Sensory uncertainty decoded from visual cortex predicts behavior

Bergen, van; Ji, Wei; Pratte, Michael S.; Jehee, Janneke

doi:10.1038/nn.4150

Cited by 188 publications

(317 citation statements)

References 33 publications

(43 reference statements)

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“…In addition to changes in precision across orientation space, previous work has shown that observers sometimes exhibit consistent biases toward or away from cardinal orientations in visual perception tasks (Andrews, 1965; Girshick et al, 2011; van Bergen, Ma, Pratte, & Jehee, 2015; Wei & Stocker, 2015). To ensure that such biases did not influence our modeling of the oblique effects in precision, we incorporated a bias term and performed model comparison both with and without this bias parameter.…”

Section: Methodsmentioning

confidence: 99%

Accounting for stimulus-specific variation in precision reveals a discrete capacity limit in visual working memory.

Pratte¹,

Park²,

Rademaker³

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

106

154

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If we view a visual scene that contains many objects, then momentarily close our eyes, some details persist while others seem to fade. Discrete models of visual working memory (VWM) assume that only a few items can be actively maintained in memory, beyond which pure guessing will emerge. Alternatively, continuous resource models assume that all items in a visual scene can be stored with some precision. Distinguishing between these competing models is challenging, however, as resource models that allow for stochastically variable precision (across items and trials) can produce error distributions that resemble random guessing behavior. Here, we evaluated the hypothesis that a major source of variability in VWM performance arises from systematic variation in precision across the stimuli themselves; such stimulus-specific variability can be incorporated into both discrete-capacity and variable-precision resource models. Participants viewed multiple oriented gratings, and then reported the orientation of a cued grating from memory. When modeling the overall distribution of VWM errors, we found that the variable-precision resource model outperformed the discrete model. However, VWM errors revealed a pronounced “oblique effect”, with larger errors for oblique than cardinal orientations. After this source of variability was incorporated into both models, we found that the discrete model provided a better account of VWM errors. Our results demonstrate that variable precision across the stimulus space can lead to an unwarranted advantage for resource models that assume stochastically variable precision. When these deterministic sources are adequately modeled, human working memory performance reveals evidence of a discrete capacity limit.

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Section: Methodsmentioning

confidence: 99%

Accounting for stimulus-specific variation in precision reveals a discrete capacity limit in visual working memory.

Pratte¹,

Park²,

Rademaker³

et al. 2017

Journal of Experimental Psychology: Human Perception and Perfor

106

154

View full text Add to dashboard Cite

show abstract

“…We adapted the decoding strategy conceived by Van Bergen et al 13 for use in a scenario where the forward model feature space is visual location instead of orientation. We converted the continuous-valued pRF estimates to a discrete spatial representation of P by P pixel values.…”

Section: Decoding Analysismentioning

confidence: 99%

“…Thus, a weight matrix W of dimensions k by P 2 described the spatial preferences of all voxels in a region of interest. All other aspects of the analysis were kept identical to the previous description 13 . Briefly, we fit a model describing the total covariance structure of voxels' responses based on the overlap in their pRFs (WW T ), as well as their noise variance and covariance.…”

Section: Decoding Analysismentioning

confidence: 99%

“…Varying the number of voxels used for the decoding analysis did not impact the pattern of decoding results, nor did varying the amount of feature space elements (pixels) used in the analysis. We did not add a spatial vicinity component in the forward model description as previous work has shown that this does not improve decoding performance 13 and ensured that the fitted covariance matrix was positive-semidefinite. This analysis is implemented in an open-source python package, available at https://github.com/tknapen/decode_encode.…”

Section: Decoding Analysismentioning

confidence: 99%

“…Specifically, we used pRFs parameter estimates from a given cortical region as an explicit encoding model. We then used a recently developed Bayesian decoding algorithm 13 to calculate the most probable stimulus pattern given the structure of BOLD responses across voxels in a test dataset, separately for every TR ( Figure 3a). Performing this analysis results in movies of inferred stimulus representations for the entire experiment (Figure 3b-c).…”

mentioning

confidence: 99%

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Mapping the Dark Side: Visual Selectivity of Default Network Deactivations

Knapen

Barendregt

2018

Preprint

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The default network (DN) The default network (DN) deactivates when participants receive sensory stimulation and focus externally to perform a task, and activates during mind-wandering 1,2 . These activations and deactivations in the DN are important in psychological disorders and alterations in cognitive state 3,4 , and are inversely correlated with responses in the multiple demand network of frontal and parietal regions 5 . Recent findings indicate that signals in the DN carry visual memory information 6,7 , but the functional role of DN deactivations remain unclear. We investigated the visual response properties of the DN by performing a visual mapping fMRI experiment at ultrahigh field (7 Tesla). In our experiment a bar-shaped stimulus systematically traversed the visual field in different directions. Participants performed a two-alternative forced-choice color discrimination task on the stimulus (Figure 1a) titrated to be equally demanding throughout the visual field (Figure 1b). Across participants, there was no difference in task performance (F(2,10) = 1.347, p = .304, η 2 = .198) or reaction time (F(2,10) = 0.121, p = .887, η 2 = .003) for V is u a l sp a ce[y ]

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Foundations of Vision

Tong

2018

Stevens' Handbook of Experimental Psychology and Cognitive Neuroscience

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This article discusses the integral nature of theology and spirituality in the writings and practices of John and Charles Wesley. It describes works of piety and works of mercy as a holistic foundation upon which the Wesleys built their movement of renewal in the Church of England in the eighteenth century. Particular attention is given to the means of grace -prayer, biblical engagement, Christian fellowship, and Eucharist, as well as the connection between the sacrament and mission. This article was the first of two lectures originally delivered at Booth College (Winnipeg) as part of the inaugural Earl Robinson Lectures series.

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Sensory uncertainty decoded from visual cortex predicts behavior

Cited by 188 publications

References 33 publications

Accounting for stimulus-specific variation in precision reveals a discrete capacity limit in visual working memory.

Accounting for stimulus-specific variation in precision reveals a discrete capacity limit in visual working memory.

Mapping the Dark Side: Visual Selectivity of Default Network Deactivations

Foundations of Vision

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