scholarly journals Prediction suppression and surprise enhancement in monkey inferotemporal cortex

2017 ◽  
Vol 118 (1) ◽  
pp. 374-382 ◽  
Author(s):  
Suchitra Ramachandran ◽  
Travis Meyer ◽  
Carl R. Olson
Keyword(s):  
Visual System ◽  
Strong Evidence ◽  
Prediction Error ◽  
Predictive Coding ◽  
Neutral Condition ◽  
Training Period ◽  
Inferotemporal Cortex ◽  
Limited Evidence ◽  
Fixed Sequence ◽  
Area Te

Exposing monkeys, over the course of days and weeks, to pairs of images presented in fixed sequence, so that each leading image becomes a predictor for the corresponding trailing image, affects neuronal visual responsiveness in area TE. At the end of the training period, neurons respond relatively weakly to a trailing image when it appears in a trained sequence and, thus, confirms prediction, whereas they respond relatively strongly to the same image when it appears in an untrained sequence and, thus, violates prediction. This effect could arise from prediction suppression (reduced firing in response to the occurrence of a probable event) or surprise enhancement (elevated firing in response to the omission of a probable event). To identify its cause, we compared firing under the prediction-confirming and prediction-violating conditions to firing under a prediction-neutral condition. The results provide strong evidence for prediction suppression and limited evidence for surprise enhancement. NEW & NOTEWORTHY In predictive coding models of the visual system, neurons carry signed prediction error signals. We show here that monkey inferotemporal neurons exhibit prediction-modulated firing, as posited by these models, but that the signal is unsigned. The response to a prediction-confirming image is suppressed, and the response to a prediction-violating image may be enhanced. These results are better explained by a model in which the visual system emphasizes unpredicted events than by a predictive coding model.

scholarly journals Prediction suppression in monkey inferotemporal cortex depends on the conditional probability between images

2016 ◽  
Vol 115 (1) ◽  
pp. 355-362 ◽  
Author(s):  
Suchitra Ramachandran ◽  
Travis Meyer ◽  
Carl R. Olson
Keyword(s):  
Conditional Probability ◽  
Joint Probability ◽  
Inferotemporal Cortex ◽  
Fixed Sequence ◽  
Weak Response ◽  
Predictive Relations ◽  
Area Te

When monkeys view two images in fixed sequence repeatedly over days and weeks, neurons in area TE of the inferotemporal cortex come to exhibit prediction suppression. The trailing image elicits only a weak response when presented following the leading image that preceded it during training. Induction of prediction suppression might depend either on the contiguity of the images, as determined by their co-occurrence and captured in the measure of joint probability P( A, B), or on their contingency, as determined by their correlation and as captured in the measures of conditional probability P( A| B) and P( B| A). To distinguish between these possibilities, we measured prediction suppression after imposing training regimens that held P( A, B) constant but varied P( A| B) and P( B| A). We found that reducing either P( A| B) or P( B| A) during training attenuated prediction suppression as measured during subsequent testing. We conclude that prediction suppression depends on contingency, as embodied in the predictive relations between the images, and not just on contiguity, as embodied in their co-occurrence.

#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 Smoking as a Common Modulator of Sensory Gating and Reward Learning in Individuals with Psychotic Disorders

2021 ◽  
Vol 11 (12) ◽  
pp. 1581
Author(s):  
Alexis E. Whitton ◽  
Kathryn E. Lewandowski ◽  
Mei-Hua Hall
Keyword(s):  
Bipolar Disorder ◽  
Prediction Error ◽  
Psychotic Disorders ◽  
Predictive Coding ◽  
Sensory Gating ◽  
Event Related Potential ◽  
Reward Learning ◽  
Smoking History ◽  
Impaired Performance ◽  
Psychotic Bipolar Disorder

Motivational and perceptual disturbances co-occur in psychosis and have been linked to aberrations in reward learning and sensory gating, respectively. Although traditionally studied independently, when viewed through a predictive coding framework, these processes can both be linked to dysfunction in striatal dopaminergic prediction error signaling. This study examined whether reward learning and sensory gating are correlated in individuals with psychotic disorders, and whether nicotine—a psychostimulant that amplifies phasic striatal dopamine firing—is a common modulator of these two processes. We recruited 183 patients with psychotic disorders (79 schizophrenia, 104 psychotic bipolar disorder) and 129 controls and assessed reward learning (behavioral probabilistic reward task), sensory gating (P50 event-related potential), and smoking history. Reward learning and sensory gating were correlated across the sample. Smoking influenced reward learning and sensory gating in both patient groups; however, the effects were in opposite directions. Specifically, smoking was associated with improved performance in individuals with schizophrenia but impaired performance in individuals with psychotic bipolar disorder. These findings suggest that reward learning and sensory gating are linked and modulated by smoking. However, disorder-specific associations with smoking suggest that nicotine may expose pathophysiological differences in the architecture and function of prediction error circuitry in these overlapping yet distinct psychotic disorders.

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 Placing meta-stable states of consciousness within the predictive coding hierarchy: The deceleration of the accelerated prediction error

2018 ◽  
Vol 63 ◽  
pp. 123-142 ◽  
Author(s):  
Amirali Shirazibeheshti ◽  
Jennifer Cooke ◽  
Srivas Chennu ◽  
Ram Adapa ◽  
David K. Menon ◽  
...  
Keyword(s):  
Prediction Error ◽  
Predictive Coding ◽  
Stable States ◽  
States Of Consciousness

scholarly journals Sexual orientation and race mute the prescriptive nature of gender stereotypes

2021 ◽  
Author(s):  
Sa-kiera Tiarra Jolynn Hudson ◽  
Asma Ghani
Keyword(s):  
Gender Differences ◽  
Sexual Orientation ◽  
Ethnic Minority ◽  
Gender Stereotypes ◽  
Strong Evidence ◽  
White Men ◽  
Limited Evidence ◽  
Total N ◽  
Men And Women ◽  
Sexual Orientations

There is substantial research on the nature and impact of gender prescriptive stereotypes. However, there has been relatively little work on whether these stereotypes are equally applicable to men and women of different identities. Across two studies (total N = 1074), we assessed gender prescriptive stereotypes intersectionality in an American context, for men and women of different sexual orientations (Study 1) and races (Study 2). Results show strong evidence of a straight-centric bias, as prescriptive stereotypes of men and women most closely aligned with those of straight men and women, but limited evidence for a White-centric bias. Furthermore, observed gender differences in prescriptive stereotypes were smaller or non-existent for sexual and ethnic minority targets compared to straight and White targets, suggesting that theories around the dyadic nature of gender stereotypes between men and women might be restricted to straight and White men and women.

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 Boredom begets creativity: a solution to the exploitation-exploration trade-off in predictive coding

2017 ◽  
Author(s):  
Jaime Gomez-Ramirez ◽  
Tommaso Costa
Keyword(s):  
Mathematical Model ◽  
Prediction Error ◽  
Predictive Coding ◽  
Error Minimization ◽  
Trade Off ◽  
Black Scholes ◽  
Intelligent Behavior ◽  
Prediction Error Minimization ◽  
Key Drivers ◽  
Technical Systems

AbstractHere, we investigate whether systems that minimize prediction error e.g. predictive coding, can also show creativity, or on the contrary, prediction error minimization unqualifies for the design of systems that respond in creative ways to non recurrent problems. We argue that there is a key ingredient that has been overlooked by researchers that needs to be incorporated to understand intelligent behavior in biological and technical systems. This ingredient is boredom. We propose a mathematical model based on the Black-Scholes-Merton equation which provides mechanistic insights into the interplay between boredom and prediction pleasure as the key drivers of behavior.

scholarly journals Magic, Bayes and wows: a Bayesian account of magic tricks

2020 ◽  
Author(s):  
Pablo Rodrigo Grassi ◽  
Andreas Bartels
Keyword(s):  
Prior Knowledge ◽  
Conceptual Understanding ◽  
Attentional Control ◽  
Prediction Error ◽  
Predictive Coding ◽  
Background Knowledge ◽  
Sensory Data ◽  
Incoming Information

Magic tricks have enjoyed an increasing interest by scientists. However, most research in magic focused on isolated aspects of it and a conceptual understanding of magic, encompassing its distinct components and varieties, is missing. Here, we present an account of magic within the theory of Bayesian predictive coding. We present the “wow” effect of magic as an increase in surprise evoked by the prediction error between expected and observed sensory data. We take into account prior knowledge of the observer, attention, and (mis-)direction of perception and beliefs by the magician to bias the observer’s predictions and present two examples for the modelling of the evoked surprise. The role of misdirection is described as everything that aims to maximize the surprise a trick evokes by the generation of novel beliefs, the exploitation of background knowledge and attentional control of the incoming information. Understanding magic within Bayesian predictive coding allows unifying all aspects of magic tricks within one framework, making it tractable, comparable and unifiable with other models in psychology and neuroscience.

scholarly journals Predictive coding as a unifying principle for explaining a broad range of brightness phenomena

2020 ◽  
Author(s):  
Alejandro Lerer ◽  
Hans Supèr ◽  
Matthias S.Keil
Keyword(s):  
Visual System ◽  
Computational Model ◽  
Predictive Coding ◽  
Gain Control ◽  
Physical World ◽  
Input Image ◽  
Visual Illusions ◽  
Contrast Effects ◽  
Dynamic Filtering ◽  
The Brain

AbstractThe visual system is highly sensitive to spatial context for encoding luminance patterns. Context sensitivity inspired the proposal of many neural mechanisms for explaining the perception of luminance (brightness). Here we propose a novel computational model for estimating the brightness of many visual illusions. We hypothesize that many aspects of brightness can be explained by a predictive coding mechanism, which reduces the redundancy in edge representations on the one hand, while non-redundant activity is enhanced on the other (response equalization). Response equalization is implemented with a dynamic filtering process, which (dynamically) adapts to each input image. Dynamic filtering is applied to the responses of complex cells in order to build a gain control map. The gain control map then acts on simple cell responses before they are used to create a brightness map via activity propagation. Our approach is successful in predicting many challenging visual illusions, including contrast effects, assimilation, and reverse contrast.Author summaryWe hardly notice that what we see is often different from the physical world “outside” of the brain. This means that the visual experience that the brain actively constructs may be different from the actual physical properties of objects in the world. In this work, we propose a hypothesis about how the visual system of the brain may construct a representation for achromatic images. Since this process is not unambiguous, sometimes we notice “errors” in our perception, which cause visual illusions. The challenge for theorists, therefore, is to propose computational principles that recreate a large number of visual illusions and to explain why they occur. Notably, our proposed mechanism explains a broader set of visual illusions than any previously published proposal. We achieved this by trying to suppress predictable information. For example, if an image contained repetitive structures, then these structures are predictable and would be suppressed. In this way, non-predictable structures stand out. Predictive coding mechanisms act as early as in the retina (which enhances luminance changes but suppresses uniform regions of luminance), and our computational model holds that this principle also acts at the next stage in the visual system, where representations of perceived luminance (brightness) are created.

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