Predictive coding as a model of biased competition in visual attention

Spratling, Michael W.

doi:10.1016/j.visres.2008.03.009

Cited by 198 publications

(213 citation statements)

References 99 publications

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“…This theory of brain function argues that probabilistic expectations about future sensory events flow backward from higher associative regions to supplement or "complete" bottom-up sensory signals. This top-down mechanism allows for optimal perceptual inference by minimizing the amount of surprise (or prediction error) left to be encoded by bottom-up signals (36) and is mathematically equivalent to computational descriptions of how attention biases visual processing (37)(38)(39). The early influence of signal probability is also supported by functional imaging studies showing increases in blood oxygen level-dependent (BOLD) signals for expected stimuli in ventral visual cortex (40,41) and stronger BOLD responses to false alarms relative to misses or correct rejections in primary visual cortex (42).…”

Section: Discussionmentioning

confidence: 99%

Dissociable prior influences of signal probability and relevance on visual contrast sensitivity

Wyart

Nobre

Summerfield

2012

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

248

262

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According to signal detection theoretical analyses, visual signals occurring at a cued location are detected more accurately, whereas frequently occurring ones are reported more often but are not better distinguished from noise. However, conventional analyses that estimate sensitivity and bias by comparing true- and false-positive rates offer limited insights into the mechanisms responsible for these effects. Here, we reassessed the prior influences of signal probability and relevance on visual contrast detection using a reverse-correlation technique that quantifies how signal-like fluctuations in noise predict trial-to-trial variability in choice discarded by conventional analyses. This approach allowed us to estimate separately the sensitivity of true and false positives to parametric changes in signal energy. We found that signal probability and relevance both increased energy sensitivity, but in dissociable ways. Cues predicting the relevant location increased primarily the sensitivity of true positives by suppressing internal noise during signal processing, whereas cues predicting greater signal probability increased both the frequency and the sensitivity of false positives by biasing the baseline activity of signal-selective units. We interpret these findings in light of “predictive-coding” models of perception, which propose separable top-down influences of expectation (probability driven) and attention (relevance driven) on bottom-up sensory processing.

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Section: Discussionmentioning

confidence: 99%

Dissociable prior influences of signal probability and relevance on visual contrast sensitivity

Wyart

Nobre

Summerfield

2012

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

248

262

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“…In models involving hierarchical predictive coding (34)(35)(36)(37)(38)(39)(40)(41)(42), visual activation feeds forward through a chain of areas leading from V1 to the ITC and beyond, with neuronal activity at successively later stages representing hypotheses about successively more global attributes of the visual stimulus. If a hypothesis represented by activity in a high-order area predicts a hypothesis represented by activity in a low-order area, rendering the latter redundant, then feedback from the high-order area induces a reduction of activity in the low-order area.…”

Section: Discussionmentioning

confidence: 99%

“…The tick marks near the top of each panel indicate those points in time at which a paired t test (n = 14, α = 0.05), applied successively to each 1-ms bin in the range from 0-1,000 ms, revealed a significant difference between voltages measured under the two conditions. local hypothesis (36,37) or of silencing neurons that otherwise would signal a signed (38)(39)(40)(41)(42) or an unsigned (34) prediction error. This form of processing, although commonly considered in relation to the prediction of local attributes by simultaneously present global attributes, also can accommodate the prediction of subsequent events by antecedent ones (39).…”

Section: Discussionmentioning

confidence: 99%

Statistical learning of visual transitions in monkey inferotemporal cortex

Meyer

Olson

2011

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

277

271

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One of the most fundamental functions of the brain is to predict upcoming events on the basis of the recent past. A closely related function is to signal when a prediction has been violated. The identity of the brain regions that mediate these functions is not known. We set out to determine whether they are implemented at the level of single neurons in the visual system. We gave monkeys prolonged exposure to pairs of images presented in fixed sequence so that each leading image became a strong predictor for the corresponding trailing image. We then monitored the responses of neurons in the inferotemporal cortex to image sequences that obeyed or violated the transitional rules imposed during training. Inferotemporal neurons exhibited a transitional surprise effect, responding much more strongly to unpredicted transitions than to predicted transitions. Thus, neurons even in the visual system make experience-based predictions and react when they fail.macaque | vision | plasticity T he inferotemporal cortex (ITC), the terminus of the ventral stream of visual areas (1), plays a critical role in object vision (2, 3). ITC neurons respond with individual patterns of selectivity to complex images (4). Training monkeys to discriminate between images (5-7), categorize them (8, 9), or form associations between them (10-12) induces functional changes among neurons in the ITC which have the effect of strengthening the representation of image attributes relevant to task performance. Even passive viewing causes changes in neuronal visual responsiveness. Repeated viewing of a single image leads to a weakening of responses to it (13,14). Repeated viewing of two images close together in time leads to pair coding: a tendency for neurons responsive to one image also to respond to the other (15-17). The effects of passive viewing, because they do not depend on task demands, fall into the category of unsupervised statistical learning.An important form of unsupervised learning not previously studied at the level of single neurons concerns transitional statistics. The learning of transitional statistics has been the focus of much behavioral study in humans because it is thought to underlie the development during infancy of the ability to perceive event boundaries including word boundaries in speech (18,19). Human infants passively exposed to a stimulus stream in which certain visual images always follow certain others automatically register the transitional rules as evidenced by their orienting preferentially to a test stream containing novel transitions (20). The adult human brain is sensitive to transitional probabilities, as evidenced by its generating strong responses to improbable transitions at the level of scalp potential and blood oxygenation measures (21-27). Monkeys, like human infants, have been reported to learn transitional probabilities and to orient preferentially to improbable transitions (28). No effort has been made as yet to characterize the underlying neuronal mechanisms (29). We hypothesized that neurons in the ITC ...

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“…Conceptually very close to our PROPRE approach is the predictive coding model originally proposed by [12] and elaborated by, e.g., [13,14]. As in our PROPRE algorithm, predictive coding implements bi-directional learning between a receptive-field-generating process and a prediction process where receptive field generation is influenced by predictability.…”

Section: Related Workmentioning

confidence: 99%

Efficient online bootstrapping of sensory representations

Gepperth

2013

Neural Networks

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This is a simulation-based contribution exploring a novel approach to the open-ended formation of multimodal representations in autonomous agents. In particular, we address the issue of transferring ("bootstrapping") feature selectivities between two modalities, from a previously learned or innate reference representation to a new induced representation. We demonstrate the potential of this algorithm by several experiments with synthetic inputs modeled after a robotics scenario where multimodal object representations are "bootstrapped" from a (reference) representation of object affordances. We focus on typical challenges in autonomous agents: absence of human supervision, changing environment statistics and limited computing power. We propose an autonomous and local neural learning algorithm termed PRO-PRE (projection-prediction) that updates induced representations based on predictability: competitive advantages are given to those feature-sensitive elements that are inferable from activities in the reference representation. PRO-PRE implements a bi-directional interaction of clustering ("projection") and inference ("prediction"), the key ingredient being an efficient online measure of predictability controlling learning in the projection step. We show that the proposed method is computationally efficient and stable, and that the multimodal transfer of feature selectivity is successful and robust under resource constraints. Furthermore, we successfully demonstrate robustness to noisy reference representations, non-stationary input statistics and uninformative inputs. to be an open-ended and largely unsupervised process, leading to internal representations of ever increasing specialization and usefulness to the agent. The labeled boxes indicate the work covered in this article: I 1 , I 2 , I 3 stand for input representations derived from sensors, N indicates the newly formed induced representation, and R stands for the reference representation that is derived from another sensory modality, controlling the bootstrapping process.

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Predictive coding as a model of biased competition in visual attention

Cited by 198 publications

References 99 publications

Dissociable prior influences of signal probability and relevance on visual contrast sensitivity

Dissociable prior influences of signal probability and relevance on visual contrast sensitivity

Statistical learning of visual transitions in monkey inferotemporal cortex

Efficient online bootstrapping of sensory representations

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