scholarly journals Prediction Error Associated with the Perceptual Segmentation of Naturalistic Events

2011 ◽  
Vol 23 (12) ◽  
pp. 4057-4066 ◽  
Author(s):  
Jeffrey M. Zacks ◽  
Christopher A. Kurby ◽  
Michelle L. Eisenberg ◽  
Nayiri Haroutunian

Predicting the near future is important for survival and plays a central role in theories of perception, language processing, and learning. Prediction failures may be particularly important for initiating the updating of perceptual and memory systems and, thus, for the subjective experience of events. Here, we asked observers to make predictions about what would happen 5 sec later in a movie of an everyday activity. Those points where prediction was more difficult corresponded with subjective boundaries in the stream of experience. At points of unpredictability, midbrain and striatal regions associated with the phasic release of the neurotransmitter dopamine transiently increased in activity. This activity could provide a global updating signal, cuing other brain systems that a significant new event has begun.

2016 ◽  
Vol 39 ◽  
Author(s):  
Giosuè Baggio ◽  
Carmelo M. Vicario

AbstractWe agree with Christiansen & Chater (C&C) that language processing and acquisition are tightly constrained by the limits of sensory and memory systems. However, the human brain supports a range of cognitive functions that mitigate the effects of information processing bottlenecks. The language system is partly organised around these moderating factors, not just around restrictions on storage and computation.


2018 ◽  
Author(s):  
Anthony I. Jang ◽  
Matthew R. Nassar ◽  
Daniel G. Dillon ◽  
Michael J. Frank

AbstractThe dopamine system is thought to provide a reward prediction error signal that facilitates reinforcement learning and reward-based choice in corticostriatal circuits. While it is believed that similar prediction error signals are also provided to temporal lobe memory systems, the impact of such signals on episodic memory encoding has not been fully characterized. Here we develop an incidental memory paradigm that allows us to 1) estimate the influence of reward prediction errors on the formation of episodic memories, 2) dissociate this influence from other factors such as surprise and uncertainty, 3) test the degree to which this influence depends on temporal correspondence between prediction error and memoranda presentation, and 4) determine the extent to which this influence is consolidation-dependent. We find that when choosing to gamble for potential rewards during a primary decision making task, people encode incidental memoranda more strongly even though they are not aware that their memory will be subsequently probed. Moreover, this strengthened encoding scales with the reward prediction error, and not overall reward, experienced selectively at the time of memoranda presentation (and not before or after). Finally, this strengthened encoding is identifiable within a few minutes and is not substantially enhanced after twenty-four hours, indicating that it is not consolidation-dependent. These results suggest a computationally and temporally specific role for putative dopaminergic reward prediction error signaling in memory formation.


2018 ◽  
Author(s):  
Michelle L. Eisenberg ◽  
Jeffrey M. Zacks ◽  
Shaney Flores

AbstractThe ability to predict what is going to happen in the near future is integral for daily functioning. Previous research suggests that predictability varies over time, with increases in prediction error at those moments that people perceive as boundaries between meaningful events. These moments also tend to be points of rapid change in the environment. Eye tracking provides a method for continuous measurement of prediction as participants watch a movie of an actor performing a series of actions. In two studies, we used eye tracking to study the time course of prediction around event boundaries. In both studies, viewers looked at objects that were about to be touched by the actor shortly before the objects were contacted, demonstrating predictive looking. However, this behavior was modulated by event boundaries: looks to to-be-contacted objects near event boundaries were less likely to be early and more likely to be late, compared to looks to objects contacted within events. This result is consistent with theories proposing that event segmentation results from transient increases in prediction error.Significance StatementThe ability to predict what will happen in the near future is integral for adaptive functioning, and although there has been extensive research on predictive processing, the dynamics of prediction at the second by second level during the perception of naturalistic activity has never been explored. The current studies therefore describe results from a novel task, the Predictive Looking at Action Task (PLAT) that can be used to investigate the dynamics of predictive processing. Demonstrating the utility of this task to investigate predictive processing, this task was applied to study the predictions made by Event Segmentation Theory, which suggests that people experience event boundaries at times of change and unpredictability in the environment. The results of these studies are of interest to communities investigating the dynamic comprehension and segmentation of naturalistic events and to communities studying visual perception of naturalistic activity.


2019 ◽  
Vol 34 (1) ◽  
pp. 205-229 ◽  
Author(s):  
Gitesh Dhairyashilrao Chavan ◽  
Ranjan Chaudhuri ◽  
Wesley J. Johnston

PurposeThe purpose of this paper is to investigate the underlying knowledge structure and evolution of industrial-buying research published between 1965 and 2015.Design/methodology/approachBibliometric analysis is performed on 357 relevant papers (using principal component analysis and natural language processing, using VantagePoint® tools, used to generate bubble maps, auto-correlation maps and Aduna cluster maps), demonstrating how various factors involved in industrial buying have evolved, their degree of correlation with each other and the interrelationships of multiple factors concerning their co-occurrences.FindingsThe systematic mapping of industrial-buying research would illustrate the development of the significant factors in industrial-buying research. This paper provides both a global perspective on the leading countries and journals in the field and a robust roadmap for further investigation in this field.Research limitations/implicationsThis paper is limited to the data considered for analysis and may, therefore, overlook or underestimate some work that has not been captured while filtering databases related to industrial buying.Practical implicationsThis paper facilitates near-future projection and trend analysis in industrial-buying research.Originality/valueThe methodology used is unique to the field of business-to-business marketing.


2011 ◽  
Vol 9 (67) ◽  
pp. 328-338 ◽  
Author(s):  
Giosué Baggio ◽  
André Fonseca

Understanding a word in context relies on a cascade of perceptual and conceptual processes, starting with modality-specific input decoding, and leading to the unification of the word's meaning into a discourse model. One critical cognitive event, turning a sensory stimulus into a meaningful linguistic sign, is the access of a semantic representation from memory. Little is known about the changes that activating a word's meaning brings about in cortical dynamics. We recorded the electroencephalogram (EEG) while participants read sentences that could contain a contextually unexpected word, such as ‘cold’ in ‘In July it is very cold outside’. We reconstructed trajectories in phase space from single-trial EEG time series, and we applied three nonlinear measures of predictability and complexity to each side of the semantic access boundary, estimated as the onset time of the N400 effect evoked by critical words. Relative to controls, unexpected words were associated with larger prediction errors preceding the onset of the N400. Accessing the meaning of such words produced a phase transition to lower entropy states, in which cortical processing becomes more predictable and more regular. Our study sheds new light on the dynamics of information flow through interfaces between sensory and memory systems during language processing.


Author(s):  
Giulia Bovolenta ◽  
Emma Marsden

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.


2021 ◽  
Vol 1 (2) ◽  
Author(s):  
Dastan Hussen Maulud ◽  
Subhi R. M. Zeebaree ◽  
Karwan Jacksi ◽  
Mohammed A. Mohammed Sadeeq ◽  
Karzan Hussein Sharif

Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.


2016 ◽  
Vol 106 (1) ◽  
pp. 31-44
Author(s):  
Ergun Biçici

Abstract Referential translation machine (RTM) is a prediction engine used for predicting the performance of natural language processing tasks including parsing, machine translation, and semantic similarity pioneering language, task, and domain independence. RTM results for predicting the performance of parsing (PPP) in out-of-domain or in-domain settings with different training sets and types of features present results independent of language or parser. RTM PPP models can be used without parsing using only text input and without any parser or language dependent information. Our results detail prediction performance, top selected features, and lower bound on the prediction error of PPP.


2021 ◽  
Vol 1 (2) ◽  
pp. 21-28
Author(s):  
Dastan Hussen Maulud ◽  
Subhi R. M. Zeebaree ◽  
Karwan Jacksi ◽  
Mohammed Mohammed Sadeeq ◽  
Karzan Hussein Sharif

Semantic analysis is an essential feature of the NLP approach. It indicates, in the appropriate format, the context of a sentence or paragraph. Semantics is about language significance study. The vocabulary used conveys the importance of the subject because of the interrelationship between linguistic classes. In this article, semantic interpretation is carried out in the area of Natural Language Processing. The findings suggest that the best-achieved accuracy of checked papers and those who relied on the Sentiment Analysis approach and the prediction error is minimal.


2014 ◽  
Vol 369 (1634) ◽  
pp. 20120394 ◽  
Author(s):  
Gary S. Dell ◽  
Franklin Chang

This article introduces the P-chain, an emerging framework for theory in psycholinguistics that unifies research on comprehension, production and acquisition. The framework proposes that language processing involves incremental prediction, which is carried out by the production system. Prediction necessarily leads to prediction error, which drives learning, including both adaptive adjustment to the mature language processing system as well as language acquisition. To illustrate the P-chain, we review the Dual-path model of sentence production, a connectionist model that explains structural priming in production and a number of facts about language acquisition. The potential of this and related models for explaining acquired and developmental disorders of sentence production is discussed.


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