An analysis of item gains and losses in retroactive interference.

The repeated-testing paradigm is used to study both retroactive interference and hypermnesia (the improvement in memory across repeated tests). Considerable theoretical progress has been made by separately analyzing the 2 components of hypermnesia: the recovery of previously unrecalled items on later tests (item gains) and the forgetting of previously recalled items on later tests (item losses). Item gains increase with increases in item-specific processing, whereas item losses decrease with increases in relational processing. The authors suggest that separate analysis of item gains and losses in retroactive interference research may also prove fruitful. Three experiments showed that an interpolated list affects item gains but not losses, whereas processing similarity between the target and interpolated lists affects losses but not gains. These results are interpreted within the relational-item-specific processing framework.     

Young infants are pro-victims,but it depends on the context

The current study examined whether and when young infants are sensitive to distressed others, using two experiments with a forced-choice paradigm. Experiment 1 showed that 5- to 9-month-old infants demonstrate a clear pro-victim preference: Infants preferred a distressed character that had been physically harmed over a matched neutral character. Experiment 2 showed that infants’ preference for a distressed other is not invariable, but rather depends on the context: Infants no longer preferred the distressed character when it expressed the exact same distress but for no apparent reason. These findings have implications for the early ontogeny of human compassion and morality, addressed in the discussion.     

Dealing with Possible Baseline Inequalities Between Experimental Groups – The Case of Motor Learning

Abstract

One important concept of experimental design is the random assignment of participants to experimental groups. This randomization process is used to prevent selection bias, as well as to provide a strong basis for a cause-and-effect relationship between the independent variable/s and the dependent variable/s. In small sample sizes, simple randomization may not provide equal groups at baseline for one or more of the variables, and therefore more restricted types of randomization, such as the stratified permuted-block randomization, can be used. A code was written to calculate the probability that simple randomization will not lead to equality between groups at baseline, and then an example of stratified permuted-block randomization was examined. The findings suggest that for certain variables that are commonly measured in experiments in motor learning, there is a relatively high probability that groups will not be equal at baseline after simple randomization. This observation reflects the small sample sizes usually found in the literature on motor learning. However, stratified permuted-block randomization does lead to greater equality among groups. Implications for researchers are discussed, and a flowchart is proposed that will allow researchers to decide whether to use simple or stratified randomization.