Core Cognitive Mechanisms Underlying Syntactic Priming: A Comparison of Three Alternative Models

Syntactic priming (SP) is the effect by which, in a dialogue, the current speaker tends to re-use the syntactic constructs of the previous speakers. SP has been used as a window into the nature of syntactic representations within and across languages. Because of its importance, it is crucial to understand the mechanisms behind it. Currently, two competing theories exist. According to the transient activation account, SP is driven by the re-activation of declarative memory structures that encode structures. According to the error-based implicit learning account, SP is driven by prediction errors while processing sentences. By integrating both transient activation and associative learning, Reitter et al.'s hybrid model 2011 assumes that SP is achieved by both mechanisms, and predicts a priming enhancement for rare or unusual constructions. Finally, a recently proposed account, the reinforcement learning account, claims that SP driven by the successful application of procedural knowledge will be reversed when the prime sentence includes grammatical errors. These theories make different assumptions about the representation of syntactic rules (declarative vs. procedural) and the nature of the mechanism that drives priming (frequency and repetition, attention, and feedback signals, respectively). To distinguish between these theories, they were all implemented as computational models in the ACT-R cognitive architecture, and their specific predictions were examined through grid-search computer simulations. Two experiments were then carried out to empirically test the central prediction of each theory as well as the individual fits of each participant's responses to different parameterizations of each model. The first experiment produced results that were best explained by the associative account, but could also be accounted for by a modified reinforcement model with a different parsing algorithm. The second experiment, whose stimuli were designed to avoid the parsing ambiguity of the first, produced somewhat weaker effects. Its results, however, were also best predicted by the model implementing the associative account. We conclude that the data overall points to SP being due to prediction violations that direct attentional resources, in turn suggesting a declarative rather than a RL based procedural representation of syntactic rules.

The development of body representations: an associative learning account

Representing one's own body is of fundamental importance to interact with our environment, yet little is known about how body representations develop. One account suggests that the ability to represent one's own body is present from birth and supports infants' ability to detect similarities between their own and others’ bodies. However, in recent years evidence has been accumulating for alternative accounts that emphasize the role of multisensory experience obtained through acting and interacting with our own body in the development of body representations. Here, we review this evidence, and propose an integrative account that suggests that through experience, infants form multisensory associations that facilitate the development of body representations. This associative account provides a coherent explanation for previous developmental findings, and generates novel hypotheses for future research.

A Neurobiological Account of False Memories

This chapter discusses human functional neuroimaging findings about how the brain creates true and false memories. These studies have shown that different brain systems contribute to the creation and retrieval of false memories, including systems for sensory perception, executive functioning and cognitive control, and the medial temporal lobe, which has long been associated with episodic and autobiographical memory formation. Many neuroimaging findings provide support for an associative account of false memories, which proposes that false memories arise from associating unrelated mental experiences in memory. At the same time, other neuroimaging findings suggest that false memory creation may depend on states of brain activity during memory encoding. Finally, the chapter briefly provides cautionary notes about using functional neuroimaging as a tool to assess private mental states in individual cases in the courtroom.

The Neural Correlates of Similarity- and Rule-based Generalization

The idea that there are multiple learning systems has become increasingly influential in recent years, with many studies providing evidence that there is both a quick, similarity-based or feature-based system and a more effortful rule-based system. A smaller number of imaging studies have also examined whether neurally dissociable learning systems are detectable. We further investigate this by employing for the first time in an imaging study a combined positive and negative patterning procedure originally developed by Shanks and Darby [Shanks, D. R., & Darby, R. J. Feature- and rule-based generalization in human associative learning. Journal of Experimental Psychology: Animal Behavior Processes, 24, 405–415, 1998]. Unlike previous related studies employing other procedures, rule generalization in the Shanks–Darby task is beyond any simple non-rule-based (e.g., associative) account. We found that rule- and similarity-based generalization evoked common activation in diverse regions including the pFC and the bilateral parietal and occipital lobes indicating that both strategies likely share a range of common processes. No differences between strategies were identified in whole-brain comparisons, but exploratory analyses indicated that rule-based generalization led to greater activation in the right middle frontal cortex than similarity-based generalization. Conversely, the similarity group activated the anterior medial frontal lobe and right inferior parietal lobes more than the rule group did. The implications of these results are discussed.