All Study Guides Intro to Semantics and Pragmatics Unit 15
🔠 Intro to Semantics and Pragmatics Unit 15 – Experimental Approaches to MeaningExperimental 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