Swaying to the Extreme: Group Relative Deprivation Predicts Voting for an Extreme Right Party in the French Presidential Election

Why do people vote for the extreme right (ER)? Despite considerable evidence suggesting the role of group relative deprivation (GRD) in accounting for prejudice, collective action and support for protest movements, there is surprisingly little research that has tested the impact of various types of relative deprivation in explaining the support for the ER. Using a large and representative sample of the French population tested before the 2012 presidential election, we hypothesised and found that GRD is a better predictor of the intention to vote for Marine Le Pen, the ER candidate, than individual relative deprivation. GRD remained a significant predictor of voting for the ER even when controlling for social dominance orientation and prejudice, while it did not predict self-placement on the left-right political continuum. Thus, the sense that the French as a group are unjustly treated compared to immigrants living in France underpins the vote for the ER but not, as we demonstrate, for any other populist party. We discuss how the rhetoric of the ER parties can appeal to voters and expand their base over and above the support coming from those who are overtly prejudice

Shape recognition: convexities, concavities and things in between.

Visual objects are effortlessly recognized from their outlines, largely irrespective of viewpoint. Previous studies have drawn different conclusions regarding the importance to shape recognition of specific shape features such as convexities and concavities. However, most studies employed familiar objects, or shapes without curves, and did not measure shape recognition across changes in scale and position. We present a novel set of random shapes with well-defined convexities, concavities and inflections (intermediate points), segmented to isolate each feature type. Observers matched the segmented reference shapes to one of two subsequently presented whole-contour shapes (target or distractor) that were re-scaled and re-positioned. For very short segment lengths, performance was significantly higher for convexities than for concavities or intermediate points and for convexities remained constant with increasing segment length. For concavities and intermediate points, performance improved with increasing segment length, reaching convexity performance only for long segments. No significant differences between concavities and intermediates were found. These results show for the first time that closed curvilinear shapes are encoded using the positions of convexities, rather than concavities or intermediate regions. A shape-template model with no free parameters gave an excellent account of the data

Learning, Social Intelligence and the Turing Test - why an "out-of-the-box" Turing Machine will not pass the Turing Test

The Turing Test (TT) checks for human intelligence, rather than any putative general intelligence. It involves repeated interaction requiring learning in the form of adaption to the human conversation partner. It is a macro-level post-hoc test in contrast to the definition of a Turing Machine (TM), which is a prior micro-level definition. This raises the question of whether learning is just another computational process, i.e. can be implemented as a TM. Here we argue that learning or adaption is fundamentally different from computation, though it does involve processes that can be seen as computations. To illustrate this difference we compare (a) designing a TM and (b) learning a TM, defining them for the purpose of the argument. We show that there is a well-defined sequence of problems which are not effectively designable but are learnable, in the form of the bounded halting problem. Some characteristics of human intelligence are reviewed including it's: interactive nature, learning abilities, imitative tendencies, linguistic ability and context-dependency. A story that explains some of these is the Social Intelligence Hypothesis. If this is broadly correct, this points to the necessity of a considerable period of acculturation (social learning in context) if an artificial intelligence is to pass the TT. Whilst it is always possible to 'compile' the results of learning into a TM, this would not be a designed TM and would not be able to continually adapt (pass future TTs). We conclude three things, namely that: a purely "designed" TM will never pass the TT; that there is no such thing as a general intelligence since it necessary involves learning; and that learning/adaption and computation should be clearly distinguished.Comment: 10 pages, invited talk at Turing Centenary Conference CiE 2012, special session on "The Turing Test and Thinking Machines

Does increased interdisciplinary contact among hard and social scientists help or hinder interdisciplinary research?

Scientists across disciplines must often work together to address pressing global issues facing our societies. For interdisciplinary projects to flourish, scientists must recognise the potential contribution of other disciplines in answering key research questions. Recent research suggested that social sciences may be appreciated less than hard sciences overall. Building on the extensive evidence of ingroup bias and ethnocentrism in intergroup relations, however, one could also expect scientists, especially those belonging to high status disciplines, to play down the contributions of other disciplines to important research questions. The focus of the present research was to investigate how hard and social scientists perceive one another and the impact of interdisciplinary collaborations on these perceptions. We surveyed 280 scientists at Wave 1 and with 129 of them followed up at Wave 2 to establish how ongoing interdisciplinary collaborations underpinned perceptions of other disciplines. Based on Wave 1 data, scientists who report having interdisciplinary experiences more frequently are also more likely to recognise the intellectual contribution of other disciplines and perceive more commonalities with them. However, in line with the intergroup bias literature, group membership in the more prestigious hard sciences is related to a stronger tendency to downplay the intellectual contribution of social science disciplines compared to other hard science disciplines. This bias was not present among social scientists who produced very similar evaluation of contribution of hard and social science disciplines. Finally, using both waves of the survey, the social network comparison of discipline pairs shows that asymmetries in the evaluation of other disciplines are only present among discipline pairs that do not have any experience of collaborating with one another. These results point to the need for policies that incentivise new collaborations between hard and social scientists and foster interdisciplinary contact

Enumerating Abelian Returns to Prefixes of Sturmian Words

We follow the works of Puzynina and Zamboni, and Rigo et al. on abelian returns in Sturmian words. We determine the cardinality of the set $\mathcal{APR}_u$ of abelian returns of all prefixes of a Sturmian word $u$ in terms of the coefficients of the continued fraction of the slope, dependingly on the intercept. We provide a simple algorithm for finding the set $\mathcal{APR}_u$ and we determine it for the characteristic Sturmian words.Comment: 19page