Attention in the real world: toward understanding its neural basis

Peelen, Marius V.; Kästner, Sabine

doi:10.1016/j.tics.2014.02.004

Cited by 193 publications

(199 citation statements)

References 100 publications

(139 reference statements)

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“…Compared with lower-level features (e.g., absolute orientation, luminance), higher-level properties (e.g., ordinal orientation, facial expression) are more invariant and categorical, and are thus easier to specify and maintain in working memory. They are also more behaviorally relevant (47). For example, the absolute orientation of a person's eyebrow varies constantly with viewers' head and eye orientations, providing little useful information.…”

Section: Discussionmentioning

confidence: 99%

Visual perception as retrospective Bayesian decoding from high- to low-level features

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Tsodyks

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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When a stimulus is presented, its encoding is known to progress from low-to high-level features. How these features are decoded to produce perception is less clear, and most models assume that decoding follows the same low-to high-level hierarchy of encoding. There are also theories arguing for global precedence, reversed hierarchy, or bidirectional processing, but they are descriptive without quantitative comparison with human perception. Moreover, observers often inspect different parts of a scene sequentially to form overall perception, suggesting that perceptual decoding requires working memory, yet few models consider how working-memory properties may affect decoding hierarchy. We probed decoding hierarchy by comparing absolute judgments of single orientations and relative/ordinal judgments between two sequentially presented orientations. We found that lower-level, absolute judgments failed to account for higher-level, relative/ordinal judgments. However, when ordinal judgment was used to retrospectively decode memory representations of absolute orientations, striking aspects of absolute judgments, including the correlation and forward/backward aftereffects between two reported orientations in a trial, were explained. We propose that the brain prioritizes decoding of higher-level features because they are more behaviorally relevant, and more invariant and categorical, and thus easier to specify and maintain in noisy working memory, and that more reliable higher-level decoding constrains less reliable lower-level decoding.Bayesian prior | interreport correlation | bidirectional tilt aftereffect | efficient coding | adaptation theory V isual stimuli evoke neuronal responses (a process termed encoding), which lead to our perceptual estimation of the stimuli (decoding). Experimental studies have firmly established that encoding is hierarchical, progressing from lower-level representations of simpler and less invariant features to higher-level representations of more complex and invariant features along visual pathways (1). Researchers have also studied decoding by using models to relate neuronal responses to perceptual estimation. Most models posit, explicitly or implicitly, that decoding follows the same low-to high-level hierarchy, often in the form of what we call the absolute-to-relative assumption (2-6). For example, these models may decode V1 responses to a line into a perceived orientation of, say 51.2°(or a distribution around it). Psychophysically, this is termed an absolute judgment. To determine the relationship between two lines, the models first decode each orientation separately and the two resulting absolute orientations are then compared. For instance, the two absolute orientations may be subtracted to obtain the angle between the lines (relative orientation), or the sign of the difference may be used to determine whether the second line is clockwise or counterclockwise from the first (ordinal orientation discrimination). Absolute orientation of a single line is a simpler, less invariant, lower-...

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Section: Discussionmentioning

confidence: 99%

Visual perception as retrospective Bayesian decoding from high- to low-level features

Ding

Cueva

Tsodyks

et al. 2017

Proc. Natl. Acad. Sci. U.S.A.

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“…We reasoned that tests of object recognition would be most likely to detect deficits in PD. In the healthy brain, object recognition includes visual search11 and object invariance 12. It is not well studied in PD, with reports limited to detection of embedded patterns13 and facial emotion recognition 14…”

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confidence: 99%

Assessing cognitive dysfunction in Parkinson's disease: An online tool to detect visuo‐perceptual deficits

et al. 2018

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Background: People with Parkinson's disease (PD) who develop visuo‐perceptual deficits are at higher risk of dementia, but we lack tests that detect subtle visuo‐perceptual deficits and can be performed by untrained personnel. Hallucinations are associated with cognitive impairment and typically involve perception of complex objects. Changes in object perception may therefore be a sensitive marker of visuo‐perceptual deficits in PD. Objective: We developed an online platform to test visuo‐perceptual function. We hypothesised that (1) visuo‐perceptual deficits in PD could be detected using online tests, (2) object perception would be preferentially affected, and (3) these deficits would be caused by changes in perception rather than response bias. Methods: We assessed 91 people with PD and 275 controls. Performance was compared using classical frequentist statistics. We then fitted a hierarchical Bayesian signal detection theory model to a subset of tasks. Results: People with PD were worse than controls at object recognition, showing no deficits in other visuo‐perceptual tests. Specifically, they were worse at identifying skewed images (P < .0001); at detecting hidden objects (P = .0039); at identifying objects in peripheral vision (P < .0001); and at detecting biological motion (P = .0065). In contrast, people with PD were not worse at mental rotation or subjective size perception. Using signal detection modelling, we found this effect was driven by change in perceptual sensitivity rather than response bias. Conclusions: Online tests can detect visuo‐perceptual deficits in people with PD, with object recognition particularly affected. Ultimately, visuo‐perceptual tests may be developed to identify at‐risk patients for clinical trials to slow PD dementia. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

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“…The vast majority of these studies made use of experimental designs entailing the manipulation of one (or more) specific stimulus or task parameters to generate a set of relevant experimental “conditions.” Changes of brain activity associated with these conditions are assessed by fitting the BOLD signal with predictors based on the type and timing of conditions (GLM: general linear model) [Friston et al, 1995]. More recently, there has been an increasing interest in the study of brain functioning using more complex and realistic stimuli, such as videos and movies [e.g., Bartels and Zeki, 2004; Fiser et al, 2004; Hasson et al, 2008; Kayser et al, 2004; Nishimoto et al, 2011; Wolf et al, 2010; see also Peelen and Kastner, 2014]. Unlike traditional paradigms, in these cases there is little a priori knowledge about the timing/occurrence of the relevant events and, hence, the construction of “predictors” for data‐fitting is challenging [but see Bartels et al, 2008; Bordier et al, 2013; Lahnakoski et al, 2012a; Ogawa et al, 2013; Raz et al, 2012].…”

Section: Introductionmentioning

confidence: 99%

Time‐resolved detection of stimulus/task‐related networks, via clustering of transient intersubject synchronization

Bordier

Macaluso

2015

Human Brain Mapping

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Several methods are available for the identification of functional networks of brain areas using functional magnetic resonance imaging (fMRI) time‐series. These typically assume a fixed relationship between the signal of the areas belonging to the same network during the entire time‐series (e.g., positive correlation between the areas belonging to the same network), or require a priori information about when this relationship may change (task‐dependent changes of connectivity). We present a fully data‐driven method that identifies transient network configurations that are triggered by the external input and that, therefore, include only regions involved in stimulus/task processing. Intersubject synchronization with short sliding time‐windows was used to identify if/when any area showed stimulus/task‐related responses. Next, a first clustering step grouped together areas that became engaged concurrently and repetitively during the time‐series (stimulus/task‐related networks). Finally, for each network, a second clustering step grouped together all the time‐windows with the same BOLD signal. The final output consists of a set of network configurations that show stimulus/task‐related activity at specific time‐points during the fMRI time‐series. We label these configurations: “brain modes” (bModes). The method was validated using simulated datasets and a real fMRI experiment with multiple tasks and conditions. Future applications include the investigation of brain functions using complex and naturalistic stimuli. Hum Brain Mapp 36:3404–3425, 2015. © 2015 The Authors Human Brain Mapping Published by Wiley Periodicals, Inc.

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Attention in the real world: toward understanding its neural basis

Cited by 193 publications

References 100 publications

Visual perception as retrospective Bayesian decoding from high- to low-level features

Visual perception as retrospective Bayesian decoding from high- to low-level features

Assessing cognitive dysfunction in Parkinson's disease: An online tool to detect visuo‐perceptual deficits

Time‐resolved detection of stimulus/task‐related networks, via clustering of transient intersubject synchronization

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