Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Boston Attention and Learning Laboratory, VA Boston Healthcare System, Boston, MA, USA; Department of Psychiatry, Harvard Medical School, Boston, MA, USA.
Cortex. 2023 Apr;161:51-64. doi: 10.1016/j.cortex.2022.12.014. Epub 2023 Feb 4.
The prevalence of developmental prosopagnosia (DP), lifelong face recognition deficits, is widely reported to be 2-2.5%. However, DP has been diagnosed in different ways across studies, resulting in differing prevalence rates. In the current investigation, we estimated the range of DP prevalence by administering well-validated objective and subjective face recognition measures to an unselected web-based sample of 3116 18-55 year-olds and applying DP diagnostic cutoffs from the last 14 years. We found estimated prevalence rates ranged from .64-5.42% when using a z-score approach and .13-2.95% when using a percentile approach, with the most commonly used cutoffs by researchers having a prevalence rate of .93% (z-score, .45% when using percentiles). We next used multiple cluster analyses to examine whether there was a natural grouping of poorer face recognizers but failed to find consistent grouping beyond those with generally above versus below average face recognition. Lastly, we investigated whether DP studies with more relaxed diagnostic cutoffs were associated with better performance on the Cambridge Face Perception Test. In a sample of 43 studies, there was a weak nonsignificant association between greater diagnostic strictness and better DP face perception accuracy (Kendall's tau-b correlation, τb =.18 z-score; τb = .11 percentiles). Together, these results suggest that researchers have used more conservative DP diagnostic cutoffs than the widely reported 2-2.5% prevalence. We discuss the strengths and weaknesses of using more inclusive cutoffs, such as identifying mild and major forms of DP based on DSM-5.
发展性面孔失认症(DP)的患病率,即终身性的面孔识别缺陷,据广泛报道为 2-2.5%。然而,由于在研究中 DP 的诊断方法不同,导致了患病率的差异。在目前的研究中,我们通过对一个未抽样的、基于网络的 3116 名 18-55 岁的样本,使用经过充分验证的客观和主观的面孔识别测试,同时应用过去 14 年的 DP 诊断标准进行测试,来估计 DP 的患病率范围。我们发现,当使用 z 分数方法时,估计的患病率范围为 0.64-5.42%;当使用百分位数方法时,估计的患病率范围为 0.13-2.95%,而研究人员最常使用的截止值的患病率为 0.93%(z 分数,当使用百分位数时为 0.45%)。接下来,我们使用多次聚类分析来检查是否存在较差的面孔识别者的自然分组,但未能发现除了那些普遍高于或低于平均面孔识别水平之外的一致性分组。最后,我们调查了具有更宽松诊断标准的 DP 研究是否与在剑桥面孔感知测试中表现更好相关。在 43 项研究的样本中,诊断标准的严格程度与 DP 面孔感知准确性之间存在微弱的非显著关联(Kendall 的 tau-b 相关系数,τb=0.18 z 分数;τb=0.11 百分位数)。总的来说,这些结果表明,研究人员使用了比广泛报道的 2-2.5%的患病率更保守的 DP 诊断标准。我们讨论了使用更具包容性的截止值的优缺点,例如根据 DSM-5 确定轻度和重度 DP。