Role of prior knowledge in implicit and explicit learning of artificial grammars

Artificial grammar learning (AGL) performance reflects both implicit and explicit processes and has typically been modeled without incorporating any influence from general world knowledge. Our research provides a systematic investigation of the implicit vs. explicit nature of general knowledge and its interaction with knowledge types investigated by past AGL research (i.e., rule- and similarity-based knowledge). In an AGL experiment, a general knowledge manipulation involved expectations being either congruent or incongruent with training stimulus structure. Inconsistent observations paradoxically led to an advantage in structural knowledge and in the use of general world knowledge in both explicit (conscious) and implicit (unconscious) cases (as assessed by subjective measures). The above findings were obtained under conditions of reduced processing time and impaired executive resources. Key findings from our work are that implicit AGL can clearly be affected by general knowledge, and implicit learning can be enhanced by the violation of expectations.     

Evidence for knowledge of the syntax of large numbers in preschoolers

The aim of this study was to provide evidence for knowledge of the syntax governing the verbal form of large numbers in preschoolers long before they are able to count up to these numbers. We reasoned that if such knowledge exists, it should facilitate the maintenance in short-term memory of lists of lexical primitives that constitute a number (e.g., three hundred forty five) compared with lists containing the same primitives but in a scrambled order (e.g., five three forty hundred). The two types of lists were given to 5-year-olds in an immediate serial recall task. As we predicted, the lists in syntactic order were easier to recall, suggesting that they match some knowledge of the way lexical primitives must be ordered to express large numerosities.     

Together we are strong: Explicit and implicit paranormal beliefs predict performance in a knowledge test of paranormal phenomena better than explicit beliefs alone

The belief in paranormal phenomena is a frequently studied topic. Studies predominantly assess explicit (i.e., conscious) parts of paranormal belief (PB) using questionnaire-based self-report measures which are prone to impression management and social desirability tendencies. In order to investigate the usefulness of measuring implicit (i.e., automatic) PB, we developed a PB Implicit Association Test (PB-IAT). Implicit PB was uncorrelated with explicit PB, but moderated the relationship between explicit PB and participants’ knowledge of paranormal phenomena. Participants with a weak implicit PB did not differ in their knowledge scores regardless of whether they had strong or weak explicit PB. But participants with strong implicit PB had higher scores when they also had strong explicit PB compared to participants with weak explicit PB. These results suggest that discrepant configurations of PB impair performance in a knowledge test about paranormal phenomena.