Name
Abstract | ||
|---|---|---|
Many natural stimuli have perceptual ambiguities that can be cognitively resolved by the surrounding context. In audition, preceding context can bias the perception of speech and non-speech stimuli. Here, we develop a neuronal network model that can account for how context affects the perception of pitch change between a pair of successive complex tones. We focus especially on an ambiguous comparison listeners experience opposite percepts (either ascending or descending) for an ambiguous tone pair depending on the spectral location of preceding context tones. We developed a recurrent, firing-rate network model, which detects frequency-change-direction of successively played stimuli and successfully accounts for the context-dependent perception demonstrated in behavioral experiments. The model consists of two tonotopically organized, excitatory populations, E-up and E-down, that respond preferentially to ascending or descending stimuli in pitch, respectively. These preferences are generated by an inhibitory population that provides inhibition asymmetric in frequency to the two populations; context dependence arises from slow facilitation of inhibition. We show that contextual influence depends on the spectral distribution of preceding tones and the tuning width of inhibitory neurons. Further, we demonstrate, using phase-space analysis, how the facilitated inhibition from previous stimuli and the waning inhibition from the just-preceding tone shape the competition between the Eup and Edown populations. In sum, our model accounts for contextual influences on the pitch change perception of an ambiguous tone pair by introducing a novel decoding strategy based on direction-selective units. The model's network architecture and slow facilitating inhibition emerge as predictions of neuronal mechanisms for these perceptual dynamics. Since the model structure does not depend on the specific stimuli, we show that it generalizes to other contextual effects and stimulus types. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.3389/fncom.2015.00101 | FRONTIERS IN COMPUTATIONAL NEUROSCIENCE |
Keywords | Field | DocType |
auditory illusion,adaptation,neuromechanistic modeling,Shepard tone,context | Population,Neuroscience,Facilitation,Computer science,Auditory illusion,Shepard tone,Stimulus (physiology),Biological neural network,Perception,Network model | Journal |
Volume | ISSN | Citations |
9 | 1662-5188 | 0 |
PageRank | References | Authors |
0.34 | 5 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| chengcheng huang | 1 | 0 | 0.34 |
| Bernhard Englitz | 2 | 2 | 0.81 |
| Shihab Shamma | 3 | 554 | 67.25 |
| John Rinzel | 4 | 459 | 219.68 |