Biology subjects

Fabio, C.

Publications and source records attributed to Fabio, C..

5 recordsLinked to original sources

bioRxiv2024

Evidence for systematic - yet task- and motor-contingent - rhythmicity of auditory perceptual judgements

Numerous studies advocate for a rhythmic mode of perception. However, the evidence in the context of auditory perception remains inconsistent. We propose that the divergent conclusions drawn from previous work stem from conceptual and methodological issues. These include ambiguous assumptions regarding the origin of perceptual rhythmicity, variations in listening tasks and attentional demands, differing analytical approaches, and the reliance on fixed participant samples for statistical testing. To systematically address these points, we conducted a series of experiments in which human participants performed auditory tasks involving monaural target sounds presented against binaural white noise backgrounds, while also recording eye movements. These experiments varied in whether stimuli were presented randomly or required motor initialization by the participants, the necessity of memory across trials and the manipulation of attentional demands across modalities. Our findings challenge the notion of universal rhythmicity in hearing, but support the existence of paradigm- and ear-specific fluctuations in perceptual sensitivity and response bias that emerge at multiple frequencies. Notably, the rhythmicity for sounds in the left and right ears appears to be largely independent among participants, and the strength of rhythmicity in behavioural data is possibly linked to oculomotor activity and attentional requirements of the task. Overall, these results may help to resolve conflicting conclusions drawn in previous work and provide specific avenues for further studies into the rhythmicity of auditory perception.

neuroscience↗

bioRxiv2022

Alpha oscillations reflect similar mapping mechanisms for localizing touch on hands and tools

Numerous studies have suggested that tools become incorporated into a representation of our body. A prominent hypothesis suggests that our brain re-uses body-based computations when we use tools. However, little is known about how this is implemented at the neural level. Here we used the ability to localize touch on both tools and body parts as a case study to fill this gap. Neural oscillations in the alpha (8-13 Hz) and beta (15-25 Hz) frequency bands are involved in mapping touch on the body in distinct reference frames. Alpha activity reflects the mapping of touch in external coordinates, whereas beta activity reflects the mapping of touch in skin-centered coordinates. Here, we aimed at pinpointing the role of these oscillations during tool-extended sensing. We recorded participants oscillatory activity while tactile stimuli were applied to either hands or the tips of hand-held rods. The posture of the hands/tool-tips was uncrossed or crossed at participants body midline in order for us to disentangle brain responses related to different coordinate systems. We found that alpha-band activity was modulated similarly across postures when localizing touch on hands and on tools, reflecting the position of touch in external space. Source reconstruction also indicated a similar network of cortical regions involved for tools and hands. Our findings strongly suggest that the brain uses similar oscillatory mechanisms for mapping touch on the body and tools, supporting the idea of neural processes being repurposed for tool-use. SIGNIFICANCE STATEMENTTool use is one of the defining traits of humankind. Tools allow us to explore our environment and expand our sensorimotor abilities. A prominent hypothesis suggests that our brain re-uses body-based neural processing to swiftly adapt to the use of tools. However, little is known about how this is implemented at the neural level. In the present study we used the ability to map touch on both tools and body parts as a case study to fill this gap. We found that the brain uses similar oscillatory mechanisms for mapping touch on a hand-held tool and on the body. These results provide novel and compelling support to the idea that neural processes devoted to body-related information are re-purposed for tool-use.

neuroscience↗

bioRxiv2021

A somatosensory computation that unifies bodies and tools

It is often claimed that tools are embodied by the user, but whether the brain actually repurposes its body-based computations to perform similar tasks with tools is not known. A fundamental computation for localizing touch on the body is trilateration. Here, the location of touch on a limb is computed by integrating estimates of the distance between sensory input and its boundaries (e.g., elbow and wrist of the forearm). As evidence of this computational mechanism, tactile localization on a limb is most precise near its boundaries and lowest in the middle. We show that the brain repurposes trilateration to localize touch on a tool. In a large sample of participants, we found that localizing touch on a tool produced the signature of trilateration, with highest precision close to the base and tip of the tool. A computational model of trilateration provided a good fit to the observed localization behavior. To further demonstrate the computational plausibility of repurposing trilateration, we implemented it in a three-layer neural network that was based on principles of probabilistic population coding. This network determined hit location in tool-centered coordinates by using a tools unique pattern of vibrations when contacting an object. Simulations demonstrated the expected signature of trilateration, in line with the behavioral patterns. Our results have important implications for how trilateration may be implemented by somatosensory neural populations. We conclude that trilateration is a fundamental spatial computation that unifies limbs and tools.

neuroscience↗

bioRxiv2021

Alpha oscillations are involved in localizing touch on hand-held tools

The sense of touch is not restricted to the body but can also extend to external objects. When we use a hand-held tool to contact an object, we feel the touch on the tool and not in the hand holding the tool. The ability to perceive touch on a tool actually extends along its entire surface, allowing the user to accurately localize where it is touched similarly as they would on their body. While the neural mechanisms underlying the ability to localize touch on the body have been largely investigated, those allowing to localize touch on a tool are still unknown. We aimed to fill this gap by recording the EEG signal of participants while they localized tactile stimuli on a hand-held rod. We focused on oscillatory activity in the alpha (7-14 Hz) and beta (15-30 Hz) range, as they have been previously linked to distinct spatial codes used to localize touch on the body. Beta activity reflects the mapping of touch in skin-based coordinates, whereas alpha activity reflects the mapping of touch in external space. We found that alpha activity was solely modulated by the location of tactile stimuli applied on a hand-held rod. Source reconstruction suggested that this alpha power modulation was localized in a network of fronto-parietal regions previously implicated in higher-order tactile and spatial processing. These findings are the first to implicate alpha oscillations in tool-extended sensing and suggest an important role for processing touch in external space when localizing touch on a tool.

neuroscience↗

bioRxiv2020

A neural surveyor in somatosensory cortex

Perhaps the most recognizable sensory map in all of neuroscience is the somatosensory homunculus. Though it seems straightforward, this simple representation belies the complex link between an activation in a somatotopic map and the associated touch location on the body. Any isolated activation is spatially ambiguous without a neural decoder that can read its position within the entire map, but how this is computed by neural networks is unknown. We propose that the somatosensory system implements multilateration, a common computation used by surveying and GPS systems to localize objects. Specifically, to decode touch location on the body, multilateration estimates the relative distance between the afferent input and the boundaries of a body part (e.g., the joints of a limb). We show that a simple feedforward neural network, which captures several fundamental receptive field properties of cortical somatosensory neurons, can implement a Bayes-optimal multilateral computation. Simulations demonstrated that this decoder produced a pattern of localization variability between two boundaries that was unique to multilateration. Finally, we identify this computational signature of multilateration in actual psychophysical experiments, suggesting that it is a candidate computational mechanism underlying tactile localization.

neuroscience↗