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Last-minute Review: Correlation, Experimentation, and Brain Micro Level

Correlation basics

  • Positive correlation: as one variable increases, the other tends to increase. Example: SAT scores and college grades tend to move together.

  • Actual real-world strength: about r \approx 0.2 \text{ to } 0.25; not a strong predictor on its own.

  • Negative correlation: as one variable increases, the other tends to decrease (e.g., higher daily alcohol intake associated with lower grades).

  • Zero correlation: no predictable relationship (e.g., hand size vs mass scores when you remove certain outliers).

  • Perfect correlation: all data points lie exactly on a straight line (rare in reality).

Correlation vs causation

  • Correlation does not imply causation; third variables can influence both.

  • Classic example: ice cream sales and deaths in different regions rise together in hot weather; heat is a third variable affecting both.

  • Smoking and cancer: correlation exists, but establishing causation requires experiments.

  • Experimentation vs correlation:

    • Correlation alone cannot establish causality.

    • Experiments isolate A causing B by manipulating A (IV) and measuring B (DV) while controlling other factors.

    • No causation without experimentation.

Experimental design essentials

  • Random assignment vs random sampling:

    • Random assignment: place participants into conditions so each has an equal chance of being in any condition (great equalizer).

    • Random sampling: how you select participants for the study (not the focus here).

  • Variables:

    • IV (independent variable): what the researcher manipulates to see what happens.

    • DV (dependent variable): what is measured to assess the effect.

    • Confounds: unwanted variables that can threaten interpretation and make results uninterpretable.

  • Control group: a baseline condition not receiving the experimental manipulation (or receiving placebo) to isolate the effect of the IV.

  • Placebo effect and deception: sometimes deception is used to preserve study validity; ethics require debriefing and often informed consent.

  • Ethical safeguards:

    • IRB/review boards to protect participants’ rights.

    • Informed consent and debriefing after the study.

  • Examples:

    • Antidepressant trial: depressed participants assessed at Time 1, receive the drug, reassessed at Time 2; multiple potential confounds (seasonal effects, therapy, placebo).

    • Beer goggles experiment: rating attractiveness before and after drinking; need a control (non-drinking) condition to separate drinking effects from other factors like lighting, mood, desperation, or ratings by different people.

    • Key issues to watch for: ensuring the same people are rated at Time 1 and Time 2; avoiding non-random assignment when comparing groups (e.g., drinkers vs non-drinkers) because it introduces confounds.

  • Study design reminders:

    • If groups differ at baseline, you cannot attribute end-of-study differences to the manipulation alone.

    • Random assignment helps balance group characteristics.

    • Some experiments involve deception; always consider whether the ends justify the means and ensure participant protection.

IVs, DVs, and confounds (quick recap)

  • IV = what you manipulate; DV = what you measure.

  • Confounds = other variables that could explain observed effects.

  • A well-designed experiment uses random assignment and a proper control condition to minimize confounds.

Building blocks of the brain: micro level (neuroscience basics)

  • Neurons are the basic building blocks of the brain and nervous system; they process and transmit information.

  • Neuron structure:

    • Dendrites: receive information from other neurons (receptors).

    • Cell body (soma): contains the nucleus; processes incoming information.

    • Nucleus: stores genetic material; part of the cell’s decision-making core.

    • Axon: transmits information away from the cell body to other neurons.

    • Axon terminal: end of the axon where signals are transmitted to the next neuron.

  • Signal transmission basics:

    • Resting potential: the neuron’s baseline electrical state, roughly -70\ \text{mV} outside and inside the neuron with a gradient of ions.

    • Ion gradients and gates: ions (sodium, potassium) move through channels; gates open in response to signals.

    • Threshold and action potential: if inputs push the neuron to threshold, an all-or-nothing spike (action potential) travels along the axon.

    • Propagation mechanism: once threshold is reached, ion channels open in sequence, creating a wave of depolarization that travels down the axon; ions are pumped back to maintain resting potential after the spike.

  • Conceptual takeaway: neurons carry and communicate information via electrical signals (action potentials) and chemical signaling at synapses; this supports information processing in the brain.

Quick reference concepts (syntax-friendly)

  • Correlation coefficient: r \approx 0.2 \text{ to } 0.25 is a weak positive relationship.

  • Resting potential: -70\ \text{mV}.

  • Action potential: an all-or-nothing electrical impulse along the axon.

  • Independent variable (IV): manipulated by the researcher.

  • Dependent variable (DV): measured to see effect of the IV.

  • Confound: a variable that undermines interpretability of results.

  • Control group: baseline condition to compare against the experimental manipulation.

  • Deception and ethics: sometimes used in social psychology; requires debriefing and protection of participants.

  • Random assignment: key method to create equivalent groups and support causal conclusions.

  • IRB and informed consent: protections for participants in research.

Macro vs micro (preview)

  • Micro: focus on neurons and cellular processes (this section).

  • Macro: to be covered later, focusing on systems and larger brain structures and networks.