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Review of Philosophy and Psychology

, Volume 7, Issue 2, pp 509–527 | Cite as

Belief Files in Theory of Mind Reasoning

  • Ágnes Melinda Kovács
Article

Abstract

Humans seem to readily track their conspecifics’ mental states, such as their goals and beliefs from early infancy. However, the underlying cognitive architecture that enables such powerful abilities remains unclear. Here I will propose that a basic representational structure, the belief file, could provide the foundation for efficiently encoding, and updating information about, others’ beliefs in online social interactions. I will discuss the representational possibilities offered by the belief file and the ways in which the repertoire of mental state reasoning is shaped by the characteristics of its constituents. A series of questions will be outlined concerning the representational skeleton of the belief file, sketching a possible structure that supports the rapid encoding and re-identification of belief related information (e.g., variables for the agent, as the belief holder and for the belief-content). After analyzing the possible limitations of the belief attribution system, I will examine some of its characteristics that might enable a flexibility that is often neglected. I will suggest that operations involving belief files are not impeded by the absence of precise first-person information regarding their contents. In fact, the system permits manipulations with “empty” belief files, allowing humans to ascribe beliefs to conspecifics based on little or no direct information regarding the content of the mental state. Such an analysis aims to advance our understanding of how spontaneous belief attribution may be performed, and to provide an insight into the possible mechanisms that allow humans to successfully navigate the social world.

Keywords

Object File Belief Attribution Shopping Cart Belief Inference Belief Representation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgments

This work was supported by the European Research Council under the European Union’s Seventh Framework Programme (FP7/2007-2013)/ERC starting grant (284236-REPCOLLAB).We thank E. Téglás, O. Mascaro, J. Michael, A. Major, G. Bródy, D. Kampis, M. Freundlieb, R. Shamsudheen, the CDC members and an anonymous reviewer for valuable comments on a previous version of the manuscript.

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Copyright information

© Springer Science+Business Media Dordrecht 2015

Authors and Affiliations

  1. 1.Cognitive Development CentreCentral European UniversityBudapestHungary

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