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Brain Structure and Function

, Volume 224, Issue 9, pp 3291–3308 | Cite as

Predictive coding of action intentions in dorsal and ventral visual stream is based on visual anticipations, memory-based information and motor preparation

  • Simona MonacoEmail author
  • Giulia Malfatti
  • Alessandro Zendron
  • Elisa Pellencin
  • Luca Turella
Original Article

Abstract

Predictions of upcoming movements are based on several types of neural signals that span the visual, somatosensory, motor and cognitive system. Thus far, pre-movement signals have been investigated while participants viewed the object to be acted upon. Here, we studied the contribution of information other than vision to the classification of preparatory signals for action, even in the absence of online visual information. We used functional magnetic resonance imaging (fMRI) and multivoxel pattern analysis (MVPA) to test whether the neural signals evoked by visual, memory-based and somato-motor information can be reliably used to predict upcoming actions in areas of the dorsal and ventral visual stream during the preparatory phase preceding the action, while participants were lying still. Nineteen human participants (nine women) performed one of two actions towards an object with their eyes open or closed. Despite the well-known role of ventral stream areas in visual recognition tasks and the specialization of dorsal stream areas in somato-motor processes, we decoded action intention in areas of both streams based on visual, memory-based and somato-motor signals. Interestingly, we could reliably decode action intention in absence of visual information based on neural activity evoked when visual information was available and vice versa. Our results show a similar visual, memory and somato-motor representation of action planning in dorsal and ventral visual stream areas that allows predicting action intention across domains, regardless of the availability of visual information.

Keywords

Functional magnetic resonance imaging (fMRI) Multivoxel pattern analysis (MVPA) Humans Actions Predictive coding Vision 

Notes

Acknowledgements

The authors would like to thank Pietro Chiesa for technical support, Jason Gallivan for providing the EBA and LO localizers, Angelika Lingnau for providing the MT localizer, and Egidio Malfatti for help with building the set-up for the LOtv localizer.

Funding

This project has received funding from the Ministero dell’istruzione, Universita’ e Ricerca under the Futuro in Ricerca 2013 grant, project RBFR132BKP to Luca Turella, and from the European Union’s Horizon 2020 research and innovation programme under the Marie Sklodowska-Curie grant agreement No 703597 to Simona Monaco.

Compliance with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in this study were in accordance with the ethical standards of the Human Research Ethics Committee of the University of Trento (protocol 2016-021) and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Supplementary material

429_2019_1970_MOESM1_ESM.docx (283 kb)
Supplementary material 1 (DOCX 282 kb)

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Authors and Affiliations

  1. 1.Center for Mind/Brain SciencesUniversity of TrentoMattarelloItaly
  2. 2.Department of Psychology and Cognitive ScienceUniversity of TrentoRoveretoItaly

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