Evidence map›Paper›PMID 27790106›Full record

ArticleFrontiers in human neuroscience2016

Differential Visual Processing of Animal Images, with and without Conscious Awareness.

Weina Zhu, Jan Drewes, Nicholas A Peatfield, David Melcher

Abstract read
In one paragraph

Article in Frontiers in human neuroscience, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it
0cells of the map it votes in
6citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

6 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. Article
  5. General and own-species attentional face biases.Attention, perception & psychophysics · 2021
    Article
  6. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Weina ZhuSchool of Information Science, Yunnan UniversityKunming, China; Department of Psychology, Giessen UniversityGiessen, Germany; Center for Mind/Brain Sciences (CIMeC), University of TrentoRovereto, Italy; Kunming Institute of Zoology, Chinese Academy of SciencesKunming, China.
Jan DrewesCenter for Mind/Brain Sciences (CIMeC), University of Trento Rovereto, Italy.
Nicholas A PeatfieldDepartment of Biomedical Physiology and Kinesiology, Simon Fraser University Burnaby, BC, Canada.
David MelcherCenter for Mind/Brain Sciences (CIMeC), University of Trento Rovereto, Italy.

Funding

European Research Council 313658
6 · The paper itself

Abstract

The human visual system can quickly and efficiently extract categorical information from a complex natural scene. The rapid detection of animals in a scene is one compelling example of this phenomenon, and it suggests the automatic processing of at least some types of categories with little or no attentional requirements (Li et al., 2002, 2005). The aim of this study is to investigate whether the remarkable capability to categorize complex natural scenes exist in the absence of awareness, based on recent reports that "invisible" stimuli, which do not reach conscious awareness, can still be processed by the human visual system (Pasley et al., 2004; Williams et al., 2004; Fang and He, 2005; Jiang et al., 2006, 2007; Kaunitz et al., 2011a). In two experiments, we recorded event-related potentials (ERPs) in response to animal and non-animal/vehicle stimuli in both aware and unaware conditions in a continuous flash suppression (CFS) paradigm. Our results indicate that even in the "unseen" condition, the brain responds differently to animal and non-animal/vehicle images, consistent with rapid activation of animal-selective feature detectors prior to, or outside of, suppression by the CFS mask.

Indexed as

animal detectionawarenesscontinous flash suppression (CFS)ERPs (event-related potentials)natural scenes

Identifiers

PMID27790106
PMCPMC5061858

What OpenQuestion holds

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.