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Jason W. Griffin

Bio: Jason W. Griffin is an academic researcher from Pennsylvania State University. The author has contributed to research in topics: Autism & Eye tracking. The author has an hindex of 6, co-authored 12 publications receiving 79 citations. Previous affiliations of Jason W. Griffin include University of Colorado Colorado Springs.
Topics: Autism, Eye tracking, Psychology, Gaze, Medicine

Papers
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Journal ArticleDOI
TL;DR: Comparing and large deficits in ASD for both face identity recognition and discrimination are found, suggesting that deficits in face identity processing may represent a core deficit in ASD.
Abstract: The ability to recognize an individual face is essential to human social interaction. Even subtle errors in this process can have huge implications for the way we relate to social partners. Because autism spectrum disorder (ASD) is characterized by deficits in social interaction, researchers have theorized about the potential role of atypical face identity processing to the symptom profile of ASD for more than 40 years. We conducted an empirical meta-analysis of this large literature to determine whether and to what extent face identity processing is atypical in ASD compared to typically developing (TD) individuals. We also tested the hypotheses that the deficit is selective to face identity recognition, not perception, and that methodological variation across studies moderates the magnitude of the estimated deficit. We identified 112 studies (5,390 participants) that generated 172 effect sizes from both recognition (k = 119) and discrimination (k = 53) paradigms. We used state-of-the-art approaches for assessing the validity and robustness of the analyses. We found comparable and large deficits in ASD for both face identity recognition (Hedge's g = -0.86) and discrimination (Hedge's g = -0.82). This means that the score of an average ASD individual is nearly 1 SD below the average TD individual on tasks assessing both aspects of face identity processing. These deficits generalize across age groups, sex, IQ scores, and task paradigms. These findings suggest that deficits in face identity processing may represent a core deficit in ASD. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

37 citations

Journal ArticleDOI
TL;DR: Examining the unique patterns of neuropsychological test performance across a battery of tests was the superior method of differentiating between competing diagnoses, and it accounted for 16-30% of the variance in diagnostic decision making.
Abstract: Objective Two main approaches to the interpretation of cognitive test performance have been utilized for the characterization of disease: evaluating shared variance across tests, as with measures of severity, and evaluating the unique variance across tests, as with pattern and error analysis Both methods provide necessary information, but the unique contributions of each are rarely considered This study compares the 2 approaches on their ability to differentially diagnose with accuracy, while controlling for the influence of other relevant demographic and risk variables Method Archival data requested from the NACC provided clinical diagnostic groups that were paired to 1 another through a genetic matching procedure For each diagnostic pairing, 2 separate logistic regression models predicting clinical diagnosis were performed and compared on their predictive ability The shared variance approach was represented through the latent phenotype δ, which served as the lone predictor in 1 set of models The unique variance approach was represented through raw score values for the 12 neuropsychological test variables comprising δ, which served as the set of predictors in the second group of models Results Examining the unique patterns of neuropsychological test performance across a battery of tests was the superior method of differentiating between competing diagnoses, and it accounted for 16-30% of the variance in diagnostic decision making Conclusion Implications for clinical practice are discussed, including test selection and interpretation (PsycINFO Database Record

30 citations

Journal ArticleDOI
TL;DR: In this article, the authors explore the power problems of subgroup analyses in more detail, using "metapower", a recently developed statistical package in R to examine power in meta-analyses, including sub-group analyses.
Abstract: One of the most used methods to examine sources of heterogeneity in meta-analyses is the so-called ‘subgroup analysis’. In a subgroup analysis, the included studies are divided into two or more subgroups, and it is tested whether the pooled effect sizes found in these subgroups differ significantly from each other. Subgroup analyses can be considered as a core component of most published meta-analyses. One important problem of subgroup analyses is the lack of statistical power to find significant differences between subgroups. In this paper, we explore the power problems of subgroup analyses in more detail, using ‘metapower’, a recently developed statistical package in R to examine power in meta-analyses, including subgroup analyses. We show that subgroup analyses require many more included studies in a meta-analysis than are needed for the main analyses. We work out an example of an ‘average’ meta-analysis, in which a subgroup analysis requires 3–4 times the number of studies that are needed for the main analysis to have sufficient power. This number of studies increases exponentially with decreasing effect sizes and when the studies are not evenly divided over the subgroups. Higher heterogeneity also requires increasing numbers of studies. We conclude that subgroup analyses remain an important method to examine potential sources of heterogeneity in meta-analyses, but that meta-analysts should keep in mind that power is very low for most subgroup analyses. As in any statistical evaluation, researchers should not rely on a test and p-value to interpret results, but should compare the confidence intervals and interpret results carefully.

26 citations

Journal ArticleDOI
01 Mar 2021
TL;DR: An introduction to power analysis is provided and a practical tutorial for calculating statistical power using the R package metapower is presented, which includes computing statistical power for summary effect sizes, tests of homogeneity, categorical moderator analysis, and subgroup analysis.
Abstract: Meta-analysis is an influential evidence synthesis technique that summarizes a body of research. Though impactful, meta-analyses fundamentally depend on the literature being sufficiently large to generate meaningful conclusions. Power analysis plays an important role in determining the number of studies required to conduct a substantive meta-analysis. Despite this, power analysis is rarely conducted or reported in published meta-analyses. A significant barrier to the widespread implementation of power analysis is the lack of available and accessible software for calculating statistical power for meta-analysis. In this paper, I provide an introduction to power analysis and present a practical tutorial for calculating statistical power using the R package metapower. The main functionality includes computing statistical power for summary effect sizes, tests of homogeneity, categorical moderator analysis, and subgroup analysis. This software is free, easy-to-use, and can be integrated into a continuous work flow with other meta-analysis packages in R.

23 citations

Journal ArticleDOI
TL;DR: The serial position effect reveals that recall of a supraspan list of words follows a predictable pattern, whereby words at the beginning and end of a list are recalled more easily than words in the middle.
Abstract: The serial position effect reveals that recall of a supraspan list of words follows a predictable pattern, whereby words at the beginning (primacy) and end (recency) of a list are recalled more easily than words in the middle. This effect has typically been studied using single list-learning trials, but in neuropsychology, multi-trial list-learning tests are more commonly used. The current study examined trends in learning for primacy, middle, and recency effects across multiple trials in younger and older age cohorts. Participants were 158 volunteers, including 79 adults aged 17-36 ("younger" group) and 79 adults aged 54-89 years ("older" group). Each participant completed four learning trials and one delayed (5-10 min) recall trial from the Memory Assessment Scales. Scores were divided into primacy (first four words), middle (middle four words), and recency (final four words) scores for all trials. For list acquisition, mixed effects modeling examined the main effects of and interactions between learning slope (logarithmic), age group, and serial position. Rate of learning increased logarithmically over four trials and varied by serial position, with growth of middle and recency word acquisition increasing more rapidly than recall of primacy words; this interaction did not differ by age group. Delayed retention differed according to age group and serial position; both older and younger adults demonstrated similar retention for primacy words, but older adults showed reduced retention for middle and recency words. Although older adults acquired less information across learning trials, the reason for this reduced acquisition was related to initial learning, not to rate of learning over time. Older compared to younger adults were less efficient at transferring middle and recency words from short-term to long-term memory.

14 citations


Cited by
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01 Jan 2006
TL;DR: It is concluded that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work, and the efficacy of this algorithm is evaluated against the variables of gender and racial origin.
Abstract: This paper details MORPH a longitudinal face database developed for researchers investigating all facets of adult age-progression, e.g. face modeling, photo-realistic animation, face recognition, etc. This database contributes to several active research areas, most notably face recognition, by providing: the largest set of publicly available longitudinal images; longitudinal spans from a few months to over twenty years; and, the inclusion of key physical parameters that affect aging appearance. The direct contribution of this data corpus for face recognition is highlighted in the evaluation of a standard face recognition algorithm, which illustrates the impact that age-progression, has on recognition rates. Assessment of the efficacy of this algorithm is evaluated against the variables of gender and racial origin. This work further concludes that the problem of age-progression on face recognition (FR) is not unique to the algorithm used in this work.

139 citations

Reference EntryDOI
15 Jul 2008

96 citations

Journal ArticleDOI
TL;DR: It is believed that voice user interfaces will play a key role in elderly people's initial acceptance and continuing engagement of social robots, and contributes to design decisions that would take individual differences into consideration.

59 citations

Journal ArticleDOI
TL;DR: Overall, the oddball FPVS paradigm is recommended as an alternative approach to behavioral or traditional event‐related potential EEG measures of face individuation, including its (a)typical development, with early studies supporting the application of this technique to clinical testing (e.g., autism spectrum disorder).
Abstract: To investigate face individuation (FI), a critical brain function in the human species, an oddball fast periodic visual stimulation (FPVS) approach was recently introduced (Liu-Shuang et al., Neuropsychologia, 2014, 52, 57). In this paradigm, an image of an unfamiliar "base" facial identity is repeated at a rapid rate F (e.g., 6 Hz) and different unfamiliar "oddball" facial identities are inserted every nth item, at a F/n rate (e.g., every 5th item, 1.2 Hz). This stimulation elicits FI responses at F/n and its harmonics (2F/n, 3F/n, etc.), reflecting neural discrimination between oddball versus base facial identities, which is quantified in the frequency domain of the electroencephalogram (EEG). This paradigm, used in 20 published studies, demonstrates substantial advantages for measuring FI in terms of validity, objectivity, reliability, and sensitivity. Human intracerebral recordings suggest that this FI response originates from neural populations in the lateral inferior occipital and fusiform gyri, with a right hemispheric dominance consistent with the localization of brain lesions specifically affecting facial identity recognition (prosopagnosia). Here, we summarize the contributions of the oddball FPVS framework toward understanding FI, including its (a)typical development, with early studies supporting the application of this technique to clinical testing (e.g., autism spectrum disorder). This review also includes an in-depth analysis of the paradigm's methodology, with guidelines for designing future studies. A large-scale group analysis compiling data across 130 observers provides insights into the oddball FPVS FI response properties. Overall, we recommend the oddball FPVS paradigm as an alternative approach to behavioral or traditional event-related potential EEG measures of face individuation.

45 citations