The results demonstrate a specific mapping of abstracted object properties, as represented by spectrotemporal coherence, and object boundaries, as represented by changes in spectrotemporal coherence, to distinct regions of auditory cortex. First, activity in auditory cortex including HG, PT, TPJ and STS increased as a function of the magnitude of the change in spectrotemporal coherence at boundaries between textures. Second, activity as a function of the absolute spectrotemporal coherence within textures increased in auditory association cortex in PT and in TPJ. Finally, increases in spectrotemporal coherence at segment boundaries were more perceptually salient than decreases in spectrotemporal coherence at object boundaries, and this was reflected by stronger neural activity at such boundaries.
While the observed parametric responses to absolute spectrotemporal coherence within textures and change in coherence between textures show some overlap in cortical resources (in PT and TPJ), they are indeed separable processes, since the experimental design orthogonalised the absolute coherence of acoustic textures and changes in coherence at boundaries. This indicates that the overlapping representations of change in coherence and absolute coherence in the non-primary auditory areas in PT and TPJ represent a distinct mapping of these two processes in similar cortical areas; these mappings could be subserved by activity within distinct units or networks in those areas (Price et al., 2005
; Nelken, 2008
; Nelken and Bar-Yosef, 2008
). Furthermore, the results are unlikely to be confounded by the behavioural task (detection of a change in spectrotemporal coherence), since a pilot study in which the absolute spectrotemporal coherence of the sounds was task-relevant yielded very similar results (Figure S1
A number of neuronal mechanisms might underlie the response to boundaries that we demonstrate across auditory cortex (including primary cortex) and acoustic texture coherence that we demonstrate in association cortex. Computationally, both boundary detection and acoustic texture analysis within boundaries must depend on the statistical properties of the stimulus over frequency-time space, since low-level acoustic features such as spectral density over time were kept constant. For the present stimuli, boundary detection requires mechanisms that do not need to assess large spectrotemporal regions but still need to assess a ‘local’ statistical rule change in the absence of any physical ‘edge’ (as would be the case in the perception of an object arising out of silence, for example, where a boundary could be defined by a discontinuity in intensity). Acoustic texture coherence analysis necessarily involves larger spectrotemporal regions than boundary detection, and the analysis of boundary before texture that we demonstrate here is consistent with the idea that more extended segments of spectrotemporal space are analysed in areas further from primary cortex. This notion is further supported by studies focusing on the time domain that suggest that the analysis of sound occurs over longer time-windows in non-primary than in primary cortex (Boemio et al., 2005
; Overath et al., 2008
). In terms of the underlying neuronal mechanism for the present stimulus, we are not aware of any studies of coherent FM in either primary or non-primary cortex. Neurons that are sensitive to the direction of single-FM sweeps have been demonstrated in the auditory cortex of rats (Ricketts et al., 1998
), cats (Mendelson and Cynader, 1985
; Heil et al., 1992
), and rhesus monkeys (Tian and Rauschecker, 2004
) (for a review see Rees and Malmierca, 2005
). In our study, the analysis of boundaries (in primary cortex and association cortex) and texture (in association cortex) could be subserved by ensembles of such units tuned to similar sweeps in different regions of frequency-time space. Alternatively, if such neurons were ever shown to exist, boundaries and acoustic texture could also be analysed by single neurons that were sensitive to coherent FM over spectrotemporal regions. In the case of both ensemble mechanisms and single-neuron mechanisms, the ‘receptive field’ of the mechanism would need to be larger for texture analysis in association areas than for boundary detection in primary areas.
The present study provides a contrasting yet complementary approach to change detection mechanisms from the classical mismatch negativity (MMN) paradigm, which is thought to reflect the violation of a previously established regularity (Näätänen and Winkler, 1999
). Both paradigms require mismatch or change detection processes in auditory cortex. However, our results suggest that, in the current stimulus paradigm, the emergence of regularity (or coherence) has a different representation to its disappearance or violation. For example, the transition from noise to a regular interval sound with pitch has a different cortical representation than the reverse transition (Krumbholz et al., 2003
). Recently, Chait and colleagues (2007
demonstrated distinct cortical mechanisms for the detection of auditory ‘edges’ based on statistical properties, where the detection of a statistical regularity (in violation of a previous irregularity) had a different cortical signature than the detection of a violation of statistical regularity. The current results support the existence of such neural and perceptual asymmetries. We propose that the degree of spectrotemporal coherence is encoded in a continuous manner, with neurons tuned to coherence levels that are equal or greater in coherence than the neurons' thresholds. Such a cumulative neural code contains an inherent asymmetry (Treisman and Gelade, 1980
; Cusack and Carlyon, 2003
): transitions to more coherent sounds excite a larger neural population, rendering them more perceptually salient. This is reflected in the neural response ().
The stimulus manipulation employed in this study addresses generic processes underlying complex auditory object analysis, but it is not intended to represent all possible auditory object classes. In speech perception, the spectrotemporal analysis necessarily spans a large frequency range over multiple temporal scales, where coherent acoustic properties (or those with ‘common fate’) need to be abstracted across the frequency-time axis; for example, formant transitions often display such coherent acoustic spectrotemporal properties (Stevens, 1998
). At the same time, there is no one ecological sound that the acoustic texture stimulus represents, but we argue here that its generic nature ensures applicability to a variety of ecological sound properties. While coherent FM is arguably a relatively weak grouping cue compared to simultaneous onset and harmonicity (Carlyon, 1991
; Summerfield and Culling, 1992
; Darwin and Carlyon, 1995
), it is nevertheless one basis upon which figure-ground selection can occur (McAdams, 1989
). It is important to note, however, that these studies generally used simple sinusoidal FM in which ‘FM coherence’ was defined as either in- or out-of-phase. The stimulus employed here is more complex in the sense that spectrotemporal coherence can only be detected as a whole, irrespective of low-level features such as phase, since FM ramps were randomly distributed in frequency-time space.
The visual depiction of the stimulus () evokes the coherent visual motion paradigm using random dot kinematograms (Newsome and Paré, 1988
; Britten et al., 1992
; Rees et al., 2000
; Braddick et al., 2001
). However, direct comparisons with the visual system based on superficial similarities are often not straightforward and need to be treated with caution (King and Nelken, 2009
). For example, objects in the visual stimulus are defined spatially while space plays a relatively minor role in the definition of auditory objects. Further, in the present case, the perceptual effect is more subtle than in the visual domain.
The data reported here move beyond the analysis of simple FM sounds to the analysis of auditory object patterns within stochastic stimuli; such auditory object analysis is dependent on mechanisms that are fundamental for the analysis of ecologically valid sounds in a dynamic auditory environment. We demonstrate a mechanism for the assessment of acoustic texture boundaries that is already present in primary auditory cortex, based on recognising changing higher-order statistical properties governing frequency-time space. Such a mechanism precedes the encoding of the absolute properties of acoustic textures in higher-level auditory association cortex.