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Ian Neath

Researcher at Memorial University of Newfoundland

Publications -  117
Citations -  4496

Ian Neath is an academic researcher from Memorial University of Newfoundland. The author has contributed to research in topics: Recall & Serial position effect. The author has an hindex of 33, co-authored 110 publications receiving 4227 citations. Previous affiliations of Ian Neath include Yale University & Purdue University.

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Manipulations of irrelevant information: suffix effects with articulatory suppression and irrelevant speech.

TL;DR: Five experiments combined three common methods of manipulating irrelevant information: requiring concurrent articulation, presenting irrelevant speech, and adding a stimulus suffix to determine how they interact and which theoretical framework most accurately and completely accounts for the data.
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Three more semantic serial position functions and a SIMPLE explanation

TL;DR: It is argued that currently available evidence suggests that serial position functions observed when recalling items that are presumably in semantic memory arise because of the same processes as those observed when recall items that is presumably in episodic memory.
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Fill-in and infill errors in order memory.

TL;DR: It is concluded that none of the currently existing models adequately accounts for fill‐in and infill errors in memory tasks.
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Modeling distributions of immediate memory effects: no strategies needed?

TL;DR: In this paper, Nairne et al. show that the feature model produces appropriate distributions of effect sizes for both the phonological confusion effect and the word-length effect without the need to assume a change in encoding or rehearsal strategy or the deployment of a different storage buffer.
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Further evidence that similar principles govern recall from episodic and semantic memory: the Canadian prime ministerial serial position function.

TL;DR: Show that free recall of the prime ministers of Canada also results in a serial position function, showing that Scale Independent Memory, Perception, and Learning (SIMPLE), a local distinctiveness model of memory that was designed to account for serial position effects in episodic memory, fit the data.