Predictive Coding System Research Articles

The illusion of intrinsic meaning: reassessing conscious experience

The illusion of intrinsic meaning in predictive coding through cognitive artifacts to minimize prediction errors points toward a functionalist attempt at understanding conscious experience. It examines how conscious experience serves functional roles in predictive coding and symbolic cognition systems within the brain. By addressing recent developments in diverse fields like cognitive neuroscience, philosophy, and artificial intelligence, we argue that conscious experience emerges from the need to construct coherent narratives for survival and decision-making. Additionally, the paper explores the implications for artificial intelligence, suggesting that artificial systems could develop analogous cognitive artifacts through predictive models without subjective awareness, contributing to a functionalist understanding of consciousness, and further advancing the discussion on the nature of conscious experience in biological and artificial systems.

Language experience shapes predictive coding of rhythmic sound sequences.

Perceptual systems heavily rely on prior knowledge and predictions to make sense of the environment. Predictions can originate from multiple sources of information, including contextual short-term priors, based on isolated temporal situations, and context-independent long-term priors, arising from extended exposure to statistical regularities. While the effects of short-term predictions on auditory perception have been well-documented, how long-term predictions shape early auditory processing is poorly understood. To address this, we recorded magnetoencephalography data from native speakers of two languages with different word orders (Spanish: functor-initial vs Basque: functor-final) listening to simple sequences of binary sounds alternating in duration with occasional omissions. We hypothesized that, together with contextual transition probabilities, the auditory system uses the characteristic prosodic cues (duration) associated with the native language's word order as an internal model to generate long-term predictions about incoming non-linguistic sounds. Consistent with our hypothesis, we found that the amplitude of the mismatch negativity elicited by sound omissions varied orthogonally depending on the speaker's linguistic background and was most pronounced in the left auditory cortex. Importantly, listening to binary sounds alternating in pitch instead of duration did not yield group differences, confirming that the above results were driven by the hypothesized long-term 'duration' prior. These findings show that experience with a given language can shape a fundamental aspect of human perception - the neural processing of rhythmic sounds - and provides direct evidence for a long-term predictive coding system in the auditory cortex that uses auditory schemes learned over a lifetime to process incoming sound sequences.

Development, Insults and Predisposing Factors of the Brain's Predictive Coding System to Chronic Perceptual Disorders-A Life-Course Examination.

The predictive coding theory is currently widely accepted as the theoretical basis of perception and chronic perceptual disorders are explained as the maladaptive compensation of the brain to a prediction error. Although this gives us a general framework to work with, it is still not clear who may be more susceptible and/or vulnerable to aberrations in this system. In this paper, we study changes in predictive coding through the lens of tinnitus and pain. We take a step back to understand how the predictive coding system develops from infancy, what are the different neural and bio markers that characterise this system in the acute, transition and chronic phases and what may be the factors that pose a risk to the aberration of this system. Through this paper, we aim to identify people who may be at a higher risk of developing chronic perceptual disorders as a reflection of aberrant predictive coding, thereby giving future studies more facets to incorporate in their investigation of early markers of tinnitus, pain and other disorders of predictive coding. We therefore view this paper to encourage the thinking behind the development of preclinical biomarkers to maladaptive predictive coding.

Mutual beta power modulation in dyadic entrainment

Across a broad spectrum of interactions, humans exhibit a prominent tendency to synchronize their movements with one another. Traditionally, this phenomenon has been explained from the perspectives of predictive coding or dynamical systems theory. While these theories diverge with respect to whether individuals hold internal models of each other, they both assume a predictive or anticipatory mechanism enabling rhythmic interactions. However, the neural bases underpinning interpersonal synchronization are still a subject under active investigation. Here we provide evidence that the brain relies on a common oscillatory mechanism to pace self-generated rhythmic movements and to track the movements produced by a partner. By performing dual-electroencephalography recordings during a joint finger-tapping task, we identified an oscillatory component in the beta range (∼ 20 Hz), which was significantly modulated by both self-generated and other-generated movement. In conditions where the partners perceived each other, we observed periodic fluctuations of beta power as a function of the reciprocal movement cycles. Crucially, this modulation occurred both in visually and in auditorily coupled conditions, and was accompanied by recurrent periods of dyadic synchronized behavior. Our results show that periodic beta power modulations may be a critical mechanism underlying interpersonal synchronization, possibly enabling mutual predictions between coupled individuals, leading to co-regulation of timing and overt mutual adaptation. Our findings thus provide a potential bridge between influential theories attempting to explain interpersonal coordination, and a concrete connection to its neurophysiological bases.

Enhanced Instructed Fear Learning in Delusion-Proneness.

Psychosis is associated with distorted perceptions and deficient bottom-up learning such as classical fear conditioning. This has been interpreted as reflecting imprecise priors in low-level predictive coding systems. Paradoxically, overly strong beliefs, such as overvalued beliefs and delusions, are also present in psychosis-associated states. In line with this, research has suggested that patients with psychosis and associated phenotypes rely more on high-order priors to interpret perceptual input. In this behavioural and fMRI study we studied two types of fear learning, i.e., instructed fear learning mediated by verbal suggestions about fear contingencies and classical fear conditioning mediated by low level associative learning, in delusion proneness—a trait in healthy individuals linked to psychotic disorders. Subjects were shown four faces out of which two were coupled with an aversive stimulation (CS+) while two were not (CS-) in a fear conditioning procedure. Before the conditioning, subjects were informed about the contingencies for two of the faces of each type, while no information was given for the two other faces. We could thereby study the effect of both classical fear conditioning and instructed fear learning. Our main outcome variable was evaluative rating of the faces. Simultaneously, fMRI-measurements were performed to study underlying mechanisms. We postulated that instructed fear learning, measured with evaluative ratings, is stronger in psychosis-related phenotypes, in contrast to classical fear conditioning that has repeatedly been shown to be weaker in these groups. In line with our hypothesis, we observed significantly larger instructed fear learning on a behavioural level in delusion-prone individuals (n = 20) compared to non-delusion-prone subjects (n = 23; n = 20 in fMRI study). Instructed fear learning was associated with a bilateral activation of lateral orbitofrontal cortex that did not differ significantly between groups. However, delusion-prone subjects showed a stronger functional connectivity between right lateral orbitofrontal cortex and regions processing fear and pain. Our results suggest that psychosis-related states are associated with a strong instructed fear learning in addition to previously reported weak classical fear conditioning. Given the similarity between nocebo paradigms and instructed fear learning, our results also have an impact on understanding why nocebo effects differ between individuals.

Are We in Time? How Predictive Coding and Dynamical Systems Explain Musical Synchrony

Humans tend to anticipate events when they synchronize their actions with sound (such as when they clap to music), which has puzzled scientists for decades. What accounts for this anticipation? We review two theoretical mechanisms for synchrony: predictive coding and dynamical systems. Both theories are grounded in neural activation patterns, but there are important distinctions. We contrast their assumptions, their computations, and their musical applications to anticipatory synchronization.

Automated decision support technologies and the legal profession

A quiet revolution is afoot in the field of law. Technical systems employing algorithms are shaping and displacing professional decision making, and they are disrupting and restructuring relationships between law firms, lawyers, and clients. Decision-support systems marketed to legal professionals to support e-discovery — generally referred to as “technology-assisted review” (TAR) — increasingly rely on “predictive coding,” machine-learning techniques to classify and predict which of the voluminous electronic documents subject to litigation should be withheld or produced to the opposing side. These systems and the companies offering them are reshaping relationships between lawyers and clients, introducing new kinds of professionals into legal practice, altering the discovery process, and shaping how lawyers construct knowledge about their cases and professional obligations. In the midst of these shifting relationships — and the ways in which these systems are shaping the construction and presentation of knowledge — lawyers are grappling with their professional obligations, ethical duties, and what it means for the future of legal practice. Through in-depth, semi-structured interviews of experts in this space — the technology company representatives who develop and sell such systems to law firms and the legal professionals who decide whether and how to use them in practice — we shed light on the organizational structures, professional rules and norms, and technical system properties that are shaping and being reshaped by predictive coding systems. Our findings show that AI-supported decision systems such as these are reconfiguring professional work practices. In particular, they highlight concerns about potential loss of professional agency and skill, limited understanding and thereby both over- and under-reliance on decision-support systems, and confusion about responsibility and accountability as new kinds of technical professionals and technologies are brought into legal practice. The introduction of predictive coding systems and the new professional and organizational arrangements they are ushering into legal practice compound general concerns over the opacity of technical systems with specific concerns about encroachments on the construction of expert knowledge, liability frameworks, and the potential (mis-)alignment of machine reasoning with professional logics and ethics. Based on our findings, we conclude that predictive coding tools — and likely other algorithmic systems lawyers use to construct knowledge and reason about legal practice — challenge the current model for evaluating whether and how tools are appropriate for legal practice. As tools become both more complex, and more consequential, it is unreasonable to rely solely on legal professionals — judges, law firms, and lawyers — to determine which technologies are appropriate for use. The legal professionals we interviewed report relying on the evaluation and judgement of a range of new technical experts within law firms and, increasingly, third-party vendors and their technical experts. This system for choosing technical systems upon which lawyers rely to make professional decisions — e.g., whether documents are responsive, whether the standard of proportionality has been met — is no longer sufficient. As the tools of medicine are reviewed by appropriate experts before they are put out for consideration and adoption by medical professionals, we argue that the legal profession must develop new processes for determining which algorithmic tools are fit to support lawyers’ decision making. Relatedly, because predictive coding systems are used to produce lawyers’ professional judgment, we argue they must be designed for contestability — providing greater transparency, interaction, and configurability around embedded choices to ensure decisions about how to embed core professional judgments, such as relevance and proportionality remain salient and demand engagement from lawyers, not just their technical experts.

Shannon + Friston = Content: Intentionality in predictive signaling systems

What is the content of a mental state? This question poses the problem of intentionality: to explain how mental states can be about other things, where being about them is understood as representing them. A framework that integrates predictive coding and signaling systems theories of cognitive processing offers a new perspective on intentionality. On this view, at least some mental states are evaluations, which differ in function, operation, and normativity from representations. A complete naturalistic theory of intentionality must account for both types of intentional state.

Interoceptive deficits in patients with obsessive-compulsive disorder in the time course of cognitive-behavioral therapy.

Interoception is impaired in different psychiatric disorders and is also associated with emotions. Only one study could show a higher interoceptive accuracy (IAcc) in patients with obsessive-compulsive disorder (OCD). Based on the predictive coding system we assume contrary results, indicating a decreased IAcc in patients with OCD. So far, there is no study investigating the effect of cognitive-behavioral therapy on IAcc in patients with OCD. Therefore, we hypothesize that patients with OCD improve their IAcc during the time course of therapy compared to healthy controls. Twenty-six patients with OCD from the Psychosomatic Clinic in Windach were examined in the time course of cognitive-behavioral therapy. They were compared to 26 matched healthy controls. IAcc via the heartbeat perception task as well as questionnaire data (OCD-, depression- and anxiety symptoms) were assessed. Results showed that IAcc, OCD-, depression- and anxiety symptoms were attenuated in patients with OCD. Patients recovered in the time course of therapy regarding OCD-, depression- and anxiety symptoms. Interoceptive deficits did not change in the time course of cognitive-behavioral therapy. We demonstrated that IAcc is affected in patients with OCD and this deficit does not change during the time course of a standardized therapy. Future studies should investigate, whether an inaccuracy in perceiving one’s bodily signals constitutes a risk factor for relapse. Further, it could be examined if IAcc can be increased via self- and body focus interventions in patients with OCD.

Speech Recognition using Linear Predictive Coding (LPC) and Adaptive Neuro-Fuzzy (ANFIS) to Control 5 DoF Arm Robot

This research shows the implementation of speech recognition to control arm robot. The method to identify the speech recognition using Linear Predictive Coding (LPC) and Adaptive Neuro-Fuzzy Inference System (ANFIS). LPC method used to feature extraction the signal of speech and ANFIS method used to learn the speech recognition. The data learning which used to ANFIS processed are 6 features. The examination system of speech identification using trained and not trained data. The result of the research shows the successful grade for trained speech data is 88.75% and not trained data is 78.78%. Identification of speech recognition system was applied to controlled arm robot based on Arduino microcontroller.

How You Move Is What I See: Planning an Action Biases a Partner's Visual Search.

Activating action representations can modulate perceptual processing of action-relevant dimensions, indicative of a common-coding of perception and action. When two or more agents work together in joint action, individual agents often need to consider not only their own actions and their effects on the world, but also predict the actions of a co-acting partner. If in these situations the action of a partner is represented in a functionally equivalent way to the agent’s own actions, one may also expect interaction effects between action and perception across jointly acting individuals. The present study investigated whether the action of a co-acting partner may modulate an agent’s perception. The “performer” prepared a grasping or pointing movement toward a physical target while the “searcher” performed a visual search task. The performer’s planned action impaired the searcher’s perceptual performance when the search target dimension was relevant to the performer’s movement execution. These results demonstrate an action-induced modulation of perceptual processes across participants and indicate that agents represent their partner’s action by employing the same perceptual system they use to represent an own action. We suggest that task representations in joint action operate along multiple levels of a cross-brain predictive coding system, which provides agents with information about a partner’s actions when they coordinate to reach a common goal.

Model for Estimation of Bounds in Digital Coding of Seabed Images

This paper proposes the novel model for estima- tion of bounds in digital coding of images. Entropy coding of images is exploited to measure the useful information content of the data. The bit rate achieved by reversible compression using the rate-distortion theory approach takes into account the contribution of the observation noise and the intrinsic information of hypothetical noise-free image. Assuming the Laplacian probability density func- tion of the quantizer input signal, SQNR gains are calcu- lated for image predictive coding system with non-adaptive quantizer for white and correlated noise, respectively. The proposed model is evaluated on seabed images. However, model presented in this paper can be applied to any signal with Laplacian distribution.

Motion trajectory information and agency influence motor learning during observational practice

Fundamental to performing actions is the acquisition of motor behaviours. We examined if motor learning, through observational practice, occurs by viewing an agent displaying naturalistic or constant velocity, and whether motion trajectory, as opposed to end-state, information is required. We also investigated if observational practice is sensitive to belief regarding the origin of an agent. Participants had to learn a novel movement sequence timing task, which required upper-limb movements to a series of targets within a pre-specified absolute and relative time goal. Experiment 1 showed learning after viewing naturalistic and constant velocity, but not end-state information. For Experiment 2, in addition to learning the movement sequence, participants observed a series of movement stimuli that were either the trained or new sequences and asked to rate their confidence on whether the observed sequence was the same or different to observational practice. The results indicated that agency belief modulates how naturalistic and constant velocity is coded. This indicated that the processes associated with belief are part of an interpretative predictive coding system where the association between belief and observed motion is determined. When motion is constant velocity, or believed to be computer-generated, coding occurs through top-down processes. When motion is naturalistic velocity, and believed to be human-generated, it is most likely coded by gaining access to bottom-up sensorimotor processes in the action-observation network.

Hybrid Neural Network Predictive-Wavelet Image Compression System

This paper considers a novel image compression technique called hybrid predictive wavelet coding. The new proposed technique combines the properties of predictive coding and discrete wavelet coding. In contrast to JPEG2000, the image data values are pre-processed using predictive coding to remove inter-pixel redundancy. The error values, which are the difference between the original and the predicted values, are discrete wavelet coding transformed. In this case, a nonlinear neural network predictor is utilised in the predictive coding system. The simulation results indicated that the proposed technique can achieve good compressed images at high decomposition levels in comparison to JPEG2000.

Analytical Model for Estimating the Bounds in Predictive Entropy Coding of Digital Speech Signals

A detailed analysis of estimating the upper bounds in predictive entropy coding of digital speech signal is carried out in this paper. Lossless speech compression is exploited to measure the useful information content of the data. The bit rate achieved by the reversible compression takes into account both the contribution of the observation noise i.e. information regarding to statistical uncertainty, which is not relevant to the user, and the intrinsic information of hypothetically noise-free speech. An entropy model of the speech source is defined in order to estimate the upper bound on SQNR gain in entropy coding of digital speech signals. The gain is calculated for speech predictive coding system with non-adaptive quantizer for white and correlated noise, respectively.

Hierarchical predictive system for the neural integration of incoming somatosensory stimulations

Hierarchical predictive system for the neural integration of incoming somatosensory stimulations

Enhancing the predictive coding efficiency with control technologies for lossless compression of images

This study applies techniques commonly used in control systems to enhance the efficiency of predictive coding in lossless compression of images for pixels around boundaries. Actually, the predictive coding system behaves just like a multi-input single-output system with the predictor itself can be regarded as the system model. Besides, the prediction error is usually feedback for the adaptation of predictor coefficients so that the prediction error of consecutive pixels can be minimised. When compared with a control system, which is to follow the system command as precisely as possible, the authors find the objective of both systems are the same. Moreover, a boundary among image pixels can be considered a step command in control systems. These observations lead to the idea of using control technologies to improve the prediction result around boundaries. To realise this idea, an adaptive Takagi–Sugeno fuzzy neural network and a proportional controller in control systems are applied as the predictor and the error compensator, respectively. To accelerate the run-time performance of the proposed system under limited resources, the online training area is even not used for network adaptation, but the performance is still comparable with state-of-the-art predictors and coders as the authors will see in the experiment.

Complementing predictive coding

GENERAL COMMENTARY article Front. Psychol., 18 December 2012Sec. Theoretical and Philosophical Psychology Volume 3 - 2012 | https://doi.org/10.3389/fpsyg.2012.00554

Low Complexity Distributed Video Coding with Golay Codes

In this paper we present a low complexity video coder working on the principle of distributed source coding that combines the principle of channel coding with source coding. In this work the encoder complexity is shifted to the decoder to support uplink friendly video applications, simultaneously achieving the rate-distortion performance of the conventional predictive coding system. In this work concept of syndrome coding with Golay codes is adopted for compression. The simulation results presented in this paper reveals the superior performance of this distributed video coder over the Intraframe coders in terms of rate distortion performance, simultaneously achieving low complexity when compared to predictive coders.

Gaussian Mixture Kalman Predictive Coding of Line Spectral Frequencies

Gaussian mixture model (GMM)-based predictive coding of line spectral frequencies (LSFs) has gained wide acceptance. In such coders, each mixture of a GMM can be interpreted as defining a linear predictive transform coder. In this paper, we use Kalman filtering principles to model each of these linear predictive transform coders to present GMM Kalman predictive coding. In particular, we show how suitable modeling of quantization noise leads to an adaptive a posteriori GMM that defines a signal-adaptive predictive coder that provides improved coding of LSFs in comparison with the baseline recursive GMM predictive coder. Moreover, we show how running the GMM Kalman predictive coders to convergence can be used to design a stationary GMM Kalman predictive coding system which again provides improved coding of LSFs but now with only a modest increase in run-time complexity over the baseline. In packet loss conditions, this stationary GMM Kalman predictive coder provides much better performance than the recursive GMM predictive coder, and in fact has comparable mean performance to a memoryless GMM coder. Finally, we illustrate how one can utilize Kalman filtering principles to design a postfilter which enhances decoded vectors from a recursive GMM predictive coder without any modifications to the encoding process.