On suppressing unwanted cues via randomization

Sensorineural Hearing Loss Diminishes Use of Temporal Envelope Cues: Evidence From Roving-Level Tone-in-Noise Detection

OBJECTIVES

The objective of our study is to understand how listeners with and without sensorineural hearing loss (SNHL) use energy and temporal envelope cues to detect tones in noise. Previous studies of low-frequency tone-in-noise detection have shown that when energy cues are made less reliable using a roving-level paradigm, thresholds of listeners with normal hearing (NH) are only slightly increased. This result is consistent with studies demonstrating the importance of temporal envelope cues for masked detection. In contrast, roving-level detection thresholds are more elevated in listeners with SNHL at the test frequency, suggesting stronger weighting of energy cues. The present study extended these tests to a wide range of frequencies and stimulus levels. The authors hypothesized that individual listeners with SNHL use energy and temporal envelope cues differently for masked detection at different frequencies and levels, depending on the degree of hearing loss.

DESIGN

Twelve listeners with mild to moderate SNHL and 12 NH listeners participated. Tone-in-noise detection thresholds at 0.5, 1, 2, and 4 kHz in 1/3 octave bands of simultaneously gated Gaussian noise were obtained using a novel, two-part tracking paradigm. A track refers to the sequence of trials in an adaptive test procedure; the signal to noise ratio was the tracked variable. Each part of the track consisted of a two-alternative, two-interval, forced-choice procedure. The initial portion of the track estimated detection threshold using a fixed masker level. When the track continued, stimulus levels were randomly varied over a 20-dB rove range (±10 dB with respect to mean masker level), and a second threshold was estimated. Rove effect (RE) was defined as the difference between thresholds for the fixed- and roving-level tests. The size of the RE indicated how strongly a listener weighted energy-based cues for masked detection. Participants were tested at one to three masker levels per frequency, depending on audibility.

RESULTS

Across all stimulus frequencies and levels, NH listeners had small REs (≈1 dB), whereas listeners with SNHL typically had larger REs. Some listeners with SNHL had larger REs at higher frequencies, where pure-tone audiometric thresholds were typically elevated. RE did not vary significantly with masker level for either group. Increased RE for the SNHL group was consistent with simulations in which energy cues were more heavily weighted than envelope cues.

CONCLUSIONS

Tone-in-noise detection thresholds in NH listeners were typically elevated only slightly by the roving-level paradigm at any frequency or level tested, consistent with the primary use of level-independent cues, such as temporal envelope cues that are conveyed by fluctuations in neural responses. In comparison, thresholds of listeners with SNHL were more affected by the roving-level paradigm, suggesting stronger weighting of energy cues. For listeners with SNHL, the largest RE was observed at 4000 Hz, for which pure-tone audiometric thresholds were most elevated. Specifically, RE size at 4000 Hz was significantly correlated with higher pure-tone audiometric thresholds at the same frequency, after controlling for the effect of age. Future studies will explore strategies for restoring or enhancing neural fluctuation cues that may lead to improved hearing in noise for listeners with SNHL.

A general formula for computing maximum proportion correct scores in various psychophysical paradigms with arbitrary probability distributions of stimulus observations

Proportion correct (Pc) is a fundamental measure of task performance in psychophysics. The maximum Pc score that can be achieved by an optimal (maximum-likelihood) observer in a given task is of both theoretical and practical importance, because it sets an upper limit on human performance. Within the framework of signal detection theory, analytical solutions for computing the maximum Pc score have been established for several common experimental paradigms under the assumption of Gaussian additive internal noise. However, as the scope of applications of psychophysical signal detection theory expands, the need is growing for psychophysicists to compute maximum Pc scores for situations involving non-Gaussian (internal or stimulus-induced) noise. In this article, we provide a general formula for computing the maximum Pc in various psychophysical experimental paradigms for arbitrary probability distributions of sensory activity. Moreover, easy-to-use MATLAB code implementing the formula is provided. Practical applications of the formula are illustrated, and its accuracy is evaluated, for two paradigms and two types of probability distributions (uniform and Gaussian). The results demonstrate that Pc scores computed using the formula remain accurate even for continuous probability distributions, as long as the conversion from continuous probability density functions to discrete probability mass functions is supported by a sufficiently high sampling resolution. We hope that the exposition in this article, and the freely available MATLAB code, facilitates calculations of maximum performance for a wider range of experimental situations, as well as explorations of the impact of different assumptions concerning internal-noise distributions on maximum performance in psychophysical experiments.

Separating the contributions of primary and unwanted cues in psychophysical studies

A fundamental issue in the design and the interpretation of experimental studies of perception relates to the question of whether the participants in these experiments could perform the perceptual task assigned to them using another feature, or cue, than that intended by the experimenter. An approach frequently used by auditory- and visual-perception researchers to guard against this possibility involves applying random variations to the stimuli across presentations or trials so as to make the "unwanted" cue unreliable for the participants. However, the theoretical basis of this widespread practice is not well developed. In this article, we describe a 2-channel model based on general principles of psychophysical signal detection theory, which can be used to assess the respective contributions of the unwanted cue and of the primary cue to performance or thresholds measured in perceptual discrimination experiments involving stimulus randomization. Example applications of the model to the analysis of results obtained in representative studies from the auditory- and visual-perception literature are provided. In several cases, the results of the model-based analyses indicate that the effectiveness of the randomization procedure was less than originally assumed by the authors of these studies. These findings underscore the importance of quantifying the potential influence of unwanted cues on the results of psychophysical experiments, even when stimulus randomization is used.

On the choice of adequate randomization ranges for limiting the use of unwanted cues in same-different, dual-pair, and oddity tasks

A major concern when designing a psychophysical experiment is that participants may use a stimulus feature (cue) other than that intended by the experimenter. One way to avoid this problem is to apply random variations to the corresponding feature across stimulus presentations to make the unwanted cue unreliable. An important question facing experimenters who use this randomization (roving) technique is how large the randomization range should be to ensure that the participants cannot achieve a certain proportion correct by using the unwanted cue, while at the same time avoiding unnecessary interference of the randomization with task performance. Previous researchers have provided formulas for the selection of adequate randomization ranges in yes-no and multiple-alternative forced choice tasks. In this article, we provide figures and tables that can be used to select randomization ranges that are better suited to experiments involving a same-different, dual-pair, or oddity task.

Limiting unwanted cues via random rove applied to the yes-no and multiple-alternative forced choice paradigms

When a random rove is used in a perceptual task to control the influence of an unwanted cue that may confound the decision strategy of primary interest, the effectiveness of the rove is determined by its range. Green [Profile Analysis (Oxford University Press, Oxford, 1988)] provided a formula which allows experimenters to determine the roving range required to ensure that the listeners relying on the unwanted cue cannot exceed a pre-defined percentage of correct responses in a two-interval, two-alternative forced-choice experiment. Here, Green's analysis is extended to the yes-no and m-alternative, forced-choice paradigms (m>2).