Perceptual prioritization of self‐associated voices

Payne, Bryony; Lavan, Nadine; Knight, Sarah; McGettigan, Carolyn

doi:10.1111/bjop.12479

Cited by 12 publications

(22 citation statements)

References 33 publications

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“…First, most studies that aimed to establish the effects of self-association on perception relied on the perceptual matching paradigm (e.g., Moradi et al, 2015; B. Payne et al, 2020; S. Payne et al, 2017; Reuther & Chakravarthi, 2017; Sui et al, 2012). However, the paradigm does not, in itself, isolate perceptual processes.…”

Section: Does Self-relevance Modulate “Perception”? the Need For A Cl...mentioning

confidence: 99%

“…However, the vast majority of studies investigated self-prioritization in unisensory, visual tasks, with only few studies providing further evidence for the beneficial effects of self-relevance on auditory or tactile processing (B. Payne et al, 2020; Schäfer, Wesslein, et al, 2016).…”

Section: Does Self-relevance Modulate “Perception”? the Need For A Cl...mentioning

confidence: 99%

“…There have been two crucial limitations in discerning perceptual contributions to SPE generation, in contrast to other, higher levels of processing. First, most studies that aimed to establish the effects of self-association on perception relied on the perceptual matching paradigm (e.g., Moradi et al, 2015;B. Payne et al, 2020;S.…”

mentioning

confidence: 99%

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Social relevance modulates multisensory integration.

Scheller¹,

Shen²

2022

Journal of Experimental Psychology: Human Perception and Perfor

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When interacting with the environment, humans exhibit robust biases toward information that pertains to themselves: Self-relevant information is processed faster and yields more accurate responses than information linked to others. Recent studies have shown that simple social associations can lead to the instant deployment of this benefit in the processing of abstract stimuli. However, how self-prioritization evolves across the processing hierarchy has been a subject of intense debate. Furthermore, there is little empirical evidence about the functional efficiency of social relevance in natural environments in which information is present across multiple senses. Across three experiments (each n = 40), the present study shows that self-prioritization effects (a) can arise in simple audio-visual numerosity judgements, (b) can be efficiently deployed across the senses by funneling perception toward self-relevant information in the more reliable sensory modality, and (c) modulate the integration of auditory and visual information into a multisensory representation. Taken together, the present findings suggest that social relevance can influence multisensory processing at both perceptual and postperceptual stages via early attentional modulations of sensory integration and later, task-dependent attentional control. Public Significance StatementThis study provides evidence that self-relevance of abstract information (temporal event numerosity) leads to changes in multisensory perception. Establishing self-relevance via social associations of oneself or another person with two visual event numerosities leads to a relative performance facilitation for self-related information that transfers to auditory and multisensory contexts. Furthermore, in relation to other-associated information, self-association with event numerosities modulates audio-visual integration by increasing sensitivity and decreasing bias in the fusion illusion. Together, the present findings suggest that social salience can influence multisensory processing via attentional modulations.

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Section: Does Self-relevance Modulate “Perception”? the Need For A Cl...mentioning

confidence: 99%

Section: Does Self-relevance Modulate “Perception”? the Need For A Cl...mentioning

confidence: 99%

See 1 more Smart Citation

Social relevance modulates multisensory integration.

Scheller¹,

Shen²

2022

Journal of Experimental Psychology: Human Perception and Perfor

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“…Finally, people who lose their voice entirely can use synthesised voices. Companies like Cereproc that synthesise characterful, engaging, and emotional voices with varying accents could help people choose a voice that they feel truly represents their 'self ' (Payne et al, 2021;Addlesee, 2023). Voice cloning is also possible, opening up the use of voice-banking technology to people at risk of losing their voice.…”

Section: Voice Assistant Accessibilitymentioning

confidence: 99%

You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification

Addlesee,

Eshghi

2024

Front. Dement.

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In spontaneous conversation, speakers seldom have a full plan of what they are going to say in advance: they need to conceptualise and plan incrementally as they articulate each word in turn. This often leads to long pauses mid-utterance. Listeners either wait out the pause, offer a possible completion, or respond with an incremental clarification request (iCR), intended to recover the rest of the truncated turn. The ability to generate iCRs in response to pauses is therefore important in building natural and robust everyday voice assistants (EVA) such as Amazon Alexa. This becomes crucial with people with dementia (PwDs) as a target user group since they are known to pause longer and more frequently, with current state-of-the-art EVAs interrupting them prematurely, leading to frustration and breakdown of the interaction. In this article, we first use two existing corpora of truncated utterances to establish the generation of clarification requests as an effective strategy for recovering from interruptions. We then proceed to report on, analyse, and release SLUICE-CR: a new corpus of 3,000 crowdsourced, human-produced iCRs, the first of its kind. We use this corpus to probe the incremental processing capability of a number of state-of-the-art large language models (LLMs) by evaluating (1) the quality of the model's generated iCRs in response to incomplete questions and (2) the ability of the said LLMs to respond correctly after the users response to the generated iCR. For (1), our experiments show that the ability to generate contextually appropriate iCRs only emerges at larger LLM sizes and only when prompted with example iCRs from our corpus. For (2), our results are in line with (1), that is, that larger LLMs interpret incremental clarificational exchanges more effectively. Overall, our results indicate that autoregressive language models (LMs) are, in principle, able to both understand and generate language incrementally and that LLMs can be configured to handle speech phenomena more commonly produced by PwDs, mitigating frustration with today's EVAs by improving their accessibility.

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“…The voice clips consisted of recordings of the word “hello” spoken in a neutral tone by four different male Southern Standard British English speakers. These were taken from a larger set of recordings collected for use in a different study ( Payne et al, 2020 ). Selection of these voice clips from this larger pool was guided by participant ratings of different traits in previous pilot work carried out online.…”

Section: Experiments 1: Trait Learning With Auditory Targetsmentioning

confidence: 99%

Instrumental learning in social interactions: Trait learning from faces and voices

Bradshaw

McGettigan

2021

Quarterly Journal of Experimental Psychology

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Recent research suggests that reinforcement learning may underlie trait formation in social interactions with faces (Hackel, Doll, & Amodio, 2015; Hackel, Mende-Siedlecki, & Amodio, 2020). The current study investigated whether the same learning mechanisms could be engaged for trait learning from voices. On each trial of a training phase, participants (N = 192) chose from pairs of human or slot machine targets that varied in the 1) reward value and 2) generosity of their payouts. Targets were either auditory (voices or tones; Experiment 1) or visual (faces or icons; Experiment 2), and were presented sequentially before payout feedback. A test phase measured participant choice behaviour, and a post-test recorded their target preference ratings. For auditory targets, we found no effect of reward or generosity on target choices, but saw higher preference ratings for generous humans and slot machines. For visual targets, participants learned about both generosity and reward, but generosity was prioritised in the human condition. These findings demonstrate that (1) reinforcement learning of trait information with visual stimuli remains intact even when sequential presentation introduces a delay in feedback and (2) learning about traits and reward in such paradigms is weakened when auditory stimuli are used.

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Perceptual prioritization of self‐associated voices

Cited by 12 publications

References 33 publications

Social relevance modulates multisensory integration.

Social relevance modulates multisensory integration.

You have interrupted me again!: making voice assistants more dementia-friendly with incremental clarification

Instrumental learning in social interactions: Trait learning from faces and voices

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