Depression and causal reasoning Flashcards

1
Q

Uncertainty coefficient

A

The per cent reduction in uncertainty about whether and/or when a predicted event will occur produced by the occurrence of a predictor event is

a broadly useable measure of contingency or association. (how much do you feel that one stimulus predict the other)

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2
Q

A normative metric for contingency

A

(normative= what ppl should be doing based on theory, not what they are doing):

Allan (1980) developed a normative metric for contingency:

Δp = P(O|R) - P(O|no R)

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3
Q

Deductive reasoning

A

Wason (1966) Selection task requires it.
Modus Ponens for picking A [if P then Q]
Modus Tollens for picking 7 [if not Q then not P]

Using logical rules to deduce true, linking premises with conclusions.

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4
Q

Modus Ponens

A

We can confirm Q by confirming P, as P implies Q;

If we know that P is true, we can conclude Q must be true. This is a valid argument and a rule of inference

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5
Q

Modus Tollens

A

We can deny P by denying Q, as not-Q implies not-P (denying the consequent).
This is a valid argument and a rule of inference.

If I am the axe murderer, then I can use an axe. (if P then Q)
I cannot use an axe. (Not Q)
Therefore, I am not an axe murderer. (therefore, Not P)

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6
Q

Dependence hypothesis

A

Checking if q depends on p

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7
Q

Independence hypothesis

A

Checking if q and p are statistically independent

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8
Q

Information gain model

A

Assumes that participants are looking for evidence that provides the most discrimination between the dependency hypothesis (that Q depends on P) and the independence hypothesis (that P and Q are statistically independent)

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9
Q

Rarity assumption

A

That the probability of the antecedent and consequent are small.
- In the world around us, the rarity assumption generally holds true because natural language divides the world up quite finely - very few things belong to any given category.
(Mckenzie & Mikkelsen (2000)) - rarity appears to be the default assumption.

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10
Q

Inter-trial interval (ITI)

A

Is a time gap programmed in by the experiment, between the trials.

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11
Q

Probability of outcome p(O)

A

Same as outcome density

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12
Q

Biases in probability estimations

A

We over-estimate the likelihood of events that have low probability and under-estimate the likelihood of events that have high probability (Lichtenstien et al. 1978)

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13
Q

Outcome density

A

Means the number of trials on which an outcome occurs = probability of outcome

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14
Q

Depressive realism

A

The hypothesis developed by Alloy and Abramson 1979 with their finding with difference between D and ND ppts.

They concluded that D ppts are “sader but wiser”

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15
Q

Are ppl irrational? Results of Wason selection taks

A

Oaksford & Chater (1994) Summarize the results of more than 30 studies from 13 authors:
- 89% choose A
- 25% choose 7
- 62% choose 4
- 16% choose D
=> We could conclude, therefore, that people are irrational!
=> Ppl select 4 b.c. Affirming the consequent: if Q then P (reverse to normal if P then Q) but not all Qs imply Ps

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16
Q

Are ppl irrational? Results of Wason selection task

A

Oaksford & Chater (1994) Summarize the results of more than 30 studies from 13 authors:
- 89% choose A
- 25% choose 7
- 62% choose 4
- 16% choose D
=> We could conclude, therefore, that people are irrational!
=> Ppl select 4 b.c. Affirming the consequent: if Q then P (reverse to normal if P then Q) but not all Qs imply Ps

17
Q

Hempel’s (1965) Black Raven Paradox

A

If it is a raven then it is black
To confirm:
More efficient to test all Ps (all ravens) than not-Qs (all black things.

18
Q

Why, hypothetically, would non-depressed people be overestimating the control over actions? (according to Alloy and Abramson, 1979)

A

Non-depressed ppl are motivated to maintain self-esteem; not having control lowers self-esteem; they over-estimate control to feel better.

Ppl with depression lack the motivation to maintain self-esteem and hence show more accurate judgements of control.

19
Q

What factors are affecting the contingency judgement? short verso deleted

A
  1. Outcome density (Alloy and Abramson, 1979 critique ish)

2. Probability of response

20
Q

What factors are affecting the contingency judgement?

A
  1. Outcome density (Alloy and Abramson, 1979 critique ish)
  2. Probability of response
  3. Duration of ITI
  4. Activity rates
21
Q

Activity rates

A

Activity rates both influence causal learning and may be influenced by causal learning, in a mutually reinforcing relationship (Byrom, Msetfi & Murphy, 2015).

While some studies suggest that accurate judgements of causal control may arise due to low activity rates associated with low mood (Blanco, Matute, Vadillo, 2012), there is also evidence of high activity rates in individuals with low mood (Byrom et al., 2015).

So mixed findings when trying to explain depressed ppl answering differently

22
Q

Associative learning theory

A

Associative learning theory explains the process of learning about contingencies through the development of associations between events an outcome

(e.g. RW 1972):
ΔV = αβ(λ – ΣV)

Mental associations gradually develop and become stronger when events and outcomes occur repeatedly at around the same time.

  • The event-outcome association is directly strengthened by co-occurrences (Cell A)
  • And weakened by the occurrence of the event on its own (Cell B)
  • Indirect influences take place when the outcome occurs unaccompanied (Cell C) - e.g., the association between context and outcome is strengthened.
  • Or when neither happens (cell D)- e.g., the association between context and outcome is weakened.

BUT because the changes to associative strength for any stimulus on any trial are governed by the total associative strength –> if you add more C cell trials and strengthen the context-outcome association, and weaken the CS- response association
AND adding more D cell trials should weak both associations