Contributions of different prefrontal cortical regions to abstract rule acquisition and reversal in monkeys (original) (raw)
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Frontiers in neuroscience, 2018
The ability to learn and follow abstract rules relies on intact prefrontal regions including the lateral prefrontal cortex (LPFC) and the orbitofrontal cortex (OFC). Here, we investigate the specific roles of these brain regions in learning rules that depend critically on the formation of abstract concepts as opposed to simpler input-output associations. To this aim, we tested monkeys with bilateral removals of either LPFC or OFC on a rapidly learned task requiring the formation of the abstract concept of same vs. different. While monkeys with OFC removals were significantly slower than controls at both acquiring and reversing the concept-based rule, monkeys with LPFC removals were not impaired in acquiring the task, but were significantly slower at rule reversal. Neither group was impaired in the acquisition or reversal of a delayed visual cue-outcome association task without a concept-based rule. These results suggest that OFC is essential for the implementation of a concept-based...
2006
Abstract The ability to use abstract rules or principles allows behavior to generalize from specific circumstances. We have previously shown that such rules are encoded in the lateral prefrontal cortex (PFC) and premotor cortex (PMC). Here, we extend these investigations to two other areas directly connected with the PFC and the PMC, the inferior temporal cortex (ITC) and the dorsal striatum (STR). Monkeys were trained to use two abstract rules:“same” or “different”.
Neural Circuits Subserving the Retrieval and Maintenance of Abstract Rules
Journal of Neurophysiology, 2003
Behavior is often governed by abstract rules or instructions for behavior that can be abstracted from one context and applied to another. Prefrontal cortex (PFC) is thought to be important for representing rules, although the contributions of ventrolateral (VLPFC) and dorsolateral (DLPFC) regions remain under-specified. In the present study, event-related fMRI was used to examine abstract rule representation in humans. Prior to scanning, subjects learned to associate unfamiliar shapes and nonwords with particular rules. During each fMRI trial, presentation of one of these cues was followed by a delay and then by sample and probe stimuli. Match and non-match rules required subjects to indicate whether or not the sample and probe matched; go rules required subjects to make a response that was not contingent on the sample/probe relation. Left VLPFC, parietal cortex, and pre-SMA exhibited sensitivity to rule type during the cue and delay periods. Delay-period activation in these regions...
Single neurons in prefrontal cortex encode abstract rules
Nature, 2001
The ability to abstract principles or rules from direct experience allows behaviour to extend beyond specific circumstances to general situations. For example, we learn the 'rules' for restaurant dining from specific experiences and can then apply them in new restaurants. The use of such rules is thought to depend on the prefrontal cortex (PFC) because its damage often results in difficulty in following rules 1. Here we explore its neural basis by recording from single neurons in the PFC of monkeys trained to use two abstract rules. ...
Abstract Rule Learning: The Differential Effects of Lesions in Frontal Cortex
Cerebral Cortex, 2013
Learning progressively more abstract stimulus-response mappings requires progressively more anterior regions of the lateral frontal cortex. Using an individual differences approach, we studied subjects with frontal lesions performing a hierarchical reinforcement-learning task to investigate how frontal cortex contributes to abstract rule learning. We predicted that subjects with lesions of the left pre-premotor (pre-PMd) cortex, a region implicated in abstract rule learning, would demonstrate impaired acquisition of second-order, as opposed to first-order, rules. We found that 4 subjects with such lesions did indeed demonstrate a second-order rule-learning impairment, but that these subjects nonetheless performed better than subjects with other frontal lesions in a second-order rule condition. This finding resulted from both their restricted exploration of the feature space and the task structure of this condition, for which they identified partially representative first-order rules. Significantly, across all subjects, suboptimal but above-chance performance in this condition correlated with increasing disconnection of left pre-PMd from the putative functional hierarchy, defined by reduced functional connectivity between left pre-PMd and adjacent nodes. These findings support the theory that activity within lateral frontal cortex shapes the search for relevant stimulus-response mappings, while emphasizing that the behavioral correlate of impairments depends critically on task structure.
Dissociable Components of Rule-Guided Behavior Depend on Distinct Medial and Prefrontal Regions
Science, 2009
Much of our behavior is guided by rules. Although human prefrontal cortex (PFC) and anterior cingulate cortex (ACC) are implicated in implementing rule-guided behavior, the crucial contributions made by different regions within these areas are not yet specified. In an attempt to bridge human neuropsychology and nonhuman primate neurophysiology, we report the effects of circumscribed lesions to macaque orbitofrontal cortex (OFC), principal sulcus (PS), superior dorsolateral PFC, ventrolateral PFC, or ACC sulcus, on separable cognitive components of a Wisconsin Card Sorting Test (WCST) analog. Only the PS lesions impaired maintenance of abstract rules in working memory; only the OFC lesions impaired rapid reward-based updating of representations of rule value; the ACC sulcus lesions impaired active reference to the value of recent choice-outcomes during rule-based decision-making.
From Rule to Response: Neuronal Processes in the Premotor and Prefrontal Cortex
Journal of Neurophysiology, 2003
The ability to use abstract rules or principles allows behavior to generalize from specific circumstances (e.g., rules learned in a specific restaurant can subsequently be applied to any dining experience). Neurons in the prefrontal cortex (PFC) encode such rules. However, to guide behavior, rules must be linked to motor responses. We investigated the neuronal mechanisms underlying this process by recording from the PFC and the premotor cortex (PMC) of monkeys trained to use two abstract rules: “same” or “different.” The monkeys had to either hold or release a lever, depending on whether two successively presented pictures were the same or different, and depending on which rule was in effect. The abstract rules were represented in both regions, although they were more prevalent and were encoded earlier and more strongly in the PMC. There was a perceptual bias in the PFC, relative to the PMC, with more PFC neurons encoding the presented pictures. In contrast, neurons encoding the beh...
Neural activity in the primate prefrontal cortex during associative learning
Neuron, 1998
CITATIONS 353 READS 43 3 authors: Some of the authors of this publication are also working on these related projects: Synchrony of beta oscillations in the frontoparietal network View project Category representations in parietal and prefrontal cortex on different levels of category abstractness
NeuroImage, 2006
It is a topic of current interest whether learning in humans relies on the acquisition of abstract rule knowledge (rule-based learning) or whether it depends on superficial item-specific information (instance-based learning). Here, we identified brain regions that mediate either of the two learning mechanisms by combining fMRI with an experimental protocol shown to be able to dissociate both learning mechanisms. Subjects had to learn object-position conjunctions in several trials and blocks. In a learning condition, either objects (Experiment 1) or positions (Experiment 2) were held constant within-blocks. In contrast to a control condition in which object -position conjunctions were trialunique, a performance increase within and across-blocks was observed in the learning condition of both experiments. We hypothesized that within-block learning mainly relies on instance-based processes, whereas across-block learning might depend on rule-based mechanisms. A within-block parametric fMRI analysis revealed a learningrelated increase of lateral prefrontal and striatal activity and a learning-related decrease of hippocampal activity in both experiments. By contrast, across-block learning was associated with an activation modulation in distinct prefrontal-striatal brain regions, but not in the hippocampus. These data indicate that hippocampal and prefrontalstriatal brain regions differentially contribute to instance-based and rule-based learning. D
Compositionality of Rule Representations in Human Prefrontal Cortex
Cerebral Cortex, 2012
Rules are widely used in everyday life to organize actions and thoughts in accordance with our internal goals. At the simplest level, single rules can be used to link individual sensory stimuli to their appropriate responses. However, most tasks are more complex and require the concurrent application of multiple rules. Experiments on humans and monkeys have shown the involvement of a frontoparietal network in rule representation. Yet, a fundamental issue still needs to be clarified: Is the neural representation of multiple rules compositional, that is, built on the neural representation of their simple constituent rules? Subjects were asked to remember and apply either simple or compound rules. Multivariate decoding analyses were applied to functional magnetic resonance imaging data. Both ventrolateral frontal and lateral parietal cortex were involved in compound representation. Most importantly, we were able to decode the compound rules by training classifiers only on the simple rules they were composed of. This shows that the code used to store rule information in prefrontal cortex is compositional. Compositional coding in rule representation suggests that it might be possible to decode other complex action plans by learning the neural patterns of the known composing elements.