Sébastien Hélie

Incubation, insight, and creative problem solving: A unified theory and a connectionist model

This article proposes a unified framework for understanding creative problem solving, namely, the explicit-implicit interaction theory. This new theory of creative problem solving constitutes an attempt at providing a more unified explanation of relevant phenomena (in part by reinterpreting/integrating various fragmentary existing theories of incubation and insight). The explicit-implicit interaction theory relies mainly on 5 basic principles, namely, (a) the coexistence of and the difference between explicit and implicit knowledge, (b) the simultaneous involvement of implicit and explicit processes in most tasks, (c) the redundant representation of explicit and implicit knowledge, (d) the integration of the results of explicit and implicit processing, and (e) the iterative (and possibly bidirectional) processing. A computational implementation of the theory is developed based on the CLARION cognitive architecture and applied to the simulation of relevant human data. This work represents an initial step in the development of process-based theories of creativity encompassing incubation, insight, and various other related phenomena.

Evidence for cortical automaticity in rule-based categorization.

There is evidence that rule-based category learning is supported by a broad neural network that includes the prefrontal cortex, the anterior cingulate cortex, the head of the caudate nucleus, and medial temporal lobe structures. Although thousands of studies have examined rule-based category learning, only a few have studied the development of automaticity in rule-based tasks. Categorizing by a newly learned rule makes heavy demands on declarative memory, but after thousands of repetitions rule-based categorizations are made with no apparent effort. Thus, it seems likely that the neural systems that mediate automatic rule-based categorization are substantially different from the systems that mediate initial learning. This research aims at identifying the neural systems responsible for early and late rule-based categorization performances. Toward this end, this article reports the results of an experiment in which human participants each practiced a rule-based categorization task for >10,000 trials distributed over 20 separate sessions. Sessions 1, 4, 10, and 20 were performed inside a magnetic resonance imaging scanner. The main findings are as follows: (1) cortical activation remained approximately constant throughout training, (2) subcortical activation increased with practice (i.e., there were more activated voxels in the striatum), and (3) only cortical activation was correlated with accuracy after extensive training. The results suggest an initial subcortical neural system centered around the head of the caudate that is gradually replaced by a cortical system centered around the ventrolateral prefrontal cortex. With extensive practice, the cortical system progressively becomes more caudal and dorsal, and is eventually centered around the premotor cortex.

Automaticity in rule-based and information-integration categorization

Three experiments studied the effects of category structure on the development of categorization automaticity. In Experiment 1, participants were each trained for over 10,000 trials in a simple categorization task with one of three category structures. Results showed that after the first few sessions, there were no significant behavioral differences between participants who learned rule-based versus information-integration category structures. Experiment 2 showed that switching the locations of the response keys after automaticity had developed caused a similar highly significant interference, regardless of category structure. In Experiment 3, a simultaneous dual task that engaged executive functions did not interfere with either rule-based or information-integration categorization. These novel results are consistent with a theory assuming separate processing pathways for initial rule-based and information-integration category learning but a common processing pathway after the development of automaticity.

Learning robust cortico-cortical associations with the basal ganglia: An integrative review

This article focuses on the interaction between the basal ganglia (BG) and prefrontal cortex (PFC). The BG are a group of nuclei at the base of the forebrain that are highly connected with cortex. A century of research suggests that the role of the BG is not exclusively motor, and that the BG also play an important role in learning and memory. In this review article, we argue that one important role of the BG is to train connections between posterior cortical areas and frontal cortical regions that are responsible for automatic behavior after extensive training. According to this view, one effect of BG trial-and-error learning is to activate the correct frontal areas shortly after posterior associative cortex activation, thus allowing for Hebbian learning of robust, fast, and efficient cortico-cortical processing. This hypothesized process is general, and the content of the learned associations depends on the specific areas involved (e.g., associations involving premotor areas would be more closely related to behavior than associations involving the PFC). We review experiments aimed at pinpointing the function of the BG and the frontal cortex and show that these results are consistent with the view that the BG is a general purpose trainer for cortico-cortical connections. We conclude with a discussion of some implications of the integrative framework and how this can help better understand the role of the BG in many different tasks.

Exploring the cognitive and motor functions of the basal ganglia: an integrative review of computational cognitive neuroscience models.

Many computational models of the basal ganglia (BG) have been proposed over the past twenty-five years. While computational neuroscience models have focused on closely matching the neurobiology of the BG, computational cognitive neuroscience (CCN) models have focused on how the BG can be used to implement cognitive and motor functions. This review article focuses on CCN models of the BG and how they use the neuroanatomy of the BG to account for cognitive and motor functions such as categorization, instrumental conditioning, probabilistic learning, working memory, sequence learning, automaticity, reaching, handwriting, and eye saccades. A total of 19 BG models accounting for one or more of these functions are reviewed and compared. The review concludes with a discussion of the limitations of existing CCN models of the BG and prescriptions for future modeling, including the need for computational models of the BG that can simultaneously account for cognitive and motor functions, and the need for a more complete specification of the role of the BG in behavioral functions.

A neurocomputational account of cognitive deficits in Parkinson's disease.

Parkinsons disease (PD) is caused by the accelerated death of dopamine (DA) producing neurons. Numerous studies documenting cognitive deficits of PD patients have revealed impairments in a variety of tasks related to memory, learning, visuospatial skills, and attention. While there have been several studies documenting cognitive deficits of PD patients, very few computational models have been proposed. In this article, we use the COVIS model of category learning to simulate DA depletion and show that the model suffers from cognitive symptoms similar to those of human participants affected by PD. Specifically, DA depletion in COVIS produced deficits in rule-based categorization, non-linear information-integration categorization, probabilistic classification, rule maintenance, and rule switching. These were observed by simulating results from younger controls, older controls, PD patients, and severe PD patients in five well-known tasks. Differential performance among the different age groups and clinical populations was modeled simply by changing the amount of DA available in the model. This suggests that COVIS may not only be an adequate model of the simulated tasks and phenomena but also more generally of the role of DA in these tasks and phenomena.

Psychologically realistic cognitive agents: taking human cognition seriously

Cognitive architectures may serve as a good basis for building mind/brain-inspired, psychologically realistic cognitive agents for various applications that require or prefer human-like behaviour and performance. This article explores a well-established cognitive architecture CLARION and shows how its behaviour and performance capture human psychology at a detailed level. The model captures many psychological quasi-laws concerning categorisation, induction, uncertain reasoning, decision-making, and so on, which indicates human-like characteristics beyond what other models have been shown to be capable of. Thus, CLARION constitutes an advance in developing more psychologically realistic cognitive agents.

Learning and Transfer of Category Knowledge in an Indirect Categorization Task

Knowledge representations acquired during category learning experiments are ‘tuned’ to the task goal. A useful paradigm to study category representations is indirect category learning. In the present article, we propose a new indirect categorization task called the “same”–“different” categorization task. The same–different categorization task is a regular same–different task, but the question asked to the participants is about the stimulus category membership instead of stimulus identity. Experiment 1 explores the possibility of indirectly learning rule-based and information-integration category structures using the new paradigm. The results suggest that there is little learning about the category structures resulting from an indirect categorization task unless the categories can be separated by a one-dimensional rule. Experiment 2 explores whether a category representation learned indirectly can be used in a direct classification task (and vice versa). The results suggest that previous categorical knowledge acquired during a direct classification task can be expressed in the same–different categorization task only when the categories can be separated by a rule that is easily verbalized. Implications of these results for categorization research are discussed.

Brain activity across the development of automatic categorization: a comparison of categorization tasks using multi-voxel pattern analysis.

Previous evidence suggests that relatively separate neural networks underlie initial learning of rule-based and information-integration categorization tasks. With the development of automaticity, categorization behavior in both tasks becomes increasingly similar and exclusively related to activity in cortical regions. The present study uses multi-voxel pattern analysis to directly compare the development of automaticity in different categorization tasks. Each of the three groups of participants received extensive training in a different categorization task: either an information-integration task, or one of two rule-based tasks. Four training sessions were performed inside an MRI scanner. Three different analyses were performed on the imaging data from a number of regions of interest (ROIs). The common patterns analysis had the goal of revealing ROIs with similar patterns of activation across tasks. The unique patterns analysis had the goal of revealing ROIs with dissimilar patterns of activation across tasks. The representational similarity analysis aimed at exploring (1) the similarity of category representations across ROIs and (2) how those patterns of similarities compared across tasks. The results showed that common patterns of activation were present in motor areas and basal ganglia early in training, but only in the former later on. Unique patterns were found in a variety of cortical and subcortical areas early in training, but they were dramatically reduced with training. Finally, patterns of representational similarity between brain regions became increasingly similar across tasks with the development of automaticity.

Simulating category learning and set shifting deficits in patients weight-restored from anorexia nervosa.

OBJECTIVE To examine set shifting in a group of women previously diagnosed with anorexia nervosa who are now weight-restored (AN-WR) and then apply a biologically based computational model (Competition between Verbal and Implicit Systems [COVIS]) to simulate the pattern of category learning and set shifting performances observed. METHOD Nineteen AN-WR women and 35 control women (CW) were administered an explicit category learning task that required rule acquisition and then a set shift following a rule change. COVIS was first fit to the behavioral results of the controls and then parameters of the model theoretically relevant to AN were altered to mimic the behavioral results. RESULTS Relative to CW, the AN-WR group displayed steeper learning curves (i.e., hyper learning) before the rule shift, but greater difficulty in learning the new categories after the rule shift (i.e., a deficit in set shifting). Hyper learning and set shifting deficits in the AN-WR group were not associated and differentially correlated with clinical measures. Hyper learning in the AN-WR group was simulated by increasing the model parameter that represents sensitivity to negative feedback (δ parameter), whereas the deficit in set shifting was simulated by altering the parameters that represent changes in rule selection and flexibility (λ and γ parameters, respectively). CONCLUSIONS These simulations suggest that multiple factors can impact category learning and set shifting in AN-WR individuals (e.g., alterations in sensitivity to negative feedback, rule selection deficits, and inflexibility) and provide an important starting point to further investigate this pervasive deficit in adult AN.