A habituation account of change detection in same/different judgments

Davelaar, Eddy J.; Tian, Xing; Weidemann, Christoph T.; Huber, David E.

doi:10.3758/s13415-011-0056-8

Cited by 21 publications

(21 citation statements)

References 73 publications

(93 reference statements)

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“…This requires consideration of the decision rule used in the same/different task. In a series of experiments, we examined repetition priming for the word matching task employed in Experiment 3 (Davelaar, Huber, Tian, & Weidemann, submitted). In different conditions, either the cue word or the target word repeated a word from the last trial.…”

Section: Discussionmentioning

confidence: 99%

Testing an associative account of semantic satiation

Tian¹,

Huber²

2010

Cognitive Psychology

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How is the meaning of a word retrieved without interference from recently viewed words? The ROUSE theory of priming assumes a discounting process to reduce source confusion between subsequently presented words. As applied to semantic satiation, this theory predicted a loss of association between the lexical item and meaning. Four experiments tested this explanation in a speeded category-matching task. All experiments used lists of 20 trials that presented a cue word for 1 second followed by a target word. Randomly mixed across the list, 10 trials used cues drawn from the same category whereas the other 10 trials used cues from 10 other categories. In Experiments 1a and 1b, the cues were repeated category labels (FRUIT-APPLE) and responses gradually slowed for the repeated category. In Experiment 2, the cues were nonrepeated exemplars (PEAR-APPLE) and responses remained faster for the repeated category. In Experiment 3, the cues were repeated exemplars in a word matching task (APPLE-APPLE) and responses again remained faster for the repeated category.

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Section: Discussionmentioning

confidence: 99%

Testing an associative account of semantic satiation

Tian¹,

Huber²

2010

Cognitive Psychology

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“…This technique allows the assessment of spatial similarity in electrophysiological studies regardless of response magnitude and estimates the similarities in underlying neural sources distribution (e.g., Luo, Tian, Song, Zhou, & Poeppel, 2013; Tian & Huber, 2013; Tian & Poeppel, 2010, 2013; Davelaar, Tian, Weidemann, & Huber, 2011; Huber, Tian, Curran, O'Reilly, & Woroch, 2008). In this method, each topographical pattern is considered as a high-dimensional vector, where the number of dimensions equals the number of sensors in recording.…”

Section: Methodsmentioning

confidence: 99%

Dynamics of Self-monitoring and Error Detection in Speech Production: Evidence from Mental Imagery and MEG

Tian¹,

Poeppel²

2015

Journal of Cognitive Neuroscience

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A critical subroutine of self-monitoring during speech production is to detect any deviance between expected and actual auditory feedback. Here we investigated the associated neural dynamics using MEG recording in mental-imagery-of-speech paradigms. Participants covertly articulated the vowel /a/; their own (individually recorded) speech was played back, with parametric manipulation using four levels of pitch shift, crossed with four levels of onset delay. A nonmonotonic function was observed in early auditory responses when the onset delay was shorter than 100 msec: Suppression was observed for normal playback, but enhancement for pitch-shifted playback; however, the magnitude of enhancement decreased at the largest level of pitch shift that was out of pitch range for normal conversion, as suggested in two behavioral experiments. No difference was observed among different types of playback when the onset delay was longer than 100 msec. These results suggest that the prediction suppresses the response to normal feedback, which mediates source monitoring. When auditory feedback does not match the prediction, an “error term” is generated, which underlies deviance detection. We argue that, based on the observed nonmonotonic function, a frequency window (addressing spectral difference) and a time window (constraining temporal difference) jointly regulate the comparison between prediction and feedback in speech.

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“…Most importantly, a growing number of studies have found these techniques to be reliable and useful (e.g., [2, 25, 26]).…”

Section: Discussionmentioning

confidence: 99%

TopoToolbox: Using Sensor Topography to Calculate Psychologically Meaningful Measures from Event-Related EEG/MEG

Tian

Poeppel

Huber

2011

Computational Intelligence and Neuroscience

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The open-source toolbox “TopoToolbox” is a suite of functions that use sensor topography to calculate psychologically meaningful measures (similarity, magnitude, and timing) from multisensor event-related EEG and MEG data. Using a GUI and data visualization, TopoToolbox can be used to calculate and test the topographic similarity between different conditions (Tian and Huber, 2008). This topographic similarity indicates whether different conditions involve a different distribution of underlying neural sources. Furthermore, this similarity calculation can be applied at different time points to discover when a response pattern emerges (Tian and Poeppel, 2010). Because the topographic patterns are obtained separately for each individual, these patterns are used to produce reliable measures of response magnitude that can be compared across individuals using conventional statistics (Davelaar et al. Submitted and Huber et al., 2008). TopoToolbox can be freely downloaded. It runs under MATLAB (The MathWorks, Inc.) and supports user-defined data structure as well as standard EEG/MEG data import using EEGLAB (Delorme and Makeig, 2004).

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A habituation account of change detection in same/different judgments

Cited by 21 publications

References 73 publications

Testing an associative account of semantic satiation

Testing an associative account of semantic satiation

Dynamics of Self-monitoring and Error Detection in Speech Production: Evidence from Mental Imagery and MEG

TopoToolbox: Using Sensor Topography to Calculate Psychologically Meaningful Measures from Event-Related EEG/MEG

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