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.
It is widely assumed in developmental biology and bioengineering that optimal understanding and control of complex living systems follows from models of molecular events. The success of reductionism ...has overshadowed attempts at top-down models and control policies in biological systems. However, other fields, including physics, engineering and neuroscience, have successfully used the explanations and models at higher levels of organization, including least-action principles in physics and control-theoretic models in computational neuroscience. Exploiting the dynamic regulation of pattern formation in embryogenesis and regeneration requires new approaches to understand how cells cooperate towards large-scale anatomical goal states. Here, we argue that top-down models of pattern homeostasis serve as proof of principle for extending the current paradigm beyond emergence and molecule-level rules. We define top-down control in a biological context, discuss the examples of how cognitive neuroscience and physics exploit these strategies, and illustrate areas in which they may offer significant advantages as complements to the mainstream paradigm. By targeting system controls at multiple levels of organization and demystifying goal-directed (cybernetic) processes, top-down strategies represent a roadmap for using the deep insights of other fields for transformative advances in regenerative medicine and systems bioengineering.
The aspiration for insight into human cognitive processing has traditionally driven research in cognitive science. With methods such as the Hidden semi-Markov Model-Electroencephalography (HsMM-EEG) ...method, new approaches have been developed that help to understand the temporal structure of cognition by identifying temporally discrete processing stages.
However, it remains challenging to assign concrete functional contributions by specific processing stages to the overall cognitive process. In this paper, we address this challenge by linking HsMM-EEG1 with cognitive modelling, with the aim of further validating the HsMM-EEG1 method and demonstrating the potential of cognitive models to facilitate functional interpretation of processing stages. For this purpose, we applied HsMM-EEG1 to data from a mental rotation task and developed an ACT-R cognitive model that is able to closely replicate human performance in this task.
Applying HsMM-EEG1 to the mental rotation experiment data revealed a strong likelihood for 6 distinct stages of cognitive processing during trials, with an additional stage for non-rotated conditions. The cognitive model predicted intra-trial mental activity patterns that project well onto the processing stages, while explaining the additional stage as a marker of non-spatial shortcut use. Thereby, this combined methodology provided substantially more information than either method by itself and suggests conclusions for cognitive processing in general.
•HsMM-EEG emerges as a powerful method to explore cognitive structure in EEG data by extracting clear-cut processing stages.•Cognitive modelling with ACT-R allows functional assumptions of intra-trial processes.•Both methods were applied to a mental rotation task well-suited for exploring spatial reasoning and strategy choice.•Model predictions correspond well to HsMM-EEG stages and allow insights not possible by either method alone.
Human memory is a complex system that works in associative ways: Reading a cue word can lead to the recollection of associated concepts. The network structure of memory recall patterns has been shown ...to contain insights about a wide variety of cognitive phenomena, including language acquisition. However, most current network approaches use pairwise connections, i.e. links between only two words at a time. This ignores the possibility that more than two concept representations might be simultaneously associated in memory. We overcome this modelling limitation by introducing cognitive hypergraphs as models of human memory. We model memory recall patterns through word associations from the Small World of Words project for N=6003 concepts (Study 1) and for N=497 concepts (Study 2). In each study we represent word associations as either a pairwise network or a hypergraph. By combining psycholinguistic norms and network centrality measures with machine learning, we quantitatively investigate whether there is any benefit to using the hypergraph model over a pairwise network in predicting test-based age of acquisition norms in children up to age 9 years (Study 1) or normative learning in toddlers up to age 30 months (Study 2, based on CHILDES data). We show that cognitive hypergraphs capture more information than pairwise networks from the same data: Cognitive hypergraphs are considerably more powerful than pairwise networks at predicting age of acquisition trends in toddlers, children and teenagers. Our studies showcase how novel approaches merging artificial intelligence and higher-order interactions can help us understand cognitive development.
We introduce mindset streams for assessing ways of bridging two target concepts in concept maps. We focus on behavioural forma mentis networks (BFMN), which map the associative and affective ...dimensions of memory recalls. Inspired by trains of thoughts taking several paths to link ideas, mindset streams are defined as BFMN subgraphs induced by all shortest paths between two target concepts, e.g. all recalls in shortest paths bridging “math” and “learning”. These streams quantify the following features of the mindset encoded in a BFMN: (i) semantic content (i.e. which ideas mediate connections between targets?), (ii) valence coherence/conflict (i.e. are connections mediated by entwining ideas perceived negatively, positively or neutrally?), and (iii) semantic relevance (i.e. are the bridges between targets peripheral or central for the connectivity/betweenness of the BFMN?). We investigate mindset streams between ‘maths”/“physics” and key motivational aspects of learning (“fun”, “work”, “failure”) in two BFMNs, encoding how 159 students and 59 experts perceived and associated concepts about Science Technology Engineering and Maths (STEM), respectively. Statistical comparisons against configuration models show that high schoolers bridge “maths” and “fun” only through overabundant levels of valence-conflicting associations, contrasting negatively perceived domain knowledge with peer-related positive experiences. This conflict is absent in the researchers’ mindset stream, which rather bridges “math” and “fun” through positive, science-related associations. The mindset streams of both groups bridge “maths” and “physics” to “work” through mostly positive career-related jargon. Students’ mindset streams of “failure” and “math”/“physics” are dominated by negative associations with test anxiety, whereas researchers integrate “failure” and “math”/“physics” in semantically richer and more positive contexts, denoting failure itself as a cornerstone of STEM learning. We discuss our findings and future research directions in view of relevant psychology/education literature.
•With few exceptions, multitasking is less efficient than single-task performance.•Cognitive and attention resources needed for multitasking are immature in children.•Media multitasking does not ...cause deficits in developing attention processes.•Practice with action video games can have a positive impact on attention systems.•Multitasking may be associated with procedural rather than declarative learning processes.•Neuroergonomics and cognitive modelling will aid design of optimal work environments.
Current work, play, and learning environments require multitasking activities from children, adolescents and adults. Advances in web-enabled and multi-function devices have created a perceived need to stay “wired” to multiple media sources. The increased demand that these activities place on information processing resources has raised concerns about the quality of learning and performance under multitasking conditions. Young children, whose attention systems and executive functions are immature, are seen to be especially at risk. To evaluate these concerns the costs and benefits of “everyday” multitasking (e.g., driving, studying, multimedia learning) are examined in relation to the classic experimental literatures on divided attention in task-switching and dual-task performance. These literatures indicate that multitasking is almost always less efficient (time, accuracy) and can result in a more superficial learning than single-task performance. Alternatively, when the cognitive, perceptual, and response requirements of the tasks are controlled by the individual, when learning platforms are developmentally appropriate, and when practice is permitted, multitasking strategies can not only be successful but can result in enhanced visual and perceptual skills and knowledge acquisition. Future progress will come from advances in cognitive and computational modelling, from training attention and brain networks, and from the neuroergonomic evaluation of performance that will enable the design of work and learning environments that are optimized for multitasking.
•For automated vehicles, understanding driver state and display design is critical.•36 participants experienced takeovers in a simulator with induced anger or neutral.•Anger influenced takeover ...quality.•Display urgency influenced takeover response time.•Response time behavior was modelled using the QN-MHP and validated.
As semi-automated vehicles become more available to the general public, it is important to investigate human factors, including both the driver side and the interface side. Despite much research on semi-automated vehicles, little research has conducted considering both driver states and takeover request display design. The present study investigated the effects of drivers’ affective states and auditory display urgency on takeover response time and performance quality. Thirty-six participants experienced takeover scenarios in a semi-automated vehicle using a driving simulator, while playing an online game. For takeover quality, angry drivers drove faster, took longer to change lanes and had lower steering wheel angles than neutral drivers, which made riskier driving. However, there was no difference in eye glance behaviors. Higher frequency and more repetitions of the auditory displays led to faster takeover reaction times, but there was no time difference between angry and neutral drivers. Drivers’ response time to takeover displays from both affect groups was modelled using the QN-MHP framework, which resulted in a R2 of 0.505 with the empirical data collected. In sum, results suggest that drivers’ anger state influenced takeover quality, while display urgency influenced takeover response time. This study is expected to make a significant contribution to research on the influence of emotion, specifically, anger on takeover performance in semi-automated vehicles as well as to the takeover display design.
Gambling disorder is a behavioral addiction that negatively impacts personal finances, work, relationships and mental health. In this pre-registered study (https://osf.io/5ptz9/) we investigated the ...impact of real-life gambling environments on two computational markers of addiction, temporal discounting and model-based reinforcement learning. Gambling disorder is associated with increased temporal discounting and reduced model-based learning. Regular gamblers (n = 30, DSM-5 score range 3-9) performed both tasks in a neutral (café) and a gambling-related environment (slot-machine venue) in counterbalanced order. Data were modeled using drift diffusion models for temporal discounting and reinforcement learning via hierarchical Bayesian estimation. Replicating previous findings, gamblers discounted rewards more steeply in the gambling-related context. This effect was positively correlated with gambling related cognitive distortions (pre-registered analysis). In contrast to our pre-registered hypothesis, model-based reinforcement learning was improved in the gambling context. Here we show that temporal discounting and model-based reinforcement learning are modulated in opposite ways by real-life gambling cue exposure. Results challenge aspects of habit theories of addiction, and reveal that laboratory-based computational markers of psychopathology are under substantial contextual control.
The decision of whether to cross a road or wait for a car to pass, humans make frequently and effortlessly. Recently, the application of drift-diffusion models (DDMs) on pedestrians’ decision-making ...has proven useful in modelling crossing behaviour in pedestrian–vehicle interactions. These models consider binary decision-making as an incremental accumulation of noisy evidence over time until one of two choice thresholds (to cross or not) is reached. One open question is whether the assumption of a kinematics-dependent drift-diffusion process, which was made in previous pedestrian crossing DDMs, is justified, with DDM-parameters varying over time according to the developing traffic situation. It is currently unknown whether kinematics-dependent DDMs provide a better model fit than conventional DDMs, which are fitted per condition. Furthermore, previous DDMs have not considered reaction times for the not-crossing option. We address these issues by a novel experimental design combined with modelling. Experimentally, we use a 2-alternative-forced-choice paradigm, where participants view videos of approaching cars from a pedestrian’s perspective and respond whether they want to cross before the car or to wait until the car has passed. Using these data, we perform thorough model comparison between kinematics-dependent and condition-wise fitted DDMs. Our results demonstrate that condition-wise fitted DDMs can show better model fits than kinematics-dependent DDMs as reflected in the mean-squared-errors. The condition-wise fitted models need considerably more parameters, but in some cases still outperform kinematics-dependent DDMs in measures that penalize the parameter number (e.g., Akaike information criterion). Introducing a starting point bias provides support for the novel hypothesis of rapid early evidence build-up from the initial view of the vehicle distance. The drift rates obtained for the condition-wise fitted models align with the assumptions in the kinematics-dependent models, confirming that pedestrians’ decision processes are kinematics-dependent. However, the partial preference for condition-wise fitted models in the model selection suggests that the correct form of kinematics-dependence has not yet been identified for all DDM-parameters, indicating room for improvement of current pedestrian crossing DDMs. Developing more accurate models of human cognitive processes will likely facilitate autonomous vehicles to understand pedestrians’ intentions as well as to show unambiguous human-like behaviour in future traffic interactions with humans.
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•Pedestrians’ road-crossing & waiting decisions measured with forced-choice paradigm.•Kinematics-dependent and condition-wise fitted drift-diffusion-models are compared.•Condition-wise fitted DDMs perform best in MSE and AIC, but not in BIC.•Cond.-wise fitted parameters confirm: crossing decisions are kinematics-dependent.•New hypothesis: Rapid early evidence buildup from first look at vehicle distance.
We propose a nonmonotonic Description Logic of typicality able to account for the phenomenon of the combination of prototypical concepts. The proposed logic relies on the logic of typicality
, whose ...semantics is based on the notion of rational closure, as well as on the distributed semantics of probabilistic Description Logics, and is equipped with a cognitive heuristic used by humans for concept composition.
We first extend the logic of typicality
by typicality inclusions of the form
, whose intuitive meaning is that 'we believe with degree
about the fact that typical Cs are Ds'. As in the distributed semantics, we define different scenarios containing only some typicality inclusions, each one having a suitable probability. We then exploit such scenarios in order to ascribe typical properties to a concept
obtained as the combination of two prototypical concepts. We also show that reasoning in the proposed Description Logic is ExpTime-complete as for the underlying standard Description Logic
.
Celotno besedilo
Dostopno za:
BFBNIB, DOBA, GIS, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, UILJ, UKNU, UL, UM, UPUK
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