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Randall C O'Reilly

Publications and source records attributed to Randall C O'Reilly.

At least 19 recordsLinked to original sources
PubMed2006

Separate neural substrates for skill learning and performance in the ventral and dorsal striatum.

It is widely accepted that the striatum of the basal ganglia is a primary substrate for the learning and performance of skills. We provide evidence that two regions of the rat striatum, ventral and dorsal, play distinct roles in instrumental conditioning (skill learning), with the ventral striatum being critical for learning and the dorsal striatum being important for performance but, notably, not for learning. This implies an actor (dorsal) versus director (ventral) division of labor, which is a new variant of the widely discussed actor-critic architecture. Our results also imply that the successful performance of a skill can ultimately result in its establishment as a habit outside the basal ganglia.

2-Amino-5-phosphonovalerate↗
PubMed2006

Testing computational models of dopamine and noradrenaline dysfunction in attention deficit/hyperactivity disorder.

We test our neurocomputational model of fronto-striatal dopamine (DA) and noradrenaline (NA) function for understanding cognitive and motivational deficits in attention deficit/hyperactivity disorder (ADHD). Our model predicts that low striatal DA levels in ADHD should lead to deficits in 'Go' learning from positive reinforcement, which should be alleviated by stimulant medications, as observed with DA manipulations in other populations. Indeed, while nonmedicated adult ADHD participants were impaired at both positive (Go) and negative (NoGo) reinforcement learning, only the former deficits were ameliorated by medication. We also found evidence for our model's extension of the same striatal DA mechanisms to working memory, via interactions with prefrontal cortex. In a modified AX-continuous performance task, ADHD participants showed reduced sensitivity to working memory contextual information, despite no global performance deficits, and were more susceptible to the influence of distractor stimuli presented during the delay. These effects were reversed with stimulant medications. Moreover, the tendency for medications to improve Go relative to NoGo reinforcement learning was predictive of their improvement in working memory in distracting conditions, suggestive of common DA mechanisms and supporting a unified account of DA function in ADHD. However, other ADHD effects such as erratic trial-to-trial switching and reaction time variability are not accounted for by model DA mechanisms, and are instead consistent with cortical noradrenergic dysfunction and associated computational models. Accordingly, putative NA deficits were correlated with each other and independent of putative DA-related deficits. Taken together, our results demonstrate the usefulness of computational approaches for understanding cognitive deficits in ADHD.

Adolescent↗
PubMed2006

Biologically based computational models of high-level cognition.

Computer models based on the detailed biology of the brain can help us understand the myriad complexities of human cognition and intelligence. Here, we review models of the higher level aspects of human intelligence, which depend critically on the prefrontal cortex and associated subcortical areas. The picture emerging from a convergence of detailed mechanistic models and more abstract functional models represents a synthesis between analog and digital forms of computation. Specifically, the need for robust active maintenance and rapid updating of information in the prefrontal cortex appears to be satisfied by bistable activation states and dynamic gating mechanisms. These mechanisms are fundamental to digital computers and may be critical for the distinctive aspects of human intelligence.

Brain↗
PubMed2006

A mechanistic account of striatal dopamine function in human cognition: psychopharmacological studies with cabergoline and haloperidol.

The authors test a neurocomputational model of dopamine function in cognition by administering to healthy participants low doses of D2 agents cabergoline and haloperidol. The model suggests that DA dynamically modulates the balance of Go and No-Go basal ganglia pathways during cognitive learning and performance. Cabergoline impaired, while haloperidol enhanced, Go learning from positive reinforcement, consistent with presynaptic drug effects. Cabergoline also caused an overall bias toward Go responding, consistent with postsynaptic action. These same effects extended to working memory and attentional domains, supporting the idea that the basal ganglia/dopamine system modulates the updating of prefrontal representations. Drug effects interacted with baseline working memory span in all tasks. Taken together, the results support a unified account of the role of dopamine in modulating cognitive processes that depend on the basal ganglia.

Adolescent↗
PubMed2006

When memory fails, intuition reigns: midazolam enhances implicit inference in humans.

People often make logically sound decisions using explicit reasoning strategies, but sometimes it pays to rely on more implicit "gut-level" intuition. The transitive inference paradigm has been widely used as a test of explicit logical reasoning in animals and humans, but it can also be solved in a more implicit manner. Some researchers have argued that the hippocampus supports relational memories required for making logical inferences. Here we show that the benzodiazepene midazolam, which inactivates the hippocampus, causes profound explicit memory deficits in healthy participants, but enhances their ability in making implicit transitive inferences. These results are consistent with neurocomputational models of the basal ganglia-dopamine system that learn to make decisions through positive and negative reinforcement. We suggest that disengaging the hippocampal explicit memory system can be advantageous for this more implicit form of learning.

Adolescent↗
PubMed2006

Neural mechanisms of cognitive control: an integrative model of stroop task performance and FMRI data.

We address the connection between conceptual knowledge and cognitive control using a neural network model. This model extends a widely held theory of cognitive control [Cohen, J. D., Dunbar, K., & McClelland, J. L. On the control of automatic processes: A parallel distributed processing model of the Stroop effect. Psychological Review, 97, 332-361, 1990] so that it can explain new empirical findings. Leveraging other computational modeling work, we hypothesize that representations used for task control are recruited from preexisting representations for categories, such as the concept of color relevant to the Stroop task we model here. This hypothesis allows the model to account for otherwise puzzling fMRI results, such as increased activity in brain regions processing to-be-ignored information. In addition, biologically motivated changes in the model's pattern of connectivity show how global competition can arise when inhibition is strictly local, as it seems to be in the cortex. We also discuss the potential for this theory to unify models of task control with other forms of attention.

Algorithms↗
PubMed2006

Making working memory work: a computational model of learning in the prefrontal cortex and basal ganglia.

The prefrontal cortex has long been thought to subserve both working memory (the holding of information online for processing) and executive functions (deciding how to manipulate working memory and perform processing). Although many computational models of working memory have been developed, the mechanistic basis of executive function remains elusive, often amounting to a homunculus. This article presents an attempt to deconstruct this homunculus through powerful learning mechanisms that allow a computational model of the prefrontal cortex to control both itself and other brain areas in a strategic, task-appropriate manner. These learning mechanisms are based on subcortical structures in the midbrain, basal ganglia, and amygdala, which together form an actor-critic architecture. The critic system learns which prefrontal representations are task relevant and trains the actor, which in turn provides a dynamic gating mechanism for controlling working memory updating. Computationally, the learning mechanism is designed to simultaneously solve the temporal and structural credit assignment problems. The model's performance compares favorably with standard backpropagation-based temporal learning mechanisms on the challenging 1-2-AX working memory task and other benchmark working memory tasks.

Algorithms↗
PubMed2005

Prefrontal cortex and flexible cognitive control: rules without symbols.

Human cognitive control is uniquely flexible and has been shown to depend on prefrontal cortex (PFC). But exactly how the biological mechanisms of the PFC support flexible cognitive control remains a profound mystery. Existing theoretical models have posited powerful task-specific PFC representations, but not how these develop. We show how this can occur when a set of PFC-specific neural mechanisms interact with breadth of experience to self organize abstract rule-like PFC representations that support flexible generalization in novel tasks. The same model is shown to apply to benchmark PFC tasks (Stroop and Wisconsin card sorting), accurately simulating the behavior of neurologically intact and frontally damaged people.

Cognition↗
PubMed2005

Serial visual search from a parallel model.

We tested a parallel neural network model of visual search, and found that it located targets more quickly when allowed to take several fast guesses. We suggest that this serially iterated parallel search may be the mode used by the visual system, in accord with theories such as the Guided Search model. Furthermore, in our model the most efficient mode of processing varied with the type of search. If the nature of visual search varies with task demands, seemingly contradictory findings can be reconciled.

Discrimination, Psychological↗
PubMed2005

When logic fails: implicit transitive inference in humans.

Transitive inference (TI) in animals (e.g., choosing A over C on the basis of knowing that A is better than B and B is better than C) has been interpreted by some as reflecting a declarative logical inference process. We invert this anthropomorphic interpretation by providing evidence that humans can exhibit TI-like behavior on the basis of simpler associative mechanisms that underlie many theories of animal learning. In this study, human participants were trained on a five-pair TI problem (A+B-, B+C-, C+D-, D+E-, E+F-) and, unlike in previous human TI studies, were prevented from becoming explicitly aware of the logical hierarchy, so they could not employ logical reasoning. They were then tested with three problems: B versus D, B versus E, and C versus E. Participants only reliably chose B over E, whereas the other test conditions yielded chance performance. This result is inconsistent with the use of logical reasoning and is instead consistent with an account developed to explain earlier TI studies with rats that found the same pattern of results. In this account, choice performance is based on differential associative strengths across the stimulus items that develop over training, despite equal overt reinforcement.

Awareness↗
PubMed2004

Hippocampus, cortex, and basal ganglia: insights from computational models of complementary learning systems.

We present a framework for understanding how the hippocampus, neocortex, and basal ganglia work together to support cognitive and behavioral function in the mammalian brain. This framework is based on computational tradeoffs that arise in neural network models, where achieving one type of learning function requires very different parameters from those necessary to achieve another form of learning. For example, we dissociate the hippocampus from cortex with respect to general levels of activity, learning rate, and level of overlap between activation patterns. Similarly, the frontal cortex and associated basal ganglia system have important neural specializations not required of the posterior cortex system. Taken together, this overall cognitive architecture, which has been implemented in functioning computational models, provides a rich and often subtle means of explaining a wide range of behavioral and cognitive neuroscience data. Here, we summarize recent results in the domains of recognition memory, contextual fear conditioning, effects of basal ganglia lesions on stimulus-response and place learning, and flexible responding.

Animals↗
PubMed2003

Transitivity, flexibility, conjunctive representations, and the hippocampus. I. An empirical analysis.

After training on a set of four ordered, simultaneous, odor discrimination problems (A+B-, B+C-, C+D-, D+E), intact rats display transitivity: When tested on the novel combination BD, they choose B. Rats with damage to the hippocampus, however, do not show transitivity (Dusek and Eichenbaum, 1997. Proc Natl Acad Sci U S A 94:7109-7114). These results have been interpreted as support for the idea that the hippocampus is a relational memory storage system that enables the subject to make comparisons among representations of the individual problems and choose based on inferential logic. We provide evidence for a simpler explanation. Specifically, subjects make their choices based on the absolute excitatory value of the individual stimuli. This value determines the ability of that stimulus to attract a response. This conclusion emerged because after training on a five-problem set (A+B-, B+C-, C+D-, D+E-, E+F-) rats preferred B when tested with BE, but not when tested with BD. The implication of these results for how to conceptualize the role of the hippocampus in transitive-like phenomena is discussed.

Animals↗
PubMed2003

Transitivity, flexibility, conjunctive representations, and the hippocampus. II. A computational analysis.

A computational neural network model is presented that explains how the hippocampus can contribute to transitive inference performance observed in rats (Dusek and Eichenbaum, 1997. Proc Natl Acad Sci U S A 94:7109-7114; Van Elzakker et al., 2003. Hippocampus 12:this issue). In contrast to existing theories that emphasize the idea that the hippocampus contributes by flexibly relating previously encoded memories, we find that the hippocampus contributes by altering the elemental associative weights of individual stimulus elements during learning. We use this model to account for a range of existing data and to make a number of distinctive predictions that clearly contrast these two views.

Animals↗
PubMed2003

Modeling hippocampal and neocortical contributions to recognition memory: a complementary-learning-systems approach.

The authors present a computational neural-network model of how the hippocampus and medial temporal lobe cortex (MTLC) contribute to recognition memory. The hippocampal component contributes by recalling studied details. The MTLC component cannot support recall, but one can extract a scalar familiarity signal from MTLC that tracks how well a test item matches studied items. The authors present simulations that establish key differences in the operating characteristics of the hippocampal-recall and MTLC-familiarity signals and identify several manipulations (e.g., target-lure similarity, interference) that differentially affect the 2 signals. They also use the model to address the stochastic relationship between recall and familiarity and the effects of partial versus complete hippocampal lesions on recognition.

Cerebral Cortex↗
PubMed2002

Memory for context is impaired by injecting anisomycin into dorsal hippocampus following context exploration.

Pre-exposure to the context facilitates the small amount of contextual fear conditioning that is normally produced by immediate shock. This context pre-exposure facilitation effect provides a convenient way to study the rat's learning about context. We recently reported that anterograde damage to dorsal hippocampus prevents this facilitation. The present experiments strengthen this conclusion by showing that the protein synthesis inhibitor, anisomycin, injected bilaterally into the dorsal hippocampus following context pre-exposure also significantly reduces the facilitation effect. The same treatment given immediately after immediate shock, however, had no effect on facilitation. These results support theories that assume that, (a) contextual fear involves two processes, acquiring and storing a conjunctive representation of a context and associating that representation with fear; and (b) the hippocampus contributes to contextual fear by participating in the storage of the memory representation of the context.

Animals↗
PubMed2002

Hippocampal formation supports conditioning to memory of a context.

It has been proposed that contextual fear conditioning depends on 2 processes: (a) construction of a conjunctive representation of the features that make up the context and (b) association of the representation with shock. Support for this view comes from studies indicating that prior exposure to the conditioning context facilitates contextual fear conditioning supported by immediate shock. Thus, conditioning produced by immediate shock is to the memory representation of the preexposed context, which is activated by retrieval cues associated with this context. The authors' experiments support this interpretation and indicate that this process depends on an intact hippocampal formation. These results support the hypothesis that the hippocampal formation supports contextual fear conditioning by storing a conjunctive representation of context.

Animals↗
PubMed2002

Prefrontal cortex and dynamic categorization tasks: representational organization and neuromodulatory control.

We present a computational model of the intradimensional/ extradimensional (ID/ED) task (a variant of the Wisconsin card sorting task) that simulates the performance of intact and frontally lesioned monkeys on three different kinds of rule changes (Dias et al., 1997, J Neurosci 17:9285-9297). Although Dias et al. interpret the lesion data as supporting a model in which prefrontal cortex is organized into different processing functions, our model suggests an alternative account based on representational content. A key aspect of the model is that prefrontal cortex representations are organized according to different levels of abstraction, with orbital areas encoding more specific featural information and dorsolateral areas encoding more abstract dimensional information. This representational scheme of the model is integrated with two additional key elements: (i) activation-based working memory representations controlled by a dynamic gating mechanism that simulates the hypothesized phasic actions of dopaminergic neuromodulation in prefrontal cortex, which acts to stabilize or destabilize frontal representations based on success in the task; and (ii) a weight-based associative learning system simulating posterior cortex and other subcortical areas, where the stimulus-response mappings are encoded. Frontal cortex contributes to the task via top-down activation-based biasing of task-appropriate features and dimensions in this posterior cortex system - this top-down biasing is specifically important for overcoming prepotent associations after a sorting rule reverses. The ability of the model to capture the double-dissociation observed by Dias et al. with orbital versus dorsolateral lesions supports the validity of these principles, many of which have also been useful in accounting for other frontal phenomena.

Animals↗