2018
DOI: 10.1101/474783
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Waving goodbye to contrast: Self-generated hand movements attenuate visual sensitivity

Abstract: It is well known that the human brain continuously predicts the sensory consequences of its own body movements, which typically results in sensory attenuation. Yet, the extent and exact mechanisms underlying sensory attenuation are still debated. To explore this issue, we asked participants to decide which of two visual stimuli was of higher contrast in a virtual reality situation where one of the stimuli could appear behind the participants' invisible moving hand or not. Over two experiments, we measured the … Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
3

Citation Types

1
8
1

Year Published

2018
2018
2022
2022

Publication Types

Select...
4
2

Relationship

1
5

Authors

Journals

citations
Cited by 7 publications
(10 citation statements)
references
References 37 publications
1
8
1
Order By: Relevance
“…The above findings and their interpretation can in principle be accommodated within a hierarchical predictive coding formulation of active inference as a form of Bayes-optimal motor control, in which proprioceptive as well as visual prediction errors can update higher-level beliefs about the state of the body and thus influence action [32][33][34] . Hierarchical predictive coding rests on a probabilistic mapping from unobservable causes (hidden states) to observable consequences (sensory states), as described by a hierarchical generative model, where each level of the model encodes conditional expectations ('beliefs') about states of the world that best explains states of affairs encoded at lower levels (i.e., sensory input).…”
mentioning
confidence: 98%
See 1 more Smart Citation
“…The above findings and their interpretation can in principle be accommodated within a hierarchical predictive coding formulation of active inference as a form of Bayes-optimal motor control, in which proprioceptive as well as visual prediction errors can update higher-level beliefs about the state of the body and thus influence action [32][33][34] . Hierarchical predictive coding rests on a probabilistic mapping from unobservable causes (hidden states) to observable consequences (sensory states), as described by a hierarchical generative model, where each level of the model encodes conditional expectations ('beliefs') about states of the world that best explains states of affairs encoded at lower levels (i.e., sensory input).…”
mentioning
confidence: 98%
“…This also implicitly minimises exteroceptive prediction error; e.g. the predicted visual action consequences 34,[40][41][42] . Crucially, all ascending prediction errors are precision-weighted based on model predictions (where precision corresponds to the inverse variance), so that a prediction error that is expected to be more precise has a stronger impact on belief updating.…”
mentioning
confidence: 99%
“…In control conditions, the targets were shown in the area reflected from the fixation point ("Ref "). C) General design for a single trial in the experiments where visual sensitivity was tested (Vasser et al, 2019). Participants were instructed to perform a trained hand movement when the fixation cross changed color.…”
Section: Suppressing Self-generated Transitionsmentioning
confidence: 99%
“…The RTs in the experimental condition ("Behind hand") were slower than in the control condition ("Reflected"). E) Results of Experiment 1 of Vasser et al (2019). X axis is the value of the test contrast, Y axis denotes the percentage of trials that were reported as higher in contrast.…”
Section: Suppressing Self-generated Transitionsmentioning
confidence: 99%
See 1 more Smart Citation