scholarly journals Looking forward does not mean forgetting about the past: ERP evidence for the interplay of predictive coding and interference during language processing

2019 ◽  
Author(s):  
Pia Schoknecht ◽  
Dietmar Roehm ◽  
Matthias Schlesewsky ◽  
Ina Bornkessel-Schlesewsky
Keyword(s):  
Language Processing ◽  
Model Updating ◽  
Time Window ◽  
Critical Word ◽  
Predictive Coding ◽  
Prediction Errors ◽  
Interference Effects ◽  
Neuronal Mechanisms ◽  
Mechanisms Of Memory ◽  
Retrieval Interference

AbstractInterference and prediction have independently been identified as crucial influencing factors during language processing. However, their interaction remains severely underinvestigated. Furthermore, the neurobiological basis of cue-based retrieval and retrieval interference during language processing remains insufficiently understood. Here, we present an ERP experiment that systematically examined the interaction of interference and prediction during language processing. We used the neurobiologically well-established predictive coding framework and insights regarding the neuronal mechanisms of memory for the theoretical framing of our study. German sentence pairs were presented word-by-word, with an article in the second sentence constituting the critical word. We analyzed mean single trial EEG activity in the N400 time window and found an interaction between interference and prediction (measured by cloze probability). Under high predictability, no interference effects were observable. Under the predictive coding account, highly predictable input is totally explained by top-down activity. Therefore the input induces no retrieval operations which could be influenced by interference. In contrast, under low predictability, conditions with high interference or with a close, low-interference distractor showed a broadly distributed negativity compared to conditions with a distant, low-interference distractor. We interpret this result as showing that when unpredicted input induces model updating, this may elicit memory retrieval including the evaluation of distractor items, thus leading to interference effects. We conclude that interference should be included in predictive coding-based accounts of language because prediction errors can trigger retrieval operations and, therefore, induce interference.

scholarly journals Focus of attention modulates the heartbeat evoked potential

2018 ◽  
Author(s):  
Frederike H. Petzschner ◽  
Lilian A. Weber ◽  
Katharina V. Wellstein ◽  
Gina Paolini ◽  
Cao Tri Do ◽  
...  
Keyword(s):  
Evoked Potential ◽  
Time Window ◽  
Predictive Coding ◽  
Sensory Information ◽  
Attentional Focus ◽  
The Body ◽  
Self Report ◽  
Prediction Errors ◽  
Theoretical Frameworks ◽  
Heartbeat Evoked Potential

AbstractTheoretical frameworks such as predictive coding suggest that the perception of the body and world – interoception and exteroception – involve intertwined processes of inference, learning, and prediction. In this framework, attention is thought to gate the influence of sensory information on perception. In contrast to exteroception, there is limited evidence for purely attentional effects on interoception. Here, we empirically tested if attentional focus modulates cortical processing of single heartbeats, using a newly-developed experimental paradigm to probe purely attentional differences between exteroceptive and interoceptive conditions in the heartbeat evoked potential (HEP). We found that the HEP is significantly higher during interoceptive compared to exteroceptive attention, in a time window of 520-580ms after the R-peak. Furthermore, this effect predicted self-report measures of autonomic system reactivity. This study thus provides direct evidence that the HEP is modulated by attention and supports recent interpretations of the HEP as a neural correlate of interoceptive prediction errors.

scholarly journals Towards a neurobiologically plausible model of language-related, negative event-related potentials

2018 ◽  
Author(s):  
Ina Bornkessel-Schlesewsky ◽  
Matthias Schlesewsky
Keyword(s):  
Model Updating ◽  
Information Source ◽  
Full Range ◽  
Predictive Coding ◽  
Event Related Potentials ◽  
Event Related Potential ◽  
Prediction Errors ◽  
Linguistic Features ◽  
Plausible Model ◽  
Related Potentials

Language-related event-related potential (ERP) components such as the N400 have traditionally been associated with linguistic or cognitive functional interpretations. By contrast, it has been considerably more difficult to relate these components to neurobiologically grounded accounts of language. Here, we propose a theoretical framework based on a predictive coding architecture, within which negative language-related ERP components such as the N400 can be accounted for in a neurobiologically plausible manner. Specifically, we posit that the amplitude of negative language-related ERP components reflects precision-weighted prediction error signals, i.e. prediction errors weighted by the relevance of the information source leading to the error. From this perspective, precision has a direct link to cue validity in a particular language and, thereby, to relevance of individual linguistic features for internal model updating. We view components such as the N400 and LAN as members of a family with similar functional characteristics and suggest that latency and topography differences between these components reflect the locus of prediction errors and model updating within a hierarchically organised cortical predictive coding architecture. This account has the potential to unify findings from the full range of the N400 literature, including word-level, sentence- and discourse-level results as well as cross-linguistic differences.

scholarly journals Faster model updating in autism during early sensory processing

2020 ◽  
Author(s):  
Judith Goris ◽  
Senne Braem ◽  
Shauni Van Herck ◽  
Eliane Deschrijver ◽  
Jan R. Wiersema ◽  
...  
Keyword(s):  
Model Updating ◽  
Predictive Coding ◽  
Control Group ◽  
Sensory Level ◽  
Prediction Errors ◽  
Context Sensitive ◽  
Core Deficit ◽  
New Information ◽  
Open Question ◽  
Sensory Prediction

AbstractBackgroundRecent theories of autism propose that a core deficit in autism would be a less context-sensitive weighting of prediction errors. There is also first support for this hypothesis on an early sensory level. However, an open question is whether this decreased context-sensitivity is caused by faster updating of one’s model of the world (i.e. higher weighting of new information), proposed by predictive coding theories, or slower model updating. Here, we differentiated between these two hypotheses by investigating how first impressions shape the mismatch negativity (MMN), reflecting early sensory prediction error processing.MethodsAn autism and matched control group (both n=27) were compared on the multi-timescale MMN paradigm, in which tones were presented that were either standard (frequently occurring) or deviant (rare), and these roles reversed every block. A well-replicated observation is that the initial model (i.e. the standard and deviant sound in the first block) influences MMN amplitudes in later blocks. If autism is characterized by faster model updating, we hypothesized that their MMN amplitudes would be less influenced by the initial context.ResultsWe found that MMN responses in the autism group did not differ between the initial deviant and initial standard sounds as they did in the control group.ConclusionsThese results show that individuals with autism are less influenced by initial contexts, confirming that autism is characterized by faster updating of sensory models, as proposed by predictive coding accounts of autism.

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.

scholarly journals Similarity-based interference and relative clauses in second language processing

2021 ◽  
pp. 026765832110635
Author(s):  
Ian Cunnings ◽  
Hiroki Fujita
Keyword(s):  
Language Processing ◽  
Relative Clause ◽  
Relative Clauses ◽  
Large Body ◽  
Interference Effects ◽  
Second Language Processing ◽  
L2 Processing ◽  
Relative Disadvantage ◽  
L1 And L2 ◽  
Object Relative

Relative clauses have long been examined in research on first (L1) and second (L2) language acquisition and processing, and a large body of research has shown that object relative clauses (e.g. ‘The boy that the girl saw’) are more difficult to process than subject relative clauses (e.g. ‘The boy that saw the girl’). Although there are different accounts of this finding, memory-based factors have been argued to play a role in explaining the object relative disadvantage. Evidence of memory-based factors in relative clause processing comes from studies indicating that representational similarity influences the difficulty associated with object relatives as a result of a phenomenon known as similarity-based interference. Although similarity-based interference has been well studied in L1 processing, less is known about how it influences L2 processing. We report two studies – an eye-tracking experiment and a comprehension task – investigating interference in the comprehension of relative clauses in L1 and L2 readers. Our results indicated similarity-based interference in the processing of object relative clauses in both L1 and L2 readers, with no significant differences in the size of interference effects between the two groups. These results highlight the importance of considering memory-based factors when examining L2 processing.

scholarly journals CNN-based Diagnosis System on Skin Cancer using Ensemble Method Weighted by Cubic Precision

2021 ◽  
Author(s):  
Pengcheng Jiang
Keyword(s):  
User Interface ◽  
Skin Cancer ◽  
Language Processing ◽  
Classification Accuracy ◽  
Model Updating ◽  
Early Stage ◽  
Model Performance ◽  
Diagnosis System ◽  
Cancer Symptoms ◽  
User Friendly

<i>Abstract</i>— One of the most prevalent diseases, skin cancer, has been proven to be treatable at an early stage. Thus, techniques that allow individuals to identify skin cancer symptoms early are in great demand. This paper proposed an interactive skin lesion diagnosis system based on the ensemble of multiple sophisticated CNN models for image classification. The performance of ResNet50, ResNeXt50, ResNeXt101, EfficientNetB4, Mobile-NetV2, MobileNetV3, and MnasNet are investigated separately as ensemble components. Then, using various criteria, we constructed ensembles and compared the accuracy they achieved. Moreover, we designed a method to update the ensemble for new data and examined its performance. In addition, a few natural language processing (NLP) techniques were used to make our system more user-friendly. To integrate all the functionalities, we built a user interface with PyQt5. As a result, MobileNetV3 achieved 91.02% as the best accuracy among all single models; ensemble weighted by cubic precision achieved 92.84% accuracy as the highest one in this study; a notable improvement in accuracy demonstrated the effectiveness of the model updating approach, and a system with all of the desired features was successfully developed. These findings benefit in two aspects. For model performance, applying cubic precisions can increase ensemble learning classification accuracy. For the developed diagnosis system, it can aid in the

scholarly journals Young Infants Process Prediction Errors at the Theta Rhythm

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.

scholarly journals Skipping a Beat: Heartbeat-Evoked Potentials Reflect Predictions during Interoceptive-Exteroceptive Integration

2020 ◽  
Vol 1 (1) ◽  
Author(s):  
Leah Banellis ◽  
Damian Cruse
Keyword(s):  
Predictive Coding ◽  
Auditory Stimuli ◽  
Prediction Errors ◽  
Self Awareness ◽  
Early Effect ◽  
Temporal Cues ◽  
Heartbeat Evoked Potential ◽  
External Signals ◽  
Potential Tool

Abstract Several theories propose that emotions and self-awareness arise from the integration of internal and external signals and their respective precision-weighted expectations. Supporting these mechanisms, research indicates that the brain uses temporal cues from cardiac signals to predict auditory stimuli and that these predictions and their prediction errors can be observed in the scalp heartbeat-evoked potential (HEP). We investigated the effect of precision modulations on these cross-modal predictive mechanisms, via attention and interoceptive ability. We presented auditory sequences at short (perceived synchronous) or long (perceived asynchronous) cardio-audio delays, with half of the trials including an omission. Participants attended to the cardio-audio synchronicity of the tones (internal attention) or the auditory stimuli alone (external attention). Comparing HEPs during omissions allowed for the observation of pure predictive signals, without contaminating auditory input. We observed an early effect of cardio-audio delay, reflecting a difference in heartbeat-driven expectations. We also observed a larger positivity to the omissions of sounds perceived as synchronous than to the omissions of sounds perceived as asynchronous when attending internally only, consistent with the role of attentional precision for enhancing predictions. These results provide support for attentionally modulated cross-modal predictive coding and suggest a potential tool for investigating its role in emotion and self-awareness.

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