An Event-related fMRI Study of Artificial Grammar Learning in a Balanced Chunk Strength Design

Matthew D. Lieberman1, Grace Y. Chang1, Joan Chiao2, Susan Y. Bookheimer1, and Barbara J. Knowlton1 Downloaded from http://mitprc.silverchair.com/jocn/article-pdf/16/3/427/1758106/089892904322926764.pdf by guest on 18 May 2021

Abstract & Artificial grammar learning (Reber, 1967) is a form of involved in using both sources of information as test stimuli implicit learning in which cognitive, rather than motor, were designed to unconfound chunk strength from rule use. implicit learning has been found. After viewing a series of Using functional connectivity analyses, the extent to which letter strings formed according to a finite state rule system, the sources of information are complementary or competi- people are able to classify new letter strings as to whether tive was also assessed. Activation in the right caudate was or not they are formed according to these grammatical rules associated with rule adherence, whereas medial temporal despite little conscious insight into the rule structure. lobe activations were associated with chunk strength. Addi- Previous research has shown that these classification judg- tionally, functional connectivity analyses revealed caudate ments are based on knowledge of abstract rules as well as and medial temporal lobe activations to be strongly superficial similarity (‘‘chunk strength’’) to training strings. negatively correlated (r = À.88) with one another during Here we used event-related fMRI to identify neural regions the performance of this task. &

INTRODUCTION ard & Knowlton, 2002; Knowlton & Squire, 1996). How- Having good intuition about ‘‘what fits’’ and ‘‘what’s ever, there is a lack of consensus regarding the brain coming next’’ is essential to achieving one’s goals and regions that support implicit learning, what these re- meeting one’s obligations efficiently and effectively. Life gions contribute computationally, and the extent to is filled with scripts and recipes that have natural which the various neurocognitive mechanisms of implic- sequences and humans routinely take advantage of the it learning operate in a competitive or complementary predictability of these sequences to coordinate their fashion. There are at least three different reasons for this thought and behavior (Lieberman, 2000; Schank & Abel- discord, each embedded in the methodologies used to son, 1977). Automating this knowledge of sequential study the neural substrates of implicit learning. regularities has the additional benefit that the relevant First, most of the existing studies have focused on representations will be activated spontaneously in the neuropsychological populations. Several of these studies presence of the sequential cues. Social interactions suggest that the basal ganglia are involved in implicit which depend on the simultaneous coordination of learning, while medial temporal areas may not be. Pa- multiple processes undoubtedly benefit from an ability tients with Parkinson’s and Huntington’s disease have to automatically infer from facial expressions and tone of increasingly disturbed basal ganglia function over time. voice a great deal of information about another’s inten- Numerous studies have shown implicit learning to be tions, evaluations, and personality (Chartrand & Jefferis, absent or impaired in both groups (Knowlton, Mangels, in press; Lieberman & Rosenthal, 2001; Ambady & Rosen- & Squire, 1996; Knowlton, Squire, Paulsen, Swerdlow, & thal, 1993; Swann, Stein-Seroussi, & McNulty, 1992). Swenson, 1996; Gabrieli, 1995; Ferraro, Balota, & Can- Implicit learning refers to the ability to learn informa- nor, 1993; Knopman & Nissen, 1991; Heindel, Salmon, tional sequences when there is no conscious intent to Shultz, Welicke, & Butters, 1989; Martone, Butters, extract this sequential information and no explicit knowl- Payne, Becker, & Sax, 1984), but spared in patients with edge that this information has been learned (Stadler & medial temp