Revision Seminar: Depressive realism & the ITI hypothesis

1. C83MLP Mechanisms of Learning and Psychopathology Revision Seminar: Depressive realism & the ITI hypothesis Dr. Mark Haselgrove

2. a + c a + b + c + d ac a + bc + d = = and how do you measure it? What is contingency Contingency in terms of responses and outcomes (Instrumental learning) This is called “delta P” ΔP = P(O|R) – P(O|~R) Can also compute the probability of the outcome occurring (p(O) or: “outcome density”) p(O)

3. 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 Depressed and Non-depressed groups

4. Alloy & Abramson (1979) Cont… • Experiment 2: 64 undergrads, divided into depressed& non-depressed groups • ΔP set to zero, P(O) varied: • 0.25 (low outcome density) • 0.75(high outcome density) • Non-depressed = Illusion of control • Depressed = Depressive realism

5. 3 s 14 s Theories of Depressive Realism Msetfi, Murphy, Simpson & Kornbrot (2005) The Inter-trial Interval (ITI) hypothesis A&A Experiment structure: Alan & Jenkins (1980): Removing the ITI, resulted in no outcome density bias Is there something special about the ITI….?

6. Theories of Depressive Realism Msetfi, Murphy, Simpson & Kornbrot (2005)

7. Theories of Depressive Realism Msetfi, Murphy, Simpson & Kornbrot (2005) Varied: ITI - Long (15 s), or Short (3 s) Depression - Depressed, or Not depressed Outcome density - High [P(O) = 0.75, or Low [P(O) = 0.15)

8. 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

10. C83MLP Mechanisms of Learning and Psychopathology Revision Seminar: SOP Dr. Mark Haselgrove

11. Wagner (1981) Response (WEEDY) W2 Pd2 A2 I Pd1 A1 P1 W1 Response (STRONG) Simulator models 100 elements moving between I, A1 and A2

12. Wagner (1981) For you to do: 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 turn it 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)