scholarly journals Oversampled and undersolved: Depressive rumination from a predictive coding perspective

2021 ◽  
Author(s):  
Max Berg ◽  
Matthias Feldmann ◽  
Tobias Kube
Keyword(s):  
Problem Solving ◽  
Predictive Coding ◽  
Instrumental Learning ◽  
Mental Simulation ◽  
Clinical Findings ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Depressive Rumination ◽  
Bayesian Sampling ◽  
Integrative View

Rumination is a widely recognized cognitive deviation in depression. An integrative view that combines clinical findings on rumination with theories of mental simulation and cognitive problem-solving could help explain the development and maintenance of rumination in a computationally and biologically plausible framework. In this review, we connect insights from neuroscience and computational psychiatry to elucidate rumination as repetitive but unsuccessful attempts at mental problem-solving. Appealing to a predictive processing account, we suggest that problem-solving is based on an algorithm that generates candidate behavior (policy primitives for problem solutions) using a Bayesian sampling approach, evaluates resulting policies for action, and then engages in instrumental learning to reduce prediction errors. We present evidence suggesting that this problem-solving algorithm is distorted in depression: Specifically, depressive rumination is regarded as excessive Bayesian sampling of candidates that is associated with high prediction errors without activation of the successive steps (policy evaluation, instrumental learning) of the algorithm. Thus, prediction errors cannot be decreased, and excessive resampling of the same problems occur. This then leads to reduced precision weighting attributed to external, “online” stimuli, low behavioral output and high opportunity costs due to the time-consuming nature of the sampling process itself. We review different computational reasons that make the proposed Bayesian sampling algorithm vulnerable to a ruminative „halting problem”. We also identify neurophysiological correlates of these deviations in pathological connectivity patterns of different brain networks. We conclude by suggesting future directions for research into behavioral and neurophysiological features of the model and point to clinical implications.

scholarly journals Constructing the hierarchy of predictive auditory sequences in the marmoset brain

2021 ◽  
Author(s):  
Yuwei Jiang ◽  
Misako Komatsu ◽  
Yuyan Chen ◽  
Ruoying Xie ◽  
Kaiwei Zhang ◽  
...  
Keyword(s):  
Auditory Processing ◽  
Time Scale ◽  
Essential Feature ◽  
Predictive Coding ◽  
Auditory Pathway ◽  
Brain Regions ◽  
Predictive Processing ◽  
Short Time Scale ◽  
Prediction Errors ◽  
Surface Areas

Our brains constantly generate predictions of sensory input that are compared with actual inputs, propagate the prediction-errors through a hierarchy of brain regions, and subsequently update the internal predictions of the world. However, the essential feature of predictive coding, the notion of hierarchical depth and its neural mechanisms, remains largely unexplored. Here, we investigated the hierarchical depth of predictive auditory processing by combining functional magnetic resonance imaging (fMRI) and high-density whole-brain electrocorticography (ECoG) in marmoset monkeys during an auditory local-global paradigm in which the temporal regularities of the stimuli were designed at two hierarchical levels. The prediction-errors and prediction updates were examined as neural responses to auditory mismatches and omissions. Using fMRI, we identified a hierarchical gradient along the auditory pathway: midbrain and sensory regions represented local, short-time-scale predictive processing followed by associative auditory regions, whereas anterior temporal and prefrontal areas represented global, long-time-scale sequence processing. The complementary ECoG recordings confirmed the activations at cortical surface areas and further differentiated the signals of prediction-error and update, which were transmitted via putatively bottom-up γ and top-down β oscillations, respectively. Furthermore, omission responses caused by absence of input, reflecting solely the two levels of prediction signals that are unique to the hierarchical predictive coding framework, demonstrated the hierarchical predictions in the auditory, temporal, and prefrontal areas. Thus, our findings support the hierarchical predictive coding framework, and outline how neural circuits and spatiotemporal dynamics are used to represent and arrange a hierarchical structure of auditory sequences in the marmoset brain.

scholarly journals Enhanced mismatch negativity responses in harmonic compared to inharmonic sound sequences

2021 ◽  
Author(s):  
David Ricardo Quiroga-Martinez ◽  
Krzysztof Basinski ◽  
Jonathan Nasielski ◽  
Barbara Tillmann ◽  
Elvira Brattico ◽  
...  
Keyword(s):  
Information Entropy ◽  
Mismatch Negativity ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Oddball Paradigm ◽  
Auditory Information ◽  
Prediction Errors ◽  
Frequency Spectra ◽  
Auditory Information Processing ◽  
The Difference

Many natural sounds have frequency spectra composed of integer multiples of a fundamental frequency. This property, known as harmonicity, plays an important role in auditory information processing. However, the extent to which harmonicity influences the processing of sound features beyond pitch is still unclear. This is interesting because harmonic sounds have lower information entropy than inharmonic sounds. According to predictive processing accounts of perception, this property could produce more salient neural responses due to the brain weighting of sensory signals according to their uncertainty. In the present study, we used electroencephalography to investigate brain responses to harmonic and inharmonic sounds commonly occurring in music: piano tones and hi-hat cymbal sounds. In a multi-feature oddball paradigm, we measured mismatch negativity (MMN) and P3a responses to timbre, intensity, and location deviants in listeners with and without congenital amusia, an impairment of pitch processing. As hypothesized, we observed larger amplitudes and earlier latencies for harmonic compared to inharmonic sounds for both MMN and P3a responses. These harmonicity effects were modulated by sound feature. Moreover, the difference in P3a latency between harmonic and inharmonic sounds was larger for controls than amusics. We propose an explanation of these results based on predictive coding and discuss the relationship between harmonicity, information entropy, and precision weighting of prediction errors.

Reward-Dependent Learning

2017 ◽  
Author(s):  
Andreas Heinz
Keyword(s):  
Mental Disorders ◽  
Dopamine Release ◽  
Computational Modelling ◽  
Instrumental Learning ◽  
Clinical Findings ◽  
Prediction Errors ◽  
Computational Approaches ◽  
The Third ◽  
Dependent Learning ◽  
Subjective Reports

In the third chapter, reward dependent instrumental learning and its computational modelling is explained. Reward prediction errors are encoded by phasic dopamine release and specific paradigms including reversal learning are described with respect to clinical findings in different mental disorders. The chapter illustrates how computational approaches avoid relying exclusively on subjective reports of patients and instead correlate specific computational steps during reward related learning with their neurobiological correlates.

A challenge for predictive coding: Representational or experiential diversity?

2020 ◽  
Vol 43 ◽  
Author(s):  
Martina G. Vilas ◽  
Lucia Melloni
Keyword(s):  
Brain Function ◽  
Predictive Coding ◽  
Predictive Processing ◽  
Process Theory

Abstract To become a unifying theory of brain function, predictive processing (PP) must accommodate its rich representational diversity. Gilead et al. claim such diversity requires a multi-process theory, and thus is out of reach for PP, which postulates a universal canonical computation. We contend this argument and instead propose that PP fails to account for the experiential level of representations.

Above and beyond the content: Feelings influence mental simulations

2020 ◽  
Vol 43 ◽  
Author(s):  
Kellen Mrkva ◽  
Luca Cian ◽  
Leaf Van Boven
Keyword(s):  
Mental Representation ◽  
Mental Simulation ◽  
Past Experience ◽  
Prediction Errors

Abstract Gilead et al. present a rich account of abstraction. Though the account describes several elements which influence mental representation, it is worth also delineating how feelings, such as fluency and emotion, influence mental simulation. Additionally, though past experience can sometimes make simulations more accurate and worthwhile (as Gilead et al. suggest), many systematic prediction errors persist despite substantial experience.

Temporal Prediction Errors Affect Short-Term Memory Scanning Response Time

2016 ◽  
Vol 63 (6) ◽  
pp. 333-342
Author(s):  
Roberto Limongi ◽  
Angélica M. Silva
Keyword(s):  
Response Time ◽  
Short Term Memory ◽  
Estimation Error ◽  
Predictive Coding ◽  
Time Estimation ◽  
Prediction Errors ◽  
Memory Scanning ◽  
Short Term ◽  
Term Memory ◽  
Temporal Prediction

Abstract. The Sternberg short-term memory scanning task has been used to unveil cognitive operations involved in time perception. Participants produce time intervals during the task, and the researcher explores how task performance affects interval production – where time estimation error is the dependent variable of interest. The perspective of predictive behavior regards time estimation error as a temporal prediction error (PE), an independent variable that controls cognition, behavior, and learning. Based on this perspective, we investigated whether temporal PEs affect short-term memory scanning. Participants performed temporal predictions while they maintained information in memory. Model inference revealed that PEs affected memory scanning response time independently of the memory-set size effect. We discuss the results within the context of formal and mechanistic models of short-term memory scanning and predictive coding, a Bayes-based theory of brain function. We state the hypothesis that our finding could be associated with weak frontostriatal connections and weak striatal activity.

THE NATURE OF DEPRESSIVE RUMINATION AND ITS CONNECTION WITH DEPRESSIVE SYMPTOMS

2020 ◽  
Vol 39 (9) ◽  
pp. 761-787
Author(s):  
Marta M. Maslej ◽  
Benoit H. Mulsant ◽  
Paul W. Andrews
Keyword(s):  
Factor Analysis ◽  
Depressive Symptoms ◽  
Problem Solving ◽  
Beck Depression Inventory ◽  
Negative Feedback ◽  
Dynamic Process ◽  
Feedback Loop ◽  
Negative Feedback Loop ◽  
Depressive Rumination ◽  
Joint Factor Analysis

Introduction: Researchers have proposed several theories of depressive rumination. To compare among them, we conducted a joint factor analysis. Methods: An online sample (n = 498) completed four rumination questionnaires and the Beck Depression Inventory. We examined associations between emerging factors and depressive symptoms. Results: Most commonly, people ruminated about solving problems in their lives, followed by the causes or consequences of negative situations. They least commonly ruminated about their symptoms and sadness. Thoughts about symptoms and causes or consequences of negative situations uniquely related to depressive symptoms. There was a circular covariance relation between depressive symptoms, thoughts about causes or consequences, and problem-solving, suggesting that symptoms are regulated by a negative feedback loop involving problem-solving. This feedback was not present unless models included thoughts about causes or consequences, suggesting that these thoughts benefit problem-solving. Discussion: Depressive rumination may be a dynamic process involving various thoughts, with different combinations of thoughts having different consequences for depression.

Extending predictive processing to the body: Emotion as interoceptive inference

2013 ◽  
Vol 36 (3) ◽  
pp. 227-228 ◽  
Author(s):  
Anil K. Seth ◽  
Hugo D. Critchley
Keyword(s):  
Psychiatric Disorders ◽  
Physiological Condition ◽  
Predictive Coding ◽  
The Body ◽  
Predictive Processing ◽  
Bayesian Brain ◽  
New View

AbstractThe Bayesian brain hypothesis provides an attractive unifying framework for perception, cognition, and action. We argue that the framework can also usefully integrate interoception, the sense of the internal physiological condition of the body. Our model of “interoceptive predictive coding” entails a new view of emotion as interoceptive inference and may account for a range of psychiatric disorders of selfhood.

The Embodiment of Concepts

2018 ◽  
pp. 640-660
Author(s):  
Michiel Van Elk ◽  
Harold Bekkering
Keyword(s):  
Prediction Error ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Conceptual Representation ◽  
Recurrent Processing ◽  
Different Dimensions ◽  
Prior Models ◽  
Processing Framework

We characterize theories of conceptual representation as embodied, disembodied, or hybrid according to their stance on a number of different dimensions: the nature of concepts, the relation between language and concepts, the function of concepts, the acquisition of concepts, the representation of concepts, and the role of context. We propose to extend an embodied view of concepts, by taking into account the importance of multimodal associations and predictive processing. We argue that concepts are dynamically acquired and updated, based on recurrent processing of prediction error signals in a hierarchically structured network. Concepts are thus used as prior models to generate multimodal expectations, thereby reducing surprise and enabling greater precision in the perception of exemplars. This view places embodied theories of concepts in a novel predictive processing framework, by highlighting the importance of concepts for prediction, learning and shaping categories on the basis of prediction errors.

Sign in / Sign up

Export Citation Format

Share Document