A critical time in computational cognitive science

Abstract

Understanding how people tick is an endeavour that has challenged us for millennia, both in informal settings and in increasingly formalized and scientific disciplines. Some are interested in the biology and others the behaviour. Some are focussed on language and others on culture or emotion. Some approach this as pure science worthy of understanding in its own right. Some approach it as applied science with value born of practical applications that improve our lifestyle and characterize our modern technological society. Cognitive Science was born as Psychologists and Linguists found that the assumptions and predictions of their theories and models were spilling outside their discipline, as Neuroscience provided a neural substrate for models of perception and cognition that obviated the need for the postulation of hypothetical daemons, as Computer Science and Mathematics provided computational tools for testing models and theories that couldn't be tested empirically in the real world. Artificial Intelligence was also born in such an environment, with the early researchers exploring models of intelligence as much as developing intelligent programs. But Computational Intelligence and Cognitive Science increasingly reflect a contrast between an applied aim of building intelligent applications and entities, and a pure aim of understanding existing intelligent entities and functions, and we seem to lack a bridge between them. Computational Cognitive Science aims to provide this bridge. Introduction and scope

Towards neuro-inspired symbolic models of cognition: linking neural dynamics to behaviors through asynchronous communications

“…Before proceeding to a detailed specification of this machine, let us briefly summarize its salient features and their relation to a possible macroscopic view of the brain:contrary to traditional stored-program computers, this machine doesn’t have an instruction register holding the current instruction being executed after its retrieval from an addressable memory; by interpreting code deduced just in time from virtual implications compiled themselves from thread configurations that are akin to brain states, the overall architecture of this system could turn out to be closer to that of a brain.virtual code implications are reminiscent of daemons that run as computer background processes and are triggered by foreground application software; daemons were in common use in the early days of the Artificial Intelligence paradigm, when Neuroscience didn’t yet provide a neural substrate for models of perception and cognition (Powers 2015). …”

“…Before proceeding to a detailed specification of this machine, let us briefly summarize its salient features and their relation to a possible macroscopic view of the brain: contrary to traditional stored-program computers, this machine doesn’t have an instruction register holding the current instruction being executed after its retrieval from an addressable memory; by interpreting code deduced just in time from virtual implications compiled themselves from thread configurations that are akin to brain states, the overall architecture of this system could turn out to be closer to that of a brain. virtual code implications are reminiscent of daemons that run as computer background processes and are triggered by foreground application software; daemons were in common use in the early days of the Artificial Intelligence paradigm, when Neuroscience didn’t yet provide a neural substrate for models of perception and cognition (Powers 2015 ). similarly to machine code compiled from application software, this new kind of daemons is compiled from thread fibers that are thus akin to cognition software finally, as described above, these daemons are triggered by local deductions within a given stream; global deductions at the model level (to be introduced below) will give access, from within any stream, to previously active threads that will thus achieve the status of a global memory (see “ Microcircuits implementing synaptic plasticity ” sections).…”

“…virtual code implications are reminiscent of daemons that run as computer background processes and are triggered by foreground application software; daemons were in common use in the early days of the Artificial Intelligence paradigm, when Neuroscience didn’t yet provide a neural substrate for models of perception and cognition (Powers 2015 ).…”