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Translating upwards: linking the neural and social sciences via neuroeconomics

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Abstract

The social and neural sciences share a common interest in understanding the mechanisms that underlie human behaviour. However, interactions between neuroscience and social science disciplines remain strikingly narrow and tenuous. We illustrate the scope and challenges for such interactions using the paradigmatic example of neuroeconomics. Using quantitative analyses of both its scientific literature and the social networks in its intellectual community, we show that neuroeconomics now reflects a true disciplinary integration, such that research topics and scientific communities with interdisciplinary span exert greater influence on the field. However, our analyses also reveal key structural and intellectual challenges in balancing the goals of neuroscience with those of the social sciences. To address these challenges, we offer a set of prescriptive recommendations for directing future research in neuroeconomics.

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Figure 1: The disciplinary connectivity of neuroeconomics research.
Figure 2: Interdisciplinary research groups occupy more central positions in the community of neuroeconomists.
Figure 3: Literature usage of 'neuroeconomic*' and 'decision neuroscience'.
Figure 4: Knowledge domains in the neuroeconomics literature.

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Acknowledgements

We thank M. Fan and H. Gold for assistance in data collection. For comments and discussions, we thank A. Beaulieu, M. Carter, L. Harris, L. Leydesdorff, P. Mehta, I. Rafols, C. Reeck, M. van Overveld, participants in the annual meetings of the Society for Neuroeconomics and anonymous reviewers. Funding for this research comes from an Incubator Award from the Duke Institute for Brain Sciences (S.A.H), a US National Institutes of Mental Health National Research Service Award F31-086255 (J.A.C.), the Erasmus Research Institute of Management (C.L. and A.S.), and the Open Research Area programme from the European Science Foundation (NESSHI 464-10-029; C.L., A.S. and P.W.).

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Correspondence to Ale Smidts or Scott A. Huettel.

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Supplementary information

Supplementary information S1 (box)

Method for the overlay of neuroeconomics publications on the map of science (PDF 104 kb)

Supplementary information S2 (box)

Choice of two interdisciplinary fields for comparison with neuroeconomics (PDF 160 kb)

Supplementary information S3 (figure)

Overlay maps of science for two reference fields (PDF 761 kb)

Supplementary information S4 (figure)

Literature usage of “neuroeconomic*” and “decision neuroscience” on ISI Web of Knowledge (PDF 271 kb)

Supplementary information S5 (box)

Literature search in neuroeconomics review articles (PDF 196 kb)

Supplementary information S6 (table)

Neuroeconomics review articles (PDF 141 kb)

Supplementary information S7 (box)

Literature search in economics articles (PDF 154 kb)

Supplementary information S8 (box)

Steps for the semantic network analysis of abstracts (PDF 161 kb)

Supplementary information S9 (figure)

Term map of economic articles making reference to biology (PDF 418 kb)

Supplementary information S10 (box)

Survey of the social network of neuroeconomists (PDF 173 kb)

Supplementary information S11 (box)

Identification of communities in the social network of neuroeconomists (PDF 153 kb)

Related links

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FURTHER INFORMATION

John A. Clithero's homepage

Duke Center for Interdisciplinary Decision Sciences

Erasmus Center for Neuroeconomics

NESSHI

Society for Neuroeconomics

Glossary

Community detection

(Also known as cluster analysis). The identification of groups of relatively tightly connected nodes in a network on the basis of an algorithmic analysis of the graph formed by the nodes and edges.

Connected component

In a network, a group of nodes that are all connected either directly or through other nodes.

Expected value

The weighted, probabilistic average of all possible values for an uncertain reward.

Natural language processing

A set of methods from computational linguistics to extract meaningful features (such as the language or the topic) of a corpus.

Out-of-sample predictive power

When fitting a model to data, the predictive power — or generalizability — of that model can be tested on data not used to estimate the model (that is, out-of-sample data).

Semantic network analytic

Application of the methods and tools of network analysis to textual data; it creates networks based on semantic relationships or co-occurrences of terms in a text corpus.

Temporal discounting

(Also known as delay discounting).The tendency to reduce the subjective value associated with rewards as the delay until their receipt increases.

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Levallois, C., Clithero, J., Wouters, P. et al. Translating upwards: linking the neural and social sciences via neuroeconomics. Nat Rev Neurosci 13, 789–797 (2012). https://doi.org/10.1038/nrn3354

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