Test Performance Checking

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Last updated: 2022-01-19

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Knit directory: cogstruct/analysis/

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tests_keyboard <- read_lines(here::here("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]$"), 
      row_number(desc(game_time)) == 1,
      row_number(desc(game_time)) <= 2
    )
  ) |> 
  ungroup()
indices <- tar_read(indices) |> 
  semi_join(validation) |> 
  mutate(across(starts_with("game_name"), ~ str_remove(.x, "[A|B]$"))) |> 
  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
  )
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 = here::here("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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: CRTPro
• Sample Size: 88
• 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()

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)

捉虫高级简版

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

Basic Information

• Abbreviation: TOVAS
• Sample Size: 89
• 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)

塔罗牌

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

Basic Information

• Abbreviation: WxPred
• Sample Size: 89
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: NsymNCmp
• Sample Size: 90
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Digit3back
• Sample Size: 83
• 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)

格子卡片

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

Basic Information

• Abbreviation: Grid2back
• Sample Size: 83
• 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)

美术卡片

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

Basic Information

• Abbreviation: Paint2back
• Sample Size: 85
• 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)

文字卡片

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

Basic Information

• Abbreviation: Verbal3back
• Sample Size: 84
• 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)

魔术师高级

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

Basic Information

• Abbreviation: Nback3
• Sample Size: 89
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: StopSigPro
• Sample Size: 88
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: ColStrpPro
• Sample Size: 90
• 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)

卡片分类PRO

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

Basic Information

• Abbreviation: CardSortPro
• Sample Size: 86
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: BirdsPro
• Sample Size: 90
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: JLO
• Sample Size: 102
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: HOP
• Sample Size: 102
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: MOTPro
• Sample Size: 102
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: AttSrc
• Sample Size: 102
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: SSTMPro
• Sample Size: 102
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: LocMemStd
• Sample Size: 102
• 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)

过目不忘PRO

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

Basic Information

• Abbreviation: FWSPro
• Sample Size: 102
• 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)

人工语言-中级

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

Basic Information

• Abbreviation: AscLanMd
• Sample Size: 102
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: LdnTwr
• Sample Size: 98
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FPTPro
• Sample Size: 102
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: SCSpan
• Sample Size: 102
• 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)

密码箱

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

Basic Information

• Abbreviation: KeepTrack
• Sample Size: 102
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: SRTPro
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: DualTaskPro
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: ConRec
• Sample Size: 99
• 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)

图片记忆

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

Basic Information

• Abbreviation: BPS
• Sample Size: 99
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: DRM
• Sample Size: 100
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: ProbRL
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Seman
• Sample Size: 99
• 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)

声调判断

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

Basic Information

• Abbreviation: Tone
• Sample Size: 98
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FlkrPro
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FacesPro
• Sample Size: 99
• 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()
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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FDSPro
• Sample Size: 99
• 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)

图形推理

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

Basic Information

• Abbreviation: NVR
• Sample Size: 99
• 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)

数字推理

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

Basic Information

• Abbreviation: DR
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: VR
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: MR3D
• Sample Size: 99
• 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)

时长分辨

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

Basic Information

• Abbreviation: DD
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: TOJ
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: RP
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: CalcSpdMed
• Sample Size: 94
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: Dual2back
• Sample Size: 99
• 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()

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)

变戏法

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

Basic Information

• Abbreviation: AntiSac
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: SchulteMed
• Sample Size: 99
• 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)

欢乐餐厅

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

Basic Information

• Abbreviation: AscMem
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: NLEMed
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: CalcMed
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: FR
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: RAT
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: OCSpan
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: RSpan
• Sample Size: 99
• 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)

雪花收藏家

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

Basic Information

• Abbreviation: DirectSrc
• Sample Size: 99
• 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.infinite(.x)),
    !(is.na(test) & is.na(retest))
  )
n_indices <- n_distinct(data$index_name)

Basic Information

• Abbreviation: MSynWin
• Sample Size: 99
• 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)

Session information

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] minqa_1.2.4         colorspace_2.0-2    ggsignif_0.6.3     
  [4] ellipsis_0.3.2      rprojroot_2.0.2     fs_1.5.2           
  [7] rstudioapi_0.13     ggpubr_0.4.0        farver_2.1.0       
 [10] bit64_4.0.5         fansi_1.0.0         lubridate_1.8.0    
 [13] xml2_1.3.3          codetools_0.2-18    splines_4.1.2      
 [16] mnormt_2.0.2        knitr_1.37          jsonlite_1.7.2     
 [19] nloptr_1.2.2.3      broom_0.7.11        dbplyr_2.1.1       
 [22] compiler_4.1.2      httr_1.4.2          backports_1.4.1    
 [25] assertthat_0.2.1    Matrix_1.4-0        fastmap_1.1.0      
 [28] cli_3.1.0           later_1.3.0         htmltools_0.5.2    
 [31] tools_4.1.2         igraph_1.2.11       gtable_0.3.0       
 [34] glue_1.6.0          Rcpp_1.0.7          carData_3.0-5      
 [37] cellranger_1.1.0    jquerylib_0.1.4     vctrs_0.3.8        
 [40] nlme_3.1-153        psych_2.1.9         xfun_0.29          
 [43] ps_1.6.0            lme4_1.1-27.1       rvest_1.0.2        
 [46] lifecycle_1.0.1     rstatix_0.7.0       getPass_0.2-2      
 [49] MASS_7.3-54         scales_1.1.1        vroom_1.5.7        
 [52] hms_1.1.1           promises_1.2.0.1    parallel_4.1.2     
 [55] RColorBrewer_1.1-2  qs_0.25.2           yaml_2.2.1         
 [58] sass_0.4.0          reshape_0.8.8       stringi_1.7.6      
 [61] highr_0.9           boot_1.3-28         rlang_0.99.0.9001  
 [64] pkgconfig_2.0.3     evaluate_0.14       lattice_0.20-45    
 [67] labeling_0.4.2      bit_4.0.4           processx_3.5.2     
 [70] tidyselect_1.1.1    here_1.0.1          GGally_2.1.2       
 [73] plyr_1.8.6          magrittr_2.0.1      R6_2.5.1           
 [76] generics_0.1.1      base64url_1.4       DBI_1.1.2          
 [79] pillar_1.6.4        haven_2.4.3         whisker_0.4        
 [82] withr_2.4.3         abind_1.4-5         modelr_0.1.8       
 [85] crayon_1.4.2        car_3.0-12          utf8_1.2.2         
 [88] tmvnsim_1.0-2       RApiSerialize_0.1.0 tzdb_0.2.0         
 [91] rmarkdown_2.11      grid_4.1.2          readxl_1.3.1       
 [94] data.table_1.14.2   callr_3.7.0         git2r_0.29.0       
 [97] reprex_2.0.1        digest_0.6.29       httpuv_1.6.5       
[100] RcppParallel_5.1.5  munsell_0.5.0       stringfish_0.15.5  
[103] bslib_0.3.1