Abstract

We have been studying on human information processing and finding out two types of feedback loop, positive and negative which are used when people understand a sentence. Former one is eidetic feedback control by visual sensory organs with encoding short-term memory (STM). Latter one is predictive feedback control by phonological imagery and schema, which help recall and reconstruction or reformation of concepts concerning with long term memory (LTM). Moreover, those strategies might be related to their behavior or attitudes. We have hypotheses that there are individual differences depending on strategies how two loops are used. Those findings must lead coordinating transformation and learning control for AI doctor or care assistive robots, which are required to interact with various types of people so that they can predict their behavior and attitudes through feedforward control.

Keywords: dual loop theory, eidetic feedback control, predictive feedback control, human information processing, human-machine interaction

## 1. Introduction

It has been becoming a key factor for artificial intelligent computers, which are composed of modern style machine learning system, how they are able to get involved with human.

Then, in our study, we have conducted experiments over a decade so that we can clarify human information processing, aiming to improve their interaction of AI doctor or support robot with human being by predicting their behavior from finding out their individual cognitive traits [1].

Specifically, we have predicted that their traits concerning with information processing would become clearer by comparing response time to short sentences between presenting with sound voice and letters. Those short sentences which are 120 questionnaires of psychological testing (YGPI) ask subjects whether they are the same or not, comparing with their daily ordinary behavior [2]. In other words, those questionnaires are concerning autobiographical memory [3], which are not effects of their knowledge or academic ability, but personality of 12 factors which divided into two factors, emotional and non-emotional [4–6].

From the results of our previous study, correlation coefficient between individual response time and the criteria of measurement (duration of each reading

questions or the number of words in one question) in the experiment by sound voice (listening) was higher than those of by letters (silent reading). And more, there was greater dispersion of response time among subjects in presenting letters experiment than former ones. From these reasons, we predicted that there would be differentiation of individual traits of information processing for letters than those of sound voice [6, 7].

We have therefore examined response time by silent reading individually and found out that there were persons of Visual type (N = 12 of 98, r < 0.3) whose correlation coefficients were much lower than those of Auditory type (N = 31 of 98, r > 0.5). In addition to this, the average of response time of Visual type persons was significantly shorter than those of Auditory type [6, 7].

Moreover, we have inspected reaction time of silent reading, especially among Intermediate type (N = 55), and found out there were another pattern of information processing between Emotional and non-Emotional questionnaires [6, 7].

In this paper, we have categorized two types, Eidetic type and Adjusting type, whose correlation coefficients and response time patters were different with each other. From these viewpoints, we had formulated a hypothesis (dual loop theory) and verified them by the experiments of practical collaborative learning in nursing class. One loop might be concerning positive feedback control (PFC) and other one might be negative feedback control (NFC) [8, 9]. Epidemic type persons might have tendency of PFC while they are solving problems. On the other hand, Adjustive type might tend coordinating two cycles (PFC and NFC) [10, 11]. We had revealed differentiations between the two types of behaviors.

Consequently, we would like to propose that the results of this study might help AI computer to learn machinery, thereby analyzing Big Data of various students' results and predicting their individual pattern of behavior so that it can support for personalized education, for instance, optimizing combination for collaborative learning.
