Decoding familiar visual object categories in the mu rhythm oscillatory response

Abstract

AbstractWhilst previous research has linked attenuation of the mu rhythm to the observation of specific visual categories, and even to a potential role in action observation via a putative mirror neuron system, much of this work has not considered what specific type of information might be coded in this oscillatory response when triggered via vision. Here, we sought to determine whether the mu rhythm contains content-specific information about the identity of familiar (and also unfamiliar) graspable objects. In the present study, right-handed participants (N=27) viewed images of both familiar (apple, wine glass) and unfamiliar (cubie, smoothie) graspable objects, whilst performing an orthogonal task at fixation. Multivariate pattern analysis (MVPA) revealed significant decoding of familiar, but not unfamiliar, visual object categories in the mu rhythm response. Thus, simply viewing familiar graspable objects may automatically trigger activation of associated tactile and/or motor properties in sensorimotor areas, reflected in the mu rhythm. In addition, we report significant attenuation in the central beta band for both familiar and unfamiliar visual objects, but not in the mu rhythm. Our findings highlight how analysing two different aspects of the oscillatory response – either attenuation or the representation of information content – provide complementary views on the role of the mu rhythm in response to viewing graspable object categories.HighlightsThe Mu Rhythm oscillation contains fine-grained information about the identity of familiar, graspable objects (but not unfamiliar)This study offers evidence of a possible oscillatory marker for cross-sensory effects involving sensorimotor and visual corticesDifferent analysis techniques (univariate vs multivariate) imply different conclusions about the role of specific oscillations in the perception of graspable objectsThe alpha rhythm at occipital sites shows greater attenuation for unfamiliar objects but better representation for familiar objects consistent with sharpening accounts of Predictive Processing