Linguistic labels cue biological motion perception and misperception

Abstract

Linguistic labels exert a particularly strong top-down influence on perception. The potency of this influence has been ascribed to their ability to evoke category-diagnostic features of concepts. In doing this, they facilitate the formation of a perceptual template concordant with those features, effectively biasing perceptual activation towards the labelled category. In this study, we employ a cueing paradigm with moving, point-light stimuli across three experiments, in order to examine how the number of biological motion features (form and kinematics) encoded in lexical cues modulates the efficacy of lexical top-down influence on perception. We find that the magnitude of lexical influence on biological motion perception rises as a function of the number of biological motion-relevant features carried by both cue and target. When lexical cues encode multiple biological motion features, this influence is robust enough to mislead participants into reporting erroneous percepts, even when a masking level yielding high performance is used.

Grounded Cognition Entails Linguistic Relativity: Response to Commentators

“…I concur with all of these points and look forward to seeing how future work on emotion concepts continues to refine them. Slivac and Flecken (2023) advance this multidisciplinary discussion even further by considering how semantic typology can be connected with predictive coding theory. This theory has been gaining prominence across the cognitive sciences, and it maintains that perception is basically the brain's "best guess" regarding the causes of its inputs, reflecting top-down knowledge-driven inference more than bottom-up sensory-driven feature processing (Clark, 2023).…”

“…Other researchers have made the same point (Barrett, 2017;Barsalou, 2009;Michel, 2022), but Slivac and Flecken's (2023, p. 659) unique contribution is to emphasize that "cross-linguistic differences in semantic … categories lead to differences in the language-induced priors that people rely on." Slivac and Flecken (2023) discuss some ways in which this connection between semantic typology and predictive coding theory has implications for linguistic relativity. Here, I will simply make one additional observation.…”

“…This theory has been gaining prominence across the cognitive sciences, and it maintains that perception is basically the brain's “best guess” regarding the causes of its inputs, reflecting top‐down knowledge‐driven inference more than bottom‐up sensory‐driven feature processing (Clark, 2023). According to Slivac and Flecken (2023, p. 659), the concepts encoded by words fit into this framework because they “function as long‐term priors similar to other types of priors that shape perception, and that develop throughout infancy and childhood as our experiences with the world grow.” Other researchers have made the same point (Barrett, 2017; Barsalou, 2009; Michel, 2022), but Slivac and Flecken's (2023, p. 659) unique contribution is to emphasize that “cross‐linguistic differences in semantic … categories lead to differences in the language‐induced priors that people rely on.”…”

Instant Effects of Semantic Information on Visual Perception

“…This cognitive impenetrability hypothesis is challenged by another view, namely predictive coding theories that posit that higher-level areas influence ongoing perceptual processing early on by sending predictions down to lower-level areas (Churchland et al, 1994;Ahissar and Hochstein, 2004;Yuille and Kersten, 2006;Friston and Kiebel, 2009;Clark, 2013;Lupyan, 2015;Thierry, 2016;Teufel and Nanay, 2017;Lupyan et al, 2020). This view is supported by differences in ratings, detection rates, or reaction times for visual stimuli depending on their emotional (e.g., Phelps et al, 2016), linguistic (e.g., Slivac et al, 2021) or semantic (e.g., Gauthier et al, 2003) content. However, some of these studies received legitimate criticism, e.g., because comparisons between critical conditions included the confound of additionally comparing different visual stimuli, or for not being able to distinguish between perceptual or post-perceptual loci of tentative top-down effects based on behavioral measures (Firestone and Scholl, 2016).…”

Linguistic Priors for Perception

“…However, given the noisiness of our everyday lives, they might make us rely on what is statistically most likely in any given context, and what is statistically most likely might very well be reinforced through language and the systematicity in the concepts and categories encoded therein. It is no surprise then that an effect of language on perception is strongest or qualitatively different when we are in the presence of ambiguous sensory information or when we are under time pressure to quickly discern what is in front of us (Slivac, Hervais-Adelman, Hagoort & Flecken (2021); Kok & Turk-Browne (2017)). In line with the subtlety of such effects, we circle back to the necessity to focus on fine-grained differences in neural activation patterns evoked by language, examinable through multivariate pattern analysis approaches, such as representational similarity analysis or machine learning.…”