Christian Forkstam

Common prefrontal activations during working memory, episodic memory, and semantic memory

Regions of the prefrontal cortex (PFC) are typically activated in many different cognitive functions. In most studies, the focus has been on the role of specific PFC regions in specific cognitive domains, but more recently similarities in PFC activations across cognitive domains have been stressed. Such similarities may suggest that a region mediates a common function across a variety of cognitive tasks. In this study, we compared the activation patterns associated with tests of working memory, semantic memory and episodic memory. The results converged on a general involvement of four regions across memory tests. These were located in left frontopolar cortex, left mid-ventrolateral PFC, left mid-dorsolateral PFC and dorsal anterior cingulate cortex. These findings provide evidence that some PFC regions are engaged during many different memory tests. The findings are discussed in relation to theories about the functional contribution of the PFC regions and the architecture of memory.

Brain imaging of human memory systems: Between-systems similarities and within-system differences

There is much evidence for the existence of multiple memory systems. However, it has been argued that tasks assumed to reflect different memory systems share basic processing components and are mediated by overlapping neural systems. Here we used multivariate analysis of PET-data to analyze similarities and differences in brain activity for multiple tests of working memory, semantic memory, and episodic memory. The results from two experiments revealed between-systems differences, but also between-systems similarities and within-system differences. Specifically, support was obtained for a task-general working-memory network that may underlie active maintenance. Premotor and parietal regions were salient components of this network. A common network was also identified for two episodic tasks, cued recall and recognition, but not for a test of autobiographical memory. This network involved regions in right inferior and polar frontal cortex, and lateral and medial parietal cortex. Several of these regions were also engaged during the working-memory tasks, indicating shared processing for episodic and working memory. Fact retrieval and synonym generation were associated with increased activity in left inferior frontal and middle temporal regions and right cerebellum. This network was also associated with the autobiographical task, but not with living/non-living classification, and may reflect elaborate retrieval of semantic information. Implications of the present results for the classification of memory tasks with respect to systems and/or processes are discussed.

Neural correlates of artificial syntactic structure classification.

The human brain supports acquisition mechanisms that extract structural regularities implicitly from experience without the induction of an explicit model. It has been argued that the capacity to generalize to new input is based on the acquisition of abstract representations, which reflect underlying structural regularities in the input ensemble. In this study, we explored the outcome of this acquisition mechanism, and to this end, we investigated the neural correlates of artificial syntactic classification using event-related functional magnetic resonance imaging. The participants engaged once a day during an 8-day period in a short-term memory acquisition task in which consonant-strings generated from an artificial grammar were presented in a sequential fashion without performance feedback. They performed reliably above chance on the grammaticality classification tasks on days 1 and 8 which correlated with a corticostriatal processing network, including frontal, cingulate, inferior parietal, and middle occipital/occipitotemporal regions as well as the caudate nucleus. Part of the left inferior frontal region (BA 45) was specifically related to syntactic violations and showed no sensitivity to local substring familiarity. In addition, the head of the caudate nucleus correlated positively with syntactic correctness on day 8 but not day 1, suggesting that this region contributes to an increase in cognitive processing fluency.

Towards an explicit account of implicit learning

Purpose of reviewThe human brain supports acquisition mechanisms that can extract structural regularities implicitly from experience without the induction of an explicit model. Reber defined the process by which an individual comes to respond appropriately to the statistical structure of the input ensemble as implicit learning. He argued that the capacity to generalize to new input is based on the acquisition of abstract representations that reflect underlying structural regularities in the acquisition input. We focus this review of the implicit learning literature on studies published during 2004 and 2005. We will not review studies of repetition priming (‘implicit memory’). Instead we focus on two commonly used experimental paradigms: the serial reaction time task and artificial grammar learning. Previous comprehensive reviews can be found in Segers 1994 article and the Handbook of Implicit Learning. Recent findingsEmerging themes include the interaction between implicit and explicit processes, the role of the medial temporal lobe, developmental aspects of implicit learning, age-dependence, the role of sleep and consolidation. SummaryThe attempts to characterize the interaction between implicit and explicit learning are promising although not well understood. The same can be said about the role of sleep and consolidation. Despite the fact that lesion studies have relatively consistently suggested that the medial temporal lobe memory system is not necessary for implicit learning, a number of functional magnetic resonance studies have reported medial temporal lobe activation in implicit learning. This issue merits further research. Finally, the clinical relevance of implicit learning remains to be determined.

The inferior frontal cortex in artificial syntax processing: An rTMS study

The human capacity to implicitly acquire knowledge of structured sequences has recently been investigated in artificial grammar learning using functional magnetic resonance imaging. It was found that the left inferior frontal cortex (IFC; Brodmanns area (BA) 44/45) was related to classification performance. The objective of this study was to investigate whether the IFC (BA 44/45) is causally related to classification of artificial syntactic structures by means of an off-line repetitive transcranial magnetic stimulation (rTMS) paradigm. We manipulated the stimulus material in a 2 x 2 factorial design with grammaticality status and local substring familiarity as factors. The participants showed a reliable effect of grammaticality on classification of novel items after 5 days of exposure to grammatical exemplars without performance feedback in an implicit acquisition task. The results show that rTMS of BA 44/45 improves syntactic classification performance by increasing the rejection rate of non-grammatical items and by shortening reaction times of correct rejections specifically after left-sided stimulation. A similar pattern of results is observed in FMRI experiments on artificial syntactic classification. These results suggest that activity in the inferior frontal region is causally related to artificial syntax processing.

Implicit Learning and Dyslexia

Several studies have reported an association between dyslexia and implicit learning deficits. It has been suggested that the weakness in implicit learning observed in dyslexic individuals may be related to sequential processing and implicit sequence learning. In the present article, we review the current literature on implicit learning and dyslexia. We describe a novel, forced‐choice structural “mere exposure” artificial grammar learning paradigm and characterize this paradigm in normal readers in relation to the standard grammaticality classification paradigm. We argue that preference classification is a more optimal measure of the outcome of implicit acquisition since in the preference version participants are kept completely unaware of the underlying generative mechanism, while in the grammaticality version, the subjects have, at least in principle, been informed about the existence of an underlying complex set of rules at the point of classification (but not during acquisition). On the basis of the “mere exposure effect,” we tested the prediction that the development of preference will correlate with the grammaticality status of the classification items. In addition, we examined the effects of grammaticality (grammatical/nongrammatical) and associative chunk strength (ACS; high/low) on the classification tasks (preference/grammaticality). Using a balanced ACS design in which the factors of grammaticality (grammatical/nongrammatical) and ACS (high/low) were independently controlled in a 2 × 2 factorial design, we confirmed our predictions. We discuss the suitability of this task for further investigation of the implicit learning characteristics in dyslexia.

Instruction effects in implicit artificial grammar learning : A preference for grammaticality

Human implicit learning can be investigated with implicit artificial grammar learning, a paradigm that has been proposed as a simple model for aspects of natural language acquisition. In the present study we compared the typical yes-no grammaticality classification, with yes-no preference classification. In the case of preference instruction no reference to the underlying generative mechanism (i.e., grammar) is needed and the subjects are therefore completely uninformed about an underlying structure in the acquisition material. In experiment 1, subjects engaged in a short-term memory task using only grammatical strings without performance feedback for 5 days. As a result of the 5 acquisition days, classification performance was independent of instruction type and both the preference and the grammaticality group acquired relevant knowledge of the underlying generative mechanism to a similar degree. Changing the grammatical stings to random strings in the acquisition material (experiment 2) resulted in classification being driven by local substring familiarity. Contrasting repeated vs. non-repeated preference classification (experiment 3) showed that the effect of local substring familiarity decreases with repeated classification. This was not the case for repeated grammaticality classifications. We conclude that classification performance is largely independent of instruction type and that forced-choice preference classification is equivalent to the typical grammaticality classification.

Sleep promotes the extraction of grammatical rules.

Grammar acquisition is a high level cognitive function that requires the extraction of complex rules. While it has been proposed that offline time might benefit this type of rule extraction, this remains to be tested. Here, we addressed this question using an artificial grammar learning paradigm. During a short-term memory cover task, eighty-one human participants were exposed to letter sequences generated according to an unknown artificial grammar. Following a time delay of 15 min, 12 h (wake or sleep) or 24 h, participants classified novel test sequences as Grammatical or Non-Grammatical. Previous behavioral and functional neuroimaging work has shown that classification can be guided by two distinct underlying processes: (1) the holistic abstraction of the underlying grammar rules and (2) the detection of sequence chunks that appear at varying frequencies during exposure. Here, we show that classification performance improved after sleep. Moreover, this improvement was due to an enhancement of rule abstraction, while the effect of chunk frequency was unaltered by sleep. These findings suggest that sleep plays a critical role in extracting complex structure from separate but related items during integrative memory processing. Our findings stress the importance of alternating periods of learning with sleep in settings in which complex information must be acquired.

Cortical brain regions associated with color processing: An FMRI study

To clarify whether the neural pathways concerning color processing are the same for natural objects, for artifacts objects and for non-objects we examined brain responses measured with functional magnetic resonance imaging (FMRI) during a covert naming task including the factors color (color vs. black&white (B&W)) and stimulus type (natural vs. artifacts vs. non-objects). Our results indicate that the superior parietal lobule and precuneus (BA 7) bilaterally, the right hippocampus and the right fusifom gyrus (V4) make part of a network responsible for color processing both for natural objects and artifacts, but not for non-objects. When color objects (both natural and artifacts) were contrasted with color non-objects we observed activations in the right parahippocampal gyrus (BA 35/36), the superior parietal lobule (BA 7) bilaterally, the left inferior middle temporal region (BA 20/21) and the inferior and superior frontal regions (BA 10/11/47). These additional activations suggest that colored objects recruit brain regions that are related to visual semantic information/retrieval and brain regions related to visuo-spatial processing. Overall, the results suggest that color information is an attribute that can improve object recognition (behavioral results) and activate a specific neural network related to visual semantic information that is more extensive than for B&W objects during object recognition.

The Interaction between Surface Color and Color Knowledge: Behavioral and Electrophysiological Evidence.

In this study, we used event-related potentials (ERPs) to evaluate the contribution of surface color and color knowledge information in object identification. We constructed two color-object verification tasks - a surface and a knowledge verification task - using high color diagnostic objects; both typical and atypical color versions of the same object were presented. Continuous electroencephalogram was recorded from 26 subjects. A cluster randomization procedure was used to explore the differences between typical and atypical color objects in each task. In the color knowledge task, we found two significant clusters that were consistent with the N350 and late positive complex (LPC) effects. Atypical color objects elicited more negative ERPs compared to typical color objects. The color effect found in the N350 time window suggests that surface color is an important cue that facilitates the selection of a stored object representation from long-term memory. Moreover, the observed LPC effect suggests that surface color activates associated semantic knowledge about the object, including color knowledge representations. We did not find any significant differences between typical and atypical color objects in the surface color verification task, which indicates that there is little contribution of color knowledge to resolve the surface color verification. Our main results suggest that surface color is an important visual cue that triggers color knowledge, thereby facilitating object identification.