What can we learn about visual attention to multiple words from the word-word interference task?

In this work, we develop an empirically driven model of visual attention to multiple words using the word-word interference (WWI) task. In this task, two words are simultaneously presented visually: a to-be-ignored distractor word at fixation, and a to-be-read-aloud target word above or below the di...

Descripción completa

Detalles Bibliográficos
Publicado en:Memory & Cognition Vol. 43; no. 1; pp. 121 - 133
Autores principales: Mulatti, Claudio, Ceccherini, Lisa, Coltheart, Max
Formato: Artículo
Publicado: Springer Nature Jan2015
Materias:
Acceso en línea:Ver este registro en EBSCOhost
fields @attributes:
  recordID: 1
pdfLink:
plink: https://search.ebscohost.com/login.aspx?direct=true&db=ssf&AN=100352380&site=ehost-live
header:
  @attributes:
    shortDbName: ssf
    uiTerm: 100352380
    longDbName: Social Sciences Full Text (H.W. Wilson)
    uiTag: AN
  controlInfo:
    bkinfo:
    jinfo:
      jid:
        0090502X
        MEG
      jtl: Memory & Cognition
      issn: 0090502X
      maglogo: N
    pubinfo:
      dt: Jan2015
      vid: 43
      iid: 1
      pid: 237
      pub: Springer Nature
    artinfo:
      ui:
        100352380
        10.3758/s13421-014-0450-x
      ppf: 121
      ppct: 12
      formats:
        fmt:
          @attributes:
            type: P
            size: 267KB
      tig:
        atl: What can we learn about visual attention to multiple words from the word-word interference task?
      aug:
        au:
          Mulatti, Claudio
          Ceccherini, Lisa
          Coltheart, Max
        affil:
          Università degli Studi di Padova, Padua Italy
          Macquarie University, Sydney Australia
      su:
        Italy
        Analysis of variance
        Attention
        Cognition
        College students
        Memory
        Reading
        Recognition (Psychology)
        Factorial experiment designs
        Visual perception
        Phonological awareness
        Descriptive statistics
      sug:
        subj:
          Analysis of variance
          Attention
          Cognition
          College students
          Memory
          Reading
          Recognition (Psychology)
          Italy
          Factorial experiment designs
          Visual perception
          Phonological awareness
          Descriptive statistics
      keyword:
        Lexical processing
        Lexical selection
        Reading Aloud
        Visual attention
        Visual Word Recognition
        Word production
        Lexical processing
        Lexical selection
        Reading Aloud
        Visual attention
        Visual Word Recognition
        Word production
      ab: In this work, we develop an empirically driven model of visual attention to multiple words using the word-word interference (WWI) task. In this task, two words are simultaneously presented visually: a to-be-ignored distractor word at fixation, and a to-be-read-aloud target word above or below the distractor word. Experiment 1 showed that low-frequency distractor words interfere more than high-frequency distractor words. Experiment 2 showed that distractor frequency (high vs. low) and target frequency (high vs. low) exert additive effects. Experiment 3 showed that the effect of the case status of the target (same vs. AlTeRnAtEd) interacts with the type of distractor (word vs. string of # marks). Experiment 4 showed that targets are responded to faster in the presence of semantically related distractors than in presence of unrelated distractors. Our model of visual attention to multiple words borrows two principles governing processing dynamics from the dual-route cascaded model of reading: cascaded interactive activation and lateral inhibition. At the core of the model are three mechanisms aimed at dealing with the distinctive feature of the WWI task, which is that two words are presented simultaneously. These mechanisms are identification, tokenization, and deactivation.
      pubtype: Academic Journal
      doctype: Article
      src: R
    language: English
    refInfo:
    copyright:
      @attributes:
        flag: N
    holdings:
      @attributes:
        islocal: N