Robustness and Idealization in Models of Cognitive Labor

Scientific research is almost always conducted by communities of scientists of varying size and complexity. Such communities are effective, in part, because they divide their cognitive labor: not every scientist works on the same project. Philip Kitcher and Michael Strevens have pioneered efforts to understand this division of cognitive labor by proposing models of how scientists make decisions about which project to work on. For such models to be useful, they must be simple enough for us to understand their dynamics, but faithful enough to reality that we can use them to analyze real scientific communities. To satisfy the first requirement, we must employ idealizations to simplify the model. The second requirement demands that these idealizations not be so extreme that we lose the ability to describe real-world phenomena. This paper investigates the status of the assumptions that Kitcher and Strevens make in their models, by first inquiring whether they are reasonable representations of reality, and then by checking the models’ robustness against weakenings of these assumptions. To do this, we first argue against the reality of the assumptions, and then develop a series of agent-based simulations to systematically test their effects on model outcomes. We find that the models are not robust against weakenings of these idealizations. In fact we find that under certain conditions, this can lead to the model predicting outcomes that are qualitatively opposite of the original model outcomes

The role and ontogeny of maps in long-distance animal navigation

Impressive feats of long-distance animal navigation raise questions relating to the proximate causes of such behaviours: what mechanisms facilitate long-distance animal navigation? What is the ontogeny of such behaviours? I focus on how animals determine their position in relation to their goals to determine goalward directions in the first step (the map step) of a two-step navigational process termed map and compass navigation. I begin by introducing a framework that deconstructs animal maps into three interlinked components: (1) map cues, (2) map structure, their organisation of spatial information, and (3) map navigation strategy, the way that animals approach goals and combine multiple map cues. I discuss the ontogeny of animal maps, clarifying the roles of inherited rules, imprinting and latent learning, and suggest that mechanisms for determining vectors of self-motion, known as path integration, could facilitate map learning by allowing animals to integrate changes in map cues with self-motion. I use two general approaches to examine animal maps. First, I employ artificial neural networks as simple computational models of animal learning. This allows me to investigate the possible ontogenies of navigational behaviours and make predictions about how animals might learn to navigate. Using this approach, I investigate how animals might learn to combine intersecting environmental gradients to navigate with a grid map. I find that neural networks adopt a mechanism of combining environmental gradient cues through first determining displacements in each gradient field independently, leading to predictable orientation errors in some environments. This work clarifies how such mechanisms might arise and change through learning, allowing me to make more nuanced predictions of how navigational mechanisms might develop through learning. Using a similar approach, I investigate how collective decision-making processes impact upon individual learning when animals navigate in groups. I find that leaders learn more quickly than followers, and that individuals in democratic groups, in which compromises are taken between individual preferences, learn by compensating for partner error unlike individuals in despotic groups and solo learners. My second general approach involves experiments on two model species of long-distance navigation. I attempt to investigate whether Manx shearwaters utilise olfactants as navigational map cues, while minimising impact on the birds, by manipulating their access to olfactory cues before release after long-distance displacement. However, I find no clear evidence that they utilise olfactory cues in their navigation, leaving the sensory basis of their navigational map an open question. I investigate navigational ontogeny in homing pigeons, examining whether passive exposure to a novel release site is sufficient for navigational learning. I find no evidence that passive exposure to the release site impacts upon initial navigational performance and hence no evidence that this is sufficient for learning. Overall, this work clarifies outstanding questions on the proximate causes of animal navigation, generates predictions on navigational learning, and produces incremental steps forward in our understanding of navigational mechanism and ontogeny in two model systems. Ultimately, advancing these theoretical and empirical approaches together will enable us to better identify and understand the proximate causes of long-distance animal navigation

Coordinating Joint Action in a Real-Life Activity: The Interplay of Explicit and Implicit Coordination

Humans engage in joint actions on a daily basis. Some of these joint actions are explicitly coordinated using, for example, speech and gesture, and the others are implicitly coordinated with the actions themselves. The first chapter of this dissertation reviews the use of speech, gesture, and intentional behavioral signals in explicit coordination of joint action and identifies three cognitive mechanisms that enable implicit coordination of joint action, namely, motor resonance, joint intentionality, and environmental and social affordance. The second chapter reports an empirical study exploring the employment of explicit and implicit coordination of joint action in a complex real-life joint activity, assembling a TV cart from its parts. We coded the content of the utterances and gestures that pairs of participants used throughout the assembly and the major and subordinate joint actions they performed. We then coded how each joint action was coordinated, that is, using speech, gestures, or action itself. The results showed speech and gesture served primarily to establish and sustain a shared mental model of the environmental affordances between the co-actors, which occurred primarily at the beginning of the task and as the participants began to attach two major parts. For both major and subordinate joint actions alike, the specifics of the joint actions such as the goal and division of labor was primarily coordinated implicitly. We argue that the shared mental model scaffolded the participants’ implicit coordination of the actions. These findings provide evidence that action itself is a communicative device and part of the conversation between co-actors of a joint activity. They also lend support to the argument that joint action cannot be fully understood on the individual level but must be interpreted as a collective of which each individual is a part

Self-organized division of cognitive labor

A menudo, los miembros de un grupo se benefician al dividir la tarea del grupo en componentes separados, donde cada miembro especializa su rol para lograr solo uno de los componentes. Si bien este fenómeno de división del trabajo se ha observado con respecto al trabajo manual y cognitivo, no existe una comprensión clara de los mecanismos cognitivos que permiten su aparición, especialmente cuando existen múltiples divisiones posibles y la comunicación es limitada. De hecho, la maximización de la utilidad esperada a menudo no diferencia entre formas alternativas en las que los individuos podrían dividir el trabajo. Desarrollamos un juego iterativo de dos personas en el que hay múltiples formas de dividir el trabajo, pero en el que no es posible negociar explícitamente una división. Implementamos el juego como una tarea experimental humana y como un modelo computacional. Nuestros resultados muestran que la mayoría de las díadas humanas pueden terminar el juego con una eficiente división del trabajo. Además, ajustamos nuestro modelo computacional a los datos de comportamiento, lo que nos permitió explicar cómo la similitud percibida entre las acciones de un jugador y los puntos focales de la tarea guió las elecciones de los jugadores de una ronda a la otra, uniendo así la dinámica de grupo y su proceso cognitivo subyacente. Las aplicaciones potenciales de este modelo fuera de la ciencia cognitiva incluyen la mejora de la cooperación en grupos humanos, sistemas de múltiples agentes, así como la colaboración entre humanos y robots.Often members of a group benefit from dividing the group’s task into separate compo nents, where each member specializes their role so as to accomplish only one of the com ponents. While this division of labor phenomenon has been observed with respect to both manual and cognitive labor, there is no clear understanding of the cognitive mechanisms allowing for its emergence, especially when there are multiple divisions possible and com munication is limited. Indeed, maximization of expected utility often does not differentiate between alternative ways in which individuals could divide labor. We developed an itera tive two-person game in which there are multiple ways of dividing labor, but in which it is not possible to explicitly negotiate a division. We implemented the game both as a human experimental task and as a computational model. Our results show that the majority of human dyads can finish the game with an efficient division of labor. Moreover, we fitted our computational model to the behavioral data, which allowed us to explain how the per ceived similarity between a player’s actions and the task’s focal points guided the players’ choices from one round to the other, thus bridging the group dynamics and its underlying cognitive process. Potential applications of this model outside cognitive science include the improvement of cooperation in human groups, multi-agent systems, as well as human-robot collaboration

Self-organized division of cognitive labor

A menudo, los miembros de un grupo se benefician al dividir la tarea del grupo en componentes separados, donde cada miembro especializa su rol para lograr solo uno de los componentes. Si bien este fenómeno de división del trabajo se ha observado con respecto al trabajo manual y cognitivo, no existe una comprensión clara de los mecanismos cognitivos que permiten su aparición, especialmente cuando existen múltiples divisiones posibles y la comunicación es limitada. De hecho, la maximización de la utilidad esperada a menudo no diferencia entre formas alternativas en las que los individuos podrían dividir el trabajo. Desarrollamos un juego iterativo de dos personas en el que hay múltiples formas de dividir el trabajo, pero en el que no es posible negociar explícitamente una división. Implementamos el juego como una tarea experimental humana y como un modelo computacional. Nuestros resultados muestran que la mayoría de las díadas humanas pueden terminar el juego con una eficiente división del trabajo. Además, ajustamos nuestro modelo computacional a los datos de comportamiento, lo que nos permitió explicar cómo la similitud percibida entre las acciones de un jugador y los puntos focales de la tarea guió las elecciones de los jugadores de una ronda a la otra, uniendo así la dinámica de grupo y su proceso cognitivo subyacente. Las aplicaciones potenciales de este modelo fuera de la ciencia cognitiva incluyen la mejora de la cooperación en grupos humanos, sistemas de múltiples agentes, así como la colaboración entre humanos y robots.Often members of a group benefit from dividing the group’s task into separate compo nents, where each member specializes their role so as to accomplish only one of the com ponents. While this division of labor phenomenon has been observed with respect to both manual and cognitive labor, there is no clear understanding of the cognitive mechanisms allowing for its emergence, especially when there are multiple divisions possible and com munication is limited. Indeed, maximization of expected utility often does not differentiate between alternative ways in which individuals could divide labor. We developed an itera tive two-person game in which there are multiple ways of dividing labor, but in which it is not possible to explicitly negotiate a division. We implemented the game both as a human experimental task and as a computational model. Our results show that the majority of human dyads can finish the game with an efficient division of labor. Moreover, we fitted our computational model to the behavioral data, which allowed us to explain how the per ceived similarity between a player’s actions and the task’s focal points guided the players’ choices from one round to the other, thus bridging the group dynamics and its underlying cognitive process. Potential applications of this model outside cognitive science include the improvement of cooperation in human groups, multi-agent systems, as well as human-robot collaboration

Self-Organized Division of Cognitive Labor

The division of labor phenomenon has been observed with re-spect to both manual and cognitive labor, but there is no clearunderstanding of the intra- and inter-individual mechanismsthat allow for its emergence, especially when there are multipledivisions possible and communication is limited. Situationsfitting this description include individuals in a group splittinga geographical region for resource harvesting without explicitnegotiation, or a couple tacitly negotiating the hour of the dayfor each to shower so that there is sufficient hot water. We stud-ied this phenomenon by means of an iterative two-person gamewhere multiple divisions are possible, but no explicit commu-nication is allowed. Our results suggest that there are a lim-ited number of biases toward divisions of labor, which serveas attractors in the dynamics of dyadic coordination. How-ever, unlike Schelling’s focal points, these biases do not attractplayers’ attention at the onset of the interaction, but are onlyrevealed and consolidated by the in-game dynamics of dyadicinteraction