78,220 research outputs found
Thoughts about disordered thinking: measuring and quantifying the laws of order and disorder
Peer ReviewedPostprint (author's final draft
Heterogeneity of Research Results: A New Perspective From Which to Assess and Promote Progress in Psychological Science
Heterogeneity emerges when multiple close or conceptual replications on the same subject produce results that vary more than expected from the sampling error. Here we argue that unexplained heterogeneity reflects a lack of coherence between the concepts applied and data observed and therefore a lack of understanding of the subject matter. Typical levels of heterogeneity thus offer a useful but neglected perspective on the levels of understanding achieved in psychological science. Focusing on continuous outcome variables, we surveyed heterogeneity in 150 meta-analyses from cognitive, organizational, and social psychology and 57 multiple close replications. Heterogeneity proved to be very high in meta-analyses, with powerful moderators being conspicuously absent. Population effects in the average meta-analysis vary from small to very large for reasons that are typically not understood. In contrast, heterogeneity was moderate in close replications. A newly identified relationship between heterogeneity and effect size allowed us to make predictions about expected heterogeneity levels. We discuss important implications for the formulation and evaluation of theories in psychology. On the basis of insights from the history and philosophy of science, we argue that the reduction of heterogeneity is important for progress in psychology and its practical applications, and we suggest changes to our collective research practice toward this end
Online discussion compensates for suboptimal timing of supportive information presentation in a digitally supported learning environment
This study used a sequential set-up to investigate the consecutive effects of timing of supportive information presentation (information before vs. information during the learning task clusters) in interactive digital learning materials (IDLMs) and type of collaboration (personal discussion vs. online discussion) in computer-supported collaborative learning (CSCL) on student knowledge construction. Students (N = 87) were first randomly assigned to the two information presentation conditions to work individually on a case-based assignment in IDLM. Students who received information during learning task clusters tended to show better results on knowledge construction than those who received information only before each cluster. The students within the two separate information presentation conditions were then randomly assigned to pairs to discuss the outcomes of their assignments under either the personal discussion or online discussion condition in CSCL. When supportive information had been presented before each learning task cluster, online discussion led to better results than personal discussion. When supportive information had been presented during the learning task clusters, however, the online and personal discussion conditions had no differential effect on knowledge construction. Online discussion in CSCL appeared to compensate for suboptimal timing of presentation of supportive information before the learning task clusters in IDLM
Neural population coding: combining insights from microscopic and mass signals
Behavior relies on the distributed and coordinated activity of neural populations. Population activity can be measured using multi-neuron recordings and neuroimaging. Neural recordings reveal how the heterogeneity, sparseness, timing, and correlation of population activity shape information processing in local networks, whereas neuroimaging shows how long-range coupling and brain states impact on local activity and perception. To obtain an integrated perspective on neural information processing we need to combine knowledge from both levels of investigation. We review recent progress of how neural recordings, neuroimaging, and computational approaches begin to elucidate how interactions between local neural population activity and large-scale dynamics shape the structure and coding capacity of local information representations, make them state-dependent, and control distributed populations that collectively shape behavior
Centralized and distributed cognitive task processing in the human connectome
A key question in modern neuroscience is how cognitive changes in a human
brain can be quantified and captured by functional connectomes (FC) . A
systematic approach to measure pairwise functional distance at different brain
states is lacking. This would provide a straight-forward way to quantify
differences in cognitive processing across tasks; also, it would help in
relating these differences in task-based FCs to the underlying structural
network. Here we propose a framework, based on the concept of Jensen-Shannon
divergence, to map the task-rest connectivity distance between tasks and
resting-state FC. We show how this information theoretical measure allows for
quantifying connectivity changes in distributed and centralized processing in
functional networks. We study resting-state and seven tasks from the Human
Connectome Project dataset to obtain the most distant links across tasks. We
investigate how these changes are associated to different functional brain
networks, and use the proposed measure to infer changes in the information
processing regimes. Furthermore, we show how the FC distance from resting state
is shaped by structural connectivity, and to what extent this relationship
depends on the task. This framework provides a well grounded mathematical
quantification of connectivity changes associated to cognitive processing in
large-scale brain networks.Comment: 22 pages main, 6 pages supplementary, 6 figures, 5 supplementary
figures, 1 table, 1 supplementary table. arXiv admin note: text overlap with
arXiv:1710.0219
Using whole body technologies to map the mobility of older adults
In this paper we describe the preliminary findings of two-year study that attempted to map the mobility of our oldest citizens using activity monitoring and location-aware technologies. We recruited a group of 100 adults aged between 72 and 92 years old, drawn from a 25 year longitudinal cohort, and collected lifestyle, nutrition, health and social engagement data. We also fitted a subset of the group with accelerometers and location-based tracking devices and asked them to wear these for a week in order that we could generate accurate, live mobility data and assess these data against self-reports. We are now using this data to describe the relationship between mobility, activity and physical and mental well-being, but in this preliminary paper, we outline some of the main challenges we encountered when trying to use these ‘whole body’ technologies to determine mobility
Cross-Recurrence Quantification Analysis of Categorical and Continuous Time Series: an R package
This paper describes the R package crqa to perform cross-recurrence
quantification analysis of two time series of either a categorical or
continuous nature. Streams of behavioral information, from eye movements to
linguistic elements, unfold over time. When two people interact, such as in
conversation, they often adapt to each other, leading these behavioral levels
to exhibit recurrent states. In dialogue, for example, interlocutors adapt to
each other by exchanging interactive cues: smiles, nods, gestures, choice of
words, and so on. In order for us to capture closely the goings-on of dynamic
interaction, and uncover the extent of coupling between two individuals, we
need to quantify how much recurrence is taking place at these levels. Methods
available in crqa would allow researchers in cognitive science to pose such
questions as how much are two people recurrent at some level of analysis, what
is the characteristic lag time for one person to maximally match another, or
whether one person is leading another. First, we set the theoretical ground to
understand the difference between 'correlation' and 'co-visitation' when
comparing two time series, using an aggregative or cross-recurrence approach.
Then, we describe more formally the principles of cross-recurrence, and show
with the current package how to carry out analyses applying them. We end the
paper by comparing computational efficiency, and results' consistency, of crqa
R package, with the benchmark MATLAB toolbox crptoolbox. We show perfect
comparability between the two libraries on both levels
Network constraints on learnability of probabilistic motor sequences
Human learners are adept at grasping the complex relationships underlying
incoming sequential input. In the present work, we formalize complex
relationships as graph structures derived from temporal associations in motor
sequences. Next, we explore the extent to which learners are sensitive to key
variations in the topological properties inherent to those graph structures.
Participants performed a probabilistic motor sequence task in which the order
of button presses was determined by the traversal of graphs with modular,
lattice-like, or random organization. Graph nodes each represented a unique
button press and edges represented a transition between button presses. Results
indicate that learning, indexed here by participants' response times, was
strongly mediated by the graph's meso-scale organization, with modular graphs
being associated with shorter response times than random and lattice graphs.
Moreover, variations in a node's number of connections (degree) and a node's
role in mediating long-distance communication (betweenness centrality) impacted
graph learning, even after accounting for level of practice on that node. These
results demonstrate that the graph architecture underlying temporal sequences
of stimuli fundamentally constrains learning, and moreover that tools from
network science provide a valuable framework for assessing how learners encode
complex, temporally structured information.Comment: 29 pages, 4 figure
Using text analysis to quantify the similarity and evolution of scientific disciplines
We use an information-theoretic measure of linguistic similarity to
investigate the organization and evolution of scientific fields. An analysis of
almost 20M papers from the past three decades reveals that the linguistic
similarity is related but different from experts and citation-based
classifications, leading to an improved view on the organization of science. A
temporal analysis of the similarity of fields shows that some fields (e.g.,
computer science) are becoming increasingly central, but that on average the
similarity between pairs has not changed in the last decades. This suggests
that tendencies of convergence (e.g., multi-disciplinarity) and divergence
(e.g., specialization) of disciplines are in balance.Comment: 9 pages, 4 figure
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