Cumplimiento de la instrucción de hormigón estructural EHE

One of the goals of improving infant ERP methodologies is to maximize the number of trials and infants that are included in the final sample of an ERP study. The meta-analysis conducted by Stets et al. (2012) investigating the factors underlying attrition rates indicated that the main factor that influences attrition rate in in- fant ERP studies is the nature of the stimuli. In experiments with visual stimuli, a combination of audio and visual stimuli had significantly lower attrition rates than experiments with purely visual stimuli. The authors’ explanation of this result in- cluded a direct relation of infants’ attrition rate with the ability of the stimulus presentation to maintain infants’ attention to the ERP stimuli. Studies of selective attention in infants that proposed and studied the intersensory redundancy hypoth- esis are also in line with these results (Bahrick & Lickliter, 2000; Bahrick, Walker, & Neisser, 1981). These studies found evidences of higher attentional resources towards a visual stimulus which was accompanied by a synchronous audio than to- wards a silent visual stimulus. On the other hand, Stets et al. (2013) supported the direct relation between attention and attrition rate not by including audio during the presentation of stimulus but by increasing the complexity of the visual stimuli by means of mixing visual stimuli from three different ERP experiments in the same session—as explained in the introduction chapter (see page 23)—although in that study full replications of prior studies did not occur for some of the experiments.

These studies suggest that stimulus sets that include audiovisual or more complex visual elements are more engaging for infant participants, which could, in turn, help create longer periods of attention. While an infant is engaged with the stimulus presentation, the infant is less likely to move and create artifacts in the EEG signal.

Artifact-free EEG data from a steady participant increases the EEG data quality and therefore it is likely that the number of valid trials for the final sample increases as well. Results of Chapter 3 support the theory of the increase in attention when the stimulus presentation combined visual with audiovisual elements—as the dynamic attention grabbers used consistently based on infants’ gaze behaviour. However, the increase in attention did not lead to a decrease in attrition rate when compared to standard ERP stimulus presentation that mainly used visual stimuli. The reason why attention did not lead to more trials included in the final sample might be the increased length of the trials due to the attention grabbers that were introduced in the gaze-contingent condition. Stets et al. (2012) also highlighted in their meta- analysis study the negative impact on the attrition rate that dynamic stimuli can produce due to the increase in trial length.

Overall, these results suggest that just the use of a more engaging stimulus pre- sentation that increases the attention is not sufficiently robust to decrease attrition rate on its own. It is likely that the sum of factors such as the nature of the stimuli, the length of the trials, and other factors still unmeasured or unknown that are re- sponsible for the final attrition rate. The unknown factors may be related with the lab environment, the experimenter’s interaction with the infant and parent or with the specific conditions of the infant at the moment of data collection such as time since last fed, sleep, age, or general health situation. Further research that helps to understand how and at what level these and other variables influence the attrition rate would be highly beneficial for the improvement of data collection methodologies in infant ERP. The improvements could go from practical aspects such as booking the appointments with parents based on the sleeping or feeding patterns of the in- fants—if these factors happen to have an effect in the likelihood of an infant being

included in the final sample—, to more theoretical aspects such as guidelines to help design a more engaging stimulus presentation with the appropriate type of stimuli, length and breaks to maximize infants’ attention span.

Infant ERP data collection is not the only part of an infant ERP study that determines attrition rate. Infant ERP editing practices can also have a direct impact on the number of trials that are valid and the number of infants that are included in the final sample. A clear example of how the editing practices influence the attrition rate of a study has been shown in Chapter 4. Each of the four editors that assessed the same infant ERP dataset included a different number of infants. The use of different editing methodologies by each of the editors created a variance in the attrition rate that ranged from the 16% for the automatic algorithm to 63% for one of the human editors. As seen in Chapter 4, the difference in the editing practices was not only observed in the agreement of the number of infants and trials included in the final sample, but also in the final morphology of the grand average and ERP component under study that each of the editing methods created. Stets and Reid (2011) also found similar results when they reanalyzed an EEG dataset and changed the criteria of the minimum number of trials to only the first three trials. With this practice, they were able to include more infants into the final sample—the attrition rate decreased—and they also found contradicting results in the morphology of the ERP component under study when different amount of trials and infants where included in the final sample.

Attrition rate may have not only practical consequences, which are mainly related to the time spent by the researcher in collecting and analyzing infant data. Attrition rate may also have crucial consequences in terms of the ERP results when trying to

get insights into cognitive development at a specific age. In most cases, the exclusion of infants from the final sample is due to an insufficient number of valid trials per condition. The practice of excluding infants that do not contribute a sufficient number of valid trials is important in order to ensure a good quality ERP but, it is unknown to what extent there might be a relation between the infants excluded and other variables that may influence results, such as their developmental stage. For example, there could be a bias in the final ERP results if only the infants that are at a more advanced developmental stage are able to sustain attention during the ERP paradigm and therefore contribute to the final sample. In general, a high attrition rate could be a sign of not having a representative sample of the population under study. The potential bias that attrition might cause in ERP developmental research would need further investigation and it raises an essential question: Is it more important to work towards infant ERP methodologies that prioritize a decrease in the attrition rate or towards ERP methodologies that prioritize that only infants that provided good enough EEG signal quality are included in the final sample? This question might be able to be answered when there is more information about how attrition rate may influence and bias the final ERP results.