Complex Cognitive Systems Research Articles

Cognitive modelling of concepts in the mental lexicon with multilayer networks: Insights, advancements, and future challenges.

The mental lexicon is a complex cognitive system representing information about the words/concepts that one knows. Over decades psychological experiments have shown that conceptual associations across multiple, interactive cognitive levels can greatly influence word acquisition, storage, and processing. How can semantic, phonological, syntactic, and other types of conceptual associations be mapped within a coherent mathematical framework to study how the mental lexicon works? Here we review cognitive multilayer networks as a promising quantitative and interpretative framework for investigating the mental lexicon. Cognitive multilayer networks can map multiple types of information at once, thus capturing how different layers of associations might co-exist within the mental lexicon and influence cognitive processing. This review starts with a gentle introduction to the structure and formalism of multilayer networks. We then discuss quantitative mechanisms of psychological phenomena that could not be observed in single-layer networks and were only unveiled by combining multiple layers of the lexicon: (i) multiplex viability highlights language kernels and facilitative effects of knowledge processing in healthy and clinical populations; (ii) multilayer community detection enables contextual meaning reconstruction depending on psycholinguistic features; (iii) layer analysis can mediate latent interactions of mediation, suppression, and facilitation for lexical access. By outlining novel quantitative perspectives where multilayer networks can shed light on cognitive knowledge representations, including in next-generation brain/mind models, we discuss key limitations and promising directions for cutting-edge future research.

Highly Demand Working Memory Intervention Weakens a Reactivated Threat Memory and the Associated Cognitive Biases

Anxiety disorders are the most frequent type of mental disorder. Threat-conditioning memory plays a central role in anxiety disorders, impacting complex cognitive systems by modifying behavioral responses to fearful stimuli and inducing an overestimation of potential threats. Here, we analyzed the reminder-dependent amnesia on physiological responses, unconditioned stimulus (US) expectancy ratings, and measures of cognitive bias towards the threat of a threat-conditioning memory.Subjects received differential threat-conditioning. Twenty-four hours later, after reactivation of the memory of threat-conditioning, one group performed a high demand working memory task (HWM) and a second group a low demand working memory task (LWM). A third group only performed the HWM task. Retention of conditioned threat memory was tested on Day 3 in an extinction session followed by a reinstatement test. Tasks targeting stimulus representation, valuation, and attentional bias towards threat were performed. We show that the reminder-dependent intervention with an HWM weakened memory retention as expressed in skin conductance response (SCR) and faded the representation and valuation towards the threat, but it did not affect US expectancy or attentional bias. Our findings provide evidence for the experimental psychopathology approach opening the possibility to weaken both Threat conditioning memory and the systems associated with the maintenance of anxiety features.

Computational and behavioral markers of model-based decision making in childhood.

Human decision-making is underpinned by distinct systems that differ in flexibility and associated cognitive cost. A widely accepted dichotomy distinguishes between a cheap but rigid model-free system and a flexible but costly model-based system. Typically, humans use a hybrid of both types of decision-making depending on environmental demands. However, children's use of a model-based system during decision-making has not yet been shown. While prior developmental work has identified simple building blocks of model-based reasoning in young children (1-4 years old), there has been little evidence of this complex cognitive system influencing behavior before adolescence. Here, by using a modified task to make engagement in cognitively costly strategies more rewarding, we show that children aged 5-11-years (N=85), including the youngest children, displayed multiple indicators of model-based decision making, and that the degree of its use increased throughout childhood. Unlike adults (N=24), however, children did not display adaptive arbitration between model-free and model-based decision-making. Our results demonstrate that throughout childhood, children can engage in highly sophisticated and costly decision-making strategies. However, the flexible arbitration between decision-making strategies might be a critically late-developing component in human development.

Predictive minds can think: addressing generality and surface compositionality of thought

Predictive processing framework (PP) has found wide applications in cognitive science and philosophy. It is an attractive candidate for a unified account of the mind in which perception, action, and cognition fit together in a single model. However, PP cannot claim this role if it fails to accommodate an essential part of cognition—conceptual thought. Recently, Williams (Synthese 1–27, 2018) argued that PP struggles to address at least two of thought’s core properties—generality and rich compositionality. In this paper, I show that neither necessarily presents a problem for PP. In particular, I argue that because we do not have access to cognitive processes but only to their conscious manifestations, compositionality may be a manifest property of thought, rather than a feature of the thinking process, and result from the interplay of thinking and language. Pace Williams, both of these capacities, constituting parts of a complex and multifarious cognitive system, may be fully based on the architectural principles of PP. Under the assumption that language presents a subsystem separate from conceptual thought, I sketch out one possible way for PP to accommodate both generality and rich compositionality.

Thoughts from French Psychoanalysis on the Absence of Corporeality in the Unconscious of Emerging Complex Cognitive System

In this text we answer at the same time to two recent interesting works of Giancarlo Minati and Luca Possati in which they both called to work on the development, one from the part of the computer side, and the other of the humanities one of an IA unconscious in complex cognitive systems as an experiment to come to more anthropomorphic machines, performance added by the unconscious will not be addressed in this paper. We gathered many sources in psychoanalysis to help us understand what could be the barriers dressed against us. In the light of Lacan, Anzieu, Leclaire and Winnicott amongst others we tried to explain how having a body, in the biological sense, makes a difference with recreating—this is a typical human preoccupation—an unconscious in IA. Of course, from a French psychoanalytic standpoint there are many conservative objections, while some can be easily overcome, the matter of innate desire and body seems an understandable concern. It is also important to consider the interesting conjecture of Possati (i.e., a computer can be a projective identification object); while we only may say that it is a transitional object in the sense of Winnicott. Also, we can study further within psychotherapy the behaviour of the patient and therapist, with an algorithm we developed. In the end we address the objection of French postructruralist psychology objections to the creation of a human-like unconscious and advise the experimenting of Possati’s theory with our device.

Working Memory Model and Language Learning

Working Memory (WM) is an essential concept of cognitive science since many aspects of human learning depend on it. Primarily proposed by Baddeley and Hitch (1974) and developed by Baddeley (1986), the concept of WM comprises the number of subsystems involved during the process and the considerable emphasis on its key role in cognitive tasks such as learning, reasoning, and comprehension. As a complex and limited cognitive system, by enabling the input to be temporarily stored, monitored and manipulated understanding the sub mechanism under the WM model is crucial in specifying the factors that affect learning languages. By taking Baddeley’s model of WM (2012) as reference, the aim of this study is to dicuss the model of WM and demonstrate the relationship between its components and the processes that play an important role in understanding the underlying cognitive processes for language learning.

The Value of Statistical Learning to Cognitive Network Science.

To study the human mind is to consider the nature of associations-how are they learned, what are their constituent parts, and how can they be severed or adjusted? The manipulation of associations stands as a pillar ofstatistical learning(SL) research, which strongly suggests that processes as diverse as word segmentation, learning of grammatical patterns, and event perception can be explained by the learner's sensitivity to simple temporal dependencies (among other regularities). Used to determine the edges of a network, associations are similarly crucial to consider when quantifying the graph-theoretical properties of various cognitive systems. With this point of convergence in mind, the present work reaffirms the unique value of network science in illuminating the broad-level architectures of complex cognitive systems. However, I also describe how insights from the SL literature, coupled with insights from psycholinguistics more broadly, offer a strong theoretical backbone upon which we can develop and study networks that reflect, as closely as possible, the psychological realities of learning.

Brain plasticity dynamics during tactile Braille learning in sighted subjects: Multi-contrast MRI approach

A growing body of empirical evidence supports the notion of diverse neurobiological processes underlying learning-induced plasticity changes in the human brain. There are still open questions about how brain plasticity depends on cognitive task complexity, how it supports interactions between brain systems and with what temporal and spatial trajectory. We investigated brain and behavioural changes in sighted adults during 8-months training of tactile Braille reading whilst monitoring brain structure and function at 5 different time points. We adopted a novel multivariate approach that includes behavioural data and specific MRI protocols sensitive to tissue properties to assess local functional and structural and myelin changes over time. Our results show that while the reading network, located in the ventral occipitotemporal cortex, rapidly adapts to tactile input, sensory areas show changes in grey matter volume and intra-cortical myelin at different times. This approach has allowed us to examine and describe neuroplastic mechanisms underlying complex cognitive systems and their (sensory) inputs and (motor) outputs differentially, at a mesoscopic level.

Complex Cognitive Systems and Their Unconscious. Related Inspired Conjectures for Artificial Intelligence

The aim of the article is to propose a conceptual framework, constructs, and conjectures to act as a guide for future, related research finalized to design and implement versions of Artificial Intelligence encompassing an artificially simulated unconscious suitable for human-like artificial cognitive processing. This article considers the concept of the unconscious in psychoanalysis. The interdisciplinary understanding of this concept is considered to be the unavoidable property of sufficiently complex, cognitive processing. We elaborate on the possibility of an artificial unconscious, able to both self-acquired properties through usage, and self-profile through a supposed implicit, parasitic usage of explicit cognitive processing. Memory activities are considered to be integrated into cognitive processing, with memory no longer only being storage and reminding no longer only being finding. We elaborate on the artificial unconscious as an implicit, usage-dependent, self-profiling, and emergent process. Conceptual characteristics of the research project are the implementation of weighted networked, fuzzified memorizations; self-generated networks of links of inter-relationships as nodes, self-variation of the intensity of the links according to the use, and activation of internal self-processes such as the introduction of fictitious links intended as variations and combinations of the current ones. Application examples suitable for experimental implementation are also discussed with reference to chatbot technology that has been extended with features of an artificial unconscious. Thus, we introduce the concept of the AU-chatbot. The main purpose is to allow the artificial cognitive processing to acquire suitable human-like attitudes in representing, interfacing, and learning, potentially important in supporting and complementing human-centered activities. Examples of expected features are the ability to combine current and unconscious links to perform cognitive processing such as representing, deciding, memorizing, and solving equivalencies, and also learning meta-profiles, such as in supporting doctor–patient interactions and educational activities. We also discuss possible technologies suitable for implementing experiments for the artificial unconscious.

Marr’s levels of analysis and the notion of copying

In this commentary, I suggest that it may be helpful to think about the formidable problem space that Westergaard’s (2021) Linguistic Proximity Model seeks to address at the three levels of analysis that Marr (1982) famously proposed are needed to understand any complex cognitive system. I argue that at the computational level of analysis, where it appears the Linguistic Proximity Model is epistemiologically situated, the notion of copying grammatical representations is unproblematic, contrary to Westergaard’s concerns.

Using neuroscience techniques to understand and improve design cognition.

Cognitive neuroscience research has traditionally focused on understanding the brain mechanisms that enable cognition by means of experimental laboratory tasks. With a budding literature, there is growing interest in the application of the related methods and findings to real-world settings. In this opinion paper we explore the potential and promise of employing current cognitive neuroscience methodologies in the field of design. We review recent evidence from preliminary studies that have employed such methods toward identifying the neural bases of design thinking and discuss their impact and limitations. Further, we highlight the importance of pairing neuroscience methods with well-established behavioral paradigms during ecologically-valid, real-world design tasks. Experimental investigations that meet these requirements can generate powerful datasets of neurocognitive measures that can offer new insights into the complex cognitive and brain systems enabling design thinking. We argue that this new knowledge can lead to the development and implementation of new techniques toward cultivating and improving design thinking in design education and professional practice.

Activation-based association profiles differentiate network roles across cognitive loads.

Working memory (WM) is a complex and pivotal cognitive system underlying the performance of many cognitive behaviors. Although individual differences in WM performance have previously been linked to the blood oxygenation level-dependent (BOLD) response across several large-scale brain networks, the unique and shared contributions of each large-scale brain network to efficient WM processes across different cognitive loads remain elusive. Using a WM paradigm and functional magnetic resonance imaging (fMRI) from the Human Connectome Project, we proposed a framework to assess the association and shared-association strength between imaging biomarkers and behavioral scales. Association strength is the capability of individual brain regions to modulate WM performance and shared-association strength measures how different regions share the capability of modulating performance. Under higher cognitive load (2-back), the frontoparietal executive control network (FPN), dorsal attention network (DAN), and salience network showed significant positive activation and positive associations, whereas the default mode network (DMN) showed the opposite pattern, namely, significant deactivation and negative associations. Comparing the different cognitive loads, the DMN and FPN showed predominant associations and globally shared-associations. When investigating the differences in association from lower to higher cognitive loads, the DAN demonstrated enhanced association strength and globally shared-associations, which were significantly greater than those of the other networks. This study characterized how brain regions individually and collaboratively support different cognitive loads.

The Double Take of Expertise: Neural Expansion Is Associated With Outstanding Performance

The performance of experts seems almost effortless. The neural-efficiency hypothesis takes this into account, suggesting that because of practice and automatization of procedures, experts require fewer brain resources. Here, I argue that the way the brain accommodates complex skills does indeed have to do with the nature of experts’ performance. However, instead of exhibiting less brain activation, experts’ performance actually engages more brain areas. Behind the seemingly effortless performance of experts lies a complex cognitive system that relies on knowledge about the domain of expertise. Unlike novices, who need to execute one process at a time, experts are able to recognize an object, retrieve its function, and connect it to another object simultaneously. The expert brain deals with this computational burden by engaging not only specific brain areas in one hemisphere but also the same (homologous) area in the opposite hemisphere. This phenomenon, which I call the double take of expertise, has been observed in a number of expertise domains. I describe it here in object- and pattern-recognition tasks in the domain of chess. I also discuss the importance of the study of expertise for our understanding of the human brain in general.

Discovery in complex adaptive systems

The distinction between ontological ground-truth phenomena and epistemic measurements of those phenomena is discussed and analyzed in the context of complex cognitive systems research. A common style of computational simulation is identified as a Dissect-the-Simulation motif. In this style, a model of cognition is designed and when it generally mimics some cognitive behavior, its own ontic processes (which are fully accessible because it is a simulation) are proposed as potential ontic processes inside actual human cognition. I propose here a second style of modeling, which might be called Measure-the-Simulation. In this style, simulated versions of coarse-grained epistemic measurements are performed on the model with the intent of discovering how those epistemic measurements themselves may transform the actual ontic data, and thus induce a variety of mis-categorizations of phenomena. When our epistemic measurements are better understood, such mis-categorizations can perhaps be avoided. As a proof of concept, Conway’s Game of Life is analyzed and measured with two different types of coarse-grained epistemic measures. Dramatic differences are identified in how a binary coarse-graining, versus a graded coarse-graining, transforms the discovery process for activity patterns on the lattice of this cellular automaton.

The complex cognitive system of executive functioning: A conceptual review

The complex cognitive system of executive functioning: A conceptual review

Theoretical study on magnetoelectric effect in multiferroic tetragonal BiMnO3

Perovskite BiMnO3 with ferroelectric and ferromagnetic ordering simultaneously, as a kind of multiferroics, can be expected to have the coupling between the magnetic and dielectric properties as well as their control by the application of electric fields. This advantage can make BiMnO3 a good candidate for an artificial synapse material. Under the framework of the density functional theory, in this paper we adopt the generalized gradient approximation (GGA+U) plane wave pseudopotential method to calculate the ferroelectricity double-well potential curves and magnetic moments of Mn of tetragonal BiMnO3, with 0.18% and 4% strain exerted in its x-y plane. The results show that the magnetic moment of Mn monotonically increases from paraelectric state to ferroelectric state. It means that the ferromagnetic property of tetragonal BiMnO3 can be controlled by the intensity of polarization. The greater the stress, the greater the range of magnetic moment is. This would imply that the multiferroic artificial synapse device based on BiMnO3 can bring another degree of freedom into designing the complex cognitive systems of artificial intelligence in the future.

The grammar of anger: Mapping the computational architecture of a recalibrational emotion

According to the recalibrational theory of anger, anger is a computationally complex cognitive system that evolved to bargain for better treatment. Anger coordinates facial expressions, vocal changes, verbal arguments, the withholding of benefits, the deployment of aggression, and a suite of other cognitive and physiological variables in the service of leveraging bargaining position into better outcomes. The prototypical trigger of anger is an indication that the offender places too little weight on the angry individual’s welfare when making decisions, i.e. the offender has too low a welfare tradeoff ratio (WTR) toward the angry individual. Twenty-three experiments in six cultures, including a group of foragers in the Ecuadorian Amazon, tested six predictions about the computational structure of anger derived from the recalibrational theory. Subjects judged that anger would intensify when: (i) the cost was large, (ii) the benefit the offender received from imposing the cost was small, or (iii) the offender imposed the cost despite knowing that the angered individual was the person to be harmed. Additionally, anger-based arguments conformed to a conceptual grammar of anger, such that offenders were inclined to argue that they held a high WTR toward the victim, e.g., “the cost I imposed on you was small”, “the benefit I gained was large”, or “I didn’t know it was you I was harming.” These results replicated across all six tested cultures: the US, Australia, Turkey, Romania, India, and Shuar hunter-horticulturalists in Ecuador. Results contradict key predictions about anger based on equity theory and social constructivism.

Reinterpretation of the Likert Scale for Public Transportation User Satisfaction: Pattern Recognition Approach

Myriad cities around the world conduct public transportation satisfaction investigations, and most of them use a linguistic Likert scale (i.e., it runs from strongly dissatisfied to strongly satisfied). Most such studies neglect two critical issues: users’ complex cognitive systems and the fundamental problem of applying the Likert scale. Thus the present study considered those issues and analyzed public transportation user satisfaction intervals of heterogeneous groups with different cognitive systems. The study used a Gaussian mixture model and a pattern recognition approach, with satisfaction data from public transportation users in South Korea. The results indicated that users expressed considerably different satisfaction intervals for the same linguistic Likert items: five intervals instead of the 7-point Likert scale in the survey. In addition, each Likert item was clustered into two distinguishable satisfaction intervals, and those heterogeneous groups were classified not randomly but systematically by sociodemographic attributes and characteristics of public transportation use. On the basis of the results, significant implications were found about public transportation satisfaction surveys and their analysis.

Evaluación de la memoria mediante realidad virtual: presente y futuro

The human memory is a complex cognitive system whose close relationship with executive functions implies that, in many occasions, a mnemonic deficit comprises difficulties to operate with correctly stored contents. Traditional memory tests, more focused in the information storage than in its processing, may be poorly sensitive both to subjects' daily life functioning and to changes originated by rehabilitation programs. In memory assessment, there is plenty evidence with regards to the need of improving it by means of tests which offer a higher ecological validity, with information that may be presented in various sensorial modalities and produced in a simultaneous way. Virtual reality reproduces three-dimensional environments with which the patient interacts in a dynamic way, with a sense of immersion in the environment similar to the presence and exposure to a real environment, and in which presentation of such stimuli, distractors and other variables may be systematically controlled. The current review aims to go deeply into the trajectory of neuropsychological assessment of memory based in virtual reality environments, making a tour through existing tests designed for assessing learning, prospective, episodic and spatial memory, as well as the most recent attempts to perform a comprehensive evaluation of all memory components.

Operationalizing Sepsis Alert Design and Clinical Decision Support: Developing Enhanced Visual Display Models

It is vital that health care providers have the tools needed for early diagnosis and treatment of sepsis to achieve improved survival outcomes. Despite advances in early warning system technology, providers still lack responsiveness in recognizing and treating septic patients. The recently introduced Centers for Medicare and Medicaid Services early management bundle for severe/septic shock highlights the need to promote bundles of care to improve guideline compliance and lower hospital mortality. Our objective is to determine the best way to provide a sepsis alert to drive compliance in the complex cognitive system that underlies the decision-making process in the dynamic and distributed health care work environment. Alert and warning complexity is especially prevalent in health care information technology. Despite this issue, there is little consensus on how alerts and warnings should be generated and displayed to the user. Effective presentation of an alert, including how and what is displayed, may offer better cognitive support during busy patient encounters and may help providers extract information quickly. We are creating a framework to evaluate sepsis alert presentation models with enhanced visual elements integrating a textual, graphical, and figure display of the PIRO (predisposition, infection, response, organ dysfunction) classification. Our alert presentation framework is tailored to clinicians with the goal to better equip clinicians to understand their patients' data through feature extraction and visualization embedded with different asynchronous alerts.