Hemodynamic Responses Link Individual Differences in Informational Masking to the Vicinity of Superior Temporal Gyrus

Abstract

Suppressing unwanted background sound is crucial for aural communication. A particularly disruptive type of background sound, informational masking (IM), often interferes in social settings. However, IM mechanisms are incompletely understood. At present, IM is identified operationally: when a target should be audible, based on suprathreshold target/masker energy ratios, yet cannot be heard because target-like background sound interferes. We here confirm that speech identification thresholds differ dramatically between low- vs. high-IM background sound. However, speech detection thresholds are comparable across the two conditions. Moreover, functional near infrared spectroscopy recordings show that task-evoked blood oxygenation changes near the superior temporal gyrus (STG) covary with behavioral speech detection performance for high-IM but not low-IM background sound, suggesting that the STG is part of an IM-dependent network. Moreover, listeners who are more vulnerable to IM show increased hemodynamic recruitment near STG, an effect that cannot be explained based on differences in task difficulty across low- vs. high-IM. In contrast, task-evoked responses near another auditory region of cortex, the caudal inferior frontal sulcus (cIFS), do not predict behavioral sensitivity, suggesting that the cIFS belongs to an IM-independent network. Results are consistent with the idea that cortical gating shapes individual vulnerability to IM.

Informational Masking Constrains Vocal Communication in Nonhuman Animals

“…Other studies have reported similarly large individual differences in speech recognition thresholds in the presence of competing speech or speech-shaped noise (Swaminathan et al, 2015;Wightman et al, 2010;Zhang et al, 2021). Large individual differences among humans in susceptibility to informational masking have been attributed to several possible factors, including differences in sensory coding, neural processing, and attentional or cognitive strategies (Lutfi et al, 2003;Swaminathan et al, 2015).…”

Combining Multiple Psychophysiological Measures of Listening Effort: Challenges and Recommendations

“…For fNIRS research, this approach has been taken recently by Zhang and colleagues who used a breath-holding task to scale the fNIRS response differences between conditions by the physiologically plausible range of the fNIRS response before performing the statistical analysis. 77 Unfortunately, information about the absolute minimum and maximum response of many of the physiological measures employed in listening effort research is often not available. For instance, no information is known regarding the physiological maximum of a skin conductance response.…”

fNIRS Dataset During Complex Scene Analysis

“…Due to its portability and low cost, fNIRS has been used in Brain-Computer Interface (BCI) applications (Naseer and Hong, 2015). Previously, fNIRS has been applied to various aspects of auditory science such as classifying different sound categories (Hong and Santosa, 2016), identifying spatial locations of noise stimuli (Tian et al, 2021), characterizing hemodynamic responses to varying auditory stimuli (Pollonini et al, 2014; Steinmetzger et al, 2020; Luke et al, 2021), and investigating informational masking (Zhang, Mary Ying and Ihlefeld, 2018; Zhang, Alamatsaz and Ihlefeld, 2021). However, to date, fNIRS has not been applied to decode auditory and visual-spatial attention during CSA, and thus, no such dataset exists yet.…”