One wouldn’t expect an expert bowler to hit only two pins: Hierarchical predictive processing of agent-caused events

Evidence is accumulating that our brains process incoming information using top-down predictions. If lower level representations are correctly predicted by higher level representations, this enhances processing. However, if they are incorrectly predicted, additional processing is required at higher levels to “explain away” prediction errors. Here, we explored the potential nature of the models generating such predictions. More specifically, we investigated whether a predictive processing model with a hierarchical structure and causal relations between its levels is able to account for the processing of agent-caused events. In Experiment 1, participants watched animated movies of “experienced” and “novice” bowlers. The results are in line with the idea that prediction errors at a lower level of the hierarchy (i.e., the outcome of how many pins fell down) slow down reporting of information at a higher level (i.e., which agent was throwing the ball). Experiments 2 and 3 suggest that this effect is specific to situations in which the predictor is causally related to the outcome. Overall, the study supports the idea that a hierarchical predictive processing model can account for the processing of observed action outcomes and that the predictions involved are specific to cases where action outcomes can be predicted based on causal knowledge.

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The Embodiment of Concepts

The Oxford Handbook of 4E Cognition ◽

10.1093/oxfordhb/9780198735410.013.34 ◽

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.

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Young Infants Process Prediction Errors at the Theta Rhythm

10.1101/2020.07.30.226902 ◽

2020 ◽

Author(s):

Moritz Köster ◽

Miriam Langeloh ◽

Christine Michel ◽

Stefanie Hoehl

Keyword(s):

Theta Rhythm ◽

Time Window ◽

Predictive Processing ◽

Prediction Errors ◽

Unexpected Events ◽

Young Infants ◽

Human Brain Development ◽

Social Events ◽

Infant Brain ◽

Process Prediction

AbstractExamining how young infants respond to unexpected events is key to our understanding of their emerging concepts about the world around them. From a predictive processing perspective, it is intriguing to investigate how the infant brain responds to unexpected events (i.e., prediction errors), because they require infants to refine their predictive models about the environment. Here, to better understand prediction error processes in the infant brain, we presented 9-month-olds (N = 36) a variety of physical and social events with unexpected versus expected outcomes, while recording their electroencephalogram. We found a pronounced response in the ongoing 4 – 5 Hz theta rhythm for the processing of unexpected (in contrast to expected) events, for a prolonged time window (2 s) and across all scalp-recorded electrodes. The condition difference in the theta rhythm was not related to the condition difference in infants’ event-related activity on the negative central (Nc) component (.4 – .6 s), which has been described in former studies. These findings constitute critical evidence that the theta rhythm is involved in the processing of prediction errors from very early in human brain development, which may support infants’ refinement of basic concepts about the physical and social environment.

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Inferring Causal Relations by Analogy

10.1093/oxfordhb/9780199399550.013.25 ◽

2017 ◽

Author(s):

Keith J. Holyoak ◽

Hee Seung Lee

Keyword(s):

Analogical Reasoning ◽

Causal Model ◽

Causal Knowledge ◽

Common Pattern ◽

Causal Relations ◽

Analogical Inference ◽

Psychological Evidence ◽

Inductive Inferences ◽

Novel Target ◽

Constituent Elements

When two situations share a common pattern of relationships among their constituent elements, people often draw an analogy between a familiar source analog and a novel target analog. This chapter reviews major subprocesses of analogical reasoning and discusses how analogical inference is guided by causal relations. Psychological evidence suggests that analogical inference often involves constructing and then running a causal model. It also provides some examples of analogies and models that have been used as tools in science education to foster understanding of critical causal relations. A Bayesian theory of causal inference by analogy illuminates how causal knowledge, represented as causal models, can be integrated with analogical reasoning to yield inductive inferences.

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Minimizing prediction errors in predictive processing: from inconsistency to non-representationalism

Phenomenology and the Cognitive Sciences ◽

10.1007/s11097-019-09649-y ◽

2019 ◽

Vol 19 (5) ◽

pp. 997-1017

Author(s):

Thomas van Es

Keyword(s):

Predictive Processing ◽

Prediction Errors

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Velocity estimation in reinforcement learning

10.1101/432492 ◽

2018 ◽

Author(s):

Carlos Velazquez ◽

Manuel Villarreal ◽

Arturo Bouzas

Keyword(s):

Reinforcement Learning ◽

Hierarchical Structure ◽

Predictive Accuracy ◽

Information Criterion ◽

Individual Variability ◽

Velocity Estimation ◽

Hyperbolic Function ◽

Prediction Errors ◽

Signal To Noise ◽

Learning Rates

The current work aims to study how people make predictions, under a reinforcement learning framework, in an environment that fluctuates from trial to trial and is corrupted with Gaussian noise. A computer-based experiment was developed where subjects were required to predict the future location of a spaceship that orbited around planet Earth. Its position was sampled from a Gaussian distribution with the mean changing at a variable velocity and four different values of variance that defined our signal-to-noise conditions. Three error-driven algorithms using a Bayesian approach were proposed as candidates to describe our data. The first is the standard delta-rule. The second and third models are delta rules incorporating a velocity component which is updated using prediction errors. The third model additionally assumes a hierarchical structure where individual learning rates for velocity and decision noise come from Gaussian distributions with means following a hyperbolic function. We used leave-one-out cross-validation and the Widely Applicable Information Criterion to compare the predictive accuracy of these models. In general, our results provided evidence in favor of the hierarchical model and highlight two main conclusions. First, when facing an environment that fluctuates from trial to trial, people can learn to estimate its velocity to make predictions. Second, learning rates for velocity and decision noise are influenced by uncertainty constraints represented by the signal-to-noise ratio. This higher order control was modeled using a hierarchical structure, which qualitatively accounts for individual variability and is able to generalize and make predictions about new subjects on each experimental condition.

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Predictive neural processing in adult zebrafish depends on shank3b

10.1101/546457 ◽

2019 ◽

Cited By ~ 2

Author(s):

Kuo‐Hua Huang ◽

Peter Rupprecht ◽

Michael Schebesta ◽

Fabrizio Serluca ◽

Kyohei Kitamura ◽

...

Keyword(s):

Neuronal Activity ◽

Sensory Input ◽

Motor Behavior ◽

Predictive Processing ◽

Prediction Errors ◽

Neuronal Responses ◽

Adult Zebrafish ◽

Disease Phenotypes ◽

Intelligent Behavior ◽

Widespread Phenomenon

SummaryIntelligent behavior requires a comparison between the predicted and the actual consequences of behavioral actions. According to the theory of predictive processing, this comparison relies on a neuronal error signal that reflects the mismatch between an internal prediction and sensory input. Inappropriate error signals may generate pathological experiences in neuropsychiatric conditions. To examine the processing of sensorimotor prediction errors across different telencephalic brain areas we optically measured neuronal activity in head-fixed, adult zebrafish in a virtual reality. Brief perturbations of visuomotor feedback triggered distinct changes in swimming behavior and different neuronal responses. Neuronal activity reflecting sensorimotor mismatch, rather than sensory input or motor output alone, was prominent throughout multiple forebrain areas. This activity preceded and predicted the transition in motor behavior. Error signals were altered in specific forebrain regions by a mutation in the autism-related gene shank3b. Predictive processing is therefore a widespread phenomenon that may contribute to disease phenotypes.

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Gestalts at threshold could reveal Gestalts as predictions

Scientific Reports ◽

10.1038/s41598-021-97878-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Thiago Leiros Costa ◽

Johan Wagemans

Keyword(s):

Predictive Processing ◽

Top Down ◽

Visual Neurons ◽

Chance Performance ◽

Response Profile ◽

Early Visual Cortex ◽

Feedback Connections ◽

And Control ◽

Contrast Response

AbstractWe review and revisit the predictive processing inspired “Gestalts as predictions” hypothesis. The study of Gestalt phenomena at and below threshold can help clarify the role of higher-order object selective areas and feedback connections in mid-level vision. In two psychophysical experiments assessing manipulations of contrast and configurality we showed that: (1) Gestalt phenomena are robust against saliency manipulations across the psychometric function even below threshold (with the accuracy gains and higher saliency associated with Gestalts being present even around chance performance); and (2) peak differences between Gestalt and control conditions happened around the time where responses to Gestalts are starting to saturate (mimicking the differential contrast response profile of striate vs. extra-striate visual neurons). In addition, Gestalts are associated with steeper psychometric functions in all experiments. We propose that these results reflect the differential engagement of object-selective areas in Gestalt phenomena and of information- or percept-based processing, as opposed to energy- or stimulus-based processing, more generally. In addition, the presence of nonlinearities in the psychometric functions suggest differential top-down modulation of the early visual cortex. We treat this as a proof of principle study, illustrating that classic psychophysics can help assess possible involvement of hierarchical predictive processing in Gestalt phenomena.

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Constructing the hierarchy of predictive auditory sequences in the marmoset brain

10.1101/2021.10.25.465732 ◽

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.

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BHi-Cect: a top-down algorithm for identifying the multi-scale hierarchical structure of chromosomes

Nucleic Acids Research ◽

10.1093/nar/gkaa004 ◽

2020 ◽

Vol 48 (5) ◽

pp. e26-e26

Author(s):

Vipin Kumar ◽

Simon Leclerc ◽

Yuichi Taniguchi

Keyword(s):

Hierarchical Structure ◽

Detection Methods ◽

Interaction Patterns ◽

Top Down ◽

Chromosome Conformation ◽

Multi Scale ◽

Topologically Associating Domains ◽

Genome Wide ◽

The Hierarchical Structure ◽

Respective Function

Abstract High-throughput chromosome conformation capture (Hi-C) technology enables the investigation of genome-wide interactions among chromosome loci. Current algorithms focus on topologically associating domains (TADs), that are contiguous clusters along the genome coordinate, to describe the hierarchical structure of chromosomes. However, high resolution Hi-C displays a variety of interaction patterns beyond what current TAD detection methods can capture. Here, we present BHi-Cect, a novel top-down algorithm that finds clusters by considering every locus with no assumption of genomic contiguity using spectral clustering. Our results reveal that the hierarchical structure of chromosome is organized as ‘enclaves’, which are complex interwoven clusters at both local and global scales. We show that the nesting of local clusters within global clusters characterizing enclaves, is associated with the epigenomic activity found on the underlying DNA. Furthermore, we show that the hierarchical nesting that links different enclaves integrates their respective function. BHi-Cect provides means to uncover the general principles guiding chromatin architecture.

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