Word and Face Recognition Processing Based on Response Times and Ex-Gaussian Components

Abstract

The face is a fundamental feature of our identity. In humans, the existence of specialized processing modules for faces is now widely accepted. However, identifying the processes involved for proper names is more problematic. The aim of the present study is to examine which of the two treatments is produced earlier and whether the social abilities are influent. We selected 100 university students divided into two groups: Spanish and USA students. They had to recognize famous faces or names by using a masked priming task. An analysis of variance about the reaction times (RT) was used to determine whether significant differences could be observed in word or face recognition and between the Spanish or USA group. Additionally, and to examine the role of outliers, the Gaussian distribution has been modified exponentially. Famous faces were recognized faster than names, and differences were observed between Spanish and North American participants, but not for unknown distracting faces. The current results suggest that response times to face processing might be faster than name recognition, which supports the idea of differences in processing nature.

Correctness and response time distributions in the MemTrax continuous recognition task: Analysis of strategies and a reverse-exponential model

“…A variety of theoretical explanations have been invoked to explain this skewed distribution for RTs across many paradigms ( Ratcliff et al, 2016 ; Moret-Tatay et al, 2021 ). However, the RevEx model provides a different and direct reflection of the massive, reciprocal processing capability of the brain, without reliance on concepts of a series of processing stages.…”

“…The present study examined RT means and their relationship to the universal observation that the averaged distribution of RTs during CRTs differs from the normal Gaussian distribution and is skewed, with absolute lower limits and less bounded upper limits. The explanation for the skewed distribution of RTs has been difficult, though numerous models have been suggested ( Burbeck and Luce, 1982 ; Hockley, 1982 , 1984a ; Moret-Tatay et al, 2018 , 2021 ; Liu et al, 2019 ). An exponentially modified Gaussian probability density function (ex-Gaussian) ( Ratcliff and Murdock, 1976 ), which provides parameters related to performance across different tests ( Hockley, 1984a ), has been widely used to model individual RTs.…”

“…Following this theoretical construct, RT during these tasks has been modeled as the convolution of an exponential function and a Gaussian function to form an exponentially modified Gaussian curve (the ex-Gaussian function) sensitive to both mechanisms. The ex-Gaussian function has been invoked to explain recognition processing based on RTs ( Moret-Tatay et al, 2021 ) and reaction time slowing in AD ( Gordon and Carson, 1990 ; Ratcliff et al, 2021 ). This distribution requires three parameters to model RT, the mean and standard deviation of the Gaussian distribution and the decay constant of the exponential component.…”

The Effect of Corrective Feedback in Basic Cognitive Tasks: A Study in Early Childhood

“…The main interest in analyzing the τ components is related to the evidence found in studies involving children with ADHD that might support that corrective feedback enhances attentional processing. Here, some literature [ 16 , 28 , 29 , 30 ] mainly found differences between children with ADHD and the control group in terms of their RT tail distributions (directly linked to τ). Moreover, a piece of research [ 31 ] found that the same τ parameter was highly positively correlated with the number of errors committed by participants.…”

Research on Library Face Book Return Model Based on Hybrid PCA and Kernel Function

“…The traditional way of returning books in university libraries is that the returner puts his valid borrowing certificate in the designated sensing area to identify his identity, returns the book, and takes it away [1][2]. The whole process of returning books in the library is cumbersome, and book returners often forget to carry valid borrowing certificates, which lead to the failure of book return and wastes the time and energy of book returners [3]. Therefore, some university administrators and technical experts propose applying advanced face recognition technology to the library return process and building a university library intelligent return model, so that book returners do not need to carry additional documents and only use their social biometric information and the book return process can be completed [4].…”