<div class="page" title="Page 1"><div class="layoutArea"><div class="column"><p><span>We are in a ‘personal data gold rush’ driven by advertising being the primary revenue source for most online companies. These companies accumulate extensive personal data about individuals with minimal concern for us, the subjects of this process. This can cause many harms: privacy infringement, personal and professional embarrassment, restricted access to labour markets, restricted access to highest value pricing, and many others. There is a critical need to provide technologies that enable alternative practices, so that individuals can par- ticipate in the collection, management and consumption of their personal data. In this paper we discuss the Databox, a personal networked device (and associated services) that col- lates and mediates access to personal data, allowing us to re- cover control of our online lives. We hope the Databox is a first step to re-balancing power between us, the data subjects, and the corporations that collect and use our data. </span></p></div></div></div>

One of the archetypal task manipulations known to depend on frontal-lobe function is reversal learning, where a dominant response must be overridden due to changes in the contingencies relating stimuli, responses, and environmental feedback. Previous studies have indicated that the lateral prefrontal cortex (LPFC), the lateral orbitofrontal cortex (LOFC), the anterior cingulate cortex (ACC), and the caudate nucleus (CN) all contribute to reversal learning. However, the exact contributions that they make during this cognitively complex task remain poorly defined. Here, using functional magnetic resonance imaging, we examine which of the cognitive processes that contribute to the performance of a reversal best predicts the pattern of activation within distinct sub-regions of the frontal lobes. We demonstrate that during reversal learning the LOFC is particularly sensitive to the implementation of the reversal, whereas the LPFC is recruited more generally during attentional control. By contrast, the ACC and CN respond when new searches are initiated regardless of whether the previous response is available, whilst medial orbitofrontal cortex (MOFC) activity is correlated with the positive affect of feedback. These results accord well with the hypothesis that distinct components of adaptable behaviour are supported by anatomically distinct components of the executive system.

Environmental stimuli constantly compete for human attention and in many cases decisions are made based on the affective meaning they convey. Although the network of structures involved in processing affective value has been well described, the specific contribution of these structures to the process by which affective value guides decision making is less well understood and is the focus of the present study. Thus, subjects read descriptions of individually tailored holidays, varying in incentive value and then made preference judgements, cognitive judgements or no decision. Choices made from an affective perspective, compared with those made from a cognitive perspective, activated a region of the anterior insula/operculum and also the anterior cingulate cortex. Furthermore, activity in perigenual, anterior cingulate cortex was correlated with subjective ratings of incentive value. In contrast, medial orbitofrontal cortex (OFC) and a region of posterior ventrolateral prefrontal cortex (PFC), bordering on the insula, were found to be more active when affective stimuli guided response selection than when no selection was made. However, only the activity in the ventrolateral PFC was specific to response selection based on affective compared with cognitive judgements. It is proposed that the necessary introspection required to make subjective preference judgements is provided by the insula and cingulate cortices, while the medial OFC and posterior ventrolateral PFC/insula cortices contribute to stimulus evaluation and motivational aspects of response selection, respectively.

In a symmetrical environment possessing no other polarizing visual cues, the spatially localized firing of hippocampal place cells can be primarily orientated by a reliable distal visual stimulus, such as a white cue card. However, if such a directional cue is made unreliable by being frequently moved in full view of the rat, the rat's internal sense of direction comes, over the course of a few days, to control the orientation of place fields instead. We investigated whether this simple form of 'cue-instability' learning would transfer to a new context, in which the firing patterns of the place cells become reorganized and in which a new spatial representation is thus active. We found that after cue-instability learning, the 'remapped' place field representation in the new environment was also orientated by the internal sense of direction of the rat rather than by the cue card, showing that the cue learning generalized from one context (and hence spatial representation) to another. This contrasts with another kind of place cell learning, in which the cells can acquire the ability to discriminate two spatial locations in one context but do not transfer this discrimination to a new context. We discuss the different effects of context changes on learned place cell activity in terms of the possible architecture of the inputs to place cells.

We discuss the problem of building a user-acceptable infrastructure for a large organisation that wishes to measure its employees' travel-to-work carbon footprint, based on the gathering of high resolution geolocation data on employees in a privacy-sensitive manner. This motivated the construction of a distributed system of personal containers in which individuals record fine-grained location information into a private data-store which they own, and from which they can trade portions of data to the organisation in return for specific benefits. This framework can be extended to gather a wide variety of personal data and facilitates the transformation of private information into a public good, with minimal and assessable loss of individual privacy. This is currently a work in progress. We report on the hardware, software and social aspects of piloting this scheme on the University of Cambridge's experimental cloud service, as well as contrasting it to a traditional centralised model.