Intro to Semantics and Pragmatics

🔠Intro to Semantics and Pragmatics Unit 15 – Experimental Approaches to Meaning

Experimental approaches to meaning in semantics and pragmatics use empirical methods to test theories about language comprehension. Researchers design controlled experiments to collect data on how people process and interpret words, sentences, and conversations in various contexts. These studies employ diverse techniques like reaction time measurements, eye-tracking, and brain imaging to investigate linguistic phenomena. By analyzing results statistically, researchers aim to provide objective evidence for theoretical claims about how we derive meaning from language.

Key Concepts and Terminology

  • Semantics studies the meaning of words, phrases, and sentences in a language
  • Pragmatics focuses on how context and speaker intentions influence meaning
  • Experimental semantics and pragmatics use empirical methods to test theories about meaning
    • Involves collecting data from human participants in controlled settings
    • Aims to provide objective evidence for or against specific hypotheses
  • Key terms include variables (independent and dependent), stimuli, conditions, and statistical significance
  • Compositionality principle states that the meaning of a complex expression is determined by the meanings of its parts and how they are combined
  • Gricean maxims describe how cooperative conversation works (quantity, quality, relation, manner)

Theoretical Background

  • Formal semantics uses logic and model theory to represent meaning
    • Focuses on truth conditions and entailment relations between sentences
  • Cognitive semantics views meaning as conceptual structures in the mind
    • Emphasizes embodied experience, metaphor, and mental spaces
  • Relevance theory in pragmatics argues that utterances raise expectations of relevance
    • Hearers interpret meaning based on maximizing relevance with minimal processing effort
  • Speech act theory classifies utterances by their intended communicative function (assertives, directives, commissives, expressives, declarations)
  • Experimental approaches aim to empirically test predictions made by these theories
    • Seek converging evidence from multiple methods (reaction times, eye-tracking, neuroimaging)

Experimental Design Basics

  • Formulate a clear research question and hypothesis based on theoretical predictions
  • Identify relevant variables to manipulate (independent) and measure (dependent)
    • Independent variables are the factors being tested (word meaning, context, speaker intention)
    • Dependent variables are the outcomes measured (reaction time, accuracy, brain activity)
  • Create stimuli that isolate the variable of interest while controlling for confounds
    • Use minimal pairs differing in one key aspect (literal vs. metaphorical, sincere vs. sarcastic)
  • Determine appropriate task for participants to perform (judgment, production, comprehension)
  • Choose between within-subjects (each participant does all conditions) and between-subjects (different groups per condition) designs
  • Decide on sample size and characteristics (age, language background) for sufficient statistical power

Data Collection Methods

  • Behavioral tasks measure overt responses from participants
    • Lexical decision task asks if a string of letters is a real word or not
    • Sentence verification task asks if a sentence is true or false given a scenario
    • Self-paced reading records time spent on each word in a sentence
  • Eye-tracking records gaze patterns while reading or viewing scenes
    • Fixation durations and saccades can indicate processing difficulty or anticipation
  • Neuroimaging techniques measure brain activity during language processing
    • Event-related potentials (ERPs) show millisecond-level changes in electrical activity
    • Functional magnetic resonance imaging (fMRI) localizes blood flow to specific brain regions
  • Collecting data online allows for larger and more diverse samples
    • Platforms like Amazon Mechanical Turk and Prolific facilitate participant recruitment and payment

Analysis Techniques

  • Preprocessing data involves filtering out incorrect responses and outliers
    • Reaction times more than 2-3 standard deviations from the mean are often excluded
  • Descriptive statistics summarize key properties of the data
    • Mean, median, standard deviation, standard error
  • Inferential statistics test for significant differences between conditions
    • T-tests compare means between two groups
    • ANOVAs test for effects of multiple factors and their interactions
    • Regression models can include continuous predictors and covariates
  • Effect sizes indicate the magnitude of differences (Cohen's d, eta-squared)
  • Data visualization through graphs and plots helps identify patterns
    • Bar plots for categorical variables, scatterplots for continuous variables

Interpreting Results

  • Determine if results support or fail to support the initial hypothesis
    • P-values less than .05 indicate statistically significant differences
    • Confidence intervals estimate the range of plausible values for an effect
  • Consider alternative explanations and potential confounds
    • Could differences be due to low-level factors (word frequency, length) rather than meaning?
  • Relate findings back to theoretical debates and previous literature
    • Do results converge with or diverge from prior studies?
    • What new questions or predictions do they generate?
  • Acknowledge limitations in generalizability and ecological validity
    • Experiments may not fully capture real-world language use
    • Samples may be skewed toward certain demographics (college students, English speakers)

Challenges and Limitations

  • Balancing experimental control with naturalness of stimuli
    • Highly controlled stimuli may lack context and pragmatic richness
    • More naturalistic stimuli may introduce confounds and variability
  • Avoiding task demands and strategies that obscure underlying processes
    • Participants may rely on surface-level heuristics rather than fully processing meaning
  • Accounting for individual differences in language processing
    • Factors like working memory, language proficiency, and cultural background can modulate effects
  • Integrating findings across different methods and modalities
    • Behavioral, eye-tracking, and neuroimaging data may not always converge
    • Different tasks may tap into different aspects of meaning processing
  • Dealing with null results and replication failures
    • Non-significant findings can be difficult to interpret and publish
    • Direct replications are necessary to establish robust effects

Real-World Applications

  • Improving natural language processing systems that can handle ambiguity and context
    • Chatbots, virtual assistants, and machine translation rely on semantic and pragmatic knowledge
  • Diagnosing and treating language disorders that affect comprehension
    • Aphasia, autism, and schizophrenia can involve deficits in processing meaning and intent
  • Enhancing education and assessment of linguistic skills
    • Experimental findings can inform language teaching methods and materials
  • Facilitating cross-cultural communication and understanding
    • Recognizing differences in pragmatic norms and expectations across languages and cultures
  • Developing more persuasive and effective messaging in advertising, politics, and health campaigns
    • Experimentally testing how wording and framing influences interpretation and behavior


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© 2024 Fiveable Inc. All rights reserved.
AP® and SAT® are trademarks registered by the College Board, which is not affiliated with, and does not endorse this website.