOR]

The parameters governing our behavior are in constant flux. Accurately capturing these dynamics in cognitive models poses a challenge to modelers. Here, we demonstrate a mapping of ACT‐R's declarative memory onto the linear ballistic accumulator (LBA), a mathematical model describing a competition between evidence accumulation processes. We show that this mapping provides a method for inferring individual ACT‐R parameters without requiring the modeler to build and fit an entire ACT‐R model. Existing parameter estimation methods for the LBA can be used, instead of the computationally expensive parameter sweeps that are traditionally done. We conduct a parameter recovery study to confirm that the LBA can recover ACT‐R parameters from simulated data. Then, as a proof of concept, we use the LBA to estimate ACT‐R parameters from an empirical dataset. The resulting parameter estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. In addition, we find that the mapping between ACT‐R and LBA lends a more concrete interpretation to ACT‐R's latency factor parameter, namely as a measure of response caution. This work contributes to a growing movement towards integrating formal modeling approaches in cognitive science. The parameters governing our behavior are in constant flux, and capturing these dynamics in cognitive models remains a challenge. We demonstrate how a mapping between ACT‐R's model of declarative memory and the linear ballistic accumulator enables efficient estimation of memory parameters from data. The resulting estimates provide a cognitively meaningful explanation for observed differences in behavior over time and between individuals. [ABSTRACT FROM AUTHOR]

We argue that cognitive models can provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms for purposes of explainable artificial intelligence (AI). Casting both the human and learner as cognitive models provides common mechanisms to compare and understand their underlying decision‐making processes. This common grounding allows us to identify divergences and explain the learner's behavior in human understandable terms. We present novel salience techniques that highlight the most relevant features in each model's decision‐making, as well as examples of this technique in common training environments such as Starcraft II and an OpenAI gridworld. We identify how cognitive models provide a common ground between human users and deep reinforcement learning (Deep RL) algorithms to compare and understand their underlying decision‐making processes, allowing us to identify divergences and explain the learner's behavior in human understandable terms. [ABSTRACT FROM AUTHOR]

Over the years, the purpose of cultural heritage (CH) sites (e.g., museums) has focused on providing personalized services to different users, with the main goal of adapting those services to the visitors' personal traits, goals, and interests. In this work, we propose a computational cognitive model that provides an artificial agent (e.g., robot, virtual assistant) with the capability to personalize a museum visit to the goals and interests of the user that intends to visit the museum by taking into account the goals and interests of the museum curators that have designed the exhibition. In particular, we introduce and analyze a special type of help (critical help) that leads to a substantial change in the user's request, with the objective of taking into account the needs that the same user cannot or has not been able to assess. The computational model has been implemented by exploiting the multi-agent oriented programming (MAOP) framework JaCaMo, which integrates three different multi-agent programming levels. We provide the results of a pilot study that we conducted in order to test the potential of the computational model. The experiment was conducted with 26 real participants that have interacted with the humanoid robot Nao, widely used in Human-Robot interaction (HRI) scenarios. [ABSTRACT FROM AUTHOR]

Cognitive architectures should make explicit the conceptual begin and end points of the agent/environment interaction cycle. Most architectures begin with the agent receiving input data representing the environment, and end with the agent sending output data. This paper suggests inverting this cycle: the agent sends output data that specifies an experiment, and receives input data that represents the result of this experiment. This complies with the embodiment paradigm because the input data does not directly represent the environment and does not amount to the agent's perception. We illustrate this in an example and propose an assessment method based upon activity-trace analysis. [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.

To characterize numerical representations, the number-line task asks participants to estimate the location of a given number on a line flanked with zero and an upper-bound number. An open question is whether estimates for symbolic numbers (e.g., Arabic numerals) and non-symbolic numbers (e.g., number of dots) rely on common processes with a common developmental pathway. To address this question, we explored whether well-established findings in symbolic number-line estimation generalize to non-symbolic number-line estimation. For exhaustive investigations without sacrificing data quality, we applied a novel Bayesian active learning algorithm, dubbed Gaussian process active learning (GPAL), that adaptively optimizes experimental designs. The results showed that the non-symbolic number estimation in participants of diverse ages (5–73 years old, n = 238) exhibited three characteristic features of symbolic number estimation. [ABSTRACT FROM AUTHOR]

E-learning is increasingly used to support student learning in higher education, facilitating administration of online formative assessments. Although providing diagnostic, actionable feedback is generally more effective, in current practice, feedback is often given in the form of a simple proportion of correctly solved items. This study shows the validation process of constructing detailed diagnostic information on a set of skills, abilities, and cognitive processes (so-called attributes) from students' item response data with diagnostic classification models. Attribute measurement in the domain of statistics education is validated based on both expert judgment and empirical student data from a think-aloud study and large-scale assessment administration. The constructed assessments provide a valid and reliable measurement of the attributes. Inferences that can be drawn from the results of these formative assessments are discussed and it is demonstrated how this information can be communicated to students via learning dashboards to allow them to make more effective learning choices. [ABSTRACT FROM AUTHOR]

Abstract: This paper describes how personality may be explained by a generic, comprehensive computational cognitive architecture. We show that a cognitive architecture by itself can serve as a generic model of personality, without any significant addition or modification. A cognitive architecture can capture the fundamental invariance within an individual in terms of behavioral inclinations as well as the inevitable variability of behaviors. Various tests and simulations have been conducted within the cognitive architecture that show that such a model is reasonably stable, is relatively flexible (in terms of person–situation interactions), captures some major personality traits (e.g., the Five-Factor Model), and accounts for a variety of empirical data. The work shows the feasibility and usefulness of integrating personality modeling with generic computational cognitive modeling (i.e., cognitive architectures). [Copyright &y& Elsevier]

Abstract: Intention recognition is one of the core components of mindreading, an important process in social cognition. Human beings, from age of 18months, have been shown to be able to extrapolate intentions from observed actions, even when the performer failed at achieving the goal. Existing accounts of intention recognition emphasize the use of an intent (plan) library, which is matched against observed actions for recognition. These therefore cannot account for recognition of failed sequences of actions, nor novel actions. In this paper, we begin to tackle these open questions by examining computational models for components of human intention recognition, which emphasize the ability of humans to detect and identify intentions in a sequence of observed actions, based solely on the rationality of movement (its efficiency). We provide a high-level overview of intention recognition as a whole, and then elaborate on two components of the model, which we believe to be at its core, namely, those of intention detection and intention prediction. By intention detection we mean the ability to discern whether a sequence of actions has any underlying intention at all, or whether it was performed in an arbitrary manner with no goal in mind. By intention prediction we mean the ability to extend an incomplete sequence of actions to its most likely intended goal. We evaluate the model, and these two components, in context of existing literature, and in a number of experiments with more than 140 human subjects. For intention detection, our model was able to attribute high levels of intention to those traces perceived by humans as intentional, and vice versa. For intention prediction as well, our model performed in a way that closely matched that of humans. The work highlights the intimate relationship between the ability to generate plans, and the ability to recognize intentions. [Copyright &y& Elsevier]

Several theories about Theory of Mind (ToM) have beenproposed. The most well-known of these are Theory Theoryand Simulation Theory, although alternative and hybridtheories do exist. One such theory, proposed by Bach (2011,2014), is based on the Structure-Mapping theory of analogy,which has been shown to play a key role in cognitivedevelopment. There is evidence that children are more likely topass false belief tasks when trained using stories that are easyto compare via structural alignment, as opposed to stories thatare difficult to compare in this way (Hoyos, Horton & Gentner,2015). This paper shows how a computational model based onBach’s account can provide an explanation for the Hoyos et al.training study and proposes directions for future research onhuman subjects.

Mathematical models of cognition measure individual differences in cognitive processes, such as processing speed, working memory capacity, and executive functions, that may underlie general intelligence. As such, cognitive models allow identifying associations between specific cognitive processes and tracking the effect of experimental interventions aimed at the enhancement of intelligence on mediating process parameters. Moreover, cognitive models provide an explicit theoretical formalization of theories regarding specific cognitive processes that may help in overcoming ambiguities in the interpretation of fuzzy verbal theories. In this paper, we give an overview of the advantages of cognitive modeling in intelligence research and present models in the domains of processing speed, working memory, and selective attention that may be of particular interest for intelligence research. Moreover, we provide guidelines for the application of cognitive models in intelligence research, including data collection, the evaluation of model fit, and statistical analyses. [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.

We argue that pretense can be viewed as analogical projection: a structural comparison between the pretend scenario and its real‐world counterpart that leads to inferences about the pretend scenario. For example, in pretending to make a phone call with a banana, a number pad might be projected on the banana's surface. We model two empirical studies of early childhood pretense, and show how successful pretense requires making and accepting such inferences, while failed pretense can be traced to failure of such projection. Other models of pretense, both theoretical and computational, and their relationships to our model, are discussed. [ABSTRACT FROM AUTHOR]

Language acquisition is supported by phonological awareness, which intentionally makes children aware of phonological units. By understanding the internal processes of children during language acquisition, this study aims to elucidate factors that can correct erroneous phonological generation. Therefore, we developed a cognitive model using innate and experiential factors of the memory retrieval in the cognitive architecture–ACT-R. Furthermore, we performed simulations using Shiritori, a Japanese word game, as an interaction task. The simulation included the observation of effects of the experiential factor of repeating a task and innate factors of different settings. It showed that repeating a single task causes incorrect convergence, and this convergence can be prevented by comprehensive activation of overall phonological knowledge during the interval of Shiritori tasks. Moreover, the simulation in specific innate settings exhibited commonalities with cases of developmental disorder by showing errors like consonant deletion. In the future, we will examine the correlation of the aforementioned findings with actual language development to realize the use of cognitive architecture in real world. [ABSTRACT FROM AUTHOR]

Reinforcement learning is an unsupervised learning algorithm, where learning is based upon feedback from the environment. Prior research has proposed cognitive (e.g., Instance-based Learning or IBL) and statistical (Q-learning) reinforcement learning algorithms. However, an evaluation of these algorithms in a single dynamic environment has not been explored. In this paper, a comparison between the statistical Q-learning algorithm and the cognitive IBL algorithm is presented. A well-known environment, “Frozen Lake,” is used to train, generalize, and scale Q-learning and IBL algorithms. For generalizing, the Q-learning and IBL agents were trained on one version of the Frozen Lake and tested on a permuted version of the same environment. For scaling, the two algorithms were tested on a larger version of the Frozen Lake environment. Results revealed that the IBL algorithm used less training time and generalized better to different environment variants. The IBL algorithm was also able to show scalability by retaining its superior performance in the larger environment. These results indicate that the IBL algorithm could be proposed as an alternative to the standard reinforcement learning algorithms based on dynamic programming such as Q-learning. The inclusion of human factors (such as memory) in the IBL algorithm makes it suitable for robust learning in complex and dynamic environments.

Abstract: The Qualitative Rectilinear Projection Calculus (QRPC), a new representation model based on planar trajectories, is presented in this work for describing qualitatively motion patterns. Several models have been developed for this purpose and by comparing them with our proposal we show that the proposed model results in an intuitive approach for representing, in any context, the kinematical behavior of two objects in motion on the plane through the possible relationships among the rectilinear projection of their trajectories. The paper is centered on the formal definition of the set of geometric relations in terms of the front–back and left–right dichotomies, and how by the composition of these relations, it can be possible enumerate an exhaustive set of qualitative states and the possible transitions among them. The complete iconic representation of the set of qualitative states defines the Conceptual Neighborhood Graph of the QRPC model. In order to illustrate how this qualitative representation can be used to analyze and describe the relative motion of two objects, some examples extracted from the traffic engineering field have been studied. [Copyright &y& Elsevier]

Abstract: Number sequence problems appear frequently in IQ tests, where the task is to extrapolate finite sequences of integers. This paper presents a computational method for solving number sequence problems appearing in IQ tests. The assumption that these problems are solvable by humans is actively exploited to keep the computational complexity manageable. The method combines elements of artificial intelligence and cognitive psychology and is referred to as anthropomorphic because it makes use of a model of human reasoning. This model features a set of cognitive resources, a repertoire of patterns that encode integer sequences, and a notion of bounded computation for decoding patterns. The model facilitates the search for patterns matching a given integer sequence by quickly discarding many patterns on the grounds that they are too demanding to decode. The computational method was implemented as a computer program called Asolver and then tested against the programs Mathematica and Maple. On the number sequence problems of the IQ test PJP, Asolver scored above IQ 140, whereas the other programs scored below IQ 100. [Copyright &y& Elsevier]

Abstract: Human decision-making can be affected by cognitive biases, and outside observers can often detect biased decision-making in others. Accordingly, intelligent agents endowed with the computational equivalent of the human mind should be able to detect biased reasoning and help people to improve their decision-making in practical applications. We are modeling bias-detection functionalities in OntoAgent, a cognitively-inspired agent environment that supports the modeling of intelligent agents with a wide range of sophisticated functionalities, including semantically-oriented language processing, decision-making, learning and collaborating with people. Within OntoAgent, different aspects of agent functionality are described using microtheories that are realized as formal computational models. This paper presents the OntoAgent model that supports the automatic detection of decision-making biases, using clinical medicine as a sample application area. It shows how an intelligent agent serving as a clinician’s assistant can follow the doctor–patient interaction and warn the doctor if it appears that his own or the patient’s decisions might be unwittingly affected by biased reasoning. [Copyright &y& Elsevier]

“We will remember this spring as a time of digital transformation in education,” these words of Valery Falkov at the opening of 2020 International Moscow Salon of Education convey an atmosphere of change that has filled the entire educational sphere. Today’s disputes on need for measuring teachers` professional deficits grow fast. Deficiency approach focused on revealing certain mismatch of teacher and his professional activity prevails in most studies that identify and assess the level of professional competences. Of course, this one of important tools for determining qualification gaps will be presented in research works, but in combination with activity-based approach which has proved its effectiveness. Action research is one that identifies and professional difficulties and need of teacher and logically determines routes of his professional development through the system of continuing professional education. It should be noticed that pandemic and transition to distant and online learning revealed lack of very simple skills in teachers such as proper organization of learning process in remote or online mode. Many teachers, students and their parents were not psychologically prepared for complete transition to digital education. In this regard, it is quite reasonable to highlight such challenge in education in digitalization process and need for overcoming it. The paper includes analysis of current satiation in school education in terms of digitalization and transition to online learning. The study brings together various views on methodology and mechanisms of digitalizing school education, and criteria of valid identification of teachers` digital competencies. The present study attempts to compile a passport of teacher’s digital competencies and approve a pilot program called “Cognitive models and algorithms for formation of teacher’s digital competence in context of digital transformation of general education”. The reported study was funded by RFBR, project number 19-29-14030.

Abstract: This paper focuses on the creation and presentation of a user-friendly experience for developing computational models of human behavior. Although computational models of human behavior have enjoyed a rich history in cognitive psychology, they have lacked widespread impact, partly due to the technical knowledge and programming required in addition to the complexities of the modeling process. We describe a modeling tool called IBLTool that is a computational implementation of the Instance-based Learning Theory (IBLT). IBLT is a theory that represents how decisions are made from experience in dynamic tasks. The IBLTool makes IBLT usable and understandable to a wider community of cognitive and behavioral scientists. The tool uses graphical user interfaces that take a modeler step-by-step through several IBLT processes and help the modeler derive predictions of human behavior in a particular task. A task would connect and interact with the IBLTool and store the decision-making data while the tool collects statistical data from the execution of a model for the task. We explain the functioning of the IBLTool and demonstrate a concrete example of the design and execution of a model for the Iowa Gambling task. The example is intended to provide a concrete demonstration of the capabilities of the IBLTool. [Copyright &y& Elsevier]

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. [ABSTRACT FROM AUTHOR]

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Abstract: Simulating consumer decision making processes involves different disciplines such as: sociology, social psychology, marketing, and computer science. In this paper, we propose an agent-based conceptual and computational model of consumer decision-making based on culture, personality and human needs. It serves as a model for individual behavior in models that investigate system-level resulting behavior. Theoretical concepts operationalized in the model are the Power Distance dimension of Hofstede’s model of national culture; Extroversion, Agreeableness and Openness of Costa and McCrae’s five-factor model of personality, and social status and social responsibility needs. These factors are used to formulate the utility function, process and update the agent state, need recognition and action estimation modules of the consumer decision process. The model was validated against data on culture, personality, wealth and car purchasing from eleven European countries. It produces believable results for the differences of consumer purchasing across eleven European countries. [Copyright &y& Elsevier]

Abstract: Experiments on visual search have demonstrated the existence of a relatively large and reliable memory for which objects have been fixated; an indication of this memory is that revisits (fixations on previously fixated objects) typically comprise only about 5% of fixations. Any cognitive architecture that supports visual search must account for where such memory resides in the system and how it can be used to guide eye movements in visual search. This paper presents a simple solution for the EPIC architecture that is consistent with the overall requirements for modeling visually-intensive tasks and other visual memory phenomena. [Copyright &y& Elsevier]

Abstract: Designing human machine interfaces that respect the ergonomic norms and following rigorous approaches constitutes a major concern for computer systems designers. The increased need on easily accessible and usable interfaces leads researchers in this domain to create methods and models that make it possible to evaluate these interfaces in terms of utility and usability. Two different approaches are currently used to evaluate human machine interfaces, empirical approaches that require user involvement in the interface development process, and analytical approaches that do not associate the user during the interface development process. This paper presents a study of user performance on two principal tasks of the contextual assistant’s interface, developed in the context of smart homes, to assist persons with cognitive disabilities. We use three different methods to analyze and evaluate this interface, focusing basically on time of execution. Two of the models developed are based on cognitive models, which are ACT-R and GOMS and the third one is based on the Fitts’ Law model. The results show that, all models give a good prediction of user performance, even if the cognitive models show better accuracy of the user performance. Furthermore, they provide a better insight into cognitive abilities required to interact with the interface. [Copyright &y& Elsevier]

This paper describes how results from the education literature have been put into practice in two projects currently underway in an engineering education center. The projects are both aimed at improving problem solving. The first is being conducted in Statics, and the second in Fluid Mechanics course in Civil Engineering. The Statics project is grounded in a model that integrates literature on problem solving, representational transformations, and prior knowledge. This model was used to analyze students' problem solving in Statics so that key difficulties could he identified. Instructional modules were then designed to address those difficulties. The design of these instructional modules served as the starting point for modules in Fluid Mechanics. The primary literature used in the development of the instructional modules focused on cognitive modeling, self-explanation, and worked examples. [ABSTRACT FROM AUTHOR]

Abstract: Theories that postulate cognitive inhibition are very common in psychology and cognitive neuroscience [e.g., Hasher, L., Lustig, C., & Zacks, R. T. (2007). Inhibitory mechanisms and the control of attention. In A. Conway, C. Jarrold, M. Kane, A. Miyake, A. Towse, & J. Towse (Eds.), Variation in working memory (pp. 227–249). New York, NY: Oxford, University Press], although they have recently been severely criticized [e.g., MacLeod, C. M., Dodd, M. D., Sheard, E. D., Wilson, D. E., & Bibi, U. (2003). In opposition to inhibition. In H. Ross (Ed.), The psychology of learning and motivation (Vol. 43, pp. 163–214). Elsevier Science]. This paper poses and attempts to answer the question whether a research program with cognitive inhibition as its main theoretical assumption is still worth pursuing. We present a set of empirical data from a modified Stroop paradigm that replicates previously reported findings. These findings refer to between-trial effects previously described in the literature on Stroop, negative priming, and inhibition-of-return. Existing theoretical accounts fail to explain all these effects in an integrated way. A repetition-suppression mechanism is proposed in order to account for these data. This mechanism is instantiated as a computational cognitive model. The theoretical implications of this model are discussed. [Copyright &y& Elsevier]

Human error while performing well-learned tasks on a com-puter is an infrequent, but pervasive problem. Such errors areoften attributed to memory deficits, such as loss of activation orinterference with other tasks (Altmann & Trafton, 2002). Weare arguing that this view neglects the role of the environment.As embodied beings, humans make extensive use of externalcues during the planning and execution of tasks. In this paper,we study how the visual interaction with a computer interfaceis linked to user errors. Gaze recordings confirm our hypoth-esis that the use of the environment increases when memorybecomes weak. An existing cognitive model of sequential ac-tion and procedural error (Halbrügge, Quade, & Engelbrecht,2015) is extended to account for the observed gaze behavior.