No evidence for general intelligence in a fish

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dataset

modified on 2022-07-05, 10:10

script (txt) and metadata (csv)

script-g.txt: script to run with program R studio

PCA-n69.csv: metadata for the PCA analysis, pairwise correlations analysis, and body length vs capture site analysis (Figs. 1, 2, S5, and S6).

Site: site of capture; crest = high fish density; lagoon = low fish density

Year: when experiments done

BW: Body Weight in gram

BL: Body Length in centimeter

Reversal learning: ranking in the reversal learning task

Detour task: ranking in the detour task

Numerical competence: ranking in the numerical competence task

Feeding against preference: ranking in the feeding against preference task

perf-n69.csv: metadata for the performance analysis across capture sites (Fig. 3).

Reversal Learning: RL_TTS = Total Trials to Succeed

Detour Task: DT_bump = number of bumps before reaching the reward plate (failed trials included)

Numerical Competence: NC_succ80 = percentage success in the last 80 trials, all combinations

Feeding Against Preference: Featen = mean number of flake items eaten prior to eat a prawn item

Site: crest = high fish density / lagoon = low fish density

transect-data2018.csv: metadata for the density analysis (Fig. S2).

day: when data collected

time: at what time the data were collected

location: lagoon = low fish density; crest = high fish density

transect: 1 transect = 150m2

n cleaners: number of cleaners seen the 150m2 transect

m2: how many m2

per 100m2: number of cleaners per 100m2

OP-n52.csv: metadata for the above chance significance.

Site: crest = high fish density; lagoon = low fish density

Percentage success: percentage success over 80 trials in object permanence.

NC.chance: metadata to verify that cleaners as a group performed above chance in the NC task

Funding

Federal Department of Economic Affairs Education and Research