What Do Cognitive Networks Do? Simulations of Spoken Word Recognition Using the Cognitive Network Science Approach

Cognitive network science is an emerging approach that uses the mathematical tools of network science to map the relationships among representations stored in memory to examine how that structure might influence processing. In the present study, we used computer simulations to compare the ability of a well-known model of spoken word recognition, TRACE, to the ability of a cognitive network model with a spreading activation-like process to account for the findings from several previously published behavioral studies of language processing. In all four simulations, the TRACE model failed to retrieve a sufficient number of words to assess if it could replicate the behavioral findings. The cognitive network model successfully replicated the behavioral findings in Simulations 1 and 2. However, in Simulation 3a, the cognitive network did not replicate the behavioral findings, perhaps because an additional mechanism was not implemented in the model. However, in Simulation 3b, when the decay parameter in spreadr was manipulated to model this mechanism the cognitive network model successfully replicated the behavioral findings. The results suggest that models of cognition need to take into account the multi-scale structure that exists among representations in memory, and how that structure can influence processing.

Cognitive Network Reconstruction in Opioid Users Compared To Non-Users: Topological Analysis of Cognitive Function Through Graph Model and Centrality Measures

Cognitive dysfunction related to opioid use disorder (OUD) requires investigation of the interconnected network of cognitive domains through behavioral experiments and graph data modeling. Here, we conducted n-back, selective and divided attention, and Wisconsin card sorting tests and then reconstructed the interactive cognitive network of subscales or domains for opioid users and non-users to identify the most central cognitive functions and their connections using graph model analysis. Then, each network was analyzed topologically based on the betweenness and closeness centrality measures. Results from the opioid users’ network show that in the divided attention module, the reaction time and the number of commission errors were the most central subscales of cognitive function. Whereas in non-users, the number of correct responses and commission errors were the most central cognitive measure. These findings corroborate that opioid users show impaired divided attention as higher reaction time and errors in performing the tasks. Divided attention is the most central cognitive function in both OUD subjects and non-users, although differences were observed between the subscales of the two groups. Therefore, divided attention is a promising target for future cognitive therapies, treatments and rehabilitation as its improvement may lead to an enhancement of overall cognitive domain performance.

Dysconnectivity between auditory-cognitive network associated with auditory GABA and glutamate levels in presbycusis patients

Accumulating studies suggest an interaction between presbycusis (PC) and cognitive impairment, which may be explained by the cognitive-ear link to a large extent. However, the neurophysiological mechanisms underlying this link are largely unknown. Here, 51 PC patients and 51 well-matched healthy controls were recruited. We combined resting-state functional MRI and edited magnetic resonance spectroscopy to investigate changes of intra- and inter-network functional connectivity and their relationships with auditory gamma-aminobutyric acid (GABA) and glutamate (Glu) levels and cognitive impairment in PC. Our study confirmed the plastic model of cognitive-ear link at the level of the large-scale brain network, including the dysconnectivity within high-order cognitive networks and between the auditory-cognitive network and overactivation between cognitive networks dependent on hearing loss, which was closely related to the cognitive impairment of PC patients. Moreover, GABA and Glu levels in the central auditory processing were abnormal in patients with PC. Importantly, reduction of GABA-mediated inhibition plays a crucial role in a dysconnectivity between the auditory-cognitive network, which may be neurochemical underpinnings of functional remodeling of cognitive-ear link in PC. Modulation of GABA neurotransmission may enable the development of new therapeutic strategies for the cognitive impairment of PC patients. Keywords: presbycusis, cognitive impairment, cognitive-ear link, GABA, resting-state networks.

Using a three-layered social-cognitive network analysis framework for understanding online collaborative discussions

Understanding the relationship between social and cognitive engagement has critical implications for collaborative learning theory, pedagogy and analytics. This study proposed a three-layered social-cognitive network analysis framework for examining the relationship between students’ social and cognitive engagement from summative, epistemic and micro-level perspectives within online collaborative discussions. A multi-method approach was used, consisting of social network analysis, quantitative content analysis, statistical analysis, epistemic network analysis and social-cognitive network visualisation. The results showed that from a summative perspective, students’ social participatory roles were critical indicators of their level of cognitive engagement. From an epistemic perspective, socially active students tended to shift towards more group-level cognitive structure, while inactive students showed a decreasing individual-level cognitive structure throughout the discussion duration. From a micro-level perspective, a large proportion of individual students showed continually changing social participatory roles with fluctuating cognitive engagement levels. The findings have implications for collaborative learning theory, pedagogy support and learning analytics. Implications for practice or policy: Researchers can use the three-layered social-cognitive network analysis framework to examine student engagement. Instructors should encourage student agency for facilitating high-quality online collaborative discussion. Instructors should consider students’ different engagement levels in online discussions.

Mapping the perception of “complex systems” across educational levels through cognitive network science

Despite recent efforts promoting complexity science across different educational contexts, there is little literature about how school students perceive complex systems. This research report aims to quantify the current perception of “complex systems” among 159 Italian high school students, providing a data-informed map of the general attitude and knowledge structure towards complexity through tools from cognitive network science. Adopting the framework of forma mentis networks, i.e. conceptual networks where words are related by memory recall patterns and labelled according to their positive/negative/neutral sentiment, the students’ mindset or forma mentis towards “complex systems” was reconstructed and compared to the mindset of 59 international postgraduate researchers working on complexity topics. Despite studying multiple scientific disciplines at the same time, students perceived complexity as an abstract and negative entity, strongly associated to “complicated” and “difficult” whereas researchers identified complexity as a positive concept, with a stronger STEM-oriented, multidisciplinary connotation towards mathematics, physics, biology and other scientific disciplines. This comparison was discussed in light of relevant literature about silo mentality in education. Mindset reconstruction through forma mentis networks opens novel ways for quantifying current perceptions of “complexity science” in mainstream educational curricula, suggesting key challenges for developing complexity education through the mindsets of complexity researchers.

Revealing semantic and emotional structure of suicide notes with cognitive network science

Understanding how people who commit suicide perceive their cognitive states and emotions represents an important open scientific challenge. We build upon cognitive network science, psycholinguistics and semantic frame theory to introduce a network representation of suicidal ideation as expressed in multiple suicide notes. By reconstructing the knowledge structure of such notes, we reveal interconnections between the ideas and emotional states of people who committed suicide through an analysis of emotional balance motivated by structural balance theory, semantic prominence and emotional profiling. Our results indicate that connections between positively- and negatively-valenced terms give rise to a degree of balance that is significantly higher than in a null model where the affective structure is randomized and in a linguistic baseline model capturing mind-wandering in absence of suicidal ideation. We show that suicide notes are affectively compartmentalized such that positive concepts tend to cluster together and dominate the overall network structure. Notably, this positive clustering diverges from perceptions of self, which are found to be dominated by negative, sad conceptual associations in analyses based on subject-verb-object relationships and emotional profiling. A key positive concept is “love”, which integrates information relating the self to others and is semantically prominent across suicide notes. The emotions constituting the semantic frame of “love” combine joy and trust with anticipation and sadness, which can be linked to psychological theories of meaning-making as well as narrative psychology. Our results open new ways for understanding the structure of genuine suicide notes and may be used to inform future research on suicide prevention.

Enhancement of Spectrum Sensing Performance via Cooperative Cognitive Radio ‎Networks at Low SNR

The inefficient use of spectrum is the key subject to overcome the upcoming spectrum crunch issue. This paper presents a study of performance of cooperative cognitive network via hard combining of decision fusion schemes. Simulation results presented different cooperative hard decision fusion schemes for cognitive network. The hard-decision fusion schemes provided different discriminations for detection levels. They also produced small values of Miss-Detection Probability at different values of Probability of False Alarm and adaptive threshold levels. The sensing performance was investigated under the influence of channel condition for proper operating conditions. An increase in the detection performance was achieved for cognitive users (secondary users) of the authorized unused dynamic spectrum holes (primary users) while operating in a very low signal-to-noise ratio with the proper condition of minimum total error rate.