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Joint statistical learning complementary SRTT hyperscanning paradigm with OSF data release

Design · All designs
Evidence · 3 claims (3 for) · 1 study (1 causal), q3 · 0 of 1 report an effect size · 3 claims rest on one study

Description

A modified serial reaction time task in which a fixed 12-location stimulus sequence is randomly divided and assigned to each co-actor for response. The experiment comprised eight blocks: Blocks 1 to 6 and 8 were "constructed from a fixed sequence of locations, while the seventh block was constructed from an interference sequence". Dyads were assigned to a joint context (both respond to pre-assigned colours) or a baseline context (actor plus passive observer), with concurrent dual-EEG recording at 512 Hz. All behaviour and EEG data, and in-house scripts, are deposited on the Open Science Framework.

Design Implications

Context

Requirements

  • Dual-EEG recording system with synchronized amplifiers
  • Paired participants randomly assigned to joint or baseline context

Constraints

  • Interference and main sequence assignment counterbalanced across dyads; only female participants recruited

Target Learners

  • undergraduate students in dyads

Learning Goals

  • implicit statistical learning of fixed stimulus sequences in joint and individual contexts

Claims

Examples

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Key Sources

  • Zheng Zheng & Jun Wang. (2024). Interbrain neural correlates of self and other integration in joint statistical learning. npj Science of Learning. https://doi.org/10.1038/s41539-024-00280-4
Page metadata
Field Value
type design
id joint-statistical-learning-complementary-srtt-hyperscanning-paradigm
title Joint statistical learning complementary SRTT hyperscanning paradigm with OSF data release
description A modified serial reaction time task in which a fixed 12-location stimulus sequence is randomly divided and assigned to each co-actor for response.
status review
generated by process:settle-candidates on 2026-10-26
trust unverified — no one has confirmed this page's content
sources 1 — zheng-zheng-jun-wang-2024

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