Effects of linguistic context and noise type on speech comprehension.

Introduction: Understanding speech in background noise is an effortful endeavor. When acoustic challenges arise, linguistic context may help us fill in perceptual gaps. However, more knowledge is needed regarding how different types of background noise affect our ability to construct meaning from perceptually complex speech input. Additionally, there is limited evidence regarding whether perceptual complexity (e.g., informational masking) and linguistic complexity (e.g., occurrence of contextually incongruous words) interact during processing of speech material that is longer and more complex than a single sentence. Our first research objective was to determine whether comprehension of spoken sentence pairs is impacted by the informational masking from a speech masker. Our second objective was to identify whether there is an interaction between perceptual and linguistic complexity during speech processing. Methods: We used multiple measures including comprehension accuracy, reaction time, and processing effort (as indicated by task-evoked pupil response), making comparisons across three different levels of linguistic complexity in two different noise conditions. Context conditions varied by final word, with each sentence pair ending with an expected exemplar (EE), within-category violation (WV), or between-category violation (BV). Forty young adults with typical hearing performed a speech comprehension in noise task over three visits. Each participant heard sentence pairs presented in either multi-talker babble or spectrally shaped steady-state noise (SSN), with the same noise condition across all three visits. Results: We observed an effect of context but not noise on accuracy. Further, we observed an interaction of noise and context in peak pupil dilation data. Specifically, the context effect was modulated by noise type: context facilitated processing only in the more perceptually complex babble noise condition. Discussion: These findings suggest that when perceptual complexity arises, listeners make use of the linguistic context to facilitate comprehension of speech obscured by background noise. Our results extend existing accounts of speech processing in noise by demonstrating how perceptual and linguistic complexity affect our ability to engage in higher-level processes, such as construction of meaning from speech segments that are longer than a single sentence.

Investigating the effects of perceptual complexity versus conceptual meaning on the object benefit in visual working memory.

Previous research has demonstrated greater visual working memory (VWM) performance for real-world objects compared with simple features. Greater amplitudes of the contralateral delay activity (CDA)-a sustained event-related potential measured during the delay period of a VWM task-have also been noted for meaningful stimuli, despite being thought of as a neural marker of a fixed working memory capacity. The current study aimed to elucidate the factors underlying improved memory performance for real-world objects by isolating the relative contributions of perceptual complexity (i.e., number of visual features) and conceptual meaning (i.e., availability of semantic, meaningful features). Participants (N = 22) performed a lateralized VWM task to test their memory of intact real-world objects, scrambled real-world objects and colours. The CDA was measured during both encoding and WM retention intervals (600-1000 ms and 1300-1700 ms poststimulus onset, respectively), and behavioural performance was estimated by using d' (memory strength in a two-alternative forced choice task). Behavioural results revealed significantly better performance within-subjects for real-world objects relative to scrambled objects and colours, with no difference between colours and scrambled objects. The amplitude of the CDA was also largest for intact real-world objects, with no difference in magnitude for scrambled objects and colours, during working memory maintenance. However, during memory encoding, both the colours and intact real-world objects had significantly greater amplitudes than scrambled objects and were comparable in magnitude. Overall, findings suggest that conceptual meaning (semantics) supports the memory benefit for real-world objects.

Characterising and dissecting human perception of scene complexity.

Humans can effortlessly assess the complexity of the visual stimuli they encounter. However, our understanding of how we do this, and the relevant factors that result in our perception of scene complexity remain unclear; especially for the natural scenes in which we are constantly immersed. We introduce several new datasets to further understanding of human perception of scene complexity. Our first dataset (VISC-C) contains 800 scenes and 800 corresponding two-dimensional complexity annotations gathered from human observers, allowing exploration for how complexity perception varies across a scene. Our second dataset, (VISC-CI) consists of inverted scenes (reflection on the horizontal axis) with corresponding complexity maps, collected from human observers. Inverting images in this fashion is associated with destruction of semantic scene characteristics when viewed by humans, and hence allows analysis of the impact of semantics on perceptual complexity. We analysed perceptual complexity from both a single-score and a two-dimensional perspective, by evaluating a set of calculable and observable perceptual features based upon grounded psychological research (clutter, symmetry, entropy and openness). We considered these factors' relationship to complexity via hierarchical regressions analyses, tested the efficacy of various neural models against our datasets, and validated our perceptual features against a large and varied complexity dataset consisting of nearly 5000 images. Our results indicate that both global image properties and semantic features are important for complexity perception. We further verified this by combining identified perceptual features with the output of a neural network predictor capable of extracting semantics, and found that we could increase the amount of explained human variance in complexity beyond that of low-level measures alone. Finally, we dissect our best performing prediction network, determining that artificial neurons learn to extract both global image properties and semantic details from scenes for complexity prediction. Based on our experimental results, we propose the "dual information" framework of complexity perception, hypothesising that humans rely on both low-level image features and high-level semantic content to evaluate the complexity of images.