scholarly journals The simultaneous recognition of multiple words: A process analysis

2021 ◽  
Author(s):  
Anne Voormann ◽  
Mikhail S. Spektor ◽  
Karl Christoph Klauer
Keyword(s):  
Word Recognition ◽  
Recognition Memory ◽  
Item Recognition ◽  
State Model ◽  
Single Word ◽  
Discrete State ◽  
Single Item ◽  
Word Test ◽  
Paired Word ◽  
Multiple Item

AbstractIn everyday life, recognition decisions often have to be made for multiple objects simultaneously. In contrast, research on recognition memory has predominantly relied on single-item recognition paradigms. We present a first systematic investigation into the cognitive processes that differ between single-word and paired-word tests of recognition memory. In a single-word test, participants categorize previously presented words and new words as having been studied before (old) or not (new). In a paired-word test, however, the test words are randomly paired, and participants provide joint old–new categorizations of both words for each pair. Across two experiments (N = 170), we found better memory performance for words tested singly rather than in pairs and, more importantly, dependencies between the two single-word decisions implied by the paired-word test. We extended two popular model classes of single-item recognition to paired-word recognition, a discrete-state model and a continuous model. Both models attribute performance differences between single-word and paired-word recognition to differences in memory-evidence strength. Discrete-state models account for the dependencies in paired-word decisions in terms of dependencies in guessing. In contrast, continuous models map the dependencies on mnemonic (Experiment 1 & 2) as well as on decisional processes (Experiment 2). However, in both experiments, model comparison favored the discrete-state model, indicating that memory decisions for word pairs seem to be mediated by discrete states. Our work suggests that individuals tackle multiple-item recognition fundamentally differently from single-item recognition, and it provides both a behavioral and model-based paradigm for studying multiple-item recognition.

Tests of a model for source and item recognition memory

2009 ◽  
Author(s):  
Michael J. Hautus ◽  
Caren M. Rotello ◽  
Neil A. MacMillan
Keyword(s):  
Recognition Memory ◽  
Item Recognition

Hippocampal, parahippocampal and occipital-temporal contributions to associative and item recognition memory: an fMRI study

Neuroreport ◽  
2001 ◽  
Vol 12 (2) ◽  
pp. 359-363 ◽  
Author(s):  
A. P. Yonelinas ◽  
J. B. Hopfinger ◽  
M. H. Buonocore ◽  
N. E. A. Kroll ◽  
K. Baynes
Keyword(s):  
Recognition Memory ◽  
Item Recognition ◽  
Fmri Study

scholarly journals How to use C-OAR-SE to design optimal standard measures

2016 ◽  
Vol 50 (11) ◽  
pp. 1924-1941 ◽  
Author(s):  
John R. Rossiter
Keyword(s):  
Future Research ◽  
Alternative Measure ◽  
Single Item ◽  
Content Type ◽  
Multiple Item ◽  
Measure Design ◽  
Alternative Measures ◽  
Accepted Practice ◽  
Meta Analyses ◽  
Convergent Validation

Purpose This paper aims to extend Rossiter’s C-OAR-SE method of measure design (IJRM, 2002, p. 19, p. 4, pp. 305-335; EJM, 2011, p. 45, p. 11, p. 12, pp. 1561-1588) by proposing five distinct construct models for designing optimally content-valid multiple-item and single-item measures. Design/methodology/approach The paper begins by dismissing convergent validation, the core procedure in Nunnally’s (1978) and Churchill’s (1979) psychometric method of measure design which allows alternative measures of the same construct. The method of dismissal is the mathematical demonstration that an alternative measure, no matter how highly its scores converge with those from the original measure, will inevitably produce different findings. The only solution to this knowledge-threatening problem is to agree on an optimal measure of each of our major constructs and to use only that measure in all future research, as is standard practice in the physical sciences. The paper concludes by proposing an extension of Rossiter’s C-OAR-SE method to design optimal standard measures of judgment constructs, the most prevalent type of construct in marketing. Findings The findings are, first, the mathematical dismissal of the accepted practice of convergent validation of alternative measures of the same construct, which paves the way for, second, the proposal of five new C-OAR-SE-based construct models for designing optimal standard measures of judgment constructs, three of which require a multiple-item measure and two of which a single-item measure. Practical implications The common practice of accepting alternative measures of the same construct causes major problems for the social sciences: when different measures are used, it becomes impossible, except by remote chance, to replicate findings; meta-analyses become meaningless because the findings are averaged over different measures; and empirical generalizations cannot be trusted when measures are changed. These problems mean that we cannot continue to accept alternative measures of the constructs and that, for each construct, an optimal standard measure must be found. Originality/value The ideas in this paper, which have untold value for the future of marketing as a legitimate science, are unique to Rossiter’s C-OAR-SE method of measure design.

Frequency-Importance and Transfer Functions for the Auditec of St. Louis Recordings of the NU-6 Word Test

1993 ◽  
Vol 36 (4) ◽  
pp. 799-807 ◽  
Author(s):  
Gerald A. Studebaker ◽  
Robert L. Sherbecoe ◽  
Christine Gilmore
Keyword(s):  
Transfer Function ◽  
Word Recognition ◽  
Transfer Functions ◽  
Normal Hearing ◽  
Word Test ◽  
Importance Function ◽  
Masking Noise

Frequency-importance and transfer functions for the Auditec of St. Louis recordings of the NU-6 word test are reported. The functions were derived from the word recognition scores of 24 subjects with normal hearing who were tested under 128 conditions of filtering and talker-spectrum-matched noise. The importance function was broader and had a lower midpoint than the NU-6 importance function reported by Schum, Matthews, and Lee (1991), but still displayed a bimodal shape. The transfer function was steeper than the transfer function reported by Schum et al., but comparable in slope to the transfer function for low-context CNC words reported by Bell, Dirks, and Trine (1992). Results from a limited set of conditions presented in quiet suggest that the use of masking noise was partly responsible for the dissimilar importance and transfer functions obtained by Schum et al. and this study. Differences in the equipment used in each experiment and in the methods used to analyze the data appear to have contributed as well.

Using Fan Passion to Predict Attendance, Media Consumption, and Social Media Behaviors

2016 ◽  
Vol 30 (3) ◽  
pp. 229-247 ◽  
Author(s):  
Kirk Wakefield
Keyword(s):  
Social Media ◽  
National Football League ◽  
Social Identification ◽  
Traditional Media ◽  
Single Item ◽  
Consumption Behavior ◽  
Sport Consumption ◽  
Multiple Item ◽  
Measurement Practices ◽  
Radio News

Passion drives sport consumption, but we lack valid relevant measures of passion. The results of two studies provide evidence of a reliable and valid multiple-item passion scale that may be used in the study of sports-related consumption behavior. In Study 1 a multi-item fan passion scale was compared with established social identification fan classification scales to provide evidence of discriminant and predictive validity. Because the passion scale outperformed other relevant fan classification measures, in Study 2 the fan passion scale was compared with current single-item measurement practices employed by National Football League and Major League Baseball teams, and some academics, to classify fans. Findings confirmed the veracity of the multi-item passion measure over categorical and interval fan avidity measures used by leagues and syndicated research providers. Taken together, the studies validate an accurate measure of fan passion that may be used to segment and predict fan behaviors, including consumption of traditional media (television, radio, news, and the team’s website) and consumption of the team’s official social media outlets.

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