Research ReportMechanisms of face specificity – Differentiating speed and accuracy in face cognition by event-related potentials of central processing
Section snippets
The speed-accuracy dichotomy in cognitive psychology
One possible explanation for the dichotomy of individual differences in object cognition lies within the different measurement approaches employed when speed and accuracy are investigated. Typically, the measurement of processing speed is conducted by administering easy tasks that pose no challenge to most participants if given enough time. In contrast, accuracy is typically measured in difficult tasks that cannot be solved perfectly by most participants, irrespective of the time accredited.
Neurofunctional correlates of cognitive processing
ERPs are complex wave shapes consisting of separate components that reflect the timing (latency) and intensity (amplitude) of various perceptual, central and motoric processes (Di Russo, Martínez, Sereno, Pitzalis, & Hillyard, 2002; Kok, 1997). One of the most intensely studied ERP component is the P300 (Polich, 2007), a positive deflection around 300 ms over central scalp sites in response to targets in a cognitive task. The P3b, a posterior subcomponent of the P300, is said to reflect
Aims of the present study
We sought to explain the divergent finding of differentiated accuracy factors and unified speed factors of FC. Theoretically, we assume that the contribution of central cognitive processes to the variance in speed and accuracy of FC was high in difficult tasks, but small in easy tasks. Therefore, we administered a recognition task with different stimulus content (faces vs. houses) and difficulty (easy vs. difficult) while recording EEG. Notably, although the construct of interest is FC, we are
Methods
The EEG dataset has been analyzed by Nowparast Rostami et al. (2017) with respect to a different research question addressed by different methods, specifically focusing on the N170 component. The tasks and part of the data preprocessing and analysis steps are similar to the procedures previously described.
In the following, we report all data exclusions, exclusion criteria, all manipulations, and all measures used in the study. Exclusion criteria were established prior to data analysis. In the
Results
The average ERPs across all tasks, separated into easy and difficult conditions, suggests that P300 latencies lie around 600 ms post-stimulus (see Fig. 2). However, there were considerable individual differences in the peak latencies of the P300 and, consequently, the C component. Fig. 3 depicts the ERP signal of individuals with relatively fast, average and relatively slow C component peak latencies in easy and difficult face and object recognition tasks, respectively.
Exploration of C
Discussion
The present study addressed discrepant findings showing that individual differences in performance accuracy during difficult tasks revealed face specificity, whereas performance speed in easy tasks showed limited specificity at best. In order to explain these puzzling findings, we investigated central cognitive sub-processes by means of ERPs by administering recognition tasks in easy and difficult conditions. We expected sub-processes underlying task performance to show no specificity for faces
Methodological considerations
In the present study, a very strict data cleaning procedure was applied. This resulted in the exclusion of 44 participants from the final sample. This was necessary because when investigating the individual differences in the latency of central processing, noise in the data would have biased the results and concealed the relationships (Astolfi et al., 2005; Babiloni et al., 2004). When estimating the models, a tradeoff was achieved between the need for many trials in order to estimate a
Summary and outlook
The present study continues a line of research on the neurocognitive correlates of face processing (e.g., Schweinberger & Neumann, 2016; Wiese et al., 2019). In order to disentangle cognitive processes underlying face specificity, we investigated face specificity in sub-components of processing as captured by the latency of central ERPs. Instead of using standard averaging procedures, we optimized the measurement of individual differences in ERP latency and amplitude by applying the RIDE
Author contributions
Authors’ contributions to the article Mechanisms of Face Specificity – Differentiating Speed and Accuracy in Face Cognition by Event-Related Potentials of Central Processing:
Kristina Meyer contributed to the study design, analyzed the data, interpreted the results and wrote the manuscript.
Hadiseh Nowparast Rostami was involved in the study design as well as data collection and analysis and revised the manuscript.
Guang Ouyang and Stefan Debener guided and supervised the analysis of the EEG data
Author Note
Kristina Meyer is now at Charité Universitätsmedizin Berlin.
Requests for stimulus materials should be addressed to Andrea Hildebrandt, Carl von Ossietzky Universität Oldenburg ([email protected]). Further research materials, analysis scripts and the parameterized data analyzed in this study are openly available: https://osf.io/ndz86/. No part of the study procedures or analysis was pre-registered prior to the research being conducted. Any other correspondence concerning this
Acknowledgments
We wish to thank Laura Kaltwasser, Danyal Ansary, Tsvetina Dimitrova, Lena Fliedner, Astrid Kiy, Nina Mader, Katarrina Mankinen, Karsten Manske, Alf Mante, Una Mikac, Friedrike Rueffer, and Susanne Stoll for their work in data collection. Furthermore, we thank Thomas Pinkpank, Ulrike Bunzenthal, and Rainer Kniesche for their help and advice in conducting the experiment.
This research was supported by a grant from the Deutsche Forschungsgemeinschaft (HI1780/2-1 & SO 177/26-1) to Andrea
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2022, CortexCitation Excerpt :After pre-processing and elimination of error trials, an average of 31 trials (range = 19–45) entered into the analyses per face/congruency/voice form condition. To manage ERP waveform blurring caused by trial-to-trial latency jitter (Ouyang, Sommer, & Zhou, 2016), the ERP data first entered RIDE (residue iteration decomposition); this method showed benefits in revealing the time-resolved dynamics that different sources play in face perception, especially when late ERP components are studied (Kashyap, Ouyang, Sommer, & Zhou, 2016; Meyer et al., 2021). If uncorrected, trial-to-trial latency variability can attenuate ERP component amplitudes and amplitude differences between conditions in relation to noise, diminishing the size of experimental effects and statistical test parameters (among other potential issues).
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