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  Inferential Pitfalls in Decoding Neural Representations.

Popov, V., Ostarek, M., & Tenison, C. (2017). Inferential Pitfalls in Decoding Neural Representations. In G. Gunzelmann, A. Howes, T. Tenbrink, & E. Davelaar (Eds.), Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017) (pp. 961-966). Austin, TX: Cognitive Science Society.

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Item Permalink: https://hdl.handle.net/11858/00-001M-0000-002D-1B44-5 Version Permalink: https://hdl.handle.net/21.11116/0000-0002-C069-F
Genre: Conference Paper

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Popov_Ostarek_Tenison_2017.pdf (Publisher version), 555KB
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 Creators:
Popov, V.1, Author
Ostarek, Markus2, 3, Author           
Tenison, C.1, Author
Affiliations:
1Department of Psychology, Carnegie Mellon University, Pittsburgh, PA, USA, ou_persistent22              
2Psychology of Language Department, MPI for Psycholinguistics, Max Planck Society, ou_792545              
3International Max Planck Research School for Language Sciences, MPI for Psycholinguistics, Max Planck Society, Nijmegen, NL, ou_1119545              

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 Abstract: A key challenge for cognitive neuroscience is to decipher the representational schemes of the brain. A recent class of decoding algorithms for fMRI data, stimulus-feature-based encoding models, is becoming increasingly popular for inferring the dimensions of neural representational spaces from stimulus-feature spaces. We argue that such inferences are not always valid, because decoding can occur even if the neural representational space and the stimulus-feature space use different representational schemes. This can happen when there is a systematic mapping between them. In a simulation, we successfully decoded the binary representation of numbers from their decimal features. Since binary and decimal number systems use different representations, we cannot conclude that the binary representation encodes decimal features. The same argument applies to the decoding of neural patterns from stimulus-feature spaces and we urge caution in inferring the nature of the neural code from such methods. We discuss ways to overcome these inferential limitations.

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Language(s): eng - English
 Dates: 20172017
 Publication Status: Published online
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Title: The 39th Annual Conference of the Cognitive Science Society (CogSci 2017)
Place of Event: London, UK
Start-/End Date: 2017-07-26 - 2017-07-29

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Title: Proceedings of the 39th Annual Conference of the Cognitive Science Society (CogSci 2017)
Source Genre: Proceedings
 Creator(s):
Gunzelmann, G., Editor
Howes, A., Editor
Tenbrink, T., Editor
Davelaar, E., Editor
Affiliations:
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Publ. Info: Austin, TX : Cognitive Science Society
Pages: - Volume / Issue: - Sequence Number: - Start / End Page: 961 - 966 Identifier: -