Learning a Generative Probabilistic Grammar of Experience: A Process‐Level Model of Language Acquisition

We introduce a set of biologically and computationally motivated design choices for modeling the learning of language, or of other types of sequential, hierarchically structured experience and behavior, and describe an implemented system that conforms to these choices and is capable of unsupervised learning from raw natural‐language corpora. Given a stream of linguistic input, our model incrementally learns a grammar that captures its statistical patterns, which can then be used to parse or generate new data. The grammar constructed in this manner takes the form of a directed weighted graph, whose nodes are recursively (hierarchically) defined patterns over the elements of the input stream. We evaluated the model in seventeen experiments, grouped into five studies, which examined, respectively, (a) the generative ability of grammar learned from a corpus of natural language, (b) the characteristics of the learned representation, (c) sequence segmentation and chunking, (d) artificial grammar learning, and (e) certain types of structure dependence. The model's performance largely vindicates our design choices, suggesting that progress in modeling language acquisition can be made on a broad front—ranging from issues of generativity to the replication of human experimental findings—by bringing biological and computational considerations, as well as lessons from prior efforts, to bear on the modeling approach.     

House sparrows use learned information selectively based on whether reward is hidden or visible

Animal Cognition - Based on past experience, food-related-cues can help foragers to predict the presence and the expected quality of food. However, when the food is already visible there is no need...     

In Eating‐Disordered Inpatient Adolescents,Self‐Criticism Predicts Nonsuicidal Self‐Injury

We examined the role of depressive traits—self‐criticism and dependency—in nonsuicidal self‐injury (NSSI) and suicidal ideation among inpatient adolescents with eating disorders. In two studies (N = 103 and 55), inpatients were assessed for depressive traits, suicidal ideations, and NSSI. In Study 2, motivation for carrying out NSSI was also assessed. In both studies, depression predicted suicidal ideation and self‐criticism predicted NSSI. In Study 2, depression and suicidal ideation also predicted NSSI. The automatic positive motivation for NSSI was predicted by dependency and depressive symptoms, and by a two‐way interaction between self‐criticism and dependency. Consistent with the “self‐punishment model,” self‐criticism appears to constitute a dimension of vulnerability for NSSI.