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Dynamic visual search task with spatiotemporally predictable targets

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

Description

An online task in which participants search for eight upward-pointing triangle targets that fade in and out among sixteen downward-pointing distractors over roughly fourteen-second trials. Four targets per trial are "spatially and temporally predictable", appearing at fixed onsets within fixed quadrants, while four appear at unpredictable times and locations; participants are not informed of the manipulation. Data, and the experimental code (PsychoPy, hosted on Pavlovia), are available at OSF (DOI: 10.17605/OSF.IO/8UPY5).

Design Implications

Context

Requirements

  • Participants used a personal computer; the experiment did not support phones or tablets
  • Normal or corrected-to-normal vision and no diagnosed psychiatric or neurological condition

Constraints

  • A well-controlled artificial environment used to maximize trial-by-trial learning of regularities, rather than realistic scenes
  • Cognitive issues were not controlled for in remote testing

Target Learners

  • Adults aged 20-80

Learning Goals

  • Learning and using spatiotemporal regularities to guide attention during dynamic visual search

Claims

Examples

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Key Sources

  • Nir Shalev, Sage Boettcher & Anna C. Nobre. (2024). Spatiotemporal predictions guide attention throughout the adult lifespan. npj Science of Learning. https://doi.org/10.1038/s41539-024-00281-3
Page metadata
Field Value
type design
id dynamic-visual-search-spatiotemporal-prediction-task
title Dynamic visual search task with spatiotemporally predictable targets
description An online task in which participants search for eight upward-pointing triangle targets that fade in and out among sixteen downward-pointing distractors over roughly fourteen-second trials.
status review
generated by process:settle-candidates on 2026-10-26
trust unverified — no one has confirmed this page's content
sources 1 — nir-shalev-2024

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