Recognition-memory models and ranking tasks: The importance of auxiliary assumptions for tests of the two-high-threshold model

ElsevierVolume 127, December 2022, 104356Journal of Memory and LanguageHighlights•

It has been argued that recognition rankings falsify two-high-threshold theory.

This claim assumes invariance of lure detection, which does not hold empirically.

Without this assumption, two-high-threshold theory explains ranking judgments.

Lure detection improves when testing lures jointly with strong (vs. weak) targets.

We conclue that measurement models need careful adaptation to new paradigms.

Abstract

The question of whether recognition memory should be measured assuming continuous memory strength (signal detection theory) or discrete memory states (threshold theory) has become a prominent point of discussion. In light of limitations associated with receiver operating characteristics, comparisons of the rival models based on simple qualitative predictions derived from their core properties were proposed. In particular, K-alternative ranking tasks (KARTs) yield a conditional probability of targets being assigned Rank 2, given that they were not assigned Rank 1, which is higher for strong than for weak targets. This finding has been argued to be incompatible with the two-high-threshold (2HT) model (Kellen & Klauer, 2014). However, we show that the incompatibility only holds under the auxiliary assumption that the probability of detecting lures is invariant under target-strength manipulations. We tested this assumption in two different ways: by developing new model versions of 2HT theory tailored to KARTs and by employing novel forced-choice-then-ranking tasks. Our results show that 2HT models can explain increases in the conditional probability of targets being assigned Rank 2 with target strength. This effect is due to larger 2HT lure-detection probabilities in test displays in which lures are ranked jointly with strong (as compared to weak) targets. We conclude that lure-detection probabilities vary with target strength and recommend that 2HT models should allow for this variation. As such models are compatible with KART performance, our work highlights the importance of carefully adapting measurement models to new paradigms.

Keywords

Recognition memory

Two-high-threshold model

Multinomial processing tree models

Ranking judgments

Model specification

© 2022 The Authors. Published by Elsevier Inc.

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