Contributions of expected learning progress and perceptual novelty to curiosity-driven exploration

Computational Learning Analytics to Estimate Location-Based Self-Regulation Process of Real-World Experiences

“…Thus, a wider range of possible values should be assumed for each of the internal parameters that drive a learner's computation. In fact, different from laboratory experiments allowing no or only a few self-determined behaviors [40], [41], [42], [43], [44], [45], learners in a natural and unconstrained situation in the world were able to execute far wider ranges of strategies to generate behavior (e.g., 115 kinds of strategies in a setting of our previous experiments [4]). Furthermore, at such a natural situation, different parameters can co-occur inside a learner, but mutual influence among co-occurring parameters is hard to discriminate without strict and artificial experimental controls.…”

“…5) Design Methodology of Computational Learning Analytics: Different from conventional computational modeling under ideal and artificial conditions [40], [41], [42], [43], [44], [45], our interest was how to model, trace, and assess the invisible and complex process of self-regulated computation by which intelligent behavior arises from learner-world interactions. By applying our real-world oriented methodology to design computational learning analytics, we theoretically devised detailed formulas to express the probabilistic regulation process of real-world behavior under grounded cognition, and made a sensing-level approximation of essential functions of the formulas.…”

“…We can learn many important lessons from conventional computational modeling that investigated behavior in indoor, online, or laboratory experiments by systematically controlling or excluding parameters (e.g., explanatory variables, extraneous variables) under ideal and artificial conditions [41], [42], [43], [44], [45], and by repeatedly investigating how each individual parameter affects other parameters [40]. However, we should consider that assessing cognitive processes in narrow and artificially isolated conditions with little ecological validity would not represent the actual functioning of cognition [46] and would not necessarily generalize to real-world cognition [47].…”

“…This idea has been recently applied in the field of computational psychiatry to investigate correlation between the generation of prosocial behavior (i.e., actions that benefit others) and the characteristics of psychiatric disorder [44]. In the setting of an online game, computational behavior modeling was used to estimate the factor (e.g., learning progress maximization, perceptual novelty, or random search) that drove a learner's exploration under the conditions of minimal task instructions and no external rewards [45].…”

“…By using the sigmoid function, the softmax function, and the reinforcement learning algorithm, the internal computation of a human can be modeled as a Bayesian decision-making system that updates his/her internal belief to generate behavior [40], [41], [42], [43], [44], [45]. Assuming that real-world learning is location-oriented computation, the present study proposes an extended model to express that the degree of a learner's belief is determined by the conditional probability and conditional expectation given by his/her stay location.…”