This possibility could be tested in divided attention experiments with synthetic textures. We explored the biological representation of sound texture using a set of generic statistics and a relatively simple auditory model, both of which could be augmented in interesting ways. The three sources of information that contributed to the present work—auditory neuroscience, natural sound GDC-0068 purchase analysis, and perceptual experiments—all provide directions for such
extensions. The auditory model of Figure 1, from which statistics are computed, captures neuronal tuning characteristics of subcortical structures. Incorporating cortical tuning properties would likely extend the range of textures we can account for. For instance, cortical receptive fields have spectral tuning that is more complex and varied than that found subcortically (Barbour and Wang, 2003 and Depireux et al., 2001), and statistics of filters modeled on their properties could capture higher-order structure that our current model does not. As discussed earlier, the correlations computed on subcortical
representations could then potentially be replaced by marginal statistics of filters at a later stage. It may also be possible to derive additional or alternative texture statistics from an analysis BMN-673 of natural sounds, similar in spirit to previous derivations Resminostat of cochlear and V1 filters from natural sounds and images (Olshausen and Field, 1996 and Smith and Lewicki, 2006), and consistent with other examples of congruence between properties of perceptual systems and natural environments (Attias and Schreiner, 1998, Garcia-Lazaro et al., 2006, Lesica and Grothe, 2008, Nelken et al.,
1999, Rieke et al., 1995, Rodríguez et al., 2010, Schwartz and Simoncelli, 2001 and Woolley et al., 2005). We envision searching for statistics that vary maximally across sounds and would thus be optimal for recognition. The sound classes for which the model failed to produce convincing synthetic examples (revealed by Experiment 4) also provide directions for exploration. Notable failures include textures involving pitched sounds, reverberation, and rhythmic structure (Figure 7, Table S1, and Figure S5). It was not obvious a priori that these sounds would produce synthesis failures—they each contain spectral and temporal structures that are stationary (given a moderately long time window), and we anticipated that they might be adequately constrained by the model statistics. However, our results show that this is not the case, suggesting that the brain is measuring something that the model is not. Rhythmic structure might be captured with another stage of envelope extraction and filtering, applied to the modulation bands. Such filters would measure “second-order” modulation of modulation (Lorenzi et al.