Code
library(tidyverse)FPT Version Five
Search Algorithm
Code
dn_dat_c <- readRDS(here::here("Data", "Denominator Neglect", "dn_dat_c.rds"))
bu_dat_h <- readRDS(here::here("Data", "Bayesian Update", "bu_dat_h.rds"))
admc.dr_dat <- readRDS(here::here("Data", "ADMC Decision Rules", "admc.dr_dat.rds"))
ns_dat <- readRDS(here::here("Data", "Number Series", "ns_dat.rds"))
dn_dat_s <- readRDS(here::here("Data", "Denominator Neglect", "dn_dat_s.rds"))
bu_dat_e <- readRDS(here::here("Data", "Bayesian Update", "bu_dat_e.rds"))
ts_dat <- readRDS(here::here("Data", "Time Series", "ts_dat.rds"))
dn_top_items_c <- readRDS(here::here("Data", "Denominator Neglect", "top_items.rds"))
dn_top_items_s <- readRDS(here::here("Data", "Denominator Neglect", "top_items_s.rds"))
startup_costs <- readRDS(here::here("Data", "startup_costs.rds"))Code
source(here::here("Scripts", "get_startup.R"))Code
dn_dat_c <- dn_dat_c |>
left_join(dn_top_items_c |>
pivot_longer(c(conf_item, harm_item),
names_to = "choice_type", values_to = "item") |>
select(item, rank),
by = "item") |>
mutate(task = "dn.c",
item = paste0(task, "_", rank),
# still need time per item
time = (3.43 - get_startup(first(task))) /
(length(unique(item)) * 2),
score = correct,
.keep = "unused") |>
select(subject_id, sscore, task, item, score, time)
dn_dat_s <- dn_dat_s |>
left_join(dn_top_items_s |>
pivot_longer(c(conf_item, harm_item),
names_to = "choice_type", values_to = "item") |>
select(item, rank),
by = "item") |>
mutate(task = "dn.s",
item = paste0(task, "_", rank),
# still need time per item
time = (4.45 - get_startup(first(task))) /
(length(unique(item)) * 2),
score = correct,
.keep = "unused") |>
select(subject_id, sscore, task, item, score, time)Code
bu_dat_h <- bu_dat_h |>
mutate(task = "bu.h",
item = paste0(task, "_", unique_trial),
time = (6.56 - get_startup(first(task))) /
(length(unique(item))),
.keep = "unused") |>
select(subject_id, sscore, task, item, score, time)
bu_dat_e <- bu_dat_e |>
mutate(task = "bu.e",
item = paste0(task, "_", unique_trial),
time = (7.26 - get_startup(first(task))) /
(length(unique(item))),
.keep = "unused") |>
select(subject_id, sscore, task, item, score, time)Code
admc.dr_dat <- admc.dr_dat |>
mutate(task = "admc.dr",
item = paste0(task, "_", parse_number(admc_id)),
time = (5.87 - get_startup(first(task))) /
(length(unique(item))),
.keep = "unused") |>
select(subject_id, sscore, task, item, score, time)Code
ns_dat <- ns_dat |>
mutate(task = "ns",
item = paste0(task, "_", parse_number(ns_id)),
time = (3.70 - get_startup(first(task))) /
(length(unique(item))),
.keep = "unused") |>
select(subject_id, sscore, task, item, score, time)Code
ts_dat <- ts_dat |>
mutate(task = "ts",
item = paste0(task, "_", ts_id),
time = (5.83 - get_startup(first(task))) /
(length(unique(item))),
.keep = "unused") |>
select(subject_id, sscore, task, item, score, time)Code
v5_dat <- rbind(dn_dat_c,
bu_dat_h,
admc.dr_dat,
ns_dat,
dn_dat_s,
bu_dat_e,
ts_dat)
v5_dat |>
filter(is.na(score)) |>
summarize(.by = c(subject_id, task),
n = n()) subject_id task n
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314 64RVc86LLeI6qccFOAs0 ts 1
315 CRGZ4TavntGH5E9LSBCX ts 1
316 DH0T6zFub589qUF0B702 ts 1
317 0i0wZSbTdy53z6Cni0Dl ts 1
318 SJdYRJAFtFnPuPXeDtZ9 ts 1
319 rhddayHF97oSYI7UrdRt ts 1
320 nabsPHGZpvr6SgYzV2am ts 1
321 5EPXWl4PDme2kqXHiOBB ts 1
322 qDI6xFzx1uEgAvcPXYms ts 2
323 ZyEbe7eQxt4YlalJLb89 ts 2
324 JhW3ezpIB8sBZJ76QQ2b ts 1
325 4CLdS8XZEmJVDlHImT7u ts 1
326 3TUTJUD20hg4uydW0nPU ts 1
327 tLdDkwsFunR4YoLKVhDr ts 1
328 PYH1CXUkU4mAxBsW5UHi ts 1
329 G2WGBZc9GyTIFNdsysZC ts 1
330 yL2xNKGqpeaOzg95CDUB ts 1
331 s0XXgsxyI4HqXAssoIP2 ts 1
332 zKG5mSoyPstUeC99enq5 ts 1
333 AsoaxzIykTyxqAoV4YQr ts 1
334 9eUKJ1MDRhFJ0kdt2ikq ts 2
335 DoFCE00qFvG0xBr3UWpI ts 1
336 bW7vftpeg6L47YzZjzWX ts 1
337 cRpM8Z159NED2UYcb3jT ts 1
338 nvEdRy29lvIepo6irKSa ts 1
339 jOtySWDeJ7rf9lxtAQzX ts 1
340 roz2c3opqAFnyNPbzR3h ts 1
341 qpQ1vSLveBFouxZzN2iu ts 1
342 076BrNISK0IP8PsESVQX ts 1
343 6rPerw3fPtggey7LPj6X ts 1
344 zdKF1SXibeBH26CpiufK ts 1
345 i8uVNjD6U5P2koFh3qw1 ts 1
346 HQtaLSwh8MGTTpLVllOE ts 2
347 qexjC6y4MMUWHAt5bqvh ts 1
348 5rHhraHsUTAqh6sjU2d1 ts 1
349 j5bxfVlajJwn5pZNQQsa ts 3
350 gYlhCVSj5pa1VFbFdicj ts 2
351 jZF8v6PoTY6RXiFVy6gY ts 3
352 hmUKEUxhdCzEzo2q7PpI ts 1
353 ArWJ1aLAwP2t66pdDGqy ts 1
354 6jOSL0IZbcNJOVzNEMr7 ts 2
355 771cxdbg1Rw25ZLqI3Ge ts 1
356 O0yVeeQVT2OJE6mbt5wl ts 1
357 tMR3y87RWvjTV5kdfi2h ts 1
358 dFi2sGOS1gEWWg2KDXcs ts 1
359 CVmfiQuobm1ux4zBp3l4 ts 1
360 kSZ3wZkDtJRrYLpi4ojs ts 1
361 LT2PWfa1d0t3mjn0w4KZ ts 1
362 j1lSAtVBMhimMpC1Ha2o ts 1
363 4MXqlRYjANDHSM003I2c ts 1
364 9NyaXPkKlfyjubys7IoE ts 1
365 g00rVq4jB6nBvbdFo9xb ts 1
366 BiyqZv07wmeZOboeTfnw ts 1
367 sUE4VAmCISZvzsFH49BK ts 1
368 imxFcTXtwyNUY9uOjkct ts 1
369 Yfk4ShUhELUtl9XwpgCa ts 6
370 vNUhkysUUHxRDi1a5BQ7 ts 1
371 9YaeRGOTkxRml6BYldlV ts 1
372 CtPuhMjKdihqEsXucpww ts 1
373 54TEF4eZN23KZglG8kHT ts 2
374 kgAJm1lblz7JIf8qQqZd ts 1
375 wQtadQgeGBUtR1EtHfet ts 2
376 TqSddjjditUnblQcWfB3 ts 2
377 azeC5Kk3x946Oj4L6HnU ts 1
378 uKYfZr65pq4KTBvCj3dE ts 1
379 hOdVHTzPBsd26UMRFL3Q ts 1
380 VLLHim4Q2j7wFMmMQbHq ts 1
381 jdJRtSyyTamNP0WYAaei ts 1
382 5akYQwXPqMXUdUwTlseA ts 1
383 3b8X79WU0YWVcsmFGeEH ts 4
384 4rY5i0hQ8qKc3mBAnAwT ts 1
385 eNXtdvWDFDTA7Xrc5UTI ts 1
386 kEsM4qtWu3c24cxLGja3 ts 2
387 a5F2j7cvabLVMfvG55nK ts 2
388 xSvzfB9wIjnw2QuEb9Ba ts 1
389 IIeCbY9dM0C04giDl66U ts 1
390 RFOrWXAQX1PO0HFZwlLL ts 2
391 Rw1m2pue8CmVp1Gbe9Aj ts 1
392 SqhyFLTUrs6u8geASTKe ts 2
393 p7jxXch3zyEvD9B92JRQ ts 1
394 mCN7iUcxW0yZqmQaO7U8 ts 2
395 ct6awrymUS8RgzTgYjKx ts 2
396 GPD6F9DZ5KZ649zqEkmE ts 2
397 JYNcLggG6xeng7vhONOS ts 1
398 QygIO2O30zG9kOeA6yaW ts 1
399 E89vWsrHcs2BX0H9x10k ts 1
400 TJQ9j6A05f8ld8OK8Mzc ts 1
401 BvKCMACzXtxloOwf0FNW ts 1
402 fkOJ1NbnRk71F9aGqXWl ts 1
403 aEalfO2zYVQmNGz6LclE ts 1
404 WTslF06a8nHFUAWr3vIT ts 1
405 GoEd9Sf53GRx8ffiYeJA ts 1
406 3G6evfXk2aaHFUO4NsWt ts 3
407 J97ljhO3gdtigy2V3olp ts 1
408 XZ6kYLrylUTUVmi9Moii ts 1
409 GexzYjhJP1cjPlyIYmBj ts 1
410 YgxWHqtXkNX2ukZ9kiCy ts 1
411 T8uRDr68JjJ4BQT3iIty ts 1
412 qXLy4pIlDDDoNenqCw3e ts 1
413 UIFwra15oVwoFsQMfzB2 ts 1
414 I6mfBMKbR5exUiCNwo3s ts 1
415 iqav0EdhDGMWKHeygaoS ts 1
416 9f0qLvOeqUpquuahieMQ ts 1
417 1EicOlsQbZR2weQYUSE4 ts 1
418 HtYDZw69CrxG7YXipN2I ts 4
419 klZuuzk47ZvpyRkPJaZf ts 1
420 yMeSz76Rr9eoJcLNUcyC ts 2
421 KDppLvc9sRPP1RRTXUbx ts 1
422 NH3PiPNoQ05mlw2FESiA ts 1
423 XcEGK0YT24vUTt758M2g ts 1
424 9nohyzDvZOZcWfHHxyyl ts 1
425 jtLHDBCHTQmFdAKk960m ts 1
426 3v8cpIfuZbk4rsihgtbl ts 1
427 qcx2RIuXihoDjqT84xfl ts 1
428 Dww6f9XWMbmfz4VOw6gr ts 2
429 SC2x1sOCmOGfCQ69ipBT ts 3
430 AMdGiZCShW6RizBlKSmC ts 1Code
v5_dat |>
filter(is.na(score)) |>
summarize(.by = subject_id,
n = n()) subject_id n
1 Ac5ZMy5LVkFjQHr2ucen 8
2 xerEAhCM3QCii740dMOT 1
3 nmy4BvIJvHgW6RKgnx5i 1
4 e8cXE9s9upZtFqszDShQ 2
5 SstzyshA6P3MsHarAJOC 1
6 R0BAq19ThveXTuFrNFWR 1
7 v8OOr4HHwaTU8MdlBiNe 96
8 fkOJ1NbnRk71F9aGqXWl 100
9 J97ljhO3gdtigy2V3olp 97
10 gAL0DiMvJc0jSvoR8Baq 96
11 LSSNI9CJdMEB7kIIdv1j 1
12 Opi65eyMrbyYjB5bwRMP 2
13 PPoF7VmcJfI5PN9okf0T 1
14 YV20vaAnaPndIGbTNCyi 96
15 FqMdeNMqacNOMwDGxovJ 2
16 L431K0ZDgV4wqUR7XTeo 1
17 A8SHBz2S8Ui095L35Wjh 48
18 1h05DmzN29B6FZZR67pP 1
19 BpXGyUgvqzapjyMB15Zy 49
20 CNX9AP2rnXxONuGYEalT 12
21 6jOSL0IZbcNJOVzNEMr7 3
22 Xf5wK1xsRt5nKLDYQwop 1
23 xSvzfB9wIjnw2QuEb9Ba 3
24 4rY5i0hQ8qKc3mBAnAwT 12
25 E3hK4Etw5ylsn1SsXH8S 97
26 0WgPYP5L2MNMyFtsWEaG 2
27 QLyEuiyGskZKXWMVQm4M 1
28 DRrgI3jzHMjID8Q48PjB 97
29 Y2lFlepXHqWUFbEKCNwq 1
30 xDJ0JpfeWhke1GvUwHem 96
31 j49ae9H3AT6mRC6ygqv8 96
32 YxsciWh4LBDtAl6lLunN 96
33 ifcG7cR8IYUd48e11ERX 6
34 hf6J6yzRivfwR2Elyo9N 2
35 0REiwYHX47zX4qbePYjN 7
36 SNsBrz8Txh3QpNgGXGtE 1
37 1E9KZrTTpuJ3KjJa8cDV 6
38 fRYcCiEcUwRJIA0mQ2o7 48
39 tRvesqPE5A0iKVY6G8g8 97
40 VwvZLU4OilnAvOArxYc7 1
41 DXeOGqQEgoEt16VOuaOn 2
42 HqfnXYULelL4tEtMA05t 2
43 CPwdHnAnqZuJJEvQdgWC 1
44 G06vCKjuln4m6u6CPQEn 1
45 ZP9yL6PFVQSvSIC7bUee 1
46 R6U0YqqdHfgqkWkuCagF 2
47 2t8eqhwiUfiSDnWfoHBW 1
48 V2ME98SReEaOQ3fhFVst 1
49 950AzNMugIUFBNRmlDCF 6
50 kiTl14IWGtRXdVVJmZgD 1
51 ADb2b1AvtBmftDmWdKWN 2
52 vbGZVDJU7u0ApalOtBTF 3
53 EECGDwmWAmWfpKKnYI7O 1
54 XjY03SGFqyp7HmUABJbW 1
55 vw9RHgQ3UaaTe1nfjJc0 2
56 IeElhKAlZlvb0pPCNyJQ 1
57 HngYolsZ7LnwhMepx4yf 1
58 383KleAMN0hXjU1qXdYJ 1
59 CKbheN9Pot7GaZpfPYe5 1
60 DoFCE00qFvG0xBr3UWpI 3
61 zANbOJqdtyEpAq00umSb 5
62 IJumhtN8oKoYEDbFfUMf 1
63 j5bxfVlajJwn5pZNQQsa 9
64 TqSddjjditUnblQcWfB3 7
65 Hy5g7Y3sJf9BcHPqEZ8p 1
66 2QJZGGcIQF8A5FhBmyuz 1
67 OJ5jre57VnjtURyBCSjz 1
68 mXMCaULEkFF6s1prItB8 1
69 771cxdbg1Rw25ZLqI3Ge 3
70 49bWZY1TC7zcMYq4FzkX 4
71 ItLyRC07hxMbcaguQBkl 1
72 A7wLmahMacmd6RniSE0h 1
73 cU37HwPJT7ClWoOdeIB0 1
74 Cxv3OL9bAloZXz0mUogg 3
75 5FK6Vbwq2sUGNtVEgaFU 1
76 fCMnEWhTh7kuGhOSMhTC 2
77 kEsM4qtWu3c24cxLGja3 4
78 mgLxjjWNdkpi3rxUB07l 2
79 17LEM8wPQyshaga81mOO 1
80 QQA4FUKgerUT0TLEFFgK 1
81 Ps3jaWnRHhoNNeL3PDa4 1
82 TJQ9j6A05f8ld8OK8Mzc 4
83 jtvtTOMSmoqa3cP4vWFw 1
84 3lGsczpaOxmDeZmuT7yz 1
85 oXDopHMLUvv9ZYWc4WY0 1
86 kovEJOLHZyADSgwnqPga 4
87 MJzHLwX8uDBLf5PVpvm3 1
88 gMVww8j5DaVLpC3Yj8nr 1
89 oy61qtPF4pg4R3ABVbJj 2
90 LxtXIECwYcSxnNetzXg7 1
91 jnddVA5OnRXjgyBHLhkr 2
92 jZF8v6PoTY6RXiFVy6gY 4
93 n76EcPNePczVQ85qQtbB 1
94 z8N4Pa7qb9TeTYKUwAeQ 8
95 IB0qJtGPs6xL8JRpYuBt 3
96 bjusHaMvMJEVS8XmZ80t 6
97 RFOrWXAQX1PO0HFZwlLL 3
98 xoO6skepz0KramQLSPL9 1
99 CBjFu6W7QeUIwHec5mC1 1
100 3lUidRlitol2ksRqEJvN 2
101 BgX9DZHT7atuF2UAoakM 4
102 yXJcaIASTUm1jt5BV2Mm 1
103 8FYM23YbIrj3Ofx7LMoo 2
104 uDwAuOeTAbfEsISlrm7L 1
105 QkP1503G1oAEIoPcmYmF 2
106 ASNPnjJhR3uNgopLHQ9Y 2
107 KVdaeOj3Eyn3hnPNiffK 1
108 vSgAkbmZBhLr5kGKihkG 1
109 fGcn13Dce53Ic5t7KUSy 2
110 yWx2uJPTKZJGfAOeaat6 2
111 0FX5rBebJhUn0z8tQid9 2
112 g3iCPU6zUjzgEz6cwB61 2
113 ym15ZZj8Df0uNAuodXn8 1
114 1dxR6XNJvb17STmiEGyG 1
115 8SvO5adegSRe2a8FwTg0 11
116 qy5e2BKyVAdHLE9Ft3KP 1
117 64RVc86LLeI6qccFOAs0 2
118 GFZDz69YiwKEpC8nh3Kc 2
119 nvEdRy29lvIepo6irKSa 2
120 vINjSqfdP97CnGjw7CAg 1
121 amrWtFuaQvSMbkuuP4XO 1
122 0BfdNKn6rbHBPfFmtL64 2
123 HFmXofKOwruJkX7ka6yA 3
124 cAYYkpIxMiubqvZJv80N 1
125 wQtadQgeGBUtR1EtHfet 3
126 WAWHoPe5JEKCIYk7egYV 1
127 JlC8vtBxKJRSYnZxy3cS 1
128 B4CiC8xT1jfHRpXt5XKT 1
129 hsTC8X9J3MHKvrWSPSGp 1
130 EJEY58XUQs9PBiy63T8T 3
131 WTslF06a8nHFUAWr3vIT 2
132 aEalfO2zYVQmNGz6LclE 4
133 kr9CWXPe1Jc4tybB5TDJ 1
134 QMFZxe3WbmxMlJ9c7oxx 48
135 xcMkFsNfZNXsd3qP1DVF 1
136 ZDYUvh7i71S87XwXaHCP 2
137 P0st9aEQhaLhgmDZBdhz 1
138 o24iqAR0WC7cR2ZkNHie 2
139 OmDvk4v7NxuhAnLBrkbW 2
140 gIVXFcb4XIgyVfkGHGYP 1
141 LGXgT64J11f1BZzL8rQf 1
142 9eUKJ1MDRhFJ0kdt2ikq 4
143 wvOpOK4esubuC1J3qRK9 1
144 jOtySWDeJ7rf9lxtAQzX 2
145 3LlGLQjPsYvg4P5YSea5 1
146 OQBbRgSRcYyepR1DfiXP 3
147 zPdJNnMEELaOlKvgj0xS 2
148 oF9hBAnlrGYSa56Xj8G1 3
149 zlPLQKWkCJ5fswxeAIsi 1
150 D8ziemVuGzthoise1ShM 48
151 Q6i1DNEvpyBBDntIgDg4 1
152 4FSjZtJEUXGQaNQXwR2P 4
153 TKO2Alhu9oJQR8IzF5eI 2
154 hI7JpYtbCLvNfC1JPRxp 3
155 Je7hQucSMLQWqrCCwqTD 2
156 NEzK3RrTXr3H711C5ner 1
157 zbI7AUMspFhHJsUyyaY2 1
158 izXbHR0eYoPO82zpM9ci 1
159 p9lkjNp3qQogD4OUaedm 1
160 iJuHugYdAhBzELotbBWp 3
161 BxdFCeWLf8nCNjrDcwKt 2
162 bRtADGpq7hsZ7dqIDhah 1
163 dDr1YyBAyPfLjg9qDv1Y 1
164 Mdmz1IMWbMN65ldqchyZ 2
165 7oqtXRTtqemWKjkCzH7m 2
166 HEX7q9dj3YP6yeKjY1io 1
167 pg3CssQWWIcR6REE3TFo 2
168 D3ShceKj3iyKrXmCrB7j 1
169 tGZegyyiWppuB0tUkXWz 1
170 omfSFxzu7MrjjSSafj2q 1
171 a5F2j7cvabLVMfvG55nK 4
172 O88gcs0iBczTV5V6mLTh 1
173 AhirSY1w8dBzmuo2t48W 1
174 Ayo5YHWlCQsHNV7XrQc7 3
175 d7pCGcyjbWTZyaOtM2w1 2
176 oEQ7OxQXOW1OrOwhfEPF 5
177 XGaVXviFC59TLyx7AKLq 1
178 fRN2BqpepgaxT82ExHgD 1
179 OcE6oQcbHzuzftyyrdB6 3
180 h1VVrAsMOlTSjgsxu07s 1
181 Ewp3WFDEQVhQx6UMUAiN 2
182 s0XXgsxyI4HqXAssoIP2 2
183 6UwmqiWhZgQPMStTzRH0 1
184 5rHhraHsUTAqh6sjU2d1 2
185 Yfk4ShUhELUtl9XwpgCa 7
186 54siyd3BYjsh1mVk7R0Z 1
187 LmdItKKcaTPAwe0Ym4Od 1
188 IOGwmjH1oX4unT6cb6gy 1
189 RsRxUuQovzhFUap8a01P 1
190 9fNzCWLAlFIBwlLJ0oFz 1
191 Bipzf7LJ6qdGqAscZzBg 5
192 5PP2t16GSzChbtte5vVw 1
193 7g48cSLs8jYPIKalXiUZ 1
194 KyeFByJ933id8W7P6j77 1
195 hSH8uwdznJPF9W8CTA2w 1
196 THM75FY0853siUB2wF5J 1
197 Mtt1LXmZWBninK3ggSK5 1
198 khYao3bQxAh4FNPIXDxa 1
199 w8EG0W8jl0IbL9HsENCg 1
200 4ErLxgNrw5giUuWzjjsm 1
201 iCXyy2xK8DDsgTtrf8kW 2
202 boffKPQBu09LdqOpXQOe 1
203 REcum50sJgQSYvBMQXwc 1
204 TKkwOc2pHd9PObQzxHDj 1
205 jyeyCfCN3CfT45Vmut9E 1
206 rwXh4OtqsjeCqJsJDrVf 2
207 EtulJVHuPSc3ZLVKBPtb 1
208 CFx2CdZ1bSxA6JPb6SA6 1
209 G6LvMujrQyj0EAuVo6Uq 1
210 4zCxjdZQSFAMBokcJ98T 1
211 NxDOJ5WGiu7vMzc0jfg5 3
212 fAPx69vDlA8qSfqNFaag 1
213 FQCQhjkNqqJrCXXHGWyM 1
214 KdXV17I3V0fPvMAidxdb 1
215 5KVDGaVVAHsiHJMjLWGH 1
216 RIcuhrLt8BIs1le0DQl9 1
217 3JAbXWzVHjOfIJvN5nIY 1
218 EjoIAEzk6UUhC7y9F0Ry 1
219 TJns5NZcCMEwMuva3lvg 1
220 nNv1U7RDvAlO10CNVduv 1
221 j3wPPP2j4S3VgnpmBFvH 1
222 OnWF9xAaNJRvOuX00KEc 2
223 wUFqN9bUAEkgPgUV5sqO 1
224 3HNTHcsl7dZ2TGlwR4g6 1
225 etnyKJ3NA8vLJWQgHIDJ 1
226 piTJXDJA6kamnbAqQVSc 1
227 YhKp6PmpkSO7X4aGjYB9 1
228 3RVHWOebZPPvYZwkVRaC 1
229 umV9t8yhNwHqLYO7llmI 2
230 LBg0xU9XZnPijXtSAo67 2
231 ekGBkEpWxMdbx2WL17xl 1
232 Q5v7vEQrx6bW7SNYTjUD 1
233 3WOzLROmRBgFy8GAfJZx 5
234 9IlG6nRRZkN1ZsoqsxCM 1
235 BdgTQ1C8QkSCuLiWUM6G 1
236 2fYxvQc1czvRDcT8JM7B 2
237 McZsAbJxyBZkxN6R06tL 1
238 Gm5YDXVt591FIqCgxMtr 16
239 Fvv0ALwZPTKLL7OVVBmn 1
240 2pScWifucpmQZvXoZCw3 1
241 tKTrELf8YVSKMU6GH0tn 1
242 s7mJzZjHp4ae1qDeD85E 1
243 03spmysv917pRTSkTcj6 1
244 6fGC9DJqZxA4MXTQX8Sd 2
245 O0NYuwahmBnGRb5IPZGa 1
246 R81zDdBsjgGbqyqUWMQT 3
247 eBDrjMp7gbOV17PYLZad 1
248 nRnUY9CTiB7LVJe7haxE 1
249 R6M8a2s8Oin7lhCtFEhQ 1
250 eO2fxGLcdC5k0aGuROZx 1
251 I9oLuP4O3GOIbYravzP8 1
252 g7rIRsOq2kkH8pYaz7fR 1
253 CRGZ4TavntGH5E9LSBCX 1
254 DH0T6zFub589qUF0B702 1
255 0i0wZSbTdy53z6Cni0Dl 1
256 SJdYRJAFtFnPuPXeDtZ9 1
257 rhddayHF97oSYI7UrdRt 1
258 nabsPHGZpvr6SgYzV2am 1
259 5EPXWl4PDme2kqXHiOBB 1
260 qDI6xFzx1uEgAvcPXYms 2
261 ZyEbe7eQxt4YlalJLb89 2
262 JhW3ezpIB8sBZJ76QQ2b 1
263 4CLdS8XZEmJVDlHImT7u 1
264 3TUTJUD20hg4uydW0nPU 1
265 tLdDkwsFunR4YoLKVhDr 1
266 PYH1CXUkU4mAxBsW5UHi 1
267 G2WGBZc9GyTIFNdsysZC 1
268 yL2xNKGqpeaOzg95CDUB 1
269 zKG5mSoyPstUeC99enq5 1
270 AsoaxzIykTyxqAoV4YQr 1
271 bW7vftpeg6L47YzZjzWX 1
272 cRpM8Z159NED2UYcb3jT 1
273 roz2c3opqAFnyNPbzR3h 1
274 qpQ1vSLveBFouxZzN2iu 1
275 076BrNISK0IP8PsESVQX 1
276 6rPerw3fPtggey7LPj6X 1
277 zdKF1SXibeBH26CpiufK 1
278 i8uVNjD6U5P2koFh3qw1 1
279 HQtaLSwh8MGTTpLVllOE 2
280 qexjC6y4MMUWHAt5bqvh 1
281 gYlhCVSj5pa1VFbFdicj 2
282 hmUKEUxhdCzEzo2q7PpI 1
283 ArWJ1aLAwP2t66pdDGqy 1
284 O0yVeeQVT2OJE6mbt5wl 1
285 tMR3y87RWvjTV5kdfi2h 1
286 dFi2sGOS1gEWWg2KDXcs 1
287 CVmfiQuobm1ux4zBp3l4 1
288 kSZ3wZkDtJRrYLpi4ojs 1
289 LT2PWfa1d0t3mjn0w4KZ 1
290 j1lSAtVBMhimMpC1Ha2o 1
291 4MXqlRYjANDHSM003I2c 1
292 9NyaXPkKlfyjubys7IoE 1
293 g00rVq4jB6nBvbdFo9xb 1
294 BiyqZv07wmeZOboeTfnw 1
295 sUE4VAmCISZvzsFH49BK 1
296 imxFcTXtwyNUY9uOjkct 1
297 vNUhkysUUHxRDi1a5BQ7 1
298 9YaeRGOTkxRml6BYldlV 1
299 CtPuhMjKdihqEsXucpww 1
300 54TEF4eZN23KZglG8kHT 2
301 kgAJm1lblz7JIf8qQqZd 1
302 azeC5Kk3x946Oj4L6HnU 1
303 uKYfZr65pq4KTBvCj3dE 1
304 hOdVHTzPBsd26UMRFL3Q 1
305 VLLHim4Q2j7wFMmMQbHq 1
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345 AMdGiZCShW6RizBlKSmC 1Code
# if you missed more than 10 items, you're getting tossed (for now)
missing_cog_items <- v5_dat |>
filter(is.na(score)) |>
summarize(.by = subject_id,
n = n()) |>
filter(n > 10) |>
pull(subject_id)
v5_dat <- v5_dat |>
filter(!(subject_id %in% missing_cog_items)) |>
mutate(score = ifelse(is.na(score), 0, score))
saveRDS(v5_dat, here::here("Data", "v5_dat.rds"))Search Algorithm: FPT Version 5
The FPT Version 5 includes five cognitive tasks.
Denominator Neglect: Combined
Bayesian Update: Hard
ADMC: Decision Rules
Number Series
Coherence Forecasting Test
Matrix Reasoning
Denominator Neglect: Separate
Bayesian Update: Easy
I don’t know what to do with the CFT as of now, so this will only have Denominator Neglect: Combined, Bayesian Update: Hard, ADMC: Decision Rules, Number Series.
I also don’t know what to do with Raven.
Going to add Time Series as well!
Methods
This algorithm seeks to maximize the \(R^2\) of predicting s-scores per minute for a set of cognitive task items of average testing time t.
Code
test <- data.frame(rep = 0,
task = "INIT",
item = "INIT",
test_r2 = 0,
time = 0,
added_r2 = 0,
added_time = 0,
r2_rate = 0)
walk(seq(1, v5_dat |> pull(item) |> unique() |> length()),
\(i) {test <<- v5_dat |>
filter(!(item %in% pull(test, item))) |>
pull(item) |>
unique() |>
map(~ rbind(v5_dat |>
filter(item == .x),
v5_dat |>
filter(item %in% pull(test, item))) |>
summarize(.by = c(task, subject_id),
score = mean(score),
sscore = first(sscore),
time = sum(time),
task = first(task)) |>
mutate(time = time + map_dbl(task, get_startup)) |>
mutate(.by = subject_id,
time = sum(time)) |>
mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
pivot_wider(names_from = task, values_from = score) |>
summarize(rep = i,
task = str_split_i(.x, "_", 1),
item = .x,
test_r2 = summary(lm(as.formula(first(formula)),
data = pick(everything())))$r.squared,
added_r2 = first(test_r2) - test |>
head(1) |>
pull(test_r2),
added_time = first(time) - test |>
head(1) |>
pull(time),
time = first(time),
r2_rate = first(added_r2 / added_time))) |>
list_rbind() |>
filter(r2_rate == max(r2_rate)) |>
rbind(test) |>
filter(item != "INIT")},
.progress = T)
test
saveRDS(test, here::here("Data", "Search Algorithms", "v5test_dat.rds"))Code
test <- readRDS(here::here("Data", "Search Algorithms", "v5test_dat.rds"))Code
v5test_plt <- test |>
ggplot(aes(x = time, y = test_r2)) +
geomtextpath::geom_textsegment(data = test |> filter(time < 10.49) |> filter(time == max(time)),
aes(x = -Inf, xend = time, y = test_r2, yend = test_r2, label = "10 MIN"),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_segment(data = test |> filter(time < 10.49) |> filter(time == max(time)),
aes(y = -Inf, yend = test_r2, x = time),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geomtextpath::geom_textsegment(data = filter(test, test_r2 == max(test_r2)),
aes(x = -Inf, xend = time, y = test_r2, yend = test_r2, label = "PEAK"),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_segment(data = filter(test, test_r2 == max(test_r2)),
aes(y = -Inf, yend = test_r2, x = time),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_line() +
geom_point(aes(color = task), size = 2) +
annotate("text", label = paste0(test |>
filter(time < 10.49) |> filter(test_r2 == max(test_r2)) |>
pull(time) |> round(1),
" minutes:\n• ",
test |> filter(time < 10.49) |>
count(task) |>
pmap_chr(\(task, n) paste(n, task)) |>
paste(collapse = "\n• ")),
x = 8.2, y = 0.2, size = 3.4, hjust = "left", color = "#202020") +
annotate("text", label = paste0(filter(test, test_r2 == max(test_r2)) |>
pull(time) |> round(1), " minutes:\n• ",
test |>
filter(time <= filter(test, test_r2 == max(test_r2)) |>
pull(time)) |>
count(task) |>
pmap_chr(\(task, n) paste(n, task)) |>
paste(collapse = "\n• ")),
x = 26.6, y = 0.25, size = 3.4, hjust = "left", color = "#202020") +
coord_cartesian(xlim = c(0, 45), ylim = c(0, 0.8), expand = c(0, 1)) +
guides(x = guide_axis(cap = "both"),
y = guide_axis(cap = "both")) +
labs(x = "Time (minutes)", y = "Test R^2", color = "Task") +
theme_classic() +
theme(
panel.background = element_rect(fill='transparent'),
plot.background = element_rect(fill='transparent', color=NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.background = element_blank())
saveRDS(v5test_plt, here::here("Figures", "Search Algorithms", "v5test_plt.rds"))
v5test_plt 
As info, this still does not have any within-task difference in item time. Currently (total time from paper - startup time) / number of items.
Same Order of Items, Randomly Selected
Code
test_random <- data.frame(rep = 0,
iter = 0,
task = "INIT",
item = "INIT",
test_r2 = 0,
time = 0,
added_r2 = 0,
added_time = 0,
r2_rate = 0)
walk(1:100,
~ {test_iter <- data.frame(rep = 0,
iter = 0,
task = "INIT",
item = "INIT",
test_r2 = 0,
time = 0,
added_r2 = 0,
added_time = 0,
r2_rate = 0)
test |>
arrange(rep) |>
select(cur_rep = rep, cur_task = task) |>
pwalk(\(cur_rep, cur_task) {
random_item <- v5_dat |>
filter(task == cur_task & !(item %in% pull(test_iter, item))) |>
pull(item) |>
unique() |>
sample(1)
test_iter <<- rbind(v5_dat |>
filter(item == random_item),
v5_dat |>
filter(item %in% pull(test_iter, item))) |>
summarize(.by = c(task, subject_id),
score = mean(score),
sscore = first(sscore),
time = sum(time),
task = first(task)) |>
mutate(time = time + map_dbl(task, get_startup)) |>
mutate(.by = subject_id,
time = sum(time)) |>
mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
pivot_wider(names_from = task, values_from = score) |>
summarize(iter = .x,
rep = cur_rep,
task = cur_task,
item = random_item,
test_r2 = summary(lm(as.formula(first(formula)),
data = pick(everything())))$r.squared,
added_r2 = first(test_r2) - test_iter |>
head(1) |>
pull(test_r2),
added_time = first(time) - test_iter |>
head(1) |>
pull(time),
time = first(time),
r2_rate = first(added_r2 / added_time)) |>
rbind(test_iter) |>
filter(item != "INIT")
})
test_random <<- rbind(test_random, test_iter) |>
filter(item != "INIT")},
.progress = T)
saveRDS(test_random, here::here("Data", "Search Algorithms", "v5test-random_dat.rds"))Code
test_random <- readRDS(here::here("Data", "Search Algorithms", "v5test-random_dat.rds"))Code
v5test_random_plt <- test_random |>
ggplot(aes(x = time, y = test_r2)) +
geomtextpath::geom_textsegment(
data = filter(test_random |>
summarize(.by = time,
test_r2 = mean(test_r2),
task = first(task)),
test_r2 == max(test_r2)),
aes(x = -Inf, xend = time, y = test_r2, yend = test_r2, label = "PEAK"),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_segment(
data = filter(test_random |>
summarize(.by = time,
test_r2 = mean(test_r2),
task = first(task)),
test_r2 == max(test_r2)),
aes(y = -Inf, yend = test_r2, x = time),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_line(aes(group = iter), alpha = .14, linewidth = .4,
color = "#202020") +
geom_line(data = test_random |> summarize(.by = time,
test_r2 = mean(test_r2)),
aes(x = time, y = test_r2), color = "#202020") +
geom_point(data = test_random |> summarize(.by = time,
test_r2 = mean(test_r2),
task = first(task)),
aes(x = time, y = test_r2, color = task),
size = 2) +
guides(x = guide_axis(cap = "both"),
y = guide_axis(cap = "both")) +
labs(x = "Time (minutes)", y = "Test R^2") +
coord_cartesian(ylim = c(0, 0.6), xlim = c(0, 45)) +
theme_classic() +
theme(
panel.background = element_rect(fill='transparent'),
plot.background = element_rect(fill='transparent', color=NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.background = element_blank())
saveRDS(v5test_random_plt, here::here("Figures", "Search Algorithms", "v5test_random_plt.rds"))
v5test_random_plt
Idea: what if as a robustness check we rerun the search alg making its first item be from a specific task. Nowhere close to all possible combinations, but might be something.
Code
v5_dat |> pull(task) |> unique() |>
walk(\(first_task) {
test <<- data.frame(first_task = "INIT",
rep = 0,
task = "INIT",
item = "INIT",
test_r2 = 0,
time = 0,
added_r2 = 0,
added_time = 0,
r2_rate = 0)
print(first_task)
walk(seq(1, v5_dat |> pull(item) |> unique() |> length()),
\(i) {
if (test |> filter(time == max(time)) |> pull(time) > 10) {Sys.sleep(0)}
else {test <<- v5_dat |>
filter(!(item %in% pull(test, item))) |>
filter((i == 1 & task == first_task) | i > 1) |>
pull(item) |>
unique() |>
map(~ rbind(v5_dat |>
filter(item == .x),
v5_dat |>
filter(item %in% pull(test, item))) |>
summarize(.by = c(task, subject_id),
score = mean(score),
sscore = first(sscore),
time = sum(time),
task = first(task)) |>
mutate(time = time + map_dbl(task, get_startup)) |>
mutate(.by = subject_id,
time = sum(time)) |>
mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
pivot_wider(names_from = task, values_from = score) |>
summarize(first_task = first_task,
rep = i,
task = str_split_i(.x, "_", 1),
item = .x,
test_r2 = summary(lm(as.formula(first(formula)),
data = pick(everything())))$r.squared,
added_r2 = first(test_r2) - test |>
head(1) |>
pull(test_r2),
added_time = first(time) - test |>
head(1) |>
pull(time),
time = first(time),
r2_rate = first(added_r2 / added_time))) |>
list_rbind() |>
filter(r2_rate == max(r2_rate)) |>
rbind(test) |>
filter(item != "INIT")}
},
.progress = T)
saveRDS(test, here::here("Data", "Search Algorithms", paste0("v5test_first-", first_task, "_dat.rds")))
})Code
v5_dat |> pull(task) |> unique() |>
walk(\(task) {
test_firsttask <- here::here("Data", "Search Algorithms",
paste0("v5test_first-", task, "_dat.rds")) |>
readRDS()
(test_firsttask |>
ggplot(aes(x = time, y = test_r2)) +
geomtextpath::geom_textsegment(data = filter(test_firsttask, test_r2 == max(test_r2)),
aes(x = -Inf, xend = time, y = test_r2, yend = test_r2, label = "PEAK"),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_segment(data = filter(test_firsttask, test_r2 == max(test_r2)),
aes(y = -Inf, yend = test_r2, x = time),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_line() +
geom_point(aes(color = task), size = 2) +
annotate("text", label = paste0(filter(test_firsttask, test_r2 == max(test_r2)) |>
pull(time) |> round(1), " minutes:\n• ",
test_firsttask |>
filter(time <= filter(test_firsttask, test_r2 == max(test_r2)) |>
pull(time)) |>
count(task) |>
pmap_chr(\(task, n) paste(n, task)) |>
paste(collapse = "\n• ")),
x = 26.6, y = 0.25, size = 3.4, hjust = "left", color = "#202020") +
coord_cartesian(xlim = c(0, 20), ylim = c(0, 0.8), expand = c(0, 1)) +
guides(x = guide_axis(cap = "both"),
y = guide_axis(cap = "both")) +
labs(x = "Time (minutes)", y = "Test R^2", color = "Task",
title = paste("Starting Task: ", task)) +
theme_classic() +
theme(
panel.background = element_rect(fill='transparent'),
plot.background = element_rect(fill='transparent', color=NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.background = element_blank(),
aspect.ratio = 1)) |>
print()
})
Mark’s idea: run with just maximizin \(R^2\) (no time in denominator)
Code
test <- data.frame(rep = 0,
task = "INIT",
item = "INIT",
test_r2 = 0,
time = 0,
added_r2 = 0,
added_time = 0,
r2_rate = 0)
walk(seq(1, v5_dat |> pull(item) |> unique() |> length()),
\(i) {test <<- v5_dat |>
filter(!(item %in% pull(test, item))) |>
pull(item) |>
unique() |>
map(~ rbind(v5_dat |>
filter(item == .x),
v5_dat |>
filter(item %in% pull(test, item))) |>
summarize(.by = c(task, subject_id),
score = mean(score),
sscore = first(sscore),
time = sum(time),
task = first(task)) |>
mutate(time = time + map_dbl(task, get_startup)) |>
mutate(.by = subject_id,
time = sum(time)) |>
mutate(formula = paste("sscore ~", task |> unique() |> paste(collapse = "+"))) |>
pivot_wider(names_from = task, values_from = score) |>
summarize(rep = i,
task = str_split_i(.x, "_", 1),
item = .x,
test_r2 = summary(lm(as.formula(first(formula)),
data = pick(everything())))$r.squared,
added_r2 = first(test_r2) - test |>
head(1) |>
pull(test_r2),
added_time = first(time) - test |>
head(1) |>
pull(time),
time = first(time),
r2_rate = first(added_r2 / added_time))) |>
list_rbind() |>
# only difference is here.
filter(added_r2 == max(added_r2)) |>
rbind(test) |>
filter(item != "INIT")},
.progress = T)
test
saveRDS(test, here::here("Data", "Search Algorithms", "v5test-max-r2_dat.rds"))Code
test_maxr2 <- readRDS(here::here("Data", "Search Algorithms", "v5test-max-r2_dat.rds"))
test_maxr2 |>
ggplot(aes(x = time, y = test_r2)) +
geomtextpath::geom_textsegment(data = test_maxr2 |> filter(time < 10.49) |> filter(time == max(time)),
aes(x = -Inf, xend = time, y = test_r2, yend = test_r2, label = "10 MIN"),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_segment(data = test_maxr2 |> filter(time < 10.49) |> filter(time == max(time)),
aes(y = -Inf, yend = test_r2, x = time),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geomtextpath::geom_textsegment(data = filter(test_maxr2, test_r2 == max(test_r2)),
aes(x = -Inf, xend = time, y = test_r2, yend = test_r2, label = "PEAK"),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_segment(data = filter(test_maxr2, test_r2 == max(test_r2)),
aes(y = -Inf, yend = test_r2, x = time),
color = scales::col_lighter("#202020", amount = 35), linewidth = .3, linetype = "longdash") +
geom_line() +
geom_point(aes(color = task), size = 2) +
annotate("text", label = paste0(test_maxr2 |>
filter(time < 10.49) |> filter(test_r2 == max(test_r2)) |>
pull(time) |> round(1),
" minutes:\n• ",
test_maxr2 |> filter(time < 10.49) |>
count(task) |>
pmap_chr(\(task, n) paste(n, task)) |>
paste(collapse = "\n• ")),
x = 8.2, y = 0.2, size = 3.4, hjust = "left", color = "#202020") +
annotate("text", label = paste0(filter(test_maxr2, test_r2 == max(test_r2)) |>
pull(time) |> round(1), " minutes:\n• ",
test_maxr2 |>
filter(time <= filter(test_maxr2, test_r2 == max(test_r2)) |>
pull(time)) |>
count(task) |>
pmap_chr(\(task, n) paste(n, task)) |>
paste(collapse = "\n• ")),
x = 26.6, y = 0.25, size = 3.4, hjust = "left", color = "#202020") +
coord_cartesian(xlim = c(0, 45), ylim = c(0, 0.8), expand = c(0, 1)) +
guides(x = guide_axis(cap = "both"),
y = guide_axis(cap = "both")) +
labs(x = "Time (minutes)", y = "Test R^2", color = "Task") +
theme_classic() +
theme(
panel.background = element_rect(fill='transparent'),
plot.background = element_rect(fill='transparent', color=NA),
panel.grid.major = element_blank(),
panel.grid.minor = element_blank(),
legend.background = element_blank())`geom_line()`: Each group consists of only one observation.
ℹ Do you need to adjust the group aesthetic?