Cultivating the Places of Knowledge

The discussion of universities anddemocracy has conventionally dealt first andforemost with the curriculum, or with thespirit of openness and tolerance whichcharacterises the scientific inquiry. In thisarticle I have added a discussion of thesituatedness of knowledge and knowledgeproduction, and, consequently, a discussion ofthe situated character of other roles of theuniversity, including the democratic role. Inthe light of the regress of political partiesand traditional popular movements – phenomenawhich seem to be true both as regardsmembership numbers and as regards level ofactivity – the role as a locally andregionally situated meeting place for avitalisation and defence of democracy seems apromising one for the contemporary university.The article has also emphasised the linksbetween culture, social capital (``the socialfabric') in the surrounding society, and theeconomic growth and welfare of the city andregion. With concepts such as Bildung (oreducation), civic competence (or civility), andculture, and I have tried to draw our attentionto the actual ``places of knowledge,' whoseimportance will certainly grow in the years tocome. The care, the cultivation, and thequalification of these places into supportinginfrastructures for cultural life, creativity,industry, and democracy should be seen as acoherent, holistic mission. It needsrecognition, and articulation, by all those whoare interested in the university as asignificant social factor: politicians,industry, the cultural sector, localcommunities, and, obviously, the academiccommunity itself.     

The variance reaction time model

The variance reaction time model (VRTM) is proposed to account for various recognition data on reaction time, the mirror effect, receiver-operating-characteristic (ROC) curves, etc. The model is based on simple and plausible assumptions within a neural network: VRTM is a two layer neural network where one layer represents items and one layer represents contexts. The recognition decision is based on a random walk of nodes activated at recognition. VRTM suggests theoretical constraints on the distributions of nodes activated at recognition and the noise in the random walk. The variability in the net inputs to nodes depends on the item frequency (the number of times that the item has been encoded) and the list length. The essential mechanism that accounts for the empirical data is a non-linear activation function. The mean activation threshold in the non-linear activation function is placed to achieve efficient discriminability between new and old items and there is variability in the activation threshold. VRTM predicts the mirror effect for low and high frequency words, a strength based mirror effect between conditions but not within one condition, appropriate ROC-curves for old/new and high/low frequency items, and list-length effects. Furthermore, it predicts appropriate means and distributions of reaction times for old/new, correct/incorrect, and high/low frequency items as well as speed/accuracy tradeoffs. VRTM has an explicit mathematical solution, it is simulated in a neural network, and it is fitted to a number of datasets.     

How something can be said about telling more than we can know: on choice blindness and introspection

The legacy of Nisbett and Wilson’s classic article, Telling More Than We Can Know: Verbal Reports on Mental Processes (1977), is mixed. It is perhaps the most cited article in the recent history of consciousness studies, yet no empirical research program currently exists that continues the work presented in the article. To remedy this, we have introduced an experimental paradigm we call choice blindness [Johansson, P., Hall, L., Sikström, S., &; Olsson, A. (2005). Failure to detect mismatches between intention and outcome in a simple decision task. Science, 310(5745), 116–119.]. In the choice blindness paradigm participants fail to notice mismatches between their intended choice and the outcome they are presented with, while nevertheless offering introspectively derived reasons for why they chose the way they did. In this article, we use word-frequency and latent semantic analysis (LSA) to investigate a corpus of introspective reports collected within the choice blindness paradigm. We contrast the introspective reasons given in non-manipulated vs. manipulated trials, but find very few differences between these two groups of reports.