Groups engaged in brainstorming undergo several types of ideation processes, some more effective than others. Current methods for characterizing the knowledge expressed in a group and its dynamics include embedding models and cosine distance between idea embeddings that do not find explicit semantic connections, or knowledge graphs that represent semantic connections but need large amounts of data to extract. We are proposing a novel idea graph model that combines embedding models and a graph representation of knowledge, together with a novel group problem-solving model. Using the graph representation and the evaluation measures, we identified and characterized more effective groups from experimental data and verified the main hypotheses derived from the theoretical model.

Studying the dynamics of research communities has been an important yet tedious topic. It is important for devising management policies that optimize the quality of research outputs by promoting a certain composition, structure, and interaction patterns for a community. While there has been significant attention to understanding the roles of individuals, there has been less focus on studying the roles that research groups perform and how roles relate to the characteristics of the community. This article presents an experimental study on the roles that research groups have in the idea flow through a community. The study analyzes two facets: the contribution of different kinds of groups to the impact research ideas make on other groups and the importance of specific groups to connect a community together. The results showed that research groups can be classified into four categories depending on the number of received citations. Groups of the mid-tier categories (called categories B and C in this article) are important in tying a community together as they improve the idea impact and bridging between groups. Policies that aggressively eliminate such groups reduce the effectiveness of idea flow, even though research communities include a certain amount of robustness for moderate levels of group removals.

Text embedding using vector space models has recently emerged as the leading way to represent text in natural language processing. These embeddings can be at the level of words, sentences, or larger textual units including entire documents. However, sentence embeddings are especially useful because sentences are the most explicitly specified elements of individual thoughts or ideas comprising a document, discussion, or conversation. Analyzing text at the sentence level thus allows access to its fine-grained semantics while preserving the semantic structure that is lost in bag-of-words approaches. Several deep learning-based models such as BERT and USE provide such contextual representations. However, the resulting embeddings are very high-dimensional, and the individual dimensions are not amenable to interpretable labels. Thus, these embeddings define a semantic space but not an explicitly useful cognitive map. In this paper, we show that an adaptive clustering approach applied to the embeddings produced by a neural network-based language model can produce much lower-dimensional, readily interpretable semantic representations, thus creating a usable cognitive map for applications such as semantic tracking and visualization of discussions, or discerning the sequential semantic structure of long documents.

Group brainstorming is a common method to generate ideas for open-ended, complex problems. Groups are known to generate fewer and less novel ideas than an equal number of individuals working separately. One cause of this is the tendency of groups to converge prematurely to a small number of ideas and neglect others. It is known that adding structure to brainstorming in the form of external hints tends to increase group performance. Our goal is to develop a cognitively-inspired feedback system that evaluates ideas in real-time and decides what kind of intervention to make in a group brainstorming session, e.g., flag an idea for further exploration, move the conversation in a new direction, etc. In this work, we develop a computational model for detecting relevant, novel, and less elaborated ideas from group messages in real-time. The flagged ideas can be fed back into the group discussion for further elaboration. We explore multiple approaches for relevance detection that do not require out-of-domain data: a probabilistic language model, a zero-shot classifier, and a one-class support vector machine. We compute two types of novelty, (1) absolute novelty with respect to domain knowledge; and (2) relative novelty with respect to previously seen ideas within the group. For absolute novelty, we explore methods to represent the domain semantic knowledge in a compressed way for faster computation. We tested our model on a dataset of ideas generated by over 50 groups. It was found that our probabilistic relevance method is the most accurate and that either topic cluster centers or common domain topics offer similar novelty results compared to full domain representation.

Text embedding has recently emerged as a very useful and successful method for semantic representation. Following initial word-level embedding methods such as Latent Semantic Analysis (LSA) and topic-based bag-of-words approaches like Latent Dirichlet Allocation (LDA), the focus has turned to language models and text encoders implemented as neural networks - ranging from word-level models to those embedding whole documents. The distinctive feature of these models is their ability to infer semantic spaces at all levels based purely on data, with no need for complexities such as syntactic analysis or ontology building. Many of these models are available pre-trained on enormous amounts of data, providing downstream applications with general-purpose semantic spaces. In particular, embedding models at the sentence level or higher are most useful in applications because the meaning of text only becomes clear at that level. Most text embedding methods produce text embeddings in high-dimensional spaces, with a dimensionality ranging from a few hundred to thousands. However, it is often useful to visualize semantic spaces in very low dimension, which requires the use of dimensionality reduction methods. It is not clear what language models and what method of dimensionality reduction would work well in these cases. In this paper, we compare four text embedding methods in combination with three methods of dimensionality reduction to map three related real-world datasets comprising textual descriptions of items in a particular domain (sports) to a 2-dimensional semantic visualization space. The results provide several insights into the utility of these methods for data of this type.

Group learning offers novel yet intriguing opportunities to improve a community's effectiveness by obtaining insight into the emergence of new design problems and concepts. This article proposes a new computational model and the related algorithmic methods to characterize circuit design communities over time. The bottom-up model explains knowledge evolution using two operators, combination/improvement for knowledge expansion, and blocking for concept elimination. New metrics were proposed to describe the effect of the two operators. Experiments considered three large data sets of circuit designs (switched-capacitor filters, CMOS OpAmp/OTAs, and ΔΣ ADCs). Opportunities to improve the communities were discussed.

In creativity research a typical problem is that of assessing the novelty of ideas or solutions generated by many people to open ended problems. For datasets larger than a few hundreds, human assessment of novelty becomes time consuming and error prone. Existing novelty detection methods such as: distance based text similarity or language model approaches do not work well for small datasets. Moreover, when compared to human novelty ratings, these approaches fail to capture the same cognitive processes or biases. We are proposing a novel cognitive model inspired by a leaky accumulator decision making models for detecting novel ideas from short text. The model is applied on a collection of ideas generated in a group brainstorming experiment. It evaluates an idea term by term and it accumulates surprise and relevance. The final novelty decision is taken at the end of each idea by means of a threshold. An important component of the model is a small domain dataset which is used to evaluate the surprise of a term's context compared to common domain knowledge. The model is compared with other methods: feature based classifiers, tf-idf similarity distance, and pretrained language models (ULMFIT).

Semantic analysis of text corpora is of broad utility, including for data from conversations, on-line chats, brainstorming sessions, comments on blogs, etc. - all of which are potentially interesting sources of information and ideas. In the present paper, we look at data from a large group brainstorming experiment that generated thousands of mostly brief statements. The ultimate goal is to detect which statements are semantically atypical within the overall corpus. In contexts such as spam detection or detection of on-line intrusions, autoencoders have been used successfully to separate typical from atypical data, and we consider this approach in the present paper. Texts are embedded in a semantic space obtained through topic analysis, and an autoencoder network is used to reconstruct each embedded text. The results show that, while difficulty of reconstruction is related to quantitative measures of atypicality in the embedding vector space, it is not well correlated with novelty assignments made by a human rater. However, this is not the case when the data is first clustered in the embedding space: The reconstruction error for each data cluster indicates that some clusters represent more novel data than others, and that the inverse size of the cluster and the mean reconstruction error of the texts in the cluster capture this well. In particular, autoencoders that enforce dimensionality reduction improve discrimination. The results also show that, in the reconstruction process, the autoencoder implicitly discovers the same clusters in the data that are discovered explicitly by an optimized k-means approach.

This paper presents InnovA, a cognitive architecture for creative problem solving in analog circuit design, e.g., topology creation (synthesis), incremental topology modification, and design knowledge identification and reuse. The architectural modules attempt to replicate cognitive human activities, like concept formation, comparison, and concept combination. The architecture uses multiple knowledge representations organized using topological similarity and causality information. Solutions are clustered, so that each cluster represents a specific set of performance tradeoffs, thus a fragment of the Pareto front in the solution space. New structural features are created through variation of existing features. New solutions are created by combining features from the same cluster, distinct clusters, and features that originate a new cluster. The related algorithms are discussed in this paper too. The architecture also incorporates modules mimicking the using of human emotions in memory formation and decision making, but these modules are still under development and are a main direction for our future work. This paper presents a number of examples to illustrate its using in various analog circuit design activities that are hard to be realized with traditional computational methods.

Label Propagation is a semi-supervised learning algorithm typically applied to partially labeled graph data sets for classifying unlabeled nodes. Similar to the Personalized PageRank algorithm, Label Propagation is in essence a random walk on a graph, resting on the assumption that similar nodes are more likely to form edges. Graph based models and analysis inform companies about their customers and help make recommendations for targeted ad placement when databases are sparse. We generalize the concept of label propagation to constrain the random walk to regions of the search space where the true solution may lie based on prior knowledge. Specifically, we reformulate the label propagation algorithm as a minimum energy control problem that embraces traditional label propagation as a special case. We apply the formulation to (i) benchmark data sets, and (ii) the Yelp challenge data set. Results indicate the approach is comparable to competing methods for the benchmark data. For the Yelp data, our experiments show a promising 20%-50% improvement over the baseline for select business features.

Mining engineering metaknowledge from electronic documents is important in devising new design solutions for emerging applications as well as in identifying new business opportunities. Continuously and comprehensively reviewing and assessing the entire technical literature to find the most relevant ideas and solutions is cumbersome as engineering innovations are constantly being proposed. In particular, high performance analog circuit design has been at the forefront of technological innovations given their importance in applications, like telecommunications, mobile applications, and health care. This paper proposes a model and mining techniques for representing analog circuit design metaknowledge. The metaknowledge representation includes three components: an associative component presenting the conceptual hierarchy of the considered circuits, a component expressing the performance capabilities (e.g., tradeoffs and bottlenecks) of the circuits, and a causal modeling part describing the more likely starting ideas and design plans used to create a circuit. Knowledge structures can be used to tackle new applications, identify opportunities to improve existing circuits, and validate design correctness. A case study illustrates the proposed knowledge structure for a set of 30 modern, high-frequency analog circuits.

This paper presents a dual-process architecture for reasoning in design innovation problems. The architecture includes (i) a uniform mechanism to create various associations among concepts and (ii) a procedure to construct rule-based reasoning flows through abstraction of associated concepts. Three reasoning procedures are discussed based on the architecture. Two case studies show that the dual-process architecture can be utilized for various innovation-related tasks, like searching the solution space search beyond the capabilities of statistical methods and identifying hints to improve the novelty of designs, hence exiting fixation.

In this paper we study the effect of external ideas on brainstorming by means of two computational models: a transient emergent attractors model (TEAM) and a probabilistic associative model (PAM). New behavioral experimental results show that hints or others' ideas can either hinder or enhance ideas generated during exposure period, while they consistently enhance the quantity of ideas produced after exposure. The TEAM model consists of a neural network of concept nodes connected by means of category membership and relatedness. Ideas emerge dynamically from the activity of the network by temporarily strengthening the connections between co-active nodes. Local inhibition inactivates current idea nodes and allows another idea to form. Active nodes prime connected inactive nodes depending on the recent activity of the node. The model shows that hindering of the number of ideas during hint presentation depends on the strength of hints and that the speed and duration of priming is essential for the long-term priming effect of hints observed in experiments. For comparison purposes, a PAM model originally proposed by Brown and Paulus (1998) is used to explain the same experimental data.

It is clear that much of the world's technological innovation originates from the domain of the startup, an arena in which the United States has historically played a major role. However, while business and other schools have traditionally offered courses in entrepreneurship, only about one third of all new businesses ever reach profitability and less than half of all new startups survive more than five years. Much of the attrition of small businesses is a result of poor preparation of the founders and the failure of academia to treat entrepreneurship as something more than a purely academic subject. A new program at Hofstra University focuses on a fresh approach to preparing Computer Science and Engineering students in entrepreneurship by providing a curriculum specifically designed to meet the myriad challenges encountered by entrepreneurs in the “real” world.

Number of college students in computing has declined since 2000 by more than 70%. Women and minorities are highly underrepresented. Students are exposed to computing education late in high-school or in college. At that point, the population sampling is already geared towards students already interested in computers. This approach results in the small number of students and more importantly in the low demographics diversity. Exposure to computing principles needs to start at a much younger age in order to spark interest in a larger and more diverse student body. This work in progress paper describes the experience of a new computing course for 7thgrade students. The novel component of the course consists in the robotics projects inspired by biological or artificial characters that seem to motivate students independent of gender.

Entrepreneurship is an integral part of the computing education at Hofstra University and consists of: (i) two optional, specialization-type programs as part of undergraduate degrees that target students with strong entrepreneurial interests, and (ii) open seminars and enrichment modules, exposing all majors to entrepreneurial ideas and activities while attempting to spark their creativity and drive for innovation. To educate students and help them understand the purpose, procedures, cost and legal implications of patenting and intellectual property issues and to give them first-hand experience in these subjects, we have included “patents and intellectual property” as one of the facets of the new programs and activities. In this paper, we present the implementation details and we summarize the experiences and practices and draw conclusions. Adoption of modules at Qatar University illustrates the generality and transferability of the approach.

Behavioral research on brainstorming discovered many cognitive, social and motivational factors that affect the idea generation process in individuals or in groups. There are still aspects not completely understood, especially the effects of sharing ideas or providing hints. In this paper we propose a conceptual dynamic model of the idea generation process to investigate the dynamics of idea generation process. The model consists of a network of fixed concept units with inter-connections determined by category membership and typicality of the unit. The novel feature of the model consists in the way ideas, represented as conceptual combinations, emerge in the conceptual network through a transient learning process. Simulation results show the effect of model parameters on idea generation process, and on convergent and divergent thinking. The model replicates results on the effect of hints presented sequentially versus simultaneously.

Understanding the neural mechanisms of the idea generation process has implications for research in brainstorming, creativity and innovation. In this paper we present a conceptual neural model for generating ideas. The model extends the associative memory model of Brown et al. (1998) by explicitly representing categories as networks of concepts and ideas as conceptual combinations. Simulation results are compared with experimental results on effects of priming on low, versus high accessibility categories.

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.

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.

Cognitive control -the ability to produce appropriate behavior in complex situations -is a fundamental aspect of intelligence. It is increasingly evident that this control arises from the interaction of dynamics in several brain regions, and depends significantly on processes of modulation and dynamical biasing. While most research has focused on explanations of behavioral responses seen in experiments and pathologies, it is reasonable to expect that internal functions such as planning and thinking would also use similar control mechanisms. In this paper, we present a connectionist model for an idea generation process that can rapidly retrieve old ideas in familiar contexts and search for novel ideas in unfamiliar ones. Based on a simple reinforcement signal, the system learns context-dependent biases that represent effective internal "response systems" for generating ideas from conceptual elements. A broad goal of the research is to show that preconfigured structural modularity, limited real-time selectivity, and adaptive modulation can interact to produce the flexible functionality necessary for cognition and intelligent behavior.

The paper presents a neural model for learning sequences of relevant patterns embedded in distractors. A contextual episode is a sequence of relevant patterns - always in the same order - intermixed with distractors. By repeated presentations of all contextual episodes, the model discovers for each episode the set of relevant patterns and their order. The problem is solved in two stages: (a) by eliminating distractors, and (b) by learning the order between relevant patterns. The model uses the concept of latent attractors - essential in creating different neural representations for same patterns in distinct episodes. No external teacher and only Hebbian type learning rules are used.

This paper presents a new technique for automatically creating analog circuit models. The method extracts piecewise linear models from trained neural networks. A model is a set of linear dependencies between circuit performances and design parameters. The paper illustrates the technique for an OTA circuit - an amplifier circuit widely used in filters and A/D converters for which models for gain and bandwidth were automatically generated. As experiments show, the obtained models have simple form that accurately fits the sampled points and the behavior of the trained neural networks. These models are useful for fast simulation of systems with non-linear behavior and performances.

Latent attractor networks have been proposed as a possible mechanism for representing episodic context in the hippocampus, and as general purpose models of episodic context-dependent encoding in neural networks. These are recurrent neural networks with attractors that never fully manifest themselves, but bias the network's response to external stimuli. While each attractor in the original latent attractor model was triggered by unique context patterns specific to the context, this model was later extended to the case where contexts were triggered progressively by the sequential presentation of several stimulus patterns without regard to order, simulating the more realistic situation where a context is identified by a sequentially scanned combination of landmarks. In this paper, we describe a network model that can select among contexts identified by overlapping sequences of different lengths, even if the relevant stimulus patterns are interspersed among patterns irrelevant to context selection.

We (1999) have proposed a paradigm called "latent attractors" where attractors embedded in a recurrent network via Hebbian learning are used to channel network response to external input rather than becoming manifest themselves. This allows the network to generate context-sensitive internal codes in complex situations. Latent attractors are particularly helpful in explaining computations within the hippocampus-a brain region of fundamental significance for memory and spatial learning. The performance of latent attractor networks depends on the number of such attractors that a network can sustain. Following methods developed for associative memory networks, we present analytical and computational results on the capacity of latent attractor networks.

Cells throughout the rodent hippocampal system show place-specific patterns of firing called place fields, creating a coarse-coded representation of location. The dependencies of this place code--or cognitive map--on sensory cues have been investigated extensively, and several computational models have been developed to explain them. However, place representations also exhibit strong dependence on spatial and behavioral context, and identical sensory environments can produce very different place codes in different situations. Several recent studies have proposed models for the computational basis of this phenomenon, but it is still not completely understood. In this article, we present a very simple connectionist model for producing context-dependent place representations in the hippocampus. We propose that context dependence arises in the dentate gyrus-hilus (DGH) system, which functions as a dynamic selector, disposing a small group of granule and pyramidal cells to fire in response to afferent stimulus while depressing the rest. It is hypothesized that the DGH system dynamics has "latent attractors," which are unmasked by the afferent input and channel system activity into subpopulations of cells in the DG, CA3, and other hippocampal regions as observed experimentally. The proposed model shows that a minimally structured hippocampus-like system can robustly produce context-dependent place codes with realistic attributes.

In many cognitive situations, context is specified globally-encompassing the entire duration of an episode, but explicitly identified only at its initiation. Examples of such contexts are social situations, spatial environments, task specifications, etc. In many such cases, a neural system needs to respond differently to the same immediate stimulus depending on the global context. Clearly, the system must be able to switch into different response regimes and remain there while the context remains fixed. Simultaneously, it must remain sensitive to external stimuli, and respond to them in a reliable and informative manner. We propose a neurally plausible approach for handling these requirements.