Need to train your rat? There is an App for that: A touchscreen behavioral evaluation system

The increasing demand for highly automated and flexible tasks capable of assessing visual learning and memory in nonhuman animals has led to the exciting development of a wide array of prefabricated touchscreen-equipped systems. However, the high cost of these prefabricated systems has led many researchers to develop or modify their own preexisting equipment. We developed a freely downloadable App, the Touchscreeen Behavioral Evaluation System (TBES) for use in conjunction with an iPad (Apple, Cupertino, California) as an alternative to prefabricated touchscreen systems. TBES allows for stimulus presentation and data collection on an iPad. The touchscreen technology offered by the iPad is attractive to researchers due to its affordability, reliability, and resistance to false inputs. We highlight these, as well as the feasibility and procedural flexibility of TBES, in an effort to promote our system as a competitive alternative to those currently available.     

Determining informative priors for cognitive models

The development of cognitive models involves the creative scientific formalization of assumptions, based on theory, observation, and other relevant information. In the Bayesian approach to implementing, testing, and using cognitive models, assumptions can influence both the likelihood function of the model, usually corresponding to assumptions about psychological processes, and the prior distribution over model parameters, usually corresponding to assumptions about the psychological variables that influence those processes. The specification of the prior is unique to the Bayesian context, but often raises concerns that lead to the use of vague or non-informative priors in cognitive modeling. Sometimes the concerns stem from philosophical objections, but more often practical difficulties with how priors should be determined are the stumbling block. We survey several sources of information that can help to specify priors for cognitive models, discuss some of the methods by which this information can be formalized in a prior distribution, and identify a number of benefits of including informative priors in cognitive modeling. Our discussion is based on three illustrative cognitive models, involving memory retention, categorization, and decision making.     

Leveling up the analysis of the reminiscence bump in autobiographical memory: A new approach based on multilevel multinomial models

In many studies of autobiographical memory, participants are asked to generate more than one autobiographical memory. The resulting data then have a hierarchical or multilevel structure, in the sense that the autobiographical memories (Level 1) generated by the same person (Level 2) tend to be more similar. Transferred to an analysis of the reminiscence bump in autobiographical memory, at Level 1 the prediction of whether an autobiographical memory will fall within the reminiscence bump is based on the characteristics of that memory. At Level 2, the prediction of whether an individual will report more autobiographical memories that fall in the reminiscence bump is based on the characteristics of the individual. We suggest a multilevel multinomial model that allows for analyzing whether an autobiographical memory falls in the reminiscence bump at both levels of analysis simultaneously. The data come from 100 older participants who reported up to 33 autobiographical memories. Our results showed that about 12% of the total variance was between persons (Level 2). Moreover, at Level 1, memories of first-time experiences were more likely to fall in the reminiscence bump than were emotionally more positive memories. At Level 2, persons who reported more emotionally positive memories tended to report fewer memories from the life period after the reminiscence bump. In addition, cross-level interactions showed that the effects at Level 1 partly depended on the Level 2 effects. We discuss possible extensions of the model we present and the meaning of our findings for two prominent explanatory approaches to the reminiscence bump, as well as future directions.     

Spatial associations in number-related tasks: A comparison of manual and pedal responses

Bimanual parity judgments of numerically small (large) digits are faster with the left (right) hand (the SNARC effect; Dehaene, Bossini, & Giraux, 1993). According to one explanation, this effect is culturally derived and reflects ontogenetic influences such as the direction of written language; it might therefore be limited to, or at least be larger with, pairs of lateralized effectors which are instrumental to the production and comprehension of written language. We report two experiments which test for SNARC effects with pedal responses, and compare these effects to manual results. Pedal responses yielded highly systematic SNARC effects; furthermore, these effects did not differ from manual SNARC effects. These results argue against accounts in which the SNARC effect is specific for effectors that are habitually associated with the production or comprehension of written language.     

Deviant Heroes: Nonconformists as Agents of Justice and Social Change

Sociologists usually define deviance as a fluid construct, relative to given social circumstances, and something that is neither negative nor positive in and of itself. Despite the rejection of absolutist approaches, the vast majority of texts and studies concerning deviant behavior implicitly or explicitly present deviance as either a morally bad or morally neutral behavior. Such literatures wrongly conflate deviant behavior with villainous actions. We argue that some of the most important deviants have been at the leading forefront of positive social change and the creation of a more just, fair, and humane society. Deviant heroes are those individuals who violate unjust norms and laws, facing the repercussions of social control, while simultaneously effecting positive social change. This article considers the theoretical role of the deviant hero within classical and contemporary sociological traditions and identifies new directions for social research.     

Idealness and similarity in goal-derived categories: A computational examination

The finding that the typicality gradient in goal-derived categories is mainly driven by ideals rather than by exemplar similarity has stood uncontested for nearly three decades. Due to the rather rigid earlier implementations of similarity, a key question has remained—that is, whether a more flexible approach to similarity would alter the conclusions. In the present study, we evaluated whether a similarity-based approach that allows for dimensional weighting could account for findings in goal-derived categories. To this end, we compared a computational model of exemplar similarity (the generalized context model; Nosofsky, Journal of Experimental Psychology. General 115:39–57, 1986) and a computational model of ideal representation (the ideal-dimension model; Voorspoels, Vanpaemel, & Storms, Psychonomic Bulletin & Review 18:1006-114, 2011) in their accounts of exemplar typicality in ten goal-derived categories. In terms of both goodness-of-fit and generalizability, we found strong evidence for an ideal approach in nearly all categories. We conclude that focusing on a limited set of features is necessary but not sufficient to account for the observed typicality gradient. A second aspect of ideal representations—that is, that extreme rather than common, central-tendency values drive typicality—seems to be crucial.