- [[20211025_001450 SV modeling results - all subjects]]
# k ~ target
![[k 5.jpg]]
Study 1
```r
term results
1: (Intercept) b = 0.08, SE = 0.01, t(164) = 5.67, p < .001, r = 0.41
2: targetCharity b = 0.02, SE = 0.01, t(122) = 1.74, p = .084, r = 0.16
```
Study 2
```r
F(2, 186) = 13.18, p < .001
# self as reference
term results
1: (Intercept) b = 0.15, SE = 0.03, t(136) = 5.73, p < .001, r = 0.44
2: targetCharity b = 0.06, SE = 0.02, t(186) = 2.87, p = .004, r = 0.21
3: targetIntragroup stranger b = 0.10, SE = 0.02, t(186) = 5.12, p < .001, r = 0.35
# charity as reference
term results
1: (Intercept) b = 0.21, SE = 0.03, t(136) = 7.85, p < .001, r = 0.56
2: targetSelf b = −0.06, SE = 0.02, t(186) = −2.87, p = .004, r = 0.21
3: targetIntragroup stranger b = 0.04, SE = 0.02, t(186) = 2.25, p = .026, r = 0.16
```
# b
![[b 5.jpg]]
# winning model: separate k, one b
$sv = reward \times (1 - k \times effort)$
Mean SV across participants
![[linear_function 7.jpg]]