Chunking on the Fly in Working Memory and its Relationship to Intelligence

The Information-Theoretic and Algorithmic Approach to Human, Animal, and Artificial Cognition

“…The fact that the theory is based on the Turing machine model is irrelevant and cannot be used as an objection as shown, for example, by the Invariance Theorem [Solomonoff 1964, Levin 1974, which not only also applies to algorithmic probability but Levin showed that disregarding the computational model the algorithmic probability converges and the only universal semi-measure dominates [Kirchherr et al 1997]. We have adopted this powerful theory and used it to advance the algorithmic cognition approach we have devised specific tools with a wide range of applications Villarreal-Zapata 2013, Zenil 2010] that also conform to animal and human behavior , Gauvrit et al 2014a, Gauvrit et al 2014b, Gauvrit et al 2014c, Kempe et al 2015, Mathy et al 2014.…”

“…Indeed, within cognitive science, the study of working memory, of probabilistic reasoning, the emergence of structure in language, strategic response, and navigational behavior is cutting-edge research. In all these areas we have made contributions , Gauvrit et al 2014a, Gauvrit et al 2014b, Gauvrit et al 2014c, Kempe et al 2015, Mathy et al 2014] based upon algorithmic complexity as a useful normative tool, shedding light on mechanisms of cognitive processes. We have proceeded by taking the mind as a form of algorithmic compressor, for the reasons provided in this survey emerging from the simple fact that the mind operates otherwise than through a lookup table and the ways in which the mind manifests biases that seem not to be accounted for but by algorithmic probability based purely on computability theory.…”

“…Humans too display biases in the same algorithmic direction, from their motion trajectories [Peng et al 2014] to their perception of reality [Chater 1999]. Indeed, we have shown that cognition, including visual perception and the generation of subjective randomness, shows a bias that can be accounted for with the seminal concept of algorithmic probability [Gauvrit et al 2014a, Gauvrit et al 2014b, Gauvrit et al 2014c, Kempe et al 2015, Mathy et al 2014. Using a computer to look at human behavior in a novel fashion, specifically by using a reverse Turing test where what is assessed is the human mind and an "average" Turing machine or computer program implementing any possible compression algorithm, we will show that the human mind behaves more like a machine.…”

“…Recent studies are perfectly in line with this idea. Mathy et al (2014) used a Simon R , a popular 80s game in which one must reproduce series of colors (chosen from among a set of 4 colors) of increasing length, echoing classical short-term memory tasks. They show that the algorithmic complexity of a series is a better predictor of correct recall than the length of the string.…”