scholarly journals Conflicting Bottom-up and Top-down Signals during Misrecognition of Visual Objects

2019 ◽  
Author(s):  
Mohamed Abdelhack ◽  
Yukiyasu Kamitani
Keyword(s):  
Visual Information ◽  
Visual Recognition ◽  
Brain Activity ◽  
Sensory Information ◽  
Visual Input ◽  
Neural Representation ◽  
Perceptual Decision Making ◽  
Inference Process ◽  
Top Down ◽  
Bottom Up

AbstractVisual recognition involves integrating visual information with other sensory information and prior knowledge. In accord with Bayesian inference under conditions of unreliable visual input, the brain relies on the prior as a source of information to achieve the inference process. This drives a top-down process to improve the neural representation of visual input. However, the extent to which non-stimulus-driven top-down information affects processing in the ventral stream is still unclear. We conducted a perceptual decision-making task using blurred images, while conducting functional magnetic resonance imaging. We then transformed brain activity into deep neural network features to distinguish bottom-up and top-down signals. We found that top-down information unrelated to the stimulus had a minimal effect on lower-level visual processes. The neural representations of degraded stimuli that were misrecognized were still correlated with the correct object category in the lower levels of processing. In contrast, activity in the higher cognitive areas was more strongly correlated with recognition reported by the subjects. The results indicated a discrepancy between the results of processing at the lower and higher levels, indicating the existence of a stimulus-independent top-down signal flowing back down the hierarchy. These findings suggest that integration between bottom-up and top-down information takes the form of competing evidence in higher visual areas between prior-driven top-down and stimulus-driven bottom-up signals. These findings could provide important insight into the different modes of integration of neural signals in the visual cortex that contribute to the visual inference process.

Neural substrates of visual percepts, imagery, and hallucinations

2002 ◽  
Vol 25 (2) ◽  
pp. 194-195
Author(s):  
Stephen Grossberg
Keyword(s):  
Visual Attention ◽  
Normal Control ◽  
Mental Imagery ◽  
Visual Information ◽  
Neural Substrates ◽  
Visual Hallucinations ◽  
Neural Models ◽  
Top Down ◽  
Bottom Up

Recent neural models clarify many properties of mental imagery as part of the process whereby bottom-up visual information is influenced by top-down expectations, and how these expectations control visual attention. Volitional signals can transform modulatory top-down signals into supra-threshold imagery. Visual hallucinations can occur when the normal control of these volitional signals is lost.

Top-Down Predictions Determine Perceptions

2017 ◽  
Author(s):  
Martin V. Butz ◽  
Esther F. Kutter
Keyword(s):  
Graphical Models ◽  
Information Integration ◽  
Visual Processing ◽  
Visual Information ◽  
Generative Models ◽  
Sources Of Information ◽  
Multiple Sources ◽  
Top Down ◽  
Bottom Up ◽  
The Brain

While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain should not be viewed as a classification system, but rather as a generative system, which perceives something by integrating sensory evidence with the available, learned, predictive knowledge about that thing. The involved generative models continuously produce expectations over time, across space, and from abstracted encodings to more concrete encodings. Bayesian information processing is the key to understand how information integration must work computationally – at least in approximation – also in the brain. Bayesian networks in the form of graphical models allow the modularization of information and the factorization of interactions, which can strongly improve the efficiency of generative models. The resulting generative models essentially produce state estimations in the form of probability densities, which are very well-suited to integrate multiple sources of information, including top-down and bottom-up ones. A hierarchical neural visual processing architecture illustrates this point even further. Finally, some well-known visual illusions are shown and the perceptions are explained by means of generative, information integrating, perceptual processes, which in all cases combine top-down prior knowledge and expectations about objects and environments with the available, bottom-up visual information.

scholarly journals The Bipolar Logic of Feedforward and Feedback Circuits

2021 ◽  
Author(s):  
Arturo Tozzi
Keyword(s):  
Visual Recognition ◽  
Coarse Grained ◽  
William Of Ockham ◽  
Top Down ◽  
Object Persistence ◽  
Bottom Up ◽  
Bipolar Mode ◽  
External Sources ◽  
Drug Resistant Epilepsy ◽  
Work Up

Instead of the conventional 0 and 1 values, bipolar reasoning uses -1, 0, +1 to describe double-sided judgements in which neutral elements are halfway between positive and negative evaluations (e.g., “uncertain” lies between “impossible” and “totally sure”). We discuss the state-of-the-art in bipolar logics and recall two medieval forerunners, i.e., William of Ockham and Nicholas of Autrecourt, who embodied a bipolar mode of thought that is eminently modern. Starting from the trivial observation that “once a wheat sheaf is sealed and tied up, the packed down straws display the same orientation”, we work up a new theory of the bipolar nature of networks, suggesting that orthodromic (i.e., feedforward, bottom-up) projections might be functionally coupled with antidromic (i.e., feedback, top-down) projections via the mathematical apparatus of presheaves/globular sets. When an entrained oscillation such as a neuronal spike propagates from A to B, changes in B might lead to changes in A, providing unexpected antidromic effects. Our account points towards the methodological feasibility of novel neural networks in which message feedback is guaranteed by backpropagation mechanisms endowed in the same feedforward circuits. Bottom-up/top-down transmission at various coarse-grained network levels provides fresh insights in far-flung scientific fields such as object persistence, memory reinforcement, visual recognition, Bayesian inferential circuits and multidimensional activity of the brain. Implying that axonal stimulation by external sources might backpropagate and modify neuronal electric oscillations, our theory also suggests testable previsions concerning the optimal location of transcranial magnetic stimulation’s coils in patients affected by drug-resistant epilepsy.

scholarly journals Impaired and Intact Aspects of Attentional Competition and Prioritization during Visual Working Memory Encoding in Schizophrenia

2021 ◽  
Author(s):  
Catherine V Barnes ◽  
Lara Roesler ◽  
Michael Schaum ◽  
Carmen Schiweck ◽  
Benjamin Peters ◽  
...  
Keyword(s):  
Working Memory ◽  
Visual Working Memory ◽  
Visual Information ◽  
Information Needs ◽  
Detection Task ◽  
Memory Encoding ◽  
Change Detection Task ◽  
Top Down ◽  
Bottom Up ◽  
Irrelevant Distractors

Objective: People with schizophrenia (PSZ) are impaired in the attentional prioritization of non-salient but relevant stimuli over salient but irrelevant distractors during visual working memory (VWM) encoding. Conversely, the guidance of top-down attention by external predictive cues is intact. Yet, it is unknown whether this preserved ability can help PSZ overcome impaired attentional prioritization in the presence of salient distractors. Methods: We employed a visuospatial change-detection task using four Gabor Patches with differing orientations in 69 PSZ and 74 healthy controls (HCS). Two patches flickered to reflect saliency and either a predictive or a non-predictive cue was displayed resulting in four conditions. Results: Across all conditions, PSZ stored significantly less information in VWM than HCS (all p < 0.001). With a non-predictive cue, PSZ stored significantly more salient than non-salient information (t140 = 5.66, p < 0.001, dt = 0.5). With a predictive cue, PSZ stored significantly more non-salient information (t140 = 5.70, p < 0.001, dt = 0.5). Conclusion: Our findings support a bottom-up bias in schizophrenia with performance significantly better for visually salient information in the absence of a predictive cue. These results indicate that bottom-up attentional prioritization is disrupted in schizophrenia, but the top-down utilization of cues is intact. We conclude that additional top-down information significantly improves performance in PSZ when non-salient visual information needs to be encoded in working memory.

Voluntary and Stimulus-Induced Attention Detected as Motion Sensation

Perception ◽  
1993 ◽  
Vol 22 (5) ◽  
pp. 517-526 ◽  
Author(s):  
Okihide Hikosaka ◽  
Satoru Miyauchi ◽  
Shinsuke Shimojo
Keyword(s):  
Information Processing ◽  
Response Bias ◽  
Visual Information ◽  
Stimulus Change ◽  
Early Stage ◽  
Visual Information Processing ◽  
Salient Object ◽  
Voluntary Attention ◽  
Top Down ◽  
Bottom Up

Attention may be drawn passively to a visually salient object. We may also actively direct attention to an object of interest. Do the two kinds of attention, passive and active, interact and jointly influence visual information processing at some neural level? What happens if the passive and active attentions come into conflict? These questions were addressed with the aid of a novel psychophysical technique which reveals an attentional gradient as a sensation of motion in a line which is presented instantaneously. The subjects were asked to direct attention with voluntary effort: to the side opposite to a stimulus change, to an object with a predetermined colour, and to an object moving smoothly. In every case the same motion sensation was induced in the line from the attended side to the unattended side. This voluntary attention, however, can easily and quickly be distracted by a change in the periphery, though it can be regained within a period of 200 to 500 ms. The results suggest that the line motion can be induced in voluntary (top-down) as well as stimulus-driven (bottom-up) situations, thus indicating the truly attentional nature of the effect, rather than it being some kind of retinotopic sensory artifact or response bias. The results also suggest that these two kinds of attention have facilitatory effects acting together on a relatively early stage of visual information processing.

Lexical Mediation between Sight and Sound in Speechreading

1992 ◽  
Vol 45 (1) ◽  
pp. 1-20 ◽  
Author(s):  
Bruno H. Repp ◽  
Ram Frost ◽  
Elizabeth Zsiga
Keyword(s):  
Visual Information ◽  
Visual Input ◽  
Top Down ◽  
Auditory Cues ◽  
Speech Detection ◽  
Visual Match ◽  
Relative Shift ◽  
Low Level ◽  
Masking Noise ◽  
The Cross

In two experiments, we investigated whether simultaneous speech reading can influence the detection of speech in envelope-matched noise. Subjects attempted to detect the presence of a disyllabic utterance in noise while watching a speaker articulate a matching or a non-matching utterance. Speech detection was not facilitated by an audio-visual match, which suggests that listeners relied on low-level auditory cues whose perception was immune to cross-modal top-down influences. However, when the stimuli were words (Experiment 1), there was a (predicted) relative shift in bias, suggesting that the masking noise itself was perceived as more speechlike when its envelope corresponded to the visual information. This bias shift was absent, however, with non-word materials (Experiment 2). These results, which resemble earlier findings obtained with orthographic visual input, indicate that the mapping from sight to sound is lexically mediated even when, as in the case of the articulatory-phonetic correspondence, the cross-modal relationship is non-arbitrary.

scholarly journals Attentionally modulated subjective images reconstructed from brain activity

2020 ◽  
Author(s):  
Tomoyasu Horikawa ◽  
Yukiyasu Kamitani
Keyword(s):  
Image Reconstruction ◽  
Visual Experience ◽  
Brain Activity ◽  
Visual Image ◽  
Neural Representation ◽  
Visual Images ◽  
Top Down ◽  
Neural Representations ◽  
Deep Image ◽  
The Brain

SummaryVisual image reconstruction from brain activity produces images whose features are consistent with the neural representations in the visual cortex given arbitrary visual instances [1–3], presumably reflecting the person’s visual experience. Previous reconstruction studies have been concerned either with how stimulus images are faithfully reconstructed or with whether mentally imagined contents can be reconstructed in the absence of external stimuli. However, many lines of vision research have demonstrated that even stimulus perception is shaped both by stimulus-induced processes and top-down processes. In particular, attention (or the lack of it) is known to profoundly affect visual experience [4–8] and brain activity [9–21]. Here, to investigate how top-down attention impacts the neural representation of visual images and the reconstructions, we use a state-of-the-art method (deep image reconstruction [3]) to reconstruct visual images from fMRI activity measured while subjects attend to one of two images superimposed with equally weighted contrasts. Deep image reconstruction exploits the hierarchical correspondence between the brain and a deep neural network (DNN) to translate (decode) brain activity into DNN features of multiple layers, and then create images that are consistent with the decoded DNN features [3, 22, 23]. Using the deep image reconstruction model trained on fMRI responses to single natural images, we decode brain activity during the attention trials. Behavioral evaluations show that the reconstructions resemble the attended rather than the unattended images. The reconstructions can be modeled by superimposed images with contrasts biased to the attended one, which are comparable to the appearance of the stimuli under attention measured in a separate session. Attentional modulations are found in a broad range of hierarchical visual representations and mirror the brain–DNN correspondence. Our results demonstrate that top-down attention counters stimulus-induced responses and modulate neural representations to render reconstructions in accordance with subjective appearance. The reconstructions appear to reflect the content of visual experience and volitional control, opening a new possibility of brain-based communication and creation.

scholarly journals Attention modulates neural representation to render reconstructions according to subjective appearance

2022 ◽  
Vol 5 (1) ◽  
Author(s):  
Tomoyasu Horikawa ◽  
Yukiyasu Kamitani
Keyword(s):  
Deep Neural Network ◽  
State Of The Art ◽  
Brain Activity ◽  
Visual Representations ◽  
Neural Representation ◽  
Induced Responses ◽  
Top Down ◽  
Neural Representations ◽  
Visual Cortical ◽  
The Brain

AbstractStimulus images can be reconstructed from visual cortical activity. However, our perception of stimuli is shaped by both stimulus-induced and top-down processes, and it is unclear whether and how reconstructions reflect top-down aspects of perception. Here, we investigate the effect of attention on reconstructions using fMRI activity measured while subjects attend to one of two superimposed images. A state-of-the-art method is used for image reconstruction, in which brain activity is translated (decoded) to deep neural network (DNN) features of hierarchical layers then to an image. Reconstructions resemble the attended rather than unattended images. They can be modeled by superimposed images with biased contrasts, comparable to the appearance during attention. Attentional modulations are found in a broad range of hierarchical visual representations and mirror the brain–DNN correspondence. Our results demonstrate that top-down attention counters stimulus-induced responses, modulating neural representations to render reconstructions in accordance with subjective appearance.

scholarly journals Separated and overlapping neural coding of face and body identity

2021 ◽  
Author(s):  
Celia Foster ◽  
Mintao Zhao ◽  
Timo Bolkart ◽  
Michael J. Black ◽  
Andreas Bartels ◽  
...  
Keyword(s):  
Neural Coding ◽  
Brain Activity ◽  
Temporal Cortex ◽  
Occipital Cortex ◽  
Visual Input ◽  
Neural Representation ◽  
Body Images ◽  
General Role ◽  
Low Level ◽  
Person Identity

AbstractRecognising a person’s identity often relies on face and body information, and is tolerant to changes in low-level visual input (e.g. viewpoint changes). Previous studies have suggested that face identity is disentangled from low-level visual input in the anterior face-responsive regions. It remains unclear which regions disentangle body identity from variations in viewpoint, and whether face and body identity are encoded separately or combined into a coherent person identity representation. We trained participants to recognize three identities, and then recorded their brain activity using fMRI while they viewed face and body images of the three identities from different viewpoints. Participants’ task was to respond to either the stimulus identity or viewpoint. We found consistent decoding of body identity across viewpoint in the fusiform body area, right anterior temporal cortex, middle frontal gyrus and right insula. This finding demonstrates a similar function of fusiform and anterior temporal cortex for bodies as has previously been shown for faces, suggesting these regions may play a general role in extracting high-level identity information. Moreover, we could decode identity across neural activity evoked by faces and bodies in the early visual cortex, right inferior occipital cortex, right parahippocampal cortex and right superior parietal cortex, revealing a distributed network that encodes person identity abstractly. Lastly, identity decoding was consistently better when participants attended to identity, indicating that attention to identity enhances its neural representation. These results offer new insights into how the brain develops an abstract neural coding of person identity, shared by faces and bodies.

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