Biologically Inspired Cognitive Architectures

Biologically Inspired Cognitive Architectures - ISSN 2212-683X
Source Normalized Impact per Paper (SNIP): 0.666 Source Normalized Impact per Paper (SNIP):
SNIP measures contextual citation impact by weighting citations based on the total number of citations in a subject field.
SCImago Journal Rank (SJR): 0.259 SCImago Journal Rank (SJR):
SJR is a prestige metric based on the idea that not all citations are the same. SJR uses a similar algorithm as the Google page rank; it provides a quantitative and a qualitative measure of the journal’s impact.
Impact Factor: 0.787 (2017) Impact Factor:
The Impact Factor measures the average number of citations received in a particular year by papers published in the journal during the two preceding years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
5 Year Impact Factor: 0.684 (2017) Five-Year Impact Factor:
To calculate the five year Impact Factor, citations are counted in 2016 to the previous five years and divided by the source items published in the previous five years.
© 2017 Journal Citation Reports ® (Clarivate Analytics, 2017)
Volumes: Volumes 23-26
Issues: 4 issues
ISSN: 2212683X

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The focus of the journal is on the integration of many research efforts in addressing the challenge of creating a real-life computational equivalent of the human mind. Therefore, the journal publishes on the multidisciplinary study of cognitive architectures found in vivo and in silico.

To help foster a wider understanding, at a computational level, of how natural intelligent systems develop their cognitive, metacognitive, and learning functions, the journal will promote the overarching goal of creating one unifying widespread framework for the computational modeling of biologically inspired cognitive architectures.

The scope includes (but is not limited to):

  • Cognitive science, with a focus on higher cognitive functions and their cognitive architecture models: including autonomous cognition and metacognition, imagery, sensemaking, meta-learning, self-regulated learning, life-long learning and cognitive growth, "critical mass" of a learner, models of creativity, affects, emotions and feelings, emotional competence, social cognition, the self, human-like episodic memory, language perception, processing, production, acquisition, and development;
  • Computer science and engineering, with a focus on human-like artificial intelligence: cognitive architectures, virtual and physical cognitive robotics, synthetic characters, bootstrapped and human-like learning, human-computer interface, vision, computational linguistics, intelligent tutoring systems;
  • Neuroscience, with a focus on higher cognition and learning: system-level computational neuroscience, cognitive neuroscience, models of the neural substrates of semantic and episodic memory and awareness, agency, emotions and feelings, theory of mind and social cognition, language, imagery, voluntary control, goal and value systems, spatial cognition, etc.

Contributions to the journal should include a cognitive architecture element and an element of biological inspiration, the latter understood broadly (e.g., inspiration by the human cognition). Both mature and new cutting edge research are welcomed, provided they have a strong emphasis on concrete empirical or theoretical studies. Submissions of a purely philosophical nature are discouraged and will be redirected elsewhere.