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Prior research has repeatedly implicated the striatum in implicit sequence learning; however, imaging findings have been inconclusive with respect to the sub-territories and laterality involved. Using functional magnetic resonance imaging (fMRI), we studied brain activation profiles associated with performance of the serial reaction time task (SRT) in 10 normal right-handed males. Behavioral results indicate that significant implicit learning occurred, uncontaminated by significant explicit knowledge. Concatenated fMRI data from the entire cohort revealed significant right-lateralized activation in both the caudate and putamen. Analysis of fMRI data from individual subjects showed inter-individual variability as to the precise territories involved, including right as well as left caudate and putamen. Interestingly, all seven subjects who manifested robust learning effects exhibited significant activation within the putamen. Moreover, among those seven subjects, the magnitude of signal intensity change within the putamen correlated significantly with the magnitude of reaction time advantage achieved. These findings demonstrate right-sided striatal activation across subjects during implicit sequence learning, but also highlight interindividual variability with respect to the laterality and striatal subterritories involved. In particular, results from individual subjects suggest that, during the SRT, the reaction time advantage garnered via implicit sequence learning might be predominantly associated with activity within the putamen.

Abstract:The purpose of this study was to determine the mediating neuroanatomy of implicit and explicit sequence learning using a modified version of the serial reaction time (SRT) paradigm. Subjects were seven healthy, right-handed adults (three male, four female, mean age 26.7, range 1843 yr). PET data were acquired via the oxygen-15-labeled-carbon dioxide inhalation method while subjects performed the SRT. Subjects were scanned during two blocks each of I) no sequence (Random), 2 ) single-blind, 12-item sequence (Implicit), and 3) unblinded, same sequence (Explicit). Whole-brain-normalized images reflecting relative regional cerebral blood flow (rCBF) were transformed to Talairach space, and statistical parametric maps (SPMs) of z-scores were generated for comparisons of interest. The threshold for significant activation was defined as z-score 2 3.00. Behavioral data demonstrated significant learning (P < .05) for Implicit and Explicit conditions. Tests of explicit knowledge reflected non-significant explicit contamination during the Implicit condition. Foci of significant activation in the Implicit condition were found in right ventral premotor cortex, right ventral caudate/nucleus accumbens, right thalamus, and bilateral area 19; activation in the Explicit condition included primary visual cortex, peri-sylvian cortex, and cerebellar vermis. Activations in visual and language areas during the Explicit condition may reflect conscious learning strategies including covert verbal rehearsal and visual imagery. Right-sided premotor, striatal, and thalamic activations support the notion that implicit sequence learning is mediated by cortico-striatal pathways, preferentially within the right hemisphere. o 1996 Wiky-Liss, h e .

Electroencephalographic (EEG) recordings were made while 16 participants performed versions of a personal-computer-based flight simulation task of low, moderate, or high difficulty. As task difficulty increased, frontal midline theta EEG activity increased and alpha band activity decreased. A participant-specific function that combined multiple EEG features to create a single load index was derived from a sample of each participant's data and then applied to new test data from that participant. Index values were computed for every 4 s of task data. Across participants, mean task load index values increased systematically with increasing task difficulty and differed significantly between the different task versions. Actual or potential applications of this research include the use of multivariate EEG-based methods to monitor task loading during naturalistic computer-based work.

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