scholarly journals Differential neural mechanisms for early and late prediction error detection

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Rahim Malekshahi ◽  
Anil Seth ◽  
Amalia Papanikolaou ◽  
Zenon Mathews ◽  
Niels Birbaumer ◽  
...  
Keyword(s):  
Error Detection ◽  
Predictive Models ◽  
Visual Processing ◽  
Prediction Error ◽  
Early Stage ◽  
Predictive Coding ◽  
Representational Content ◽  
Saccadic Eye Movements ◽  
Spatial Prediction ◽  
Neural Responses

Abstract Emerging evidence indicates that prediction, instantiated at different perceptual levels, facilitate visual processing and enable prompt and appropriate reactions. Until now, the mechanisms underlying the effect of predictive coding at different stages of visual processing have still remained unclear. Here, we aimed to investigate early and late processing of spatial prediction violation by performing combined recordings of saccadic eye movements and fast event-related fMRI during a continuous visual detection task. Psychophysical reverse correlation analysis revealed that the degree of mismatch between current perceptual input and prior expectations is mainly processed at late rather than early stage, which is instead responsible for fast but general prediction error detection. Furthermore, our results suggest that conscious late detection of deviant stimuli is elicited by the assessment of prediction error’s extent more than by prediction error per se. Functional MRI and functional connectivity data analyses indicated that higher-level brain systems interactions modulate conscious detection of prediction error through top-down processes for the analysis of its representational content, and possibly regulate subsequent adaptation of predictive models. Overall, our experimental paradigm allowed to dissect explicit from implicit behavioral and neural responses to deviant stimuli in terms of their reliance on predictive models.

scholarly journals Prediction-error signals to violated expectations about person identity and head orientation are doubly-dissociated across the dorsal and ventral visual streams

2018 ◽  
Author(s):  
Jonathan E. Robinson ◽  
Will Woods ◽  
Sumie Leung ◽  
Jordy Kaufman ◽  
Michael Breakspear ◽  
...  
Keyword(s):  
Error Detection ◽  
Visual Processing ◽  
Prediction Error ◽  
Predictive Coding ◽  
Angular Gyrus ◽  
Head Orientation ◽  
Visual Stream ◽  
Person Identity ◽  
Violated Expectations ◽  
Double Dissociations

AbstractPredictive coding theories of perception suggest the importance of constantly updated internal models of the world in predicting future sensory inputs. One implication of such models is that cortical regions whose function is to resolve particular stimulus attributes should also signal prediction violations with respect to those same stimulus attributes. Previously, through carefully designed experiments, we have demonstrated early-mid latency EEG/MEG prediction-error signals in the dorsal visual stream to violated expectations about stimulus orientation/trajectory, with localisations consistent with cortical areas processing motion and orientation. Here we extend those methods to simultaneously investigate the predictive processes in both dorsal and ventral visual streams. In this MEG study we employed a contextual trajectory paradigm that builds expectations using a series of image presentations. We created expectations about both face orientation and identity, either of which can subsequently be violated. Crucially this paradigm allows us to parametrically test double dissociations between these different types of violations. The study identified double dissociations across the type of violation in the dorsal and ventral visual streams, such that the right fusiform gyrus showed greater evidence of prediction-error signals to Identity violations than to Orientation violations, whereas the left angular gyrus and postcentral gyrus showed the opposite pattern of results. Our results suggest comparable processes for error checking and context updating in high-level expectations instantiated across both perceptual streams. Perceptual prediction-error signalling is initiated in regions associated with the processing of different stimulus properties.Significance StatementVisual processing occurs along ‘what’ and ‘where’ information streams that run, respectively along the ventral and dorsal surface of the posterior brain. Predictive coding models of perception imply prediction-error detection processes that are instantiated at the level where particular stimulus attributes are parsed. This implies that, for instance, when considering face stimuli, signals arising through violated expectations about the person identity of the stimulus should localise to the ventral stream, whereas signals arising through violated expectations about head orientation should localise to the dorsal stream. We test this in a magnetoencephalography source localisation study. The analysis confirmed that prediction-error signals to identity versus head-orientation occur with similar latency, but activate doubly-dissociated brain regions along ventral and dorsal processing streams.

scholarly journals Predictive Neural Computations Support Spoken Word Recognition: Evidence from MEG and Competitor Priming

2020 ◽  
Author(s):  
Yingcan Carol Wang ◽  
Ediz Sohoglu ◽  
Rebecca A. Gilbert ◽  
Richard N. Henson ◽  
Matthew H. Davis
Keyword(s):  
Word Recognition ◽  
Prediction Error ◽  
Spoken Word Recognition ◽  
Predictive Coding ◽  
Spoken Word ◽  
Speech Comprehension ◽  
Speech Sounds ◽  
Neural Responses ◽  
Perceptual Inference ◽  
Neural Computations

AbstractHuman listeners achieve quick and effortless speech comprehension through computations of conditional probability using Bayes rule. However, the neural implementation of Bayesian perceptual inference remains unclear. Competitive-selection accounts (e.g. TRACE) propose that word recognition is achieved through direct inhibitory connections between units representing candidate words that share segments (e.g. hygiene and hijack share /haid3/). Manipulations that increase lexical uncertainty should increase neural responses associated with word recognition when words cannot be uniquely identified (during the first syllable). In contrast, predictive-selection accounts (e.g. Predictive-Coding) proposes that spoken word recognition involves comparing heard and predicted speech sounds and using prediction error to update lexical representations. Increased lexical uncertainty in words like hygiene and hijack will increase prediction error and hence neural activity only at later time points when different segments are predicted (during the second syllable). We collected MEG data to distinguish these two mechanisms and used a competitor priming manipulation to change the prior probability of specific words. Lexical decision responses showed delayed recognition of target words (hygiene) following presentation of a neighbouring prime word (hijack) several minutes earlier. However, this effect was not observed with pseudoword primes (higent) or targets (hijure). Crucially, MEG responses in the STG showed greater neural responses for word-primed words after the point at which they were uniquely identified (after /haid3/ in hygiene) but not before while similar changes were again absent for pseudowords. These findings are consistent with accounts of spoken word recognition in which neural computations of prediction error play a central role.Significance StatementEffective speech perception is critical to daily life and involves computations that combine speech signals with prior knowledge of spoken words; that is, Bayesian perceptual inference. This study specifies the neural mechanisms that support spoken word recognition by testing two distinct implementations of Bayes perceptual inference. Most established theories propose direct competition between lexical units such that inhibition of irrelevant candidates leads to selection of critical words. Our results instead support predictive-selection theories (e.g. Predictive-Coding): by comparing heard and predicted speech sounds, neural computations of prediction error can help listeners continuously update lexical probabilities, allowing for more rapid word identification.

scholarly journals Rapid computations of spectrotemporal prediction error support perception of degraded speech

eLife ◽  
2020 ◽  
Vol 9 ◽  
Author(s):  
Ediz Sohoglu ◽  
Matthew H Davis
Keyword(s):  
Speech Perception ◽  
Prediction Error ◽  
Predictive Coding ◽  
Signal Quality ◽  
Neural Responses ◽  
Perceptual Inference ◽  
Written Text ◽  
Degraded Speech ◽  
Neural Representations ◽  
Brain Responses

Human speech perception can be described as Bayesian perceptual inference but how are these Bayesian computations instantiated neurally? We used magnetoencephalographic recordings of brain responses to degraded spoken words and experimentally manipulated signal quality and prior knowledge. We first demonstrate that spectrotemporal modulations in speech are more strongly represented in neural responses than alternative speech representations (e.g. spectrogram or articulatory features). Critically, we found an interaction between speech signal quality and expectations from prior written text on the quality of neural representations; increased signal quality enhanced neural representations of speech that mismatched with prior expectations, but led to greater suppression of speech that matched prior expectations. This interaction is a unique neural signature of prediction error computations and is apparent in neural responses within 100 ms of speech input. Our findings contribute to the detailed specification of a computational model of speech perception based on predictive coding frameworks.

scholarly journals Covert spatial attention speeds target individuation

2019 ◽  
Author(s):  
Joshua J. Foster ◽  
Emma M. Bsales ◽  
Edward Awh
Keyword(s):  
Spatial Attention ◽  
Visual Processing ◽  
Object Representation ◽  
Target Location ◽  
Response Selection ◽  
Early Stage ◽  
Covert Attention ◽  
Object Representations ◽  
Neural Responses ◽  
Object Individuation

AbstractCovert spatial attention has long been thought to speed visual processing. Psychophysics studies have shown that target information accrues faster at attended locations than at unattended locations. However, with behavioral evidence alone, it is difficult to determine whether attention speeds visual processing of the target, or subsequent post-perceptual stages of processing (e.g. decision making and response selection). Moreover, while many studies have shown that that attention can boost the amplitude of visually-evoked neural responses, no effect has been observed on the latency of those neural responses. Here, we offer new evidence that may reconcile the neural and behavioral findings. Our study focused on the N2pc, an EEG marker of visual selection that has been linked with object individuation – the formation of an object representation that is distinct from the background and from other objects. In two experiments, we manipulated whether or not covert attention was precisely deployed to the location of an impending search target. We found that the target-evoked N2pc onset approximately 20 ms earlier when the target location was cued than when it was not cued. Thus, although attention may not speed the earliest stages of sensory processing, attention does speed the critical transition between raw sensory encoding and the formation of individuated object representations.Significance StatementCovert spatial attention improves processing at attended locations. Past behavioral studies have shown that information about visual targets accrues faster at attended than at unattended locations. However, it has remained unclear whether attention speeds perceptual analysis or subsequent post-perceptual stages of processing. Here we present robust evidence that attention speeds the N2pc, an electrophysiological signal that indexes the formation of individuated object representations. Our findings show that attention speeds a relatively early stage of perceptual processing, while also elucidating the specific perceptual process that is speeded.

scholarly journals Rapid computations of spectrotemporal prediction error support perception of degraded speech

2020 ◽  
Author(s):  
Ediz Sohoglu ◽  
Matthew H. Davis
Keyword(s):  
Speech Perception ◽  
Prediction Error ◽  
Predictive Coding ◽  
Signal Quality ◽  
Neural Responses ◽  
Perceptual Inference ◽  
Written Text ◽  
Degraded Speech ◽  
Neural Representations ◽  
Brain Responses

AbstractHuman speech perception can be described as Bayesian perceptual inference but how are these Bayesian computations instantiated neurally? We use magnetoencephalographic recordings of brain responses to degraded spoken words as a function of signal quality and prior knowledge to demonstrate that spectrotemporal modulations in speech are more clearly represented in neural responses than alternative speech representations (e.g. spectrogram or articulatory features). We found an interaction between speech signal quality and expectations from prior written text on the quality of neural representations; increased signal quality enhanced neural representations of speech that mismatched with prior expectations, but led to greater suppression of speech that matched prior expectations. This interaction is a unique neural signature of prediction error computations and already apparent in neural responses within 250 ms of speech input. Our findings contribute towards the detailed specification of a computational model of speech perception based on predictive coding frameworks.

Computing with Self-Excitatory Cliques: A Model and an Application to Hyperacuity-Scale Computation in Visual Cortex

1999 ◽  
Vol 11 (1) ◽  
pp. 21-66 ◽  
Author(s):  
Douglas A. Miller ◽  
Steven W. Zucker
Keyword(s):  
Visual Processing ◽  
Network Architecture ◽  
Early Stage ◽  
Cortical Cell ◽  
Pyramidal Cells ◽  
Direct Calculation ◽  
Formal Theory ◽  
Cortical Cells ◽  
Cell Counts ◽  
Spatiotemporal Response

We present a model of visual computation based on tightly inter-connected cliques of pyramidal cells. It leads to a formal theory of cell assemblies, a specific relationship between correlated firing patterns and abstract functionality, and a direct calculation relating estimates of cortical cell counts to orientation hyperacuity. Our network architecture is unique in that (1) it supports a mode of computation that is both reliable and efficent; (2) the current-spike relations are modeled as an analog dynamical system in which the requisite computations can take place on the time scale required for an early stage of visual processing; and (3) the dynamics are triggered by the spatiotemporal response of cortical cells. This final point could explain why moving stimuli improve vernier sensitivity.

The “Mosaic Stage” in Amodal Completion as Characterized by Magnetoencephalography Responses

2006 ◽  
Vol 18 (8) ◽  
pp. 1394-1405 ◽  
Author(s):  
Gijs Plomp ◽  
Lichan Liu ◽  
Cees van Leeuwen ◽  
Andreas A. Ioannides
Keyword(s):  
Neural Activity ◽  
Visual Processing ◽  
Early Stage ◽  
Amodal Completion ◽  
Peak Latency ◽  
Per Se

We investigated the process of amodal completion in a same-different experiment in which test pairs were preceded by sequences of two figures. The first of these could be congruent to a global or local completion of an occluded part in the second figure, or a mosaic interpretation of it. We recorded and analyzed the magnetoencephalogram for the second figures. Compared to control conditions, in which unrelated primes were shown, occlusion and mosaic primes reduced the peak latency and amplitude of neural activity evoked by the occlusion patterns. Compared to occlusion primes, mosaic ones reduced the latency but increased the amplitude of evoked neural activity. Processes relating to a mosaic interpretation of the occlusion pattern, therefore, can dominate in an early stage of visual processing. The results did not provide evidence for the presence of a functional “mosaic stage” in completion per se, but characterize the mosaic interpretation as a qualitatively special one that can rapidly emerge in visual processing when context favors it.

#3083 Fibromyalgia and myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS): an interoceptive predictive coding model of pain and fatigue expression

2021 ◽  
Vol 92 (8) ◽  
pp. A3.3-A4
Author(s):  
Harriet Sharp ◽  
Kristy Themelis ◽  
Marisa Amato ◽  
Andrew Barritt ◽  
Kevin Davies ◽  
...  
Keyword(s):  
Prediction Error ◽  
Predictive Coding ◽  
Chronic Fatigue ◽  
Self Report ◽  
Subjective Experiences ◽  
Double Blind ◽  
Pain Thresholds ◽  
Pressure Pain Thresholds ◽  
Interoceptive Accuracy ◽  
Bodily States

IntroductionThe aetiology and pathophysiology of fibromyalgia and ME/CFS are poorly characterised but altered inflammatory, autonomic and interoceptive processes have been implicated. Interoception has been conceptualised as a predictive coding process; where top-down prediction signals compare to bottom-up afferents, resulting in prediction error signals indicating mismatch between expected and actual bodily states. Chronic dyshomeostasis and elevated interoceptive prediction error signals have been theorised to contribute to the expression of pain and fatigue in fibromyalgia and ME/CFS.Objectives/AimsTo investigate how altered interoception and prediction error relates to baseline expression of pain and fatigue in fibromyalgia and ME/CFS and in response to an inflammatory challenge.MethodsSixty-five patients with fibromyalgia and/or ME/CFS diagnosis and 26 matched controls underwent baseline assessment: self-report questionnaires assessing subjective pain and fatigue and objective measurements of pressure-pain thresholds. Participants received injections of typhoid (inflammatory challenge) or saline (placebo) in a randomised, double-blind, crossover design, then completed heartbeat tracking task (assessing interoceptive accuracy). Porges Body Questionnaire assessed interoceptive sensibility. Interoceptive prediction error (IPE) was calculated as discrepancy between objective accuracy and subjective sensibility.ResultsPatients with fibromyalgia and ME/CFS had significantly higher IPE (suggesting tendency to over-estimate interoceptive ability) and interoceptive sensibility, despite no differences in interoceptive accuracy. IPE and sensibility correlated positively with all self-report fatigue and pain measures, and negatively with pain thresholds. Following inflammatory challenge, IPE correlated negatively with the mismatch between subjective and objective measures of pain induced by inflammation.ConclusionsThis is the first study to reveal altered interoception processes in patients with fibromyalgia and ME/CFS, who are known to have dysregulated autonomic function. Notably, we found elevated IPE in patients, correlating with their subjective experiences of pain and fatigue. We hypothesise a predictive coding model, where mismatch between expected and actual internal bodily states (linked to autonomic dysregulation) results in prediction error signalling which could be metacognitively interpreted as chronic pain and fatigue. Our results demonstrate potential for further exploration of interoceptive processing in patients with fibromyalgia and ME/CFS, aiding understanding of these poorly defined conditions and providing potential new targets for diagnostic and therapeutic intervention.

scholarly journals Neural Responses to Ambiguity Involve Domain-general and Domain-specific Emotion Processing Systems

2013 ◽  
Vol 25 (4) ◽  
pp. 547-557 ◽  
Author(s):  
Maital Neta ◽  
William M. Kelley ◽  
Paul J. Whalen
Keyword(s):  
Decision Making ◽  
Facial Expressions ◽  
Visual Processing ◽  
Neural Responses ◽  
Social Signals ◽  
Emotional Facial Expressions ◽  
Biologically Relevant ◽  
Domain Specific ◽  
Specific Emotion ◽  
On Line

Extant research has examined the process of decision making under uncertainty, specifically in situations of ambiguity. However, much of this work has been conducted in the context of semantic and low-level visual processing. An open question is whether ambiguity in social signals (e.g., emotional facial expressions) is processed similarly or whether a unique set of processors come on-line to resolve ambiguity in a social context. Our work has examined ambiguity using surprised facial expressions, as they have predicted both positive and negative outcomes in the past. Specifically, whereas some people tended to interpret surprise as negatively valenced, others tended toward a more positive interpretation. Here, we examined neural responses to social ambiguity using faces (surprise) and nonface emotional scenes (International Affective Picture System). Moreover, we examined whether these effects are specific to ambiguity resolution (i.e., judgments about the ambiguity) or whether similar effects would be demonstrated for incidental judgments (e.g., nonvalence judgments about ambiguously valenced stimuli). We found that a distinct task control (i.e., cingulo-opercular) network was more active when resolving ambiguity. We also found that activity in the ventral amygdala was greater to faces and scenes that were rated explicitly along the dimension of valence, consistent with findings that the ventral amygdala tracks valence. Taken together, there is a complex neural architecture that supports decision making in the presence of ambiguity: (a) a core set of cortical structures engaged for explicit ambiguity processing across stimulus boundaries and (b) other dedicated circuits for biologically relevant learning situations involving faces.

scholarly journals Visual search for object categories is predicted by the representational architecture of high-level visual cortex

2017 ◽  
Vol 117 (1) ◽  
pp. 388-402 ◽  
Author(s):  
Michael A. Cohen ◽  
George A. Alvarez ◽  
Ken Nakayama ◽  
Talia Konkle
Keyword(s):  
Visual Cortex ◽  
Visual Search ◽  
Visual System ◽  
Visual Processing ◽  
Search Task ◽  
Visual Search Task ◽  
Visual Object ◽  
Neural Responses ◽  
Object Categories ◽  
High Level

Visual search is a ubiquitous visual behavior, and efficient search is essential for survival. Different cognitive models have explained the speed and accuracy of search based either on the dynamics of attention or on similarity of item representations. Here, we examined the extent to which performance on a visual search task can be predicted from the stable representational architecture of the visual system, independent of attentional dynamics. Participants performed a visual search task with 28 conditions reflecting different pairs of categories (e.g., searching for a face among cars, body among hammers, etc.). The time it took participants to find the target item varied as a function of category combination. In a separate group of participants, we measured the neural responses to these object categories when items were presented in isolation. Using representational similarity analysis, we then examined whether the similarity of neural responses across different subdivisions of the visual system had the requisite structure needed to predict visual search performance. Overall, we found strong brain/behavior correlations across most of the higher-level visual system, including both the ventral and dorsal pathways when considering both macroscale sectors as well as smaller mesoscale regions. These results suggest that visual search for real-world object categories is well predicted by the stable, task-independent architecture of the visual system. NEW & NOTEWORTHY Here, we ask which neural regions have neural response patterns that correlate with behavioral performance in a visual processing task. We found that the representational structure across all of high-level visual cortex has the requisite structure to predict behavior. Furthermore, when directly comparing different neural regions, we found that they all had highly similar category-level representational structures. These results point to a ubiquitous and uniform representational structure in high-level visual cortex underlying visual object processing.

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