673,754 research outputs found
Yesterday Is History, Tomorrow Is a Mystery: An Eye-Tracking Investigation of the Processing of Past and Future Time Reference During Sentence Reading
published Online First August 23, 2021The ability to think about nonpresent time is a crucial aspect of human cognition. Both the past and
future imply a temporal displacement of an event outside the “now.” They also intrinsically differ: The
past refers to inalterable events; the future to alterable events, to possible worlds. Are the past and future
processed similarly or differently? In this study, we addressed this question by investigating how
Spanish speakers process past/future time reference violations during sentence processing, while recording
eye movements. We also investigated the role of verbs (in isolation; within sentences) and adverbs
(deictic; nondeictic) during time processing. Existing accounts propose that past processing, which
requires a link to discourse, is more complex than future processing, which—like the present—is locally
bound. Our findings show that past and future processing differs, especially at early stages of verb processing,
but this difference is not limited to the presence/absence of discourse linking. We found earlier
mismatch effects for past compared to future time reference in incongruous sentences, in line with previous
studies. Interestingly, it took longer to categorize the past than the future tense when verbs were
presented in isolation. However, it took longer to categorize the future than the past when verbs were
presented in congruous sentences, arguably because the future implies alterable worlds. Finally, temporal
adverbs were found to play an important role in reinspection and reanalysis triggered by the presence
of undefined time frames (nondeictic adverbs) or incongruences (mismatching verbs).This research is supported by the Basque Government through the
BERC 2018-2021 program, by the Spanish State Research Agency through
BCBL Severo Ochoa excellence accreditation SEV-2015-0490. Simona
Mancini was supported by Grants RYC-2017-22015, FFI2016-76432-
P_LAMPT (Ministerio de Economía y Competitividad, Agencia Estatal de
Investigación & Fondo Europeo de Desarrollo Regional) and partially by
Grants IN [18_HMS_LIN_0058 (BBVA Foundation) and PIBA_2020_I_
0024 from the Basque Governmen
Knowledge-infused and Consistent Complex Event Processing over Real-time and Persistent Streams
Emerging applications in Internet of Things (IoT) and Cyber-Physical Systems
(CPS) present novel challenges to Big Data platforms for performing online
analytics. Ubiquitous sensors from IoT deployments are able to generate data
streams at high velocity, that include information from a variety of domains,
and accumulate to large volumes on disk. Complex Event Processing (CEP) is
recognized as an important real-time computing paradigm for analyzing
continuous data streams. However, existing work on CEP is largely limited to
relational query processing, exposing two distinctive gaps for query
specification and execution: (1) infusing the relational query model with
higher level knowledge semantics, and (2) seamless query evaluation across
temporal spaces that span past, present and future events. These allow
accessible analytics over data streams having properties from different
disciplines, and help span the velocity (real-time) and volume (persistent)
dimensions. In this article, we introduce a Knowledge-infused CEP (X-CEP)
framework that provides domain-aware knowledge query constructs along with
temporal operators that allow end-to-end queries to span across real-time and
persistent streams. We translate this query model to efficient query execution
over online and offline data streams, proposing several optimizations to
mitigate the overheads introduced by evaluating semantic predicates and in
accessing high-volume historic data streams. The proposed X-CEP query model and
execution approaches are implemented in our prototype semantic CEP engine,
SCEPter. We validate our query model using domain-aware CEP queries from a
real-world Smart Power Grid application, and experimentally analyze the
benefits of our optimizations for executing these queries, using event streams
from a campus-microgrid IoT deployment.Comment: 34 pages, 16 figures, accepted in Future Generation Computer Systems,
October 27, 201
The ERP correlates of self-knowledge: Are assessments of one’s past, present, and future traits closer to semantic or episodic memory?
Self-knowledge concerns one’s own preferences and personality. It pertains to the self (similar to episodic memory), yet does not concern events. It is factual (like semantic memory), but also idiosyncratic. For these reasons, it is unclear where self-knowledge might fall on a continuum in relation to semantic and episodic memory. In this study, we aimed to compare the event-related potential (ERP) correlates of self-knowledge to those of semantic and episodic memory, using N400 and Late Positive Component (LPC) as proxies for semantic and episodic processing, respectively. We considered an additional factor: time perspective. Temporally distant selves have been suggested to be more semantic compared to the present self, but thinking about one’s past and future selves may also engage episodic memory. Twenty-eight adults answered whether traits (e.g., persistent) were true of most people holding an occupation (e.g. soldiers; semantic memory condition), or true of themselves 5 years ago, in the present, or 5 years from now (past, present, and future self-knowledge conditions). The study ended with an episodic recognition memory task for previously seen traits. Present self-knowledge produced mean LPC amplitudes at posterior parietal sites that fell between semantic and episodic memory. Mean LPC amplitudes for past and future self-knowledge were greater than for semantic memory, and not significantly different from episodic memory. Mean N400 amplitudes for the self-knowledge conditions were smaller than for semantic memory at sagittal sites. However, this N400 effect was not separable from a preceding P200 effect at these same electrode sites. This P200 effect can be interpreted as reflecting the greater emotional salience of self as compared to general knowledge, which may have facilitated semantic processing. Overall, our findings are consistent with a distinction between knowledge of others and self-knowledge, but the closeness of self-knowledge’s neural correlates to either semantic or episodic memory appears to depend to some extent on time perspective
Extending Event-Driven Architecture for Proactive Systems
ABSTRACT Proactive Event-Driven Computing is a new paradigm, in which a decision is not made due to explicit users' requests nor is it made as a response to past events. Rather, the decision is autonomously triggered by forecasting future states. Proactive event-driven computing requires a departure from current event-driven architectures to ones capable of handling uncertainty and future events, and real-time decision making. We present a proactive event-driven architecture for Scalable Proactive Event-Driven Decision-making (SPEEDD), which combines these capabilities. The proposed architecture is composed of three main components: complex event processing, real-time decision making, and visualization. This architecture is instantiated by a real use case from the traffic management domain. In the future, the results of actual implementations of the use case will help us revise and refine the proposed architecture
Transcranial Direct Corrent stimulation (tDCS) of the anterior prefrontal cortex (aPFC) modulates reinforcement learning and decision-making under uncertainty: A doubleblind crossover study
Reinforcement learning refers to the ability to acquire
information from the outcomes of prior choices (i.e.
positive and negative) in order to make predictions on the
effect of future decision and adapt the behaviour basing on
past experiences. The anterior prefrontal cortex (aPFC) is considered
to play a key role in the representation of event value,
reinforcement learning and decision-making. However, a
causal evidence of the involvement of this area in these processes
has not been provided yet. The aim of the study was to
test the role of the orbitofrontal cortex in feedback processing,
reinforcement learning and decision-making under uncertainly.
Eighteen healthy individuals underwent three sessions of
tDCS over the prefrontal pole (anodal, cathodal, sham) during
a probabilistic learning (PL) task. In the PL task, participants
were invited to learn the covert probabilistic stimulusoutcome
association from positive and negative feedbacks in
order to choose the best option. Afterwards, a probabilistic
selection (PS) task was delivered to assess decisions based
on the stimulus-reward associations acquired in the PL task.
During cathodal tDCS, accuracy in the PL task was reduced
and participants were less prone to maintain their choice after
positive feedback or to change it after a negative one (i.e., winstay
and lose-shift behavior). In addition, anodal tDCS affected
the subsequent PS task by reducing the ability to choose the
best alternative during hard probabilistic decisions. In conclusion,
the present study suggests a causal role of aPFC in feedback
trial-by-trial behavioral adaptation and decision-making
under uncertainty
Challenging the challenge: handling data in the Gigabit/s range
The ALICE experiment at CERN will propose unprecedented requirements for
event building and data recording. New technologies will be adopted as well as
ad-hoc frameworks, from the acquisition of experimental data up to the transfer
onto permanent media and its later access. These issues justify a careful,
in-depth planning and preparation. The ALICE Data Challenge is a very important
step of this development process where simulated detector data is moved from
dummy data sources up to the recording media using processing elements and
data-paths as realistic as possible. We will review herein the current status
of past, present and future ALICE Data Challenges, with particular reference to
the sessions held in 2002 when - for the first time - streams worth one week of
ALICE data were recorded onto tape media at sustained rates exceeding 300 MB/s.Comment: Talk from the 2003 Computing in High Energy and Nuclear Physics
(CHEP03), La Jolla, Ca, USA, March 2003, 9 pages, PDF. PSN MOGT00
Predictive Processing and the Phenomenology of Time Consciousness: A Hierarchical Extension of Rick Grush’s Trajectory Estimation Model
This chapter explores to what extent some core ideas of predictive processing can be applied to the phenomenology of time consciousness. The focus is on the experienced continuity of consciously perceived, temporally extended phenomena (such as enduring processes and successions of events). The main claim is that the hierarchy of representations posited by hierarchical predictive processing models can contribute to a deepened understanding of the continuity of consciousness. Computationally, such models show that sequences of events can be represented as states of a hierarchy of dynamical systems. Phenomenologically, they suggest a more fine-grained analysis of the perceptual contents of the specious present, in terms of a hierarchy of temporal wholes. Visual perception of static scenes not only contains perceived objects and regions but also spatial gist; similarly, auditory perception of temporal sequences, such as melodies, involves not only perceiving individual notes but also slightly more abstract features (temporal gist), which have longer temporal durations (e.g., emotional character or rhythm). Further investigations into these elusive contents of conscious perception may be facilitated by findings regarding its neural underpinnings. Predictive processing models suggest that sensorimotor areas may influence these contents
An Annotation Scheme for Reichenbach's Verbal Tense Structure
In this paper we present RTMML, a markup language for the tenses of verbs and
temporal relations between verbs. There is a richness to tense in language that
is not fully captured by existing temporal annotation schemata. Following
Reichenbach we present an analysis of tense in terms of abstract time points,
with the aim of supporting automated processing of tense and temporal relations
in language. This allows for precise reasoning about tense in documents, and
the deduction of temporal relations between the times and verbal events in a
discourse. We define the syntax of RTMML, and demonstrate the markup in a range
of situations
Clustering-Based Predictive Process Monitoring
Business process enactment is generally supported by information systems that
record data about process executions, which can be extracted as event logs.
Predictive process monitoring is concerned with exploiting such event logs to
predict how running (uncompleted) cases will unfold up to their completion. In
this paper, we propose a predictive process monitoring framework for estimating
the probability that a given predicate will be fulfilled upon completion of a
running case. The predicate can be, for example, a temporal logic constraint or
a time constraint, or any predicate that can be evaluated over a completed
trace. The framework takes into account both the sequence of events observed in
the current trace, as well as data attributes associated to these events. The
prediction problem is approached in two phases. First, prefixes of previous
traces are clustered according to control flow information. Secondly, a
classifier is built for each cluster using event data to discriminate between
fulfillments and violations. At runtime, a prediction is made on a running case
by mapping it to a cluster and applying the corresponding classifier. The
framework has been implemented in the ProM toolset and validated on a log
pertaining to the treatment of cancer patients in a large hospital
Visual object imagery and autobiographical memory: object imagers are better at remembering their personal past
In the present study we examined whether higher levels of object imagery, a stable characteristic that reflects the ability and preference in generating pictorial mental images of objects, facilitate involuntary and voluntary retrieval of autobiographical memories (ABMs). Individuals with high (High-OI) and low (Low-OI) levels of object imagery were asked to perform an involuntary and a voluntary ABM task in the laboratory. Results showed that High-OI participants generated more involuntary and voluntary ABMs than Low-OI, with faster retrieval times. High-OI also reported more detailed memories compared to Low-OI and retrieved memories as visual images. Theoretical implications of these findings for research on voluntary and involuntary ABMs are discussed
- …