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Four situational inhibition training games (button-selection and voice-recognition)

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 causal), q3 · 1 of 1 report an effect size · 1 claim rests on one study

Description

A series of four digital inhibition training games developed with Construct 3 per the situational cognitive training game framework. As printed, "the game system comprises two types of games: button-selection and voice-recognition games". Button-selection games are based on the Simon task and require suppressing automatic responses across three difficulty stages; voice-recognition games are based on the Stroop task and require verbally responding while inhibiting conflicting visual and textual information. Situations include soccer, baseball, sushi-eating, and pet training, and the system provides immediate feedback and accurate response-time recording.

Design Implications

Context

Requirements

  • Built with Construct 3 for digitalized delivery with precise response-time recording
  • Tasks derived from classic inhibition trainings (Simon and Stroop tasks)

Constraints

  • Voice recognition accuracy was noted by participants as needing improvement

Target Learners

  • university students

Learning Goals

  • inhibition ability (executive function)

Claims

Examples

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Key Sources

  • Sora Chi-Fang Huang, Zhi-Hong Chen, Yu-Tzu Lin. (2026). Enhancing inhibition ability through situational training games: effectiveness, motivation and experience. Education Tech Research Dev 74. https://doi.org/10.1007/s11423-026-10593-3
Page metadata
Field Value
type design
id situational-inhibition-training-games-element
title Four situational inhibition training games (button-selection and voice-recognition)
description A series of four digital inhibition training games developed with Construct 3 per the situational cognitive training game framework.
status review
generated by process:settle-candidates on 2026-10-28
trust unverified — no one has confirmed this page's content
sources 1 — sora-chi-fang-huang-2026

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