A Test of Discrete State Models of Recognition Memory: How Do Participants Use a Continuous Response Scale?

57 Ss scaled 20 statements which expressed attitudes toward labor unions. The scaling task required S to first judge to which half of a continuum, ranging from favorable to unfavorable, the statement belonged. By regulating rate of tapping a telegraph key which referred to the chosen segment of the scale, S indicated the distance between the statement and the neutral origin of the scale. The statements were also scaled by 53 Ss who followed a conventional successive-intervals procedure. An r of .94 was reported between scale values obtained from the two scaling procedures. Evidence concerning equal-interval properties and inter-rater agreement for the continuous response scale is presented.

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Bias in confidence: A critical test for discrete-state models of change detection

10.31234/osf.io/6cmsp ◽

2020 ◽

Author(s):

David Kellen ◽

Samuel Winiger ◽

Henrik Singmann

Keyword(s):

Working Memory ◽

Change Detection ◽

High Fidelity ◽

Critical Test ◽

Discrete State ◽

The Core ◽

Confidence Judgments ◽

Memory Representations ◽

State Models ◽

Biased Judgments

Ongoing discussions on the nature of storage in visual working memory have mostlyfocused on two theoretical accounts: On one hand we have a discrete-state accountpostulating that information in working memory is supported with high fidelity for alimited number of discrete items by a given number of “slots”, with no informationbeing retained beyond these. In contrast with this all-or-nothing view, we have acontinuous account arguing that information can be degraded in a continuous manner, reflecting the amount of resources dedicated to each item. It turns out that the core tenets of this discrete-state account constrain the way individuals can express confidence in their judgments, excluding the possibility of biased confidence judgments. Importantly, these biased judgments are expected when assuming a continuous degradation of information. We report two studies showing that biased confidence judgments can be reliably observed, a finding that rejects a large number of discrete-state models, dismissing the idea that change-detection judgments consist of a mixture of guesses and high-fidelity memory representations.

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Ensemble perception: extracting the average of perceptual vs numerical stimuli

10.31234/osf.io/emw2y ◽

2020 ◽

Author(s):

David Mitrani Rosenbaum ◽

Vincent de Gardelle ◽

Marius Usher

Keyword(s):

Experimental Design ◽

Computational Modeling ◽

Response Scale ◽

Normal Distributions ◽

Perceptual Features ◽

Set Size ◽

Continuous Response ◽

Ensemble Perception ◽

Temporal Sequences ◽

Gabor Elements

Recent research has established that humans can extract average of perceptual features from sets of briefly and simultaneously presented elements or the average of rapid temporal sequences of numerical values. Here we compare the extraction of the average of simultaneously presented sets of perceptual features (orientations) and of numerical values (1-9 digits), using an identical experimental design. Arrays of Gabor elements or digits are simultaneously presented for 300 ms and the observers are required to estimate the average on a continuous response scale. In each trial the elements were sampled from normal distributions (of various means) and we varied the set-size (4-12), in order to compare the averaging mechanism in terms of its efficiency or capacity (the number of items one can pool from). We find that while for orientation the averaging precision remained constant with set-size, for numbers it decreased with set-size. Using computational modeling we extracted capacity parameters. Despite marked heterogeneity between the observers, the capacity was larger for orientations compared with numbers (which was close to WM-limitation of 3-4). This supports the idea that numbers more than perceptual features are subject to attentional limitations at encoding.

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