A Model of Event Knowledge

We present a connectionist model of event knowledge that is trained on examples of sequences of activities that are not explicitly labeled as events. The model learns co-occurrence patterns among the components of activities as they occur in the moment (entities, actions, and contexts), and also learns to predict sequential patterns of activities. In so doing, the model displays behaviors that in humans have been characterized as exemplifying inferencing of unmentioned event components, the prediction of upcoming components (which may or may not ever happen or be mentioned), reconstructive memory, and the ability to flexibly accommodate novel variations from previously encountered experiences. All of these behaviors emerge from what the model learns

A Neural Attention Model for Categorizing Patient Safety Events

Medical errors are leading causes of death in the US and as such, prevention of these errors is paramount to promoting health care. Patient Safety Event reports are narratives describing potential adverse events to the patients and are important in identifying and preventing medical errors. We present a neural network architecture for identifying the type of safety events which is the first step in understanding these narratives. Our proposed model is based on a soft neural attention model to improve the effectiveness of encoding long sequences. Empirical results on two large-scale real-world datasets of patient safety reports demonstrate the effectiveness of our method with significant improvements over existing methods.Comment: ECIR 201

Prediction-Based Learning and Processing of Event Knowledge.

Knowledge of common events is central to many aspects of cognition. Intuitively, it seems as though events are linear chains of the activities of which they are comprised. In line with this intuition, a number of theories of the temporal structure of event knowledge have posited mental representations (data structures) consisting of linear chains of activities. Competing theories focus on the hierarchical nature of event knowledge, with representations comprising ordered scenes, and chains of activities within those scenes. We present evidence that the temporal structure of events typically is not well-defined, but it is much richer and more variable both within and across events than has usually been assumed. We also present evidence that prediction-based neural network models can learn these rich and variable event structures and produce behaviors that reflect human performance. We conclude that knowledge of the temporal structure of events in the human mind emerges as a consequence of prediction-based learning

The Wind Chilled the Spectators, but the Wine Just Chilled: Sense, Structure, and Sentence Comprehension

Anticipation plays a role in language comprehension. In this article, we explore the extent to which verb sense influences expectations about upcoming structure. We focus on change of state verbs like shatter, which have different senses that are expressed in either transitive or intransitive structures, depending on the sense that is used. In two experiments we influence the interpretation of verb sense by manipulating the thematic fit of the grammatical subject as cause or affected entity for the verb, and test whether readers’ expectations for a transitive or intransitive structure change as a result. This sense-biasing context influenced reading times in the postverbal regions. Reading times for transitive sentences were faster following good-cause than good-theme subjects, but the opposite pattern was found for intransitive sentences. We conclude that readers use sense-contingent subcategorization preferences during on-line comprehension

Post-traumatic stress symptoms in pathological gambling: Potential evidence of anti-reward processes

Excessive gambling is considered to be a part of the addiction spectrum. Stress-like emotional states are a key feature both of pathological gambling (PG) and of substance addiction. In substance addiction, stress symptomatology has been attributed in part to “anti-reward” allostatic neuroadaptations, while a potential involvement of anti-reward processes in the course of PG has not yet been investigated. Methods To that end, individuals with PG (n = 22) and mentally healthy subjects (n = 13) were assessed for trauma exposure and post-traumatic stress symptomatology (PTSS) using the Life Events Checklist and the Civilian Mississippi Scale, respectively. Results In comparison with healthy subjects, individuals with PG had significantly greater PTSS scores including greater physiological arousal sub-scores. The number of traumatic events and their recency were not significantly different between the groups. In the PG group, greater gambling severity was associated with more PTSS, but neither with traumatic events exposure nor with their recency. Conclusions Our data replicate prior reports on the role of traumatic stress in the course of PG and extend those findings by suggesting that the link may be derived from the anti-reward-type neuroadaptation rather than from the traumatic stress exposure per se

Efficacy of Elaborated Semantic Features Analysis in Aphasia: a quasi-randomised controlled trial

Background: Word finding difficulty is one of the most common features of aphasia. Semantic Features Analysis (SFA) directly aims to improve word finding in people with aphasia. Evidence from systematic reviews suggests that SFA leads to positive outcomes, yet the evidence comprises single case studies and case series. There is a need to evaluate the efficacy of SFA in controlled group studies/trials. Aims: To evaluate the efficacy of Elaborated Semantic Feature Analysis (ESFA) for word finding in people with aphasia. We investigated: (a) the efficacy of ESFA versus a delayed therapy/control, (b) the efficacy of two therapy approaches– individual versus a combination of individual and group therapy. Methods and procedures: We ran a multi-centre, quasi-randomised controlled trial, nested in a larger study (Thales-Aphasia). Participants were recruited from community settings. They had to be people with aphasia due to stroke at least four months post-onset. Participants were randomized to individual vs combination vs delayed therapy/control groups. Both therapy groups had three hours of ESFA per week for 12 weeks. Delayed therapy/control group had no intervention for 12 weeks and were then randomized to either individual or combination therapy. The primary outcome was confrontation naming. Secondary outcomes were the Boston Naming Test, Discourse, the Functional Assessment of Communication Skills for adults (ASHA–FACS), the Stroke and Aphasia Quality of Life scale (SAQOL-39g), the General Health Questionnaire-12 item, and the EQ-5D. Outcomes and Results: Of the 72 participants of the Thales-Aphasia project, 58 met eligibility criteria for speech-language therapy and 39 were allocated to ESFA. The critical p-value was adjusted for multiple comparisons (.005). For the therapy versus control comparison, there was a significant main effect of time on the primary outcome (p<.001, η2p=.42) and a significant interaction effect (p=.003, η2p=.21). An interaction effect for the SAQOL-39g (p=.015, η2p=.11) and its psychosocial domain (p=.013, η2p=.12) did not remain significant after Bonferroni adjustment. For the individual versus combination ESFA comparison, there were significant main effects of time on the primary outcome (p<.001, η2p=.49), the BNT (p<.001, η2p=.29) and the ASHA-FACS (p=.001, η2p=.18). Interaction and group effects were not significant. Conclusion: Though underpowered, this study provides evidence on the efficacy of ESFA to improve word finding in aphasia, with gains similar in the two therapy approaches. Trial registration: ISRCTN71455409, https://doi.org/10.1186/ISRCTN7145540

Is the structure of 42Si understood?

A more detailed test of the implementation of nuclear forces that drive shell evolution in the pivotal nucleus \nuc{42}{Si} -- going beyond earlier comparisons of excited-state energies -- is important. The two leading shell-model effective interactions, SDPF-MU and SDPF-U-Si, both of which reproduce the low-lying \nuc{42}{Si}($2^+_1$) energy, but whose predictions for other observables differ significantly, are interrogated by the population of states in neutron-rich \nuc{42}{Si} with a one-proton removal reaction from \nuc{43}{P} projectiles at 81~MeV/nucleon. The measured cross sections to the individual \nuc{42}{Si} final states are compared to calculations that combine eikonal reaction dynamics with these shell-model nuclear structure overlaps. The differences in the two shell-model descriptions are examined and linked to predicted low-lying excited $0^+$ states and shape coexistence. Based on the present data, which are in better agreement with the SDPF-MU calculations, the state observed at 2150(13)~keV in \nuc{42}{Si} is proposed to be the ($0^+_2$) level.Comment: accepted in Physical Review Letter

Discrete and fuzzy dynamical genetic programming in the XCSF learning classifier system

A number of representation schemes have been presented for use within learning classifier systems, ranging from binary encodings to neural networks. This paper presents results from an investigation into using discrete and fuzzy dynamical system representations within the XCSF learning classifier system. In particular, asynchronous random Boolean networks are used to represent the traditional condition-action production system rules in the discrete case and asynchronous fuzzy logic networks in the continuous-valued case. It is shown possible to use self-adaptive, open-ended evolution to design an ensemble of such dynamical systems within XCSF to solve a number of well-known test problems