D L Boyer

A N Srdic

S A Smirnov

D B Haidvogel

J Sommeria

Aspects of oscillatory along-shelf flow in the vicinity of an isolated submarine canyon

Selected results are presented from a closely coupled laboratory-numerical model study of the oscillatory flow of a linearly stratified fluid in the vicinity of an isolated submarine canyon. It is shown that the boundary condition applied along the model floor in the numerical experiments is critically important in having the numerical model simulate the laboratory experiments. Furthermore, it is shown that enhanced viscosities, which assure the numerical stability, must be applied with care because they may change the flow fields. A scaling argument is advanced which predicts the strength of the time-mean flow generated by the background current. The sensitivity of the flow field to changes in some of the system parameters is discussed. Finally, some initial results for flows generated in the presence of boundary turbulence are also discussed.

INTRODUCTION

In recent communications by three of us, comparisons of the flow fields obtained by a series of laboratory experiments and an associated numerical model were made with the goal of determining the degree to which laboratory experiments could be used in the development of numerical models of coastal currents; see Pérenne et al. 2001 and[START_REF] Boyer | Laboratory-numerical model comparisons of canyon flows: A parameter study[END_REF], henceforth designated as PHB and BHP, respectively. The motivations for these studies were based on (i) the recognition that current numerical models when applied to the same forcing and boundary conditions can provide enormously different results when strict one to one comparisons are made (see, Haidvogel & Beckmann (1998)); (ii) the difficulty of field programs providing adequate data in space and time to test the models; (iii) the understanding that most of the physics of the coastal oceans can be modeled to some degree in the laboratory; and (iv) the realization that modern data acquisition techniques such as Particle tracking velocimetry (PTV) and particle image velocimetry (PIV) can provide data sets having characteristics similar to those generated by numerical models.

The idealized physical system considered is given schematically in Figure 1. An annular coast (vertical), shelf (horizontal) and continental slope model, incised by a single submarine canyon, is placed in the central portion of a circular test cell; the deep ocean is between the continental slope and the outside wall of the test cell. The test cell is filled with a linearly stratified fluid whilst rotating and the initial background rotation is set at f /2, where f is the Coriolis parameter. After the fluid has reached the state of a solid-body rotation, experiments are initiated by modulating harmonically the test cell with an amplitude and a period T about the background rotation rate f /2.

A dimensional analysis shows that four dynamical (the Rossby, Ro; temporal Rossby, Ro t ; Burger, Bu; and Ekman, E, numbers) and five geometrical parameters characterize the system. Laboratory experiments for laminar flows have been conducted in a 1.8 m diameter test cell at Arizona State University in Tempe, Arizona, while transitional and fully developed turbulent flows employed the 13 m diameter tank of the Laboratoire des Ecoulements Geophysiques et Industriels (LEGI) in Grenoble, France. The Tempe, Arizona, and Grenoble, France, experiments used PTV and PIV, techniques, to obtain the time dependent horizontal velocity fields at selected levels. The numerical model employed was the Spectral Element Ocean Model (SEOM) described in Haidvogel & Beckmann (1999). The SEOM used a parameterized shear stress condition for most of the experiments and, to assure numerical stability, employed enhanced horizontal viscosities (i.e., 100 times larger than the kinematic viscosity of the water). Table 1 provides definitions of the various dimensionless parameters as well as their values and/or ranges for experiments in Tempe and Grenoble as well as in the ocean. This communication will touch briefly three aspects of the overall study. These include (i) the reasons why the numerical simulations in PHB do not fall within the error bars of the physical experiments, (ii) the sensitivity of the time mean flows to the changes in the system parameters, and (iii) a discussion of the preliminary experiments for turbulent flows emphasizing similarities and differences between their laminar counterparts.

SELECTED RESULTS

Variance between laboratory and numerical models

The PHB study exhibited substantial differences between the laboratory and numerical models. These model-model differences can be shown to owe to the lack of sufficient resolution of the Ekman layers along the model floor. Even with the Ekman layers adequately resolved, the numerical solutions are not totally satisfactory in that enhanced viscosities must be employed for both the parameterized shear stress and resolved Ekman layer (no-slip) boundary conditions. Presumably, if the resolution of the numerical models could be increased, the SEOM model would presumably give a satisfactory simulation of the flow. Figures 2a,b, c are illustrations of the velocity fields for (i) the laboratory experiments, (ii) the SEOM model using the shear stress parameterization and (iii) the SEOM model using the no slip condition, respectively. The SEOM model with the stress boundary conditions gives a good simulation qualitatively but on a closer look the quantitative differences are well above the error bars of the laboratory measurements. Additionally the laboratory vorticity and divergence fields are more symmetric about the canyon axis than the numerical model using the shear stress condition. Figures 2c for the SEOM model for which the no-slip condition has been applied, does a better job of simulating the across-axis symmetry of the observations. Furthermore the magnitudes of the maximum vorticity and divergence are correct within the uncertainties of the experiments.The real difficulty is that the numerics required enhanced horizontal viscosities to maintain the numerical stability. This viscosity coupled with the no-slip condition leads to thicker aerodynamic boundary layers along the canyon walls. Such layers, of course, are not found in nature. Presumably, given sufficient resolution, the SEOM model should be able to satisfactorily simulate the laboratory experiment.

Sensitivity to parameter variations

The PHB study focused on a single set of system parameters, which we note as the central case. The question arises as to what degree does the flow field change with variations in the system parameters. Let us first discuss a number of case studies and choose as an observable the time mean or residual flow in the horizontal plane of the shelf break.

Figures 3a,b,c, d depict the velocity and vorticity fields for the laboratory experiments (left) and shearstress condition SEOM numerical model runs (right) for the plane of the shelf break for the central case and three other cases to be described below. Figure 3a is for the central case and shows a rather strong region of cyclonic vorticity in the head of the canyon. The only parameter changed in Figure 3b from that of 3a is the Burger number that has been reduced a factor of 4. Thus, Figure 3b can be considered identical to 3a, although having a significantly weaker stratification. This, in turn, leads to a much stronger mean or residual flow; the flow distribution for the turbulent case is also more uniform in the vertical direction for the less stratified case.

The parameter values for Figure 3c are the same as those for Figure 3a with the exception that Ro t = 0.25 in 3c rather than 0.52 in 3a. This illustration shows that the characteristic speed of the shelf break vorticity is less than that for 3a, in keeping with the scaling argument given below. Figure 3d corresponds to Ro t = 1.25 and gives the vorticity and velocity fields for the superinertial counterpart of 3a, which shows that the cyclonic vorticity at the shelf break level is also stronger for the lower temporal Rossby number case. One notes, that the nature of the multicell mean flow regime for the super-inertial case is not captured by the SEOM model. The reason for this is not apparent at the present moment.

A numerical model comparison for the energetics of increasing Ro numbers, other parameters held fixed, was conducted by the SEOM model and showed a strong increase with Ro. Furthermore, a laboratory experiment was conducted in the 13.5 m diameter tank of LEGI in which a scaled up version of the model being considered was investigated. This experiment demonstrated that although the Ekman number becomes increasingly small, its effects on the flow dynamics does not become negligible.

BHP advanced a scaling argument for the magnitude of the normalized time mean flow speed based on the system parameters for the magnitude of the normalized time mean flow. The scaling argument used the notion that the vortex tubes with a vertical orientation are stretched in passing from the shelf, across the shelf break and into the canyon. During one oscillation cycle, this stretching leads to a predominantly cyclonic vorticity; the cyclonic vorticity production Z during this period can be shown to scale as where h D = deep-ocean depth, h S = the depth at the shelf, L = the length of the continental slope, u 0 = the characteristic velocity at the shelf-break.

This vorticity production per cycle must be dissipated in each cycle. To this end, it is assumed that the dissipation, D, takes place on the boundaries of the canyon by Ekman suction; the scaling for Ekman suction can be written as [START_REF] Pedlosky | Geophysical Fluid Dynamics[END_REF] where ζ B = the basin-scale vorticity, W = the width of the canyon. Equating (2.1) and (2.2), one obtains Figure 4 is a plot of the square root of the mean kinetic energy per unit mass normalized by the amplitude of the forcing current, U 1 /u 0 , against the scaling parameter λ defined in (2.3); the data include those obtained from laboratory experiments, labeled L and from numerical experiments, labeled N. We conclude that there is a good support to the scaling arguments advanced. Table 1.

Some experiments with boundary layer turbulence

Motion fields in the ocean are inherently turbulent and it is thus important to address the question of the modeling of inherently turbulent flows. Figures 5a,b and 6a, b are the plots of the time mean flow and vorticity fields at the shelf break levels z/h D = -0.2 and z/h D = -0.4 of the time mean flow for the case in which the topography is smooth and for the case in which roughness elements of the size 3 cm cubed (separated by a spacing of 15 cm) are placed on the model floor (see Fig. 1b), respectively. Qualitative comparisons between these Figures and those for the laminar experiments show that the mean flows are qualitatively similar to the turbulent flows. In particular, there is a flow into the canyon from the deep water and flow away from the canyon along its flanks in the upper level. The rectified flows in the upper layers are also predominantly cyclonic and are characterized by jet-like motions along the canyon walls with the coastline on the right facing downstream. As for the laminar flows, there is also a strong time mean flow directed downstream along the continental slope isobaths; i.e., with the coast on the right. Quantitative comparisons of these turbulent flows will be made in a later communication that is now in preparation.

Figure 1 .

 1 Figure 1. Experimental set-up (a) schematic of the canyon model, (b) rotating tank and the canyon model with roughness elements in Grenoble.

Figure 2 .

 2 Figure 2. Vorticity (left) and horizontal divergence (right) fields for the central experiment discussed by PHB as obtained from (a) the laboratory, (b) the SEOM model using a parameterized shear-strees condition along the model floor and (c) the SEOM model using a no-slip condition, including a highly resolved Ekman layer, along the model floor. Parameters: Ro = 0.1, Ro t = 0.52, Bu = 10, E = 3.2 × 10 -3 .

Figure 3 .

 3 Figure 3. Velocity and vorticity fields for laboratory (left) and SEOM (right) models at the shelf-break level z/h D = -0.2 for (a) Ro = 0.1, Ro t = 0.52, Bu = 10, E = 3.2 × 10 -3 ; (b) Ro = 0.1, Ro t = 0.52, Bu = 2.5, E = 3.2 × 10 -3 ; (c) Ro = 0.1, Ro t = 0.25, Bu = 10, E = 3.2 × 10 -3 ; (d) Ro = 0.1, Ro t = 1.25, Bu = 10, E = 3.2 × 10 -3 .

Figure 4 .Figure 5 .

 45 Figure 4. Characteristic speed of the normalized time-mean flow at the shelf break level as obtained from the laboratory experiments and the numerical model against the scaling relation. The symbols near the data points correspond to either laboratory (L) or numerical (N) experiments. The dashed line is the best fit U 1 /u 0 = (0.9λ + 12.7) × 10 -2 .

Figure 6 .

 6 Figure 6. Velocity and vorticity fields for laboratory experiments in Grenoble at the level (a) z/h D = -0.2, (b) z/h D = -0.4. The case with roughness elements. Parameters are given inTable 1.

Table 1 .

 1 Dimensionless parameters.

	Parameter	Tempe	Grenoble	Ocean
	Rossby number	0.1	0.14	0.14-0.43
	Temporal Rossby 0.25-1.25	0.5	<1
	number
	Burger number	2.5-10	1.35	5-18
	Ekman number	3.2 × 10 -3	1.2 × 10 -4	4 × 10 -7


Kerri M Bailey

Bruno L Giordano

Amanda L Kaas

Fraser W Smith
email: fraser.smith@uea.ac.uk

Decoding hand-object sounds in primary somatosensory cortex Decoding the sound of hand-object interactions in primary somatosensory cortex

Keywords: cortical feedback, multi-voxel pattern analysis, multisensory, S1. (4)

published or not. The documents may come

Decoding hand-object sounds in primary somatosensory cortex Much traditional neuroscientific research has investigated the function of the primary sensory brain areas (e.g. primary visual, somatosensory, and auditory cortices) with respect to how sensory input is processed within its corresponding sensory modality (see [START_REF] Carandini | Do We Know What the Early Visual System Does?[END_REF] for such an example of the visual system). However, it is well known that the majority of input to neurons, even within the primary sensory areas, comes from other cortical sources: either via local or long-range connections (for a review of the visual system, see [START_REF] Muckli | Network interactions: Non-geniculate input to V1[END_REF]. We have previously shown that non-stimulated early visual brain regions contain information transmitted via such connections about the surrounding context of natural visual scenes [START_REF] Smith | Nonstimulated early visual areas carry information about surrounding context[END_REF][START_REF] Muckli | Contextual Feedback to Superficial Layers of V1[END_REF]. Such early sensory neurons are not only subject to a great deal of contextual influences within their respective sensory modality, however, but also via across modality connections (for reviews, see [START_REF] Driver | Multisensory Interplay Reveals Crossmodal Influences on "Sensory-Specific" Brain Regions, Neural Responses, and Judgments[END_REF][START_REF] Ghazanfar | Is neocortex essentially multisensory?[END_REF]. For example, in a recent study we have shown that different visual images of familiar but not unfamiliar object categories could be discriminated in primary somatosensory cortex -SI, despite the absence of any tactile stimulation during the experiment [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. In the present work, we investigate whether such effects might also exist when participants are presented with sounds depicting familiar hand-object interactions. We expected this to be possible due to preexisting associative links that are formed from prior experience of both sensory aspects (i.e. auditory and somatosensory) of interacting with objects (e.g. the sound and haptic stimulation of typing keys on a computersee [START_REF] Meyer | Convergence and divergence in a neural architecture for recognition and memory[END_REF][START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF].

Support for this hypothesis is found in several additional studies that used multi-voxel pattern analysis (MVPA) to reveal content-specific effects in primary sensory cortices of a distal sensory modality to which the stimuli are presented. For example, [START_REF] Meyer | Predicting visual stimuli on the basis of activity in auditory cortices[END_REF] showed that simply viewing a silent yet sound-implying video clip transmits content-specific activity to primary auditory cortex (A1) even in the absence of auditory stimulation. Further . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online [START_REF] Clark | Whatever next? Predictive brains, situated agents, and the future of cognitive[END_REF] Decoding hand-object sounds in primary somatosensory cortex work has shown viewing video clips conveying object interactions with the hands can elicit different patterns of activity in SI in the absence of external tactile stimulation [START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF]. Additionally, when hearing the sounds of rich visual scenes, content-specific activity can be discriminated in early visual cortex, particularly in regions representing the periphery of visual space [START_REF] Vetter | Decoding sound and imagery content in early visual cortex[END_REF]). Finally, a set of classic multisensory studies has shown that primary sensory areas are subject to modulatory influences from other modalities (e.g. [START_REF] Calvert | Activation of auditory cortex during silent lip-reading[END_REF][START_REF] Mcintosh | Analysis of Neural Interactions Explains the Activation of Occipital Cortex by an Auditory Stimulus[END_REF]; see [START_REF] Driver | Multisensory Interplay Reveals Crossmodal Influences on "Sensory-Specific" Brain Regions, Neural Responses, and Judgments[END_REF] for a review).

Here we expand on this body of literature by investigating, for the first time, whether content-specific information can be sent to S1 when beginning from the auditory domain.

Therefore, in the present study, participants listened to sound clips of familiar hand-object interactions (e.g. bouncing a ball, typing on a keyboard), in addition to two control categories (pure tones and animal vocalizations), in an event-related functional magnetic resonance imaging (fMRI) experiment. We predicted that MVPA would show significant decoding of sound identity for hand-object interaction sounds in SI, particularly in independently localized hand-sensitive voxels, but not for the two control categories.

Materials and Methods

Participants

Self-reported right handed healthy participants (N = 10; 3 male), with an age range of 18-25 years (M = 22.7, SD = 2.41), participated in this experiment. All participants reported normal or corrected-to-normal vision, and normal hearing, and were deemed eligible after meeting MRI screening criteria, approved by the Scannexus MRI centre in Maastricht.

Written consent was obtained in accordance with approval from the Research Ethics . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online [START_REF] Clark | Whatever next? Predictive brains, situated agents, and the future of cognitive[END_REF] Decoding hand-object sounds in primary somatosensory cortex Committee of the School of Psychology at the University of East Anglia. Participants received €24 euros (equivalent to £20 sterling British pounds) for their time.

Stimuli & Design

Three different categories of auditory stimuli were used in a rapid event-related design; sounds depicting hand-object interactions, animal vocalizations, and pure tones.

There were five different sub-categories within each of these categories, with two exemplars of each sub-category, thus resulting in 30 individual stimuli in total. The five hand-object interaction sub-categories consisted of bouncing a basketball, knocking on a door, typing on a keyboard, crushing paper, and sawing wood. These were chosen for the reason that participants should have previously either directly experienced rich haptic interactions with such objects, or observed such interactions. Two control categories were also used. Firstly, animal vocalizations were used as familiar sounds not directly involving interactions with the hands. These consisted of birds chirping, a dog barking, a fly buzzing, a frog croaking, and a rooster crowing. An independent ratings experiment confirmed these sounds were matched to the hand-object interactions for familiarity. Sounds from these two categories were downloaded from SoundSnap.com, YouTube.com, and a sound database taken from [START_REF] Giordano | Hearing living symbols and nonliving icons: Category specificities in the cognitive processing of environmental sounds[END_REF]. The second control category were nonmeaningful sounds, defined as pure tones. These consisted of pure tones of five different frequencies (400Hz, 800Hz, 1600Hz, 3200Hz, and 6400Hz), created in MatLab. All sounds were stored in WAV format, and were cut to exactly 2000ms using Audacity 2.1.2, with sound filling the entire duration. Finally, all sounds were normalised to the root mean square (RMS) level [START_REF] Giordano | Abstract encoding of auditory objects in cortical activity patterns[END_REF]). More information regarding how these sounds were selected, including pilot experiments and ratings for the sounds, can be seen in Supplementary Text and Supplementary Table 1.

. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online Aug. 13, 2019;

Decoding hand-object sounds in primary somatosensory cortex Each run began and ended with 12s silent blocks of fixation. After the initial 12s fixation, 60 individual stimuli were played, with each unique sound presented twice per run.

Stimuli were played in a pseudo-randomly allocated order at 2s duration with a 3s ISI (5s trial duration). At random intervals, 15 null trials (duration 5s) were interspersed where no sound was played. This resulted in a total run time of 399s. After the main experiment, a somatosensory localiser was included to map the hand region in the somatosensory cortex (see [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. Piezo-electric Stimulator pads (Dancer Design, UK) were placed against the participant's index finger, ring finger, and palm of each hand using Velcro (six pads total, three per hand; see Supplementary Figure 1 for a visual example on one hand). Each pad contained a 6mm diameter disk centred in an 8 mm diameter static aperture.

The disks stimulated both hands simultaneously with a 25Hz vibration in a direction normal to the surface of the disk and skin, at an amplitude within the range of ±0.5mm. Localiser runs consisted of 15 stimulation blocks and 15 baseline blocks (block design, 12s on, 12s off, 366s total run time). Note that for the first two participants, a slightly modified timing was employed (block design, 30s on, 30s off; 10 stimulation blocks, 9 baseline blocks).

Procedure

After signing informed consent, each participant was trained on the experimental procedure on a trial set of stimuli not included in the main experiment, before entering the scan room. Participants were instructed to keep fixated on a black and white central fixation cross presented against a grey background whilst listening carefully to the sounds, which were played at a self-reported comfortable level (as in [START_REF] Leaver | Cortical Representation of Natural Complex Sounds: Effects of Acoustic Features and Auditory Object Category[END_REF][START_REF] Man | Convergent and invariant object representations for sight, sound, and touch[END_REF][START_REF] Man | Sight and sound converge to form modalityinvariant representations in temporoparietal cortex[END_REF][START_REF] Meyer | Predicting visual stimuli on the basis of activity in auditory cortices[END_REF]. Participants performed a one-back repetition counting task, and hence counted the number of times they heard any particular sound repeated twice in a row, for example, two . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online Aug. 13, 2019;

Decoding hand-object sounds in primary somatosensory cortex sounds each of a dog barking (randomly allocated from 2 to 6 per run). We chose this task as it was important that the task did not require an explicit motor action such as pressing a button, to prevent a possible confound in somatosensory cortex activity (see [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. Thus, participants verbally stated the number of counted repetitions they heard at the end of each run, and they were explicitly asked to not make any movements in the scanner unless necessary. Overall, most participants completed either 8 or 9 runs (with the exception of one participant, who completed 7), thus, participants were exposed to approximately 32-36 repetitions per stimulus, and 16-18 repetitions per unique sound exemplar. After the main experiment, participants took part in the somatosensory mapping localiser. Participants were not informed about this part of the experiment until all main experimental runs had been completed. Each participant completed 1 (N = 2) or 2 (N = 8) somatosensory mapping runs, and kept their eyes fixated on a black and white central fixation cross presented against a grey background for the duration of each run. Participants were debriefed after completion of all scanning sessions.

MRI Data Acquisition

Structural and functional MRI data was collected using a high-field 3-Tesla MR scanner (Siemens Prisma, 64 channel head coil, Scannexus, Maastricht, Netherlands). High resolution T1 weighted anatomical images of the entire brain were obtained with a threedimensional magnetization-prepared rapid-acquisition gradient echo (3D MPRAGE) sequence (192 volumes, 1mm isotropic). Blood-oxygen level dependent (BOLD) signals were recorded using a multiband echo-planar imaging (EPI) sequence: (400 volumes, TR = 1000ms; TE = 30ms; flip angle 77; 36 oblique slices, matrix 78 x 78; voxel size = 2.5mm 3 ; slice thickness 2.5mm; interslice gap 2.5mm; field of view 196; multiband factor 2). A short five volume posterior-anterior opposite phase encoding direction scan was acquired before . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online Aug. 13, 2019;

Decoding hand-object sounds in primary somatosensory cortex the main functional scans, to allow for subsequent EPI distortion correction [START_REF] Jezzard | Correction for geometric distortion in echo planar images from B0 field variations[END_REF][START_REF] Fritz | Comparison of EPI distortion correction methods at 3T and 7T[END_REF]. Slices were positioned to cover somatosensory, auditory, visual, and frontal cortex. Sounds were presented via an in-ear hi-fi audio system (Sensimetrics, Woburn MA, USA), and the visual display was rear projected onto a screen behind the participant via an LCD projector. Finally, a miniature Piezo Tactile Stimulator (mini-PTS; developed by Dancer Design, UK) was used to deliver vibro-tactile stimulation to the hands, using the same fMRI sequence with a modified number of volumes (366s for the majority, slightly longer for the first two participants due to slightly different designsee

Stimuli & Design above).

MRI Data Pre-processing

Functional data for each main experimental run, in addition to somatosensory localiser runs, was pre-processed in Brain Voyager 20.4 (Brain Innovation, Maastricht, The Netherlands; [START_REF] Goebel | Analysis of functional image analysis contest (FIAC) data with Brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis[END_REF], using defaults for slice scan time correction, 3D rigid body motion correction, and temporal filtering. Functional data were intra-session aligned to the pre-processed functional run closest to the anatomical scan of each participant. Distortion correction was applied using COPE 1.0 [START_REF] Fritz | Comparison of EPI distortion correction methods at 3T and 7T[END_REF]), using the 5 volume scan acquired in the opposite phase encode direction (posterior to anterior) for each participant. Voxel displacement maps (VDM)'s were created for each participant, which were applied for EPI distortion correction to each run in turn. Functional data were then coregistered to the participant's ACPC anatomical scan. Note no Talairach transformations were applied, since such a transformation would remove valuable fine-grained pattern information from the data that may be useful for MVPA analysis [START_REF] Fischl | High-resolution intersubject averaging and a coordinate system for the cortical surface[END_REF][START_REF] Argall | Simplified intersubject averaging on the cortical surface using SUMA[END_REF][START_REF] Goebel | Analysis of functional image analysis contest (FIAC) data with Brainvoyager QX: From single-subject to cortically aligned group general linear model analysis and self-organizing group independent component analysis[END_REF][START_REF] Kriegeskorte | Analyzing for information, not activation, to exploit high-resolution fMRI[END_REF]. For the main MVPA analyses (described further below) we conducted a GLM analysis independently per run per Decoding hand-object sounds in primary somatosensory cortex participant, with a different predictor coding stimulus onset for each stimulus presentation convolved with a standard double gamma model of the haemodynamic response function (see [START_REF] Greening | Spatially generalizable representations of facial expressions: Decoding across partial face samples[END_REF][START_REF] Smith | Nonstimulated early visual areas carry information about surrounding context[END_REF]. The resulting beta-weight estimates are the input to the pattern classification algorithm described below (see Multi-Voxel Pattern Analysis).

The somatosensory mapping localiser data was analysed using a GLM approach, with one predictor defining stimulation onset convolved with the standard double gamma model of the haemodynamic response function. The t-values were defined from the somatosensory localiser by taking the contrast of stimulation vs baseline in each participant. This allowed us to define the 100 voxels showing the strongest hand-related response in each individual's hand-drawn anatomical mask (see below) of the post-central gyrus (PCG).

Anatomical mask of Post-Central Gyri

In order to accurately capture the potential contribution from each sub-region of SIi.e. area 3a, 3b, 1 or 2, hand-drawn masks of the PCG were created in each individual participant [START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF][START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. This allowed us to go beyond the capabilities of the somatosensory localiser alone, by enabling inclusion of all the information potentially availablei.e. both tactile and proprioceptive -in SI for the pattern classification algorithms (see [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF] for further information). Furthermore, such an approach concords exactly with the key previous studies in this area [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF][START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF], since anatomical masks were defined in both these studies. Anatomical masks were created using MRIcron 6 [START_REF] Rorden | Improving Lesion-Symptom Mapping[END_REF]) using each participant's anatomical MRI scan in ACPC space. As in [START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF] and [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF], the latero-inferior border was taken to be the last axial slice where the corpus callosum was not visible. From anterior to posterior the masks were defined by the . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online Aug. 13, 2019;

Decoding hand-object sounds in primary somatosensory cortex floors of the central and post-central sulci. Furthermore, masks did not extend to the medial wall in either hemisphere [START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF][START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. This resulted in an average of 41 slices (total range 39 to 46) for each hemisphere per participant (see Figure 1 for an example). The average voxel count was 1969 (SD = 229) for the right PCG, and 2106 (SD = 215) for the left PCG, which did not significantly differ from one another (p = .084).

The size of each mask per participant is reported in Supplementary Table 2.

FIGURE 1 HERE

Additional Regions of Interest

Additional regions of interest (ROIs) were created using the Jüelich Anatomy toolbox [START_REF] Eickhoff | A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data[END_REF] as in [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. Regions included Primary Auditory Cortex [START_REF] Morosan | Human primary auditory cortex: Cytoarchitectonic subdivisions and mapping into a spatial reference system[END_REF][START_REF] Rademacher | Probabilistic mapping and volume measurement of human primary auditory cortex[END_REF], Pre-Motor Cortex [START_REF] Geyer | The microstructural border between the motor and the cognitive domain in the human cerebral cortex[END_REF], Primary Motor Cortex [START_REF] Geyer | Two different areas within the primary motor cortex of man[END_REF], and Primary Visual Cortex [START_REF] Amunts | Brodmann's areas 17 and 18 brought into stereotaxic space -Where and how variable?[END_REF].

We used the 30% probability cut-off for each map as this produces a roughly comparable number of voxels as in the anatomical masks of PCG [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF][START_REF] Eickhoff | A new SPM toolbox for combining probabilistic cytoarchitectonic maps and functional imaging data[END_REF]. See Supplementary Figure 2 for examples of the masks.

Multi-Voxel Pattern Analysis

For the multi-voxel pattern analysis (MVPA; e.g. Haynes, 2015), we trained a linear support vector machine (SVM) to learn the mapping between the spatial patterns of brain activation generated in response to each of the five different sounds within a particular sound category (for example: for hand-object interactions, the classifier was trained on a five way discrimination between each relevant sub-category: typing on a keyboard, knocking on a door, crushing paper and so on; [START_REF] Greening | Spatially generalizable representations of facial expressions: Decoding across partial face samples[END_REF][START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]Smith & . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online Aug. 13, 2019;

Decoding hand-object sounds in primary somatosensory cortex [START_REF] Smith | Nonstimulated early visual areas carry information about surrounding context[END_REF][START_REF] Vetter | Decoding sound and imagery content in early visual cortex[END_REF]. The classifier was trained and tested on independent data, using a leave one run out cross-validation procedure [START_REF] Smith | Nonstimulated early visual areas carry information about surrounding context[END_REF][START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. The input to the classifier was always single trial brain activity patterns (beta weights) from a particular ROI while the independent test data consisted of an average activity pattern taken across the repetitions of specific exemplars in the left out run (e.g. the single trial beta weights of the four presentations of 'bouncing a ball' in the left out run were averaged). We have used this approach successfully in previous studies, as averaging effectively increases the signal to noise of the patterns [START_REF] Smith | Nonstimulated early visual areas carry information about surrounding context[END_REF][START_REF] Vetter | Decoding sound and imagery content in early visual cortex[END_REF][START_REF] Muckli | Contextual Feedback to Superficial Layers of V1[END_REF]. For similar approaches applied to EEG and MEG data, see [START_REF] Smith | Decoding the dynamic representation of facial expressions of emotion in explicit and incidental tasks[END_REF] and [START_REF] Grootswagers | Decoding Dynamic Brain Patterns from Evoked Responses: A Tutorial on Multivariate Pattern Analysis Applied to Time Series Neuroimaging Data[END_REF] respectively. We note that a supplementary analysis using single trial activity patterns as the test data revealed a very similar pattern of results but lower accuracies as expected. These are reported in the Supplementary Results, and Supplementary Figure 3.

Finally, we used the LIBSVM toolbox [START_REF] Chang | LIBSVM: a library for support vector machines[END_REF] to implement the linear SVM algorithm, using default parameters (C = 1). The activity pattern estimates (beta weights) within each voxel in the training data was normalised within a range of -1 to 1, prior to input to the SVM. The test data were also normalised using the same parameters as in the training set, in order to optimise classification performance. To test whether group level decoding accuracy was significantly above chance, we performed non-parametric Wilcoxon signed-rank tests using exact method on all MVPA analyses, against the expected chance level of 1/5 [START_REF] Formisano | Who" is saying "what"? Brainbased decoding of human voice and speech[END_REF][START_REF] Greening | Spatially generalizable representations of facial expressions: Decoding across partial face samples[END_REF], with all significance values reported two-tailed. Effect sizes for the Wilcoxon tests are calculated as r = Z / √N, when N = number of observations [START_REF] Rosenthal | Meta-analytic procedures for social research[END_REF], to be identified as small (> .1), moderate (> .3), and large (> .5), according to [START_REF] Cohen | Statistical power analysis for the behavioral sciences[END_REF] classification of effect sizes.

. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online Aug. 13, 2019;

Decoding hand-object sounds in primary somatosensory cortex

Results

Bilateral anatomical masks of the lateral post-central gyri (PCG) were defined in each participant (see Figure 1). We then computed cross-validated decoding performance of sound identity for each sound category (familiar hand-object interactions, animal vocalizations, and pure tones) independently in right, left and pooled PCG.

Post-Central Gyri

As expected, significantly above-chance decoding was found for hand-object interaction sounds in right PCG (Med = 23.65%; Z = -2.199, p = .025, r = .492), left PCG (Med = 30.56%; Z = -2.383, p = .016, r = .533), and pooled PCG (Med = 28.75%; Z = -2.490, p = .012, r = .557); signed rank, two-tailed test, chance = 20% (see Figure 2A). Crucially, the same analyses for our two control categories of familiar animal vocalizations and unfamiliar pure tones did not show any significant decoding above chance in right, left, or pooled PCG (all p's > .4). Further pairwise comparisons revealed decoding performance for hand-object interactions was significantly higher than pure tones in pooled PCG (Z = -2.380, p = .016, r = .532), showing the same trend in left PCG (p = .053). Decoding accuracies across the right and left hemisphere were not significantly different from one another for hand-object interaction sounds (p = .105). Thus, the PCG carries content-specific information only for the familiar hand-object interaction sounds which convey haptic properties with the hands, regardless of hemisphere.

FIGURE 2 HERE

In subsequent analyses, when selecting the top 100 most active voxels in PCG from the somatosensory hand localiser, significant decoding for hand-object interactions was found Decoding hand-object sounds in primary somatosensory cortex only in left PCG (Med = 29.45%; Z = -2.504, p = .008, r = .560); signed rank, two-tailed test, chance = 20% (see Figure 2B; see also Figure 2G for single participant data). Critically, decoding accuracies for hand-object interactions in left PCG were significantly higher than both control categories (Hands vs Animals: Z = -2.346, p = .016, r = .525; Hands vs Tones: Z = -2.603, p = .006, r = .582). In addition, decoding of hand-object interactions was significantly higher in the left than the right PCG (Z = -2.199, p = .027, r = .492). These results show the classifier could reliably decode hand-object interaction sounds above chance when constrained to the hand-sensitive voxels in left PCG, which were significantly higher than both control categories. Thus sound identity was reliably decoded above chance when restricting the MVPA analysis to voxels with high responses to tactile stimulation of the right, but not left, hand.

Additional Regions of Interest

Primary Auditory Cortex. As would be expected, decoding in primary auditory cortex (A1) was robustly significant for all sound categories (all Meds ≥ 64.72%, all Z's ≤ -2.601, all p's ≤ .002, all r's ≥ .627; signed rank, two-tailed test, chance = 20%; see Figure 2C).

Further pairwise comparisons showed in right A1, decoding of pure tones (Med = 83.65%) was significantly higher than both animal vocalizations (Med = 71.25%, Z = -2.431, p = .012, r = .544) and hand-object interactions (Med = 66.25%, Z = -2.666, p = .004, r = .596), in addition to animal vocalizations being significantly higher than hand-object interactions (Z = -2.668, p = .004, r = .597). In pooled A1, pure tones (Med = 84.29%) were decoded significantly better than hand-object interactions (Med = 73.75%, Z = -2.552, p =.008, r = .571), and animal vocalizations (Med = 84.45%) were decoded significantly better than handobject interactions (Z = -2.243, p = .023, r = .502). Thus in A1, all sound categories were highly discriminated with the specific pattern of decoding performance being the opposite to Decoding hand-object sounds in primary somatosensory cortex that in PCG, with better decoding of pure tones, followed by animal vocalizations, then handobject interaction sounds.

Pre-Motor Cortex. In pre-motor cortex (PMC), significantly above chance decoding was found for hand-object interactions in right PMC (Med = 30.56%; Z = -2.601, p = .006, r = .582), left PMC (Med = 24.72%, Z -2.527, p = .008, r = .565) and pooled PMC (Med = 31.25%, Z = -2.666, p = .004, r = .596); signed rank, two-tailed test, chance = 20% (see Figure 2D). Interestingly, further tests showed decoding for hand-object interactions was significantly higher than pure tones in right PMC (Z = -2.449, p = .012, r = .548), left PMC (Z = -2.197, p = .031, r = .491), and pooled PMC (Z = -2.807, p = .002, r = .628). Finally there was a weak effect of decoding animal sounds in pooled PMC (Med = 27.22%, Z = -1.963, p = .047, r = .439). Supplementary analysesusing single trial test dataalso supported the robustness of this effect. Thus overall it appears that PMC may contain information about both types of familiar sound, but not the pure tone control category.

Primary Motor Cortex.

Decoding accuracies in primary motor cortex (M1) revealed significant decoding for hand-object interactions only in left M1 (Med = 27.09, Z = -2.245, p = .021, r = .502; signed rank, two-tailed, chance = 20%; see Figure 2E). There were no reliable differences in decoding across categories or hemispheres, however hand-object interactions showed a strong trend to be significantly higher than pure tones (Med = 20.00%) in left M1 (p = .064, two-tailed). Supplementary analyses also showed weak evidence for decoding of animal calls in left M1.

Primary Visual Cortex.

Decoding accuracies in primary visual cortex (V1) revealed no significant decoding. However, hand-object interactions in pooled V1 revealed a non-Decoding hand-object sounds in primary somatosensory cortex significant trend for above-chance decoding performance (Med = 24.72%, p = .061; signed rank, two-tailed, chance = 20%; see Figure 2F).

Discussion

In the present study we show that hearing sounds depicting familiar hand-object interactions elicit significantly different patterns of activity in primary somatosensory cortex (SI), despite the complete absence of external tactile stimulation. Crucially, such decoding was not found for two control categories of familiar animal vocalizations, and unfamiliar pure tones. Moreover, further analysis showed decoding accuracies were significantly higher for hand-object interaction sounds to both control categories when restricted to the hand-sensitive voxels of the left PCG. Thus these results strongly suggest cross-modal connections from audition to SI transmit content-specific information about familiar handobject sounds. We further show that pre-motor cortex (PMC) also contains content-specific information about sounds depicting familiar hand-object interactions, as well as weaker evidence for a similar effect with animal vocalizations.

Triggering cross-modal content-specific information in SI from audition

The present study agrees with a set of studies that show cross-modal connections can trigger content-specific activity even in the earliest regions of supposedly unimodal sensory cortex, across multiple sensory domains [START_REF] Meyer | Predicting visual stimuli on the basis of activity in auditory cortices[END_REF][START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF][START_REF] Vetter | Decoding sound and imagery content in early visual cortex[END_REF][START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. Our results significantly extend this previous body of work by demonstrating, for the first time, that a similar effect is present across the domains of audition and touch for particular sound categories. Similar to [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF], we demonstrate that such effects are not present for all sound categories, but only for specific categories where some correspondence is known to exist across sensory modalities (i.e. hand-. CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online Aug. 13, 2019;

Decoding hand-object sounds in primary somatosensory cortex object sounds activate lateral PCG where hand sensitive voxels are located). In fact, we demonstrate greater decoding of hand-object interaction sounds than both pure tones and animal vocalizations in the hand sensitive voxels of left PCG. It is important to note, furthermore, that the pattern of decoding performance was different in PCG and in auditory cortex, with better decoding of hand sounds than pure tones in PCG (and also in PMC) but vice versa in auditory cortex. Taken together this suggests that the present results in PCG reflect high-level information being activated in this region, as opposed to passive relay of low level acoustic features from auditory cortex.

The current results expand on our earlier study that showed simply viewing images of familiar graspable objects led to reliable decoding in SI [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]; see also the related study of [START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF]. Thus we show either viewing images or hearing sounds of appropriate objects/eventsi.e. those related to haptic interactions with the handstriggers content-specific activity in the earliest regions of somatosensory cortex. Crucially, both the current study and [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF] demonstrate that these effects are present when the analyses are limited to independently-localized hand-(or finger)-sensitive voxels in the PCG, and that the effects are higher in magnitude than for appropriate control categories.

What then might be the function of this cross-sensory information being present in SI? If Predictive Coding is assumed to be the general computational function of the brain [START_REF] Friston | Predictive coding under the free-energy principle[END_REF][START_REF] Clark | Whatever next? Predictive brains, situated agents, and the future of cognitive[END_REF], then it may be the case that either seeing a familiar graspable object, or hearing the sounds associated with a familiar hand-object interaction, leads to contentspecific activity in SI that is useful for future (or concurrent) interaction with the specific object. It should be possible to directly test Predictive Coding accounts by using appropriately designed paradigms where specific sensory cues (e.g. visual or auditory) predict forthcoming 3D objects (see e.g. [START_REF] Rossit | FMRI reveals a lower visual field preference for hand actions in human superior parieto-occipital cortex (SPOC) and precuneus[END_REF] in a target modality such as somatosensory cortex (see [START_REF] Kok | Less Is More: Expectation Sharpens Representations in the Primary Visual Cortex[END_REF] . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online [START_REF] Clark | Whatever next? Predictive brains, situated agents, and the future of cognitive[END_REF] Decoding hand-object sounds in primary somatosensory cortex see also [START_REF] Zhou | Visuo-tactile cross-modal associations in cortical somatosensory cells[END_REF]. Alternatively it might be the case that the activation present in SI is not useful for future object interaction but rather reflects the broader representation of object knowledge in the haptic domain (e.g. [START_REF] Man | Sight and sound converge to form modalityinvariant representations in temporoparietal cortex[END_REF][START_REF] Martin | GRAPES-Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain. Decoding hand-object sounds in primary somatosensory cortex[END_REF][START_REF] Meyer | Convergence and divergence in a neural architecture for recognition and memory[END_REF]. Such accounts propose that the representation of object concepts is distributed across the perceptual, action and emotion systems in the brain [START_REF] Martin | GRAPES-Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain. Decoding hand-object sounds in primary somatosensory cortex[END_REF], and that conceptual processing involves neural re-use of the same systems for perception and for conceptual processing [START_REF] Barsalou | On Staying Grounded and Avoiding Quixotic Dead Ends[END_REF]. While these accounts do not generally invoke the primary sensory areas as being involved in the representation of object knowledge, [START_REF] Martin | GRAPES-Grounding representations in action, perception, and emotion systems: How object properties and categories are represented in the human brain. Decoding hand-object sounds in primary somatosensory cortex[END_REF] account, for example, proposes that such regions may become involved under specific task conditions. Such accounts would predict that the effects we report in SI should be modified as a function of task constraintswith stronger effects for tasks where somatosensory properties of objects and/or actions are more vs less prominent.

How does auditory stimulation lead to content-specific information being present in SI? There are several possible routes through which this could be accomplished (see e.g. [START_REF] Driver | Multisensory Interplay Reveals Crossmodal Influences on "Sensory-Specific" Brain Regions, Neural Responses, and Judgments[END_REF]. First, auditory information may first arrive at high level multisensory convergence zones, such as pSTS, posterior parietal cortex or ventral premotor areas, before such areas then send feedback to SI (see [START_REF] Driver | Multisensory Interplay Reveals Crossmodal Influences on "Sensory-Specific" Brain Regions, Neural Responses, and Judgments[END_REF][START_REF] Meyer | Convergence and divergence in a neural architecture for recognition and memory[END_REF]; see also Smith & Goodale, 2015, for a comparable view). Second, auditory information may be directly projected to SI, without passing through such higher multisensory regions. Such direct connections have been found in animal models between certain sensory pairings: e.g. from primary auditory to primary visual cortex (e.g. [START_REF] Falchier | Anatomical evidence of multimodal integration in primate striate cortex[END_REF][START_REF] Falchier | Projection from visual areas V2 and prostriata to caudal auditory cortex in the monkey[END_REF] and between primary auditory and somatosensory cortex [START_REF] Budinger | Multisensory processing via early cortical stages: connections of the primary auditory cortical field with other sensory systems[END_REF]; see also [START_REF] Cappe | Heteromodal connections supporting multisensory integration at low levels of cortical processing in the monkey[END_REF]. However it has been proposed that such direct connections are relatively sparse as opposed to the amount of feedback arriving from higher multisensory areas [START_REF] Driver | Multisensory Interplay Reveals Crossmodal Influences on "Sensory-Specific" Brain Regions, Neural Responses, and Judgments[END_REF]. Finally a . CC-BY-NC-ND 4.0 International license It is made available under a (which was not peer-reviewed) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.

The copyright holder for this preprint . http://dx.doi.org/10.1101/732669 doi: bioRxiv preprint first posted online Aug. 13, 2019;

Decoding hand-object sounds in primary somatosensory cortex third possibility is the involvement of lower tier multisensory regions [START_REF] Driver | Multisensory Interplay Reveals Crossmodal Influences on "Sensory-Specific" Brain Regions, Neural Responses, and Judgments[END_REF] that are anatomically located next to primary sensory areas: for instance auditory regions located close to SII may be bimodal responding to both auditory and tactile information (see e.g. [START_REF] Cappe | Heteromodal connections supporting multisensory integration at low levels of cortical processing in the monkey[END_REF][START_REF] Wallace | A revised view of sensory cortical parcellation[END_REF]. Indeed recent studies in humans have indicated the presence of auditory frequency information in SII area OP4 [START_REF] Pérez-Bellido | Auditory Frequency Representations in Human Somatosensory Cortex[END_REF] and the influence of auditory information at very fast time-scales in SI indicative of a feedforward effect [START_REF] Sugiyama | Effect of acceleration of auditory inputs on the primary somatosensory cortex in humans[END_REF]. From the present data we can only speculate on the involvement of these potential routes by which information could be transmitted from auditory to somatosensory cortex. We note that high temporal resolution techniquessuch as EEG or MEGmay reveal differences in the timing of cross-sensory effects that would indicate the involvement of direct or feedback-mediated mechanisms (see e.g. [START_REF] Sugiyama | Effect of acceleration of auditory inputs on the primary somatosensory cortex in humans[END_REF]. In addition, laminar fMRI may be used to indicate which layers of SI manifest cross-sensory effects, with implications for the relative role of feedforward vs feedback-mediated connections (see e.g. [START_REF] Lawrence | Laminar fMRI: Applications for cognitive neuroscience[END_REF][START_REF] Muckli | Contextual Feedback to Superficial Layers of V1[END_REF].

Hemispheric Differences in auditory and visually triggered cross-sensory information in SI

We note that both [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF] and [START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF] used visual stimuli (either images of familiar graspable objects, or videos depicting haptic explorations of objects with the hands, respectively) and found stronger decoding accuracies in the right hemisphere PCG whereas in the present study with auditory stimuli we found stronger decoding in the left hemisphere PCG. There are several potential reasons for the greater involvement of hand-sensitive voxels in the left PCG in the present study. First, some of our sounds depict bimanual actions (e.g. typing on a keyboard) and previous studies have found greater activation in the left hemisphere for bimanual action sounds [START_REF] Aziz-Zadeh | Left hemisphere motor facilitation in response to manual action sounds[END_REF].

Decoding hand-object sounds in primary somatosensory cortex In addition, much research concerning the neural processing of tools has reported strong left lateralization of the tool network in right-handed participants (as our participants were; [START_REF] Ishibashi | Decoding hand-object sounds in primary somatosensory cortex cognition: An activation likelihood estimation meta-analysis of 70 neuroimaging contrasts[END_REF][START_REF] Lewis | Distinct Cortical Pathways for Processing Tool versus Animal Sounds[END_REF][START_REF] Lewis | Lefties Get It "Right" When Hearing Tool Sounds[END_REF] although this would suggest left-lateralization for both sounds and images/videos. However it may be the case that such left lateralization depends upon object directed action content being present (or strongly implied), as was the case in the current study. Finally one further important difference between [START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF] and the present study is that in the earlier study we mapped hand (finger) sensitive voxels in SI for each hand independently, which permitted considering the relative influence of contra-and ipsi-lateral influences, whereas in the present study both hands were mapped simultaneously. Hence in the present study selected voxels may have reflected a stronger contra-lateral bias, and therefore reflect the relatively earlier sub-regions of SI, such as area 3b. Future work will be necessary, ideally in the same participants with the same localizers, to determine the relative role of hand sensitive voxels in left and right SI to visually and auditory triggered information.

Decoding action related information in Pre-Motor Cortices

In pre-motor cortex (PMC), we also found reliable decoding of hand-object interaction sounds in both the left and right hemisphere. We also found greater decoding of such sounds compared to pure tones. Finally, decoding of animal vocalizations was significant in PMC when pooling across hemispheres (and in further analyses using single trials as test data, the effect was robustly present in each hemisphere). PMC is known to play a large role in processing action related information [START_REF] Gallese | Action recognition in the premotor Decoding hand-object sounds in primary somatosensory cortex cortex[END_REF]. For instance, PMC has been found to be preferentially activated for object-related hand actions and non-objectrelated mouth actions [START_REF] Buccino | Action observation activates premotor and parietal areas in a somatotopic manner: An FMRI study[END_REF]). Both our familiar sounds of hand-object Decoding hand-object sounds in primary somatosensory cortex interactions and animal vocalizations imply such an action, therefore it would seem reasonable for them to trigger activity in pre-motor areas.

In addition, decoding in PMC for both hand-object interactions and animal vocalizations could be part of a somatotopic auditory mirror neuron system, since PMC has previously been found to be active in response to both performing an action and hearing the corresponding action sound [START_REF] Kohler | Hearing sounds, understanding actions: Action representation in mirror neurons[END_REF]. Furthermore, [START_REF] Gazzola | Empathy and the Somatotopic Auditory Mirror System in Humans[END_REF] found overlap at the voxel level between left PMC activation when human participants executed a motor action, or listened to the sound of the action. Crucially, they found a somatotopic pattern, whereby a dorsal cluster within PMC was involved in listening to and executing hand actions, and a ventral cluster within PMC was involved in listening to and executing mouth actions. Therefore, both hand-and mouth-specific clusters within PMC may contribute to the decoding found in the present experiment. We would predict no significant decoding for the hand-object sounds if PMC analyses were limited to mouthselective voxels in PMC, and likewise for animal vocalizations in hand-selective voxels in PMC. Hence in future work it would be optimal to include an additional mouth (and hand) movement localizer to test these predictions. Overall, the decoding effects we see for actionrelated information in PMC (and also M1) suggest these regions also receive content-specific information regarding the action properties of familiar hand-object interaction sounds (with weaker evidence of an effect also for animal vocalizations).

Conclusion

We have shown that the identity of familiar hand-object interaction sounds can be discriminated in SI, in the absence of any concurrent tactile stimulation. Thus cross-modal connections from audition to SI transmit content-specific information about such sounds. Our work provides converging evidence that activity in supposedly modality-specific primary Decoding hand-object sounds in primary somatosensory cortex

Figure 1 .

 1 Figure 1. Anatomical masks of the lateral post-central gyrus for a representative participant.

Decoding hand-object sounds in primary somatosensory cortex sensory areas can be shaped in a content-specific manner by relevant contextual information transmitted across sensory modalities [START_REF] Meyer | Predicting visual stimuli on the basis of activity in auditory cortices[END_REF][START_REF] Meyer | Seeing touch is correlated with content-specific activity in primary somatosensory cortex[END_REF][START_REF] Vetter | Decoding sound and imagery content in early visual cortex[END_REF][START_REF] Smith | Decoding visual object categories in early somatosensory cortex[END_REF]. Such an effect is in keeping with the rich range of contextual effects expected in primary sensory areas under the Predictive Processing framework [START_REF] Clark | Whatever next? Predictive brains, situated agents, and the future of cognitive[END_REF].

Decoding hand-object sounds in primary somatosensory cortex