本論文では, 状況的行為 (Situated Action: 以下SA) アプローチと記号処理アプローチとの違いについて, 記号の概念, 表象, プラン, インタラクションの4点で検討する.
第一に, SA派は物理記号系仮説が人間の日常の認知には不適切だと主張しているが, 彼らは記号概念を誤解している. 物理記号系においては, 記号とは「何かを指示するパターン」である. それは感覚刺激から形成され, 記憶に貯えられ, 処理され, 運動を生じるものである. ところがSA派は, 物理記号系の「記号」は言語のような明示的な意味解釈を受けるものに限られており, 道の曲がり具合や温度計の水銀柱の高さといった日常生活で扱われている非言語的な「信号」を扱うことができないと誤解している.
第二に, SA派は表象が不要であると主張する. 確かにある種の状況においては行為者の持つ表象は単純なもので間に合うが, 表象を持たずに知的な行為をするシステムは未だかつて記述されたことがない. 少なくとも行為が合目的的である限り, 目標や目標と状況や行為との関係については表象されていなければならない. さもなければ, 行動主義心理学に戻ってしまう. また, 生態学的アプローチでは, 行為者と環境との関係である「アフォーダンス」は直接に知覚され, 何のプロセスも必要としないという. しかし, メンタル・プロセスが無意識あるいは意識下で生じるということは, それらが記号処理プロセスでないという証拠にはならない. また, 行為が柔軟であるということは, すべてが新しく生成されていることを意味しない.
第三に, SA派の言うように行為はしばしばプランなしに行われる. しかし, それは人間がプランを全く用いていないことを意味しない. プランは常に抽象的であり, 実行時に下位レベルの実装を必要とする. また, プラン通りに遂行されるかどうかは, その遂行途中で生じる出来事に依存している. プランの重要な特徴は, 状況的な行為を必要とするような機会を最小化することにある. ゆえに, 人間がプランを形成し, 行動がそれに影響されることと, 多くの行為が状況的であることとの間には, 何の矛盾もない.
第四に, SA派はインタラクションを重視し, アフォーダンスを引き出して適切に反応することによって, 環境に適応できるのだと主張する. しかし, それは環境が比較的独立な構成要素に分割でき, システムが備える方略で間に合うほど環境が単純である場合に限られる. 方略が用意されていない状況は無視されてしまうため, システムは不適応な行動をとることになる.
結局, SA派のように, もし研究の目的がSuchmanらが研究したようなコピーをとるというような課題を活動として理解することならば, 活動に注目するのは適切であり, 人間は活動の中の相互作用的構成要素として研究されれば十分である. しかし, その状況の中で人間は思考している. われわれの目的が, 人間が外界とどのように相互作用するかを理解することであるなら, 思考のプロセスに注目することが重要である. 記号派とSA派を分けているのは異なる課題と行動への注目である. 問題は両者の知見をどう統合するかであり, 両者を共約不可能なパラダイムと考えていては認知の理解は進まない.

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コンピュータは記憶を助ける道具である一方, 人間の記憶負荷を驚くほど増加させてもいる. 「Forget-me-not」は, エピソード記憶に似た方法でデータを体制化し, 日常記憶の課題を支援するために開発中のコンピューティング・モデルである. 身につけることの可能なこのモデルは, 利用者とともに移動し, 利用者が遭遇する人や機器からの信号を記録することで利用者の出会ったエピソードを記憶する. 利用者は記憶の断片から「Forget-me-not」の検索機能を使って, 自分でも忘れかけている出来事を思い出すことができる. 本稿では開発中のモデルの仕様と実際の使用例について紹介する.

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In this paper, the structure of non-mathematicians' knowledge of probability is argued. Based on the analysis of the conceptual model (teachers' model) of probability, it is hypothesized that the mental model (non-mathematicians' model) of probability might consist of three different understandings (views), the case view, the frequency view, and the subjective view. In people's explanations of probabilistic statements about some concrete events, the hypothesized views are identified, and it is shown that an individual's explanations are often based on two or more views. The relationship between mental models and problem solving processes is discussed. It is demonstrated that the view that a problem solver took influenced problem solving processes. There are some cases where the frequency view has advantages in problem solving. The implication of multiple views of probability for mathematics education is also argued.

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In this paper, role of computer-based tools is discussed from a standpoint of situated learning theory, which considers learning as a process of enculturation to a community of practice. The authors propose utilizing computer-based tool for supporting collaborative learning and mention AlgoBlock, a tangible programming language developed by the authors, as an example of computer-based tools for facilitating learners' conversation and cooperation.
AlgoBlock is a set of physical blocks. Each block corresponds to a command of a Logo-like programming language, therefore, learners can connect those tangible blocks manually to form a program and they can share commands and programs on physical work space. It works as an open tool that enables learners to monitor each other's intention, and to make use of eye lines and body movements as resources for collaboration control, thus this tool supports social interaction among learners. By using this tool within adequate classroom settings, the authors believe, learners can improve their skills for programming and logical thinking through conversation and cooperation. To show the positive effect of the tool on learners' collaboration, the result of observational sessions, in which elementary students engaged in group programming works using AlgoBlock, is described.

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This paper discusses fundamental issues on representation and human interface problems, and proposes some methodologies for establishing macroscopic cognitive engineering to contribute organizational and social problem solving. Through experience of human interface evaluation and designs in information and telecommunication systems, we have been confronted with the need to develop systematic and macroscopic Cognitive Engineering (CE), understandable not only for individuals but also for organizations, for resolving representation and human interface (HI) problems, such as HM (Machine) I, HG (Graphical representation) I, HH (Human) I, HE (Environment) I, HT (Task) I, HJ (Job) I, HO (Organization) I, HS (Society) I, etc., from viewpoints of individuals and organizations or societies. It is important to research and develop for new CE methodologies to bridge between microscopic view and macroscopic view for harmonious development of cognitive artifacts and humans and organizations. As for CE methodologies, SPSC (Social Problem Solving CE), PRFC (Problem Representation Facilitating CE), MYTC (Myself-Yourself-Task Communicating CE), CMOC (Cerebellum Mode Operating CE), BECC (Behavior-Emotion-Cognition Systems CE), MMBC (Microscopic-Macroscopic Bridging CE), IECC (Internal-External Considering CE), CARC (Cognitive Artifacts Reflecting CE), and HDEC (Harmonious Development Evolving CE) are needed for solving fundamental representation and human interface problems.

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This paper examines the structure of grammatical knowledge through analyzing the acceptability of English parasitic gap constructions. Judgments on 100 parasitic gap constructions are collected with questionnaires from fifteen native speakers of English. The judgments are analyzed by multiple regression analysis. On the basis of the analysis, this paper claims that plural syntactic constraints simultaneously bear with different weights on the acceptability of parasitic gap constructions. A linear equation that predicts the acceptability of a given parasitic gap construction is proposed. As a general implication, this paper suggests that syntactic constraints as part of the language faculty are additive and with different importance.

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We propose an information-media theory of learning, and to incorporate the learning mechanism of human beings into machine learning. In the theory, a learning is regarded as communication by using multiple information-media, each of which includes a specific past experience forming imagery. This paper reports an experimental investigation of human learning processes dynamically selecting information-media, and raises a research issue on learning mechanisms dealing with multiple information-media.
The authors perform psychological experiments, in which subjects learn the programming language Prolog, and are asked to solve some questions. The subjects are divided into three groups according to their textbooks: (A) one containing only the specification of Prolog language, (B) the specification and an information-media explained in sentences, and (C) those explained in sentences and diagrams. Their incorrect answers, protocols, and elapsed time for solving questions are analyzed to clarify timing introducing a new information-medium, their selected media, and the effect of diagrams.
Based on the experiment, the authors constructed a cognitive model for simulating the behavior of subjects. The model is programmed in Prosit, by which each medium is handled as a situation. It confirms the information-media theory, and is a prototype towards a knowledge-intensive and robust machine learning mechanism.

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Previous studies of human device operation learning seem to assume that subjects acquire mental models of the target system by means of instruction prior to learning sessions. The researchers assume that when subjects are not provided such models, they would learn procedural knowledge, which imply that they would not have mental models of the system. However, in the first experiment, we analyzed verbal protocols of subjects controlling a water tank system without prior knowledge about the system, and found that they spontaneously formed several mental models, and that these models which were used by outperformed subjects were equivalent to the target system functionally, rather than structurally. In the second experiment, we provided subjects with such a functionally equivalent model and showed that it was as effective as structurally equivalent one.

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