Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling

Research output: Contribution to journalJournal articleResearchpeer-review

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Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling. / Nordfang, Maria; Dyrholm, Mads; Bundesen, Claus.

In: Journal of Experimental Psychology: General, Vol. 142, No. 2, 2013, p. 510-537.

Research output: Contribution to journalJournal articleResearchpeer-review

Harvard

Nordfang, M, Dyrholm, M & Bundesen, C 2013, 'Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling', Journal of Experimental Psychology: General, vol. 142, no. 2, pp. 510-537. https://doi.org/10.1037/a0029631

APA

Nordfang, M., Dyrholm, M., & Bundesen, C. (2013). Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling. Journal of Experimental Psychology: General, 142(2), 510-537. https://doi.org/10.1037/a0029631

Vancouver

Nordfang M, Dyrholm M, Bundesen C. Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling. Journal of Experimental Psychology: General. 2013;142(2):510-537. https://doi.org/10.1037/a0029631

Author

Nordfang, Maria ; Dyrholm, Mads ; Bundesen, Claus. / Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling. In: Journal of Experimental Psychology: General. 2013 ; Vol. 142, No. 2. pp. 510-537.

Bibtex

@article{cf44f01eba00488eb6f0ef868132cdc9,
title = "Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling",
abstract = "The attentional weight of a visual object depends on the contrast of the features of the object to its local surroundings (feature contrast) and the relevance of the features to one{\textquoteright}s goals (feature relevance). We investigated the dependency in partial report experiments with briefly presented stimuli but unspeededresponses. The task was to report the letters from a mixture of letters (targets) and digits (distractors). Color was irrelevant to the task, but many stimulus displays contained an item (target or distractor) in a deviant color (a color singleton). The results showed concurrent effects of feature contrast (colorsingleton vs. nonsingleton) and relevance (target vs. distractor). A singleton target had a higher probability of being reported than did a nonsingleton target, and a singleton distractor interfered more strongly with report of targets than did a nonsingleton distractor. Measured by use of Bundesen{\textquoteright}s (1990) computational theory of visual attention, the attentional weight of a singleton object was nearlyproportional to the weight of an otherwise similar nonsingleton object, with a factor of proportionality that increased with the strength of the feature contrast of the singleton. This result is explained by generalizing the weight equation of Bundesen{\textquoteright}s (1990) theory of visual attention such that the attentional weight of an object becomes a product of a bottom-up (feature contrast) and a top-down (feature relevance) component.",
keywords = "Faculty of Social Sciences, visual attention, stimulus-driven, goal-driven, theory of visual attention, computational modeling",
author = "Maria Nordfang and Mads Dyrholm and Claus Bundesen",
year = "2013",
doi = "10.1037/a0029631",
language = "English",
volume = "142",
pages = "510--537",
journal = "Journal of Experimental Psychology: General",
issn = "0096-3445",
publisher = "American Psychological Association",
number = "2",

}

RIS

TY - JOUR

T1 - Identifying Bottom-Up and Top-Down Components of Attentional Weight by Experimental Analysis and Computational Modeling

AU - Nordfang, Maria

AU - Dyrholm, Mads

AU - Bundesen, Claus

PY - 2013

Y1 - 2013

N2 - The attentional weight of a visual object depends on the contrast of the features of the object to its local surroundings (feature contrast) and the relevance of the features to one’s goals (feature relevance). We investigated the dependency in partial report experiments with briefly presented stimuli but unspeededresponses. The task was to report the letters from a mixture of letters (targets) and digits (distractors). Color was irrelevant to the task, but many stimulus displays contained an item (target or distractor) in a deviant color (a color singleton). The results showed concurrent effects of feature contrast (colorsingleton vs. nonsingleton) and relevance (target vs. distractor). A singleton target had a higher probability of being reported than did a nonsingleton target, and a singleton distractor interfered more strongly with report of targets than did a nonsingleton distractor. Measured by use of Bundesen’s (1990) computational theory of visual attention, the attentional weight of a singleton object was nearlyproportional to the weight of an otherwise similar nonsingleton object, with a factor of proportionality that increased with the strength of the feature contrast of the singleton. This result is explained by generalizing the weight equation of Bundesen’s (1990) theory of visual attention such that the attentional weight of an object becomes a product of a bottom-up (feature contrast) and a top-down (feature relevance) component.

AB - The attentional weight of a visual object depends on the contrast of the features of the object to its local surroundings (feature contrast) and the relevance of the features to one’s goals (feature relevance). We investigated the dependency in partial report experiments with briefly presented stimuli but unspeededresponses. The task was to report the letters from a mixture of letters (targets) and digits (distractors). Color was irrelevant to the task, but many stimulus displays contained an item (target or distractor) in a deviant color (a color singleton). The results showed concurrent effects of feature contrast (colorsingleton vs. nonsingleton) and relevance (target vs. distractor). A singleton target had a higher probability of being reported than did a nonsingleton target, and a singleton distractor interfered more strongly with report of targets than did a nonsingleton distractor. Measured by use of Bundesen’s (1990) computational theory of visual attention, the attentional weight of a singleton object was nearlyproportional to the weight of an otherwise similar nonsingleton object, with a factor of proportionality that increased with the strength of the feature contrast of the singleton. This result is explained by generalizing the weight equation of Bundesen’s (1990) theory of visual attention such that the attentional weight of an object becomes a product of a bottom-up (feature contrast) and a top-down (feature relevance) component.

KW - Faculty of Social Sciences

KW - visual attention

KW - stimulus-driven

KW - goal-driven

KW - theory of visual attention

KW - computational modeling

U2 - 10.1037/a0029631

DO - 10.1037/a0029631

M3 - Journal article

C2 - 22889161

VL - 142

SP - 510

EP - 537

JO - Journal of Experimental Psychology: General

JF - Journal of Experimental Psychology: General

SN - 0096-3445

IS - 2

ER -

ID: 40663225