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

Abstract

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Formal language hierarchy reflects different levels of cognitive complexity.

“…A question that makes formal language hierarchy interesting to cognitive science—and that remains open—is whether these different levels capture discontinuities in human computational power for language (Fitch & Friederici, 2012), that is, (a) whether adjacent dependencies are unquestionably easier to process than nested and (b) whether nested are easier than crossed. In the present study, we addressed these two open questions, equating computational power (ease of processing) with implicit learnability, that is, the ability to learn without intention and without awareness of what has been learned (Ziori et al, 2014).…”

“…One exception was second-pass reading (eye tracking), which lost sensitivity when we considered the learning outcomes of purely implicit learners. Therefore, we then raised the hypothesis that second-pass reading could reflect explicit influences on a process designed to be implicit but that is nevertheless susceptible to these (Ziori et al, 2014). Maximizing the identification of possible explicit-knowledge influences was, thus, another reason for using eye-tracking measures in the present study.…”

Facial beauty affects implicit and explicit learning of men and women differently

“…The AGL task has traditionally used strings of letters, shapes and other symbols involving no meaningful pre-existing associations. More recent variations employed strings of stimuli eliciting people’s prior knowledge and expectations, and showed that implicit AGL reflects selective processes and may be affected by people’s prior (geography) knowledge ( Ziori et al, 2014 ), goals ( Eitam et al, 2009 ), and motivational relevance ( Eitam and Higgins, 2010 , 2014 ; Eitam et al, 2013 ; cf Eitam et al, 2014 for evidence of implicit learning of faces when they were task irrelevant). The present work aims to extend the above AGL research in a different knowledge-rich context and examine whether the processing of beautiful faces, which have been shown to trigger the reward center of brain, is associated more with implicit or with explicit knowledge.…”

Wordless wisdom: The dominant role of tacit knowledge in true and fake news discrimination.

“…There are various ways to measure tacit and explicit knowledge (Ellis & Roever, 2021). One is through self-report measures sometimes used in implicit learning experiments (Dienes & Scott, 2005; Neil & Higham, 2012; Scott & Dienes, 2008; Ziori et al, 2014). In implicit learning studies that use artificial grammars, participants are first exposed to multiple exemplars of letter strings (e.g., MVXRT) that conform to a finite state grammar.…”

“…After each veracity rating, participants indicated the decision strategy they used, which could be one of the following: guess , intuition , familiarity , prior knowledge , rule , or other . The guess , intuition , rule , and familiarity decision strategies were adapted from Scott and Dienes (2008), and the prior knowledge decision strategy was adapted from Ziori et al (2014). We added other in case participants used a decision strategy we had not considered.…”