Last updated: 2022-01-19

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Rmd 02fac49 Liang Zhang 2022-01-19 wflow_publish("analysis/test_checking.Rmd") 
tests_keyboard <- read_lines("config/require_keybord.txt")
validation <- tar_read(device_info) |> 
  mutate(valid_device = !(game_name %in% tests_keyboard & used_mouse)) |> 
  inner_join(tar_read(data_validation)) |> 
  filter(valid_device & valid_version) |> 
  group_by(user_id, game_name) |> 
  filter(
    if_else(
      str_detect(game_name_abbr, "[A|B]$"), 
      row_number(desc(game_time)) == 1,
      row_number(desc(game_time)) <= 2
    )
  ) |> 
  ungroup()
Joining, by = c("game_name_abbr", ".id", "user_id", "course_name", "game_id",
"game_name", "game_time", "game_duration")
indices <- tar_read(indices) |> 
  semi_join(validation) |> 
  mutate(across(starts_with("game_name"), ~ str_remove(.x, "[A|B]$"))) |> 
  group_by(user_id, game_name_abbr, game_name, index_name) |> 
  mutate(occasion = recode(row_number(game_time), `1` = "test", `2` = "retest")) |> 
  ungroup() |> 
  pivot_wider(
    id_cols = c(user_id, game_name, game_name_abbr, index_name), 
    names_from = occasion,
    values_from = score
  )
Joining, by = c("game_name_abbr", ".id", "user_id", "course_name", "game_id",
"game_name", "game_time", "game_duration")
tests_included <- deframe(distinct(indices, game_name_abbr, game_name))
render_content <- function(file, ...) {
  knitr::knit(
    text = knitr::knit_expand(file, ...),
    quiet = TRUE
  )
}
purrr::imap_chr(
  tests_included,
  ~ render_content(
    file = "archetypes/child_check_index.Rmd",
    game_name_abbr = .x,
    game_name = .y
  )
) |> 
  str_c(collapse = "\n\n") |> 
  cat()

快速归类PRO

data <- indices |> 
  filter(
    game_name_abbr == "CRTPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: CRTPro
  • Sample Size: 126
  • Index Names:
    • nc
    • mrt
    • rtsd

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()
Registered S3 method overwritten by 'GGally':
  method from   
  +.gg   ggplot2

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

捉虫高级简版

data <- indices |> 
  filter(
    game_name_abbr == "TOVAS",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: TOVAS
  • Sample Size: 329
  • Index Names:
    • nc
    • mrt
    • rtsd
    • dprime
    • c
    • commissions
    • omissions

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

塔罗牌

data <- indices |> 
  filter(
    game_name_abbr == "WxPred",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: WxPred
  • Sample Size: 47
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

数感

data <- indices |> 
  filter(
    game_name_abbr == "NsymNCmp",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: NsymNCmp
  • Sample Size: 141
  • Index Names:
    • pc
    • mrt
    • w

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

数字卡片

data <- indices |> 
  filter(
    game_name_abbr == "Digit3back",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: Digit3back
  • Sample Size: 160
  • Index Names:
    • pc
    • mrt
    • dprime
    • c

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.00420999 (tol = 0.002, component 1)
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

格子卡片

data <- indices |> 
  filter(
    game_name_abbr == "Grid2back",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: Grid2back
  • Sample Size: 152
  • Index Names:
    • pc
    • mrt
    • dprime
    • c

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

美术卡片

data <- indices |> 
  filter(
    game_name_abbr == "Paint2back",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: Paint2back
  • Sample Size: 160
  • Index Names:
    • pc
    • mrt
    • dprime
    • c

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

文字卡片

data <- indices |> 
  filter(
    game_name_abbr == "Verbal3back",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: Verbal3back
  • Sample Size: 148
  • Index Names:
    • pc
    • mrt
    • dprime
    • c

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

魔术师高级

data <- indices |> 
  filter(
    game_name_abbr == "Nback3",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: Nback3
  • Sample Size: 188
  • Index Names:
    • pc
    • mrt
    • dprime
    • c

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

变色魔块PRO

data <- indices |> 
  filter(
    game_name_abbr == "StopSigPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: StopSigPro
  • Sample Size: 225
  • Index Names:
    • rt_nth
    • ssrt
    • pc_all
    • pc_go
    • pc_stop

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

多彩文字PRO

data <- indices |> 
  filter(
    game_name_abbr == "ColStrpPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: ColStrpPro
  • Sample Size: 282
  • Index Names:
    • mrt_inc
    • mrt_con
    • pc_inc
    • pc_con
    • cong_eff_rt
    • cong_eff_pc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

卡片分类PRO

data <- indices |> 
  filter(
    game_name_abbr == "CardSortPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: CardSortPro
  • Sample Size: 258
  • Index Names:
    • mrt_repeat
    • mrt_switch
    • pc_repeat
    • pc_switch
    • switch_cost_rt_spe
    • switch_cost_pc_spe

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

候鸟迁徙PRO

data <- indices |> 
  filter(
    game_name_abbr == "BirdsPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: BirdsPro
  • Sample Size: 552
  • Index Names:
    • mrt_inc
    • mrt_con
    • pc_inc
    • pc_con
    • cong_eff_rt
    • cong_eff_pc
    • mrt_repeat
    • mrt_switch
    • pc_repeat
    • pc_switch
    • switch_cost_rt_spe
    • switch_cost_pc_spe

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

方向临摹

data <- indices |> 
  filter(
    game_name_abbr == "JLO",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: JLO
  • Sample Size: 135
  • Index Names:
    • nc
    • mean_ang_err
    • mean_log_err

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

按图索骥

data <- indices |> 
  filter(
    game_name_abbr == "HOP",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: HOP
  • Sample Size: 276
  • Index Names:
    • mean_dist_err_allo
    • mean_dist_err_both
    • mean_dist_err_ego
    • mean_log_err_allo
    • mean_log_err_both
    • mean_log_err_ego

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

萤火虫PRO

data <- indices |> 
  filter(
    game_name_abbr == "MOTPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: MOTPro
  • Sample Size: 180
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

火眼金睛

data <- indices |> 
  filter(
    game_name_abbr == "AttSrc",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: AttSrc
  • Sample Size: 46
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

位置记忆PRO

data <- indices |> 
  filter(
    game_name_abbr == "SSTMPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: SSTMPro
  • Sample Size: 180
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

宇宙黑洞

data <- indices |> 
  filter(
    game_name_abbr == "LocMemStd",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: LocMemStd
  • Sample Size: 196
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

过目不忘PRO

data <- indices |> 
  filter(
    game_name_abbr == "FWSPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: FWSPro
  • Sample Size: 180
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

人工语言-中级

data <- indices |> 
  filter(
    game_name_abbr == "AscLanMd",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: AscLanMd
  • Sample Size: 45
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

各得其所

data <- indices |> 
  filter(
    game_name_abbr == "LdnTwr",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: LdnTwr
  • Sample Size: 92
  • Index Names:
    • prop_perfect
    • mrt_init

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
Warning in checkConv(attr(opt, "derivs"), opt$par, ctrl = control$checkConv, :
Model failed to converge with max|grad| = 0.0044457 (tol = 0.002, component 1)
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

连点成画PRO

data <- indices |> 
  filter(
    game_name_abbr == "FPTPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: FPTPro
  • Sample Size: 46
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

蝴蝶照相机

data <- indices |> 
  filter(
    game_name_abbr == "SCSpan",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: SCSpan
  • Sample Size: 180
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

密码箱

data <- indices |> 
  filter(
    game_name_abbr == "KeepTrack",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: KeepTrack
  • Sample Size: 180
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

超级秒表PRO

data <- indices |> 
  filter(
    game_name_abbr == "SRTPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: SRTPro
  • Sample Size: 102
  • Index Names:
    • mrt
    • rtsd

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

一心二用PRO

data <- indices |> 
  filter(
    game_name_abbr == "DualTaskPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: DualTaskPro
  • Sample Size: 350
  • Index Names:
    • nc
    • mrt
    • rtsd
    • dprime
    • c
    • commissions
    • omissions

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

连续再认

data <- indices |> 
  filter(
    game_name_abbr == "ConRec",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: ConRec
  • Sample Size: 350
  • Index Names:
    • nc
    • mrt
    • rtsd
    • dprime
    • c
    • commissions
    • omissions

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

图片记忆

data <- indices |> 
  filter(
    game_name_abbr == "BPS",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: BPS
  • Sample Size: 250
  • Index Names:
    • pc
    • p_sim_foil
    • p_sim_lure
    • p_sim_target
    • bps_score

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

言语记忆

data <- indices |> 
  filter(
    game_name_abbr == "DRM",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: DRM
  • Sample Size: 200
  • Index Names:
    • tm_dprime
    • tm_bias
    • fm_dprime
    • fm_bias

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

强化学习

data <- indices |> 
  filter(
    game_name_abbr == "ProbRL",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: ProbRL
  • Sample Size: 50
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

语义判断

data <- indices |> 
  filter(
    game_name_abbr == "Seman",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: Seman
  • Sample Size: 343
  • Index Names:
    • nc
    • mrt
    • rtsd
    • dprime
    • c
    • commissions
    • omissions

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

声调判断

data <- indices |> 
  filter(
    game_name_abbr == "Tone",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: Tone
  • Sample Size: 343
  • Index Names:
    • nc
    • mrt
    • rtsd
    • dprime
    • c
    • commissions
    • omissions

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

太空飞船PRO

data <- indices |> 
  filter(
    game_name_abbr == "FlkrPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: FlkrPro
  • Sample Size: 300
  • Index Names:
    • mrt_inc
    • mrt_con
    • pc_inc
    • pc_con
    • cong_eff_rt
    • cong_eff_pc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

察颜观色PRO

data <- indices |> 
  filter(
    game_name_abbr == "FacesPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: FacesPro
  • Sample Size: 600
  • Index Names:
    • mrt_inc
    • mrt_con
    • pc_inc
    • pc_con
    • cong_eff_rt
    • cong_eff_pc
    • mrt_repeat
    • mrt_switch
    • pc_repeat
    • pc_switch
    • switch_cost_rt_spe
    • switch_cost_pc_spe

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

顺背数PRO

data <- indices |> 
  filter(
    game_name_abbr == "FDSPro",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: FDSPro
  • Sample Size: 196
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

图形推理

data <- indices |> 
  filter(
    game_name_abbr == "NVR",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: NVR
  • Sample Size: 49
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

数字推理

data <- indices |> 
  filter(
    game_name_abbr == "DR",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: DR
  • Sample Size: 49
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

文字推理

data <- indices |> 
  filter(
    game_name_abbr == "VR",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: VR
  • Sample Size: 49
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

三维心理旋转测试

data <- indices |> 
  filter(
    game_name_abbr == "MR3D",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: MR3D
  • Sample Size: 49
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

时长分辨

data <- indices |> 
  filter(
    game_name_abbr == "DD",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: DD
  • Sample Size: 35
  • Index Names:
    • threshold

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

时间顺序判断

data <- indices |> 
  filter(
    game_name_abbr == "TOJ",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: TOJ
  • Sample Size: 35
  • Index Names:
    • threshold

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

节奏感知

data <- indices |> 
  filter(
    game_name_abbr == "RP",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: RP
  • Sample Size: 35
  • Index Names:
    • threshold

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

专注大师_中级

data <- indices |> 
  filter(
    game_name_abbr == "CalcSpdMed",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: CalcSpdMed
  • Sample Size: 32
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

眼耳并用

data <- indices |> 
  filter(
    game_name_abbr == "Dual2back",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: Dual2back
  • Sample Size: 431
  • Index Names:
    • pc_aud
    • pc_both
    • pc_vis
    • mrt_aud
    • mrt_both
    • mrt_vis
    • dprime_aud
    • dprime_both
    • dprime_vis
    • c_aud
    • c_both
    • c_vis

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()
Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value
Warning: Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).
Warning: Removed 1 rows containing non-finite values (stat_density).
Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removing 1 row that contained a missing value
Warning: Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).
Removed 1 rows containing missing values (geom_point).

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

变戏法

data <- indices |> 
  filter(
    game_name_abbr == "AntiSac",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: AntiSac
  • Sample Size: 36
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

舒尔特方格（中级）

data <- indices |> 
  filter(
    game_name_abbr == "SchulteMed",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: SchulteMed
  • Sample Size: 35
  • Index Names:
    • nc_cor

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
boundary (singular) fit: see ?isSingular
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

欢乐餐厅

data <- indices |> 
  filter(
    game_name_abbr == "AscMem",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: AscMem
  • Sample Size: 35
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

登陆月球（中级）

data <- indices |> 
  filter(
    game_name_abbr == "NLEMed",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: NLEMed
  • Sample Size: 70
  • Index Names:
    • mean_abs_err
    • mean_log_err

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

速算师（中级）

data <- indices |> 
  filter(
    game_name_abbr == "CalcMed",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: CalcMed
  • Sample Size: 35
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

图形折叠

data <- indices |> 
  filter(
    game_name_abbr == "FR",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: FR
  • Sample Size: 35
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

远距离联想

data <- indices |> 
  filter(
    game_name_abbr == "RAT",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: RAT
  • Sample Size: 35
  • Index Names:
    • nc

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

幸运小球

data <- indices |> 
  filter(
    game_name_abbr == "OCSpan",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: OCSpan
  • Sample Size: 140
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

打靶场

data <- indices |> 
  filter(
    game_name_abbr == "RSpan",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: RSpan
  • Sample Size: 138
  • Index Names:
    • nc
    • max_span
    • mean_span_pcu
    • mean_span_anu

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()
Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removed 2 rows containing missing values
Warning: Removed 2 rows containing missing values (geom_point).
Warning: Removed 2 rows containing non-finite values (stat_density).
Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removed 2 rows containing missing values

Warning in ggally_statistic(data = data, mapping = mapping, na.rm = na.rm, :
Removed 2 rows containing missing values
Warning: Removed 2 rows containing missing values (geom_point).
Removed 2 rows containing missing values (geom_point).

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

雪花收藏家

data <- indices |> 
  filter(
    game_name_abbr == "DirectSrc",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: DirectSrc
  • Sample Size: 35
  • Index Names:
    • nc_cor

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)

我是大厨

data <- indices |> 
  filter(
    game_name_abbr == "MSynWin",
    across(contains("test"), is.finite),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

  • Abbreviation: MSynWin
  • Sample Size: 140
  • Index Names:
    • score_total
    • score_aud
    • score_mem
    • score_vis

Pairwise Correlation of indices

data |> 
  pivot_wider(
    id_cols = user_id,
    names_from = index_name, 
    values_from = test
  ) |> 
  select(-user_id) |> 
  GGally::ggpairs()

Test-Retest

data_test_retest <- drop_na(data)
reliability <- data_test_retest |> 
  group_by(index_name) |> 
  group_modify(
    ~ tibble(
      icc = .x |> 
        select(contains("test")) |> 
        psych::ICC() |> 
        pluck("results", "ICC", 3),
      r = cor(.x$test, .x$retest)
    )
  ) |> 
  ungroup()
data_test_retest |> 
  ggpubr::ggscatter("test", "retest") +
  geom_text(
    data = reliability,
    aes(
      x = -Inf, y = Inf, 
      label = str_glue("r = {round(r, 2)}\nicc = {round(icc, 2)}")
    ),
    hjust = -0.1, vjust = 1.1
  ) +
  facet_wrap(~ index_name, ncol = 1, scales = "free") +
  theme(aspect.ratio = 1)


sessionInfo()
R version 4.1.2 (2021-11-01)
Platform: i386-w64-mingw32/i386 (32-bit)
Running under: Windows 10 x64 (build 22000)

Matrix products: default

locale:
[1] LC_COLLATE=Chinese (Simplified)_China.936 
[2] LC_CTYPE=Chinese (Simplified)_China.936   
[3] LC_MONETARY=Chinese (Simplified)_China.936
[4] LC_NUMERIC=C                              
[5] LC_TIME=Chinese (Simplified)_China.936    

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
 [1] targets_0.10.0   forcats_0.5.1    stringr_1.4.0    dplyr_1.0.7     
 [5] purrr_0.3.4      readr_2.1.1      tidyr_1.1.4.9000 tibble_3.1.6    
 [9] ggplot2_3.3.5    tidyverse_1.3.1  workflowr_1.7.0 

loaded via a namespace (and not attached):
  [1] nlme_3.1-153        fs_1.5.2            lubridate_1.8.0    
  [4] bit64_4.0.5         RColorBrewer_1.1-2  httr_1.4.2         
  [7] rprojroot_2.0.2     tools_4.1.2         backports_1.4.1    
 [10] bslib_0.3.1         utf8_1.2.2          R6_2.5.1           
 [13] DBI_1.1.2           colorspace_2.0-2    withr_2.4.3        
 [16] mnormt_2.0.2        tidyselect_1.1.1    GGally_2.1.2       
 [19] processx_3.5.2      bit_4.0.4           compiler_4.1.2     
 [22] git2r_0.29.0        cli_3.1.0           rvest_1.0.2        
 [25] xml2_1.3.3          labeling_0.4.2      stringfish_0.15.5  
 [28] sass_0.4.0          scales_1.1.1        psych_2.1.9        
 [31] callr_3.7.0         digest_0.6.29       minqa_1.2.4        
 [34] rmarkdown_2.11      pkgconfig_2.0.3     htmltools_0.5.2    
 [37] lme4_1.1-27.1       highr_0.9           dbplyr_2.1.1       
 [40] fastmap_1.1.0       rlang_0.99.0.9001   readxl_1.3.1       
 [43] rstudioapi_0.13     farver_2.1.0        jquerylib_0.1.4    
 [46] generics_0.1.1      RApiSerialize_0.1.0 jsonlite_1.7.2     
 [49] vroom_1.5.7         car_3.0-12          magrittr_2.0.1     
 [52] Matrix_1.4-0        Rcpp_1.0.7          munsell_0.5.0      
 [55] fansi_1.0.0         abind_1.4-5         lifecycle_1.0.1    
 [58] stringi_1.7.6       whisker_0.4         yaml_2.2.1         
 [61] carData_3.0-5       MASS_7.3-54         plyr_1.8.6         
 [64] grid_4.1.2          parallel_4.1.2      promises_1.2.0.1   
 [67] crayon_1.4.2        lattice_0.20-45     splines_4.1.2      
 [70] haven_2.4.3         hms_1.1.1           tmvnsim_1.0-2      
 [73] knitr_1.37          ps_1.6.0            pillar_1.6.4       
 [76] ggpubr_0.4.0        igraph_1.2.11       boot_1.3-28        
 [79] ggsignif_0.6.3      base64url_1.4       codetools_0.2-18   
 [82] reprex_2.0.1        glue_1.6.0          evaluate_0.14      
 [85] getPass_0.2-2       data.table_1.14.2   RcppParallel_5.1.5 
 [88] modelr_0.1.8        nloptr_1.2.2.3      vctrs_0.3.8        
 [91] tzdb_0.2.0          httpuv_1.6.5        cellranger_1.1.0   
 [94] gtable_0.3.0        reshape_0.8.8       qs_0.25.2          
 [97] assertthat_0.2.1    xfun_0.29           broom_0.7.11       
[100] rstatix_0.7.0       later_1.3.0         ellipsis_0.3.2