Mapping the Semantic Structure of Cognitive Neuroscience

Abstract

Abstract■ Cognitive neuroscience, as a discipline, links the biological systems studied by neuroscience to the processing constructs studied by psychology. By mapping these relations throughout the literature of cognitive neuroscience, we visualize the semantic structure of the discipline and point to directions for future research that will advance its integrative goal. For this purpose, network text analyses were applied to an exhaustive corpus of abstracts collected from five major journals over a 30-month period, including every study that used fMRI to investigate psychological processes. From this, we generate network maps that illustrate the relationships among psychological and anatomical terms, along with centrality statistics that guide inferences about network structure. Three terms-prefrontal cortex, amygdala, and anterior cingulate cortex-dominate the network structure with their high frequency in the literature and the density of their connections with other neuroanatomical terms. From network statistics, we identify terms that are understudied compared with their importance in the network (e.g., insula and thalamus), are underspecified in the language of the discipline (e.g., terms associated with executive function), or are imperfectly integrated with other concepts (e.g., subdisciplines like decision neuroscience that are disconnected from the main network). Taking these results as the basis for prescriptive recommendations, we conclude that semantic analyses provide useful guidance for cognitive neuroscience as a discipline, both by illustrating systematic biases in the conduct and presentation of research and by identifying directions that may be most productive for future research. ■

Revealing Relationships Among Cognitive Functions Using Functional Connectivity and a Large-Scale Meta-Analysis Database

“…Thus, our relational mapping of cognitive functions provides a whole picture of cognition which is feasible because it includes many known neurocognitive relationships. A study to survey relationships among cognitive functions whose aim was similar to ours was conducted using text analysis of neuroscience literature (Beam et al, 2014). In this study, the authors identified networks among 100 cognitive concepts, among 100 anatomical regions, and among combinations of both on the basis of the co-occurrences of the terms in the texts.…”

Choosing experiments to accelerate collective discovery

“…Variations of this network metaphor for knowledge have appeared in philosophy (9), social studies of science (10-12), artificial intelligence (13), complex systems research (14), and the natural sciences (7,15,16). Nevertheless, networks have rarely been used to measure scientific content (2,11,17,18) and never to evaluate the efficiency of scientific problem selection.In this paper, we build a model of scientific investigation that allows us to measure collective research behavior in a large corpus of scientific texts and then compare this inferred behavior with more and less efficient alternatives. We define an explicit objective function to quantify the efficiency of a research strategy adopted by the scientific community: the total number of experiments performed to discover a given portion of an unknown knowledge graph.…”

“…Variations of this network metaphor for knowledge have appeared in philosophy (9), social studies of science (10)(11)(12), artificial intelligence (13), complex systems research (14), and the natural sciences (7,15,16). Nevertheless, networks have rarely been used to measure scientific content (2,11,17,18) and never to evaluate the efficiency of scientific problem selection.…”

Predicting research trends with semantic and neural networks with an application in quantum physics

“…The authors used the network to draw connections between human innovation process and random walks. In the field of neuroscience, semantic networks have been used to map the landscape of the field [11,12]. Papers from the interdisciplinary journal PNAS have been used to investigate sociological properties such as inter-disciplinary research [13].…”