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)