Abstract:Neuroimaging and neuropsychological studies have identified the involvement of the right posterior region in the processing of visual words. Interestingly, in contrast, ERP studies of the N170 typically demonstrate selectivity for words more strikingly over the left hemisphere. Why is right hemisphere selectivity for words during the N170 epoch typically not observed, despite the clear involvement of this region in word processing? One possibility is that amplitude differences measured on averaged ERPs in prev… Show more
For various reasons, the brain response activities in EEG signals are not perfectly synchronized from trial to trial with respect to event markers -a problem commonly referred to as ERP latency jitter. EEG experimental technologies have been greatly advanced to reduce technical timing errors so as to reduce the jitter. However, there are intrinsic sources of jitter that are difficult, if not impossible, to remove. The problem becomes more complicated when facing multiple sub-components with different jitter. The jitter issue renders trial-averaged ERP inaccurate at best and misleading at worst. Effectively correcting the jitter has profound significance in brain research. I present a simple method and easy-to-use toolbox ReSync for correcting ERP jitters based on signal processing theories and techniques. ReSync can be used to correct multiple overlapping ERP sub-components with different degrees of jitter without affecting each other (including the static one). The theories, principles, technical details, and limitations of ReSync were presented in this paper, along with a series of simulation and real data examples for evaluating and validating the method.
For various reasons, the brain response activities in EEG signals are not perfectly synchronized from trial to trial with respect to event markers -a problem commonly referred to as ERP latency jitter. EEG experimental technologies have been greatly advanced to reduce technical timing errors so as to reduce the jitter. However, there are intrinsic sources of jitter that are difficult, if not impossible, to remove. The problem becomes more complicated when facing multiple sub-components with different jitter. The jitter issue renders trial-averaged ERP inaccurate at best and misleading at worst. Effectively correcting the jitter has profound significance in brain research. I present a simple method and easy-to-use toolbox ReSync for correcting ERP jitters based on signal processing theories and techniques. ReSync can be used to correct multiple overlapping ERP sub-components with different degrees of jitter without affecting each other (including the static one). The theories, principles, technical details, and limitations of ReSync were presented in this paper, along with a series of simulation and real data examples for evaluating and validating the method.
“…Scholars have demonstrated that not only faces but also Chinese characters may generate an N170 component. One previous study indicated that N170 is not constant for facial representation, suggesting that the cognitive tasks may significantly affect the N170 amplitude [38]. The N170 amplitude may be a common reaction to highly familiar visual stimuli [38].…”
Section: Hypothesesmentioning
confidence: 95%
“…One previous study indicated that N170 is not constant for facial representation, suggesting that the cognitive tasks may significantly affect the N170 amplitude [38]. The N170 amplitude may be a common reaction to highly familiar visual stimuli [38]. For instance, scholars compare the N170 component elicited by human faces with the ERPs elicited by ape faces, birds, cars, and items of furniture, in order to explore face specificity in visual processing.…”
Section: Hypothesesmentioning
confidence: 99%
“…The N170 components can be induced not only by face stimuli but also by stimuli such as cars, houses, shoes, furniture, tools, road signs, flowers, and printed words [37]. The larger N170 elicited by faces may be influenced by their expertise in recognizing faces [38]. The customer's preferences and requirements may be presented by the similarity in the cognition information of the individual, which may generate the ERP component, such as N170.…”
This paper has developed a neuromarketing framework measuring the relationship between products and services in product–service systems (PSSs), particularly regarding its impact on PSS decision making. We divided the PSSs into different levels of product and service combinations in order to identify the impact of the various elements in PSS on decision making, particularly the key factor that induces significant variation in the purchase rate. The experiments showed the neural mechanisms behind the value perception of PSSs; this has been indicated by the appearance of N170, which is related to the cognition processing of familiarity and similarity. It is concluded that the perceived value of the product-oriented PSS is mainly determined by the product attribute, as the promotional effect of service has been clarified. The results explain the psychological and neurological activities that take place when consumers are browsing product–service bundles, which may help corporations better understand the relationships among the components in product–service bundles, providing insight for PSS innovation and service design.
“…In the study of (Yang et al, 2017), the authors examined the possibility whether the right hemisphere is responding selectivity for words during the N170 epoch. The authors used single-trial analysis.…”
The current study manipulated according to perceptual load theory (PLT) by increasing the letters' numbers of Arabic words and pseudowords to examine the effect of selective attention on the orthographic complexity of Arabic words. The current study's objective is to investigate the effect of selective attention on Event-Related Potentials components associated with orthographic codes such as N170. Participants were requested to perform lexical decision task by identifying words vs. pseudowords. Results showed that there is a significant effect of selective attention on N170 component, which reflects orthographic coding of words with increase negatively for three-letters words compared to six-letters words or nine-letters words. Interestingly, Selective attention does not affect the pseudowords, in that there are no differences between different pseudowords according to length. These findings suggested that selective attention influenced both words and pseudo-words. These findings showed that orthographic coding stages strongly depend on selective attention.
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