Computational Cognitive Model Research Articles

Implementations are not specifications: Specification, replication and experimentation in computational cognitive modeling

Contemporary methods of computational cognitive modeling have recently been criticized by Addyman and French (2012) on the grounds that they have not kept up with developments in computer technology and human–computer interaction. They present a manifesto for change according to which, it is argued, modelers should devote more effort to making their models accessible, both to non-modelers (with an appropriate easy-to-use user interface) and modelers alike. We agree that models, like data, should be freely available according to the normal standards of science, but caution against confusing implementations with specifications. Models may embody theories, but they generally also include implementation assumptions. Cognitive modeling methodology needs to be sensitive to this. We argue that specification, replication and experimentation are methodological approaches that can address this issue.

Using model-based functional MRI to locate working memory updates and declarative memory retrievals in the fronto-parietal network

In this study, we used model-based functional MRI (fMRI) to locate two functions of the fronto-parietal network: declarative memory retrievals and updating of working memory. Because regions in the fronto-parietal network are by definition coherently active, locating functions within this network is difficult. To overcome this problem, we applied model-based fMRI, an analysis method that uses predictions of a computational model to inform the analysis. We applied model-based fMRI to five previously published datasets with associated computational cognitive models, and subsequently integrated the results in a meta-analysis. The meta-analysis showed that declarative memory retrievals correlated with activity in the inferior frontal gyrus and the anterior cingulate, whereas updating of working memory corresponded to activation in the inferior parietal lobule, as well as to activation around the inferior frontal gyrus and the anterior cingulate.

A Cognitive Modeling Approach to Decision Support Tool Design for Anesthesia Provider Crisis Management

Prior research has revealed existing operating room (OR) patient monitors to provide limited support for prompt and accurate decision making by anesthesia providers during crises. Decision support tools (DSTs) developed for this purpose typically alert the anesthesia provider to existence of a problem but do not recommend a treatment plan. There is a need for a human-centered approach to the design and development of a crisis management DST. A hierarchical task analysis was conducted to identify anesthesia provider procedures in detecting, diagnosing, and treating a critical incident and a cognitive task analysis to elicit goals, decisions, and information requirements. This information was coded in a computational cognitive model using GOMS (Goals, Operators, Methods, Selection rules) Language. An OR monitor interface was prototyped to present output from the cognitive model following ecological interface design principles. A preliminary assessment of the DST was performed with anesthesiology and usability experts. The anesthesiologists indicated they would use the tool in the perioperative environment and would recommend its use by junior anesthesia providers. Future research will focus on formal validation of the DST design approach and comparison of tool output to actual anesthesia provider decisions in real or simulated crises.

Exploring the cognitive and motor functions of the basal ganglia: an integrative review of computational cognitive neuroscience models

Many computational models of the basal ganglia (BG) have been proposed over the past twenty-five years. While computational neuroscience models have focused on closely matching the neurobiology of the BG, computational cognitive neuroscience (CCN) models have focused on how the BG can be used to implement cognitive and motor functions. This review article focuses on CCN models of the BG and how they use the neuroanatomy of the BG to account for cognitive and motor functions such as categorization, instrumental conditioning, probabilistic learning, working memory, sequence learning, automaticity, reaching, handwriting, and eye saccades. A total of 19 BG models accounting for one or more of these functions are reviewed and compared. The review concludes with a discussion of the limitations of existing CCN models of the BG and prescriptions for future modeling, including the need for computational models of the BG that can simultaneously account for cognitive and motor functions, and the need for a more complete specification of the role of the BG in behavioral functions.

A Computational Cognitive Model of Information Search in Textual Materials

Document foraging for information is a crucial and increasingly prevalent activity nowadays. We designed a computational cognitive model to simulate the oculomotor scanpath of an average web user searching for specific information from textual materials. In particular, the developed model dynamically combines visual, semantic, and memory processes to predict the user’s focus of attention during information seeking from paragraphs of text. A series of psychological experiments was conducted using eye-tracking techniques in order to validate and refine the proposed model. Comparisons between model simulations and human data are reported and discussed taking into account the strengths and shortcomings of the model. The proposed model provides a unique contribution to the investigation of the cognitive processes involved during information search and bears significant implications for web page design and evaluation.

An Accessible Cognitive Modeling Tool for Evaluation of Pilot–Automation Interaction

Various cognitive modeling techniques and tools have been developed to support description and prediction of human behavior in complex systems. GOMS (Goals, Operators, Methods and Selection rules) modeling methods have been used in human–computer interaction (HCI) analysis for many years and are considered easy to learn. GOMS has several limitations, including representing only expert behavior in tasks and not supporting detailed modeling of visual and motor operations or parallel processing. Another limitation is that operation time estimates are deterministic. This research developed an enhanced GOMS language and computational cognitive modeling tool to address the existing GOMS limitations to aid cockpit automation designers in assessing the potential for automation-induced pilot performance problems. Output of the tool for a specific flight and automation use scenario was compared with experiment data for validation purposes. Results demonstrated significant correlations of model-based pilot performance and cognitive workload predictions with observations on pilots using a flight simulator. The new enhanced cognitive modeling approach is expected to provide accurate explanations and predictions of user behaviors during the design of complex systems and interfaces in various domains involving interactive task performance.

What is the nature of the mind's pattern-recognition process?

If we look at the human mind as a pattern-recognition device, what is the nature of its pattern-recognizing? And how does it differ from the majority of pattern-recognition methods we have collectively devised over the decades? These broad philosophical questions emerge from the studies of chess thought, and we propose that a major task of the mind is to engage in “experience recognition” (Linhares & Freitas, 2010). One of the basic tenets of that proposal is that pattern recognition, in cognitive science and related disciplines, does not accurately reflect human psychology. As an example, the well-known article by Chase and Simon, “perception in chess”, and the benchmark cognitive computational models of chess, by Gobet et al. were criticized. Lane and Gobet (2011) provide serious skepticism concerning some of those arguments, and here we take the opportunity to respond and expand the theoretical constructs of “experience recognition”. We postulate that the mind's pattern-recognizing process holds the following properties: it is a highly path-dependent process; it prioritizes internal encodings; it is a self-organizing process in constant change; and it constructs its future information-processing pathways by continuously recognizing the possibilities that lie within the adjacent possible.

Explaining Embodied Cognition Results

From the late 1950s until 1975, cognition was understood mainly as disembodied symbol manipulation in cognitive psychology, linguistics, artificial intelligence, and the nascent field of Cognitive Science. The idea of embodied cognition entered the field of Cognitive Linguistics at its beginning in 1975. Since then, cognitive linguists, working with neuroscientists, computer scientists, and experimental psychologists, have been developing a neural theory of thought and language (NTTL). Central to NTTL are the following ideas: (a) we think with our brains, that is, thought is physical and is carried out by functional neural circuitry; (b) what makes thought meaningful are the ways those neural circuits are connected to the body and characterize embodied experience; (c) so-called abstract ideas are embodied in this way as well, as is language. Experimental results in embodied cognition are seen not only as confirming NTTL but also explained via NTTL, mostly via the neural theory of conceptual metaphor. Left behind more than three decades ago is the old idea that cognition uses the abstract manipulation of disembodied symbols that are meaningless in themselves but that somehow constitute internal "representations of external reality" without serious mediation by the body and brain. This article uniquely explains the connections between embodied cognition results since that time and results from cognitive linguistics, experimental psychology, computational modeling, and neuroscience.

The World Within Wikipedia: An Ecology of Mind

Human beings inherit an informational culture transmitted through spoken and written language. A growing body of empirical work supports the mutual influence between language and categorization, suggesting that our cognitive-linguistic environment both reflects and shapes our understanding. By implication, artifacts that manifest this cognitive-linguistic environment, such asWikipedia, should represent language structure and conceptual categorization in a way consistent with human behavior. We use this intuition to guide the construction of a computational cognitive model, situated in Wikipedia, that generates semantic association judgments. Our unsupervised model combines information at the language structure and conceptual categorization levels to achieve state of the art correlation with human ratings on semantic association tasks including WordSimilarity-353, semantic feature production norms, word association, and false memory.

Introduction: Essays in honour of Doug Mewhort.

On June 7, 2012, a meeting was held at the University of Manitoba in Winnipeg, Canada to honour Professor Douglas John Kerr Mewhort, a recognised authority on human memory and computational cognitive modelling. This issue is a collection of articles from that meeting. Each article in this issue is written by a student or colleague. The common themes amongst the articles- computational theory, empirical rigour, numeric sophistication, and respect for the priority of experimental data over theory-are characteristically Doug. On behalf of all contributors to this special issue, we wish Doug productive and happy years to come. There is little doubt that he will continue to influence and challenge our science.

Neurophysiological correlates of age-related changes in working memory updating

Normal aging is associated with a gradual decline in executive functions such as set-shifting, inhibition, and updating, along with a progressive decline of neurotransmitter systems including the dopamine system. Modulation from the dopamine system is thought to be critical for the gating of information during working memory. Given the known relationships between executive aging, cognition, and dopamine, this study aims to explore the neurobiology underlying age-related changes in working memory updating using fMRI with healthy subjects from across the adult age spectrum. Our results indicate that older age is associated with poorer performance, reduced meso-cortico-striatal activation, and reduced functional coupling between the caudate and the VLPFC during the updating task. Additionally, caudate activation is associated with improved accuracy and VLPFC activation with faster reaction times in the full sample. Thus, older subjects' under-recruitment of and reduced functional coupling between these regions may specifically underlie age-related changes in working memory updating. These results are consistent with computational models of executive cognition and dopamine-mediated age-related cognitive decline.

Cognitive Computational Models of Emotions and Affective Behaviors

Emotions are one of the important subconscious mechanisms that influence human behaviors, attentions, and decision making. The emotion process helps to determine how humans perceive their internal status and needs in order to form consciousness of an individual. Emotions have been studied from multidisciplinary perspectives and covered a wide range of empirical and psychological topics, such as understanding the emotional processes, creating cognitive and computational models of emotions, and applications in computational intelligence. This paper presents a comprehensive survey of cognitive and computational models of emotions resulted from multidisciplinary studies. It explores how cognitive models serve as the theoretical basis of computational models of emotions. The mechanisms underlying affective behaviors are examined as important elements in the design of these computational models. A comparative analysis of current approaches is elaborated based on recent advances towards a coherent cognitive computational model of emotions, which leads to the machine simulated emotions for cognitive robots and autonomous agent systems in cognitive informatics and cognitive computing.

Towards Computational Cognitive Modeling of Mental Imagery

Mental imagery is the human ability to imagine and reason with visuo-spatial information. It is crucial for everyday tasks such as describing a route or remembering the form of objects. The so-called imagery debate has been centered around the question how mental imagery is realized, i.e., what structures and algorithms can plausibly explain and model mental imagery. There is, however, little progress on a coherent theory that can sufficiently cover the diversity of the empirical data. This article presents a new theory of mental imagery, which in contrast to other contemporary theories is formalized as a computational cognitive model. We will compare this theory to the contemporary theories using two representative phenomena of mental imagery. We will argue that formalized theories can advance the currently stagnant imagery debate.

Metaphysics and Computational Cognitive Science: Let’s Not Let the Tail Wag the Dog

David Chalmers characterizes the central commitments of computational cognitive science in terms of two theses: computational suf ficiency , the idea that the right kind of computational structure suffices for the possession of a mind, and computational explanation, the idea that computation provides a general framework for the explanation of cognitive processes and behavior. The computational program has been challenged by Hilary Putnam (1988) and John Searle (1991), who argue that every physical system implements every computation, with the consequence that any computational ‘explanation’ of cognition is utterly trivial. What is needed, according to Chalmers, is an account of implementation, which would both answer the Searle/Putnam challenge and provide a foundation for computational cognitive theorizing. In this paper I argue that computational cognitive models typically do not satisfy Chalmers’ notion of implementation, and so his account does not provide a conceptual foundation for computational theorizing as it is actually practiced. I argue further that the ‘in-principle’ possibility of deviant implementations of the Putnam/Searle sort does not undermine that practice – it does not make computational explanation trivial – though seeing why it doesn’t requires that we take account of the use to which a computation is put in the exercise of a cognitive capacity.

On costs and benefits of n−2 repetitions in task switching: towards a behavioural marker of cognitive inhibition

Inhibition in task switching is inferred from slower reaction times returning to a recently performed task after one intervening trial (i.e. an ABA sequence) compared to returning to a task not recently performed (CBA sequence). These n-2 repetition costs are thought to reflect the persisting inhibition of a task after its disengagement. As such, the n-2 repetition cost is an attractive tool for the researcher interested in inhibitory functioning in clinical/neurological/neuroscience disciplines. In the literature, an absence of this cost is often interpreted as an absence of inhibition, an assumption with strong implications for researchers. The current paper argues that this is not necessarily an accurate interpretation, as an absence of inhibition should lead to an n-2 repetition benefit as a task's activation level will prime performance. This argument is supported by three instances of a computational cognitive model varying the degree of inhibition present. An inhibition model fits human n-2 repetition costs well. Removal of the inhibition-the activation-only model-predicts an n-2 repetition benefit. For the model to produce a null n-2 repetition cost, small amounts of inhibition were required-the reduced-inhibition model. The authors also demonstrate that a lateral-inhibition locus of the n-2 repetition cost cannot account for observed human data. The authors conclude that a null n-2 repetition cost provides no evidence on its own for an absence of inhibition, and propose reporting of a significant n-2 repetition benefit to be the best evidence for a lack of inhibition. Implications for theories on task switching are discussed.

A Computational Model of “Active Vision” for Visual Search in Human–Computer Interaction

Human visual search plays an important role in many human–computer interaction (HCI) tasks. Better models of visual search are needed not just to predict overall performance outcomes, such as whether people will be able to find the information needed to complete an HCI task, but to understand the many human processes that interact in visual search, which will in turn inform the detailed design of better user interfaces. This article describes a detailed instantiation, in the form of a computational cognitive model, of a comprehensive theory of human visual processing known as “active vision” (Findlay & Gilchrist, 2003). The computational model is built using the Executive Process-Interactive Control cognitive architecture. Eye-tracking data from three experiments inform the development and validation of the model. The modeling asks—and at least partially answers—the four questions of active vision: (a) What can be perceived in a fixation? (b) When do the eyes move? (c) Where do the eyes move? (d) What information is integrated between eye movements? Answers include: (a) Items nearer the point of gaze are more likely to be perceived, and the visual features of objects are sometimes misidentified. (b) The eyes move after the fixated visual stimulus has been processed (i.e., has entered working memory). (c) The eyes tend to go to nearby objects. (d) Only the coarse spatial information of what has been fixated is likely maintained between fixations. The model developed to answer these questions has both scientific and practical value in that the model gives HCI researchers and practitioners a better understanding of how people visually interact with computers, and provides a theoretical foundation for predictive analysis tools that can predict aspects of that interaction.

Memory systems within a cognitive architecture

This article addresses the division of memory systems in relation to an overall cognitive architecture. As understanding the architecture is essential to understanding the mind, developing computational cognitive architectures is an important enterprise in computational psychology (computational cognitive modeling). The article proposes a set of hypotheses concerning memory systems from the standpoint of a cognitive architecture, in particular, the four-way division of memory (including explicit and implicit procedural memory and explicit and implicit declarative memory). It then discusses in detail how these hypotheses may be validated through examining qualitatively the literature on memory. A quick review follows of computational simulations of a variety of quantitative data (which are not limited to narrowly conceived “memory tasks”). Results of accounting for both qualitative and quantitative data point to the promise of this approach.

Visual Search Versus Memory in a Paired Associate Task

The Digit Symbol Substitution Test (DSST) has been used to study various effects, like aging and fatigue. Multiple cognitive and perceptual processes, like associative memory and visual search, are prominently utilized in the DSST. Understanding how these processes contribute to execution of tasks like the DSST is important to human factors research, as moderators like age and fatigue may differentially affect these processes. This study investigates performance on variants of the DSST that emphasize either visual search or associative memory with experimentation and computational cognitive modeling. While there are similarities in performance across task variants, the observed data suggests that, when visual search is possible, people appear to not utilize memory to the extent they would if relying on memory alone. The modeling suggests that behavior differences in the DSST variants results partly from procedural (i.e. strategic) choices and partly from the demands of the tasks.

Understanding Cognitive Strategy With Adaptive Automation in Dual-Task Performance Using Computational Cognitive Models

The objectives of this study were to investigate the effects of advance auditory cuing of control mode changes in an adaptively automated system on human performance and to explain cognitive behaviors at mode changes by using a computational cognitive model. A dual-task piloting simulation, involving tracking and tactical decision making, was developed to collect human performance data with auditory cuing or no cuing of mode transitions in the tactical task. Computational GOMS (goal, operators, methods, and selection) language models were coded to simulate user behavior on the basis of expectation of increased memory transactions (between long-term and working stores) at mode transitions. The models were applied to the same task simulation and scenarios performed by the humans. Human performance data did not reveal differences between cued and no-cue trials possibly because of distraction from the tracking (secondary loading) task. Comparison of results for human and model output demonstrated the model to be descriptive of the pattern of human performance across conditions but not accurate in predicting timing of memory use in preparing for manual control. A refined GOMS language model was coded on the basis of a modified assumption that memory stores are used on an ad hoc basis after high-workload mode transitions and with consideration of human parallel processing in dual-task performance. Results revealed the refined model to have greater plausibility for representing actual behavior. The manner of operator use of memory stores for controlling an adaptive system provides insight into the impact of cuing of mode transitions and a basis for future systems design.

A cognitive model’s view of animal cognition

Abstract Although it is a relatively new field of study, the animal cognition literature is quite extensive and difficult to synthesize. This paper explores the contributions a comprehensive, computational, cognitive model can make toward organizing and assimilating this literature, as well as toward identifying important concepts and their interrelations. Using the LIDA model as an example, a framework is described within which to integrate the diverse research in animal cognition. Such a framework can provide both an ontology of concepts and their relations, and a working model of an animal’s cognitive processes that can compliment active empirical research. In addition to helping to account for a broad range of cognitive processes, such a model can help to comparatively assess the cognitive capabilities of different animal species. After deriving an ontology for animal cognition from the LIDA model, we apply it to develop the beginnings of a database that maps the cognitive facilities of a variety of animal species. We conclude by discussing future avenues of research, particularly the use of computational models of animal cognition as valuable tools for hypotheses generation and testing.