Robustness of the rule-learning effect in 7-month-old infants: A close, multicenter replication of Marcus et al. (1999)

We conducted a close replication of the seminal work by Marcus and colleagues from 1999, which showed that after a brief auditory exposure phase, 7-month-old infants were able to learn and generalize a rule to novel syllables not previously present in the exposure phase. This work became the foundation for the theoretical framework by which we assume that infants are able to learn abstract representations and generalize linguistic rules. While some extensions on the original work have shown evidence of rule learning, the outcomes are mixed, and an exact replication of Marcus et al.'s study has thus far not been reported. A recent meta-analysis by Rabagliati and colleagues brings to light that the rule-learning effect depends on stimulus type (e.g., meaningfulness, speech vs. nonspeech) and is not as robust as often assumed. In light of the theoretical importance of the issue at stake, it is appropriate and necessary to assess the replicability and robustness of Marcus et al.'s findings. Here we have undertaken a replication across four labs with a large sample of 7-month-old infants (N = 96), using the same exposure patterns (ABA and ABB), methodology (Headturn Preference Paradigm), and original stimuli. As in the original study, we tested the hypothesis that infants are able to learn abstract “algebraic” rules and apply them to novel input. Our results did not replicate the original findings: infants showed no difference in looking time between test patterns consistent or inconsistent with the familiarization pattern they were exposed to

Robustness of the rule-learning effect in 7-month-old infants: A close, multicenter replication of Marcus et al. (1999)

We conducted a close replication of the seminal work by Marcus and colleagues from 1999, which showed that after a brief auditory exposure phase, 7-month-old infants were able to learn and generalize a rule to novel syllables not previously present in the exposure phase. This work became the foundation for the theoretical framework by which we assume that infants are able to learn abstract representations and generalize linguistic rules. While some extensions on the original work have shown evidence of rule learning, the outcomes are mixed, and an exact replication of Marcus et al.'s study has thus far not been reported. A recent meta-analysis by Rabagliati and colleagues brings to light that the rule-learning effect depends on stimulus type (e.g., meaningfulness, speech vs. nonspeech) and is not as robust as often assumed. In light of the theoretical importance of the issue at stake, it is appropriate and necessary to assess the replicability and robustness of Marcus et al.'s findings. Here we have undertaken a replication across four labs with a large sample of 7-month-old infants (N = 96), using the same exposure patterns (ABA and ABB), methodology (Headturn Preference Paradigm), and original stimuli. As in the original study, we tested the hypothesis that infants are able to learn abstract “algebraic” rules and apply them to novel input. Our results did not replicate the original findings: infants showed no difference in looking time between test patterns consistent or inconsistent with the familiarization pattern they were exposed to

Daan van Renswoude's Quick Files

The Quick Files feature was discontinued and it’s files were migrated into this Project on March 11, 2022. The file URL’s will still resolve properly, and the Quick Files logs are available in the Project’s Recent Activity

The Developmental Object Familiarity Inventory (DOFI)

Numerous studies have used object familiarity as an independent variable without ever actually defining the construct. Instead, it has been used as a measure of exposure - more exposure of a stimulus making it more familiar. Yet, we argue, that being familiar with an object is more than simple exposure: different levels of experience with and knowledge about an object should be integrated into the construct. Thus, we created the Developmental Object Familiarity Inventory (DOFI) as a new parent report measure to evaluate object familiarity on a six-point scale. The scale points are: (1)"My child has never seen this object before", (2)"My child has paid attention to this object before", (3)"My child has shown interest to explore the object at least once", (4)"My child has some knowledge about the object's use", (5)"My child can indicate where the object is when asked for it", and (6)"My child has and uses a consistent word for the object". This way, real-life familiarity about real-life objects can be measured, as well as, other research questions related to object familiarity (e.g., van Renswoude et al., 2019). Items were 76 objects covering six object categories following the CDI's organization (Fenson et al., 1993): 'vehicles' (9 items), 'toys' (9 items), 'food' (11 items), 'clothing' (14 items), 'household' (18 items), and 'furniture' (15 items) based on the Dutch CDI (N-CDI; Zink & Lejaeger, 2002). In three studies the new measure's reliability and validity was investigated by collecting data about infants between the age of 6 and 24 months. A first study (N = 10 infant reports, M = 15,37; SD = 3,98) shows that the six scale points indeed follow a strict order for all parents for all items. A second study (N = 28 complete infant reports, M = 13,5; SD = 5,5) was aimed at testing the internal structure of the questionnaire (Messick, 1995; Downing, 2003). The survey had great internal consistency (Cronbach's alpha is .991) and a good interitem correlation (d = .63). The scale's ordinality was to be analyzed with a graded response model (GRM, Samejima, 2011; Figure 1). When including age and gender as covariates in the model, age has a significant, positive effect on the latent trait (β = 1.17, t = 2.67, p < .05). Twelve participants responded to the test-retest survey. On average, the interval between measurements was 23 days (min = 9; max = 57; SD = 12,64). With a correlation of r = .86 (p < .001) the test-retest reliability was good. In the third, pilot study, an eye-tracking validation (N = 6 infants between the ages of 7,3 and 22 months, M = 14,4; SD = 4,52), the familiarity scores obtained in the DOFI were attempted to be related to preferential looking. We gathered important information to consider when designing future studies implementing the DOFI (in progress). In conclusion, the DOFI shows great potential to be used in object familiarity related research and might revolutionize the field towards a more natural and generalizable manner of research

Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions

Circular data are encountered throughout a variety of scientific disciplines, such as in eye movement research as the direction of saccades. Motivated by such applications, mixtures of peaked circular distributions are developed. The peaked distributions are a novel family of Batschelet-type distributions, where the shape of the distribution is warped by means of a transformation function. Because the Inverse Batschelet distribution features an implicit inverse that is not computationally feasible for large or complex data, an alternative called the Power Batschelet distribution is introduced. This distribution is easy to compute and mimics the behavior of the Inverse Batschelet distribution. Inference is performed in both the frequentist framework, through Expectation–Maximization (EM) and the bootstrap, and the Bayesian framework, through MCMC. All parameters can be fixed, which may be done by assumption to reduce the number of parameters. Model comparison can be performed through information criteria or through bridge sampling in the Bayesian framework, which allows performing a wealth of hypothesis tests through the Bayes factor. An R package, flexcircmix, is available to perform these analyses

WALD-EM: Wald Accumulation for Locations and Durations of Eye Movements

Describing, analyzing, and explaining patterns in eye movement behavior is crucial for understanding visual perception. Further, eye movements are increasingly used in informing cognitive process models. In this article, we start by reviewing basic characteristics and desiderata for models of eye movements. Specifically, we argue that there is a need for models combining spatial and temporal aspects of eye-tracking data (i.e., fixation durations and fixation locations), that formal models derived from concrete theoretical assumptions are needed to inform our empirical research, and custom statistical models are useful for detecting specific empirical phenomena that are to be explained by said theory. In this article, we develop a conceptual model of eye movements, or specifically, fixation durations and fixation locations, and from it derive a formal statistical model—meeting our goal of crafting a model useful in both the theoretical and empirical research cycle. We demonstrate the use of the model on an example of infant natural scene viewing, to show that the model is able to explain different features of the eye movement data, and to showcase how to identify that the model needs to be adapted if it does not agree with the data. We conclude with discussion of potential future avenues for formal eye movement models

Infants’ center bias in free viewing of real-world scenes

This study examines how salience and a center bias drive infants’ first fixation while looking at complex scenes. Adults are known to have a strong center bias, their first point of gaze is nearly always in the center of the scene. The center bias is likely to be a strategic bias, as looking towards the center minimizes the distance to other parts of the scene and important objects are often located at the center. In an experimental design varying salience regions of scenes and start positions we examined infants’ (N = 48, Age = 5–20-month-olds) first fixation after scene onset. The pre-registered hypothesis that infants also have a center bias while looking at real-world scenes was confirmed. The strength of the center bias is correlated with the saliency distribution such that the bias is weaker when the strongest salience is peripheral rather central. In the absence of clear salient regions there still was a strong center bias. These results suggests there is a competition between stimulus-driven factors and a center bias in steering attention from a young age onwards

Mixtures of peaked power Batschelet distributions for circular data with application to saccade directions

Circular data are encountered throughout a variety of scientific disciplines, such as in eye movement research as the direction of saccades. Motivated by such applications, mixtures of peaked circular distributions are developed. The peaked distributions are a novel family of Batschelet-type distributions, where the shape of the distribution is warped by means of a transformation function. Because the Inverse Batschelet distribution features an implicit inverse that is not computationally feasible for large or complex data, an alternative called the Power Batschelet distribution is introduced. This distribution is easy to compute and mimics the behavior of the Inverse Batschelet distribution. Inference is performed in both the frequentist framework, through Expectation–Maximization (EM) and the bootstrap, and the Bayesian framework, through MCMC. All parameters can be fixed, which may be done by assumption to reduce the number of parameters. Model comparison can be performed through information criteria or through bridge sampling in the Bayesian framework, which allows performing a wealth of hypothesis tests through the Bayes factor. An R package, flexcircmix, is available to perform these analyses