Single trial ERP amplitudes reveal the time course of acquiring representations of novel faces in individual participants

12/01/2020
by   W. Sommer, et al.
0

The neural correlates of face individuation - the acquisition of memory representations for novel faces - have been studied only in coarse detail and disregarding individual differences between learners. In their seminal study, (Tanaka, Curran, Porterfield, Collins, 2006) required the identification of a particular novel face across 70 trials and found that the N250 component in the ERP became more negative from the first to the second half of the experiment, where it reached a similar amplitude as a well-known face. We were unable to directly replicate this finding in our study when we used the original split of trials. However, when we applied a different split of trials we observed very similar changes in N250 amplitude. Then, we developed and applied a new two-step explorative-confirmative non-parametric method based on permutation testing to determine the time course of face individuation in individual participants based on single-trial N250 amplitudes. We show that the assumption of a steep initial increase of N250 amplitude across multiple presentations of the target face, followed by a plateau, yields plausible results in fitting linear trends for most participants. The transition point from initial acquisition to the plateau phase differed strongly between participants and tended to be earlier when performance in target face recognition was better. Hence, face individuation may be accounted for by a biphasic process of early, fast acquisition, followed by a slower, asymptotic consolidation or maintenance phase. The current approach might be fruitfully applied to further investigations into face individuation and their neural correlates

READ FULL TEXT
research
01/19/2019

Face Detection and Face Recognition In the Wild Using Off-the-Shelf Freely Available Components

This paper presents an easy and efficient face detection and face recogn...
research
01/21/2022

Reliable Detection of Doppelgängers based on Deep Face Representations

Doppelgängers (or lookalikes) usually yield an increased probability of ...
research
09/13/2023

Anytime-valid inference in N-of-1 trials

App-based N-of-1 trials offer a scalable experimental design for assessi...
research
09/12/2023

Multimodal Outcomes in N-of-1 Trials: Combining Unsupervised Learning and Statistical Inference

N-of-1 trials are randomized multi-crossover trials in single participan...
research
02/24/2021

Estimating Vaccine Efficacy Over Time After a Randomized Study is Unblinded

The COVID-19 pandemic due to the novel coronavirus SARS CoV-2 has inspir...
research
07/31/2022

Neural Correlates of Face Familiarity Perception

In the domain of face recognition, there exists a puzzling timing discre...
research
07/01/2022

How trial-to-trial learning shapes mappings in the mental lexicon: Modelling Lexical Decision with Linear Discriminative Learning

Priming and antipriming can be modelled with error-driven learning (Mars...

Please sign up or login with your details

Forgot password? Click here to reset