12.7 Natural language understanding
10 min read•august 21, 2024
is a complex process involving multiple cognitive and linguistic components. It forms the foundation of human communication, integrating phonological, lexical, syntactic, and semantic processing to comprehend spoken and written language.
The psychology of language explores how humans process and interpret language in real-time. This field investigates the roles of context, , and individual differences in shaping our ability to understand and use language effectively.
Foundations of natural language understanding
- Natural language understanding forms the core of human communication and cognition, integrating multiple linguistic and cognitive processes
- Psychology of Language explores how humans comprehend and produce language, providing insights into the complex mechanisms underlying verbal interactions
Components of language comprehension
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- involves recognizing and interpreting speech sounds (phonemes)
- retrieves word meanings from mental lexicon based on auditory or visual input
- analyzes sentence structure to determine grammatical relationships between words
- combines word meanings and syntactic information to construct overall sentence meaning
- links sentences together to form coherent representations of larger texts or conversations
Levels of linguistic analysis
- Phonetics examines the physical properties of speech sounds produced by human vocal tract
- Morphology studies the internal structure of words and how they are formed from smaller meaningful units (morphemes)
- Syntax investigates rules governing sentence structure and word order in languages
- Semantics focuses on the meaning of words, phrases, and sentences in isolation
- explores how context and speaker intentions influence language interpretation
Role of context in understanding
- Situational context provides information about the physical environment and social setting of communication
- Linguistic context includes preceding and following words, sentences, or discourse that shape interpretation
- Background knowledge allows listeners to fill in gaps and make inferences based on prior experiences
- Cultural context influences interpretation of idioms, metaphors, and other culturally-specific expressions
- Emotional context affects how listeners perceive and respond to language, including tone and prosody
Cognitive processes in language understanding
- Language comprehension involves multiple interacting cognitive processes that work together to extract meaning from linguistic input
- Psychology of Language research investigates how these processes unfold in real-time and interact with other cognitive systems (memory, attention)
Parsing and syntactic processing
- constructs syntactic structures as words are encountered in real-time
- Garden-path sentences (The horse raced past the barn fell) demonstrate challenges in initial parsing decisions
- involves choosing between multiple possible syntactic interpretations
- Working memory constraints influence the complexity of syntactic structures that can be processed
- Prosodic cues (intonation, stress patterns) can guide syntactic parsing in spoken language comprehension
Semantic interpretation
- selects appropriate word meanings based on context (bank as financial institution vs. river edge)
- determines the semantic relationships between words in a sentence (agent, patient, instrument)
- combines word meanings to derive sentence-level interpretations
- Event structure representation constructs mental models of described situations or actions
- involves mapping conceptual domains to understand figurative language
Pragmatic inference
- categorizes utterances based on their intended function (assertions, questions, commands)
- derives additional meaning beyond literal sentence content (Can you pass the salt? implies a request)
- integrates implied information into the common ground between speakers
- identifies the intended referents of pronouns and other referring expressions
- Indirect speech acts interpret non-literal meanings based on context and social conventions
Models of language comprehension
- Theoretical models in Psychology of Language aim to explain how humans process and understand language
- These models provide frameworks for generating hypotheses and interpreting experimental findings in language research
Bottom-up vs top-down processing
- builds meaning from individual linguistic units (phonemes, words) to larger structures
- uses context and prior knowledge to guide interpretation of incoming linguistic input
- propose that both bottom-up and top-down processes operate simultaneously during comprehension
- Predictive processing suggests that listeners/readers actively generate expectations about upcoming linguistic input
- Evidence from eye-tracking studies supports the influence of both bottom-up and top-down factors in real-time comprehension
Interactive models
- (Marslen-Wilson) proposes that word recognition activates multiple lexical candidates that compete for selection
- (McClelland & Elman) simulates interactive activation between phonological, lexical, and word levels
- emphasize the integration of multiple sources of information to resolve ambiguities
- Distributed cohort model incorporates semantic and syntactic constraints in addition to phonological information
- (Kintsch) describes text comprehension as a process of building and refining mental representations
Connectionist approaches
- represent language knowledge as patterns of activation across interconnected units
- capture temporal dependencies in language processing through feedback connections
- Long short-term memory (LSTM) networks model long-range dependencies in language comprehension
- (BERT, GPT) use attention mechanisms to process entire sequences of text simultaneously
- Connectionist models can simulate language acquisition and processing without explicit rule-based representations
Challenges in natural language understanding
- Natural language presents numerous challenges for both human comprehension and artificial intelligence systems
- Psychology of Language research investigates how humans overcome these challenges and the limitations of current computational approaches
Ambiguity resolution
- Lexical ambiguity occurs when words have multiple meanings (homonyms, polysemes)
- Syntactic ambiguity arises from multiple possible sentence structures (The man saw the woman with the telescope)
- Scope ambiguity involves different interpretations of quantifiers and modifiers (Every student read two books)
- Referential ambiguity emerges when pronouns or referring expressions have multiple potential antecedents
- Context-dependent ambiguity requires consideration of broader discourse or situational factors for resolution
Figurative language interpretation
- Metaphors map conceptual domains to convey abstract ideas through concrete imagery (Life is a journey)
- Idioms have meanings that cannot be derived from their individual components (Kick the bucket)
- Sarcasm and irony involve saying one thing while meaning the opposite, often for humorous or critical effect
- Metonymy uses associated concepts to refer to entities (The White House announced...)
- Hyperbole employs exaggeration for emphasis or effect (I've told you a million times)
Cross-linguistic differences
- Syntactic variation across languages affects word order and grammatical structure (Subject-Object-Verb vs. Subject-Verb-Object)
- Morphological complexity differs between languages (agglutinative vs. isolating languages)
- Lexical gaps occur when concepts exist in one language but lack direct translations in another
- Phonological systems vary in the number and types of speech sounds used across languages
- Pragmatic norms for politeness, indirectness, and turn-taking differ across cultures and languages
Neural basis of language understanding
- Neurolinguistics investigates the biological foundations of language comprehension and production
- Psychology of Language research integrates neuroimaging and behavioral methods to study brain-language relationships
Brain regions involved
- Broca's area (left inferior frontal gyrus) contributes to syntactic processing and speech production
- Wernicke's area (left posterior superior temporal gyrus) supports lexical-semantic processing and speech comprehension
- Arcuate fasciculus connects Broca's and Wernicke's areas, facilitating information exchange
- Angular gyrus integrates multimodal information and supports semantic processing
- Anterior temporal lobe serves as a hub for conceptual knowledge and semantic memory
Neuroimaging studies
- Functional magnetic resonance imaging (fMRI) measures blood flow changes associated with neural activity during language tasks
- Event-related potentials (ERPs) capture electrical brain activity with high temporal resolution, revealing stages of language processing
- Magnetoencephalography (MEG) combines temporal precision with spatial localization of language-related brain activity
- Diffusion tensor imaging (DTI) maps white matter tracts involved in language networks
- Transcranial magnetic stimulation (TMS) allows causal inferences about brain region functions in language processing
Language disorders and comprehension
- Aphasia results from brain damage and can impair various aspects of language comprehension and production
- Specific language impairment (SLI) affects language development in children without other cognitive deficits
- Dyslexia involves difficulties in reading and phonological processing despite normal intelligence
- Autism spectrum disorders often include pragmatic language deficits and difficulties with figurative language interpretation
- Primary progressive aphasia causes gradual deterioration of language abilities due to neurodegenerative processes
Computational approaches
- Computational models in Psychology of Language aim to simulate human language processing and understanding
- Natural language processing (NLP) techniques provide tools for analyzing and generating human language
Natural language processing techniques
- breaks text into individual words or subword units for further processing
- assigns grammatical categories (noun, verb, adjective) to words in context
- identifies and classifies proper names (persons, organizations, locations) in text
- analyzes grammatical structure by identifying relationships between words in sentences
- determines which words or phrases refer to the same entities across a text
Machine learning in language understanding
- Supervised learning algorithms train on labeled data to perform tasks like or text classification
- Unsupervised learning techniques discover patterns in text data without predefined categories (topic modeling)
- Transfer learning applies knowledge from one language task to improve performance on related tasks
- Deep learning models (convolutional neural networks, transformers) achieve state-of-the-art performance on many NLP tasks
- Reinforcement learning enables language models to improve through interaction and feedback
Limitations of current AI systems
- Lack of common sense reasoning hinders AI systems from understanding implicit knowledge humans take for granted
- Difficulty with context-dependent interpretation limits AI's ability to grasp nuanced meanings in different situations
- Bias in training data can lead to unfair or inaccurate language processing for certain groups or topics
- Explainability challenges make it difficult to understand how AI systems arrive at their language understanding decisions
- Brittleness to adversarial examples demonstrates vulnerabilities in AI language models to carefully crafted inputs
Individual differences in comprehension
- Psychology of Language research examines how personal factors influence language understanding
- Understanding individual variations in comprehension informs educational practices and clinical interventions
Cognitive factors
- Working memory capacity affects the ability to process complex sentences and maintain discourse coherence
- Processing speed influences real-time language comprehension and production efficiency
- Executive function skills (inhibition, task-switching) support comprehension monitoring and ambiguity resolution
- Attention control modulates the allocation of cognitive resources during language processing
- Metacognitive abilities enable readers/listeners to monitor their own comprehension and employ strategies when needed
Language proficiency
- Vocabulary size correlates strongly with overall language comprehension abilities
- Syntactic knowledge supports accurate parsing and interpretation of complex sentence structures
- Pragmatic competence enables appropriate interpretation of non-literal language and social cues
- Reading fluency affects the speed and accuracy of written language comprehension
- Multilingualism can enhance cognitive flexibility and metalinguistic awareness
Cultural influences
- Schema theory suggests that cultural background knowledge shapes expectations and interpretations of text
- Collectivist vs. individualist cultural orientations may influence pragmatic inference and communication styles
- Cultural literacy affects understanding of references, allusions, and culturally-specific concepts in language
- Politeness norms vary across cultures, impacting interpretation of indirect speech acts and requests
- Narrative structures and rhetorical styles differ across cultures, influencing comprehension of texts and discourse
Applications of natural language understanding
- Insights from Psychology of Language research inform practical applications across various domains
- Natural language understanding technologies continue to evolve, impacting daily life and professional practices
Human-computer interaction
- Voice assistants (Siri, Alexa) use natural language understanding to interpret user commands and queries
- Chatbots employ NLP techniques to engage in text-based conversations for customer service or information retrieval
- Sentiment analysis tools analyze social media posts and customer reviews to gauge public opinion
- Language generation systems produce human-like text for various applications (news articles, product descriptions)
- Augmentative and alternative communication (AAC) devices assist individuals with speech impairments
Language education
- Intelligent tutoring systems provide personalized feedback and instruction based on learner's language proficiency
- Automated essay scoring uses NLP to evaluate written compositions and provide feedback to students
- Computer-assisted language learning (CALL) applications leverage speech recognition for pronunciation practice
- Adaptive reading technologies adjust text difficulty based on reader's comprehension level
- Corpus linguistics tools analyze large language datasets to inform curriculum design and teaching materials
Clinical assessment and intervention
- Computerized language assessments use NLP to evaluate various aspects of language functioning
- Text analysis tools assist in diagnosing language disorders by identifying linguistic patterns associated with specific conditions
- Speech recognition technology supports therapy for articulation disorders and accent modification
- Augmentative and alternative communication (AAC) devices enable individuals with severe language impairments to communicate
- Language rehabilitation apps provide targeted exercises for individuals recovering from aphasia or traumatic brain injury
Future directions in research
- Psychology of Language continues to evolve, incorporating new technologies and interdisciplinary approaches
- Emerging research areas address complex challenges in language understanding and its applications
Emerging technologies
- Brain-computer interfaces may enable direct neural decoding of language comprehension processes
- Virtual and augmented reality environments create immersive contexts for studying situated language use
- Quantum computing could potentially solve complex language processing tasks more efficiently
- Neuromorphic computing architectures aim to mimic brain-like processing for improved language understanding
- Multimodal language processing integrates visual, auditory, and other sensory information with linguistic input
Interdisciplinary approaches
- Cognitive neuroscience methods (optogenetics, calcium imaging) provide new insights into neural mechanisms of language
- Computational cognitive science combines behavioral experiments with computational modeling to test theories
- Developmental robotics explores how language acquisition can be modeled in artificial agents
- Psycholinguistics and second language acquisition research inform each other to understand bilingual processing
- Evolutionary linguistics investigates the origins and development of human language capacities
Ethical considerations
- Privacy concerns arise from the collection and analysis of large-scale language data
- Bias mitigation in language models requires addressing systemic inequalities reflected in training data
- Transparency in AI language systems is crucial for understanding their decision-making processes
- Accountability for AI-generated content raises questions about authorship and responsibility
- Potential misuse of advanced language technologies (deepfakes, disinformation) necessitates ethical guidelines and safeguards
Key Terms to Review (41)
Bottom-up processing: Bottom-up processing is a cognitive approach where perception starts with the incoming sensory information and builds up to a final interpretation. This method emphasizes how we piece together individual components, such as sounds or letters, to form a complete understanding of language and meaning. It plays a crucial role in how we comprehend spoken words, interpret context, and recognize speech patterns, forming the foundation for more complex processes involved in understanding discourse and natural language.
Cognitive processes: Cognitive processes refer to the mental activities involved in acquiring, processing, storing, and using information. These processes include perception, memory, reasoning, and decision-making, playing a crucial role in how individuals understand and interpret language. They influence how people derive meaning from words and sentences, affecting comprehension and natural language understanding.
Cohort Model: The cohort model is a theoretical framework used in psycholinguistics to explain how listeners identify words during speech processing. It suggests that as a person hears the initial sounds of a word, they activate a set of potential candidates (the cohort) that could match those sounds, narrowing down the possibilities as more phonetic information is received. This model highlights the dynamic and interactive nature of language processing, where context and prior knowledge influence understanding.
Compositional semantics: Compositional semantics is the study of how the meaning of complex expressions is derived from the meanings of their parts and the rules used to combine them. This concept emphasizes that the meaning of a sentence or phrase is not just a sum of its parts but is influenced by the way those parts interact within a specific grammatical structure, highlighting its significance in understanding language meaning.
Constraint-based models: Constraint-based models are theoretical frameworks used in natural language understanding that emphasize how multiple constraints interact to determine the meaning of a sentence. These models focus on the idea that sentence interpretation is not just a linear process but involves the simultaneous consideration of various linguistic factors, such as syntax, semantics, and contextual information, to narrow down possible interpretations.
Construction-integration model: The construction-integration model is a theoretical framework used to understand how individuals comprehend language by constructing meaning from text and integrating it with prior knowledge. This model emphasizes the dual processes of constructing a mental representation of the information presented and integrating that representation with existing knowledge, making it crucial for both natural language understanding and discourse processing.
Contextual understanding: Contextual understanding refers to the ability to comprehend language not just by the words themselves, but also by considering the surrounding circumstances, cultural background, and prior knowledge that influence meaning. This understanding is crucial in natural language processing as it helps systems accurately interpret and generate human language in ways that make sense based on context.
Conversational implicature: Conversational implicature refers to the way in which speakers imply meaning through their statements without explicitly stating it. It relies on the context of the conversation and the shared knowledge between the speakers, often guided by cooperative principles such as relevance and quantity. This phenomenon plays a crucial role in communication, enhancing our understanding of what is meant beyond the literal interpretation of words.
Coreference resolution: Coreference resolution is the task of determining when two or more expressions in a text refer to the same entity. This process is essential for understanding and interpreting natural language, as it helps identify relationships between different parts of a sentence or between sentences in a larger context.
Dependency parsing: Dependency parsing is a process in natural language processing that involves analyzing the grammatical structure of a sentence by establishing relationships between words, where one word is dependent on another. This technique helps in understanding how different components of a sentence interact and provides insights into its meaning, making it essential for various applications in natural language understanding, such as machine translation and information retrieval.
Discourse processing: Discourse processing refers to the cognitive processes involved in understanding and interpreting language beyond the sentence level, focusing on how we comprehend larger units of communication like conversations, narratives, and texts. It encompasses how individuals derive meaning from context, connect ideas, and maintain coherence across multiple sentences, enabling effective communication and comprehension in real-world situations.
Incremental parsing: Incremental parsing refers to the process of interpreting a sentence as it is being spoken or written, rather than waiting for the entire sentence to be completed before analysis begins. This approach allows for real-time comprehension and facilitates communication by enabling listeners to start making sense of the information immediately, which is crucial in natural language understanding.
Interactive Models: Interactive models are frameworks that describe how various cognitive processes work together simultaneously during language comprehension, speech recognition, and natural language understanding. These models emphasize that understanding language is not a linear process; rather, multiple sources of information, such as context and prior knowledge, influence how we interpret spoken or written language in real-time.
Lexical access: Lexical access is the process through which individuals retrieve and recognize words from their mental lexicon when they hear or see them. This retrieval is crucial for understanding spoken or written language, as it allows us to connect words with their meanings and grammatical roles, which plays a significant role in various aspects of language processing, including comprehension and production.
Lexical ambiguity resolution: Lexical ambiguity resolution refers to the process by which a listener or reader determines the intended meaning of a word that has multiple meanings based on the context in which it is used. This process is crucial for effective communication, as it allows individuals to navigate words that can represent different concepts or ideas, depending on how they are used in sentences. Understanding this concept is essential for grasping how meaning is derived from language and how people make sense of ambiguous phrases in natural conversation.
Long short-term memory networks: Long short-term memory (LSTM) networks are a type of recurrent neural network (RNN) architecture designed to model sequences and learn from time-dependent data. They are particularly effective in tasks involving natural language understanding, as they can retain information over long periods and selectively forget irrelevant data. This ability to manage memory and maintain context is crucial for applications like language translation, speech recognition, and text generation.
Metaphor comprehension: Metaphor comprehension refers to the cognitive process through which individuals interpret and understand metaphorical language, recognizing the underlying meanings and relationships between different concepts. This process is crucial for effective communication, as it allows people to grasp abstract ideas and emotions through more familiar or concrete terms. Understanding metaphors enhances natural language processing and contributes to richer language use in social interactions.
Named Entity Recognition: Named Entity Recognition (NER) is a natural language processing (NLP) task that involves identifying and classifying key information in text into predefined categories such as names of people, organizations, locations, dates, and other entities. This process is crucial in natural language understanding as it helps machines to comprehend and organize the vast amounts of unstructured data found in human language.
Natural language understanding: Natural language understanding (NLU) is a subfield of artificial intelligence that focuses on enabling machines to comprehend and interpret human language in a way that is meaningful. This involves not only recognizing words and sentences but also grasping the intent behind the language, context, and nuances, allowing for effective communication between humans and computers.
Noam Chomsky: Noam Chomsky is a renowned linguist, cognitive scientist, and philosopher, widely considered the father of modern linguistics. His groundbreaking theories on language acquisition and structure have profoundly influenced our understanding of how humans learn language and the innate capacities that facilitate this process.
Parallel distributed processing models: Parallel distributed processing models, often referred to as connectionist models, are computational frameworks that simulate cognitive processes by using networks of interconnected nodes. These models represent knowledge and learning through patterns of activation across the network, allowing for simultaneous processing of information. This approach is particularly relevant in understanding how natural language is processed and understood, as it mirrors the complex, interconnected nature of human cognition.
Part-of-speech tagging: Part-of-speech tagging is the process of assigning a specific grammatical category, such as noun, verb, or adjective, to each word in a text. This helps in understanding the structure and meaning of sentences, as well as facilitating further analysis in language processing tasks. Accurate part-of-speech tagging is crucial for tasks like text-to-speech synthesis and natural language understanding, as it informs systems about how words function within context, enhancing their ability to interpret and generate language effectively.
Phonological Processing: Phonological processing is the ability to recognize and manipulate the sound structures of spoken language, including phonemes, syllables, and rhymes. This skill is essential for reading and writing, as it underpins the ability to decode written words and connect them to their spoken forms, influencing various aspects of language function and comprehension.
Polysemy: Polysemy refers to the phenomenon where a single word or phrase has multiple meanings or senses that are related by extension. This characteristic highlights the flexibility of language, allowing words to convey different ideas depending on context, which is crucial for understanding meaning in various applications, including interpreting semantics, translating languages, and enhancing natural language processing.
Pragmatics: Pragmatics is the branch of linguistics that studies the context-dependent aspects of meaning in language, focusing on how people use language in social situations to convey meaning beyond the literal interpretation of words. It considers factors like speaker intent, context, and social norms, which all influence how language is understood in communication. By examining these elements, pragmatics helps to bridge the gap between the literal meanings of words and their intended meanings in various interactions.
Presupposition accommodation: Presupposition accommodation is the process by which a speaker or listener adjusts their understanding of a statement to resolve any conflicts with existing beliefs or assumptions. This mechanism allows individuals to make sense of new information, even when it challenges what they previously thought was true, thus maintaining coherence in communication.
Recurrent neural networks: Recurrent neural networks (RNNs) are a class of artificial neural networks designed to recognize patterns in sequences of data, such as time series or natural language. They are unique because they have loops in their architecture, allowing them to maintain a form of memory of previous inputs. This feature makes RNNs particularly effective for tasks like natural language understanding, where context and the order of words are crucial for comprehension.
Reference resolution: Reference resolution is the process by which a system identifies and connects pronouns or other referring expressions to the entities they denote in a given context. This is essential for understanding language, as it helps determine what 'he', 'she', 'it', or 'they' refer to in sentences. The ability to accurately resolve references is crucial for natural language understanding, enabling both humans and machines to make sense of conversations and written texts.
Semantic integration: Semantic integration refers to the cognitive process through which individuals combine and synthesize different pieces of information to form a coherent understanding of language. This involves not just the recognition of individual words but also the interplay between their meanings, context, and how they fit together within sentences or discourse. It plays a vital role in how we comprehend complex language structures and derive meaning from conversations or texts.
Semantic representation: Semantic representation refers to the way in which meaning is encoded and understood within a language, often through structured formats like propositions or semantic networks. It plays a critical role in natural language understanding by allowing systems to grasp the meaning of words, sentences, and the relationships between them, thus enabling effective communication and interpretation.
Sentiment Analysis: Sentiment analysis is the computational method used to determine the emotional tone behind a body of text, helping to understand the attitudes, opinions, and feelings expressed within it. This process involves natural language understanding techniques to classify text as positive, negative, or neutral, and is essential for businesses and researchers to gauge public opinion and customer sentiment effectively.
Speech act theory: Speech act theory is a framework in linguistics and philosophy that examines how language can be used not just to convey information, but also to perform actions. This theory highlights that when people communicate, they are often doing more than just stating facts; they are also making requests, giving orders, offering apologies, or expressing intentions, which depends heavily on context and meaning.
Steven Pinker: Steven Pinker is a prominent cognitive psychologist and linguist known for his theories on language acquisition, evolution of language, and the cognitive processes underlying communication. His work emphasizes the innate aspects of language and the interplay between nature and nurture in language development, which ties into understanding individual differences in how people acquire language, the origins of human language, and natural language understanding.
Structural ambiguity resolution: Structural ambiguity resolution refers to the process by which individuals interpret sentences that can have more than one grammatical structure, leading to different meanings. This phenomenon occurs in natural language understanding when a single sentence can be parsed in multiple ways due to its syntax, which can result in confusion or misinterpretation if not resolved properly. The ability to resolve structural ambiguities is essential for effective communication and comprehension in language processing.
Syntactic Parsing: Syntactic parsing is the process of analyzing the structure of sentences in a language to understand their grammatical composition and meaning. It plays a crucial role in how we comprehend language, as it helps us identify relationships between words, phrases, and clauses, enabling effective communication. By breaking down sentences into their constituent parts, syntactic parsing contributes to various aspects of language processing, including reading, comprehension, and natural language understanding.
Syntax parsing: Syntax parsing is the process of analyzing a sequence of symbols, typically in the form of sentences, to determine its grammatical structure according to a given set of rules. This analysis is crucial for understanding how words combine to form meaningful phrases and sentences, enabling natural language processing systems to derive meaning from text.
Thematic role assignment: Thematic role assignment is the process of determining the specific roles that participants play in an event described by a sentence, such as who is the agent, patient, or experiencer. This process helps in understanding the meaning of a sentence by identifying how different entities are involved in the action and their relationships to one another. Assigning these roles accurately is crucial for effective communication and comprehension in natural language understanding.
Tokenization: Tokenization is the process of breaking down a text into smaller units called tokens, which can be words, phrases, or symbols. This technique is essential in various applications where understanding and processing natural language is crucial, enabling systems to analyze text data accurately and efficiently. Tokenization helps in preparing textual data for tasks such as translation, speech synthesis, and understanding context within sentences.
Top-down processing: Top-down processing is a cognitive process that begins with higher-level mental functions, such as expectations and prior knowledge, influencing how we perceive and understand information. This type of processing emphasizes the role of context and experience in interpreting sensory input, allowing for quicker and more efficient language comprehension, speech recognition, and natural language understanding.
Trace model: The trace model is a theoretical framework in speech perception that emphasizes how the brain processes spoken language by maintaining a representation of sound features over time. This model proposes that speech perception is not a linear process but rather involves the simultaneous activation of multiple sound representations, allowing for flexibility and context in understanding spoken words.
Transformer models: Transformer models are a type of neural network architecture designed to handle sequential data, particularly in the field of natural language processing. They utilize mechanisms such as self-attention and positional encoding to process input data in parallel, allowing for more efficient handling of context and dependencies in language tasks. This innovation has greatly improved performance in tasks like translation, summarization, and question answering.