Pre-perceptual grouping of auditory scenes explains contextual biases in the perception of ambiguous tonal shifts. Vincent Adam1, Claire Chambers2, Claire Peloﬁ2, Daniel Pressnitzer2, Maneesh Sahani1 Gatsby Computational Neuroscience Unit, University College London Ecole Normale Superieure, Cognitive Studies Department Background Shepard Tones Base frequency fb, and octave related Bell-shaped envelope on log-frequency Evoke a non ambiguous pitch Shift Ambiguity Small Physical upward shift fb ↑leads to upward percieved shift Octave shift = Identity Half-Octave Shift → Ambiguous percept Biasing Pitch Shift Perception (Chambers and Pressnitzer) [3] Psychophysical Results Demonstration of half-octave ambiguity. (a,b) A single Shepard tone played shortly prior to the ambiguous pair can induce a strong bias, toward the shift that encompasses that tone. (c,d) Build up of the bias with the number of preceding tones (e,f) A simple adaptation mechanism? Adaptation is usually local tonotopically local adaptation eﬀects falls oﬀto 0 with distance →Here bias is strongest in the middle of the region Psychophysical manifestations of adaptation are usually repulsive Englitz et al [2], showed repulsion in a decoding paradigm repulsion predicts the opposite eﬀect More that Average Spectrum of Context Sparsely dropping individual pure tones removes the bias References [1] R. Shepard, JASA 1964 [2] B. Englitz, et al Basic Aspects of Hearing 2013 [3] C. Chambers, et al submitted 2015 [4] A. Bregman, Auditory scene analysis 1994 [5] Z. Ghahramani, Jordan, M Machine learning 1997 Model Concept: Preperceptual Grouping Inspiration: perceptual grouping in stream segretation [4]. Items of an auditory scene can be segregated in multiple distinct perceptual objects Preperceptual grouping Items of an auditory scene can be grouped in multiple distinct pre-perceptual tracks based on spectro-temporal continuity Helps solving the attribution problem (prevents misbinding) Assumption reﬂects natural sounds statistics Model Presentation Model assumptions sensory noise corrupt frequency representation track spectro-temporal continuity tracks persist in time tracks generate tones close to their mean (std σC) sensory noise ∝ 1 √ duration T (0) t T (0) t+1 T (K) t T (K) t+1 Ct Ct+1 ηt ηt+1 ... Tracks’ means (inferred) Components’ attributions (inferred) Components Formalism: Factorial Hidden Markov Model [5] Probabilistic inference: Online inference of the tracks underlying the auditory scene sensed tones are attributed to tracks (clustered) beliefs on tracks center frequency are updated Variational Approximation: p  T (i) t |Ht  ≈N  T (i) t |µ(i) t , σ(i) t  Tone Attribution: rl k(Y ) = p(ηl = k|Y, H) Tracks update: µ(i) ← µ(i) σ(i)2 + PL l=0 rl kck σ2 C ! / 1 σ(i)2 + PL l=0 rl k σ2 C ! σ(i)2 ← 1 σ(i)2 + PL l=0 rl k σ2 C !−1 From pre-perceptual tracks to perception: Track-local features are extracted and combined to give rise to a percept. Here, track-local shifts are linearily combined to give rise to the global shift percept. Model Fitting Results The model predictions qualitatively and quantitatively match the psychophysical results. It explains perceived shift both in the presence and the absence of context. Discussion Summary Chambers et al. presented a contextual bias in auditory perception We suggest that a simple adaptive mechanism cannot account for the bias We propose a model relying on pre-perceptual tracking of auditory scenes to account for the bias and show it matches psychophysical results Pre-perceptual Grouping, a general mechanism? This work was funded by the Gatsby Charitable Foundation. Contact: vincenta@gatsby.ucl.ac.uk