Attentional and Contextual Priors in Sound Perception

Abstract

Behavioral and neural studies of selective attention have consistently demonstrated that explicit attentional cues to particular perceptual features profoundly alter perception and performance. The statistics of the sensory environment can also provide cues about what perceptual features to expect, but the extent to which these more implicit contextual cues impact perception and performance, as well as their relationship to explicit attentional cues, is not well understood. In this study, the explicit cues, or attentional prior probabilities, and the implicit cues, or contextual prior probabilities, associated with different acoustic frequencies in a detection task were simultaneously manipulated. Both attentional and contextual priors had similarly large but independent impacts on sound detectability, with evidence that listeners tracked and used contextual priors for a variety of sound classes (pure tones, harmonic complexes, and vowels). Further analyses showed that listeners updated their contextual priors rapidly and optimally, given the changing acoustic frequency statistics inherent in the paradigm. A Bayesian Observer model accounted for both attentional and contextual adaptations found with listeners. These results bolster the interpretation of perception as Bayesian inference, and suggest that some effects attributed to selective attention may be a special case of contextual prior integration along a feature axis.

Mice tune out not in: Violation of prediction drives auditory saliency

“…While both primates and carnivores might use their auditory 296 senses to tune in and follow potential prey or conspecific communication signals, 297 tracking of the statistics in mice -a prey species -may predominantly serve to 298 detect sudden, potentially dangerous changes in the environment. Interestingly 299 and consistent with that hypothesis, while implicit cueing in humans usually 300 enhances perception of high-probability signals (Girshick et al, 2011;Scharf et al, 301 1987;Wolmetz and Elhilali, 2016), threatening stimuli are best perceived if they In summary, our study provides the first evidence for animal detection behavior In total 14 adult male mice bred at the University of Oldenburg animal facilities 325 were used in the experiments. All mice had a C57BL/6.CAST-Cdh23 Ahl+ background 326 (the Jackson laboratory, #002756) and were between 3 and 9 month old.…”

“…x-axis: hit rate when the stimulus in the trial before was drawn from the same class as the current stimulus ('repetition'), y-axis: hit rate to stimuli that were preceded by a stimulus from another class ('switch'). 196 Stimulus statistics in auditory scenes have been suggested to shape auditory 197 perception in two contrary ways: (1) A focus on novelty detection, favouring low-198 probability sounds (Khouri and Nelken, 2015) and (2) (Scharf et al, 1987;Wolmetz and Elhilali, 2016). Here we tested whether attention 201 in mice is drawn rather towards low-or high-probability target sounds.…”

“…Greenberg and Larkin, 1968). This form of selective auditory 40 attention does not require awareness of the subject and is driven by unconscious 41 expectations (Wolmetz and Elhilali, 2016). Within this framework, the 42 improvement of detectability is based on the expectation as an implicit cue and 43 serves as an internal reward-maximizing strategy that drives the attention towards 44 the expectation (Girshick et al, 2011).…”

“…This is probably due to its pre-attentive nature -deviance detection is 276 observable in passively listening subjects (Tiitinen et al, 1994) as well as in 277 anaesthetized animals (Antunes et al, 2010). In active listening tasks, implicit 278 cueing could then use the predictive signal to channel selective attention to high-279 probability sounds (Wolmetz and Elhilali, 2016) by inverting the sign. The (Bendixen, 2014;Sussman, 2007).…”

Selective attentional processes in cochlear implant recipients: Measurements of the attentional filter

“…However, s uch cue-driven attentional processes could potentially aid in the detection o f repeated or expected signals in the presence of background noise. There is evidence that cued detection could play a role in auditory scene analysis and that listeners can use acoustic cues to dynamically track a particular speaker in the presence of competing voices (Wolmetz andElhilali, 2016 , Woods andMcDermott, 2015 ).…”