Cogs 14a midterm 2 study guide

COGS 14A - Midterm 2 Study Guide

General Exam Information

  • The exam is open-book and open-note, but you must work alone.

  • The test consists of multiple-choice, fill-in-the-blank, and short-answer questions.

  • Time limit: 55 minutes.

  • The best way to study: review lecture materials, quizzes, and assigned readings.


Key Topics from Lectures

Lecture 9: The Research Process and Methods

  • Steps in conducting scientific research:

    • Identify a topic: Determine an area of interest or a research question.

    • Learn about the topic: Conduct literature reviews to gather existing knowledge.

    • Form a hypothesis: Develop a testable prediction.

    • Design the study: Establish the methodology, including independent and dependent variables.

    • Collect data: Gather empirical evidence through experiments, surveys, or observations.

    • Analyze data: Use statistical methods to evaluate results.

    • Interpret results: Compare findings with existing theories.

    • Communicate results: Present in a scientific format, typically following APA guidelines.

  • Pilot Studies: Small-scale preliminary studies used to test feasibility and refine methodologies before full-scale research.

  • Ecological vs. External Validity:

    • Ecological Validity: The extent to which findings apply to real-world settings.

    • External Validity: The ability to generalize findings to different populations or conditions.

  • Blind Review in Data Analysis: Preventing experimenter bias by keeping analysts unaware of study conditions.

  • Scientific Communication:

    • APA format: Abstract, Introduction, Methods, Results, Discussion.

    • Peer-reviewed journals and their impact factors.

  • Challenges of Measurement in Cognitive Science: Different disciplines (e.g., psychology, neuroscience) use distinct measurement techniques.

Lecture 10: Measurement Methods in Cognitive Science

  • Non-experimental Methods:

    • Field Studies: Observing behavior in natural environments.

    • Surveys and Interviews: Collecting qualitative and quantitative responses.

    • Longitudinal Studies: Tracking changes in individuals/groups over time.

    • Case Studies: In-depth analysis of unique individuals (e.g., brain injury patients).

  • Experimental Methods:

    • Direct manipulation of independent variables to observe effects.

    • Requires control groups and random assignment to ensure validity.

  • Neuroscientific Methods:

    • Reaction Time Studies: Measuring cognitive processing speed through response times.

    • Eye Tracking: Using infrared cameras to analyze gaze patterns and attention.

    • EEG (Electroencephalography): Recording brain wave activity to study neural processes.

    • ERP (Event-Related Potentials): Averaging EEG responses to specific stimuli.

    • MEG (Magnetoencephalography): Measuring magnetic fields from neural activity for high temporal precision.

    • CT (Computed Tomography): Structural imaging technique using X-rays.

    • PET (Positron Emission Tomography): Tracking metabolic activity via radioactive tracers.

    • fMRI (Functional MRI): Measuring blood oxygen levels to infer brain activity.

    • DTI (Diffusion Tensor Imaging): Mapping white matter pathways in the brain.

Lecture 11: Ethics in Research

  • Unethical Research Cases:

    • Tuskegee Syphilis Study: Withheld treatment from Black men to observe disease progression.

    • Henrietta Lacks: Her cancer cells were taken without consent and used for research.

    • Project MKUltra: CIA-funded experiments involving mind control and drug testing.

  • Key Ethical Guidelines:

    • Nuremberg Code: Established voluntary consent as a research requirement.

    • Declaration of Helsinki: Guidelines for ethical medical research.

    • Belmont Report: Defined principles of respect, beneficence, and justice.

  • Institutional Review Boards (IRB):

    • Committees ensuring research adheres to ethical standards.

    • Review studies to minimize risks and ensure informed consent.

Lecture 13: Literature Searches, Measurement, and Statistics

  • Boolean Search Methods:

    • Using "AND" to narrow results.

    • Using "OR" to expand results.

    • Using "NOT" to exclude certain terms.

  • Role of Citations:

    • Preventing plagiarism and acknowledging sources.

    • Difference between reference sections (cited works) and bibliographies (all consulted works).

  • Operational Definitions:

    • Defining abstract concepts through measurable variables.

Lecture 14: The Role of Statistics in Research

  • Population vs. Sample:

    • Population: Entire group of interest.

    • Sample: Subset used for research.

  • Parameters vs. Statistics:

    • Parameter: Numerical characteristic of a population.

    • Statistic: Estimate derived from a sample.

  • Sampling Error & Variability:

    • Differences between population parameters and sample statistics.

  • Normal Distribution Characteristics:

    • Symmetrical, bell-shaped curve where most values cluster around the mean.

Lecture 15: Measures for Comparing Groups

  • Types of Measurement Scales:

    • Nominal: Categories (e.g., eye color, gender).

    • Ordinal: Ordered categories without equal intervals (e.g., ranking in a race).

    • Interval: Ordered values with equal spacing but no true zero (e.g., IQ scores, Celsius temperature).

    • Ratio: Interval scale with a meaningful zero point (e.g., height, reaction time).

  • Measures of Central Tendency:

    • Mean: Arithmetic average, best for normally distributed data.

    • Median: Middle value, best for skewed distributions.

    • Mode: Most frequently occurring value.

  • Measures of Dispersion:

    • Variance and Standard Deviation: Indicate data spread around the mean.

Lecture 16: Correlations vs. Controlled Between-Group Designs

  • Correlations vs. Experiments:

    • Correlations: Measure relationships without causation (e.g., height and weight).

    • Experiments: Manipulate variables to establish cause-effect relationships.

  • Common Statistical Tests:

    • Chi-Square Test: Analyzes categorical data.

    • T-Test: Compares two means for significant differences.

    • ANOVA (F-Test): Compares multiple groups.

  • Pearson’s Correlation Coefficient (r):

    • Measures strength/direction of linear relationships (-1 to +1).

  • Regression Analysis:

    • Predicting dependent variables based on independent variables.


Study Strategies

  1. Review Lecture Slides & Notes: Focus on key definitions and concepts.

  2. Practice with Quizzes: Look at past questions to understand question format.

  3. Create Flashcards: Memorize key terms and concepts.

  4. Explain Concepts to a Friend: Teaching reinforces understanding.

  5. Take Timed Practice Tests: Simulate the exam environment.

Good luck with your midterm! 🎯📚


robot