“Response Strength”
• Consistent with most observations:
• Instrumental learning: Reinf:  p(Resp)
• Similar to classical conditioning: but effects of
stimuli, not responses
–
–
–
–
Intensity effects (“stronger” US  “stronger” CS)
V in Rescorla-Wagner model: V=k(λ-V)
Blocking and overshadowing
Extinction
Instrumental learning:
Strength: Reinf:  p(Resp)
Magnitude effect: amount:  p(Resp)
Frequency effect: freq:  p(Resp)
Mazes: Reinf :  speed, latency
Behavioral contrast: relative amounts of reinf.
Matching: “Response Strength Equation”: Rel.
rates of behavior = Rel. rates of reinf.
• Contiguity effects: Delay of reinf. gradient
•  Delay of Reinf   p(Reinf) (slow learning)
•
•
•
•
•
“Behavioral Momentum” measured
by “Resistance to Change”
• Two 18-wheelers drive down interstate,
same speed, but different loads.
• Momentum = velocity x mass
• Velocity = response rate
• Resistance to Change = mass = strength
• Can separate response rate from Pavlovian?
• RTC measures S O relationship, not
response rate?
Measuring resistance-to-change in a
multiple schedule
BO
RED
CRF
1.5 min
3 Min
BO GREEN
VR-4
1.5 min
3 min
BO
1.5 min
RED
CRF
3 min
BO
GREEN
VR-4
1.5 min
Free food presented during Blackout in Resistance-to-Change Tests
3 min
Now, consider the strength of
responses within response
sequences
Delay-of-reinforcement gradient
Food
Strength
or
Value
R1
R2
Time
R3
Reid (1994, Behavioural Processes)
Which would be easier to learn?
Training Sequence
R1 R2 R3
R1* R2 R3
Different
response
required in 1st
position
New
*
R
R
R
Target
1
2
3
Sequence
Different
response
required in 3rd
position
Trials Containing the Same Responses
as in Training Sequence
32
28
24
20
16
12
8
4
0
32
28
24
20
16
12
8
4
0
32
28
24
20
16
12
8
4
0
First Response Position
Middle Response Position
Same Required
Different Required
Last Response Position
0
30
60
90 120 150 180 210 240 270 300
Trials
The last response
in the sequence
was the most
sensitive to the
change in
contingency.