118 research outputs found
A morphospace of functional configuration to assess configural breadth based on brain functional networks
The best approach to quantify human brain functional reconfigurations in
response to varying cognitive demands remains an unresolved topic in network
neuroscience. We propose that such functional reconfigurations may be
categorized into three different types: i) Network Configural Breadth, ii)
Task-to-Task transitional reconfiguration, and iii) Within-Task
reconfiguration. In order to quantify these reconfigurations, we propose a
mesoscopic framework focused on functional networks (FNs) or communities. To do
so, we introduce a 2D network morphospace that relies on two novel mesoscopic
metrics, Trapping Efficiency (TE) and Exit Entropy (EE), which capture topology
and integration of information within and between a reference set of FNs. In
this study, we use this framework to quantify the Network Configural Breadth
across different tasks. We show that the metrics defining this morphospace can
differentiate FNs, cognitive tasks and subjects. We also show that network
configural breadth significantly predicts behavioral measures, such as episodic
memory, verbal episodic memory, fluid intelligence and general intelligence. In
essence, we put forth a framework to explore the cognitive space in a
comprehensive manner, for each individual separately, and at different levels
of granularity. This tool that can also quantify the FN reconfigurations that
result from the brain switching between mental states.Comment: main article: 24 pages, 8 figures, 2 tables. supporting information:
11 pages, 5 figure
Dietary Adaptations and Intra- and Interspecific Variation in Dental Occlusal Shape in Hominin and Non-hominin Primates
Dental morphology and tooth shape have been used to recreate the
dietary adaptations for extinct species, and thus dental variation can provide
information on the relationship between fossil species and their
paleoenvironments. Variation in living species with known behaviors can provide
a baseline for interpreting morphology, and behavior, in the fossil record.
Tooth occlusal surface outlines in hominins and non-hominin primates, and other
mammals, have been used for assessments of taxonomic significance, with
variability often considered as being primarily phylogenetic. Few studies have
attempted to assess how diet might influence the pattern of variability in closely
related species. Here the occlusal surface shape variability in anterior and postcanine
maxillary dentition in primates is measured to assess whether the
relationship between diet and variability is consistent.
Data were collected from five non-hominin primates in a range of dietary
categories, as well as two hominin species, including the derived Paranthropus
robustus and a gracile australopith. Mapping a series of 50 sliding semilandmarks
based on 2-D photographs using tpsDig software, occlusal surfaces
were outlined. Thereafter, outline shapes were quantified using Elliptical Fourier
Functional Analysis, and principle components and multivariate analyses were
preformed to explore the pattern of intra and interspecific variability in occlusal
outlines.These results suggest that there is not a clear relationship between dietary
feeding adaptations for all categories examined and selection for larger
premolars and molars, as well as smaller incisors, led to less variation in both
anterior and post-canine teeth of the fossil hominin Paranthropus robustus
The evolutionary origins of volition
It appears to be a straightforward implication of distributed cognition principles that there is no integrated executive control system (e.g. Brooks 1991, Clark 1997). If distributed cognition is taken as a credible paradigm for cognitive science this in turn presents a challenge to volition because the concept of volition assumes integrated information processing and action control. For instance the process of forming a goal should integrate information about the available action options. If the goal is acted upon these processes should control motor behavior. If there were no executive system then it would seem that processes of action selection and performance couldnât be functionally integrated in the right way. The apparently centralized decision and action control processes of volition would be an illusion arising from the competitive and cooperative interaction of many relatively simple cognitive systems. Here I will make a case that this conclusion is not well-founded. Prima facie it is not clear that distributed organization can achieve coherent functional activity when there are many complex interacting systems, there is high potential for interference between systems, and there is a need for focus. Resolving conflict and providing focus are key reasons why executive systems have been proposed (Baddeley 1986, Norman and Shallice 1986, Posner and Raichle 1994). This chapter develops an extended theoretical argument based on this idea, according to which selective pressures operating in the evolution of cognition favor high order control organization with a âhighest-orderâ control system that performs executive functions
Embodied Artificial Intelligence through Distributed Adaptive Control: An Integrated Framework
In this paper, we argue that the future of Artificial Intelligence research
resides in two keywords: integration and embodiment. We support this claim by
analyzing the recent advances of the field. Regarding integration, we note that
the most impactful recent contributions have been made possible through the
integration of recent Machine Learning methods (based in particular on Deep
Learning and Recurrent Neural Networks) with more traditional ones (e.g.
Monte-Carlo tree search, goal babbling exploration or addressable memory
systems). Regarding embodiment, we note that the traditional benchmark tasks
(e.g. visual classification or board games) are becoming obsolete as
state-of-the-art learning algorithms approach or even surpass human performance
in most of them, having recently encouraged the development of first-person 3D
game platforms embedding realistic physics. Building upon this analysis, we
first propose an embodied cognitive architecture integrating heterogenous
sub-fields of Artificial Intelligence into a unified framework. We demonstrate
the utility of our approach by showing how major contributions of the field can
be expressed within the proposed framework. We then claim that benchmarking
environments need to reproduce ecologically-valid conditions for bootstrapping
the acquisition of increasingly complex cognitive skills through the concept of
a cognitive arms race between embodied agents.Comment: Updated version of the paper accepted to the ICDL-Epirob 2017
conference (Lisbon, Portugal
Comparative Connectomics.
We introduce comparative connectomics, the quantitative study of cross-species commonalities and variations in brain network topology that aims to discover general principles of network architecture of nervous systems and the identification of species-specific features of brain connectivity. By comparing connectomes derived from simple to more advanced species, we identify two conserved themes of wiring: the tendency to organize network topology into communities that serve specialized functionality and the general drive to enable high topological integration by means of investment of neural resources in short communication paths, hubs, and rich clubs. Within the space of wiring possibilities that conform to these common principles, we argue that differences in connectome organization between closely related species support adaptations in cognition and behavior.We thank Lianne Scholtens, Jim Rilling, Tom Schoenemann for discussions and comments. MPvdH was supported by a VENI (# 451-12-001) grant from the Netherlands Organization for Scientific Research (NWO) and a Fellowship of MQ.This is the author accepted manuscript. The final version is available from Elsevier via https://doi.org/10.1016/j.tics.2016.03.00
Measuring the Complexity of Consciousness
The quest for a scientific description of consciousness has given rise to new
theoretical and empirical paradigms for the investigation of phenomenological
contents as well as clinical disorders of consciousness. An outstanding
challenge in the field is to develop measures that uniquely quantify global
brain states tied to consciousness. In particular, information-theoretic
complexity measures such as integrated information have recently been proposed
as measures of conscious awareness. This suggests a new framework to
quantitatively classify states of consciousness. However, it has proven
increasingly difficult to apply these complexity measures to realistic brain
networks. In part, this is due to high computational costs incurred when
implementing these measures on realistically large network dimensions.
Nonetheless, complexity measures for quantifying states of consciousness are
important for assisting clinical diagnosis and therapy. This article is meant
to serve as a lookup table of measures of consciousness, with particular
emphasis on clinical applicability of these measures. We consider both,
principle-based complexity measures as well as empirical measures tested on
patients. We address challenges facing these measures with regard to realistic
brain networks, and where necessary, suggest possible resolutions.Comment: 9 page
- âŠ