When the happy victimizer says sorry: Children's understanding of apology and emotion

Previous research suggests that children gradually understand the mitigating effects of apology on damage to a transgressor's reputation. However, little is known about young children's insights into the central emotional implications of apology. In two studies, children ages 4–9 heard stories about moral transgressions in which the wrongdoers either did or did not apologize. In Study 1, children in the no‐apology condition showed the classic pattern of ‘happy victimizer’ attributions by expecting the wrongdoer to feel good about gains won via transgression. By contrast, in the apology condition, children attributed negative feelings to the transgressor and improved feelings to the victim. In Study 2, these effects were found even when the explicit emotion marker ‘sorry’ was removed from the apology exchange. Thus, young children understand some important emotional functions of apology.     

Hope as a Predictor of Interpersonal Suicide Risk

The current study hypothesized that (1) hope would negatively predict burdensomeness, thwarted belongingness, and acquired capability to enact lethal injury; (2) hope would negatively predict suicidal ideation; and (3) the interpersonal suicide risk factors would predict suicidal ideation. Results indicated that hope negatively predicted burdensomeness and thwarted belongingness, but positively predicted acquired capability to enact suicide. Contrary to our second hypothesis, hope did not predict suicidal ideation, but interpersonal risk factors for suicide predicted suicidal ideation. Results are discussed in terms of implications for hope theory and Joiner's (2005) interpersonal risk factors for suicide, and for clinical practice.     

Two ways of learning associations

How people learn chunks or associations between adjacent items in sequences was modelled. Two previously successful models of how people learn artificial grammars were contrasted: the CCN, a network version of the competitive chunker of Servan‐Schreiber and Anderson [J. Exp. Psychol.: Learn. Mem. Cogn. 16 (1990) 592], which produces local and compositionally‐structured chunk representations acquired incrementally; and the simple recurrent network (SRN) of Elman [Cogn. Sci. 14 (1990) 179], which acquires distributed representations through error correction. The models' susceptibility to two types of interference was determined: prediction conflicts, in which a given letter can predict two other letters that appear next with an unequal frequency; and retroactive interference, in which the prediction made by a letter changes in the second half of training. The predictions of the models were determined by exploring parameter space and seeing howdensely different regions of the space of possible experimental outcomes were populated by model outcomes. For both types of interference, human data fell squarely in regions characteristic of CCN performance but not characteristic of SRN performance.