# Pattern notebook

## TV 3

The sample size:

```(len (ssi sb (= \$tv 3)))
```
 15

How many subjects answered each quiz question correctly:

```(.sum (getl quiz (ssi sb (= \$tv 3))))
```
I value
better-amount 14
better-prob 15
immediate 11
faster-completion 15

The number of subjects who answered every question correctly:

```(setv s-tv3-all-correct (list
(. (ss (.all (getl quiz (ssi sb (= \$tv 3))) 1) \$) index)))
(len s-tv3-all-correct)
```
 10

Number of I choices, all TV 3 subjects (recall that there are 60 trials):

```(.sort-values (.sum :level "sn"
(= (getl ts (ssi sb (= \$tv 3)) "choice") "I")))
```
sn choice
s037 0
s038 0
s040 0
s041 0
s032 1
s035 2
s033 3
s036 3
s028 5
s026 11
s039 14
s031 20
s030 27
s029 30
s027 35

Number of I choices, only TV 3 subjects who answered every quiz question correctly:

```(.sort-values (.sum :level "sn"
(= (getl ts s-tv3-all-correct "choice") "I")))
```
sn choice
s037 0
s038 0
s040 0
s041 0
s032 1
s036 3
s028 5
s026 11
s031 20
s029 30

Quantiles of the above (expressed as proportions out of 60):

```(rd 2 (.quantile
(.mean (= (getl ts s-tv3-all-correct "choice") "I") :level "sn")
[.25 .5 .75]))
```
I choice
0.25 0.00
0.50 0.03
0.75 0.16

For comparison, the corresponding quantiles in the 49 subjects of Luhmann, Ishida, and Hajcak (2011) are .17, .43, and .63.

Also for comparison, here's the MTurk subjects:

```(.sort-values (.sum :level "sn"
(= (getl ts (ssi sb (< \$tv 3)) "choice") "I")))
```
sn choice
s022 0
s017 1
s019 1
s021 1
s023 3
s018 4
s016 6
s003 8
s006 14
s024 27

Choices of I after D has become available are still rare. This is the complete list:

```(ss
(getl ts (ssi sb (<= \$tv 3)))
(& (> \$rt \$dwait) (= \$choice "I")))
```
sn block trial dwait choice rt won tt
s024 3 1 11.568 I 13.188 4 6
s030 2 1 6.392 I 11.830 0 3
s030 3 3 7.716 I 9.756 0 8
s030 16 3 14.593 I 14.729 0 47
s039 9 2 6.995 I 8.186 4 25
s039 10 3 6.849 I 9.395 0 29

## TV 4

For TV 4, I changed the gamble amounts and probabilities and switched to hypothetical rewards. For TV 5, I reworded the first quiz question.

```(.sort-values (.sum :level "sn"
(= (getl ts (ssi sb (.isin \$tv [4 5])) "choice") "I")))
```
sn choice
s049 2
s046 17
s048 18
s047 32
s044 33
s045 33
s042 36
s043 42
```(rd 2 (.quantile
(.mean :level "sn"
(= (getl ts (ssi sb (.isin \$tv [4 5])) "choice") "I"))
[.25 .5 .75]))
```
I choice
0.25 0.30
0.50 0.54
0.75 0.56
```(learn-scatter (ssi sb (.isin \$tv [4 5])))
```

Here, black dots are I choices and white dots are D choices.

## TV 6

Same as TV 5, but with real rewards again.

```(.sort-values (.sum :level "sn"
(= (getl ts (ssi sb (= \$tv 6)) "choice") "I")))
```
sn choice
s054 1
s057 2
s055 8
s060 22
s050 24
s052 25
s059 27
s053 32
s058 36
s062 36
s051 39
```(rd 2 (.quantile
(.mean :level "sn"
(= (getl ts (ssi sb (= \$tv 6)) "choice") "I"))
[.25 .5 .75]))
```
I choice
0.25 0.25
0.50 0.42
0.75 0.57

Maybe a little less than TV 5, but not bad.

```(learn-scatter (ssi sb (= \$tv 6)))
```

## TV 7

Now we're randomly assigning all 4 conditions.

Choices of I after D has already been available for 2 s:

```(setv n-late (.sum (.groupby :level "sn" (wc
(getl ts s-final)
(& (> \$rt (+ \$dwait 2)) (= \$choice "I"))))))
(.sort-values :ascending F (get n-late (> n-late 0)))
```
sn value
s081 22
s160 5
s222 4
s245 3
s244 3
s242 3
s234 2
s080 2
s214 2
s127 2
s104 2
s095 2
s082 2
s164 2
s184 1
s233 1
s230 1
s235 1
s195 1
s068 1
s165 1
s161 1
s152 1
s147 1
s143 1
s142 1
s130 1
s114 1
s107 1
s093 1
s158 1

Let's exclude subjects who made 3 or more such errors, or who were inattentive according to the experimenter's notes.

```(setv s-late (set (. (get n-late (>= n-late 3)) index)))
(setv s-inattentive #{"s069" "s185"})
(setv s-good (sorted (- (set s-final) s-late s-inattentive)))
(valcounts (getl sb s-good "cond"))
```
I cond
control 42
within_pattern 43
across_pattern 42
across_force_d 42

The number of subjects who got each possible number of quiz questions wrong:

```(.sort-index (valcounts (\$ (getl sb s-final) quiz-qs-wrong)))
```
I quiz-qs-wrong
0 94
1 48
2 29
3 3
4 3

And here's the number of subjects who got each quiz question wrong:

```(.sum (~ (getl quiz s-final)))
```
I value
better-amount 39
better-prob 15
immediate 33
faster-completion 40

Subjects per condition getting no quiz questions wrong and satisfying the above criteria:

```(valcounts (\$ (ss (getl sb s-good) (= \$quiz-qs-wrong 0)) cond))
```
I cond
control 23
within_pattern 21
across_pattern 23
across_force_d 22

### Plots

Here we have the proportion of D choices considering only free-choice trials, which are all 60 trials for control subjects, 30 trials for across-forced subjects, and 20 trials for within-forced subjects. Plots are per condition, with means marked by vertical lines.

```(d-prop-dotplot "control")
```
```(d-prop-dotplot "within_pattern")
```
```(d-prop-dotplot "across_pattern")
```
```(d-prop-dotplot "across_force_d")
```

#### Difference from first to last third

```(d-prop-dotplot "control" :diffs T)
```
```(d-prop-dotplot "within_pattern" :diffs T)
```
```(d-prop-dotplot "across_pattern" :diffs T)
```
```(d-prop-dotplot "across_force_d" :diffs T)
```

#### Last third only

```(d-prop-dotplot "control" :late-only T)
```
```(d-prop-dotplot "within_pattern" :late-only T)
```
```(d-prop-dotplot "across_pattern" :late-only T)
```
```(d-prop-dotplot "across_force_d" :late-only T)
```

## Paper intro notes

• discuss self-control problems and notions of self-control
• introduce teleological behaviorism (Rachlin (2017) - describes teleological behaviorism; Rachlin (2019) - theory: behavioral evolution can select behavior patterns that are beneficial as a pattern but comprise deleterious individual actions )

### Hard commitments

Rogers, Milkman, and Volpp (2014) Bryan, Karlan, and Nelson (2010) are review articles about the value of commitment devices

• Read and van Leeuwen (1998) demonstrates hot-to-cold as well as cold-to-hot empathy gaps
• Bryan et al. (2010): rotating savings and credit associations (ROSCAs) can help people to repeatedly choose to save money by committing them to it
• Read, Loewenstein, and Kalyanaraman (1999): when people chose three movies to watch in advance, rather than choosing each movie before watching it, they were more likely to choose highbrow movies. Likewise, they were likelier to choose prize-draw lottery tickets over instant-win tickets when choosing in advance.
• Giné, Karlan, and Zinman (2010): when smokers were given the opportunity to put money in a savings account that they only got back if they stopped smoking, they stopped at a higher rate than controls
• Ariely and Wertenbroch (2002): sort of relevant. Students did better when giving themselves future deadlines. This is like pattern-setting because it's a commitment to future virtuous behavior.

### Soft commitments

• Possibly relevant: Bryan et al.'s (2010) definitions: "We refer to commitment devices that call for real economic penalties for failure, or rewards for success, hard commitments. And we refer to any device that has primarily psychological consequences a soft commitment."
• discuss Rachlin's idea of soft commitment for setting the pattern (Rachlin (2016) - describes pattern-setting via "soft commitment" (7 citations, but hasn't been tried) ; Rachlin (2014) is the pop version) - this is a somewhat different definiton of "soft committment" from Bryan et al. (2010); Howie says "Soft commitment is another name for patterning behavior over time so that it may come into contact with temporally distant or extended contingencies."
• Siegel and Rachlin (1995) can be seen as a partial test, but doesn't really test the idea of pattern-setting to improve self-control or increase LL choices - Siegel and Rachlin (1995) found that 4 pigeons who preferred an SS reward over an LL one would instead go for LL if they had to peck keys 31 times instead of 1 time to get an outcome, even though the outcome only depended on the 31st choice. Siegel and Rachlin described this as a (soft) committment although it's clearly different from a case in which the pigeons had to make one choice on whether they were getting SS or LL for a number of remaining trials.

### Others?

Other possible relevant empirical results to look for or discuss:

• Studies of gym memberships. Do longer memberships increase exercise usage?
• Commitments to diets or swearing off drugs.

Park, Kim, Jhang, Lee, and Lee (2022): a weird paper that's probably not relevant. The gist is that COVID-19 threat pushes people to be more likely to make sequence of choices that make a nice pattern, compared to less patterned sequences.

## References

Ariely, D., & Wertenbroch, K. (2002). Procrastination, deadlines, and performance: Self-control by precommitment. Psychological Science, 13(3), 219–224. doi:10.1111/1467-9280.00441

Bryan, G., Karlan, D., & Nelson, S. (2010). Commitment devices. Annual Review of Economics, 2(1), 671–698. doi:10.1146/annurev.economics.102308.124324

Ciria, L. F., Quintero, M. J., López, F. J., Luque, D., Cobos, P. L., & Morís, J. (2021). Intolerance of uncertainty and decisions about delayed, probabilistic rewards: A replication and extension of Luhmann, C. C., Ishida, K., & Hajcak, G. (2011). PLOS ONE. doi:10.1371/journal.pone.0256210

Giné, X., Karlan, D., & Zinman, J. (2010). Put your money where your butt is: A commitment contract for smoking cessation. American Economic Journal: Applied Economics, 2(4), 213–235. doi:10.1257/app.2.4.213

Luhmann, C. C., Ishida, K., & Hajcak, G. (2011). Intolerance of uncertainty and decisions about delayed, probabilistic rewards. Behavior Therapy, 42, 378–386. doi:10.1016/j.beth.2010.09.002. Retrieved from http://www.psychology.stonybrook.edu/cluhmann-/papers/luhmann-2011-bt.pdf

Park, J., Kim, J., Jhang, J., Lee, J. C., & Lee, J. (2022). The impact of infectious disease threat on consumers' pattern-seeking in sequential choices. Psychology and Marketing, 39(2), 370–389. doi:10.1002/mar.21602

Rachlin, H. (2014, July 24). If you're so smart, why aren't you happy? Retrieved from http://blog.oup.com/2014/07/psychology-self-monitor-abstract-particular

Rachlin, H. (2016). Self-control based on soft commitment. Behavior Analyst, 39(2), 259–268. doi:10.1007/s40614-016-0054-9

Rachlin, H. (2017). In defense of teleological behaviorism. Journal of Theoretical and Philosophical Psychology, 37(2), 65. doi:10.1037/teo0000060

Rachlin, H. (2019). Group selection in behavioral evolution. Behavioural Processes, 161, 65–72. doi:10.1016/j.beproc.2017.09.005

Read, D., Loewenstein, G., & Kalyanaraman, S. (1999). Mixing virtue and vice: Combining the immediacy effect and the diversification heuristic. Journal of Behavioral Decision Making, 12(4), 257–273. doi:10.1002/(SICI)1099-0771(199912)12:4<257::AID-BDM327>3.0.CO;2-6

Read, D., & van Leeuwen, B. (1998). Predicting hunger: The effects of appetite and delay on choice. Organizational Behavior and Human Decision Processes, 76, 189–205. doi:10.1006/obhd.1998.2803

Rogers, T., Milkman, K. L., & Volpp, K. G. (2014). Commitment devices: Using initiatives to change behavior. JAMA, 311(20), 2065–2066. doi:10.1001/jama.2014.3485

Siegel, E., & Rachlin, H. (1995). Soft commitment: Self-control achieved by response persistence. Journal of the Experimental Analysis of Behavior, 64(2), 117–128. doi:10.1901/jeab.1995.64-117