Laxmi R. Iyer

2009 Special Issue: Neural dynamics of idea generation and the effects of priming

Idea generation is a fundamental attribute of the human mind, but the cognitive and neural mechanisms underlying this process remain unclear. In this paper, we present a dynamic connectionist model for the generation of ideas within a brainstorming context. The key hypothesis underlying the model is that ideas emerge naturally from itinerant attractor dynamics in a multi-level, modular semantic space, and the potential surface underlying this dynamics is itself shaped dynamically by task context, ongoing evaluative feedback, inhibitory modulation, and short-term synaptic modification. While abstract, the model attempts to capture the interplay between semantic representations, working memory, attentional selection, reinforcement signals, and modulation. We show that, once trained on a set of contexts and ideas, the system can rapidly recall stored ideas in familiar contexts, and can generate novel ideas by efficient, multi-level dynamical search in both familiar and unfamiliar contexts. We also use a simplified continuous-time instantiation of the model to explore the effect of priming on idea generation. In particular, we consider how priming low-accessible categories in a connectionist semantic network can lead to the generation of novel ideas. The mapping of the model onto various regions and modulatory processes in the brain is also discussed briefly.

2012 Special Issue: Connectivity and thought: The influence of semantic network structure in a neurodynamical model of thinking

Understanding cognition has been a central focus for psychologists, neuroscientists and philosophers for thousands of years, but many of its most fundamental processes remain very poorly understood. Chief among these is the process of thought itself: the spontaneous emergence of specific ideas within the stream of consciousness. It is widely accepted that ideas, both familiar and novel, arise from the combination of existing concepts. From this perspective, thought is an emergent attribute of memory, arising from the intrinsic dynamics of the neural substrate in which information is embedded. An important issue in any understanding of this process is the relationship between the emergence of conceptual combinations and the dynamics of the underlying neural networks. Virtually all theories of ideation hypothesize that ideas arise during the thought process through association, each one triggering the next through some type of linkage, e.g., structural analogy, semantic similarity, polysemy, etc. In particular, it has been suggested that the creativity of ideation in individuals reflects the qualitative structure of conceptual associations in their minds. Interestingly, psycholinguistic studies have shown that semantic networks across many languages have a particular type of structure with small-world, scale free connectivity. So far, however, these related insights have not been brought together, in part because there has been no explicitly neural model for the dynamics of spontaneous thought. Recently, we have developed such a model. Though simplistic and abstract, this model attempts to capture the most basic aspects of the process hypothesized by theoretical models within a neurodynamical framework. It represents semantic memory as a recurrent semantic neural network with itinerant dynamics. Conceptual combinations arise through this dynamics as co-active groups of neural units, and either dissolve quickly or persist for a time as emergent metastable attractors and are recognized consciously as ideas. The work presented in this paper describes this model in detail, and uses it to systematically study the relationship between the structure of conceptual associations in the neural substrate and the ideas arising from this systems dynamics. In particular, we consider how the small-world and scale-free characteristics influence the effectiveness of the thought process under several metrics, and show that networks with both attributes indeed provide significant advantages in generating unique conceptual combinations.

Effects of relevant and irrelevant primes on idea generation: A computational model

Brainstorming is the process of generating ideas in a specific task or problem context.We have previously presented a connectionist framework to study the dynamics of idea generation in individuals. In this paper, we develop this model further, and apply it to studying qualitatively the effects of priming on the process of ideation. Motivated by experimental data from a previous study, we explore the differential effects of relevant and irrelevant primes on productivity of idea generation in specific problem/task contexts. Simulations using our model suggest that even irrelevant primes can provide a modest productivity boost in contexts that are familiar or are similar to familiar contexts, but no benefit when the context is unfamiliar. We propose possible explanations for these results and make predictions for future experiments.

Modularity and Self-Organized Functional Architectures in the Brain

It is generally believed that cognition involves the self-organization of coherent dy- namic functional networks across several brain regions in response to incoming stimulus and internal modulation. These context-dependent networks arise continually from the spatiotemporally multi-scale structural substrate of the brain configured by evolution, development and previous experience, persisting for 100–200 ms and generating re- sponses such as imagery, recall and motor action. In the current paper, we show that a system of interacting modular attractor networks can use a selective mechanism for assembling functional networks from the modular substrate. We use the approach to develop a model of idea-generation in the brain. Ideas are modeled as combinations of concepts organized in a recurrent network that reflects previous associations between them. The dynamics of this network, resulting in the transient co-activation of concept groups, is seen as a search through the space of ideas, and attractor dynamics is used to “shape” this search. The process is required to encompass both rapid retrieval of old ideas in familiar contexts and efficient search for novel ones in unfamiliar situations (or during brainstorming). The inclusion of an adaptive modulatory mechanism allows the network to balance the competing requirements of exploiting previous learning and exploring new possibilities as needed in different contexts.

A synergistic view of autonomous cognitive systems

As advances in neuroscience, cognitive science and robotics continue to elucidate the physical basis of autonomous behavior in animals, many deep questions remain open, including: What sort of system can evolve and support perception, cognition and action? What general principles or attributes can be defined for such a system? If so, how do these principles and attributes make complex behavior possible? In this paper, we report on the early stages of research to address these questions from a complex systems and systems biology perspective. We argue that flexible modularity and hierarchical interaction of synergies in a modular system are fundamental to the possibility of cognition. We describe a minimal model comprising three interacting core subsystems that embodies the principles of flexible modularity and synergy. Finally, we present a simple neural model of action generation based on the principles of interacting modular synergies.

A dynamical connectionist model of idea generation

In this paper, we present a model for the generation of ideas within a creative thinking/brainstorming context. In the model, ideas emerge as conceptual combinations from the interaction of complex dynamics at several semantic levels: Features, concepts, categories, and previously generated ideas. This dynamics is shaped by external information on task context, constraints and goals, and is modulated by evaluative feedback from an internal critic working through reinforcement. While the model is abstract, it attempts to capture the interplay between semantic representations in the temporal, frontal and parietal cortices, working memory in the prefrontal cortex, attentional selection by the basal ganglia, and modulation from the dopaminergic reward system. We show that a context-specific itinerant search for novel but meaningful conceptual combinations (ideas) emerges naturally from the dynamics of this system. We also briefly describe a computational model for ideation in groups using a multi-agent formalism. The initial focus of this model is on studying the potential benefits of cognitive diversity in agent groups, e.g., the presence of convergent and divergent thinkers, or agents with different semantic organizations.

Neurocognitive spotlights: Configuring domains for ideation

Creativity is an important attribute of the human mind, and shows itself in all aspects of its function. However, its neural basis remains poorly understood. In this paper, we explore two issues with regard to creativity in the semantic domain: 1) What neural mechanism enable the brain to construct context-specific semantic spaces to facilitate the generation of relevant ideas? and 2) Can these mechanisms support greater creativity simply by exploring unusual semantic spaces? We use a variant of our previously developed neural model of ideation to show that a dynamical modular neural system can, indeed, learn to configure context-appropriate semantic domains based on experience, and that exploratory dynamics within this system can lead to the unmasking of novel emergent ideas.

A neurodynamical model of context-dependent category learning

The abstraction of patterns from data and the formation of categories is a hallmark of human cognitive ability. As such, it has been studied from many different perspectives by researchers, and these studies have led to several explanatory models. In this paper, we consider the inference of categorical representations for the purpose of producing task-specific responses. Task-relevant responses require a knowledge repertoire that is organized to allow efficient access to useful information. We present a neurodynamical system that infers functionally coherent categories from semantic inputs (or concepts) presented sequentially in different contexts, and encodes them as attractors in a two-dimensional topological feature space. The resulting category representations can then act as pointers in a larger system for semantic cognition. The system allows controlled hierarchical organization and functional segregation of the inferred categories.