Competency Implications of Changing Human Resource Roles

[Excerpt] The present study examines which competencies will be necessary to perform key human resource roles over the next decade at Eastman Kodak Company. This project was a critical component of an ongoing quality process to improve organizational capability. The results establish a platform that will enable Kodak to better assess, plan, develop, and measure the capability of human resource staff

An integrated theory of language production and comprehension

Currently, production and comprehension are regarded as quite distinct in accounts of language processing. In rejecting this dichotomy, we instead assert that producing and understanding are interwoven, and that this interweaving is what enables people to predict themselves and each other. We start by noting that production and comprehension are forms of action and action perception. We then consider the evidence for interweaving in action, action perception, and joint action, and explain such evidence in terms of prediction. Specifically, we assume that actors construct forward models of their actions before they execute those actions, and that perceivers of others' actions covertly imitate those actions, then construct forward models of those actions. We use these accounts of action, action perception, and joint action to develop accounts of production, comprehension, and interactive language. Importantly, they incorporate well-defined levels of linguistic representation (such as semantics, syntax, and phonology). We show (a) how speakers and comprehenders use covert imitation and forward modeling to make predictions at these levels of representation, (b) how they interweave production and comprehension processes, and (c) how they use these predictions to monitor the upcoming utterances. We show how these accounts explain a range of behavioral and neuroscientific data on language processing and discuss some of the implications of our proposal

Towards a complete multiple-mechanism account of predictive language processing [Commentary on Pickering & Garrod]

Although we agree with Pickering & Garrod (P&G) that prediction-by-simulation and prediction-by-association are important mechanisms of anticipatory language processing, this commentary suggests that they: (1) overlook other potential mechanisms that might underlie prediction in language processing, (2) overestimate the importance of prediction-by-association in early childhood, and (3) underestimate the complexity and significance of several factors that might mediate prediction during language processing

Interactive inference: a multi-agent model of cooperative joint actions

We advance a novel computational model of multi-agent, cooperative joint actions that is grounded in the cognitive framework of active inference. The model assumes that to solve a joint task, such as pressing together a red or blue button, two (or more) agents engage in a process of interactive inference. Each agent maintains probabilistic beliefs about the goal of the joint task (e.g., should we press the red or blue button?) and updates them by observing the other agent's movements, while in turn selecting movements that make his own intentions legible and easy to infer by the other agent (i.e., sensorimotor communication). Over time, the interactive inference aligns both the beliefs and the behavioral strategies of the agents, hence ensuring the success of the joint action. We exemplify the functioning of the model in two simulations. The first simulation illustrates a ''leaderless'' joint action. It shows that when two agents lack a strong preference about their joint task goal, they jointly infer it by observing each other's movements. In turn, this helps the interactive alignment of their beliefs and behavioral strategies. The second simulation illustrates a "leader-follower" joint action. It shows that when one agent ("leader") knows the true joint goal, it uses sensorimotor communication to help the other agent ("follower") infer it, even if doing this requires selecting a more costly individual plan. These simulations illustrate that interactive inference supports successful multi-agent joint actions and reproduces key cognitive and behavioral dynamics of "leaderless" and "leader-follower" joint actions observed in human-human experiments. In sum, interactive inference provides a cognitively inspired, formal framework to realize cooperative joint actions and consensus in multi-agent systems.Comment: 32 pages, 16 figure

Theory of mind and decision science: Towards a typology of tasks and computational models

The ability to form a Theory of Mind (ToM), i.e., to theorize about others’ mental states to explain and predict behavior in relation to attributed intentional states, constitutes a hallmark of human cognition. These abilities are multi-faceted and include a variety of different cognitive sub-functions. Here, we focus on decision processes in social contexts and review a number of experimental and computational modeling approaches in this field. We provide an overview of experimental accounts and formal computational models with respect to two dimensions: interactivity and uncertainty. Thereby, we aim at capturing the nuances of ToM functions in the context of social decision processes. We suggest there to be an increase in ToM engagement and multiplexing as social cognitive decision-making tasks become more interactive and uncertain. We propose that representing others as intentional and goal directed agents who perform consequential actions is elicited only at the edges of these two dimensions. Further, we argue that computational models of valuation and beliefs follow these dimensions to best allow researchers to effectively model sophisticated ToM-processes. Finally, we relate this typology to neuroimaging findings in neurotypical (NT) humans, studies of persons with autism spectrum (AS), and studies of nonhuman primates

A dynamic neural field approach to natural and efficient human-robot collaboration

A major challenge in modern robotics is the design of autonomous robots that are able to cooperate with people in their daily tasks in a human-like way. We address the challenge of natural human-robot interactions by using the theoretical framework of dynamic neural fields (DNFs) to develop processing architectures that are based on neuro-cognitive mechanisms supporting human joint action. By explaining the emergence of self-stabilized activity in neuronal populations, dynamic field theory provides a systematic way to endow a robot with crucial cognitive functions such as working memory, prediction and decision making . The DNF architecture for joint action is organized as a large scale network of reciprocally connected neuronal populations that encode in their firing patterns specific motor behaviors, action goals, contextual cues and shared task knowledge. Ultimately, it implements a context-dependent mapping from observed actions of the human onto adequate complementary behaviors that takes into account the inferred goal of the co-actor. We present results of flexible and fluent human-robot cooperation in a task in which the team has to assemble a toy object from its components.The present research was conducted in the context of the fp6-IST2 EU-IP Project JAST (proj. nr. 003747) and partly financed by the FCT grants POCI/V.5/A0119/2005 and CONC-REEQ/17/2001. We would like to thank Luis Louro, Emanuel Sousa, Flora Ferreira, Eliana Costa e Silva, Rui Silva and Toni Machado for their assistance during the robotic experiment

Foreseeing the dynamics of strategy : an anticipatory systems perspective

The paper explores firms as complex anticipatory systems which construct dynamic strategic configurations based on anticipation of their future possible states within the competitive environment. We argue that firm’s performance depends on (a) its strategy making process based on anticipation, and (b) its managerial capabilities which effectuate the anticipatory process in the following four stages: search across anticipated “what-if” resource configurations, the articulation and conversion of their meaning, and the finding and evolution of strategic patterns and courses of action for environmental fit. We performed an in-depth exploratory study with a group of senior managers in a pharmaceutical firm to uncover diverse anticipatory capabilities. The study was based on the development and re-assessment of a product market strategy for a new drug launch without and with the use of a simulation-based learning environment. The results show the existence of heterogeneous anticipatory process, which we name search-articulate-find-evolve of alternative resource configuration sets, determining the managerial dynamic capabilities related particularly to managerial cognition and decision making. We propose anticipation enhanced by modelling and simulation can improve managers’ mental processes and help them to overcome cognitive limitations when dealing with real-world complexities