On the Relation between Institutional Statuses and Technical Artifacts: A Proposed Taxonomy of Social Kinds

Technical artifacts do not seem particularly continuous with institutional statuses. If statuses are defined in terms of their constitutive rules, as Searle maintains, then disassociation is always possible – someone or something can satisfy those rules without being able to realize the functional effects that are associated with that status. The gap between technical artifacts and Searlean statuses suggests the possibility of an additional social kind, which I call, following Muhammad Ali Khalidi, a ‘real social kind’. However, the placement of real social kinds between technical artifacts and statuses recommends a reconfiguration of Khalidi’s most abstract characterization of the notion. This reconfiguration also lends support to his surprising claim that money is a real social kind.     

The curse of knowledge: first language knowledge impairs adult learners' use of novel statistics for word segmentation

We investigated whether adult learners' knowledge of phonotactic restrictions on word forms from their first language impacts their ability to use statistical information to segment words in a novel language. Adults were exposed to a speech stream where English phonotactics and phoneme co-occurrence information conflicted. A control where these did not conflict was also run. Participants chose between words defined by novel statistics and words that are phonotactically possible in English, but had much lower phoneme contingencies. Control participants selected words defined by statistics while experimental participants did not. This result held up with increases in exposure and when segmentation was aided by telling participants a word prior to exposure. It was not the case that participants simply preferred English-sounding words, however, when the stimuli contained very short pauses, participants were able to learn the novel words despite the fact that they violated English phonotactics. Results suggest that prior linguistic knowledge can interfere with learners' abilities to segment words from running speech using purely statistical cues at initial exposure.     

From exemplar to grammar: a probabilistic analogy-based model of language learning

While rules and exemplars are usually viewed as opposites, this paper argues that they form end points of the same distribution. By representing both rules and exemplars as (partial) trees, we can take into account the fluid middle ground between the two extremes. This insight is the starting point for a new theory of language learning that is based on the following idea: If a language learner does not know which phrase-structure trees should be assigned to initial sentences, s/he allows (implicitly) for all possible trees and lets linguistic experience decide which is the "best" tree for each sentence. The best tree is obtained by maximizing "structural analogy" between a sentence and previous sentences, which is formalized by the most probable shortest combination of subtrees from all trees of previous sentences. Corpus-based experiments with this model on the Penn Treebank and the Childes database indicate that it can learn both exemplar-based and rule-based aspects of language, ranging from phrasal verbs to auxiliary fronting. By having learned the syntactic structures of sentences, we have also learned the grammar implicit in these structures, which can in turn be used to produce new sentences. We show that our model mimicks children's language development from item-based constructions to abstract constructions, and that the model can simulate some of the errors made by children in producing complex questions.     

Experience and sentence processing: statistical learning and relative clause comprehension

Many explanations of the difficulties associated with interpreting object relative clauses appeal to the demands that object relatives make on working memory. MacDonald and Christiansen [MacDonald, M. C., & Christiansen, M. H. (2002). Reassessing working memory: Comment on Just and Carpenter (1992) and Waters and Caplan (1996). Psychological Review, 109, 35-54] pointed to variations in reading experience as a source of differences, arguing that the unique word order of object relatives makes their processing more difficult and more sensitive to the effects of previous experience than the processing of subject relatives. This hypothesis was tested in a large-scale study manipulating reading experiences of adults over several weeks. The group receiving relative clause experience increased reading speeds for object relatives more than for subject relatives, whereas a control experience group did not. The reading time data were compared to performance of a computational model given different amounts of experience. The results support claims for experience-based individual differences and an important role for statistical learning in sentence comprehension processes.     

How to build a brain: from function to implementation

To have a fully integrated understanding of neurobiological systems, we must address two fundamental questions: 1. What do brains do (what is their function)? and 2. How do brains do whatever it is that they do (how is that function implemented)? I begin by arguing that these questions are necessarily inter-related. Thus, addressing one without consideration of an answer to the other, as is often done, is a mistake. I then describe what I take to be the best available approach to addressing both questions. Specifically, to address 2, I adopt the Neural Engineering Framework (NEF) of Eliasmith &; Anderson [Neural engineering: Computation representation and dynamics in neurobiological systems. Cambridge, MA: MIT Press, 2003] which identifies implementational principles for neural models. To address 1, I suggest that adopting statistical modeling methods for perception and action will be functionally sufficient for capturing biological behavior. I show how these two answers will be mutually constraining, since the process of model selection for the statistical method in this approach can be informed by known anatomical and physiological properties of the brain, captured by the NEF. Similarly, the application of the NEF must be informed by functional hypotheses, captured by the statistical modeling approach.     

Social context influences infants’ ability to extract statistical information from a sequence of gestures

Infants’ social environment is rich of complex sequences of events and actions. This study investigates whether 12-month-old infants are able to learn statistical regularities from a sequence of human gestures and whether this ability is affected by a social vs non-social context. Using a visual familiarization task, infants were familiarized to a continuous sequence of eight videos in which two women imitated each other performing arm gestures. The sequence of videos in which the two women performed imitative gestures was organized into 4 different gesture units. Videos within a gesture unit had a highly predictable transitional probability, while such transition was less predictable between gesture units. The social context was manipulated varying the mutual gaze of the actors and their body orientation. At test, infants were able to discriminate between the high- and low-predictable gesture units in the social, but not in the non-social condition. Results demonstrate that infants are capable to detect statistical regularities from a sequence of human gestures performed by two different individuals. Moreover, our findings indicate that salient social cues can modulate infants’ ability to extract statistical information from a sequence of gestures.     

Cross-situational Learning From Ambiguous Egocentric Input Is a Continuous Process: Evidence Using the Human Simulation Paradigm

Recent laboratory experiments have shown that both infant and adult learners can acquire word-referent mappings using cross-situational statistics. The vast majority of the work on this topic has used unfamiliar objects presented on neutral backgrounds as the visual contexts for word learning. However, these laboratory contexts are much different than the real-world contexts in which learning occurs. Thus, the feasibility of generalizing cross-situational learning beyond the laboratory is in question. Adapting the Human Simulation Paradigm, we conducted a series of experiments examining cross-situational learning from children's egocentric videos captured during naturalistic play. Focusing on individually ambiguous naming moments that naturally occur during toy play, we asked how statistical learning unfolds in real time through accumulating cross-situational statistics in naturalistic contexts. We found that even when learning situations were individually ambiguous, learners’ performance gradually improved over time. This improvement was driven in part by learners’ use of partial knowledge acquired from previous learning situations, even when they had not yet discovered correct word-object mappings. These results suggest that word learning is a continuous process by means of real-time information integration.     

Cross‐Situational Learning of Minimal Word Pairs

Cross‐situational statistical learning of words involves tracking co‐occurrences of auditory words and objects across time to infer word‐referent mappings. Previous research has demonstrated that learners can infer referents across sets of very phonologically distinct words (e.g., WUG, DAX), but it remains unknown whether learners can encode fine phonological differences during cross‐situational statistical learning. This study examined learners’ cross‐situational statistical learning of minimal pairs that differed on one consonant segment (e.g., BON–TON), minimal pairs that differed on one vowel segment (e.g., DEET–DIT), and non‐minimal pairs that differed on two or three segments (e.g., BON–DEET). Learners performed above chance for all pairs, but performed worse on vowel minimal pairs than on consonant minimal pairs or non‐minimal pairs. These findings demonstrate that learners can encode fine phonetic detail while tracking word‐referent co‐occurrence probabilities, but they suggest that phonological encoding may be weaker for vowels than for consonants.     

A matter of style? Testing the moderating effect of driving styles on the relationship between job strain and work-related crashes of professional drivers

Different empirical studies suggest that both job strain and driving styles are significant contributors to the work-related traffic crashes suffered by professional drivers. Nevertheless, the current evidence falls considerably short when explaining why driving styles may modify (or not) the relationship between occupational stressors and professional drivers’ safety outcomes. The aim of this study was to examine whether driving styles moderate the effect of job strain on professional drivers’ Work Traffic Crashes (WTCs). This research was performed using the data collected from a sample of 753 professional drivers, responding to a self-report questionnaire on job strain (work stress indicator of the Job Demand-Control model), driving styles and work-traffic safety outcomes suffered in the past two years. Regression-based moderation analyses suggest that the job strain of professional drivers is positively associated with the occupational traffic crashes they suffer, and that such association is stronger in drivers with “reckless & careless”, “anxious”, and “angry & hostile” driving styles. Meanwhile, the “patient & careful” (positive) driving style was not associated with a lower risk of suffering a WTC, nor with a lower vulnerability to stress-related WTCs. The results of this study support the hypothesis that driving styles exert a statistical moderation between the job strain and the occupational traffic crashes suffered by professional drivers. These findings may support the design of evidence-based interventions in both the organizational and individual levels, focused stress-related factors and driving styles as predictors of work traffic crashes.