Holistic Ensemble Perception

Han, Linfeng; Leib, Allison Yamanashi; Budish, Danielle; Whitney, David

doi:10.1167/19.10.194b

Cited by 3 publications

(8 citation statements)

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“…According to previous studies, correlations in the ensemble rating tasks depend on the noise level of the image (Han et al, 2020) and the inconsistency of observers' answers (Yamanashi . Texform images are noisy; therefore, it is significantly harder to rate them even in the object rating task.…”

Section: Discussionmentioning

confidence: 98%

“…The series of Experiments 1 is based on the paradigms of previous studies investigating ensemble representation for complex visual properties (Yamanashi Leib et al, 2016Han et al, 2020). In Experiment 1A, observers completed two tasks: the object rating task (Figure 1B) and the ensemble rating task (Figure 1C).…”

Section: Experiments 1amentioning

confidence: 99%

“…The analysis of the ensemble rating task was conducted in accordance with the analysis pipeline reported in previous studies (Yamanashi Leib et al, 2016Han et al, 2020). We estimated two types of correlations: first, between observer ratings in the ensemble rating task and the number of animate images, and second, between observer ratings in the ensemble rating task and mean individual rating (see General Method).…”

Section: Ensemble Rating Taskmentioning

confidence: 99%

“…Importantly, the number of animate images and mean individual rating were calculated as if the whole set was visible. This method, already described in previous studies (Piazza, et al, 2013;Sweeny et al, 2013Sweeny et al, , 2014Sweeny et al, , 2015Wolfe et al, 2015;Yamanashi Leib et al, 2014, 2016, 2020Han et al, 2020), allowed us to simulate various sampling strategies and estimate their performance.…”

Section: Experiments 1cmentioning

confidence: 99%

“…Recent studies have shown that observers can easily extract summary statistics for mid-level features such as object size (Ariely, 2001;Chong & Treisman, 2003;Tiurina & Utochkin, 2019;Markov & Tiurina, 2021;Haberman & Suresh, 2021), shape (Sweeny, Bates, & Elias, 2021;Khayat, Fusi, & Hochstein, 2021) and texture (Koenderink, Doorn, & Pont, 2004;Cant & Xu, 2012, 2017. Observers could also estimate summary statistics for high-level visual features: emotion, gender, gaze direction, and head rotation (Haberman & Whitney, 2007, 2009Sweeny & Whitney 2014;Yamanashi Leib et al, 2014;Florey et al, 2016;Han et al, 2020); categorical characteristics (Khayat & Hochstein, 2019); economic value (Yamanashi , and animacy (Yamanashi Leib et al, 2016). However, it is still unclear whether semantic processing is required to evaluate statistics for such complex abstract visual features as animacy or lifelikeness.…”

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confidence: 99%

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Ensemble Representation of Animacy Is Based on Mid-level Visual Features

Tiurina¹,

Markov²

2022

Preprint

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Studies suggest that mid-level features could underlie object animacy perception. In the current research, we tested whether ensemble animacy perception is based on high or mid-level features. We used five types of images of animals and inanimate objects: color, greyscale, silhouettes, texforms – unrecognizable images which preserve mid-level texture and shape information – and scrambled images. In the series of Experiments 1, we asked participants to evaluate the animacy of single images and sets of eight images using a 10-point scale. In the series of Experiments 2, participants were shown two sets of eight images and had to choose a more animate one in the 2AFC task. We found that in both paradigms, observers could report the mean animacy of the set of texform images without direct access to information about high- level features. We hypothesize that ensemble animacy perception can be explained only by mid-level features such as shape and texture.

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Section: Discussionmentioning

confidence: 98%

Section: Experiments 1amentioning

confidence: 99%

Section: Ensemble Rating Taskmentioning

confidence: 99%

Section: Experiments 1cmentioning

confidence: 99%

mentioning

confidence: 99%

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Ensemble Representation of Animacy Is Based on Mid-level Visual Features

Tiurina¹,

Markov²

2022

Preprint

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Task-irrelevant inputs alter ensemble representations of faces within the spatial focus of attention

Störmer,

Sayed

2024

Preprint

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Spatial attention enhances processing of information, but how does unattended and task-irrelevant information influence visual representations within the focus of attention? We tested this by asking participants to extract the average emotional expression of a set of faces while simultaneously presenting task-irrelevant faces at a spatially unattended location. Across several experiments we found that participants’ reports of the emotional expression of faces were biased towards the task-irrelevant faces. For example, if happier faces were presented at the unattended location, participants were biased to perceive the attended faces as happier. These biases towards unattended inputs were not modulated by the feature similarity between the face sets, suggesting that higher-level categorical spreading of attention underlies these effects. Together, these results indicate that unattended inputs do not only affect the efficiency of target processing but can alter the representational content of spatially attended objects, fundamentally changing our conscious visual experience.

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Ensemble representation of animacy could be based on mid-level visual features

Tiurina,

Markov

2024

Atten Percept Psychophys

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Holistic Ensemble Perception

Cited by 3 publications

References 0 publications

Ensemble Representation of Animacy Is Based on Mid-level Visual Features

Ensemble Representation of Animacy Is Based on Mid-level Visual Features

Task-irrelevant inputs alter ensemble representations of faces within the spatial focus of attention

Ensemble representation of animacy could be based on mid-level visual features

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