Effects of Language on Visual Perception

Lupyan, Gary; Rahman, Rasha Abdel; Boroditsky, Lera; Clark, Andy

doi:10.1016/j.tics.2020.08.005

Cited by 154 publications

(120 citation statements)

References 138 publications

(156 reference statements)

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“…For instance, an individual will report hearing a speech sound that has been replaced by noise (e.g., hearing the phoneme /s/ in the frame legi_lature, where the critical phoneme has been replaced by a cough; Warren, 1970), and an individual's estimate of an object's size can be influenced by the width between a person's hands (Stefanucci & Geuss, 2009). While such context effects are ubiquitous across a range of domains in cognitive psychology, an ongoing debate--whether in the domain of language (e.g., Magnuson, Mirman, Luthra, Strauss, & Harris, 2018;Norris, McQueen, & Cutler, 2018) or the domain of vision (e.g., Firestone & Scholl, 2014Gilbert & Li, 2013;Lupyan, Abdel Rahman, Boroditsky, & Clark, 2020;Schnall, 2017aSchnall, , 2017b)--centers on how contextual information is integrated with sensory signals. In particular, do contextual effects on sensory processing reflect influences on perception itself, or does context only affect an individual's postperceptual decisions?…”

Section: Introductionmentioning

confidence: 99%

Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again

et al. 2021

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A long‐standing question in cognitive science is how high‐level knowledge is integrated with sensory input. For example, listeners can leverage lexical knowledge to interpret an ambiguous speech sound, but do such effects reflect direct top‐down influences on perception or merely postperceptual biases? A critical test case in the domain of spoken word recognition is lexically mediated compensation for coarticulation (LCfC). Previous LCfC studies have shown that a lexically restored context phoneme (e.g., /s/ in Christma#) can alter the perceived place of articulation of a subsequent target phoneme (e.g., the initial phoneme of a stimulus from a tapes‐capes continuum), consistent with the influence of an unambiguous context phoneme in the same position. Because this phoneme‐to‐phoneme compensation for coarticulation is considered sublexical, scientists agree that evidence for LCfC would constitute strong support for top–down interaction. However, results from previous LCfC studies have been inconsistent, and positive effects have often been small. Here, we conducted extensive piloting of stimuli prior to testing for LCfC. Specifically, we ensured that context items elicited robust phoneme restoration (e.g., that the final phoneme of Christma# was reliably identified as /s/) and that unambiguous context‐final segments (e.g., a clear /s/ at the end of Christmas) drove reliable compensation for coarticulation for a subsequent target phoneme. We observed robust LCfC in a well‐powered, preregistered experiment with these pretested items (N = 40) as well as in a direct replication study (N = 40). These results provide strong evidence in favor of computational models of spoken word recognition that include top–down feedback.

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Section: Introductionmentioning

confidence: 99%

Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again

et al. 2021

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“…The Bayesian brain-framework and previous empirical evidence contradict the notion of purely “bottom-up perception” (A. Clark, 2013; Lupyan et al, 2020; Lupyan & Clark, 2015). Perception is subject to top-down modulations already during early visual processing (Abdel Rahman & Sommer, 2008; Bar, 2004; Boutonnet & Lupyan, 2015; Collins & Olson, 2014; de Lange et al, 2018; Gandolfo & Downing, 2019; T. S. Lee & Mumford, 2003; Maier & Abdel Rahman, 2018, 2019; Press & Yon, 2019; Rabovsky et al, 2012; Samaha et al, 2018; Weller et al, 2019): For instance, semantic knowledge and conceptual-linguistic categories influence object perception as early as in the P1-component of the event-related potential (ERP), i.e.…”

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confidence: 82%

Conceptual knowledge affects early stages of visual mental imagery and object perception

Maier

Frömer

et al. 2020

Preprint

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When we imagine an object and when we actually see that object, similar brain regions become active. Yet, the time course of neurocognitive mechanisms that support imagery is still largely unknown. The current view holds that imagery does not share early perceptual mechanisms, but starts with high-level visual representations. However, evidence of early shared mechanisms is difficult to obtain because imagery and perception tasks typically differ in visual input. We therefore tracked electrophysiological brain responses while fully controlling visual input, (1) comparing imagery and perception of objects with varying amounts of associated knowledge, and (2) comparing the time courses of successful and incomplete imagery. Imagery and perception were similarly influenced by knowledge already at early stages, revealing shared mechanisms during low-level visual processing. It follows that imagery is not merely perception in reverse; instead, both are active and constructive processes, based on shared mechanisms starting at surprisingly early stages. Keywords: mental imagery, early visual processing, event-related potentials, semantic knowledge, P1 component MENTAL IMAGERY: TIME COURSE, COGNITIVE MECHANISMS 3 Time course and shared neurocognitive mechanisms of mental imagery and visual perception A growing body of research suggests that seeing something with the mind's eyemental imagery-may not be all that different from seeing something with one's physical eyes. Indeed, imagery and perception recruit overlapping neural circuits, including primary visual areas 1-8 , and the vividness of imagination correlates with the similarity of brain activities accompanying imagery and perception 9 . Predictive processing accounts posit that perception arises from hierarchical Bayesian predictions-essentially imaginations-that are constrained by bottom-up sensory input 10-14 .This theoretical framework is neurally plausible 15-20 and supported by evidence that even early stages of perception are subject to top-down influences 15,[21][22][23][24][25][26][27][28][29][30][31] . This suggests that initial aspects of imagery could be fast enough to generate early top-down effects.This suggestion contrasts with alternative accounts assuming that perception first runs through a strictly hierarchical succession of increasingly complex visual representations, with early stages mainly driven by bottom-up sensory processes. At later stages, recurrent feedback from higher-level brain areas is assumed to enable stabilization of visual representations and, eventually, conscious access 32,33 . Based on this account of perception, recent work has mapped out how visual imagery could follow a reverse hierarchy of activation compared to perception 6,7,[34][35][36] . Under these assumptions, imagery would not rely on early perceptual mechanisms like feature processing but start relatively late, with entire visual representations that bring several levels of the visual hierarchy into concert 6,7,35 . In support of this idea, Dijkstra, et al. 35 found ...

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“…The processes described by the Bayesian and rate distortion models of categorical perception can be motivated by predictive coding, the idea that human cognition aims to minimize its surprise about its environment (Clark, 2013;Friston & Kiebel, 2009), viewing perception as a constructive inferential process affected by those expectations (Barlow, 1990). Such a system uses any resource that helps it to predict the upcoming information, including perceptual features of an object or its categorical label (Lupyan, 2015;Lupyan & Clark, 2015;Lupyan et al, 2020). Optimizing one's prediction naturally leads to biases in these perceptual predictions, well described by the aforementioned computational models, depending on the assumed task structure and resource limitations.…”

Section: 123mentioning

confidence: 99%

The Influences of Category Learning on Perceptual Reconstructions

Dubova

Goldstone

2021

Cognitive Science

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We explore different ways in which the human visual system can adapt for perceiving and categorizing the environment. There are various accounts of supervised (categorical) and unsupervised perceptual learning, and different perspectives on the functional relationship between perception and categorization. We suggest that common experimental designs are insufficient to differentiate between hypothesized perceptual learning mechanisms and reveal their possible interplay. We propose a relatively underutilized way of studying potential categorical effects on perception, and we test the predictions of different perceptual learning models using a two-dimensional, interleaved categorizationplus-reconstruction task. We find evidence that the human visual system adapts its encodings to the feature structure of the environment, uses categorical expectations for robust reconstruction, allocates encoding resources with respect to categorization utility, and adapts to prevent miscategorizations.

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Effects of Language on Visual Perception

Cited by 154 publications

References 138 publications

Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again

Robust Lexically Mediated Compensation for Coarticulation: Christmash Time Is Here Again

Conceptual knowledge affects early stages of visual mental imagery and object perception

The Influences of Category Learning on Perceptual Reconstructions

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