心理科学
心理科學
심이과학
Psychological Science
2006年
1期
9~13
,共null页
时间关系推理 心理模型 形式规划 日常生活事件
時間關繫推理 心理模型 形式規劃 日常生活事件
시간관계추리 심리모형 형식규화 일상생활사건
temporal relation reasoning, mental model, formal rule, daily events
对时间推理的研究主要有两种范式,习俗周期性时间推理和时间关系推理。以往大多效研究集中在习俗周期性时间推理上,而时间关系推理研究则相对薄弱。本研究在以往研究的基础上,对日常生活事件的时间关系推理进行了研究。实验设计考虑了推理任务的模型数量、前提数量、无关前提以及有无肯定答案等因素。形成了六类推理任务。研究发现:推理任务的模型数量、前提数量、无关前提、是否有肯定答案以及推理者时间维度上的人格特征等都会影响个体的时间关系推理;被试一般是以呈现的前提顺序来表征推理任务的。但是,当被试意识到无关信息时,可以从整体上表征问题。
對時間推理的研究主要有兩種範式,習俗週期性時間推理和時間關繫推理。以往大多效研究集中在習俗週期性時間推理上,而時間關繫推理研究則相對薄弱。本研究在以往研究的基礎上,對日常生活事件的時間關繫推理進行瞭研究。實驗設計攷慮瞭推理任務的模型數量、前提數量、無關前提以及有無肯定答案等因素。形成瞭六類推理任務。研究髮現:推理任務的模型數量、前提數量、無關前提、是否有肯定答案以及推理者時間維度上的人格特徵等都會影響箇體的時間關繫推理;被試一般是以呈現的前提順序來錶徵推理任務的。但是,噹被試意識到無關信息時,可以從整體上錶徵問題。
대시간추리적연구주요유량충범식,습속주기성시간추리화시간관계추리。이왕대다효연구집중재습속주기성시간추리상,이시간관계추리연구칙상대박약。본연구재이왕연구적기출상,대일상생활사건적시간관계추리진행료연구。실험설계고필료추리임무적모형수량、전제수량、무관전제이급유무긍정답안등인소。형성료륙유추리임무。연구발현:추리임무적모형수량、전제수량、무관전제、시부유긍정답안이급추리자시간유도상적인격특정등도회영향개체적시간관계추리;피시일반시이정현적전제순서래표정추리임무적。단시,당피시의식도무관신식시,가이종정체상표정문제。
Temporal reasoning is one of the most important components in daily events. This research has two kinds of main paradigms- periodical temporal reasoning and temporal relation reasoning. Most of the previous studies are focused on periodical temporal reasoning, but not on temporal relation reasoning. In this study, we explored the temporal relation reasoning of daily events with college students as subjects. The number of models, the number of premises, irrelevant premises, etc. were all considered in the experiment design. With the two experiment results, we have concluded as follows: The number of models, the number of premises, irrelevant premises, valid or invalid answers to reasoning tasks and personality characteristics in time can affect the individual temporal relation reasoning; The reasoning tasks are generally represented in the premises sequence of presentation by subjects. But when a subject is aware of irrelevant information, the tasks can be represented as a whole, and the multiple-model problem based on four premises can be dynamically predigested as one-model.