Computational Cognitive Model Research Articles

Understanding Narrative-Driven Intergroup Conflict Dynamics Using Computational Cognitive Coherence Maps

Humans make sense of the world through narratives. Therefore, adversaries often use conflict-sustaining narratives to maintain dominance and delegitimize the actions of the rivals. To better understand narratives’ role and influence in such intractable conflicts, a computational framework and methodology are introduced. The computational cognitive model and its underlying inference mechanism allow analysts to simulate and analyze narratives in relation to opposing narratives. The ability to simulate the interaction of adversarial stories with a set of micronarratives shared by members of a group opens new avenues to counter conflict-sustaining narratives. The methodology and its application to a concrete conflict scenario demonstrate how to conduct simulation-driven exploratory analysis over a complex adaptive narrative space to discern how narratives are matched to unfolding events and how they can be used to facilitate favorable change.

How Children Process Reduced Forms: A Computational Cognitive Modeling Approach to Pronoun Processing in Discourse.

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.

Anxious and obsessive-compulsive traits are independently associated with valuation of noninstrumental information.

Aversion to uncertainty about the future has been proposed as a transdiagnostic trait underlying psychiatric diagnoses including obsessive-compulsive disorder and generalized anxiety. This association might explain the frequency of pathological information-seeking behaviors such as compulsive checking and reassurance-seeking in these disorders. Here we tested the behavioral predictions of this model using a noninstrumental information-seeking task that measured preferences for unusable information about future outcomes in different payout domains (gain, loss, and mixed gain/loss). We administered this task, along with a targeted battery of self-report questionnaires, to a general-population sample of 146 adult participants. Using computational cognitive modeling of choices to test competing theories of information valuation, we found evidence for a model in which preferences for costless and costly information about future outcomes were independent, and in which information preference was modulated by both outcome mean and outcome variance. Critically, we also found positive associations between a model parameter controlling preference for costly information and individual differences in latent traits of both anxiety and obsessive-compulsion. These associations were invariant across different payout domains, providing evidence that individuals high in obsessive-compulsive and anxious traits show a generalized increase in willingness-to-pay for unusable information about uncertain future outcomes, even though this behavior reduces their expected future reward. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A Computational Model of Focused Attention Meditation and Its Transfer to a Sustained Attention Task

Meditation has been shown to aid with the management of affective disorders through improving emotion regulation. Here we begin to develop a theory of meditation by creating a computational cognitive model of focused attention meditation. Our model was created within Prims, a derivative of the ACT-R cognitive architecture. We implemented a model based on an extensive literature review of how the meditation experience unfolds over time. We then tested the Prims model in a sustained attention task, intending to capture a faculty that may be trained with meditation practice. The model was significantly better able to maintain focus after the meditation practice than before. These results agree qualitatively with empirical findings of a longitudinal study on the effects of meditation conducted in 2010. The central mechanism for increasing task relevant focus in the model seems to be a feedback loop. The meditation and mind-wandering processes reinforce themselves and weaken the other. However, this reinforcement is more dispersed in the more elaborate mind-wandering process, which causes it to decrease over time. We speculate that observed improvements in emotion regulation observed after meditation arise from the ability to maintain focus, because it allows the practitioner to avoid emotions spiraling out of control.

Cross-linguistic scope ambiguity: An investigation of English, Spanish, and Mandarin

Faced with a sentence like Every horse didn't jump over the fence as a description of a scenario in which one out of two horses jumped, adults readily endorse the utterance as a good description, while children overwhelmingly reject it. However, systematic changes to the task setup lead to marked increases in children's endorsement rates (Musolino & Lidz 2006; Viau et al. 2010). Savinelli et al.(2017) use a computational cognitive model of utterance endorsement in truth-value judgment tasks to analytically demonstrate that both children and adults' interpretation behavior is affected by pragmatic manipulations. We test a clear prediction of these models: manipulating the conversational goal (or Question Under Discussion) should lead to clear effects on utterance endorsement. In addition to investigating the predictions for English, we also investigate Spanish and Mandarin, where the status of the relevant ambiguity may be less clear.

Data-driven experimental design and model development using Gaussian process with active learning

Interest in computational modeling of cognition and behavior continues to grow. To be most productive, modelers should be equipped with tools that ensure optimal efficiency in data collection and in the integrity of inference about the phenomenon of interest. Traditionally, models in cognitive science have been parametric, which are particularly susceptible to model misspecification because their strong assumptions (e.g. parameterization, functional form) may introduce unjustified biases in data collection and inference. To address this issue, we propose a data-driven nonparametric framework for model development, one that also includes optimal experimental design as a goal. It combines Gaussian Processes, a stochastic process often used for regression and classification, with active learning, from machine learning, to iteratively fit the model and use it to optimize the design selection throughout the experiment. The approach, dubbed Gaussian process with active learning (GPAL), is an extension of the parametric, adaptive design optimization (ADO) framework (Cavagnaro, Myung, Pitt, & Kujala, 2010). We demonstrate the application and features of GPAL in a delay discounting task and compare its performance to ADO in two experiments. The results show that GPAL is a viable modeling framework that is noteworthy for its high sensitivity to individual differences, identifying novel patterns in the data that were missed by the model-constrained ADO. This investigation represents a first step towards the development of a data-driven cognitive modeling framework that serves as a middle ground between raw data, which can be difficult to interpret, and parametric models, which rely on strong assumptions.

Robots as Mirrors of the Human Mind

Robots are currently the center of attention in various fields of research because of their potential use as assistants for daily living. In this article, I highlight a different role that robots can play—being a tool for understanding human cognition. I provide examples in which robots have been used in experimental psychology to study sociocognitive mechanisms such as joint attention and sense of agency. I also discuss the issue of whether and when robots (especially those that resemble humans) are perceived through a human-centered lens with anthropomorphic attributions. In the final section, I describe approaches in which the robots’ embodiment has been used for the implementation of computational models of human cognition. In sum, the collection of studies presented here shows that robots can be an extremely useful tool for scientific inquiry in the areas of experimental psychology and cognitive science.

Conjoining Meanings: Semantics without Truth Values

<i>Conjoining Meanings: Semantics without Truth Values</i>

A computational cognitive model of judgments of relative direction

In the past several decades, considerable theoretical progress has been made in understanding the role of reference frames in the encoding and retrieval of spatial information about the environment. Many of these insights have come from participants making judgments of relative direction using their memories of spatial layouts. In this task, participants are asked to imagine standing at a given location and facing a certain direction, and to point to a target location. Although this task has been widely and productively used, a computational cognitive model of judgments of relative direction has yet to be introduced. Computational modeling of judgments of relative direction is a critical next step to formulating and testing hypotheses about the cognitive processes involved in establishing and using spatial reference frames. We present an initial attempt to model judgments of relative direction and fit the model to two datasets exhibiting behavioral patterns commonly observed in the spatial memory literature. The model was able to predict many important features of these data, most notably alignment effects. We discuss directions for future modeling efforts.

Meta-control of the exploration-exploitation dilemma emerges from probabilistic inference over a hierarchy of time scales

Cognitive control is typically understood as a set of mechanisms that enable humans to reach goals that require integrating the consequences of actions over longer time scales. Importantly, using routine behaviour or making choices beneficial only at short time scales would prevent one from attaining these goals. During the past two decades, researchers have proposed various computational cognitive models that successfully account for behaviour related to cognitive control in a wide range of laboratory tasks. As humans operate in a dynamic and uncertain environment, making elaborate plans and integrating experience over multiple time scales is computationally expensive. Importantly, it remains poorly understood how uncertain consequences at different time scales are integrated into adaptive decisions. Here, we pursue the idea that cognitive control can be cast as active inference over a hierarchy of time scales, where inference, i.e., planning, at higher levels of the hierarchy controls inference at lower levels. We introduce the novel concept of meta-control states, which link higher-level beliefs with lower-level policy inference. Specifically, we conceptualize cognitive control as inference over these meta-control states, where solutions to cognitive control dilemmas emerge through surprisal minimisation at different hierarchy levels. We illustrate this concept using the exploration-exploitation dilemma based on a variant of a restless multi-armed bandit task. We demonstrate that beliefs about contexts and meta-control states at a higher level dynamically modulate the balance of exploration and exploitation at the lower level of a single action. Finally, we discuss the generalisation of this meta-control concept to other control dilemmas.

Research work in the Applied Cognitive Modelling Lab

Dr Junya Morita is based at the Applied Cognitive Modelling Laboratory (ACML) within the Department of Behavior Informatics at Shizuoka University in Japan. His team is conducting investigations that use computational models in an effort to improve our understanding of human minds and their inner workings. There are currently two directions of study underway at ACML. The first is concerned with theoretical studies of cognitive modelling, where the team try to construct models that explain human minds as computational and algorithmic levels. The second direction of study is the application of computational cognitive models. Morita and his team believe that there are fundamental values within the basic endeavours of cognitive science and are working to prove these values exist and are valid. Current topics of application include education, driving, entertainment, graphic design, language development, web navigation and mental illness.

The effect of intrinsic image memorability on recollection and familiarity

Many photographs of real-life scenes are very consistently remembered or forgotten by most people, making these images intrinsically memorable or forgettable. Although machine vision algorithms can predict a given image’s memorability very well, nothing is known about the subjective quality of these memories: are memorable images recognized based on strong feelings of familiarity or on recollection of episodic details? We tested people’s recognition memory for memorable and forgettable scenes selected from image memorability databases, which contain memorability scores for each image, based on large-scale recognition memory experiments. Specifically, we tested the effect of intrinsic memorability on recollection and familiarity using cognitive computational models based on receiver operating characteristics (ROCs; Experiment 1 and 2) and on remember/know (R/K) judgments (Experiment 2). The ROC data of Experiment 2 indicated that image memorability boosted memory strength, but did not find a specific effect on recollection or familiarity. By contrast, ROC data from Experiment 2, which was designed to facilitate encoding and, in turn, recollection, found evidence for a specific effect of image memorability on recollection. Moreover, R/K judgments showed that, on average, memorability boosts recollection rather than familiarity. However, we also found a large degree of variability in these judgments across individual images: some images actually achieved high recognition rates by exclusively boosting familiarity rather than recollection. Together, these results show that current machine vision algorithms that can predict an image’s intrinsic memorability in terms of hit rates fall short of describing the subjective quality of human memories.

Computational Cognitive Modeling of Human Calibration and Validity Response Scoring for the Graduate Record Examinations (GRE)

Most research on skill acquisition and retention focuses on the individual being tested. Yet sometimes another person is responsible for evaluating the individual’s performance. Here, we study the acquisition and retention of rater skill using data for the Graduate Record Examinations (GRE). Our work is based on the idea that response scoring, like other cognitive skills, will gradually improve with amount of practice, and decline with elapsed time since that practice occurred. These classic findings are the focus of a computational cognitive model called the Predictive Performance Equation (PPE). However, the generalizability of these findings to response scoring and the applicability of PPE to that domain have not yet been demonstrated. To address this issue, we leveraged a naturalistic dataset containing rating performance from over 23,000 sessions. Our analyses provide empirical support for PPE and establish a basis for using a model like PPE to personalize rater training requirements.

Cognitive neurorobotics and self in the shared world, a focused review of ongoing research

Through brain-inspired modeling studies, cognitive neurorobotics aims to resolve dynamics essential to different emergent phenomena at the level of embodied agency in an object environment shared with human beings. This article is a review of ongoing research focusing on model dynamics associated with human self-consciousness. It introduces the free energy principle and active inference in terms of Bayesian theory and predictive coding, and then discusses how directed inquiry employing analogous models may bring us closer to representing the sense of self in cognitive neurorobots. The first section quickly locates cognitive neurorobotics in the broad field of computational cognitive modeling. The second section introduces principles according to which cognition may be formalized, and reviews cognitive neurorobotics experiments employing such formalizations. The third section interprets the results of these and other experiments in the context of different senses of self, both “minimal” and “narrative” self. The fourth section considers model validity and discusses what we may expect ongoing cognitive neurorobotics studies to contribute to scientific explanation of cognitive phenomena including the senses of minimal and narrative self.

Pronoun Processing and Interpretation by L2 Learners of Italian: Perspectives from Cognitive Modelling

How do second language learners acquire form-meaning associations in the second language that are inconsistent with their first language? In this study, we focus on subject pronouns in Italian and Dutch. A native speaker of the non-null subject language Dutch learning the null subject language Italian as a second language will not only have to learn to use and comprehend null pronouns, but will also have to learn to use and comprehend overt pronouns differently in the L2 than in the L1. The interpretation of Italian overt pronouns, but not of Dutch overt pronouns or Italian null pronouns, has been argued to require perspective taking, specifically the use of hypotheses about the conversational partner’s communicative choices to guide one’s own choices. Therefore, a related question is how perspective taking and cognitive constraints influence L2 acquisition of such forms. Using computational cognitive modelling, this study explores two learning scenarios. In cognitive model 1, second language acquisition proceeds in the same way as first language acquisition and is based on the same grammar. In cognitive model 2, second language acquisition differs from first language acquisition and involves the construction of a partly different grammar. Our results suggest that the second scenario may be cognitively more plausible than the first one. Furthermore, our models explain why second language learners of Italian perform less native-like on overt pronouns than on null pronouns.

A Structured Approach for Display of the Most Practical Theories in ELT

A flowchart is just a diagrammatic approach to capturing and managing information. The present study investigates astructured approach to display some theoretical frameworks based on ‘Computational Cognitive Model’ by Dubey et al.(2008) and visual attention model proposed by Koch and Ullman (1985) and implemented by Itti, Koch and Neibur (1998).To this end, 10 practical theories are selected by the researcher to explain the sequence of steps followed by their framework.Since flowcharts may be used for diagnosing a malfunction or logic of solving a problem. This study can indicate thedifferent parts of the theories to troubleshoot the problems as well as quick reference to previous steps. The researcher tries toget an agreement on confirmation of combinatory theoretical diagram from the 7 faculty members in Ruskin university ofLondon. At the end of the statements, the exhibitive design of the practical theories selected in this study breaks complicatedprocesses down into a series of smaller, more manageable steps. This schematically design can boost students' interests muchbetter than text only and impart knowledge better since students can understand faster.

Simulating Code-switching Using a Neural Network Model of Bilingual Sentence Production

Code-switching is the alternation from one language to the other during bilingual speech. We present a novel method of researching this phenomenon using computational cognitive modeling. We trained a neural network of bilingual sentence production to simulate early balanced Spanish–English bilinguals, late speakers of English who have Spanish as a dominant native language, and late speakers of Spanish who have English as a dominant native language. The model produced code-switches even though it was not exposed to code-switched input. The simulations predicted how code-switching patterns differ between early balanced and late non-balanced bilinguals; the balanced bilingual simulation code-switches considerably more frequently, which is in line with what has been observed in human speech production. Additionally, we compared the patterns produced by the simulations with two corpora of spontaneous bilingual speech and identified noticeable commonalities and differences. To our knowledge, this is the first computational cognitive model simulating the code-switched production of non-balanced bilinguals and comparing the simulated production of balanced and non-balanced bilinguals with that of human bilinguals.

A Computational Study of Executive Dysfunction in Amyotrophic Lateral Sclerosis.

Executive dysfunction is a well-documented, yet nonspecific corollary of various neurological diseases and psychiatric disorders. Here, we applied computational modeling of latent cognition for executive control in amyotrophic lateral sclerosis (ALS) patients. We utilized a parallel reinforcement learning model of trial-by-trial Wisconsin Card Sorting Test (WCST) behavior. Eighteen ALS patients and 21 matched healthy control participants were assessed on a computerized variant of the WCST (cWCST). ALS patients showed latent cognitive symptoms, which can be characterized as bradyphrenia and haphazard responding. A comparison with results from a recent computational Parkinson’s disease (PD) study (Steinke et al., 2020, J Clin Med) suggests that bradyphrenia represents a disease-nonspecific latent cognitive symptom of ALS and PD patients alike. Haphazard responding seems to be a disease-specific latent cognitive symptom of ALS, whereas impaired stimulus-response learning seems to be a disease-specific latent cognitive symptom of PD. These data were obtained from the careful modeling of trial-by-trial behavior on the cWCST, and they suggest that computational cognitive neuropsychology provides nosologically specific indicators of latent facets of executive dysfunction in ALS (and PD) patients, which remain undiscoverable for traditional behavioral cognitive neuropsychology. We discuss implications for neuropsychological assessment, and we discuss opportunities for confirmatory computational brain imaging studies.

A Compositional Neural Architecture for Language

Hierarchical structure and compositionality imbue human language with unparalleled expressive power and set it apart from other perception-action systems. However, neither formal nor neurobiological models account for how these defining computational properties might arise in a physiological system. I attempt to reconcile hierarchy and compositionality with principles from cell assembly computation in neuroscience; the result is an emerging theory of how the brain could convert distributed perceptual representations into hierarchical structures across multiple timescales while representing interpretable incremental stages of (de)compositional meaning. The model's architecture-a multidimensional coordinate system based on neurophysiological models of sensory processing-proposes that a manifold of neural trajectories encodes sensory, motor, and abstract linguistic states. Gain modulation, including inhibition, tunes the path in the manifold in accordance with behavior and is how latent structure is inferred. As a consequence, predictive information about upcoming sensory input during production and comprehension is available without a separate operation. The proposed processing mechanism is synthesized from current models of neural entrainment to speech, concepts from systems neuroscience and category theory, and a symbolic-connectionist computational model that uses time and rhythm to structure information. I build on evidence from cognitive neuroscience and computational modeling that suggests a formal and mechanistic alignment between structure building and neural oscillations, and moves toward unifying basic insights from linguistics and psycholinguistics with the currency of neural computation.

Computational cognitive modeling and validation of Dp140 induced alteration of working memory in Duchenne Muscular Dystrophy

Duchenne Muscular Dystrophy has emerged as a model to assess cognitive domains. The DMD gene variant location and its association with variable degrees of cognitive impairment necessitate identification of a common denominator. Computer architectures provide a framework to delineate the mechanisms involved in the cognitive functioning of the human brain. Copy number variations in the 79 exons of DMD gene were screened in 84 DMD subjects by Multiplex Ligation-dependent Probe Amplification (MLPA). DMD subjects were categorized based on the presence or absence of DP140 isoform. The cognitive and neuropsychological assessments were carried out as per inclusion criteria using standard scales. Instance-based learning theory (IBLT) based on the partial matching process was developed to mimic Stroop Color and Word Task (SCWT) performance on Adaptive Control of Thought-Rational (ACT-R) cognitive architecture based on IBLT. Genotype–phenotype correlation was conducted based on the mutation location in DMD gene. Assessment of specific cognitive domains in DP140 − ve group corresponded to the involvement of multiple brain lobes including temporal (verbal and visual learning and memory), parietal (visuo-conceptual and visuo-constructive abilities) and frontal (sustained and focused attention, verbal fluency, cognitive control). Working memory axis was found to be the central domain through tasks including RAVLT trial 1, recency effect, digit span backward, working memory index, arithmetic subtests in the Dp140 − ve group. IBLT validated the non-reliance of DMD subjects on recency indicating affected working memory domain. Modeling strategy revealed altered working memory processes in DMD cases with affected Dp140 isoform. DMD brain was observed to rely on primacy than the recency suggesting alterations in working memory capacity. Modeling revealed lowered activation of DMD brain with Dp140 − ve in order to retrieve the instances.