Brain bases for navigating acoustic features
Billig, A. J.; Sedley, W.; Gander, P. E.; Kumar, S.; Lad, M. J.; Chait, M.; Mohammadi, Y.; Berger, J. I.; Griffiths, T.
Show abstract
Whether physical navigation shares neural substrates with mental travel in other behaviourally relevant domains is debated. With respect to sound, pure-tone working memory in humans elicits hippocampal as well as auditory cortical and inferior frontal activity, and rodent work suggests that hippocampal cells that usually track an animals physical location can also map to tone frequency when task-relevant. We generated a sound dimension based on the density of random-frequency tones in a stack, resulting in a percept ranging from low- ("beepy") to high-density ("noisy"). We established that unlike tone frequency, which listeners automatically associate with vertical position, this density dimension elicited no consistent spatial mapping. During functional magnetic resonance imaging, human participants held in mind the density of a series of tone stacks and, after a short maintenance period, adjusted further stacks to match the target ("navigation"). Density of the currently heard sound was represented most strongly in bilateral non-primary auditory cortex, specifically bilateral planum polare, while density of the maintained target was represented in right anterior hippocampus and left inferior temporal gyrus. Encoding and maintenance activity in bilateral hippocampus, inferior frontal gyrus, planum polare and posterior cingulate was positively associated with subsequent navigation success. Bilateral inferior frontal gyrus and hippocampus were among regions with elevated activity during adjustment, compared to a parity-judgment condition with closely matched acoustics and motor demands. Bilateral orbitofrontal cortex was more active when navigation was toward a target density than when participants adjusted density in a control condition with no particular target. We find that self-initiated travel along a non-spatial auditory dimension engages a brain system overlapping with that supporting physical navigation. Key PointsO_LIWork in rodents suggests that navigation in physical space and the active analysis of sounds share a neural substrate in the hippocampus, supporting the use of common computational mechanisms. C_LIO_LIWe examined the human brain system for navigation through an acoustic environment to a remembered target. C_LIO_LIIn addition to high-level auditory cortex we demonstrate involvement of the hippocampus in acoustic navigation along with other sites in frontal and cingulate cortex that also support physical navigation. C_LI
Matching journals
The top 4 journals account for 50% of the predicted probability mass.
Similar papers in this journal
- Metamodal Coupling of Vibrotactile and Auditory Speech Processing Systems Through Matched Stimulus Representations 96%
- Discovery, Interruption, and Updating of Auditory Regularities in Memory: Evidence from Low-Frequency Brain Dynamics in Human MEG 96%
- Task dependence of neural representations of scene attributes but not scene categories in the prefrontal cortex 96%
Similar papers in this journal
Similar papers in this journal
- Language beyond the language system: dorsal visuospatial pathways support processing of demonstratives and spatial language during naturalistic fast fMRI 97%
- Impulse perturbation reveals cross-modal access to sensory working memory through learned associations 97%
- Disentangling the roles of neocortical alpha/beta and hippocampal theta/gamma oscillations in human episodic memory formation 96%
Similar papers in this journal
- Spatial predictive context speeds up visual search by biasing local attentional competition 96%
- Abstract neural representations of category membership beyond information coding stimulus or response 96%
- Spatial and temporal context jointly modulate the sensory response within the ventral visual stream 96%
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.