Different roles of similarity and predictability in auditory stream segregation

Sound sources often emit trains of discrete sounds, such as a series of footsteps. Previously, two dif¬ferent principles have been suggested for how the human auditory system binds discrete sounds to¬gether into perceptual units. The feature similarity principle is based on linking sounds with similar characteristics over time. The predictability principle is based on linking sounds that follow each other in a predictable manner. The present study compared the effects of these two principles. Participants were presented with tone sequences and instructed to continuously indicate whether they perceived a single coherent sequence or two concurrent streams of sound. We investigated the influence of separate manipulations of similarity and predictability on these perceptual reports. Both grouping principles affected perception of the tone sequences, albeit with different characteristics. In particular, results suggest that whereas predictability is only analyzed for the currently perceived sound organization, feature similarity is also analyzed for alternative groupings of sound. Moreover, changing similarity or predictability within an ongoing sound sequence led to markedly different dynamic effects. Taken together, these results provide evidence for different roles of similarity and predictability in auditory scene analysis, suggesting that forming auditory stream representations and competition between alter¬natives rely on partly different processes

Stable individual characteristics in the perception of multiple embedded patterns in multistable auditory stimuli

The ability of the auditory system to parse complex scenes into component objects in order to extract information from the environment is very robust, yet the processing principles underlying this ability are still not well understood. This study was designed to investigate the proposal that the auditory system constructs multiple interpretations of the acoustic scene in parallel, based on the finding that when listening to a long repetitive sequence listeners report switching between different perceptual organizations. Using the ‘ABA-’ auditory streaming paradigm we trained listeners until they could reliably recognise all possible embedded patterns of length four which could in principle be extracted from the sequence, and in a series of test sessions investigated their spontaneous reports of those patterns. With the training allowing them to identify and mark a wider variety of possible patterns, participants spontaneously reported many more patterns than the ones traditionally assumed (Integrated vs. Segregated). Despite receiving consistent training and despite the apparent randomness of perceptual switching, we found individual switching patterns were idiosyncratic; i.e. the perceptual switching patterns of each participant were more similar to their own switching patterns in different sessions than to those of other participants. These individual differences were found to be preserved even between test sessions held a year after the initial experiment. Our results support the idea that the auditory system attempts to extract an exhaustive set of embedded patterns which can be used to generate expectations of future events and which by competing for dominance give rise to (changing) perceptual awareness, with the characteristics of pattern discovery and perceptual competition having a strong idiosyncratic component. Perceptual multistability thus provides a means for characterizing both general mechanisms and individual differences in human perception

The role of perceived source location in auditory stream segregation: separation affects sound organization, common fate does not

The human auditory system is capable of grouping sounds originating from different sound sources into coherent auditory streams, a process termed auditory stream segregation. Several cues can influence auditory stream segregation, but the full set of cues and the way in which they are integrated is still unknown. In the current study, we tested whether auditory motion can serve as a cue for segregating sequences of tones. Our hypothesis was that, following the principle of common fate, sounds emitted by sources moving together in space along similar trajectories will be more likely to be grouped into a single auditory stream, while sounds emitted by independently moving sources will more often be heard as two streams. Stimuli were derived from sound recordings in which the sound source motion was induced by walking humans. Although the results showed a clear effect of spatial separation, auditory motion had a negligible influence on stream segregation. Hence, auditory motion may not be used as a primitive cue in auditory stream segregation

Predictive regularity representations in deviance detection and auditory stream segregation: from conceptual to computational models

Predictive accounts of perception have received increasing attention in the past twenty years. Detecting violations of auditory regularities, as reflected by the Mismatch Negativity (MMN) auditory event-related potential, is amongst the phenomena seamlessly fitting this approach. Largely based on the MMN literature, we propose a psychological conceptual framework called the Auditory Event Representation System (AERS), which is based on the assumption that auditory regularity violation detection and the formation of auditory perceptual objects are based on the same predictive regularity representations. Based on this notion, a computational model of auditory stream segregation, called CHAINS, has been developed. In CHAINS, the auditory sensory event representation of each incoming sound is considered for being the continuation of likely combinations of the preceding sounds in the sequence, thus providing alternative interpretations of the auditory input. Detecting repeating patterns allows predicting upcoming sound events, thus providing a test and potential support for the corresponding interpretation. Alternative interpretations continuously compete for perceptual dominance. In this paper, we briefly describe AERS and deduce some general constraints from this conceptual model. We then go on to illustrate how these constraints are computationally specified in CHAINS

Dynamical state variables.

<p>Eight continuous variables (functions of time) that describe the dynamical state of chain at any given moment. Note that all variables except are always non-negative, and . All dynamical variables are initialised to zero upon a chain entering the competition. (See the “Chain Dynamics” section in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002925#s2" target="_blank">Models</a> for details.).</p

Illustration of the role of some model parameters.

<p>The left columns show the proportion of time spent in the segregated organisation separately for the first and subsequent phases, while the right columns display the durations of all perceptual phases (again, in separate columns for the first and subsequent phases). A) <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002925#s3" target="_blank">Results</a> obtained with the original parameter set, specified in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002925#pcbi-1002925-t001" target="_blank">Tables 1</a> and <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002925#pcbi-1002925-t003" target="_blank">3</a>. These charts are identical to panels C) and D) of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002925#pcbi-1002925-g008" target="_blank">Figure 8</a> and the same panels of <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002925#pcbi-1002925-g009" target="_blank">Figure 9</a>, respectively. B) Chain building parameter is changed from 0.00015 to 0.00075. Increasing the effect of rate-of-change on the inclusion probability renders it more difficult to form the ABA chain and thus the segregated percept is more prominent (especially with small and large in the first phase and small and in subsequent phases). C) Chain building parameter is changed from 0.0055 to 0.0035. Decreasing the probability of skipping over auditory events promotes the chains of the integrated organization, especially when rate of change is small. D) The weighting coefficient of success rate is changed from 3.8 to 3.9. As the number of successful predictions a chain makes in unit time have a larger effect on its excitation, the integrated percept (with the highest success rate) is more dominant in subsequent phases than with the original parameter value. E) The weighting coefficient of the inhibitory signals towards the excitatory populations is changed from 8.1 to 8.2. The resulting increase in the effectiveness of collisions in lowering chain excitation is manifested by a small bias towards the segregated percept (whose corresponding chains incur fewer collisions) in subsequent phases. Further, switches are less probable, i.e. phase durations are higher, when inhibition is more efficient in suppressing momentarily non-dominant chains. F) The weighting coefficient of noise is changed from 3.4 to 3.0. As noise is responsible for the perceptual switches, decreasing its contribution to the excitation of the chains lengthens subsequent phases (especially when is small). Note that adjusting the weighting coefficients of the dynamical state variables in panels D), E), and F) has no influence on the first phases (that is governed exclusively by chain discovery). Colour calibration is shown on top.</p

Dynamical switching.

<p>The <i>left panels</i> show the excitation and other dynamical state variables of the chains that arise in response to a four-minute long ABA− sequence with , . The excitation variables () alternate at random intervals between two stable organisations once they are both discovered (at around 40 seconds): “integrated” (blue only) and “segregated” (red [“B”] and green [“A”] together). The percepts that would correspond to the chain with maximum momentary excitation are plotted above, calculated from low-pass filtered excitation time-courses (to avoid bouncing). Segregation dominates 74% of the time; the mean phase duration is 23.7 s. The <i>right panels</i> plot the changes in the state variables during a perceptual switch at 110 seconds on a magnified time-scale. The corresponding time period in the left panels is highlighted in bright yellow. Chain excitations are modulated by the noise variables (not shown). The inhibitory populations (with activities ) serve to achieve exclusivity of the stable organisations by suppressing chains colliding with the dominant one. The adaptation and self-excitation state variable () renders switches in close succession unlikely (self-excitation) while increasing the probability of a switch as the duration of the perceptual phase grows (adaptation). The probabilistic rediscovery of a chain supports its excitation through the rediscovery rate ().</p

The time course of the probability of segregation.

<p>A) Four curves showing the group-average probability by which listeners () reported hearing the segregated percept at various times during a trial. The parameter combinations for each coloured curve are shown on the side map. B) The corresponding results from the Chains model (15 simulations). The probability of the streaming percept is always zero at the onset of the stimulus train as there is a delay to the first reported/modelled percept. This does not mean that listeners necessarily report (or that the model would find) the integrated percept before the segregated one (i.e., the probability of the integrated percept is also zero at the onset of the stimulus train).</p

Dynamical system parameters.

<p>Parameters that control the dynamics of competing chains. The parameters control the magnitude of the corresponding effect, and the corresponding parameters are time constants associated with the same effects. (See the “Chain Dynamics” section in <a href="http://www.ploscompbiol.org/article/info:doi/10.1371/journal.pcbi.1002925#s2" target="_blank">Models</a> for details.).</p

Phase length distributions.

<p>A) Distribution of the perceptual phase durations obtained from the perceptual experiment data with and (110 phases from 15 participants). B) Distribution of the “perceptual” phase durations obtained from the model for the same and parameters as in panel A) (53 phases from 15 simulations). Note that a small number of outliers are not visible (a 213 and a 223 seconds-long perceptual phase on panel A and a single 179 seconds-long phase on panel B). ^*Empirical phases exclude “both” and “neither” responses.</p