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.

The Role of Prior Event Retrieval in Encoding Changed Event Features

2020 ◽  
Author(s):  
Mary Hermann ◽  
Timothy Alexander ◽  
Christopher N. Wahlheim ◽  
Jeffrey M. Zacks
Keyword(s):  
Prediction Error ◽  
Prediction Errors ◽  
Event Memory ◽  
Episodic Retrieval ◽  
Everyday Activities ◽  
Previous Event ◽  
New Learning ◽  
Retrieval Mechanism ◽  
Prior Event

When people experience everyday activities, their comprehension can be shaped by expectations that derive from similar recent experiences, which can affect the encoding of the new experience into memory. When a new experience includes changes—such as a driving route being blocked by construction—this can lead to interference in subsequent memory. However, theories based on prediction-error-driven learning propose that unpredicted changes can lead to facilitation rather than interference. One potential mechanism of effective encoding of event changes is the retrieval of related features from previous events. Another such mechanism is the generation of a prediction error when a predicted feature is contradicted. In two experiments, we tested for effects of these two mechanisms on memory for changed features in movies of everyday activities. Participants viewed movies of an actor performing everyday activities across two fictitious days. Some event features changed across the days, and some features violated viewers’ predictions. Retrieval of previous event features while viewing the second movie was associated with better subsequent memory, providing evidence for the retrieval mechanism. Contrary to our hypotheses, there was not support for the error mechanism: Prediction error was not associated with better memory when it was observed correlationally (Experiment 1) or directly manipulated (Experiment 2). These results support a key role for episodic retrieval in the encoding of new events. They also indicate boundary conditions on the role of prediction errors in driving new learning. Both findings have clear implications for theories of event memory.

scholarly journals Dopamine, Prediction Error and Beyond

2020 ◽  
pp. 107385842090759
Author(s):  
Kelly M. J. Diederen ◽  
Paul C. Fletcher
Keyword(s):  
Prediction Error ◽  
Large Body ◽  
Theoretical Models ◽  
Optimal Choice ◽  
Reward Processing ◽  
Prediction Errors ◽  
The Difference ◽  
Past Experiences ◽  
Mental Pathology

A large body of work has linked dopaminergic signaling to learning and reward processing. It stresses the role of dopamine in reward prediction error signaling, a key neural signal that allows us to learn from past experiences, and that facilitates optimal choice behavior. Latterly, it has become clear that dopamine does not merely code prediction error size but also signals the difference between the expected value of rewards, and the value of rewards actually received, which is obtained through the integration of reward attributes such as the type, amount, probability and delay. More recent work has posited a role of dopamine in learning beyond rewards. These theories suggest that dopamine codes absolute or unsigned prediction errors, playing a key role in how the brain models associative regularities within its environment, while incorporating critical information about the reliability of those regularities. Work is emerging supporting this perspective and, it has inspired theoretical models of how certain forms of mental pathology may emerge in relation to dopamine function. Such pathology is frequently related to disturbed inferences leading to altered internal models of the environment. Thus, it is critical to understand the role of dopamine in error-related learning and inference.

scholarly journals Event-related potentials reflect prediction errors and pop-out during comprehension of degraded speech

2020 ◽  
Vol 2020 (1) ◽  
Author(s):  
Leah Banellis ◽  
Rodika Sokoliuk ◽  
Conor J Wild ◽  
Howard Bowman ◽  
Damian Cruse
Keyword(s):  
Prediction Error ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Prediction Errors ◽  
Top Down ◽  
Degraded Speech ◽  
Related Potentials ◽  
Task Goals ◽  
Post Hoc

Abstract Comprehension of degraded speech requires higher-order expectations informed by prior knowledge. Accurate top-down expectations of incoming degraded speech cause a subjective semantic ‘pop-out’ or conscious breakthrough experience. Indeed, the same stimulus can be perceived as meaningless when no expectations are made in advance. We investigated the event-related potential (ERP) correlates of these top-down expectations, their error signals and the subjective pop-out experience in healthy participants. We manipulated expectations in a word-pair priming degraded (noise-vocoded) speech task and investigated the role of top-down expectation with a between-groups attention manipulation. Consistent with the role of expectations in comprehension, repetition priming significantly enhanced perceptual intelligibility of the noise-vocoded degraded targets for attentive participants. An early ERP was larger for mismatched (i.e. unexpected) targets than matched targets, indicative of an initial error signal not reliant on top-down expectations. Subsequently, a P3a-like ERP was larger to matched targets than mismatched targets only for attending participants—i.e. a pop-out effect—while a later ERP was larger for mismatched targets and did not significantly interact with attention. Rather than relying on complex post hoc interactions between prediction error and precision to explain this apredictive pattern, we consider our data to be consistent with prediction error minimization accounts for early stages of processing followed by Global Neuronal Workspace-like breakthrough and processing in service of task goals.

scholarly journals PREDICTION AND ERROR-BASED LEARNING IN L2 PROCESSING AND ACQUISITION

2021 ◽  
pp. 1-26
Author(s):  
Giulia Bovolenta ◽  
Emma Marsden
Keyword(s):  
Language Acquisition ◽  
Computational Modeling ◽  
Language Learning ◽  
Empirical Evidence ◽  
Language Processing ◽  
Prediction Error ◽  
Predictive Processing ◽  
L2 Processing ◽  
L1 And L2

Abstract There is currently much interest in the role of prediction in language processing, both in L1 and L2. For language acquisition researchers, this has prompted debate on the role that predictive processing may play in both L1 and L2 language learning, if any. In this conceptual review, we explore the role of prediction and prediction error as a potential learning aid. We examine different proposed prediction mechanisms and the empirical evidence for them, alongside the factors constraining prediction for both L1 and L2 speakers. We then review the evidence on the role of prediction in learning languages. We report computational modeling that underpins a number of proposals on the role of prediction in L1 and L2 learning, then lay out the empirical evidence supporting the predictions made by modeling, from research into priming and adaptation. Finally, we point out the limitations of these mechanisms in both L1 and L2 speakers.

scholarly journals Young Children Integrate Current Observations, Priors and Agent Information to Build Predictive Models of Others’ Actions

2018 ◽  
Author(s):  
E. Kayhan ◽  
L. Heil ◽  
J. Kwisthout ◽  
I. van Rooij ◽  
S. Hunnius ◽  
...  
Keyword(s):  
Young Children ◽  
Bayesian Network ◽  
Computational Model ◽  
Predictive Models ◽  
Predictive Processing ◽  
Prediction Errors ◽  
Model Based ◽  
Pupillary Responses ◽  
Causal Bayesian Network ◽  
Processing Framework

AbstractFrom early on in life, children are able to use information from their environment to form predictions about events. For instance, they can use statistical information about a population to predict the sample drawn from that population and infer an agent’s preferences from systematic violations of random sampling. We investigated how young children build and update models of an agent’s sampling actions over time, and whether a computational model based on the causal Bayesian network formalization of predictive processing can explain this process.We formalized three hypotheses about how different explanatory variables (i.e., prior probabilities, current observations, and agent characteristics) are used to build predictive models of others’ actions. We measured pupillary responses as a behavioral marker of ‘prediction errors’ (i.e., the perceived mismatch between what one’s model of an agent predicts and what the agent actually does), as described in the predictive processing framework. Pupillary responses of 24-month-olds, but not 18-month-olds, showed that young children integrated information about current observations, priors and agents to generate predictive models of agents and their actions.These findings shed light on the mechanisms behind toddlers’ inferences about agent-caused events. To our knowledge, this is the first study in which young children’s pupillary responses are used as markers of prediction errors, and explained by a computational model based on the causal Bayesian network formalization of predictive processing. We argue that the predictive processing framework provides a promising explanation of the way in which young children process other persons’ actions.HighlightsWe present three formalized hypotheses on how young children generate predictive models of others’ sampling actions.We measured pupillary responses of children as a behavioral marker of prediction errors as described in the predictive processing framework.Results showed that young children integrated information about current observations, prior probabilities and agents to generate predictive models about others’ actions.A computational model based on the causal Bayesian network formalization of predictive processing can explain this process.

scholarly journals Causal evidence supporting the proposal that dopamine transients function as a temporal difference prediction error

2019 ◽  
Author(s):  
Etienne JP Maes ◽  
Melissa J Sharpe ◽  
Matthew P.H. Gardner ◽  
Chun Yun Chang ◽  
Geoffrey Schoenbaum ◽  
...  
Keyword(s):  
Prediction Error ◽  
Second Order ◽  
Prediction Errors ◽  
Temporal Difference ◽  
Reward Prediction ◽  
Order Conditioning ◽  
Central Tenet

Reward-evoked dopamine is well-established as a prediction error. However the central tenet of temporal difference accounts – that similar transients evoked by reward-predictive cues also function as errors – remains untested. To address this, we used two phenomena, second-order conditioning and blocking, in order to examine the role of dopamine in prediction error versus reward prediction. We show that optogenetically-shunting dopamine activity at the start of a reward-predicting cue prevents second-order conditioning without affecting blocking. These results support temporal difference accounts by providing causal evidence that cue-evoked dopamine transients function as prediction errors.

Useful misrepresentation: perception as embodied proactive inference

2022 ◽  
Author(s):  
Joshua Martin
Keyword(s):  
Prediction Error ◽  
Predictive Processing ◽  
Predictive Information ◽  
Objective Truth ◽  
Embodied Action ◽  
Processing Framework

According to the predictive processing framework, perception is geared to represent the environment in terms of embodied action opportunities as opposed to objective truth. Here, we argue that such an optimisation is reflected by biases in expectations (i.e., prior predictive information) that facilitate ‘useful’ inferences of external sensory causes. To support this, we highlight a body of literature suggesting that perception is systematically biased away from accurate estimates under conditions where utility and accuracy conflict with one another. We interpret this to reflect the brain’s attempt to adjudicate between conflicting sources of prediction error, as external accuracy is sacrificed to facilitate actions that proactively avoid physiologically surprising outcomes. This carries important theoretical implications and offers new insights into psychopathology.

Introduction

2019 ◽  
pp. 1-6
Author(s):  
Matteo Colombo ◽  
Liz Irvine ◽  
Mog Stapleton
Keyword(s):  
Distributed Processing ◽  
Extended Mind ◽  
Predictive Processing ◽  
Andy Clark ◽  
Cognitive Scientist ◽  
Mind And Brain ◽  
Opening Up ◽  
Intense Debate ◽  
Processing Framework

Andy Clark is a leading philosopher and cognitive scientist. His work has been wide-ranging and inspiring. The extended mind hypothesis, the power of parallel distributed processing, the role of language in opening up novel paths for thinking, the flexible interface between biological minds and artificial technologies, the significance of representation in explanations of intelligent behaviour, the promise of the predictive processing framework to unify the cognitive sciences: these are just some of the ideas illuminated by Clark’s work that have sparked intense debate across the sciences of mind and brain. This introduction puts into focus some of the major motifs running through Clark’s work and outlines the content and structure of the volume.

Mediofrontal Negativity Signals Unexpected Timing of Salient Outcomes

2017 ◽  
Vol 29 (4) ◽  
pp. 718-727 ◽  
Author(s):  
Sara Garofalo ◽  
Christopher Timmermann ◽  
Simone Battaglia ◽  
Martin E. Maier ◽  
Giuseppe di Pellegrino
Keyword(s):  
Prefrontal Cortex ◽  
Computational Model ◽  
Medial Prefrontal Cortex ◽  
Prediction Error ◽  
Critical Role ◽  
Aversive Conditioning ◽  
Prediction Errors ◽  
Per Se ◽  
Future Outcomes

The medial prefrontal cortex (mPFC) and ACC have been consistently implicated in learning predictions of future outcomes and signaling prediction errors (i.e., unexpected deviations from such predictions). A computational model of ACC/mPFC posits that these prediction errors should be modulated by outcomes occurring at unexpected times, even if the outcomes themselves are predicted. However, unexpectedness per se is not the only variable that modulates ACC/mPFC activity, as studies reported its sensitivity to the salience of outcomes. In this study, mediofrontal negativity, a component of the event-related brain potential generated in ACC/mPFC and coding for prediction errors, was measured in 48 participants performing a Pavlovian aversive conditioning task, during which aversive (thus salient) and neutral outcomes were unexpectedly shifted (i.e., anticipated or delayed) in time. Mediofrontal ERP signals of prediction error were observed for outcomes occurring at unexpected times but were specific for salient (shock-associated), as compared with neutral, outcomes. These findings have important implications for the theoretical accounts of ACC/mPFC and suggest a critical role of timing and salience information in prediction error signaling.

scholarly journals Nucleus-accumbens dopamine tracks aversive stimulus duration and prediction but not value or prediction error

2021 ◽  
Author(s):  
J.N. Goedhoop ◽  
B.J.G. van den Boom ◽  
T. Arbab ◽  
I. Willuhn
Keyword(s):  
Nucleus Accumbens ◽  
Prediction Error ◽  
Aversive Stimulus ◽  
Simultaneous Presentation ◽  
Signal Recovery ◽  
Prediction Errors ◽  
Nucleus Accumbens Core ◽  
Aversive Stimuli ◽  
Reward Conditioning

ABSTRACTThe role of dopamine in processing aversive stimuli is under debate: Credits range from no involvement at all, to acting as a punishment-prediction error (PPE) signal. Here, we systematically investigated dopamine release in the nucleus-accumbens core (NAC), which is closely linked to reward-prediction errors, in rats that were exposed to white noise (WN), a versatile, underutilized aversive stimulus, and its predictive cues. Both induced a negative dopamine ramp, followed by slow signal recovery upon stimulus cessation. In contrast to reward conditioning, dopamine was unaffected by WN value, context valence, or probabilistic contingencies, and the WN dopamine-response shifted only partially towards its predictive cue. However, unpredicted WN provoked slower post-stimulus signal recovery than predicted WN. Despite differing signal qualities, dopamine responses to simultaneous presentation of rewarding and aversive stimuli were additive. Together, our findings indicate that instead of a PPE, NAC dopamine primarily tracks prediction and duration of punishment.

Sign in / Sign up

Export Citation Format

Share Document