The Effect of Visual Perceptual Load on Auditory Processing

Many fundamental aspects of auditory processing occur even when we are not attending to the auditory environment. This has led to a popular belief that auditory signals are analysed in a largely pre-attentive manner, allowing hearing to serve as an early warning system. However, models of attention highlight that even processes that occur by default may rely on access to perceptual resources, and so can fail in situations when demand on sensory systems is particularly high. If this is the case for auditory processing, the classic paradigms employed in auditory attention research are not sufficient to distinguish between a process that is truly automatic (i.e., will occur regardless of any competing demands on sensory processing) and one that occurs passively (i.e., without explicit intent) but is dependent on resource-availability. An approach that addresses explicitly whether an aspect of auditory analysis is contingent on access to capacity-limited resources is to control the resources available to the process; this can be achieved by actively engaging attention in a different task that depletes perceptual capacity to a greater or lesser extent. If the critical auditory process is affected by manipulating the perceptual demands of the attended task this suggests that it is subject to the availability of processing resources; in contrast a process that is automatic should not be affected by the level of load in the attended task. This approach has been firmly established within vision, but has been used relatively little to explore auditory processing. In the experiments presented in this thesis, I use MEG, pupillometry and behavioural dual-task designs to explore how auditory processing is impacted by visual perceptual load. The MEG data presented illustrate that both the overall amplitude of auditory responses, and the computational capacity of the auditory system are affected by the degree of perceptual load in a concurrent visual task. These effects are mirrored by the pupillometry data in which pupil dilation is found to reflect both the degree of load in the attended visual task (with larger pupil dilation to the high compared to the low load visual load task), and the sensory processing of irrelevant auditory signals (with reduced dilation to sounds under high versus low visual load). The data highlight that previous assumptions that auditory processing can occur automatically may be too simplistic; in fact, though many aspects of auditory processing occur passively and benefit from the allocation of spare capacity, they are not strictly automatic. Moreover, the data indicate that the impact of visual load can be seen even on the early sensory cortical responses to sound, suggesting not only that cortical processing of auditory signals is dependent on the availability of resources, but also that these resources are part of a global pool shared between vision and audition

Less is more: latent learning is maximized by shorter training sessions in auditory perceptual learning

Background: The time course and outcome of perceptual learning can be affected by the length and distribution of practice, but the training regimen parameters that govern these effects have received little systematic study in the auditory domain. We asked whether there was a minimum requirement on the number of trials within a training session for learning to occur, whether there was a maximum limit beyond which additional trials became ineffective, and whether multiple training sessions provided benefit over a single session. Methodology/Principal Findings: We investigated the efficacy of different regimens that varied in the distribution of practice across training sessions and in the overall amount of practice received on a frequency discrimination task. While learning was relatively robust to variations in regimen, the group with the shortest training sessions (~8 min) had significantly faster learning in early stages of training than groups with longer sessions. In later stages, the group with the longest training sessions (>1 hr) showed slower learning than the other groups, suggesting overtraining. Between-session improvements were inversely correlated with performance; they were largest at the start of training and reduced as training progressed. In a second experiment we found no additional longer-term improvement in performance, retention, or transfer of learning for a group that trained over 4 sessions (~4 hr in total) relative to a group that trained for a single session (~1 hr). However, the mechanisms of learning differed; the single-session group continued to improve in the days following cessation of training, whereas the multi-session group showed no further improvement once training had ceased. Conclusions/Significance: Shorter training sessions were advantageous because they allowed for more latent, between-session and post-training learning to emerge. These findings suggest that efficient regimens should use short training sessions, and optimized spacing between sessions

Auditory Discrimination Learning:Role of Working Memory

Perceptual training is generally assumed to improve perception by modifying the encoding or decoding of sensory information. However, this assumption is incompatible with recent demonstrations that transfer of learning can be enhanced by across-trial variation of training stimuli or task. Here we present three lines of evidence from healthy adults in support of the idea that the enhanced transfer of auditory discrimination learning is mediated by working memory (WM). First, the ability to discriminate small differences in tone frequency or duration was correlated with WM measured with a tone n-back task. Second, training frequency discrimination around a variable frequency transferred to and from WM learning, but training around a fixed frequency did not. The transfer of learning in both directions was correlated with a reduction of the influence of stimulus variation in the discrimination task, linking WM and its improvement to across-trial stimulus interaction in auditory discrimination. Third, while WM training transferred broadly to other WM and auditory discrimination tasks, variable-frequency training on duration discrimination did not improve WM, indicating that stimulus variation challenges and trains WM only if the task demands stimulus updating in the varied dimension. The results provide empirical evidence as well as a theoretic framework for interactions between cognitive and sensory plasticity during perceptual experience

Feedback valence affects auditory perceptual learning independently of feedback probability.

Previous studies have suggested that negative feedback is more effective in driving learning than positive feedback. We investigated the effect on learning of providing varying amounts of negative and positive feedback while listeners attempted to discriminate between three identical tones; an impossible task that nevertheless produces robust learning. Four feedback conditions were compared during training: 90% positive feedback or 10% negative feedback informed the participants that they were doing equally well, while 10% positive or 90% negative feedback informed them they were doing equally badly. In all conditions the feedback was random in relation to the listeners' responses (because the task was to discriminate three identical tones), yet both the valence (negative vs. positive) and the probability of feedback (10% vs. 90%) affected learning. Feedback that informed listeners they were doing badly resulted in better post-training performance than feedback that informed them they were doing well, independent of valence. In addition, positive feedback during training resulted in better post-training performance than negative feedback, but only positive feedback indicating listeners were doing badly on the task resulted in learning. As we have previously speculated, feedback that better reflected the difficulty of the task was more effective in driving learning than feedback that suggested performance was better than it should have been given perceived task difficulty. But contrary to expectations, positive feedback was more effective than negative feedback in driving learning. Feedback thus had two separable effects on learning: feedback valence affected motivation on a subjectively difficult task, and learning occurred only when feedback probability reflected the subjective difficulty. To optimize learning, training programs need to take into consideration both feedback valence and probability.The research was funded by the Medical Research Council, UK (Grant U135097130; http://www.mrc.ac.uk/), which supported SA, DRM and KM through intramural funding

Does training with amplitude modulated tones affect tone-vocoded speech perception?

Temporal-envelope cues are essential for successful speech perception. We asked here whether training on stimuli containing temporal-envelope cues without speech content can improve the perception of spectrally-degraded (vocoded) speech in which the temporal-envelope (but not the temporal fine structure) is mainly preserved. Two groups of listeners were trained on different amplitude-modulation (AM) based tasks, either AM detection or AM-rate discrimination (21 blocks of 60 trials during two days, 1260 trials; frequency range: 4Hz, 8Hz, and 16Hz), while an additional control group did not undertake any training. Consonant identification in vocoded vowel-consonant-vowel stimuli was tested before and after training on the AM tasks (or at an equivalent time interval for the control group). Following training, only the trained groups showed a significant improvement in the perception of vocoded speech, but the improvement did not significantly differ from that observed for controls. Thus, we do not find convincing evidence that this amount of training with temporal-envelope cues without speech content provide significant benefit for vocoded speech intelligibility. Alternative training regimens using vocoded speech along the linguistic hierarchy should be explored

Does training with amplitude modulated tones affect tone-vocoded speech perception?

Temporal-envelope cues are essential for successful speech perception. We asked here whether training on stimuli containing temporal-envelope cues without speech content can improve the perception of spectrally-degraded (vocoded) speech in which the temporal-envelope (but not the temporal fine structure) is mainly preserved. Two groups of listeners were trained on different amplitude-modulation (AM) based tasks, either AM detection or AM-rate discrimination (21 blocks of 60 trials during two days, 1260 trials; frequency range: 4Hz, 8Hz, and 16Hz), while an additional control group did not undertake any training. Consonant identification in vocoded vowel-consonant-vowel stimuli was tested before and after training on the AM tasks (or at an equivalent time interval for the control group). Following training, only the trained groups showed a significant improvement in the perception of vocoded speech, but the improvement did not significantly differ from that observed for controls. Thus, we do not find convincing evidence that this amount of training with temporal-envelope cues without speech content provide significant benefit for vocoded speech intelligibility. Alternative training regimens using vocoded speech along the linguistic hierarchy should be explored

Comparison of learning rates in the later stage of training.

<p>Group mean DLFs after the first 800 trials for groups T800, T400 and T200 from Experiment 1 (see Fig. 5B), and the T800 m group from Experiment 2. Data points are mean thresholds for 100 trials each, and solid lines are least squares logarithmic fits. Error bars were omitted, since analyses compared slopes not individual points.</p

Progress of learning over training days.

<p>Group mean DLFs for each training day. DLFs from block 1 are plotted at the far left, followed by daily DLFs for each training day (note that the block 1 DLFs were not reused in calculating the mean for Day 1). Solid lines are least squares logarithmic fits plotted on a log-log scale to appear linear. Error bars were omitted, since analyses compared slopes not individual points.</p

Comparison of single and multi-session training for learning, retention and transfer of learning.

<p>(A) Group mean thresholds for training. Solid lines are least squares logarithmic fits. Error bars were omitted for clarity as they overlapped at each point. Note that the pre-test (where groups were initially matched) is not included in this figure or in fitting the learning curves (B) Pre-, post- and retention tests at the trained frequency (1 kHz), adjusted for individual differences in pre-test performance at 1 kHz. (C) Pre-, post- and retention tests at the untrained frequency (4 kHz), adjusted for individual differences in pre-test performance at 4 kHz. Error bars in panels B and C show ±SEM.</p