2: Theories and Questions

Question 1

what is the distortion rule and what are sources of distortions

Answer 1

• Distortion rule: Procedures used to make our observations should not introduce distortions.
• Sources of distortions:
  1. From instruments (apparatus) used for measurements
  2. From observer / experimenter (observer bias)
  3. From sampling procedures
  4. From the environment (more so if not controlled, i.e.,
  outside the laboratory)

Question 2

when and how does observer bias occur

Answer 2

• During the experimentation or observation: giving clues to the subject, misinterpreting behaviours.
• During recording or analysis of the behaviour/ data (especially during times of uncertainty).
• Often unconscious

Question 3

11 sacred principles of science

Answer 3

replication, speculation, hypothesis, data, questions, Paradigms, theories, models, principles,
rules, laws & hypotheses

Question 4

replication

Answer 4

• The basic idea of scientific experimental research is replication (to judge reliability), not statistical significance.
• Relevant to n-of-1 or case study research.
• Claude Bernard (1865): “Introduction à l’étude de la médecine expérimentale”.
• Experimentation = provoked observation.
• “Show me the data!”

Question 5

Speculation, hypothesis, data

Answer 5

• Idiographic (the specific) or nomothetic (the general)?
• “The plural of anecdote is data” (Ray Wolfinger, circa
  1969-1970).
• Datum vs. data: Anecdotes / n of 1 observations have no scientific value?
• Certainly debatable in neuroscience, ethology, clinical neuroscience or psychology… and most purely idiographic sciences (e.g., astronomy).

Question 6

what are paradigms

Answer 6

Set of laws, theories, methods and applications that
form a scientific research tradition

Question 7

what are theories

Answer 7

A collection of hypotheses about a specific phenomenon. A set of assumptions about the causes of a behaviour and the rules that specify how the causes operate. Metaphor: Theory ≈ Map

Question 8

what is a model

Answer 8

a specific implementation of a theory

Question 9

what is a principle

Answer 9

a generally accepted ‘fact,’ not always tested

Question 10

what is a rule

Answer 10

a generally accepted process or pattern, sometimes mathematically defined

Question 11

what is a law

Answer 11

substantially verified theory

Question 12

what is a hypothesis

Answer 12

Statement used to test a theory or model. A testable
statement about the relation between variables.
Paradigms, theories, models, principles,
rules, laws & hypotheses

Question 13

what are qualitative theories

Answer 13

verbal statements, discourse-
based. Variables can be discussed, but are not
necessarily mathematically evaluated.

Question 14

what are quantitative theories

Answer 14

Mathematically / statistically
inspired. Relationship between variables and
constants are investigated. Rules, formulas,
computational models are used

Question 15

what are descriptive theories

Answer 15

Describe relationships
between variables, no explanations given

Question 16

what are analogical theories

Answer 16

The relationships between
variables are explained via analogies or
metaphors

Question 17

what are fundamental theories

Answer 17

Complex new constructs
and concepts are suggested

Question 18

domain or scope of theories

Answer 18

theory of evolution
learning theory
neural network theory
super-male (testosterone) theory of autism

Question 19

what is the theory of evolution

Answer 19

broad scope for biology (evolutionary
biology), neuroscience (comparative neuroscience,
neuroethology, neurobiology), psychology (evolutionary
psychology, animal psychology), anthropology (biological
anthropology, primatology), etc

Question 20

what is learning theory

Answer 20

skinnerian principles

Question 21

other examples of theories

Answer 21

• Information theory // Communication theory
• Decision theory / Signal detection theory
• Psychoanalytical theories (Freud, Jung, Adler)
• Attachment theory (Bowlby, Ainsworth, etc.)
• The “Theory of Mind” theory
• Triune Brain theory (MacLean)
• Hebbian theory (Hebb’s rule or Cell Assembly Theory)
• Ramachandran // Michael Persinger: Neurotheology?

Question 22

roles of theory in science

Answer 22

• Describing phenomena
• Understanding phenomena: Finding the cause
• Predicting phenomena
• Explaining phenomena: Organizing and interpreting research results
• Generating research: Heuristic value of theories.

Question 23

a good theory…

Answer 23

1. Can account for the data collected.
2. Has explanatory relevance (logical soundness).
3. Is testable: Can be verified / confirmed or disconfirmed (i.e., falsifiable)*. Testable = falsifiable.
4. Predicts novel events. Prediction
5. Is parsimonious. Parsimony

• No “proof” in science (except mathematics?), only plausibility

Question 24

steps in developing theories

Answer 24

1. Defining the scope of the theory
2. Reviewing the literature
3. Formulating the theory
4. Establishing predictive validity
5. Testing the theory empirically

Question 25

hypothetical construct

Answer 25

Cronbach and Meehl (1948), McCorquodale and Meehl (1948)

• Inferred, but untested. Not operationally defined.
• Cannot be observed directly.
• Properties and implications not demonstrated in empirical research.
• “Intelligence”, “motivation”, “personality”, “stress”, “emotions”, “moods”

Question 26

intervening variables

Answer 26

From Tolman (1938)

• Hypothetical internal, covert, implicit state
• Inferred, summary of empirical data; operationally defined.
• Cannot be observed directly, at least, not initially: “valence” (Lewin), “habit strength” (Hull), etc.
• The variable used to address an hypothetical construct

Question 27

hypotheses

Answer 27

• “Asking questions” = “formulating hypotheses”.
• Experimental psychology and neuroscience being mainly an hypothetico-deductive sciences, hypotheses are very central to the research process.
• Hypothesis: Tentative explanation.
• The tentative explanation often includes a statement about the relationship between two or more variables.
• The hypothesis should be testable.

Question 28

steps in Experimental Research

Answer 28

1. Ask a question: from data, observations, theories, etc. Develop a research idea.
2. Make preliminary observations (pilot project) and start to formulate hypotheses.
3. Make predictions from hypotheses that can be easily tested empirically.
4. Identify the variables that need to be measured; define the problem: give operational and/or ostensive definitions (defined later).
5. Select a research approach (correlational, observational, experimental, hybrid) and design.
6. Select subjects / participants / instances / model system: human participants, animal subjects (what species?), cell lines, etc.
7. Observations and measurements / measures: Choose a suitable research environment, necessary equipment, and choose the right sampling, recording & scoring methods, etc.
8. Collect sufficient data and make sure you have enough subjects / participants / observations in order to validate or invalidate your hypotheses.
9. Use the appropriate statistical data analysis (or analyses): exploratory or confirmatory data analysis; descriptive and/or inferential data analyses.

10.Report your results / dissemination: Talks, posters, papers, book chapters, etc

Question 29

alternative approaches

Answer 29

• Bayesian approach: Prior probabilities (as opposed to the “frequentist” approach [Neyman–Pearson]). The “new
  inductivism”.
• Strong inference (J.R. Platt, 1964): Inductive inference;
  more below.
• New experimentalism (Ackermann,1989): A-theoretical. For example:
  * Severe experimentalism
  (Deborah Mayo, 1996);
  more below.
  • New statistics (Cumming,
    2012): Estimation thinking;
    Effect sizes and CI’s.

Question 30

strong inference, who suggested it, process of elimination, works well with…

Answer 30

• Strong inference was suggested by Platt (1964):
• Process of elimination: Develop several alternative explanations (leading to testable predictions) and test all the predictions.
• Works well with high control in an experimental context. For example, popular in molecular biology.
• Works well with precise predictions (i.e., clear precise outcomes).

Question 31

strong inference strategy

Answer 31

• Devise alternative hypotheses (note the plural).
• Devise an experiment (or experiments), with alternative possible outcomes. Ideally, these outcomes will help you reject one or more of your
  hypotheses.
• Carry-out the experiment(s).
• Repeat the procedure, devising sub-hypotheses or sequential hypotheses in order to define and refine the possibilities that remain.

quote from platt (1964)
1) Devising alternative hypotheses;

2) Devising a crucial experiment (or several of them), with alternative possible outcomes, each of which will, as nearly as possible, exclude one or more of the hypotheses;

3) Carrying out the experiment so as to get a clean result;

1’) Recycling the procedure, making sub-hypotheses or sequential hypotheses to refine the possibilities that remain; and so on.

Question 32

what does strong inference reduce the chances of

Answer 32

confirmation bias:
* Tendency to look for confirmatory information and ignore contradictory information.

Question 33

strong inference has a method to devise alternative hypotheses: what two preliminary phases are used before starting the experiment

Answer 33

• Exploratory phase: preliminary observations, tests
• Pilot phase: mini-experiment (with small sample)

Question 34

Severe experimental testing (Mayo), error, progress, and strategies

Answer 34

Error detection and error correction

Progress from the identification of errors (learning from our mistakes): Induction as a self-correcting process

Strategies:
* Practical interventions
* Cross-checking
* Error control and elimination, error statistics