Gains and losses in action memory.

Enacting action phrases (SPT for subject-performed task) produces better free recall than only learning the phrases verbally (VT for verbal task). A widespread explanation of the enactment effect is based on the distinction between item-specific and relational information. There is widespread agreement that the main reason is the excellent item-specific encoding by enactment. However, there is little direct evidence in the case of free recall. The role of relational information is less clear. We suggest that content-based relational encoding is better in VTs than in SPTs. In three experiments, in which multiple free recall testing used item gains and losses as indices of item-specific and content-based relational encoding, respectively, these assumptions were confirmed. Consistently more gains (indexing better item-specific encoding) and more losses (indexing poorer relational encoding) were observed in SPTs than in VTs (Experiments 1 and 2). Furthermore, it was demonstrated that the content-based relational information underlying losses is not identical with order-relational information (Experiment 2). In Experiment 3, it was shown that an item-specific orienting task for VTs produced an equivalent number of item gains and losses as did the SPT condition.     

Social Identity and Corporate Mergers

Corporate mergers require proper human resources management to reach their financial and strategic objectives and minimize negative consequences for employee well‐being. Understanding the antecedents of employees’ identification with the merged organization during the corporate merger is crucial, because stronger post‐merger identification results in less conflict and higher levels of motivation. Unfortunately, employees often identify more strongly with their pre‐merger organizations than with the merged organization. One influential approach to understanding the processes underlying organizational identification is the social identity approach ( Tajfel & Turner, 1986 ; Turner, Hogg, Oakes, Reicher, & Wetherell, 1987 ). Research applying this perspective to organizational mergers shows that levels of identification with the merged organization are partly explained by status and dominance differences of the involved organizations, by motivational threats and uncertainties during the merger, and by the representation of the post‐merger identity. Leaders and managers of corporate mergers are able to influence these processes and, thus, to provide a path for successful merger integration.     

A Computational and Empirical Investigation of Graphemes in Reading

It is often assumed that graphemes are a crucial level of orthographic representation above letters. Current connectionist models of reading, however, do not address how the mapping from letters to graphemes is learned. One major challenge for computational modeling is therefore developing a model that learns this mapping and can assign the graphemes to linguistically meaningful categories such as the onset, vowel, and coda of a syllable. Here, we present a model that learns to do this in English for strings of any letter length and any number of syllables. The model is evaluated on error rates and further validated on the results of a behavioral experiment designed to examine ambiguities in the processing of graphemes. The results show that the model (a) chooses graphemes from letter strings with a high level of accuracy, even when trained on only a small portion of the English lexicon; (b) chooses a similar set of graphemes as people do in situations where different graphemes can potentially be selected; (c) predicts orthographic effects on segmentation which are found in human data; and (d) can be readily integrated into a full‐blown model of multi‐syllabic reading aloud such as CDP++ (Perry, Ziegler, & Zorzi, 2010). Altogether, these results suggest that the model provides a plausible hypothesis for the kind of computations that underlie the use of graphemes in skilled reading.