Attention as Inference: Selection Is Probabilistic; Responses Are All-or-None Samples

Autor/inn/en	Vul, Edward; Hanus, Deborah; Kanwisher, Nancy
Titel	Attention as Inference: Selection Is Probabilistic; Responses Are All-or-None Samples
Quelle	In: Journal of Experimental Psychology: General, 138 (2009) 4, S.546-560 (15 Seiten) PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0096-3445
DOI	10.1037/a0017352
Schlagwörter	Attention; Probability; Inferences; Experimental Psychology; Cognitive Processes; Short Term Memory; Visual Stimuli; Cognitive Psychology + Suchen Sie Ihr Suchwort? Aufmerksamkeit; Wahrscheinlichkeitsrechnung; Wahrscheinlichkeitstheorie; Inference; Inferenz; Experimentelle Psychologie; Cognitive process; Kognitiver Prozess; Kurzzeitgedächtnis; Kognitive Psychologie
Abstract	Theories of probabilistic cognition postulate that internal representations are made up of multiple simultaneously held hypotheses, each with its own probability of being correct (henceforth, "probability distributions"). However, subjects make discrete responses and report the phenomenal contents of their mind to be all-or-none states rather than graded probabilities. How can these 2 positions be reconciled? Selective attention tasks, such as those used to study crowding, the attentional blink, rapid serial visual presentation, and so forth, were recast as probabilistic inference problems and used to assess how graded, probabilistic representations may produce discrete subjective states. The authors asked subjects to make multiple guesses per trial and used 2nd-order statistics to show that (a) visual selective attention operates in a graded fashion in time and space, selecting multiple targets to varying degrees on any given trial; and (b) responses are generated by a process of sampling from the probabilistic states that result from graded selection. The authors concluded that although people represent probability distributions, their discrete responses and conscious states are products of a process that samples from these probabilistic representations. (Contains 15 figures and 2 footnotes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10