revision seminar: depressive realism & the iti hypothesis c83mlp mechanisms of learning and...
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Revision Seminar:
Depressive realism & the ITI hypothesis
C83MLP Mechanisms of Learning and Psychopathology
Dr. Mark Haselgrove
What is contingency and how do you measure it?
Contingency in terms of responses and outcomes (Instrumental learning)
ΔP = P(O|R) – P(O|~R)
a + c a + b + c + d =
This is called “delta P”
Can also compute the probability of the outcome occurring (p(O) or: “outcome density”)
p(O)
a c a + b c + d=
Alloy & Abramson (1979)
- 40 discrete trials (each lasting 3 s) in which ss could press a button (R) or not (~R). Green light was then illuminated (O) or not (~O)
- At the end of 40 trials, ss rated the control they had over the light: (0= no control, 100= complete control)
- Experiment 1: 96 undergrads, divided into depressed & non-depressed groups Beck Depression Inventory (Beck, 1967)
No difference between Depressedand Non-depressed groups
Alloy & Abramson (1979) Cont…
- Experiment 2: 64 undergrads, divided into depressed & non-depressed groupsΔP set to zero, P(O) varied:
0.25 (low outcome density) 0.75 (high outcome density)
- Non-depressed = Illusion of control- Depressed = Depressive realism
Theories of Depressive Realism
Msetfi, Murphy, Simpson & Kornbrot (2005)
The Inter-trial Interval (ITI) hypothesis
3 s
14 s
A&A Experiment structure:
Alan & Jenkins (1980): Removing the ITI, resulted in no outcome density bias
Is there something special about the ITI….?
Theories of Depressive Realism
Msetfi, Murphy, Simpson & Kornbrot (2005)
Theories of Depressive Realism
Msetfi, Murphy, Simpson & Kornbrot (2005)
Varied: ITI - Long (15 s), or Short (3 s)Depression - Depressed, or Not depressedOutcome density - High [P(O) = 0.75, or Low [P(O) = 0.15)
Theories of Depressive Realism
Msetfi, Murphy, Simpson & Kornbrot (2005)
What is it about depression and the ITI?
Morrow & Nolen- Hoeksema (1990) – Depressed people spend time ruminating about their feelings and symptoms
Perhaps attention is diverted during ITI in depressed people
- Do not use information provided by the ITI, or (background context) to evaluate the relationship between noR and noO
Revision Seminar:
SOP
C83MLP Mechanisms of Learning and Psychopathology
Dr. Mark Haselgrove
A1
I
A2
P1
Pd2
Pd1
W1
W 2
Response (STRONG)
Response (WEEDY)
Wagner (1981)
Simulator models 100 elements moving between I, A1 and A2
Wagner (1981)
For you to do:
(1) Turn on X for 15 epochs. Record the output at each epoch. Input into Excel. Reset and repeat the simulation 5 times. Calculate an average across simulations. Draw a line graph. Marvel at its beauty.
(2) Turn on X for 15 epochs, then turn it off for N* epochs, and then turnit on for one more epoch. Record the output. Input into Excel. Reset and repeat the simulation 5 times. Calculate an average across simulations. Draw a line graph. *Where N = 5,10, 15 epochs.
(3) Repeat stage 2 but with a simulation in which X is much less intense (simulate this by reducing P1 to say, 0.3). Do you get the same pattern of results?
Think about when the model generates short term habituation, and sensitization. Is there any evidence (in the literature) of stimulus intensity impacting on habituation?(e.g. Turk-Brown, Yi, Leber & Chun (2007) Visual Quality Determines the Direction of Neural Repetition Effects. P. 429)