Interactive technology assessment (iTA) provides an answer to the ethical problem of normative bias in evaluation research. This normative bias develops when relevant perspectives on the evaluand (the thing being evaluated) are neglected. In iTA this bias is overcome by incorporating different perspectives into the assessment. As a consequence, justification of decisions based on the assessment is provided by stakeholders having achieved agreement. In this article, agreement is identified with wide reflective equilibrium to show that it indeed has the potential (...) of justifying decisions. We work out several conditions for this agreement to be achievable and just. (shrink)

Research on social influences often distinguishes between social and quality incentives to ascribe meaning to the value that popularity conveys. This study examines the neural correlates of those incentives through which popularity influences preferences. This research reports an functional magnetic resonance imaging experiment and a behavioral task in which respondents evaluated popular products with three focus perspectives; unspecified focus, focus on social aspects, and focus on quality. The results show that value derived with a social focus reflects inferences of approval (...) and reward value, and positively affects preferences. Value derived with a quality focus reflects inferences of quality and negatively affects preferences. This study provides evidence of two distinct inferential routes on both a neurological level, represented by different regions in the brain, and a behavioral level. These results provide the first evidence that a single popularity cue can in different ways influence the value derived from product popularity. (shrink)

In this issue of topiCS, we present the best papers from the ICCM meeting. These best papers represent advances in the state of the art in cognitive modeling. Since ICCM was for the first time also held jointly with the Society for Mathematical Psychology, we use this preface to also reflect on the similarities and differences between mathematical psychology and cognitive modeling.

When producing a description of a target referent in a visual context, speakers need to choose a set of properties that distinguish it from its distractors. Computational models of language production/generation usually model this as a search process and predict that the time taken will increase both with the number of distractors in a scene and with the number of properties required to distinguish the target. These predictions are reminiscent of classic findings in visual search; however, unlike models of reference (...) production, visual search models also predict that search can become very efficient under certain conditions, something that reference production models do not consider. This paper investigates the predictions of these models empirically. In two experiments, we show that the time taken to plan a referring expression—as reflected by speech onset latencies—is influenced by distractor set size and by the number of properties required, but this crucially depends on the discriminability of the properties under consideration. We discuss the implications for current models of reference production and recent work on the role of salience in visual search. (shrink)

We report two experiments that investigated the widely held assumption that speakers use the addressee’s discourse model when choosing referring expressions (e.g., Ariel, 1990; Chafe, 1994; Givón, 1983; Prince, 1985), by manipulating whether the addressee could hear the immediately preceding linguistic context. Experiment 1 showed that speakers increased pronoun use (and decreased noun phrase use) when the referent was mentioned in the immediately preceding sentence compared to when it was not, even though the addressee did not hear the preceding sentence, (...) indicating that speakers used their own, privileged discourse model when choosing referring expressions. The same pattern of results was found in Experiment 2. Speakers produced more pronouns when the immediately preceding sentence mentioned the referent than when it mentioned a referential competitor, regardless of whether the sentence was shared with their addressee. Thus, we conclude that choice of referring expression is determined by the referent’s accessibility in the speaker’s own discourse model rather than the addressee’s. (shrink)

We apply an online optimization process based on machine learning to the production of Bose-Einstein condensates. BEC is typically created with an exponential evaporation ramp that is optimal for ergodic dynamics with two-body s-wave interactions and no other loss rates, but likely sub-optimal for real experiments. Through repeated machine-controlled scientific experimentation and observations our ’learner’ discovers an optimal evaporation ramp for BEC production. In contrast to previous work, our learner uses a Gaussian process to develop a statistical model of the (...) relationship between the parameters it controls and the quality of the BEC produced. We demonstrate that the Gaussian process machine learner is able to discover a ramp that produces high quality BECs in 10 times fewer iterations than a previously used online optimization technique. Furthermore, we show the internal model developed can be used to determine which parameters are essential in BEC creation and which are unimportant, providing insight into the optimization process of the system. (shrink)