The California Cognitive Science Conference at UC Berkeley is an annual all-day symposium bringing together hundreds of students, researchers, & members of the general public from around the world who are passionate about the interdisciplinary field of Cognitive Science for a day of talks & research presentations. We feature talks given by prominent scientists & thinkers from a wide variety of disciplines, & our acclaimed poster session provides undergraduates with the opportunity to present their original research alongside graduate students & professional researchers. The theme for this year's annual conference is "Forging Connections: Bonding & the Brain." Through this event, our speakers will explore the important roles of social connection in today's rapidly changing world: from the neurobiological & psychological implications of bonding to the consequences of technology, & much more. The conference provides attendees with a glimpse into the latest research in all the fields that comprise Cognitive Science, including but not limited to Psychology, Neuroscience, Computer Science/Artificial Intelligence, Linguistics, Philosophy, & the Social Sciences.

Cognitive agents such as humans and robots perceive their environment through an abundance of sensors producing streams of data that need to be processed to generate intelligent behavior. A key question of cognition-enabled and AI-driven robotics is how to organize and manage knowledge efficiently in a cognitive robot control architecture. We argue, that memory is a central active component of such architectures that mediates between semantic and sensorimotor representations, orchestrates the flow of data streams and events between different processes and provides the components of a cognitive architecture with data-driven services for the abstraction of semantics from sensorimotor data, the parametrization of symbolic plans for execution and prediction of action effects. Based on related work, and the experience gained in developing our ARMAR humanoid robot systems, we identified conceptual and technical requirements of a memory system as central component of cognitive robot control architecture that facilitate the realization of high-level cognitive abilities such as explaining, reasoning, prospection, simulation and augmentation. Conceptually, a memory should be active, support multi-modal data representations, associate knowledge, be introspective, and have an inherently episodic structure. Technically, the memory should support a distributed design, be access-efficient and capable of long-term data storage. We introduce the memory system for our cognitive robot control architecture and its implementation in the robot software framework ArmarX. We evaluate the efficiency of the memory system with respect to transfer speeds, compression, reproduction and prediction capabilities.

Cognitive Computing (COC) aims to build highly cognitive machines with low computational resources that respond in real-time. However, scholarly literature shows varying research areas and various interpretations of COC. This calls for a cohesive architecture that delineates the nature of COC. We argue that if Herbert Simon considered the design science is the science of artificial, cognitive systems are the products of cognitive science or 'the newest science of the artificial'. Therefore, building a conceptual basis for COC is an essential step into prospective cognitive computing-based systems. This paper proposes an architecture of COC through analyzing the literature on COC using a myriad of statistical analysis methods. Then, we compare the statistical analysis results with previous qualitative analysis results to confirm our findings. The study also comprehensively surveys the recent research on COC to identify the state of the art and connect the advances in varied research disciplines in COC. The study found that there are three underlaying computing paradigms, Von-Neuman, Neuromorphic Engineering and Quantum Computing, that comprehensively complement the structure of cognitive computation. The research discuss possible applications and open research directions under the COC umbrella.

This document presents endeavors to represent emotion in a computational cognitive architecture. The first part introduces research organizing with two axes of emotional affect: pleasantness and arousal. Following this basic of emotional components, the document discusses an aspect of emergent properties of emotion, showing interaction studies with human users. With these past author's studies, the document concludes that the advantage of the cognitive human-agent interaction approach is in representing human internal states and processes.

Machine learning, artificial intelligence, natural language processing, deep learning, robotics, and several other technologies have enabled businesses to leverage human intelligence and evaluate inputs for maximum accuracy and precision. For example, you now have image recognition software that acts as a scanner and finds the best search options on Google after interpreting what the image is. So, the application is based on ML, natural language processing, and artificial intelligence. It imitates a human who uses the item or object through the eyes and interprets the results in mind. Although all these disruptive technologies are individually the best in their field, combining them, it's a challenge.

When should an online reinforcement learning-based frequency agile cognitive radar be expected to outperform a rule-based adaptive waveform selection strategy? We seek insight regarding this question by examining a dynamic spectrum access scenario, in which the radar wishes to transmit in the widest unoccupied bandwidth during each pulse repetition interval. Online learning is compared to a fixed rule-based sense-and-avoid strategy. We show that given a simple Markov channel model, the problem can be examined analytically for simple cases via stochastic dominance. Additionally, we show that for more realistic channel assumptions, learning-based approaches demonstrate greater ability to generalize. However, for short time-horizon problems that are well-specified, we find that machine learning approaches may perform poorly due to the inherent limitation of convergence time. We draw conclusions as to when learning-based approaches are expected to be beneficial and provide guidelines for future study.

In this study, conversations between humans and avatars are linguistically, organizationally, and structurally analyzed, focusing on what is necessary for creating face-to-face multimodal interfaces for machines. We videorecorded thirty-four human-avatar interactions, performed complete linguistic microanalysis on video excerpts, and marked all the occurrences of multimodal actions and events. Statistical inferences were applied to data, allowing us to comprehend not only how often multimodal actions occur but also how multimodal events are distributed between the speaker (emitter) and the listener (recipient). We also observed the distribution of multimodal occurrences for each modality. The data show evidence that double-loop feedback is established during a face-to-face conversation. This led us to propose that knowledge from Conversation Analysis (CA), cognitive science, and Theory of Mind (ToM), among others, should be incorporated into the ones used for describing human-machine multimodal interactions. Face-to-face interfaces require an additional control layer to the multimodal fusion layer. This layer has to organize the flow of conversation, integrate the social context into the interaction, as well as make plans concerning 'what' and 'how' to progress on the interaction. This higher level is best understood if we incorporate insights from CA and ToM into the interface system.

Computers are quickly gaining on us. Artificial systems are now exceeding the performance of human experts in several domains. However, we do not yet have a deep definition of expertise. This paper examines the nature of expertise and presents an abstract knowledge-level and skill-level description of expertise. A new level lying above the Knowledge Level, called the Expertise Level, is introduced to describe the skills of an expert without having to worry about details of the knowledge required. The Model of Expertise is introduced combining the knowledge-level and expertise-level descriptions. Application of the model to the fields of cognitive architectures and human cognitive augmentation is demonstrated and several famous intelligent systems are analyzed with the model.

We will soon be surrounded by artificial systems capable of cognitive performance rivaling or exceeding a human expert in specific domains of discourse. However, these "cogs" need not be capable of full general artificial intelligence nor able to function in a stand-alone manner. Instead, cogs and humans will work together in collaboration each compensating for the weaknesses of the other and together achieve synthetic expertise as an ensemble. This paper reviews the nature of expertise, the Expertise Level to describe the skills required of an expert, and knowledge stores required by an expert. By collaboration, cogs augment human cognitive ability in a human/cog ensemble. This paper introduces six Levels of Cognitive Augmentation to describe the balance of cognitive processing in the human/cog ensemble. Because these cogs will be available to the mass market via common devices and inexpensive applications, they will lead to the Democratization of Expertise and a new cognitive systems era promising to change how we live, work, and play. The future will belong to those best able to communicate, coordinate, and collaborate with cognitive systems.

There is a research trend in cognitive science that shifts from a top-down direction (guided by hypothesis-driven testing of cognitive theories) towards a bottom-up approach (enabled by data-drivcen pattern discovery of cognition-related properties). The emergence of high-throughput data collection techniques provides cognitive scientists rich research substances of labelled behavioral data, from one's digital traces on a social media, to large-scale crowdsourcing of experimental responses to well-defined cognitive tasks [1]. Riding along the big data era of cognitive science is the advanced developments of artificial intelligence (AI) methods that is capable of performing components of cognitive functions at human-level or superhuman-level performances. With the new directions, comes new challenges. As the study of the essence, tasks and functions of cognition, how can we as cognitive scientists reshape the field using these new sources of data and new tools of analytical methods, such that it maintains a coherent core as the classical theory-driven studies of cognitive science? To better formulate this challenge, we categorizes the interactions between the concepts of AI and those of the human cognition into three main types (Figure 1). First, we have the computational inference, the process of utilizing machine learning models as a prediction or inference engines to map from measurable signals to the cognitive properties. The second direction is to use the cognitive theory as a prior to build AI. This approach can be dated as early as the symbolic cognitive architectures in 1970s [2, 3], where major cognitive processes such as knowledge representation, memory, learning and control are explicitly mapped into computational components.