Visual Statistical Learning (Shape Patterns)

Platform: HTML · English · verified 89d ago

This is a task in the LoL Gamer Cognitive Study study. Click to know more.

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Description

Reimplemented from the LoL battery's Shape-Patterns mini-game (Brain & Learning Lab, UNIGE). Participants passively watch a continuous stream of abstract shapes built from a fixed set of hidden triplets — within-triplet conditional probability is 1.0, between-triplet ~0.33 — for ~10 minutes. Two test phases follow: 34 two-alternative recognition trials (pick the familiar 3-shape pattern from a foil) and 8 pattern-completion trials (pick the missing shape from a row of options). Accuracy above chance indexes implicit learning of the embedded transitional probabilities. The original 16 abstract-shape stimuli, the 4 hidden triplets, and the 42 question files are reused verbatim from the LoL battery; only the harness (PHP/jQuery) was reimplemented in jsPsych for stand-alone browser use.

Contact & Lab

Lab

Brain and Learning Lab

unige.ch

Principal investigator

Daphné Bavelier

University of Geneva

Task contact

daphne.bavelier@unige.ch

Contributors

Daphné Bavelier

University of Geneva

Supervision

Benoît Bediou

University of Geneva

Conceptualization

Supervision

Hugo Farajallah

University of Geneva

Software

Data Curation

Citation

Fiser, J., & Aslin, R. N. (2002). Statistical learning of new visual feature combinations by infants. Proceedings of the National Academy of Sciences, 99(24), 15822–15826. https://doi.org/10.1073/pnas.232472899
Turk-Browne, N. B., Jungé, J. A., & Scholl, B. J. (2005). The automaticity of visual statistical learning. Journal of Experimental Psychology: General, 134(4), 552–564. https://doi.org/10.1037/0096-3445.134.4.552

Credits

Adapted from the Brain and Learning Lab (UNIGE) LoL Gamer Cognitive Study battery.

Domains

Learning

Part of suite:

LoL Gamer Cognitive Study

Target Population

Age Categories

young adults

middle age

Clinical Status

non-clinical

Additional Notes

Originally validated with adult gamers (18+). Long passive-viewing phase — verify attention with seated, uninterrupted setup.

Languages

English

Run under

Engine

HTML / JavaScript

Format

Online + in-lab

Modalities

behavioral

Task Outputs

What it measures

Measure implicit learning of transitional probabilities in a visual stream via two-alternative recognition and pattern-completion tests.

Output formats

csv

One CSV per participant, one row per test trial (familiarization is passive).

Experimental design

Trial-based

42 trials

Within-subject: phase

Conditions

phase

recognition

completion

— Test phase. 34 recognition + 8 completion trials.

Key measures for analysis

Recognition accuracy (% above 25–50% chance, depending on number of options per item)
Completion accuracy (% above ~33% chance — 3 options per item)
Combined statistical-learning index

Recorded variables

Variable	Role	Type	Description	Unit
`trial_id`	metadata	number	Test-trial index within phase (0-based)	-
`phase`	independent	string	Test sub-phase Values: recognition, completion	-
`question_file`	metadata	string	Source question file (e.g., questions/12.txt)	-
`options`	metadata	string	Shuffled option list as JSON (sequence indices for recognition, shape numbers for completion)	-
`response`	dependent	string	Selected option (1-based row label for recognition, shape filename index for completion)	-
`correct`	dependent	boolean	Whether the response matches the embedded triplet	-
`rt`	dependent	number	RT from question onset to response	ms