Signal detection theory (SDT) is a powerful, statistically based research tool that, if used properly, can enhance modern research. However, if used improperly, SDT, like other important research tools (e.g., inferential statistical tests), can lead to incorrect conclusions. Unfortunately, SDT is poorly understood by many modern researchers and thus is sometimes used inappropriately to support or reject important conclusions. Many of the problems with modern uses of SDT, as well as recent arguments against the use of SDT (e.g., Norris, 1995), appear to stem from misunderstandings about the nature of the underspecified decision variable and about the assumptions underlying specific descriptive statistics. For example, having explicit assumptions for d9 and b, the respective descriptive statistics for sensitivity and bias that are most typically associated with SDT is apparently of great concern to some researchers, with many opting to use A9 and B 0 because these alternative measures are assumed to be nonparametric and thus free from assumptions about distribution. The goal of the present work is to examine the nature of the aspects of SDT that seem to be most misunderstood in modern research. Origin of SDT: Statistical Decision TheoryIn all aspects of psychology, the researcher is faced with problems that stem from the need to use simple behavioral measures to study the complex processes involved in all human behavior. Furthermore, all decisions that lead to overt behavioral responses are based on internal (thus unobservable) and largely unspecified multidimensional sources of evidence, and they are influenced by strategic and motivationalconsiderations.Although the many problems faced by the researcher seem obvious for complex behaviors, the same problems exist with supposedly simple tasks such as the detection of a stimulus, where the observable physical dimensions are anything but isomorphic with unobservable sensory, perceptual, or cognitive dimensions. A major simplifying strategy throughout psychology is to model behavior and decision-making statistically, with the actual observer viewed as a less than perfect, and sometimes biased, version of a computer or an ideal observer modeled to process the same information. If the multidimensional evidence were precise (without variability or uncertainty), the decision would not be statistical. Because the processes being studied are characterized statistically, it makes sense to use descriptive statistics to characterize the behavior of the processes and to use inferential statistics to test hypotheses about the processes.The authors thank John Wixted, John Irwin, Robert Sorkin, and an anonymous reviewer for comments and suggestions that were extremely helpful in our preparing this manuscript in a form that allows difficult material to be presented in a clear and easily followed manner. Preparation of this paper was supported in part by a grant from National Science Foundation. Correspondence should be addressed to R. E. Pastore, Department of Psychology, Binghamt...
The effects of ambient odor (lavender, neroli or placebo) and suggestions related to the effects of an odor (relaxing, stimulating or none) on mood were explored. Mood of 90 undergraduate women was assessed using physiological measures (heart rate and skin conductance) and the self-report Profile of Mood States questionnaire. Analysis indicated that physiological measures were influenced by suggestion in predictable directions. Relaxing odors yielded decreases in heart rate and skin conductance, with stimulating odors yielding the reverse effects under equivalent conditions. These data further support the notion that expectations play a significant role in mediating odor-evoked mood changes.
This study evaluated the relationship between primitive and scheme-driven grouping (A. S. Bregman, 1990) by comparing the ability of different listeners to detect single note changes in 3-voice musical compositions. Primitive grouping was manipulated by the use of 2 distinctly different compositional styles (homophony and polyphony). The effects of scheme-driven processes were tested by comparing performance of 2 groups of listeners (musicians and nonmusicians) and by varying task demands (integrative and selective listening). Following previous studies, which had tested only musically trained participants, several variables were manipulated within each compositional style. The results indicated that, although musicians demonstrated a higher sensitivity to changes than did nonmusicians, the 2 groups exhibited similar patterns of sensitivity under a variety of conditions.
Word recognition, semantic priming, and cognitive impenetrability research have used signal detection theory (SDT) measures to separate perceptual and postperceptual processes. In the D. Norris (1986) checking model and model simulation (D. Norris, 1995), priming alters only postperceptual word decision criteria: Stimulus-related priming reduces uncertainty, increasing sensitivity; stimulus-unrelated priming increases false alarms more than hits, reducing sensitivity. This work is cited as strong evidence that criterion changes can alter perceptual sensitivity and that SDT is inappropriate for investigating complex cognitive processes. The authors' current SDT ideal observer analysis of the model demonstrates that related priming does not directly alter sensitivity and that unrelated priming increases only false-alarm rate, reducing sensitivity. This analysis provides new perspectives on SDT concepts of complex decision processing.
This study explores complexity, musical dimensions, and the use of music for cognitive, emotional, or background purposes. In the pilot study, participants rated the complexity of 70 song excerpts representing the Reflective and Complex, Upbeat and Conventional, Intense and Rebellious, and Energetic and Rhythmic dimensions. In the main study, participants listened to 30 songs that were rated as high, moderate, or low complexity in the pilot study. They rated their use of each song on a modified version of the Uses of Music Inventory. The results indicated that highly complex music is used more for cognitive purposes and low complexity music is used more for emotional purposes. Ultimately, these findings confirmed that use of music is influenced by complexity.Much research on music has examined music preferences and music uses in relation to personality and situational factors. For example, Rentfrow and Gosling (2003) identified a factor structure to categorize musical preferences and associated this structure with personality characteristics. Other researchers have examined the qualities of music (e.g., complexity) and music uses (Chamorro-Although past research has studied the connection between music qualities (e.g., complexity) and music preferences, there is a paucity of research examining how the qualities of the music affect the manner in which the music is used (
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
This site is protected by reCAPTCHA and the Google Privacy Policy and Terms of Service apply.
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.