Assessing Children's Racial Attitudes via a Signal Detection Model

1973 ◽  
Vol 36 (2) ◽  
pp. 587-598 ◽  
Author(s):  
Kathryn H. Williams ◽  
John E. Williams ◽  
Robert C. Beck
Keyword(s):  
Preschool Children ◽  
Signal Detection ◽  
Response Bias ◽  
Skin Color ◽  
Absolute Judgment ◽  
Positive Signal ◽  
Detection Model ◽  
Negative Adjective ◽  
Response Biases ◽  
Negative Signal

Previous research indicated that, in relative choice situations, both Caucasian and Negro preschool children tend to associate positive evaluative adjectives with light-skinned human figures and negative evaluative adjectives with dark-skinned ones. The present study utilized a signal-detection model with figures presented singly so that response bias, as well as sensitivity to the color-signal, could be evaluated. 30 Caucasian and 30 Negro preschool children were given 48 trials on which either a dark-skinned or light-skinned figure was accompanied by a story containing a positive or a negative adjective and were asked if the story described the figure. Dark-skinned figures carried a negative “signal” for Ss of both races. While the light-skinned figure carried a positive signal for the Caucasian Ss, the evidence for Negro Ss was unclear. The data also showed strong acquiescent (“yea-saying”) response biases, i.e., the children tended to respond “yes” much more frequently than “no,” regardless of the type of adjective employed or the skin-color of the presented figure. It was concluded that the basic phenomena previously shown with the relative choice methodology can also be shown with the absolute judgment methodology of the signal-detection model.

Cultural Deprivation and Response Biases of Preschool Children for Complex and Novel Stimuli

1975 ◽  
Vol 40 (3) ◽  
pp. 999-1003 ◽  
Author(s):  
Robert A. Hicks ◽  
Steven Dockstader ◽  
Mary Parker-Schumacher
Keyword(s):  
Preschool Children ◽  
Signal Detection ◽  
Response Bias ◽  
Cultural Deprivation ◽  
Response Biases ◽  
Familiar Stimuli

As a test of the hypothesis that a consequence of cultural deprivation is the avoidance of arousal-provoking stimuli the response biases to complex/novel and simple/familiar stimuli of 29 deprived and 35 non-deprived preschoolers were determined using a signal detection-like procedure. Consistent with the hypothesis, the deprived Ss demonstrated a pronounced response bias for the simple/familiar stimuli. With qualification, it was suggested that the response bias of the deprived group might stem from possible aversive effects of complex/novel stimuli on these Ss.

scholarly journals Predictive cues reduce but do not eliminate intrinsic response bias

2018 ◽  
Author(s):  
Mingjia Hu ◽  
Dobromir Rahnev
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Signal Detection Theory ◽  
Detection Theory ◽  
Stimulus Category ◽  
Biasing Effects ◽  
Original Size ◽  
Response Biases ◽  
Criterion Location

Predictive cues induce large changes in people’s choices by biasing responses towards the expected stimulus category. At the same time, even in the absence of predictive cues, humans often exhibit substantial intrinsic response biases. Despite the ubiquity of both of these biasing effects, it remains unclear how predictive cues interact with intrinsic bias. To understand the nature of this interaction, we examined data across three previous experiments that featured a combination of neutral cues (revealing intrinsic biases) and predictive cues. We found that predictive cues decreased the intrinsic bias to about half of its original size. This result held both when bias was quantified as the criterion location estimated using signal detection theory and as the probability of choosing a particular stimulus category. Our findings demonstrate that predictive cues reduce but do not eliminate intrinsic response bias, testifying to both the malleability and rigidity of intrinsic biases.

Recognition Memory for Self-Relevant Personality-Trait Words

2009 ◽  
Vol 68 (3) ◽  
pp. 133-142 ◽  
Author(s):  
Marek Nieznański
Keyword(s):  
Recognition Memory ◽  
Signal Detection ◽  
Response Bias ◽  
Personality Trait ◽  
Recognition Task ◽  
The Self ◽  
Rating Task ◽  
Detection Model ◽  
Self Study ◽  
Trait Words

Two studies based on a signal detection model of recognition memory are presented. Both studies investigated detection and response bias in the recognition of personality-trait words with respect to their relation to the self. Study 1 employed a yes-no task in which participants were to recognize trait adjectives. Study 2 employed a rating task in which participants declared their confidence with respect to whether a noun or an adjective had been presented. After the recognition task, participants selected personality-trait words that described them and ones that described other people. The results indicated a stronger tendency to say “old” for self-descriptive than for non-self-descriptive adjectives and nouns. Study 2 suggested that self-descriptive nouns are better detected than non-self-descriptive nouns.

Signal-Detection Analysis of the Müller-Lyer and the Horizontal-Vertical Illusions

1994 ◽  
Vol 79 (3) ◽  
pp. 1299-1304 ◽  
Author(s):  
Gary M. Brosvic ◽  
Nancy A. Civale ◽  
Patricia Long ◽  
Deborah Kieley ◽  
Kathryn Kristoff ◽  
...  
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Signal Strength ◽  
Perceptual Error ◽  
Lyer Illusion ◽  
Detection Analysis ◽  
Signal Detection Analysis ◽  
Response Biases

Perceptual error in the Müller-Lyer and the Horizontal-Vertical illusions was quantified using nonparametric signal-detection measures of sensitivity and response bias. Sensitivity scores were positively related to signal strength with the greatest values observed for the strongest signals. Sensitivity at each signal strength did not differ between the two illusions. Response-bias scores were inversely related to signal strength, with the most conservative biases observed for the strongest signals. Response biases for each signal strength were significantly more conservative for the Horizontal-Vertical than for the Müller-Lyer illusion.

scholarly journals Using the Theory of Signal Detection to Improve Ad Recognition Testing

1986 ◽  
Vol 23 (4) ◽  
pp. 327-336 ◽  
Author(s):  
Surendra N. Singh ◽  
Gilbert A. Churchill
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Recognition Testing ◽  
Response Biases

Recognition tests are a very popular means of assessing the memory effectiveness of advertisements. Unfortunately the recognition scores obtained by current methods reflect both the memory for an advertisement and the response biases of the respondents. The authors introduce the theory of signal detection (TSD) which can be used to secure independent estimates of memory and response bias in recognition tests. They discuss how TSD can be used to improve ad recognition testing.

scholarly journals Failure to Apply Signal Detection Theory to the Montreal Battery of Evaluation of Amusia May Misdiagnose Amusia

2012 ◽  
Vol 30 (5) ◽  
pp. 480-496 ◽  
Author(s):  
Molly J. Henry ◽  
J. Devin McAuley
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Signal Detection Theory ◽  
Roc Curves ◽  
Detection Theory ◽  
Performance Measure ◽  
Evaluation Of Performance ◽  
N 93 ◽  
Response Biases ◽  
Large Response

This article considers a signal detection theory (SDT) approach to evaluation of performance on the Montreal Battery of Evaluation of Amusia (MBEA). One hundred fifty-five individuals completed the original binary response version of the MBEA (n = 62) or a confidence rating version (MBEA-C; n = 93). Confidence ratings afforded construction of empirical receiver operator characteristic (ROC) curves and derivation of bias-free performance measures against which we compared the standard performance metric, proportion correct (PC), and an alternative signal detection metric, d ′. Across the board, PC was tainted by response bias and underestimated performance as indexed by Az, a nonparametric ROC-based performance measure. Signal detection analyses further revealed that some individuals performing worse than the standard PC-based cutoff for amusia diagnosis showed large response biases. Given that PC is contaminated by response bias, this suggests the possibility that categorizing individuals as having amusia or not, using a PC-based cutoff, may inadvertently misclassify some individuals with normal perceptual sensitivity as amusic simply because they have large response biases. In line with this possibility, a comparison of amusia classification using d ′- and PC-based cutoffs showed potential misclassification of 33% of the examined cases.

Lax Decision Criteria Lead to Negativity Bias: Evidence from the Emotional Stroop Task

2014 ◽  
Vol 114 (3) ◽  
pp. 896-912 ◽  
Author(s):  
Guofang Liu ◽  
Ziqiang Xin ◽  
Chongde Lin
Keyword(s):  
Signal Detection ◽  
Response Bias ◽  
Stroop Task ◽  
Negativity Bias ◽  
Emotional Stroop ◽  
Decision Criteria ◽  
Emotional Words ◽  
Emotional Stroop Task ◽  
Detection Analysis ◽  
Response Biases

Negativity bias means that negative information is usually given more emphasis than comparable positive information. Under signal detection theory, recent research found that people more frequently and incorrectly identify negative task-related words as having been presented originally than positive words, even when they were not presented. That is, people have lax decision criteria for negative words. However, the response biases for task-unrelated negative words and for emotionally important words are still unclear. This study investigated response bias for these two kinds of words. Study 1 examined the response bias for task-unrelated negative words using an emotional Stroop task. Proportions of correct recognition to negative and positive words were assessed by non-parametric signal detection analysis. Participants have lower (i.e., more lax) decision criteria for task-unrelated negative words than for positive words. Study 2 supported and expanded this result by investigating participants' response bias for highly emotional words. Participants have lower decision criteria for highly emotional words than for less emotional words. Finally, possible evolutionary sources of the response bias were discussed.

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