New approaches to the single-interval adjustment matrix yes-no task

Two adaptations of the Single-Interval Adjust-Matrix Yes-No (SIAM-YN) task, designed to increase the efficiency of absolute threshold estimation, are described. The first, the SIAM Twin Track (SIAM-TT) task, consists of two interleaved tracks of the standard SIAM-YN that are run in the same trial with a single response. The second new task modifies the binary SIAM-YN task by using a six-point rating-scale (SIAM-Rating). In Experiment 1, data from three tasks estimating absolute thresholds were obtained using a 10-ms tone, the 2-IFC up-down procedure, SIAM-YN task, and the SIAM-TT task. The data support the use of the SIAM-TT as an alternative to the conventional two-interval and one-interval (SIAM-YN) tasks when used to estimate absolute thresholds. By presenting two interleaved SIAM-YN tracks on a single experimental trial, the SIAM-TT task possesses greater efficiency alongside its signal-detection tradition which confers less response bias. Similarly, in Experiment 2, which compared the 2-IFC adaptive, SIAM-YN, and SIAM-Rating tasks, there was no main effect of task upon threshold estimates. The findings replicate previous studies supporting the validity and efficiency of the SIAM-YN task, and extends the SIAM-YN toolbox to efficiently facilitate the generation of psychometric functions (the SIAM-TT task) and Receiver Operating Characteristic Curves (the SIAM-Rating task).

Authors

Institutions

Publication Details

Journal
Frontiers in Psychology
Published
2024-11-21
DOI
https://doi.org/10.3389/fpsyg.2024.1416188
Primary Topic
Hearing Loss and Rehabilitation
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

New approaches to the single-interval adjustment matrix yes-no task

Michael J. Hautus, Daniel Shepherd
Frontiers in Psychology
Hearing Loss and Rehabilitation
article

New approaches to the single-interval adjustment matrix yes-no task

Michael J. Hautus, Daniel Shepherd
article en

Abstract

Two adaptations of the Single-Interval Adjust-Matrix Yes-No (SIAM-YN) task, designed to increase the efficiency of absolute threshold estimation, are described. The first, the SIAM Twin Track (SIAM-TT) task, consists of two interleaved tracks of the standard SIAM-YN that are run in the same trial with a single response. The second new task modifies the binary SIAM-YN task by using a six-point rating-scale (SIAM-Rating). In Experiment 1, data from three tasks estimating absolute thresholds were obtained using a 10-ms tone, the 2-IFC up-down procedure, SIAM-YN task, and the SIAM-TT task. The data support the use of the SIAM-TT as an alternative to the conventional two-interval and one-interval (SIAM-YN) tasks when used to estimate absolute thresholds. By presenting two interleaved SIAM-YN tracks on a single experimental trial, the SIAM-TT task possesses greater efficiency alongside its signal-detection tradition which confers less response bias. Similarly, in Experiment 2, which compared the 2-IFC adaptive, SIAM-YN, and SIAM-Rating tasks, there was no main effect of task upon threshold estimates. The findings replicate previous studies supporting the validity and efficiency of the SIAM-YN task, and extends the SIAM-YN toolbox to efficiently facilitate the generation of psychometric functions (the SIAM-TT task) and Receiver Operating Characteristic Curves (the SIAM-Rating task).

Frontiers in PsychologyVol. 15
University of Auckland (NZ), Auckland University of Technology (NZ)
Openalex Percentile: Top 79%
Hearing Loss and Rehabilitation
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.