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Whitford, T. J.

Publications and source records attributed to Whitford, T. J..

2 recordsLinked to original sources

Sensory prediction errors predict motor prediction errors

When sensory inputs can be predicted by an organisms own actions or external environmental cues, neural activity is often attenuated compared to sensory inputs that are unpredictable. We have recently demonstrated that attenuation to predictable inputs is also observed when stimulating the motor system with transcranial magnetic stimulation (TMS). Akin to sensory attenuation, predictable TMS excites the motor system less effectively than unpredicted TMS. However, it remains unclear whether these motor prediction signals are related to, or even dependent on, sensory prediction. Using dual-site TMS to target two brain regions, we arranged different warning cues to predict different regions of stimulation and measured motor attenuation using motor-evoked potentials. We found that expecting TMS over the motor cortex produced stronger attenuation than expecting TMS over a non-motor region, confirming that the attenuation observed is directly linked to activation of the motor system and not due to the sensory by-products of TMS. Using combined TMS-EEG, we measured motor attenuation with motor-evoked potentials, and simultaneously measured sensory attenuation to the sound of TMS (a coil "click") with auditory-evoked potentials. We found that both motor and auditory potentials were attenuated to predictable TMS compared to unpredictable TMS. Critically, the magnitude of auditory attenuation predicted the magnitude of motor attenuation. Our results reveal a close correspondence between error processing in the sensory and motor systems. The findings provide compelling evidence that predictive coding is governed by domain-general properties across distinct neural systems, which share common mechanisms responsible for all forms of predictive learning.

neuroscience↗

The functional anatomy of self-generated and predictable speech

Sensory attenuation refers to the cortical suppression of self-generated sensations relative to externally-generated sensations. This attenuation of cortical responsiveness is the result of internal forward models which make precise predictions about forthcoming sensations. Forward models of sensory attenuation in the auditory domain are thought to operate along auditory white matter pathways such as the arcuate fasciculus and the frontal aslant. The aim of this study was to investigate whether brain regions that are structurally connected via these white matter pathways are also effectively connected during overt speech, as well as as when listening to externally-generated speech that is temporally predictable via a visual cue. Using Electroencephalography (EEG) and Dynamic Causal Modelling (DCM) we investigated network models that link the primary auditory cortex to Wernickes and Brocas area either directly or indirectly through Geschwinds territory, which are structurally connected via the arcuate fasciculus. Connections between Brocas and supplementary motor area, which are structurally connected via the frontal aslant, were also included. Our results revealed that bilateral areas interconnected by indirect and direct pathways of the arcuate fasciculus, in addition to regions interconnected by the frontal aslant best explain the EEG responses to both self-generated speech, and speech that is externally-generated but temporally predictable. These findings indicate that structurally connected brain regions thought to be involved in auditory attenuation are also effectively connected. Critically, our findings expand on the notion of internal forward models, whereby sensory consequences of our actions are internally predicted and reflected in reduced cortical responsiveness to these sensations.\n\nHighlightsO_LIEffective connectivity of auditory attenuation to self-generated and predictable speech\nC_LIO_LIEEG and DCM were used to investigate several plausible network models\nC_LIO_LIStructurally connected brain areas of auditory attenuation are effectively connected\nC_LIO_LIInternal forward models modulate self-generated and predictable speech\nC_LI

neuroscience↗