Navigating Concepts in the Human Mind Unravels the Latent Geometry of Its Semantic Space

Mechanisms for retrieving basic knowledge in the human mind are still unknown. Exploration is usually represented by cognitive units, i.e., concepts, linked together by associative relationships forming semantic networks. However, understanding how humans navigate such networks remains elusive, because the underlying topology of concepts cannot be observed directly, and only functional representations are accessible. Here, we overcome those limitations and show that the hypothesis of an underlying, latent geometry characterizing the human mind is plausible. We characterize this geometry by means of adequate descriptors for exploring and navigating dynamics, demonstrating that they can capture the differences between healthy subjects and patients at different stages of dementia. Our results provide the first fundamental step to develop a new unifying conceptual and computational framework that can be used to support the assessment of neurodegenerative diseases from language and semantic memory retrieval tasks, as well as helping develop targeted nonpharmacological therapies to maintain residual cognitive capacity.

Metaphors’ effect on off-line cognition: A preliminary study based on a reaction time task

The modality of apprehension and processing of metaphorical expressions in comparison with non-metaphorical ones has hitherto captivated numerous researchers in manifold fields of study, such as linguistics, psychology, and cognitive sciences. More specially, metaphors used in a one-sentence paragraph have been the subjects of many studies. However, cognitive functions of structural metaphors haven’t been entirely noteworthy in contrast with non-metaphorical expressions employed in textual context. In this study, the interrelationship between memory and conceptual metaphor in significant cognitive processes has been examined in a textual context. In this respect, the hypothesis, that conceptual metaphor as a value can assist with the recognition and recollection process and incorporate the quintessence of our cerebrations, has been put to test. To evaluate this assumption, the reaction time task is used. Each testable case has been subjected to analysis within two analogous contexts, in a metaphorical and non-metaphorical manner. Afterwards, terms were displayed, and the subjects needed to determine as swiftly as possible whether these vocabularies were exemplified or not. The results indicated that the terms pertaining to the schema and other terms included in metaphorical context would be processed faster than the one with non-metaphorical context. With regard to the obtained data, it seems that the conceptual metaphor generates semantic networks in the mind which will be more accessible to memory upon information retrieval.

K-clique percolation in free association networks. The mechanism behind the 7 ± 2 law?

It is important to reveal the mechanisms of propagation in different cognitive networks. In this study we discuss the k-clique percolation phenomenon on the free association networks including "English Small World of Words project" (SWOW-EN). We compare different semantic networks and networks of free associations for different languages. Surprisingly it turned out that k-clique percolation for all k < k c = (6 − 7) is possible on free association networks of different languages. Our analysis suggests the new universality patterns for a community organization of free association networks. We conjecture that our result can provide the qualitative explanation of the Miller’s 7 ± 2 rule for the capacity limit of working memory. The new model of network evolution extending the preferential attachment is suggested which provides the observed value of k c .

Cognitive Assessment of Knowledge Consolidation in a Course on the Diagnostic Evaluation of Learning Disorders in Psychology Students

This study explored the cognitive changes to long-term memory as a result of academic learning in 43 Psychology undergraduate students (91% female and 9% male). The participants carried out a conceptual definition task based on the Natural Semantic Networks technique. They defined ten target concepts related to the diagnostic evaluation of learning disorders using verbs, nouns, or adjectives as definers. After, students weighed the quality of each definer through a ten-point scale. The higher the score, the greater the conceptual relationship between the definer and the target. The data of this study (the schema behavior) was subjected to a computational simulation. Finally, the participants carried out a lexical decision task based on the semantic priming paradigm, they read pairs of words that may or may not be related to the measured knowledge schema. The task was to judge whether the second word in each pair was spelled correctly or incorrectly. The results indicated an increase in the level of conceptual accessibility derived from learning. In addition, the participants accessed their knowledge schema, first through general nodes and then retrieved through the more specific nodes. The neurocomputational activation pattern suggested that learning experiences changed the meaning of concepts for each participant along the course. At the end of the course, the students seemed to reach a schematic consolidation. The diagnosis of these cognitive characteristics through the formation of academic schemas can provide learning opportunities closer to each student’s cognitive profile to increase the effectiveness of instruction.

Using Denotational Mathematics for the Formal Description of Home UbiHealth Decision-Support Systems With Knowledge Flow

Home UbiHealth applications require support from decision-making and decision-support components. The decision process must possess a holistic perspective about the individual's healthcare condition and needs at home. Also, it shall consider and evaluate the available data, producing cognitive intelligence through appropriate processing. The present work describes a model for decision-making and decision-support in UbiHealth environments based on denotational mathematics. The decision-making component of the model provides the necessary actions to handle the connected devices. The decision-support component suggests actions to the user (physician). The decisions are drawn from a simulated operation of cooperating and co-existing semantic networks. The model is thoroughly described, and its positive impact is explained for all participating stakeholders including medical professionals, patients, information technology scientists, and technical personnel.

Small-world Structure in Children’s Featured Semantic Networks

Background: Knowing the development pattern of children’s language is applicable in developmental psychology. Network models of language are helpful for the identification of these patterns. Objectives: We examined the small-world properties of featured semantic networks of developing children. Materials & Methods: In this longitudinal study, the featured semantic networks of children aged 18-30 months were obtained using R software version 3.5.2 and the igraph software package. The data of 2000 English (British)-speaking children, half boy and half girls, were gathered from existing databases of MCDI (between 2000 and 2007) and McRae feature norms. The growth pattern of these networks was illustrated by graph measures. Comparing these measures with those of the reference random networks, the small-world structure can be examined. Results: To have a comparison between path length and clustering coefficient of featured semantic networks with those of random networks, we computed the Q quotient. The results showed that the values of the Q quotient at 18, 22, 26, and 30 months of age were all more than 1, which confirms the small-world characteristic of the networks. Conclusion: Featured semantic networks of children exhibited a small-world structure, in which there was a local structure in the form of clusters of words. For global access, some words act as bridges connecting semantically distant clusters. These networks possess small-world property from the early months of age. The small-world structure cannot be seen in the less dense networks built with a higher cut-off threshold.