Publications

Abstract

Purpose of review: The 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 Seger's 1994 article and the Handbook of Implicit Learning. Recent findings: Emerging 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. Summary: The 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.

Abstract

In this paper we investigate the neural correlates of syntactic classification of an acquired grammatical sequence structure in an event-related FMRI study. During acquisition, participants were engaged in an implicit short-term memory task without performance feedback. We manipulated the statistical frequency-based and rule-based characteristics of the classification stimuli independently in order to investigate their role in artificial grammar acquisition. The participants performed reliably above chance on the classification task. We observed a partly overlapping corticostriatal processing network activated by both manipulations including inferior prefrontal, cingulate, inferior parietal regions, and the caudate nucleus. More specifically, the left inferior frontal BA 45 and the caudate nucleus were sensitive to syntactic violations and endorsement, respectively. In contrast, these structures were insensitive to the frequency-based manipulation.

Abstract

The posterior medial parietal cortex and left prefrontal cortex (PFC) have both been implicated in the recollection of past episodes. In a previous study, we found the posterior precuneus and left lateral inferior frontal cortex to be activated during episodic source memory retrieval. This study further examines the role of posterior precuneal and left prefrontal activation during episodic source memory retrieval using a similar source memory paradigm but with longer latency between encoding and retrieval. Our results suggest that both the precuneus and the left inferior PFC are important for regeneration of rich episodic contextual associations and that the precuneus activates in tandem with the left inferior PFC during correct source retrieval. Further, results suggest that the left ventro-lateral frontal region/ frontal operculum is involved in searching for task-relevant information (BA 47) and subsequent monitoring or scrutiny (BA 44/45) while regions in the dorsal inferior frontal cortex are important for information selection (BA 45/46).

Abstract

Recent FMRI studies indicate that language related brain regions are engaged in artificial grammar (AG) processing. In the present study we investigate the Reber grammar by means of formal analysis and network simulations. We outline a new method for describing the network dynamics and propose an approach to grammar extraction based on the state-space dynamics of the network. We conclude that statistical frequency-based and rule-based acquisition procedures can be viewed as complementary perspectives on grammar learning, and more generally, that classical cognitive models can be viewed as a special case of a dynamical systems perspective on information processing

Abstract

We present two simple arguments for the potential relevance of a neurobiological analogue of the finite-state architecture. The first assumes the classical cognitive framework, is wellknown, and is based on the assumption that the brain is finite with respect to its memory organization. The second is formulated within a general dynamical systems framework and is based on the assumption that the brain sustains some level of noise and/or does not utilize infinite precision processing. We briefly review the classical cognitive framework based on Church–Turing computability and non-classical approaches based on analog processing in dynamical systems. We conclude that the dynamical neurobiological analogue of the finitestate architecture appears to be relevant, at least at an implementational level, for cognitive brain systems

Abstract

A recurrently connected attractor neural network with a Hebbian learning rule is currently our best ANN analogy for a piece cortex. Functionally biological memory operates on a spectrum of time scales with regard to induction and retention, and it is modulated in complex ways by sub-cortical neuromodulatory systems. Moreover, biological memory networks are commonly believed to be highly distributed and engage many co-operating cortical areas. Here we focus on the temporal aspects of induction and retention of memory in a connectionist type attractor memory model of a piece of cortex. A continuous time, forgetful Bayesian-Hebbian learning rule is described and compared to the characteristics of LTP and LTD seen experimentally. More generally, an attractor network implementing this learning rule can operate as a long-term, intermediate-term, or short-term memory. Modulation of the print-now signal of the learning rule replicates some experimental memory phenomena, like e.g. the von Restorff effect.

Abstract

It has previously been suggested that the activity in sensory regions of the brain can be modulated by attentional mechanisms during parallel cognitive processing. To investigate whether such attention-related modulations are present in the processing of pain, the regional cerebral blood ¯ow was measured using [15O]butanol and positron emission tomography in conditions involving both pain and parallel cognitive demands. The painful stimulus consisted of the standard cold pressor test and the cognitive task was a computerised perceptual maze test. The activations during the maze test reproduced findings in previous studies of the same cognitive task. The cold pressor test evoked signi®cant activity in the contralateral S1, and bilaterally in the somatosensory association areas (including S2), the ACC and the mid-insula. The activity in the somatosensory association areas and periaqueductal gray/midbrain were significantly modified, i.e. relatively decreased, when the subjects also were performing the maze task. The altered activity was accompanied with significantly lower ratings of pain during the cognitive task. In contrast, lateral orbitofrontal regions showed a relative increase of activity during pain combined with the maze task as compared to only pain, which suggests the possibility of the involvement of frontal cortex in modulation of regions processing pain

Abstract

Capacity limited memory systems need to gradually forget old information in order to avoid catastrophic forgetting where all stored information is lost. This can be achieved by allowing new information to overwrite old, as in the so-called palimpsest memory. This paper describes a new such learning rule employed in an attractor neural network. The network does not exhibit catastrophic forgetting, has a capacity dependent on the learning time constant and exhibits recency e!ects in retrieval

Abstract

Understanding the role of the medial temporal lobe (MTL) in learning and memory is an important problem in cognitive neuroscience. Memory and learning processes that depend on the function of the MTL and related diencephalic structures (e.g., the anterior and mediodorsal thalamic nuclei) are defined as declarative. We have studied the MTL activity as indicated by regional cerebral blood flow with positron emission tomography and statistical parametric mapping during recall of abstract designs in a less practiced memory state as well as in a well-practiced (well-encoded) memory state. The results showed an increased activity of the MTL bilaterally (including parahippocampal gyrus extending into hippocampus proper, as well as anterior lingual and anterior fusiform gyri) during retrieval in the less practiced memory state compared to the well-practiced memory state, indicating a dynamic role of the MTL in retrieval during the learning processes. The results also showed that the activation of the MTL decreases as the subjects learn to draw abstract designs from memory, indicating a changing role of the MTL during recall in the earlier stages of acquisition compared to the well-encoded declarative memory state.