Search

During decision − making, animals consider not only the current but also the past quality of options. For example, when humans evaluate performance (e.g. sales) of employees, they do not only consider the average performance but also the trend of performance; ascending performance is often viewed as more favorable than descending performance. In our study, we test if non-human animals have a similar bias when they are evaluating options using house-hunting by the acorn ant, Temnothorax curvispinosus, as our model system. Our data show that when nest-site quality is static over time, ant colonies tend to prefer the nest site which was better (i.e. darker) between two nest options. However, when the nest quality changes over time—one improves and the other worsens—more colonies choose the low-quality, but improving, nest than the high-quality, but worsening, nest. These results suggest that a continuous change of option quality may influence evaluation. We discuss alternative explanations for our results, possible mechanisms, and potential ecological benefits for keeping track of the nest-site quality. [ABSTRACT FROM AUTHOR]

Summary: Garg et al. (2023, Proceedings of the National Academy of Sciences, 120[6], e2207959120) build simulation models to understand how turtle ants collectively find efficient paths through branched networks, highlighting the importance of bidirectional traffic, leakage of ants at junctions, and the ability to increase flow as key components for efficiency. Their findings provide new, biologically realistic mechanisms that could improve applications in our own engineered networks. [ABSTRACT FROM AUTHOR]

This paper focuses on the collective cognition by robotic swarms. The robotic swarms expected to perform tasks that are beyond the capability of a single robot by collective behavior that emerge from local interactions, similar to biological swarms. However, the robotic swarms have to rely on the collective cognition more than biological swarms when considering the limitation in sensory capabilities and the cost of each robot. In this paper, we develop controllers for a robotic swarm to accomplish a foraging task that requires collective cognition. In this task, robots have to both collectively distinguish two objects, namely food and poison, and cooperatively transport food objects to the nest. We applied an evolutionary robotics approach with the covariance matrix adaptation evolution strategy to develop controllers for robotic swarms. The results of computer simulations show that collective cognition was successfully developed, which allows the robots to transport only food objects. In addition, we also perform experiments to examine the scalability and the flexibility of the developed controllers. [ABSTRACT FROM AUTHOR]

Mandevillian intelligence is a specific form of collective intelligence in which individual cognitive vices (i.e., shortcomings, limitations, constraints and biases) are seen to play a positive functional role in yielding collective forms of cognitive success. The present paper introduces the concept of mandevillian intelligence and reviews a number of strands of empirical research that help to shed light on the phenomenon. The paper also attempts to highlight the value of the concept of mandevillian intelligence from a philosophical, scientific and engineering perspective. Inasmuch as we accept the notion of mandevillian intelligence, then it seems that the cognitive and epistemic value of a specific social or technological intervention will vary according to whether our attention is focused at the individual or collective level of analysis. This has a number of important implications for how we think about the design and evaluation of collective cognitive systems. For example, the notion of mandevillian intelligence forces us to take seriously the idea that the exploitation (or even the accentuation) of individual cognitive shortcomings could, in some situations, provide a productive route to collective forms of cognitive and epistemic success. [ABSTRACT FROM AUTHOR]

While most technological positioning studies were traditionally addressed by comparing firms technological patents classes and portfolios, only a few of them adopted science mapping patent co-citation techniques and none of these seeks to understand the impact of collective cognition on the technology structure of an entire industry. What is the firms technological positioning landscape within an high collective cognition sector? What is the groups technological positioning evolution? How do technology structures shift according to different economic scenarios? Through a strategic lens we contribute to technology strategy literatures by proposing an invention behavior map of automotive actors at a firm, groups and industry level. From Derwent Innovation Index, about 581,000 patents, 1,309,356 citations and 1,287,594 co-citations relationships between (a) the main 49 firms assignees of 1991-2013 and (b) the main 28 or 34 groups assignees by considering three timespan 1991-1997, 1998-2004, 2005-2013, were collected. Results: (1) most of the companies are located close together, depicting the sector technology structure as highly dense; (2) the market leaders do not coincide with technology production leaders and not necessarily occupy central technological positions; (3) the automotive groups considerably varies in the three timespan in terms of position and composition; (4) the market leaders groups occupy technological remoteness positions during economic growth timespan; (5) the sector technology structure is highly dense during growth, strongly scattered and lacking of technologically center positioned actors after economic decline. Finally, strategic implications supporting central locating or suburb R&D positioning planning and M&As recombinational partners decision making are discussed. [ABSTRACT FROM AUTHOR]

This case study examined team-based computer-game design efforts by children with diverse abilities to explore the nature of their collective design actions and cognitive processes. Ten teams of middle-school children, with a high percentage of minority students, participated in a 6-weeks, computer-assisted math-game-design program. Essential processes of collective design cognition and operation emerged from the data: (a) collective exploration of design constraints during problem framing, (b) aggregation of identity, experience, and memory for collective solution generation, and (c) development of coalition and task interdependence during design execution. Salient contexts supporting collective design included team- role fulfillment with presence of leadership and scaffolding for mutuality in design talk. The study findings also indicated perceived learning of school children during collaborative math-game design. [ABSTRACT FROM AUTHOR]

I start with a brief assessment of the implications of Sterelny's anti-individualist, anti-internalist apprentice learning model for a more historical and interdisciplinary cognitive science. In a selective response I then focus on two core features of his constructive account: collaboration and skill. While affirming the centrality of joint action and decision making, I raise some concerns about the fragility of the conditions under which collaborative cognition brings benefits. I then assess Sterelny's view of skill acquisition and performance, which runs counter to dominant theories that stress the automaticity of skill. I suggest that it may still overestimate the need for and ability of experts to decompose and represent the elements of their own practical knowledge. [ABSTRACT FROM AUTHOR]

Today, much problem solving is performed by teams, rather than individuals. The complexity of these problems has exceeded the cognitive capacity of any individual and requires a team of members to solve them. The success of solving these complex problems not only relies on individual team members who possess different but complementary expertise, but more importantly, their collective problem solving ability. To better conceptualize large scale complex problem solving, an understanding of collective cognitive components and processes during team-based complex problem solving is necessary. This paper offers a conceptual discussion about complex problem solving from a collective cognition perspective. The types of cognitive processing and cognitive components of team-based problem solving (TBPS) as well as the cognitive states of collective emergent cognitive states and the interactive mechanisms will be discussed. Also, implications from the model for assessing TBPS performance and suggestions for future research will be offered. [ABSTRACT FROM AUTHOR]