{"id":12055,"date":"2026-04-16T12:55:02","date_gmt":"2026-04-16T04:55:02","guid":{"rendered":"https:\/\/www.invbus.com\/blog\/?p=12055"},"modified":"2026-04-16T12:57:13","modified_gmt":"2026-04-16T04:57:13","slug":"%e8%ae%a4%e7%9f%a5%e5%81%8f%e8%af%af%e5%88%97%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/www.invbus.com\/blog\/?p=12055","title":{"rendered":"\u8ba4\u77e5\u504f\u8bef\u5217\u8868"},"content":{"rendered":"\n<h2 class=\"wp-block-heading\" id=\"\u951a\u5b9a\u504f\u8bef-Anchoring-bias\">\u951a\u5b9a\u504f\u8bef (Anchoring bias)<\/h2>\n\n\n\n<p>\u951a\u5b9a\u504f\u8bef (Anchoring bias) \uff0c\u6216\u79f0\u805a\u7126\u6548\u5e94 (focalism) \uff0c\u662f\u6307\u5728\u505a\u51b3\u7b56\u65f6\u8fc7\u5ea6\u4f9d\u8d56\u67d0\u4e00\u4e2a\u7279\u5f81\u6216\u4fe1\u606f\u2014\u2014\u5373\u201c\u951a\u5b9a\u201d\u5728\u7279\u5b9a\u4fe1\u606f\u4e0a\uff08\u901a\u5e38\u662f\u6700\u5148\u83b7\u5f97\u7684\u4fe1\u606f\uff09\u3002<\/p>\n\n\n\n<p>\u951a\u5b9a\u504f\u8bef\u5305\u542b\u6216\u6d89\u53ca\u4ee5\u4e0b\u5185\u5bb9\uff1a<\/p>\n\n\n\n<p><strong>\u5171\u540c\u6765\u6e90\u504f\u8bef<\/strong>\u00a0(Common source bias)\uff1a\u503e\u5411\u4e8e\u5408\u5e76\u6216\u6bd4\u8f83\u6765\u81ea\u540c\u4e00\u6765\u6e90\u7684\u7814\u7a76\uff0c\u6216\u8005\u6765\u81ea\u4f7f\u7528\u76f8\u540c\u65b9\u6cd5\u6216\u6570\u636e\u7684\u6765\u6e90\u7684\u7814\u7a76\u3002<\/p>\n\n\n\n<p><strong>\u4fdd\u5b88\u504f\u8bef<\/strong>\u00a0(Conservatism bias)\uff1a\u5f53\u6709\u65b0\u8bc1\u636e\u51fa\u73b0\u65f6\uff0c\u503e\u5411\u4e8e\u5bf9\u5df2\u6709\u4fe1\u5ff5\u8fdb\u884c\u4e0d\u8db3\u7684\u4fee\u6b63\u3002<\/p>\n\n\n\n<p><strong>\u529f\u80fd\u56fa\u7740<\/strong>\u00a0(Functional fixedness)\uff1a\u503e\u5411\u4e8e\u5c06\u67d0\u4e2a\u7269\u54c1\u5c40\u9650\u4e8e\u5176\u4f20\u7edf\u7528\u9014\uff0c\u800c\u65e0\u6cd5\u7075\u6d3b\u8fd0\u7528\u3002<\/p>\n\n\n\n<p><strong>\u5de5\u5177\u5b9a\u5f8b<\/strong>\u00a0(Law of the instrument)\uff1a\u8fc7\u5ea6\u4f9d\u8d56\u719f\u6089\u7684\u5de5\u5177\u6216\u65b9\u6cd5\uff0c\u800c\u5ffd\u89c6\u6216\u4f4e\u4f30\u66ff\u4ee3\u65b9\u6848\u3002\u201c\u5982\u679c\u4f60\u624b\u91cc\u53ea\u6709\u4e00\u628a\u9524\u5b50\uff0c\u770b\u4ec0\u4e48\u90fd\u50cf\u9489\u5b50\u3002\u201d<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u89c6\u9519\u89c9-Apophenia\">\u89c6\u9519\u89c9 (Apophenia)<\/h2>\n\n\n\n<p>\u6307\u4e2a\u4f53\u503e\u5411\u4e8e\u5728\u65e0\u5173\u4e8b\u7269\u4e4b\u95f4\u611f\u77e5\u5230\u6709\u610f\u4e49\u7684\u8054\u7cfb\u3002 \u89c6\u9519\u89c9\u7684\u7c7b\u578b\u5305\u62ec\uff1a<\/p>\n\n\n\n<p><strong>\u805a\u7c7b\u9519\u89c9<\/strong>\u00a0(Clustering illusion)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u968f\u673a\u6570\u636e\u4e2d\u5c0f\u8303\u56f4\u7684\u8fde\u7eed\u3001\u91cd\u590d\u6216\u805a\u96c6\u73b0\u8c61\u7684\u91cd\u8981\u6027\uff08\u5373\u770b\u5230\u865a\u5e7b\u7684\u6a21\u5f0f\uff09\u3002<\/p>\n\n\n\n<p><strong>\u865a\u5047\u5173\u8054<\/strong>\u00a0(Illusory correlation)\uff1a\u503e\u5411\u4e8e\u9519\u8bef\u5730\u611f\u77e5\u4e24\u4e2a\u65e0\u5173\u4e8b\u4ef6\u4e4b\u95f4\u5b58\u5728\u8054\u7cfb\u3002<\/p>\n\n\n\n<p><strong>\u7a7a\u60f3\u6027\u9519\u89c9<\/strong>\u00a0(Pareidolia)\uff1a\u503e\u5411\u4e8e\u5c06\u6a21\u7cca\u4e14\u968f\u673a\u7684\u523a\u6fc0\uff08\u901a\u5e38\u662f\u56fe\u50cf\u6216\u58f0\u97f3\uff09\u611f\u77e5\u4e3a\u91cd\u8981\u4fe1\u606f\uff0c\u4f8b\u5982\u5728\u4e91\u4e2d\u770b\u5230\u52a8\u7269\u6216\u4eba\u8138\uff0c\u5728\u6708\u7403\u8868\u9762\u770b\u5230\u4eba\u50cf\uff0c\u4ee5\u53ca\u5728\u5012\u653e\u7684\u97f3\u4e50\u4e2d\u542c\u5230\u5e76\u4e0d\u5b58\u5728\u7684\u9690\u85cf\u4fe1\u606f\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u53ef\u5f97\u6027\u542f\u53d1\u6cd5-Availability-heuristic\">\u53ef\u5f97\u6027\u542f\u53d1\u6cd5 (Availability heuristic)<\/h2>\n\n\n\n<p>\u53ef\u5f97\u6027\u542f\u53d1\u6cd5\uff08\u4ea6\u79f0\u53ef\u5f97\u6027\u504f\u5dee\uff09\u662f\u6307\u4eba\u4eec\u503e\u5411\u4e8e\u9ad8\u4f30\u8bb0\u5fc6\u4e2d\u66f4\u201c\u5bb9\u6613\u83b7\u5f97\u201d\u7684\u4e8b\u4ef6\u53d1\u751f\u7684\u53ef\u80fd\u6027\uff0c\u800c\u8fd9\u79cd\u201c\u53ef\u5f97\u6027\u201d\u53d7\u5230\u8bb0\u5fc6\u7684\u8fd1\u671f\u6027\u3001\u4e8b\u4ef6\u7684\u7279\u6b8a\u6027\u6216\u60c5\u7eea\u5f71\u54cd\u7a0b\u5ea6\u7684\u5f71\u54cd\u3002\u53ef\u5f97\u6027\u542f\u53d1\u6cd5\u5305\u62ec\u4ee5\u4e0b\u5185\u5bb9\uff1a<\/p>\n\n\n\n<p><strong>\u4ee5\u4eba\u4e3a\u4e2d\u5fc3\u7684\u601d\u7ef4<\/strong>\u00a0(Anthropocentric thinking)\uff1a\u503e\u5411\u4e8e\u4f7f\u7528\u4e0e\u4eba\u7c7b\u76f8\u5173\u7684\u7c7b\u6bd4\u4f5c\u4e3a\u63a8\u7406\u5176\u4ed6\u8f83\u4e0d\u719f\u6089\u7684\u751f\u7269\u73b0\u8c61\u7684\u57fa\u7840\u3002<\/p>\n\n\n\n<p><strong>\u62df\u4eba\u5316<\/strong>\u00a0(Anthropomorphism)\uff1a\u5c06\u52a8\u7269\u3001\u7269\u4f53\u548c\u62bd\u8c61\u6982\u5ff5\u8d4b\u4e88\u4eba\u7c7b\u7684\u7279\u5f81\u3001\u60c5\u611f\u6216\u610f\u56fe\u3002 \u76f8\u53cd\u7684\u504f\u5dee\u662f\u975e\u4eba\u5316\u77e5\u89c9 (dehumanised perception) \uff0c\u5373\u4e0d\u5c06\u60c5\u611f\u6216\u601d\u7ef4\u5f52\u4e8e\u4ed6\u4eba\uff0c\u8fd9\u662f\u4e00\u79cd\u5ba2\u4f53\u5316\u7684\u8868\u73b0\u3002<\/p>\n\n\n\n<p><strong>\u6ce8\u610f\u504f\u5dee<\/strong>\u00a0(Attentional bias)\uff1a\u77e5\u89c9\u5bb9\u6613\u53d7\u5230\u53cd\u590d\u51fa\u73b0\u7684\u60f3\u6cd5\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n<p><strong>\u9891\u7387\u9519\u89c9<\/strong>\u00a0(Frequency illusion) \u6216\u5df4\u5fb7-\u8fc8\u56e0\u970d\u592b\u73b0\u8c61 (Baader\u2013Meinhof phenomenon)\uff1a<\/p>\n\n\n\n<p>\u9891\u7387\u9519\u89c9\u6307\u4e00\u65e6\u6ce8\u610f\u5230\u67d0\u4e2a\u4e8b\u7269\uff0c\u5c31\u4f1a\u9891\u7e41\u5bdf\u89c9\u5230\u5b83\uff0c\u4ece\u800c\u8bef\u4ee5\u4e3a\u5b83\u7684\u51fa\u73b0\u9891\u7387\u5f88\u9ad8\uff08\u5c5e\u4e8e\u9009\u62e9\u6027\u504f\u5dee\u7684\u4e00\u79cd\uff09\u3002<\/p>\n\n\n\n<p>\u5df4\u5fb7-\u8fc8\u56e0\u970d\u592b\u73b0\u8c61\u662f\u4e00\u79cd\u9519\u89c9\uff0c\u5373\u67d0\u4ef6\u6700\u8fd1\u5f15\u8d77\u6ce8\u610f\u7684\u4e8b\u60c5\uff0c\u968f\u540e\u4f3c\u4e4e\u4ee5\u6781\u9ad8\u7684\u9891\u7387\u51fa\u73b0\u3002\u8be5\u73b0\u8c61\u56e0\u5df4\u5fb7-\u8fc8\u56e0\u970d\u592b\u7ec4\u7ec7 (Baader\u2013Meinhof Group) \u66fe\u88ab\u9891\u7e41\u63d0\u53ca\u800c\u5f97\u540d\u3002<\/p>\n\n\n\n<p><strong>\u5185\u9690\u8054\u7ed3<\/strong>\u00a0(Implicit association)\uff1a\u4e2a\u4f53\u5bf9\u8bcd\u8bed\u5339\u914d\u901f\u5ea6\u7684\u5feb\u6162\u53d6\u51b3\u4e8e\u5b83\u4eec\u5728\u5fc3\u7406\u4e0a\u7684\u5173\u8054\u7a0b\u5ea6\u3002<\/p>\n\n\n\n<p><strong>\u663e\u8457\u6027\u504f\u5dee<\/strong>\u00a0(Salience bias)\uff1a\u503e\u5411\u4e8e\u5173\u6ce8\u66f4\u52a0\u7a81\u51fa\u6216\u60c5\u7eea\u5316\u7684\u4e8b\u7269\uff0c\u800c\u5ffd\u7565\u90a3\u4e9b\u5e73\u6de1\u65e0\u5947\u7684\u4e8b\u7269\uff0c\u5373\u4f7f\u8fd9\u4e9b\u5dee\u5f02\u5728\u5ba2\u89c2\u6807\u51c6\u4e0a\u5e76\u65e0\u5b9e\u9645\u610f\u4e49\u3002\u53c2\u89c1\u51af\u00b7\u96f7\u65af\u6258\u592b\u6548\u5e94 (von Restorff effect)\u3002<\/p>\n\n\n\n<p><strong>\u9009\u62e9\u504f\u5dee<\/strong>\u00a0(Selection bias)\uff1a\u5f53\u7edf\u8ba1\u6837\u672c\u7684\u6210\u5458\u5e76\u975e\u5b8c\u5168\u968f\u673a\u9009\u53d6\u65f6\uff0c\u4f1a\u5bfc\u81f4\u6837\u672c\u4e0d\u80fd\u4ee3\u8868\u603b\u4f53\u3002<\/p>\n\n\n\n<p><strong>\u5e78\u5b58\u8005\u504f\u5dee<\/strong>\u00a0(Survivorship bias)\uff1a\u503e\u5411\u4e8e\u5173\u6ce8\u90a3\u4e9b\u201c\u5e78\u5b58\u201d\u4e8e\u67d0\u4e00\u8fc7\u7a0b\u7684\u4eba\u6216\u4e8b\u7269\uff0c\u800c\u65e0\u610f\u4e2d\u5ffd\u7565\u4e86\u672a\u80fd\u5b58\u7eed\u7684\u4e2a\u4f53\uff0c\u56e0\u4e3a\u5b83\u4eec\u8f83\u5c11\u88ab\u770b\u89c1\u3002<\/p>\n\n\n\n<p><strong>\u91cf\u5316\u504f\u5dee<\/strong>\u00a0(Quantification bias)\uff1a\u503e\u5411\u4e8e\u7ed9\u4e88\u53ef\u6d4b\u91cf\u6216\u91cf\u5316\u7684\u6307\u6807\u66f4\u591a\u7684\u6743\u91cd\uff0c\u800c\u5ffd\u89c6\u4e0d\u53ef\u91cf\u5316\u7684\u4ef7\u503c\u3002 \u53c2\u89c1\u9ea6\u514b\u7eb3\u9a6c\u62c9\u8c2c\u8bef (McNamara fallacy)\u3002<\/p>\n\n\n\n<p><strong>\u5e38\u8d70\u4e4b\u8def\u6548\u5e94<\/strong>\u00a0(Well-travelled road effect)\uff1a\u503e\u5411\u4e8e\u4f4e\u4f30\u884c\u9a76\u719f\u6089\u8def\u7ebf\u6240\u9700\u7684\u65f6\u95f4\uff0c\u800c\u9ad8\u4f30\u884c\u9a76\u4e0d\u719f\u6089\u8def\u7ebf\u6240\u9700\u7684\u65f6\u95f4\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u8ba4\u77e5\u5931\u8c03-Cognitive-dissonance\">\u8ba4\u77e5\u5931\u8c03 (Cognitive dissonance)<\/h2>\n\n\n\n<p>\u8ba4\u77e5\u5931\u8c03\u6307\u4e2a\u4f53\u5728\u611f\u77e5\u5230\u77db\u76fe\u4fe1\u606f\u65f6\u6240\u7ecf\u5386\u7684\u5fc3\u7406\u51b2\u7a81\u53ca\u5176\u5e26\u6765\u7684\u5fc3\u7406\u8d1f\u62c5\u3002<\/p>\n\n\n\n<p><strong>\u6b63\u5e38\u6027\u504f\u5dee<\/strong>\u00a0(Normalcy bias)\uff1a\u8ba4\u77e5\u5931\u8c03\u7684\u4e00\u79cd\u8868\u73b0\uff0c\u6307\u4e2a\u4f53\u62d2\u7edd\u4e3a\u4ece\u672a\u53d1\u751f\u8fc7\u7684\u707e\u96be\u505a\u51c6\u5907\u6216\u4f5c\u51fa\u53cd\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u52aa\u529b\u5408\u7406\u5316<\/strong>\u00a0(Effort justification)\uff1a\u4e2a\u4f53\u503e\u5411\u4e8e\u5bf9\u81ea\u5df1\u82b1\u8d39\u52aa\u529b\u83b7\u5f97\u7684\u7ed3\u679c\u8d4b\u4e88\u66f4\u9ad8\u7684\u4ef7\u503c\uff0c\u5373\u4f7f\u8be5\u7ed3\u679c\u7684\u5b9e\u9645\u4ef7\u503c\u5e76\u6ca1\u6709\u90a3\u4e48\u9ad8\u3002\u4f8b\u5982\uff0c\u201c\u5b9c\u5bb6\u6548\u5e94 (IKEA effect) \u201d\u6307\u4eba\u4eec\u4f1a\u5bf9\u81ea\u5df1\u90e8\u5206\u7ec4\u88c5\u7684\u7269\u54c1\uff08\u5982\u5b9c\u5bb6\u5bb6\u5177\uff09\u8d4b\u4e88\u4e0d\u6210\u6bd4\u4f8b\u7684\u9ad8\u4ef7\u503c\uff0c\u800c\u4e0d\u7ba1\u6700\u7ec8\u6210\u54c1\u7684\u8d28\u91cf\u5982\u4f55\u3002<\/p>\n\n\n\n<p><strong>\u672c\u00b7\u5bcc\u5170\u514b\u6797\u6548\u5e94<\/strong>\u00a0(Ben Franklin effect)\uff1a\u6307\u5f53\u4e00\u4e2a\u4eba\u4e3a\u4ed6\u4eba\u63d0\u4f9b\u8fc7\u4e00\u6b21\u5e2e\u52a9\u540e\uff0c\u6bd4\u8d77\u63a5\u53d7\u5bf9\u65b9\u7684\u5e2e\u52a9\uff0c\u66f4\u53ef\u80fd\u518d\u6b21\u5e2e\u52a9\u8be5\u4eba\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u786e\u8ba4\u504f\u8bef-Confirmation-bias\">\u786e\u8ba4\u504f\u8bef (Confirmation bias)<\/h2>\n\n\n\n<p>\u786e\u8ba4\u504f\u8bef\u6307\u4e2a\u4f53\u503e\u5411\u4e8e\u4ee5\u652f\u6301\u81ea\u5df1\u5148\u5165\u4e3a\u4e3b\u89c2\u5ff5\u7684\u65b9\u5f0f\u6765\u641c\u7d22\u3001\u89e3\u91ca\u3001\u5173\u6ce8\u548c\u8bb0\u5fc6\u4fe1\u606f\u3002\u5176\u4ed6\u6d89\u53ca\u6216\u5c5e\u4e8e\u786e\u8ba4\u504f\u8bef\u7684\u8ba4\u77e5\u504f\u5dee\u5305\u62ec\uff1a<\/p>\n\n\n\n<ul class=\"wp-block-list\">\n<li><strong>\u53cd\u5f39\u6548\u5e94<\/strong>\u00a0(Backfire effect)\uff1a\u5f53\u4e2a\u4f53\u9762\u5bf9\u4e0e\u81ea\u8eab\u4fe1\u5ff5\u76f8\u6096\u7684\u8bc1\u636e\u65f6\uff0c\u53cd\u800c\u4f1a\u52a0\u6df1\u539f\u6709\u4fe1\u5ff5\u7684\u503e\u5411\u3002<\/li>\n\n\n\n<li><strong>\u4e00\u81f4\u6027\u504f\u8bef<\/strong>\u00a0(Congruence bias)\uff1a\u503e\u5411\u4e8e\u4ec5\u901a\u8fc7\u76f4\u63a5\u9a8c\u8bc1\u6765\u68c0\u9a8c\u5047\u8bbe\uff0c\u800c\u4e0d\u8003\u8651\u6d4b\u8bd5\u53ef\u80fd\u7684\u66ff\u4ee3\u5047\u8bbe\u3002<\/li>\n\n\n\n<li><strong>\u5b9e\u9a8c\u8005\u504f\u8bef<\/strong>\u00a0(Experimenter\u2019s bias) \u6216\u671f\u671b\u504f\u8bef (Expectation bias)\uff1a\u5b9e\u9a8c\u8005\u503e\u5411\u4e8e\u76f8\u4fe1\u3001\u786e\u8ba4\u5e76\u53d1\u8868\u7b26\u5408\u5176\u9884\u671f\u5b9e\u9a8c\u7ed3\u679c\u7684\u6570\u636e\uff0c\u540c\u65f6\u6000\u7591\u3001\u5ffd\u89c6\u6216\u964d\u4f4e\u4e0e\u5176\u671f\u671b\u76f8\u6096\u7684\u6570\u636e\u7684\u6743\u91cd\u3002<\/li>\n\n\n\n<li><strong>\u89c2\u5bdf\u8005\u671f\u671b\u6548\u5e94<\/strong>\u00a0(Observer-expectancy effect)\uff1a\u5f53\u7814\u7a76\u8005\u9884\u671f\u67d0\u4e2a\u7ed3\u679c\u65f6\uff0c\u53ef\u80fd\u4f1a\u65e0\u610f\u8bc6\u5730\u64cd\u7eb5\u5b9e\u9a8c\u6216\u8bef\u89e3\u6570\u636e\uff0c\u4ee5\u652f\u6301\u5176\u671f\u671b\u7684\u7ed3\u8bba\u3002\u53c2\u89c1\u88ab\u8bd5\u671f\u671b\u6548\u5e94 (subject-expectancy effect) \u3002<\/li>\n\n\n\n<li><strong>\u9009\u62e9\u6027\u77e5\u89c9<\/strong>\u00a0(Selective perception)\uff1a\u4e2a\u4f53\u7684\u671f\u671b\u4f1a\u5f71\u54cd\u5176\u5bf9\u4e8b\u7269\u7684\u611f\u77e5\u3002<\/li>\n\n\n\n<li><strong>\u68ee\u6885\u5c14\u7ef4\u65af\u53cd\u5c04<\/strong>\u00a0(Semmelweis reflex)\uff1a\u6307\u4e2a\u4f53\u503e\u5411\u4e8e\u62d2\u7edd\u4e0e\u65e2\u5b9a\u89c2\u5ff5\u76f8\u77db\u76fe\u7684\u65b0\u8bc1\u636e\u3002<\/li>\n<\/ul>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u81ea\u6211\u4e2d\u5fc3\u504f\u8bef-Egocentric-bias\">\u81ea\u6211\u4e2d\u5fc3\u504f\u8bef (Egocentric bias)<\/h2>\n\n\n\n<p>\u81ea\u6211\u4e2d\u5fc3\u504f\u8bef\u6307\u4e2a\u4f53\u8fc7\u5ea6\u4f9d\u8d56\u81ea\u8eab\u89c6\u89d2\uff0c\u6216\u5bf9\u81ea\u8eab\u76f8\u8f83\u4e8e\u4ed6\u4eba\u7684\u8ba4\u77e5\u6709\u6240\u4e0d\u540c\u7684\u503e\u5411\u3002 \u4ee5\u4e0b\u662f\u81ea\u6211\u4e2d\u5fc3\u504f\u8bef\u7684\u5177\u4f53\u8868\u73b0\u5f62\u5f0f\uff1a<\/p>\n\n\n\n<p><strong>\u504f\u8bef\u76f2\u70b9<\/strong>\u00a0(Bias blind spot)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u81ea\u5df1\u6bd4\u4ed6\u4eba\u66f4\u4e0d\u53d7\u504f\u89c1\u5f71\u54cd\uff0c\u6216\u8005\u66f4\u5bb9\u6613\u8bc6\u522b\u4ed6\u4eba\u8eab\u4e0a\u7684\u8ba4\u77e5\u504f\u8bef\uff0c\u800c\u975e\u81ea\u8eab\u7684\u504f\u8bef\u3002<\/p>\n\n\n\n<p><strong>\u865a\u5047\u5171\u8bc6\u6548\u5e94<\/strong>\u00a0(False consensus effect)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u4ed6\u4eba\u4e0e\u81ea\u5df1\u89c2\u70b9\u4e00\u81f4\u7684\u7a0b\u5ea6\u3002<\/p>\n\n\n\n<p><strong>\u865a\u5047\u72ec\u7279\u6027\u504f\u8bef<\/strong>\u00a0(False uniqueness bias)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u81ea\u5df1\u7684\u9879\u76ee\u6216\u81ea\u8eab\u6bd4\u5b9e\u9645\u66f4\u72ec\u7279\u3002<\/p>\n\n\n\n<p><strong>\u798f\u52d2\u6548\u5e94<\/strong>\u00a0(Forer effect) \u6216\u5df4\u7eb3\u59c6\u6548\u5e94 (Barnum effect)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u6a21\u7cca\u3001\u5bbd\u6cdb\u7684\u4e2a\u6027\u63cf\u8ff0\u9ad8\u5ea6\u51c6\u786e\u5730\u9002\u7528\u4e8e\u81ea\u5df1\uff0c\u5c3d\u7ba1\u8fd9\u4e9b\u63cf\u8ff0\u5b9e\u9645\u4e0a\u9002\u7528\u4e8e\u5927\u591a\u6570\u4eba\u3002\u8fd9\u79cd\u6548\u5e94\u90e8\u5206\u89e3\u91ca\u4e86\u5360\u661f\u672f\u3001\u7b97\u547d\u3001\u7b14\u8ff9\u5b66\u4ee5\u53ca\u67d0\u4e9b\u7c7b\u578b\u7684\u6027\u683c\u6d4b\u8bd5\u4e3a\u4f55\u80fd\u88ab\u5e7f\u6cdb\u63a5\u53d7\u3002<\/p>\n\n\n\n<p><strong>\u4e0d\u5bf9\u79f0\u6d1e\u5bdf\u9519\u89c9<\/strong>\u00a0(Illusion of asymmetric insight)\uff1a\u4e2a\u4f53\u503e\u5411\u4e8e\u8ba4\u4e3a\u81ea\u5df1\u5bf9\u4ed6\u4eba\u7684\u4e86\u89e3\u8d85\u8fc7\u4ed6\u4eba\u5bf9\u81ea\u5df1\u7684\u4e86\u89e3\u3002<\/p>\n\n\n\n<p><strong>\u63a7\u5236\u9519\u89c9<\/strong>\u00a0(Illusion of control)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u81ea\u5df1\u5bf9\u5916\u90e8\u4e8b\u4ef6\u7684\u5f71\u54cd\u529b\u3002<\/p>\n\n\n\n<p><strong>\u900f\u660e\u5ea6\u9519\u89c9<\/strong>\u00a0(Illusion of transparency)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u4ed6\u4eba\u5bf9\u81ea\u5df1\u5fc3\u7406\u72b6\u6001\u7684\u4e86\u89e3\u7a0b\u5ea6\uff0c\u540c\u65f6\u9ad8\u4f30\u81ea\u5df1\u5bf9\u4ed6\u4eba\u5fc3\u7406\u72b6\u6001\u7684\u7406\u89e3\u80fd\u529b\u3002<\/p>\n\n\n\n<p><strong>\u6709\u6548\u6027\u9519\u89c9<\/strong>\u00a0(Illusion of validity)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u81ea\u5df1\u5224\u65ad\u7684\u51c6\u786e\u6027\uff0c\u5c24\u5176\u662f\u5728\u4fe1\u606f\u4e00\u81f4\u6216\u76f8\u4e92\u5173\u8054\u65f6\u3002<\/p>\n\n\n\n<p><strong>\u865a\u5047\u4f18\u8d8a\u611f<\/strong>\u00a0(Illusory superiority)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u81ea\u8eab\u4f18\u826f\u54c1\u8d28\uff0c\u4f4e\u4f30\u81ea\u8eab\u7f3a\u70b9\u3002\u6b64\u6548\u5e94\u4ea6\u79f0 \u201c\u83b1\u514b\u6c83\u8d1d\u8d21\u6548\u5e94 (Lake Wobegon effect)\u201d\u3001\u201c\u4f18\u4e8e\u5e73\u5747\u6548\u5e94 (better-than-average effect)\u201d \u6216 \u201c\u4f18\u8d8a\u6027\u504f\u8bef (superiority bias) \u201d\u3002<\/p>\n\n\n\n<p><strong>\u5929\u771f\u6124\u4e16\u5ac9\u4fd7<\/strong>\u00a0(Na\u00efve cynicism)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u4ed6\u4eba\u6bd4\u81ea\u5df1\u66f4\u52a0\u81ea\u79c1\u6216\u5e26\u6709\u81ea\u6211\u4e2d\u5fc3\u504f\u8bef\u3002<\/p>\n\n\n\n<p><strong>\u5929\u771f\u73b0\u5b9e\u4e3b\u4e49<\/strong>\u00a0(Na\u00efve realism)\uff1a\u8ba4\u4e3a\u81ea\u5df1\u6240\u770b\u5230\u7684\u73b0\u5b9e\u662f\u5ba2\u89c2\u4e14\u65e0\u504f\u89c1\u7684\uff0c\u6240\u6709\u7406\u6027\u7684\u4eba\u90fd\u4f1a\u8ba4\u540c\u81ea\u5df1\u7684\u89c2\u70b9\uff0c\u82e5\u6709\u4eba\u4e0d\u540c\u610f\uff0c\u5219\u4e00\u5b9a\u662f\u56e0\u4e3a\u4ed6\u4eec\u65e0\u77e5\u3001\u61d2\u60f0\u3001\u4e0d\u7406\u6027\u6216\u6301\u6709\u504f\u89c1\u3002<\/p>\n\n\n\n<p><strong>\u8fc7\u5ea6\u81ea\u4fe1\u6548\u5e94<\/strong>\u00a0(Overconfidence effect)\uff1a\u503e\u5411\u4e8e\u5bf9\u81ea\u5df1\u7684\u7b54\u6848\u8fc7\u5ea6\u81ea\u4fe1\u3002\u4f8b\u5982\uff0c\u5bf9\u4e8e\u67d0\u4e9b\u7c7b\u578b\u7684\u95ee\u9898\uff0c\u4e2a\u4f53\u8ba4\u4e3a\u81ea\u5df1\u201c99% \u786e\u5b9a\u201d\u7684\u7b54\u6848\uff0c\u5b9e\u9645\u4e0a\u6709 40% \u7684\u53ef\u80fd\u662f\u9519\u8bef\u7684\u3002<\/p>\n\n\n\n<p><strong>\u8ba1\u5212\u8c2c\u8bef<\/strong>\u00a0(Planning fallacy)\uff1a\u503e\u5411\u4e8e\u4f4e\u4f30\u5b8c\u6210\u4e00\u9879\u4efb\u52a1\u6240\u9700\u7684\u65f6\u95f4\u3002<\/p>\n\n\n\n<p><strong>\u514b\u5236\u504f\u8bef<\/strong>\u00a0(Restraint bias)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u81ea\u5df1\u5728\u9762\u5bf9\u8bf1\u60d1\u65f6\u7684\u81ea\u63a7\u80fd\u529b\u3002<\/p>\n\n\n\n<p><strong>\u7279\u8d28\u5f52\u56e0\u504f\u8bef<\/strong>\u00a0(Trait ascription bias)\uff1a\u8ba4\u4e3a\u81ea\u5df1\u5728\u6027\u683c\u3001\u884c\u4e3a\u548c\u60c5\u7eea\u65b9\u9762\u8f83\u4e3a\u591a\u53d8\uff0c\u800c\u4ed6\u4eba\u5219\u8f83\u4e3a\u53ef\u9884\u6d4b\u3002<\/p>\n\n\n\n<p><strong>\u7b2c\u4e09\u4eba\u6548\u5e94<\/strong>\u00a0(Third-person effect)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u5927\u4f17\u4f20\u5a92\u7684\u4fe1\u606f\u5bf9\u4ed6\u4eba\u7684\u5f71\u54cd\u529b\u5927\u4e8e\u5bf9\u81ea\u5df1\u7684\u5f71\u54cd\u529b\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u6269\u5c55\u5ffd\u89c6-Extension-neglect\">\u6269\u5c55\u5ffd\u89c6 (Extension neglect)<\/h2>\n\n\n\n<p>\u6269\u5c55\u5ffd\u89c6\u6307\u5728\u8bc4\u4f30\u7ed3\u679c\u3001\u76f8\u5173\u6027\u6216\u5224\u65ad\u65f6\uff0c\u672a\u80fd\u5145\u5206\u8003\u8651\u6837\u672c\u91cf\u7684\u5f71\u54cd\u3002\u4ee5\u4e0b\u662f\u6269\u5c55\u5ffd\u89c6\u7684\u5177\u4f53\u8868\u73b0\u5f62\u5f0f\uff1a<\/p>\n\n\n\n<p><strong>\u57fa\u7840\u7387\u8c2c\u8bef<\/strong>\u00a0(Base rate fallacy) \u6216\u57fa\u7840\u7387\u5ffd\u89c6 (Base rate neglect)\uff1a\u503e\u5411\u4e8e\u5ffd\u7565\u4e00\u822c\u6027\u4fe1\u606f\uff0c\u800c\u53ea\u5173\u6ce8\u7279\u5b9a\u6848\u4f8b\u7684\u4fe1\u606f\uff0c\u5373\u4f7f\u4e00\u822c\u4fe1\u606f\u66f4\u4e3a\u91cd\u8981\u3002<\/p>\n\n\n\n<p><strong>\u540c\u60c5\u5fc3\u8870\u51cf<\/strong>\u00a0(Compassion fade)\uff1a\u5bf9\u5c11\u6570\u53ef\u8bc6\u522b\u7684\u53d7\u5bb3\u8005\u8868\u73b0\u51fa\u66f4\u5f3a\u7684\u540c\u60c5\u5fc3\uff0c\u800c\u5bf9\u5927\u91cf\u533f\u540d\u53d7\u5bb3\u8005\u7684\u540c\u60c5\u5fc3\u8f83\u5f31\u3002<\/p>\n\n\n\n<p><strong>\u5408\u53d6\u8c2c\u8bef<\/strong>\u00a0(Conjunction fallacy)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u5177\u4f53\u6761\u4ef6\u6bd4\u540c\u6837\u6761\u4ef6\u7684\u4e00\u822c\u7248\u672c\u66f4\u53ef\u80fd\u53d1\u751f\u3002<\/p>\n\n\n\n<p><strong>\u6301\u7eed\u65f6\u95f4\u5ffd\u89c6<\/strong>\u00a0(Duration neglect)\uff1a\u5728\u8bc4\u4f30\u4f53\u9a8c\u7684\u6574\u4f53\u4ef7\u503c\u65f6\uff0c\u5ffd\u89c6\u8be5\u4f53\u9a8c\u6301\u7eed\u7684\u65f6\u95f4\u3002<\/p>\n\n\n\n<p><strong>\u53cc\u66f2\u8d34\u73b0<\/strong>\u00a0(Hyperbolic discounting)\uff1a\u6307\u4e2a\u4f53\u503e\u5411\u4e8e\u66f4\u770b\u91cd\u5373\u65f6\u56de\u62a5\uff0c\u800c\u975e\u672a\u6765\u56de\u62a5\uff0c\u5bfc\u81f4\u51b3\u7b56\u968f\u65f6\u95f4\u63a8\u79fb\u53d8\u5f97\u4e0d\u4e00\u81f4\u3002\u5373\uff0c\u4eba\u4eec\u4eca\u5929\u505a\u51fa\u7684\u9009\u62e9\u53ef\u80fd\u662f\u672a\u6765\u7684\u81ea\u5df1\u6240\u4e0d\u613f\u610f\u505a\u51fa\u7684\uff0c\u5373\u4f7f\u5f53\u65f6\u7684\u63a8\u7406\u65b9\u5f0f\u76f8\u540c\u3002 \u4e5f\u79f0\u4e3a\u5f53\u524d\u65f6\u523b\u504f\u8bef (current moment bias) \u6216\u5f53\u4e0b\u504f\u8bef (present bias)\uff0c\u4e0e\u52a8\u6001\u4e0d\u4e00\u81f4\u6027 (Dynamic inconsistency) \u76f8\u5173\u3002\u4f8b\u5982\uff0c\u4e00\u9879\u7814\u7a76\u663e\u793a\uff0c\u5f53\u4e3a\u4e0b\u5468\u9009\u62e9\u98df\u7269\u65f6\uff0c74% \u7684\u53c2\u4e0e\u8005\u9009\u62e9\u4e86\u6c34\u679c\uff0c\u800c\u5f53\u4e3a\u5f53\u5929\u9009\u62e9\u98df\u7269\u65f6\uff0c70% \u7684\u4eba\u9009\u62e9\u4e86\u5de7\u514b\u529b\u3002<\/p>\n\n\n\n<p><strong>\u5bf9\u6837\u672c\u91cf\u7684\u4e0d\u654f\u611f<\/strong>\u00a0(Insensitivity to sample size)\uff1a\u503e\u5411\u4e8e\u4f4e\u4f30\u5c0f\u6837\u672c\u4e2d\u7684\u53d8\u5f02\u6027\u3002<\/p>\n\n\n\n<p><strong>\u5c11\u5373\u662f\u591a\u6548\u5e94<\/strong>\u00a0(Less-is-better effect)\uff1a\u5f53\u5206\u522b\u8bc4\u4f30\u65f6\uff0c\u4eba\u4eec\u503e\u5411\u4e8e\u66f4\u559c\u6b22\u8f83\u5c0f\u7684\u96c6\u5408\uff0c\u800c\u975e\u8f83\u5927\u7684\u96c6\u5408\uff0c\u4f46\u5728\u8054\u5408\u8bc4\u4f30\u65f6\u5219\u4e0d\u4f1a\u51fa\u73b0\u8fd9\u79cd\u60c5\u51b5\u3002<\/p>\n\n\n\n<p><strong>\u6982\u7387\u5ffd\u89c6<\/strong>\u00a0(Neglect of probability)\uff1a\u5728\u4e0d\u786e\u5b9a\u73af\u5883\u4e0b\u505a\u51b3\u7b56\u65f6\uff0c\u5b8c\u5168\u5ffd\u89c6\u6982\u7387\u3002<\/p>\n\n\n\n<p><strong>\u89c4\u6a21\u5ffd\u89c6<\/strong>\u00a0(Scope neglect) \u6216\u89c4\u6a21\u4e0d\u654f\u611f (Scope insensitivity)\uff1a\u5728\u8bc4\u4f30\u95ee\u9898\u65f6\uff0c\u503e\u5411\u4e8e\u5bf9\u95ee\u9898\u89c4\u6a21\u7684\u4e0d\u654f\u611f\u3002\u4f8b\u5982\uff0c\u4eba\u4eec\u613f\u610f\u652f\u4ed8\u76f8\u540c\u91d1\u989d\u6765\u62ef\u6551 2000 \u540d\u513f\u7ae5\u548c 20000 \u540d\u513f\u7ae5\u3002<\/p>\n\n\n\n<p><strong>\u96f6\u98ce\u9669\u504f\u8bef<\/strong>\u00a0(Zero-risk bias)\uff1a\u503e\u5411\u4e8e\u4f18\u5148\u9009\u62e9\u5c06\u4e00\u4e2a\u8f83\u5c0f\u7684\u98ce\u9669\u964d\u81f3\u96f6\uff0c\u800c\u975e\u5927\u5e45\u51cf\u5c11\u66f4\u5927\u7684\u98ce\u9669\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u9519\u8bef\u5148\u9a8c-False-priors\">\u9519\u8bef\u5148\u9a8c (False priors)<\/h2>\n\n\n\n<p>\u9519\u8bef\u5148\u9a8c\u662f\u6307\u4e2a\u4f53\u6700\u521d\u7684\u4fe1\u5ff5\u548c\u77e5\u8bc6\u5f71\u54cd\u4e86\u5bf9\u5ba2\u89c2\u4e8b\u5b9e\u7684\u516c\u6b63\u8bc4\u4f30\uff0c\u4ece\u800c\u5bfc\u81f4\u9519\u8bef\u7684\u7ed3\u8bba\u3002\u57fa\u4e8e\u9519\u8bef\u5148\u9a8c\u7684\u504f\u8bef\u5305\u62ec\uff1a<\/p>\n\n\n\n<p><strong>\u4ee3\u7406\u68c0\u6d4b\u504f\u8bef<\/strong>\u00a0(Agent detection bias)\uff1a\u503e\u5411\u4e8e\u5047\u8bbe\u67d0\u4e2a\u4e8b\u4ef6\u662f\u7531\u6709\u610f\u8bc6\u6216\u667a\u80fd\u7684\u4ee3\u7406\u6709\u610f\u4e3a\u4e4b\u7684\u3002<\/p>\n\n\n\n<p><strong>\u81ea\u52a8\u5316\u504f\u8bef<\/strong>\u00a0(Automation bias)\uff1a\u8fc7\u5ea6\u4f9d\u8d56\u81ea\u52a8\u5316\u7cfb\u7edf\uff0c\u5bfc\u81f4\u9519\u8bef\u7684\u81ea\u52a8\u5316\u4fe1\u606f\u8986\u76d6\u6b63\u786e\u7684\u51b3\u7b56\u3002<\/p>\n\n\n\n<p><strong>\u6027\u522b\u504f\u89c1<\/strong>\u00a0(Gender bias)\uff1a\u4e00\u7ec4\u5e7f\u6cdb\u5b58\u5728\u7684\u9690\u6027\u504f\u89c1\uff0c\u5bf9\u7279\u5b9a\u6027\u522b\u5b58\u5728\u6b67\u89c6\u3002\u4f8b\u5982\uff0c\u5047\u8bbe\u5973\u6027\u4e0d\u9002\u5408\u9700\u8981\u9ad8\u5ea6\u667a\u529b\u7684\u5de5\u4f5c\u3002 \u6216\u8005\u5728\u6ca1\u6709\u6027\u522b\u6307\u793a\u7684\u60c5\u51b5\u4e0b\uff0c\u9ed8\u8ba4\u5047\u8bbe\u67d0\u4eba\u6216\u67d0\u52a8\u7269\u4e3a\u7537\u6027\u3002<\/p>\n\n\n\n<p><strong>\u6027\u5174\u8da3\u8fc7\u5ea6\u611f\u77e5\u504f\u8bef<\/strong>\u00a0(Sexual overperception bias)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u4ed6\u4eba\u5bf9\u81ea\u5df1\u7684\u6027\u5174\u8da3\uff1b\u6027\u5174\u8da3\u4f4e\u4f30\u504f\u8bef (Sexual underperception bias)\u5219\u6307\u503e\u5411\u4e8e\u4f4e\u4f30\u8fd9\u79cd\u5174\u8da3\u3002<\/p>\n\n\n\n<p><strong>\u523b\u677f\u5370\u8c61<\/strong>\u00a0(Stereotyping)\uff1a\u5728\u7f3a\u4e4f\u5177\u4f53\u4fe1\u606f\u7684\u60c5\u51b5\u4e0b\uff0c\u9884\u8bbe\u67d0\u4e2a\u7fa4\u4f53\u7684\u6210\u5458\u5177\u6709\u7279\u5b9a\u7279\u5f81\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u6846\u67b6\u6548\u5e94-Framing-effect\">\u6846\u67b6\u6548\u5e94 (Framing effect)<\/h2>\n\n\n\n<p>\u6846\u67b6\u6548\u5e94\u6307\u4e2a\u4f53\u5bf9\u76f8\u540c\u4fe1\u606f\u7684\u89e3\u8bfb\u4f1a\u56e0\u4fe1\u606f\u7684\u5448\u73b0\u65b9\u5f0f\u4e0d\u540c\u800c\u5f97\u51fa\u4e0d\u540c\u7684\u7ed3\u8bba\u3002\u6846\u67b6\u6548\u5e94\u7684\u5177\u4f53\u8868\u73b0\u5305\u62ec\uff1a<\/p>\n\n\n\n<p><strong>\u5bf9\u6bd4\u6548\u5e94<\/strong>\u00a0(Contrast effect)\uff1a\u5f53\u67d0\u4e2a\u523a\u6fc0\u4e0e\u6700\u8fd1\u89c2\u5bdf\u5230\u7684\u5bf9\u6bd4\u5bf9\u8c61\u8fdb\u884c\u6bd4\u8f83\u65f6\uff0c\u5176\u611f\u77e5\u53ef\u80fd\u88ab\u589e\u5f3a\u6216\u524a\u5f31\u3002<\/p>\n\n\n\n<p><strong>\u8bf1\u9975\u6548\u5e94<\/strong>\u00a0(Decoy effect)\uff1a\u5f53\u9009\u9879 C \u88ab\u5f15\u5165\u65f6\uff0c\u4eba\u4eec\u5bf9\u9009\u9879 A \u6216 B \u7684\u504f\u597d\u4f1a\u503e\u5411\u4e8e B\uff0c\u800c C \u5728\u6240\u6709\u65b9\u9762\u90fd\u660e\u663e\u52a3\u4e8e B\uff0c\u5e76\u4e14\u5728\u67d0\u4e9b\u65b9\u9762\u52a3\u4e8e A\u3002<\/p>\n\n\n\n<p><strong>\u9ed8\u8ba4\u6548\u5e94<\/strong>\u00a0(Default effect)\uff1a\u5728\u591a\u4e2a\u9009\u9879\u4e2d\uff0c\u4eba\u4eec\u503e\u5411\u4e8e\u9009\u62e9\u9ed8\u8ba4\u9009\u9879\u3002<\/p>\n\n\n\n<p><strong>\u9762\u989d\u6548\u5e94<\/strong>\u00a0(Denomination effect)\uff1a\u5f53\u91d1\u94b1\u88ab\u5206\u6210\u8f83\u5c0f\u9762\u989d\uff08\u5982\u786c\u5e01\uff09\u65f6\uff0c\u4eba\u4eec\u66f4\u503e\u5411\u4e8e\u82b1\u8d39\u5b83\uff0c\u800c\u5f53\u4ee5\u5927\u9762\u989d\uff08\u5982\u7eb8\u5e01\uff09\u5448\u73b0\u65f6\uff0c\u5219\u66f4\u503e\u5411\u4e8e\u4fdd\u7559\u3002<\/p>\n\n\n\n<p><strong>\u533a\u5206\u504f\u8bef<\/strong>\u00a0(Distinction bias)\uff1a\u5f53\u540c\u65f6\u8bc4\u4f30\u4e24\u4e2a\u9009\u9879\u65f6\uff0c\u4eba\u4eec\u503e\u5411\u4e8e\u8ba4\u4e3a\u5b83\u4eec\u7684\u5dee\u5f02\u6bd4\u5355\u72ec\u8bc4\u4f30\u65f6\u66f4\u5927\u3002<\/p>\n\n\n\n<p><strong>\u9886\u57df\u5ffd\u89c6\u504f\u8bef<\/strong>\u00a0(Domain neglect bias)\uff1a\u5728\u89e3\u51b3\u8de8\u5b66\u79d1\u95ee\u9898\u65f6\uff0c\u4eba\u4eec\u503e\u5411\u4e8e\u5ffd\u89c6\u76f8\u5173\u9886\u57df\u7684\u77e5\u8bc6\u3002<\/p>\n\n\n\n<p><strong>\u60c5\u5883\u5ffd\u89c6\u504f\u8bef<\/strong>\u00a0(Context neglect bias)\uff1a\u5728\u5904\u7406\u6280\u672f\u6311\u6218\u65f6\uff0c\u503e\u5411\u4e8e\u5ffd\u89c6\u5176\u6240\u5904\u7684\u4eba\u7c7b\u60c5\u5883\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u903b\u8f91\u8c2c\u8bef-Logical-fallacy\">\u903b\u8f91\u8c2c\u8bef (Logical fallacy)<\/h2>\n\n\n\n<p><strong>\u4f2f\u514b\u68ee\u6096\u8bba<\/strong>\u00a0(Berkson\u2019s paradox)\uff1a\u503e\u5411\u4e8e\u8bef\u89e3\u6d89\u53ca\u6761\u4ef6\u6982\u7387\u7684\u7edf\u8ba1\u5b9e\u9a8c\u3002<\/p>\n\n\n\n<p><strong>\u627f\u8bfa\u5347\u7ea7\u8c2c\u8bef<\/strong>\u00a0(Escalation of commitment)\uff0c\u53c8\u79f0\u975e\u7406\u6027\u5347\u7ea7\u6216\u6c89\u6ca1\u6210\u672c\u8c2c\u8bef (Sunk cost fallacy)\uff1a\u4eba\u4eec\u57fa\u4e8e\u5148\u524d\u7684\u6295\u8d44\u6765\u4e3a\u51b3\u7b56\u589e\u52a0\u6295\u5165\uff0c\u5373\u4f7f\u6709\u65b0\u7684\u8bc1\u636e\u8868\u660e\u8be5\u51b3\u7b56\u53ef\u80fd\u662f\u9519\u8bef\u7684\u3002<\/p>\n\n\n\n<p><strong>G.I. Joe\u8c2c\u8bef<\/strong>\u00a0(G. I. Joe fallacy)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u4e86\u89e3\u8ba4\u77e5\u504f\u8bef\u5c31\u8db3\u4ee5\u514b\u670d\u5b83\u3002<\/p>\n\n\n\n<p><strong>\u8d4c\u5f92\u8c2c\u8bef<\/strong>\u00a0(Gambler\u2019s fallacy)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u672a\u6765\u7684\u6982\u7387\u4f1a\u53d7\u5230\u8fc7\u53bb\u4e8b\u4ef6\u7684\u5f71\u54cd\uff0c\u5b9e\u9645\u4e0a\u8fd9\u4e9b\u6982\u7387\u662f\u6ca1\u6709\u6539\u53d8\u7684\u3002\u8be5\u8c2c\u8bef\u6e90\u4e8e\u5bf9\u5927\u6570\u6cd5\u5219\u7684\u9519\u8bef\u7406\u89e3\u3002\u4f8b\u5982\uff1a\u201c\u6211\u8fde\u7eed\u4e94\u6b21\u6295\u63b7\u786c\u5e01\u90fd\u662f\u6b63\u9762\uff0c\u6240\u4ee5\u7b2c\u516d\u6b21\u6295\u63b7\u51fa\u73b0\u53cd\u9762\u7684\u6982\u7387\u6bd4\u6b63\u9762\u5927\u5f97\u591a\u3002\u201d<\/p>\n\n\n\n<p><strong>\u70ed\u624b\u8c2c\u8bef<\/strong>\u00a0(Hot-hand fallacy)\uff0c\u4e5f\u79f0\u70ed\u624b\u73b0\u8c61 (hot hand phenomenon) \u6216\u70ed\u624b\u6548\u5e94 (hot hand)\uff1a\u8ba4\u4e3a\u7ecf\u5386\u8fc7\u968f\u673a\u4e8b\u4ef6\u6210\u529f\u7684\u4eba\u5728\u63a5\u4e0b\u6765\u7684\u5c1d\u8bd5\u4e2d\u66f4\u53ef\u80fd\u7ee7\u7eed\u6210\u529f\u3002<\/p>\n\n\n\n<p><strong>\u8ba1\u5212\u7ee7\u7eed\u504f\u8bef<\/strong>\u00a0(Plan continuation bias)\uff1a\u672a\u80fd\u8ba4\u8bc6\u5230\u539f\u6709\u7684\u884c\u52a8\u8ba1\u5212\u5df2\u4e0d\u518d\u9002\u7528\u4e8e\u53d8\u5316\u7684\u60c5\u5883\u6216\u4e0e\u9884\u671f\u4e0d\u540c\u7684\u60c5\u5883\u3002<\/p>\n\n\n\n<p><strong>\u5b50\u52a0\u6027\u6548\u5e94<\/strong>\u00a0(Subadditivity effect)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u6574\u4f53\u7684\u6982\u7387\u4f4e\u4e8e\u5404\u4e2a\u90e8\u5206\u7684\u6982\u7387\u4e4b\u548c\u3002<\/p>\n\n\n\n<p><strong>\u8282\u7701\u65f6\u95f4\u504f\u8bef<\/strong>\u00a0(Time-saving bias)\uff1a\u4f4e\u4f30\u4ece\u76f8\u5bf9\u8f83\u4f4e\u901f\u5ea6\u589e\u52a0\uff08\u6216\u51cf\u5c11\uff09\u65f6\u8282\u7701\u7684\u65f6\u95f4\uff0c\u4e14\u9ad8\u4f30\u4ece\u76f8\u5bf9\u8f83\u9ad8\u901f\u5ea6\u589e\u52a0\uff08\u6216\u51cf\u5c11\uff09\u65f6\u8282\u7701\u7684\u65f6\u95f4\u3002<\/p>\n\n\n\n<p><strong>\u96f6\u548c\u504f\u8bef<\/strong>\u00a0(Zero-sum bias)\uff1a\u9519\u8bef\u5730\u8ba4\u4e3a\u67d0\u79cd\u60c5\u51b5\u7c7b\u4f3c\u96f6\u548c\u535a\u5f08\uff08\u5373\uff0c\u4e00\u4e2a\u4eba\u7684\u83b7\u76ca\u5fc5\u5b9a\u4ee5\u53e6\u4e00\u4e2a\u4eba\u7684\u635f\u5931\u4e3a\u4ee3\u4ef7\uff09\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u524d\u666f\u7406\u8bba-Prospect-theory\">\u524d\u666f\u7406\u8bba (Prospect theory)<\/h2>\n\n\n\n<p>\u4ee5\u4e0b\u662f\u4e0e\u524d\u666f\u7406\u8bba\u76f8\u5173\u7684\u5185\u5bb9\uff1a<\/p>\n\n\n\n<p><strong>\u6a21\u7cca\u6548\u5e94<\/strong>\u00a0(Ambiguity effect)\uff1a\u503e\u5411\u4e8e\u56de\u907f\u90a3\u4e9b\u65e0\u6cd5\u786e\u5b9a\u6709\u5229\u7ed3\u679c\u6982\u7387\u7684\u9009\u9879\u3002<\/p>\n\n\n\n<p><strong>\u5904\u7f6e\u6548\u5e94<\/strong>\u00a0(Disposition effect)\uff1a\u503e\u5411\u4e8e\u51fa\u552e\u5df2\u7ecf\u589e\u503c\u7684\u8d44\u4ea7\uff0c\u800c\u6297\u62d2\u51fa\u552e\u5df2\u7ecf\u8d2c\u503c\u7684\u8d44\u4ea7\u3002<\/p>\n\n\n\n<p><strong>\u6050\u60e7\u56de\u907f<\/strong>\u00a0(Dread aversion)\uff1a\u5c31\u50cf\u635f\u5931\u5bf9\u60c5\u611f\u7684\u5f71\u54cd\u662f\u6536\u76ca\u7684\u4e24\u500d\uff0c\u6050\u60e7\u5bf9\u60c5\u611f\u7684\u5f71\u54cd\u4e5f\u662f\u4eab\u4e50\u7684\u4e24\u500d\u3002<\/p>\n\n\n\n<p><strong>\u7980\u8d4b\u6548\u5e94<\/strong>\u00a0(Endowment effect)\uff1a\u4eba\u4eec\u503e\u5411\u4e8e\u8981\u6c42\u6bd4\u4ed6\u4eec\u613f\u610f\u652f\u4ed8\u7684\u66f4\u9ad8\u4ef7\u683c\u6765\u653e\u5f03\u4e00\u4ef6\u7269\u54c1\u3002<\/p>\n\n\n\n<p><strong>\u635f\u5931\u538c\u6076<\/strong>\u00a0(Loss aversion)\uff1a\u653e\u5f03\u67d0\u7269\u7684\u611f\u77e5\u635f\u5931\u6bd4\u83b7\u5f97\u8be5\u7269\u7684\u611f\u77e5\u6536\u76ca\u8981\u5927\u3002\u53c2\u89c1\u6c89\u6ca1\u6210\u672c\u8c2c\u8bef\u3002<\/p>\n\n\n\n<p><strong>\u4f2a\u786e\u5b9a\u6548\u5e94<\/strong>\u00a0(Pseudocertainty effect)\uff1a\u5982\u679c\u9884\u671f\u7684\u7ed3\u679c\u662f\u6b63\u9762\u7684\uff0c\u4eba\u4eec\u503e\u5411\u4e8e\u505a\u51fa\u98ce\u9669\u538c\u6076\u7684\u9009\u62e9\uff1b\u4f46\u4e3a\u4e86\u907f\u514d\u8d1f\u9762\u7ed3\u679c\uff0c\u5219\u503e\u5411\u4e8e\u505a\u51fa\u98ce\u9669\u5bfb\u6c42\u7684\u9009\u62e9\u3002<\/p>\n\n\n\n<p><strong>\u73b0\u72b6\u504f\u8bef<\/strong>\u00a0(Status quo bias)\uff1a\u503e\u5411\u4e8e\u504f\u597d\u4e8b\u7269\u4fdd\u6301\u76f8\u5bf9\u4e0d\u53d8\u3002<\/p>\n\n\n\n<p><strong>\u7cfb\u7edf\u6b63\u5f53\u5316<\/strong>\u00a0(System justification)\uff1a\u503e\u5411\u4e8e\u634d\u536b\u548c\u652f\u6301\u73b0\u72b6\u3002\u73b0\u6709\u7684\u793e\u4f1a\u3001\u7ecf\u6d4e\u548c\u653f\u6cbb\u5b89\u6392\u5f80\u5f80\u88ab\u504f\u597d\uff0c\u800c\u66ff\u4ee3\u65b9\u6848\u5219\u88ab\u8d2c\u4f4e\uff0c\u6709\u65f6\u751a\u81f3\u4ee5\u727a\u7272\u4e2a\u4eba\u548c\u96c6\u4f53\u5229\u76ca\u4e3a\u4ee3\u4ef7\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u81ea\u6211\u8bc4\u4f30-Self-assessment\">\u81ea\u6211\u8bc4\u4f30 (Self-assessment)<\/h2>\n\n\n\n<p><strong>\u9093\u5b81-\u514b\u9c81\u683c\u6548\u5e94<\/strong>\u00a0(Dunning\u2013Kruger effect)\uff1a\u4e0d\u719f\u7ec3\u7684\u4e2a\u4f53\u503e\u5411\u4e8e\u9ad8\u4f30\u81ea\u5df1\u7684\u80fd\u529b\uff0c\u800c\u4e13\u5bb6\u5219\u503e\u5411\u4e8e\u4f4e\u4f30\u81ea\u5df1\u7684\u80fd\u529b\u3002<\/p>\n\n\n\n<p><strong>\u51b7\u70ed\u5171\u60c5\u5dee\u8ddd<\/strong>\u00a0(Hot-cold empathy gap)\uff1a\u503e\u5411\u4e8e\u4f4e\u4f30\u751f\u7406\u9a71\u52a8\u56e0\u7d20\u5bf9\u4e2a\u4eba\u6001\u5ea6\u3001\u504f\u597d\u548c\u884c\u4e3a\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n<p><strong>\u96be\u6613\u6548\u5e94<\/strong>\u00a0(Hard\u2013easy effect)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u81ea\u5df1\u5b8c\u6210\u56f0\u96be\u4efb\u52a1\u7684\u80fd\u529b\uff0c\u4f4e\u4f30\u81ea\u5df1\u5b8c\u6210\u7b80\u5355\u4efb\u52a1\u7684\u80fd\u529b\u3002<\/p>\n\n\n\n<p><strong>\u89e3\u91ca\u6df1\u5ea6\u9519\u89c9<\/strong>\u00a0(Illusion of explanatory depth)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u81ea\u5df1\u5bf9\u67d0\u4e2a\u8bdd\u9898\u7684\u7406\u89e3\u8fdc\u6bd4\u5b9e\u9645\u7406\u89e3\u5f97\u66f4\u6df1\u3002\u8fd9\u4e00\u6548\u5e94\u5bf9\u89e3\u91ca\u6027\u77e5\u8bc6\u6700\u4e3a\u660e\u663e\uff0c\u800c\u4eba\u4eec\u901a\u5e38\u5728\u7a0b\u5e8f\u6027\u3001\u53d9\u4e8b\u6027\u6216\u4e8b\u5b9e\u6027\u77e5\u8bc6\u7684\u81ea\u6211\u8bc4\u4f30\u4e0a\u8868\u73b0\u5f97\u66f4\u597d\u3002<\/p>\n\n\n\n<p><strong>\u5192\u5145\u8005\u7efc\u5408\u75c7<\/strong>\u00a0(Impostor Syndrome)\uff1a\u4e2a\u4f53\u6000\u7591\u81ea\u5df1\u7684\u6280\u80fd\u3001\u624d\u534e\u6216\u6210\u5c31\uff0c\u5e76\u4e14\u6709\u4e00\u79cd\u6301\u7eed\u7684\u5185\u5fc3\u6050\u60e7\uff0c\u62c5\u5fc3\u81ea\u5df1\u4f1a\u88ab\u63ed\u9732\u4e3a\u4e00\u4e2a\u9a97\u5b50\u3002\u4e5f\u79f0\u4e3a\u5192\u5145\u8005\u73b0\u8c61\u3002<\/p>\n\n\n\n<p><strong>\u5ba2\u89c2\u6027\u9519\u89c9<\/strong>\u00a0(Objectivity illusion)\uff1a\u4eba\u4eec\u503e\u5411\u4e8e\u8ba4\u4e3a\u81ea\u5df1\u6bd4\u4ed6\u4eba\u66f4\u5ba2\u89c2\u548c\u516c\u6b63\u3002\u8fd9\u79cd\u504f\u8bef\u4e5f\u9002\u7528\u4e8e\u81ea\u5df1\u2014\u2014\u4eba\u4eec\u80fd\u591f\u5bdf\u89c9\u5230\u4ed6\u4eba\u53d7\u5ba2\u89c2\u6027\u9519\u89c9\u7684\u5f71\u54cd\uff0c\u5374\u65e0\u6cd5\u770b\u5230\u81ea\u5df1\u5b58\u5728\u8fd9\u4e00\u504f\u8bef\u3002\u53c2\u89c1\u504f\u8bef\u76f2\u70b9 (Bias blind spot)\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u771f\u7406\u5224\u65ad-Truth-judgment\">\u771f\u7406\u5224\u65ad (Truth judgment)<\/h2>\n\n\n\n<p><strong>\u4fe1\u5ff5\u504f\u8bef<\/strong>\u00a0(Belief bias)\uff1a\u5f53\u4e00\u4e2a\u4eba\u5bf9\u8bba\u8bc1\u7684\u903b\u8f91\u5f3a\u5ea6\u7684\u8bc4\u4f30\u53d7\u5230\u7ed3\u8bba\u53ef\u4fe1\u5ea6\u7684\u504f\u5f71\u54cd\u65f6\uff0c\u51fa\u73b0\u7684\u6548\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u865a\u5047\u771f\u7406\u6548\u5e94<\/strong>\u00a0(Illusory truth effect)\uff1a\u503e\u5411\u4e8e\u76f8\u4fe1\u67d0\u4e2a\u9648\u8ff0\u4e3a\u771f\uff0c\u7279\u522b\u662f\u5f53\u5b83\u66f4\u5bb9\u6613\u5904\u7406\u6216\u88ab\u591a\u6b21\u9648\u8ff0\u65f6\uff0c\u800c\u4e0d\u7ba1\u5176\u5b9e\u9645\u771f\u5b9e\u6027\u3002\u8fd9\u4e9b\u662f\u201c\u771f\u5b9e\u6027\u201d (truthiness)\u7684\u7279\u5b9a\u8868\u73b0\u5f62\u5f0f\u3002<\/p>\n\n\n\n<p><strong>\u62bc\u97f5\u5373\u7406\u7531\u6548\u5e94<\/strong>\u00a0(Rhyme as reason effect)\uff1a\u5f53\u62bc\u97f5\u7684\u9648\u8ff0\u88ab\u8ba4\u4e3a\u66f4\u771f\u5b9e\u65f6\uff0c\u51fa\u73b0\u7684\u6548\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u4e3b\u89c2\u9a8c\u8bc1<\/strong>\u00a0(Subjective validation)\uff1a\u5f53\u67d0\u4e2a\u9648\u8ff0\u7b26\u5408\u4e3b\u4f53\u7684\u4fe1\u5ff5\u65f6\uff0c\u4e2a\u4f53\u503e\u5411\u4e8e\u8ba4\u4e3a\u8be5\u9648\u8ff0\u4e3a\u771f\u3002\u6b64\u6548\u5e94\u8fd8\u4f1a\u8d4b\u4e88\u5de7\u5408\u4e4b\u95f4\u7684\u4e3b\u89c2\u770b\u6cd5\u548c\u8fde\u63a5\u3002\u53c2\u89c1\u786e\u8ba4\u504f\u8bef\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u5176\u4ed6-Other\">\u5176\u4ed6 (Other)<\/h2>\n\n\n\n<p><strong>\u884c\u52a8\u504f\u8bef<\/strong>\u00a0(Action bias)\uff1a\u5f53\u9762\u5bf9\u95ee\u9898\u65f6\uff0c\u503e\u5411\u4e8e\u91c7\u53d6\u884c\u52a8\uff0c\u5373\u4f7f\u4e0d\u884c\u52a8\u53ef\u80fd\u66f4\u6709\u6548\uff0c\u6216\u8005\u5f53\u6ca1\u6709\u660e\u663e\u95ee\u9898\u65f6\u4e5f\u4f1a\u91c7\u53d6\u884c\u52a8\u3002<\/p>\n\n\n\n<p><strong>\u52a0\u6cd5\u504f\u8bef<\/strong>\u00a0(Additive bias)\uff1a\u503e\u5411\u4e8e\u901a\u8fc7\u52a0\u6cd5\u6765\u89e3\u51b3\u95ee\u9898\uff0c\u5373\u4f7f\u51cf\u6cd5\u53ef\u80fd\u662f\u66f4\u597d\u7684\u65b9\u6cd5\u3002<\/p>\n\n\n\n<p><strong>\u5c5e\u6027\u66ff\u4ee3<\/strong>\u00a0(Attribute substitution)\uff1a\u5f53\u9700\u8981\u5bf9\u4e00\u4e2a\u8ba1\u7b97\u590d\u6742\u7684\u76ee\u6807\u5c5e\u6027\u505a\u51fa\u5224\u65ad\u65f6\uff0c\u4e2a\u4f53\u4f1a\u7528\u66f4\u5bb9\u6613\u8ba1\u7b97\u7684\u542f\u53d1\u5f0f\u5c5e\u6027\u6765\u66ff\u4ee3\u3002\u8fd9\u4e2a\u66ff\u4ee3\u8fc7\u7a0b\u88ab\u8ba4\u4e3a\u53d1\u751f\u5728\u81ea\u52a8\u7684\u76f4\u89c9\u5224\u65ad\u7cfb\u7edf\u4e2d\uff0c\u800c\u4e0d\u662f\u66f4\u81ea\u89c9\u7684\u53cd\u601d\u7cfb\u7edf\u4e2d\u3002<\/p>\n\n\n\n<p><strong>\u77e5\u8bc6\u7684\u8bc5\u5492<\/strong>\u00a0(Curse of knowledge)\uff1a\u5f53\u4fe1\u606f\u66f4\u4e30\u5bcc\u7684\u4eba\u53d1\u73b0\u5f88\u96be\u4ece\u4fe1\u606f\u8f83\u5c11\u7684\u4eba\u7684\u89c6\u89d2\u601d\u8003\u95ee\u9898\u65f6\u53d1\u751f\u7684\u73b0\u8c61\u3002<\/p>\n\n\n\n<p><strong>\u8870\u9000\u4e3b\u4e49<\/strong>\u00a0(Declinism)\uff1a\u503e\u5411\u4e8e\u5c06\u8fc7\u53bb\u89c6\u4e3a\u79ef\u6781\u7684\uff08\u7f8e\u597d\u56de\u5fc6\uff09\uff0c\u800c\u5c06\u672a\u6765\u89c6\u4e3a\u6d88\u6781\u7684\u3002<\/p>\n\n\n\n<p><strong>\u5386\u53f2\u7ec8\u7ed3\u9519\u89c9<\/strong>\u00a0(End-of-history illusion)\uff1a\u4e00\u79cd\u5e74\u9f84\u65e0\u5173\u7684\u4fe1\u5ff5\uff0c\u5373\u8ba4\u4e3a\u81ea\u5df1\u5728\u672a\u6765\u7684\u53d8\u5316\u4f1a\u6bd4\u8fc7\u53bb\u7684\u53d8\u5316\u5c0f\u3002<\/p>\n\n\n\n<p><strong>\u5938\u5927\u9884\u671f<\/strong>\u00a0(Exaggerated expectation)\uff1a\u503e\u5411\u4e8e\u9884\u671f\u6216\u9884\u6d4b\u6bd4\u5b9e\u9645\u53d1\u751f\u7684\u7ed3\u679c\u66f4\u6781\u7aef\u7684\u540e\u679c\u3002<\/p>\n\n\n\n<p><strong>\u5f62\u5f0f\u529f\u80fd\u5f52\u56e0\u504f\u8bef<\/strong>\u00a0(Form function attribution bias)\uff1a\u5728\u4eba\u7c7b\u4e0e\u673a\u5668\u4eba\u4e92\u52a8\u4e2d\uff0c\u503e\u5411\u4e8e\u5728\u4e0e\u673a\u5668\u4eba\u4e92\u52a8\u65f6\u72af\u7cfb\u7edf\u6027\u9519\u8bef\u3002\u4eba\u4eec\u53ef\u80fd\u4f1a\u6839\u636e\u673a\u5668\u4eba\u7684\u5916\u89c2\uff08\u5f62\u5f0f\uff09\u6765\u8bbe\u5b9a\u4ed6\u4eec\u7684\u671f\u671b\u548c\u611f\u77e5\uff0c\u8d4b\u4e88\u4e0d\u4e00\u5b9a\u53cd\u6620\u673a\u5668\u771f\u5b9e\u529f\u80fd\u7684\u529f\u80fd\u3002<\/p>\n\n\n\n<p><strong>\u57fa\u7840\u75db\u82e6\u504f\u8bef<\/strong>\u00a0(Fundamental pain bias)\uff1a\u4eba\u4eec\u503e\u5411\u4e8e\u8ba4\u4e3a\u4ed6\u4eec\u51c6\u786e\u62a5\u544a\u4e86\u81ea\u5df1\u7684\u75bc\u75db\u6c34\u5e73\uff0c\u540c\u65f6\u62b1\u6709\u76f8\u53cd\u7684\u4fe1\u5ff5\uff0c\u5373\u4ed6\u4eba\u5938\u5927\u4e86\u75db\u82e6\u7684\u7a0b\u5ea6\u3002<\/p>\n\n\n\n<p><strong>\u4eab\u4e50\u56de\u5fc6\u504f\u8bef<\/strong>\u00a0(Hedonic recall bias)\uff1a\u5bf9\u81ea\u5df1\u7684\u85aa\u8d44\u6ee1\u610f\u7684\u4eba\u503e\u5411\u4e8e\u9ad8\u4f30\u81ea\u5df1\u5b9e\u9645\u7684\u6536\u5165\uff0c\u800c\u4e0d\u6ee1\u610f\u7684\u4eba\u5219\u503e\u5411\u4e8e\u4f4e\u4f30\u81ea\u5df1\u7684\u6536\u5165\u3002<\/p>\n\n\n\n<p><strong>\u540e\u89c1\u504f\u8bef<\/strong>\u00a0(Hindsight bias)\uff1a\u6709\u65f6\u88ab\u79f0\u4e3a\u201c\u6211\u65e9\u5c31\u77e5\u9053\u4e86\u201d\u6548\u5e94\uff0c\u6216\u201c\u540e\u89c1\u4e4b\u660e\u662f 20\/20\u201d\u6548\u5e94\uff0c\u6307\u4eba\u4eec\u503e\u5411\u4e8e\u8ba4\u4e3a\u8fc7\u53bb\u7684\u4e8b\u4ef6\u5728\u53d1\u751f\u4e4b\u524d\u662f\u53ef\u4ee5\u9884\u6d4b\u7684\u3002<\/p>\n\n\n\n<p><strong>\u5f71\u54cd\u504f\u8bef<\/strong>\u00a0(Impact bias)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u672a\u6765\u60c5\u7eea\u72b6\u6001\u7684\u6301\u7eed\u65f6\u95f4\u6216\u5f3a\u5ea6\u3002<\/p>\n\n\n\n<p><strong>\u4fe1\u606f\u504f\u8bef<\/strong>\u00a0(Information bias)\uff1a\u5373\u4f7f\u4fe1\u606f\u65e0\u6cd5\u5f71\u54cd\u884c\u52a8\uff0c\u4eba\u4eec\u4ecd\u503e\u5411\u4e8e\u53bb\u5bfb\u6c42\u4fe1\u606f\u3002<\/p>\n\n\n\n<p><strong>\u5185\u611f\u504f\u8bef\u6216\u9965\u997f\u6cd5\u5b98\u6548\u5e94<\/strong>\u00a0(Interoceptive bias or Hungry judge effect)\uff1a\u8eab\u4f53\u81ea\u8eab\u7684\u611f\u89c9\u8f93\u5165\u503e\u5411\u4e8e\u5f71\u54cd\u4eba\u4eec\u5bf9\u5916\u90e8\u65e0\u5173\u60c5\u5883\u7684\u5224\u65ad\u3002\u4f8b\u5982\uff0c\u5ba1\u5047\u91ca\u7684\u6cd5\u5b98\u5728\u8fdb\u98df\u548c\u4f11\u606f\u65f6\u901a\u5e38\u66f4\u5bbd\u5bb9\u3002<\/p>\n\n\n\n<p><strong>\u8d27\u5e01\u9519\u89c9<\/strong>\u00a0(Money illusion)\uff1a\u503e\u5411\u4e8e\u5173\u6ce8\u8d27\u5e01\u7684\u9762\u503c\uff08\u540d\u4e49\u4ef7\u503c\uff09\uff0c\u800c\u975e\u5176\u8d2d\u4e70\u529b\u7684\u5b9e\u9645\u4ef7\u503c\u3002<\/p>\n\n\n\n<p><strong>\u9053\u5fb7\u51ed\u8bc1\u6548\u5e94<\/strong>\u00a0(Moral credential effect)\uff1a\u5f53\u4e00\u4e2a\u4eba\u505a\u4e86\u597d\u4e8b\u540e\uff0c\u4ed6\u4eec\u4f1a\u8ba4\u4e3a\u81ea\u5df1\u53ef\u4ee5\u5728\u672a\u6765\u505a\u51fa\u4e0d\u90a3\u4e48\u597d\u6216\u8005\u4e0d\u9053\u5fb7\u7684\u884c\u4e3a\u3002<\/p>\n\n\n\n<p><strong>\u975e\u9002\u5e94\u6027\u9009\u62e9\u5207\u6362<\/strong>\u00a0(Non-adaptive choice switching)\uff1a\u5728\u7ecf\u5386\u4e00\u6b21\u7cdf\u7cd5\u7684\u51b3\u7b56\u7ed3\u679c\u540e\uff0c\u9762\u5bf9\u540c\u6837\u7684\u51b3\u7b56\u95ee\u9898\u65f6\uff0c\u503e\u5411\u4e8e\u56de\u907f\u4e4b\u524d\u505a\u51fa\u7684\u9009\u62e9\uff0c\u5373\u4f7f\u8be5\u9009\u62e9\u662f\u6700\u4f18\u7684\u3002\u4e5f\u79f0\u4e3a\u201c\u5403\u4e00\u5811\u957f\u4e00\u667a\u201d\u6548\u5e94\u6216\u201c\u70eb\u624b\u6548\u5e94\u201d\u3002<\/p>\n\n\n\n<p><strong>\u4ec5\u56e0\u63a5\u89e6\u6548\u5e94<\/strong>\u00a0(Mere exposure effect) \u6216\u719f\u6089\u6027\u539f\u5219 (familiarity principle)\uff08\u5728\u793e\u4f1a\u5fc3\u7406\u5b66\u4e2d\uff09\uff1a\u503e\u5411\u4e8e\u56e0\u4e0e\u67d0\u4e8b\u7269\u7684\u719f\u6089\u800c\u5bf9\u5176\u8868\u73b0\u51fa\u8fc7\u5ea6\u7684\u559c\u7231\u3002<\/p>\n\n\n\n<p><strong>\u9057\u6f0f\u504f\u8bef<\/strong>\u00a0(Omission bias)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u6709\u5bb3\u7684\u884c\u4e3a\uff08\u4f5c\u4e3a\u4e3b\u52a8\u884c\u4e3a\uff09\u6bd4\u540c\u6837\u6709\u5bb3\u7684\u4e0d\u4f5c\u4e3a\uff08\u9057\u6f0f\u884c\u4e3a\uff09\u66f4\u7cdf\u7cd5\uff0c\u6216\u8005\u66f4\u4e0d\u9053\u5fb7\u3002<\/p>\n\n\n\n<p><strong>\u4e50\u89c2\u504f\u8bef<\/strong>\u00a0(Optimism bias)\uff1a\u503e\u5411\u4e8e\u8fc7\u5ea6\u4e50\u89c2\uff0c\u6781\u5927\u5730\u4f4e\u4f30\u4e0d\u826f\u7ed3\u679c\u53d1\u751f\u7684\u6982\u7387\uff0c\u5e76\u9ad8\u4f30\u6709\u5229\u548c\u4ee4\u4eba\u6109\u5feb\u7684\u7ed3\u679c\u53d1\u751f\u7684\u6982\u7387\u3002\u53c2\u89c1\u613f\u671b\u601d\u7ef4\u3001\u6548\u4ef7\u6548\u5e94\u3001\u79ef\u6781\u7ed3\u679c\u504f\u8bef\uff0c\u5e76\u4e0e\u60b2\u89c2\u504f\u8bef\u4f5c\u5bf9\u6bd4\u3002<\/p>\n\n\n\n<p><strong>\u9e35\u9e1f\u6548\u5e94<\/strong>\u00a0(Ostrich effect)\uff1a\u5ffd\u89c6\u660e\u663e\u7684\u8d1f\u9762\u60c5\u51b5\u3002<\/p>\n\n\n\n<p><strong>\u7ed3\u679c\u504f\u8bef<\/strong>\u00a0(Outcome bias)\uff1a\u503e\u5411\u4e8e\u6839\u636e\u6700\u7ec8\u7ed3\u679c\u6765\u5224\u65ad\u51b3\u7b56\uff0c\u800c\u4e0d\u662f\u6839\u636e\u505a\u51fa\u51b3\u7b56\u65f6\u7684\u8d28\u91cf\u3002<\/p>\n\n\n\n<p><strong>\u60b2\u89c2\u504f\u8bef<\/strong>\u00a0(Pessimism bias)\uff1a\u67d0\u4e9b\u4eba\uff0c\u5c24\u5176\u662f\u6291\u90c1\u75c7\u60a3\u8005\uff0c\u503e\u5411\u4e8e\u9ad8\u4f30\u8d1f\u9762\u4e8b\u4ef6\u53d1\u751f\u7684\u53ef\u80fd\u6027\u3002 \uff08\u4e0e\u4e50\u89c2\u504f\u8bef\u5bf9\u6bd4\u3002\uff09<\/p>\n\n\n\n<p><strong>\u5f53\u524d\u504f\u8bef<\/strong>\u00a0(Present bias)\uff1a\u4eba\u4eec\u5728\u8003\u8651\u4e24\u79cd\u672a\u6765\u65f6\u523b\u4e4b\u95f4\u7684\u6743\u8861\u65f6\uff0c\u503e\u5411\u4e8e\u66f4\u91cd\u89c6\u4e0e\u5f53\u524d\u65f6\u95f4\u66f4\u63a5\u8fd1\u7684\u56de\u62a5\u3002<\/p>\n\n\n\n<p><strong>\u690d\u7269\u5931\u660e<\/strong>\u00a0(Plant blindness)\uff1a\u503e\u5411\u4e8e\u5ffd\u89c6\u5468\u56f4\u73af\u5883\u4e2d\u7684\u690d\u7269\uff0c\u5e76\u672a\u80fd\u610f\u8bc6\u5230\u548c\u6b23\u8d4f\u690d\u7269\u5bf9\u5730\u7403\u751f\u547d\u7684\u4f5c\u7528\u3002<\/p>\n\n\n\n<p><strong>\u9884\u9632\u504f\u8bef<\/strong>\u00a0(Prevention bias)\uff1a\u5728\u6295\u8d44\u4fdd\u62a4\u98ce\u9669\u65f6\uff0c\u51b3\u7b56\u8005\u503e\u5411\u4e8e\u8ba4\u4e3a\u82b1\u8d39\u4e00\u7f8e\u5143\u8fdb\u884c\u9884\u9632\u80fd\u5e26\u6765\u6bd4\u82b1\u8d39\u4e00\u7f8e\u5143\u7528\u4e8e\u53ca\u65f6\u68c0\u6d4b\u548c\u54cd\u5e94\u66f4\u591a\u7684\u5b89\u5168\u6027\uff0c\u5373\u4f7f\u8fd9\u4e24\u8005\u7684\u6548\u679c\u76f8\u7b49\u3002<\/p>\n\n\n\n<p><strong>\u6982\u7387\u5339\u914d<\/strong>\u00a0(Probability matching)\uff1a\u5728\u968f\u673a\u60c5\u5883\u4e2d\uff0c\u9009\u62e9\u7684\u6982\u7387\u4e0e\u5956\u52b1\u7684\u6982\u7387\u5339\u914d\u4e0d\u5f53\u3002<\/p>\n\n\n\n<p><strong>\u4eb2\u521b\u65b0\u504f\u8bef<\/strong>\u00a0(Pro-innovation bias)\uff1a\u5bf9\u53d1\u660e\u6216\u521b\u65b0\u5728\u793e\u4f1a\u4e2d\u7684\u6709\u7528\u6027\u8fc7\u4e8e\u4e50\u89c2\uff0c\u901a\u5e38\u5ffd\u89c6\u5176\u5c40\u9650\u6027\u548c\u5f31\u70b9\u3002<\/p>\n\n\n\n<p><strong>\u6295\u5c04\u504f\u8bef<\/strong>\u00a0(Projection bias)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u672a\u6765\u7684\u81ea\u5df1\u4f1a\u4e0e\u5f53\u524d\u7684\u81ea\u5df1\u6709\u591a\u5c11\u76f8\u540c\u7684\u504f\u597d\u3001\u601d\u60f3\u548c\u4ef7\u503c\u89c2\uff0c\u4ece\u800c\u5bfc\u81f4\u6b21\u4f18\u9009\u62e9\u3002<\/p>\n\n\n\n<p><strong>\u6bd4\u4f8b\u504f\u8bef<\/strong>\u00a0(Proportionality bias)\uff1a\u6211\u4eec\u5929\u751f\u503e\u5411\u4e8e\u8ba4\u4e3a\u91cd\u5927\u4e8b\u4ef6\u6709\u91cd\u5927\u7684\u539f\u56e0\uff0c\u8fd9\u4e5f\u53ef\u4ee5\u89e3\u91ca\u6211\u4eec\u503e\u5411\u4e8e\u63a5\u53d7\u9634\u8c0b\u8bba\u3002<\/p>\n\n\n\n<p><strong>\u8fd1\u671f\u9519\u89c9<\/strong>\u00a0(Recency illusion)\uff1a\u8ba4\u4e3a\u67d0\u4e2a\u81ea\u5df1\u6700\u8fd1\u6ce8\u610f\u5230\u7684\u73b0\u8c61\u5c31\u662f\u65b0\u7684\u73b0\u8c61\u3002\u5e38\u7528\u6765\u6307\u4ee3\u8bed\u8a00\u73b0\u8c61\uff1b\u8ba4\u4e3a\u4e00\u4e2a\u81ea\u5df1\u6700\u8fd1\u6ce8\u610f\u5230\u7684\u8bcd\u6216\u8bed\u8a00\u4f7f\u7528\u65b9\u5f0f\u662f\u521b\u65b0\u7684\uff0c\u5b9e\u9645\u4e0a\u5b83\u5df2\u7ecf\u5b58\u5728\u5f88\u4e45\uff08\u53c2\u89c1\u9891\u7387\u9519\u89c9\uff09\u3002\u6b64\u5916\uff0c\u8fd1\u671f\u504f\u8bef\u662f\u504f\u5411\u8fd1\u671f\u4e8b\u4ef6\u800c\u975e\u5386\u53f2\u4e8b\u4ef6\u7684\u4e00\u79cd\u8ba4\u77e5\u504f\u8bef\u3002\u8bb0\u5fc6\u504f\u8bef\uff0c\u8fd1\u671f\u504f\u8bef\u7ed9\u4e88\u201c\u6700\u8fd1\u4e8b\u4ef6\u66f4\u5927\u7684\u91cd\u8981\u6027\u201d\uff0c\u4f8b\u5982\u966a\u5ba1\u56e2\u5728\u88ab\u8981\u6c42\u4f11\u5ead\u524d\u542c\u5230\u7684\u6700\u540e\u4e00\u4f4d\u5f8b\u5e08\u7684\u7ed3\u6848\u9648\u8bcd\u3002<\/p>\n\n\n\n<p><strong>\u7cfb\u7edf\u6027\u504f\u8bef<\/strong>\u00a0(Systematic bias)\uff1a\u5f53\u5dee\u5f02\u5224\u65ad\u7684\u76ee\u6807\u6210\u4e3a\u56de\u5f52\u6548\u5e94\u7684\u5bf9\u8c61\u65f6\u6240\u4ea7\u751f\u7684\u5224\u65ad\uff0c\u8fd9\u4e9b\u56de\u5f52\u6548\u5e94\u662f\u4e0d\u76f8\u7b49\u7684\u3002<\/p>\n\n\n\n<p><strong>\u98ce\u9669\u8865\u507f\u6548\u5e94\u6216\u4f69\u5c14\u8328\u66fc\u6548\u5e94<\/strong>\u00a0(Risk compensation or Peltzman effect)\uff1a\u5f53\u611f\u77e5\u5230\u5b89\u5168\u6027\u589e\u52a0\u65f6\uff0c\u4eba\u4eec\u503e\u5411\u4e8e\u627f\u62c5\u66f4\u5927\u7684\u98ce\u9669\u3002<\/p>\n\n\n\n<p><strong>\u4ee3\u7406\u66ff\u4ee3<\/strong>\u00a0(Surrogation)\uff1a\u5931\u53bb\u8861\u91cf\u6307\u6807\u6240\u4ee3\u8868\u7684\u6218\u7565\u6784\u60f3\uff0c\u5e76\u56e0\u6b64\u8868\u73b0\u5f97\u597d\u50cf\u8be5\u8861\u91cf\u6807\u51c6\u5c31\u662f\u6240\u5173\u6ce8\u7684\u6784\u60f3\u3002<\/p>\n\n\n\n<p><strong>\u76ee\u7684\u8bba\u504f\u8bef<\/strong>\u00a0(Teleological Bias)\uff1a\u503e\u5411\u4e8e\u5c06\u76ee\u7684\u8fc7\u5ea6\u5f52\u56e0\u4e8e\u90a3\u4e9b\u5e76\u975e\u6e90\u4e8e\u76ee\u6807\u5bfc\u5411\u884c\u52a8\u3001\u8bbe\u8ba1\u6216\u57fa\u4e8e\u529f\u80fd\u6548\u679c\u7684\u5b9e\u4f53\u548c\u4e8b\u4ef6\u3002<\/p>\n\n\n\n<p><strong>\u706b\u9e21\u9519\u89c9<\/strong>\u00a0(Turkey illusion)\uff1a\u5728\u8fde\u7eed\u53d1\u5c55\u4e2d\u6ca1\u6709\u9884\u671f\u5230\u8d8b\u52bf\u7684\u7a81\u53d8\u3002<\/p>\n\n\n\n<p><strong>\u65e0\u610f\u8bc6\u504f\u8bef\u6216\u9690\u6027\u504f\u8bef<\/strong>\u00a0(Unconscious bias or implicit bias)\uff1a\u4eba\u4eec\u65e0\u610f\u8bc6\u4e2d\u5bf9\u53e6\u4e00\u4e2a\u4eba\u6216\u7fa4\u4f53\u6240\u6301\u7684\u6f5c\u5728\u6001\u5ea6\u548c\u523b\u677f\u5370\u8c61\uff0c\u8fd9\u4e9b\u6001\u5ea6\u548c\u523b\u677f\u5370\u8c61\u5f71\u54cd\u4e86\u4ed6\u4eec\u5bf9\u4ed6\u4eba\u7684\u7406\u89e3\u548c\u4e92\u52a8\u3002\u8bb8\u591a\u7814\u7a76\u4eba\u5458\u8ba4\u4e3a\uff0c\u65e0\u610f\u8bc6\u504f\u8bef\u662f\u81ea\u52a8\u53d1\u751f\u7684\uff0c\u56e0\u4e3a\u5927\u8111\u57fa\u4e8e\u8fc7\u53bb\u7684\u7ecf\u9a8c\u548c\u80cc\u666f\u8fc5\u901f\u505a\u51fa\u5224\u65ad\u3002<\/p>\n\n\n\n<p><strong>\u5355\u4f4d\u504f\u8bef<\/strong>\u00a0(Unit bias)\uff1a\u6807\u51c6\u5efa\u8bae\u7684\u6d88\u8d39\u91cf (\u4f8b\u5982\u98df\u7269\u7684\u4efd\u91cf) \u88ab\u8ba4\u4e3a\u662f\u9002\u5f53\u7684\uff0c\u5373\u4f7f\u5bf9\u7279\u5b9a\u4e2a\u4f53\u6765\u8bf4\u53ef\u80fd\u662f\u8fc7\u91cf\u7684\uff0c\u4e5f\u4f1a\u5168\u90e8\u6d88\u8d39\u3002<\/p>\n\n\n\n<p><strong>\u4ef7\u503c\u9009\u62e9\u504f\u8bef<\/strong>\u00a0(Value selection bias)\uff1a\u5728\u964c\u751f\u7684\u60c5\u5883\u4e2d\u63a8\u7406\u65f6\uff0c\u503e\u5411\u4e8e\u4f9d\u8d56\u73b0\u6709\u7684\u6570\u503c\u6570\u636e\uff0c\u5373\u4f7f\u9700\u8981\u8fdb\u884c\u8ba1\u7b97\u6216\u6570\u503c\u5904\u7406\u3002<\/p>\n\n\n\n<p><strong>\u97e6\u4f2f-\u8d39\u5e0c\u7eb3\u5b9a\u5f8b<\/strong>\u00a0(Weber\u2013Fechner law)\uff1a\u5728\u5927\u91cf\u6570\u91cf\u4e2d\u6bd4\u8f83\u5c0f\u5dee\u5f02\u7684\u56f0\u96be\u3002<\/p>\n\n\n\n<p><strong>\u5973\u6027\u662f\u7f8e\u597d\u6548\u5e94<\/strong>\u00a0(Women are wonderful effect)\uff1a\u503e\u5411\u4e8e\u5c06\u66f4\u591a\u79ef\u6781\u7684\u5c5e\u6027\u4e0e\u5973\u6027\u5173\u8054\uff0c\u800c\u975e\u4e0e\u7537\u6027\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u793e\u4f1a-Social\">\u793e\u4f1a (Social)<\/h2>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"\u5173\u8054\u8c2c\u8bef-Association-fallacy\">\u5173\u8054\u8c2c\u8bef (Association fallacy)<\/h3>\n\n\n\n<p>\u5173\u8054\u8c2c\u8bef\u5305\u62ec\uff1a<\/p>\n\n\n\n<p><strong>\u6743\u5a01\u504f\u8bef<\/strong>\u00a0(Authority bias)\uff1a\u503e\u5411\u4e8e\u8d4b\u4e88\u6743\u5a01\u4eba\u7269\u7684\u89c2\u70b9\u66f4\u9ad8\u7684\u51c6\u786e\u6027\uff08\u4e0e\u5176\u5185\u5bb9\u65e0\u5173\uff09\uff0c\u5e76\u66f4\u5bb9\u6613\u53d7\u5230\u8be5\u89c2\u70b9\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n<p><strong>\u5566\u5566\u961f\u6548\u5e94<\/strong>\u00a0(Cheerleader effect)\uff1a\u4eba\u4eec\u5728\u7fa4\u4f53\u4e2d\u6bd4\u72ec\u81ea\u65f6\u770b\u8d77\u6765\u66f4\u6709\u5438\u5f15\u529b\u3002<\/p>\n\n\n\n<p><strong>\u5149\u73af\u6548\u5e94<\/strong>\u00a0(Halo effect)\uff1a\u4e00\u4e2a\u4eba\u79ef\u6781\u6216\u6d88\u6781\u7684\u7279\u8d28\u4f1a\u5728\u4ed6\u4eba\u5bf9\u5176\u7684\u611f\u77e5\u4e2d\u201c\u8513\u5ef6\u201d\u5230\u5176\u4ed6\u4e2a\u6027\u9886\u57df\uff08\u53c2\u89c1\u5916\u8c8c\u5438\u5f15\u529b\u523b\u677f\u5370\u8c61\uff09\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"\u5f52\u56e0\u504f\u8bef-Attribution-bias\">\u5f52\u56e0\u504f\u8bef (Attribution bias)<\/h3>\n\n\n\n<p>\u5f52\u56e0\u504f\u8bef\u5305\u62ec\uff1a<\/p>\n\n\n\n<p><strong>\u884c\u4e3a\u8005-\u89c2\u5bdf\u8005\u504f\u8bef<\/strong>\u00a0(Actor-observer bias)\uff1a\u5f53\u89e3\u91ca\u4ed6\u4eba\u884c\u4e3a\u65f6\uff0c\u8fc7\u5ea6\u5f3a\u8c03\u5176\u4e2a\u6027\u5f71\u54cd\u800c\u5ffd\u89c6\u5176\u60c5\u5883\u5f71\u54cd\uff08\u53c2\u89c1\u57fa\u672c\u5f52\u56e0\u9519\u8bef\uff09\uff1b\u800c\u89e3\u91ca\u81ea\u5df1\u7684\u884c\u4e3a\u65f6\uff0c\u5219\u76f8\u53cd\uff0c\u8fc7\u5ea6\u5f3a\u8c03\u60c5\u5883\u5f71\u54cd\u800c\u5ffd\u89c6\u4e2a\u6027\u5f71\u54cd\u3002<\/p>\n\n\n\n<p><strong>\u9632\u5fa1\u6027\u5f52\u56e0\u5047\u8bbe<\/strong>\u00a0(Defensive attribution hypothesis)\uff1a\u5f53\u7ed3\u679c\u53d8\u5f97\u66f4\u4e25\u91cd\u6216\u4e0e\u53d7\u5bb3\u8005\u7684\u4e2a\u4eba\u6216\u60c5\u5883\u76f8\u4f3c\u65f6\uff0c\u503e\u5411\u4e8e\u5c06\u66f4\u591a\u7684\u8d23\u4efb\u5f52\u548e\u4e8e\u4f24\u5bb3\u8005\u3002<\/p>\n\n\n\n<p><strong>\u5916\u90e8\u6fc0\u52b1\u504f\u8bef<\/strong>\u00a0(Extrinsic incentives bias)\uff1a\u4e0e\u57fa\u672c\u5f52\u56e0\u9519\u8bef\u7684\u4f8b\u5916\uff0c\u8ba4\u4e3a\u4ed6\u4eba\u5177\u6709\u5916\u90e8\u60c5\u5883\u52a8\u673a\uff0c\u800c\u81ea\u5df1\u5219\u5177\u5907\u5185\u5728\u52a8\u673a\u3002<\/p>\n\n\n\n<p><strong>\u57fa\u672c\u5f52\u56e0\u9519\u8bef<\/strong>\u00a0(Fundamental attribution error)\uff1a\u4eba\u4eec\u503e\u5411\u4e8e\u8fc7\u5ea6\u5f3a\u8c03\u4e2a\u6027\u5316\u7684\u89e3\u91ca\u6765\u89e3\u91ca\u4ed6\u4eba\u884c\u4e3a\uff0c\u800c\u4f4e\u4f30\u60c5\u5883\u56e0\u7d20\u5bf9\u540c\u4e00\u884c\u4e3a\u7684\u5f71\u54cd\u3002\u53c2\u89c1\u884c\u4e3a\u8005-\u89c2\u5bdf\u8005\u504f\u8bef\u3001\u7fa4\u4f53\u5f52\u56e0\u9519\u8bef\u3001\u79ef\u6781\u6548\u5e94\u3001\u6d88\u6781\u6548\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u7fa4\u4f53\u5f52\u56e0\u9519\u8bef<\/strong>\u00a0(Group attribution error)\uff1a\u504f\u89c1\u7684\u4fe1\u5ff5\uff0c\u8ba4\u4e3a\u4e2a\u4f53\u7fa4\u4f53\u6210\u5458\u7684\u7279\u5f81\u53cd\u6620\u4e86\u6574\u4e2a\u7fa4\u4f53\uff0c\u6216\u8005\u503e\u5411\u4e8e\u5047\u8bbe\u7fa4\u4f53\u51b3\u7b56\u7684\u7ed3\u679c\u53cd\u6620\u4e86\u7fa4\u4f53\u6210\u5458\u7684\u504f\u597d\uff0c\u5373\u4f7f\u6709\u660e\u663e\u7684\u8bc1\u636e\u8868\u660e\u76f8\u53cd\u3002<\/p>\n\n\n\n<p><strong>\u654c\u610f\u5f52\u56e0\u504f\u8bef<\/strong>\u00a0(Hostile attribution bias)\uff1a\u5373\u4f7f\u884c\u4e3a\u6a21\u7cca\u6216\u65e0\u5bb3\uff0c\u4e5f\u503e\u5411\u4e8e\u5c06\u4ed6\u4eba\u884c\u4e3a\u89e3\u91ca\u4e3a\u654c\u610f\u3002<\/p>\n\n\n\n<p><strong>\u610f\u56fe\u504f\u8bef<\/strong>\u00a0(Intentionality bias)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u4eba\u7c7b\u7684\u884c\u4e3a\u662f\u6545\u610f\u7684\uff0c\u800c\u975e\u5076\u7136\u7684\u3002<\/p>\n\n\n\n<p><strong>\u516c\u6b63\u4e16\u754c\u5047\u8bbe<\/strong>\u00a0(Just-world hypothesis)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u4e16\u754c\u672c\u8d28\u4e0a\u662f\u516c\u6b63\u7684\uff0c\u56e0\u6b64\u4ed6\u4eec\u4f1a\u5c06\u65e0\u6cd5\u89e3\u91ca\u7684\u4e0d\u516c\u6b63\u884c\u4e3a\u5408\u7406\u5316\u4e3a\u53d7\u5bb3\u8005\u5e94\u5f97\u7684\u3002<\/p>\n\n\n\n<p><strong>\u9053\u5fb7\u8fd0\u6c14<\/strong>\u00a0(Moral luck)\uff1a\u503e\u5411\u4e8e\u6839\u636e\u4e8b\u4ef6\u7ed3\u679c\u6765\u5224\u65ad\u4ed6\u4eba\u9053\u5fb7\u4e0a\u7684\u9ad8\u4f4e\u3002<\/p>\n\n\n\n<p><strong>\u6e05\u6559\u5f92\u504f\u8bef<\/strong>\u00a0(Puritanical bias)\uff1a\u503e\u5411\u4e8e\u5c06\u4e2a\u4f53\u7684\u4e0d\u826f\u7ed3\u679c\u6216\u4e0d\u9053\u5fb7\u884c\u4e3a\u5f52\u56e0\u4e8e\u9053\u5fb7\u7f3a\u5931\u6216\u7f3a\u4e4f\u81ea\u63a7\u529b\uff0c\u800c\u4e0d\u8003\u8651\u66f4\u5e7f\u6cdb\u7684\u793e\u4f1a\u51b3\u5b9a\u56e0\u7d20\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n<p><strong>\u81ea\u5229\u504f\u8bef<\/strong>\u00a0(Self-serving bias)\uff1a\u503e\u5411\u4e8e\u5c06\u6210\u529f\u5f52\u56e0\u4e8e\u81ea\u8eab\uff0c\u800c\u5c06\u5931\u8d25\u5f52\u56e0\u4e8e\u5916\u90e8\u56e0\u7d20\u3002\u4e5f\u8868\u73b0\u4e3a\u4eba\u4eec\u503e\u5411\u4e8e\u4ee5\u6709\u5229\u4e8e\u81ea\u5df1\u5229\u76ca\u7684\u65b9\u5f0f\u8bc4\u4f30\u6a21\u7cca\u7684\u4fe1\u606f\uff08\u53c2\u89c1\u7fa4\u4f53\u670d\u52a1\u504f\u8bef\uff09\u3002<\/p>\n\n\n\n<p><strong>\u6700\u7ec8\u5f52\u56e0\u9519\u8bef<\/strong>\u00a0(Ultimate attribution error)\uff1a\u4e0e\u57fa\u672c\u5f52\u56e0\u9519\u8bef\u7c7b\u4f3c\uff0c\u5728\u8fd9\u79cd\u9519\u8bef\u4e2d\uff0c\u4e2a\u4f53\u66f4\u503e\u5411\u4e8e\u5c06\u6574\u4e2a\u7fa4\u4f53\u7684\u884c\u4e3a\u5f52\u56e0\u4e8e\u7fa4\u4f53\u5185\u90e8\u7684\u4e2a\u4f53\uff0c\u800c\u975e\u4e2a\u4f53\u672c\u8eab\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"\u4ece\u4f17-Conformity\">\u4ece\u4f17 (Conformity)<\/h3>\n\n\n\n<p>\u4ece\u4f17\u6d89\u53ca\u4ee5\u4e0b\u5185\u5bb9\uff1a<\/p>\n\n\n\n<p><strong>\u53ef\u5f97\u6027\u7ea7\u8054\u6548\u5e94<\/strong>\u00a0(Availability cascade)\uff1a\u4e00\u79cd\u81ea\u6211\u5f3a\u5316\u8fc7\u7a0b\uff0c\u901a\u8fc7\u516c\u5171\u8bdd\u8bed\u4e2d\u7684\u4e0d\u65ad\u91cd\u590d\uff0c\u4f7f\u5f97\u96c6\u4f53\u4fe1\u5ff5\u53d8\u5f97\u8d8a\u6765\u8d8a\u53ef\u4fe1\uff08\u5373\u201c\u91cd\u590d\u8db3\u591f\u957f\u65f6\u95f4\uff0c\u5b83\u5c31\u4f1a\u53d8\u5f97\u771f\u5b9e\u201d\uff09\u3002 \u53c2\u89c1\u53ef\u5f97\u6027\u542f\u53d1\u6cd5\u3002<\/p>\n\n\n\n<p><strong>\u8ddf\u98ce\u6548\u5e94<\/strong>\u00a0(Bandwagon effect)\uff1a\u7531\u4e8e\u8bb8\u591a\u4eba\u505a\uff08\u6216\u76f8\u4fe1\uff09\u67d0\u4e8b\uff0c\u4e2a\u4f53\u4e5f\u503e\u5411\u4e8e\u505a\uff08\u6216\u76f8\u4fe1\uff09\u540c\u6837\u7684\u4e8b\u60c5\u3002\u4e0e\u7fa4\u4f53\u601d\u7ef4\u548c\u7f8a\u7fa4\u884c\u4e3a\u76f8\u5173\u3002<\/p>\n\n\n\n<p><strong>\u793c\u8c8c\u504f\u8bef<\/strong>\u00a0(Courtesy bias)\uff1a\u503e\u5411\u4e8e\u7ed9\u51fa\u6bd4\u81ea\u5df1\u771f\u5b9e\u60f3\u6cd5\u66f4\u7b26\u5408\u793e\u4f1a\u671f\u671b\u7684\u610f\u89c1\uff0c\u4ee5\u907f\u514d\u5192\u72af\u4ed6\u4eba\u3002<\/p>\n\n\n\n<p><strong>\u7fa4\u4f53\u601d\u7ef4<\/strong>\u00a0(Groupthink)\uff1a\u7fa4\u4f53\u6210\u5458\u4e4b\u95f4\u4e3a\u4e86\u548c\u8c10\u6216\u4ece\u4f17\u800c\u4f5c\u51fa\u4e0d\u7406\u6027\u6216\u4e0d\u5065\u5168\u7684\u51b3\u7b56\u3002\u7fa4\u4f53\u6210\u5458\u8bd5\u56fe\u51cf\u5c11\u51b2\u7a81\uff0c\u8fbe\u6210\u5171\u8bc6\u51b3\u7b56\uff0c\u800c\u4e0d\u6279\u5224\u6027\u5730\u8bc4\u4f30\u66ff\u4ee3\u89c2\u70b9\uff0c\u79ef\u6781\u538b\u5236\u5f02\u8bae\uff0c\u5e76\u5c06\u81ea\u5df1\u4e0e\u5916\u90e8\u5f71\u54cd\u9694\u79bb\u3002<\/p>\n\n\n\n<p><strong>\u7fa4\u4f53\u8f6c\u5411<\/strong>\u00a0(Groupshift)\uff1a\u5f53\u7fa4\u4f53\u5df2\u7ecf\u6709\u504f\u5411\u67d0\u4e2a\u65b9\u5411\u65f6\uff0c\u7fa4\u4f53\u7684\u51b3\u7b56\u5f80\u5f80\u6bd4\u6574\u4e2a\u7fa4\u4f53\u7684\u503e\u5411\u66f4\u4e3a\u5192\u9669\u6216\u4fdd\u5b88\u3002<\/p>\n\n\n\n<p><strong>\u793e\u4f1a\u671f\u671b\u504f\u8bef<\/strong>\u00a0(Social desirability bias)\uff1a\u503e\u5411\u4e8e\u8fc7\u5ea6\u62a5\u544a\u81ea\u5df1\u5728\u793e\u4f1a\u4e0a\u88ab\u8ba4\u4e3a\u662f\u53ef\u53d6\u7684\u7279\u5f81\u6216\u884c\u4e3a\uff0c\u800c\u4f4e\u4f30\u81ea\u5df1\u4e0d\u88ab\u793e\u4f1a\u671f\u671b\u7684\u7279\u5f81\u6216\u884c\u4e3a\u3002 \u53c2\u89c1\uff1a\u793c\u8c8c\u504f\u8bef\u3002<\/p>\n\n\n\n<p><strong>\u771f\u76f8\u504f\u8bef<\/strong>\u00a0(Truth bias)\uff1a\u4eba\u4eec\u503e\u5411\u4e8e\u76f8\u4fe1\u4ed6\u4eba\u7684\u6c9f\u901a\uff0c\u4e0d\u8bba\u8be5\u4eba\u662f\u5426\u5728\u6492\u8c0e\u6216\u4e0d\u771f\u5b9e\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"\u5185\u7fa4\u4f53\u504f\u8bef-Ingroup-bias\">\u5185\u7fa4\u4f53\u504f\u8bef (Ingroup bias)<\/h3>\n\n\n\n<p>\u5185\u7fa4\u4f53\u504f\u8bef\u662f\u6307\u4e2a\u4f53\u503e\u5411\u4e8e\u5bf9\u4ed6\u4eec\u8ba4\u4e3a\u662f\u81ea\u5df1\u7fa4\u4f53\u6210\u5458\u7684\u4eba\u7ed9\u4e88\u4f18\u5f85\u3002\u8fd9\u4e0e\u4ee5\u4e0b\u5185\u5bb9\u76f8\u5173\uff1a<\/p>\n\n\n\n<p><strong>\u4e0d\u5728\u6b64\u53d1\u660e<\/strong>\u00a0(Not invented here)\uff1a\u5bf9\u5916\u90e8\u7fa4\u4f53\u5f00\u53d1\u7684\u4ea7\u54c1\u3001\u7814\u7a76\u3001\u6807\u51c6\u6216\u77e5\u8bc6\u7684\u538c\u6076\u3002<\/p>\n\n\n\n<p><strong>\u5916\u7fa4\u4f53\u540c\u8d28\u6027\u504f\u8bef<\/strong>\u00a0(Outgroup homogeneity bias)\uff1a\u4e2a\u4f53\u8ba4\u4e3a\u5176\u4ed6\u7fa4\u4f53\u6210\u5458\u6bd4\u81ea\u5df1\u7fa4\u4f53\u7684\u6210\u5458\u66f4\u4e3a\u5355\u4e00\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"\u5176\u4ed6\u793e\u4f1a\u504f\u8bef-Other-social-biases\">\u5176\u4ed6\u793e\u4f1a\u504f\u8bef (Other social biases)<\/h3>\n\n\n\n<p><strong>\u5047\u5b9a\u76f8\u4f3c\u6027\u504f\u8bef<\/strong>\u00a0(Assumed similarity bias)\uff1a\u4e2a\u4f53\u503e\u5411\u4e8e\u8ba4\u4e3a\u4ed6\u4eba\u4e0e\u81ea\u5df1\u6709\u66f4\u591a\u76f8\u4f3c\u4e4b\u5904\uff0c\u800c\u5b9e\u9645\u60c5\u51b5\u5e76\u975e\u5982\u6b64\u3002<\/p>\n\n\n\n<p><strong>\u5916\u7fa4\u4f53\u504f\u597d<\/strong>\u00a0(Outgroup favoritism)\uff1a\u4e00\u4e9b\u793e\u4f1a\u5f31\u52bf\u7fa4\u4f53\u4f1a\u5bf9\u5176\u4ed6\u793e\u4f1a\u3001\u6587\u5316\u6216\u65cf\u88d4\u7fa4\u4f53\u8868\u8fbe\u597d\u611f\uff08\u751a\u81f3\u504f\u597d\uff09\uff0c\u800c\u975e\u4ed6\u4eec\u81ea\u5df1\u6240\u5c5e\u7684\u7fa4\u4f53\u3002<\/p>\n\n\n\n<p><strong>\u76ae\u683c\u9a6c\u5229\u7fc1\u6548\u5e94<\/strong>\u00a0(Pygmalion effect)\uff1a\u4ed6\u4eba\u5bf9\u67d0\u4eba\u671f\u671b\u7684\u73b0\u8c61\uff0c\u5f71\u54cd\u8be5\u4eba\u7684\u8868\u73b0\u3002<\/p>\n\n\n\n<p><strong>\u53cd\u5e94\u6027<\/strong>\u00a0(Reactance) \uff1a\u51fa\u4e8e\u62b5\u6297\u88ab\u611f\u77e5\u4e3a\u9650\u5236\u81ea\u7531\u9009\u62e9\u7684\u884c\u4e3a\u7684\u9700\u6c42\uff0c\u4ea7\u751f\u505a\u4e0e\u4ed6\u4eba\u671f\u671b\u76f8\u53cd\u7684\u884c\u4e3a\u7684\u51b2\u52a8\uff08\u53c2\u89c1\u9006\u5411\u5fc3\u7406\u5b66\uff09\u3002<\/p>\n\n\n\n<p><strong>\u53cd\u5e94\u6027\u8d2c\u503c<\/strong>\u00a0(Reactive devaluation)\uff1a\u4ec5\u56e0\u4e3a\u67d0\u9879\u63d0\u6848\u88ab\u8ba4\u4e3a\u6765\u81ea\u5bf9\u7acb\u65b9\u800c\u8fdb\u884c\u8d2c\u4f4e\u3002<\/p>\n\n\n\n<p><strong>\u793e\u4f1a\u6bd4\u8f83\u504f\u8bef<\/strong>\u00a0(Social comparison bias)\uff1a\u5728\u505a\u51b3\u7b56\u65f6\uff0c\u503e\u5411\u4e8e\u504f\u7231\u90a3\u4e9b\u4e0e\u81ea\u5df1\u7279\u5b9a\u4f18\u52bf\u4e0d\u76f8\u7ade\u4e89\u7684\u6f5c\u5728\u5019\u9009\u4eba\u3002<\/p>\n\n\n\n<p><strong>\u5171\u4eab\u4fe1\u606f\u504f\u8bef<\/strong>\u00a0(Shared information bias)\uff1a\u7fa4\u4f53\u6210\u5458\u503e\u5411\u4e8e\u82b1\u8d39\u66f4\u591a\u65f6\u95f4\u548c\u7cbe\u529b\u8ba8\u8bba\u5927\u5bb6\u90fd\u5df2\u719f\u6089\u7684\u4fe1\u606f\uff08\u5373\u5171\u4eab\u4fe1\u606f\uff09\uff0c\u800c\u8f83\u5c11\u65f6\u95f4\u548c\u7cbe\u529b\u8ba8\u8bba\u4ec5\u90e8\u5206\u6210\u5458\u77e5\u9053\u7684\u4fe1\u606f\uff08\u5373\u975e\u5171\u4eab\u4fe1\u606f\uff09\u3002<\/p>\n\n\n\n<p><strong>\u6bd4\u5e73\u5747\u6c34\u5e73\u66f4\u5dee\u6548\u5e94<\/strong>\u00a0(Worse-than-average effect)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u81ea\u5df1\u5728\u96be\u5ea6\u8f83\u5927\u7684\u4efb\u52a1\u4e0a\u6bd4\u4ed6\u4eba\u505a\u5f97\u66f4\u5dee\u3002<\/p>\n\n\n\n<h2 class=\"wp-block-heading\" id=\"\u8bb0\u5fc6\u5f52\u56e0\u9519\u8bef-Misattribution-of-memory\">\u8bb0\u5fc6\u5f52\u56e0\u9519\u8bef (Misattribution of memory)<\/h2>\n\n\n\n<p>\u5728\u5fc3\u7406\u5b66\u4e2d\uff0c\u8bb0\u5fc6\u5f52\u56e0\u9519\u8bef\u6216\u6765\u6e90\u5f52\u56e0\u9519\u8bef\u662f\u6307\u56de\u5fc6\u8005\u9519\u8bef\u5730\u8bc6\u522b\u8bb0\u5fc6\u7684\u6765\u6e90\u3002\u8bb0\u5fc6\u5f52\u56e0\u9519\u8bef\u5f80\u5f80\u53d1\u751f\u5728\u4e2a\u4f53\u65e0\u6cd5\u76d1\u63a7\u548c\u63a7\u5236\u4ed6\u4eec\u5728\u56de\u5fc6\u65f6\u5bf9\u5224\u65ad\u7684\u6001\u5ea6\u5f71\u54cd\u65f6\u3002\u8bb0\u5fc6\u5f52\u56e0\u9519\u8bef\u5206\u4e3a\u4e09\u4e2a\u7ec4\u6210\u90e8\u5206\uff1a\u9690\u6027\u8bb0\u5fc6\u3001\u865a\u5047\u8bb0\u5fc6\u548c\u6765\u6e90\u6df7\u6dc6\u3002\u5b83\u6700\u521d\u88ab\u8ba4\u4e3a\u662f\u4e39\u5c3c\u5c14\u00b7\u8c22\u514b\u7279 (Daniel Schacter) \u63d0\u51fa\u7684\u4e03\u5927\u8bb0\u5fc6\u4e4b\u7f6a\u4e4b\u4e00\u3002<\/p>\n\n\n\n<p>\u5f52\u56e0\u9519\u8bef\u5305\u62ec\uff1a<\/p>\n\n\n\n<p><strong>\u9690\u6027\u8bb0\u5fc6<\/strong>\u00a0(Cryptomnesia)\uff1a\u5f53\u8bb0\u5fc6\u88ab\u8bef\u8ba4\u4e3a\u662f\u65b0\u7684\u601d\u60f3\u6216\u60f3\u8c61\u65f6\uff0c\u56e0\u4e3a\u4e2a\u4f53\u6ca1\u6709\u4e3b\u89c2\u7ecf\u9a8c\u611f\u77e5\u5b83\u662f\u8bb0\u5fc6\u3002<\/p>\n\n\n\n<p><strong>\u865a\u5047\u8bb0\u5fc6<\/strong>\u00a0(False memory)\uff1a\u5f53\u60f3\u8c61\u88ab\u8bef\u8ba4\u4e3a\u662f\u8bb0\u5fc6\u65f6\u3002<\/p>\n\n\n\n<p><strong>\u793e\u4f1a\u9690\u6027\u8bb0\u5fc6<\/strong>\u00a0(Social cryptomnesia)\uff1a\u793e\u4f1a\u548c\u4e2a\u4eba\u672a\u80fd\u8bb0\u4f4f\u67d0\u4e00\u53d8\u5316\u7684\u8d77\u6e90\uff0c\u4eba\u4eec\u77e5\u9053\u793e\u4f1a\u53d1\u751f\u4e86\u53d8\u5316\uff0c\u4f46\u5fd8\u8bb0\u4e86\u8fd9\u4e2a\u53d8\u5316\u662f\u5982\u4f55\u53d1\u751f\u7684\uff1b\u5373\uff0c\u91c7\u53d6\u4e86\u54ea\u4e9b\u6b65\u9aa4\u4ee5\u53ca\u662f\u8c01\u91c7\u53d6\u4e86\u8fd9\u4e9b\u6b65\u9aa4\u3002\u8fd9\u5bfc\u81f4\u793e\u4f1a\u5bf9\u505a\u51fa\u91cd\u5927\u727a\u7272\u5e76\u4fc3\u6210\u793e\u4f1a\u4ef7\u503c\u53d8\u5316\u7684\u5c11\u6570\u7fa4\u4f53\u7684\u793e\u4f1a\u4fe1\u7528\u964d\u4f4e\u3002<\/p>\n\n\n\n<p><strong>\u6765\u6e90\u6df7\u6dc6<\/strong>\u00a0(Source confusion)\uff1a\u5c06\u60c5\u8282\u6027\u8bb0\u5fc6\u4e0e\u5176\u4ed6\u4fe1\u606f\u6df7\u6dc6\uff0c\u5bfc\u81f4\u8bb0\u5fc6\u626d\u66f2\u3002<\/p>\n\n\n\n<p><strong>\u6697\u793a\u6027<\/strong>\u00a0(Suggestibility)\uff1a\u5f53\u63d0\u95ee\u8005\u63d0\u51fa\u7684\u60f3\u6cd5\u88ab\u8bef\u8ba4\u4e3a\u662f\u8bb0\u5fc6\u65f6\u3002<\/p>\n\n\n\n<p><strong>\u4f69\u5c14\u57fa\u6548\u5e94<\/strong>\u00a0(The Perky effect)\uff1a\u771f\u5b9e\u7684\u56fe\u50cf\u53ef\u4ee5\u5f71\u54cd\u60f3\u8c61\u4e2d\u7684\u56fe\u50cf\uff0c\u6216\u8005\u88ab\u8bef\u8bb0\u4e3a\u60f3\u8c61\u4e2d\u7684\uff0c\u800c\u4e0d\u662f\u73b0\u5b9e\u7684\u3002<\/p>\n\n\n\n<h3 class=\"wp-block-heading\" id=\"\u5176\u4ed6\u8bb0\u5fc6\u504f\u8bef-Other-memory-biases\">\u5176\u4ed6\u8bb0\u5fc6\u504f\u8bef (Other memory biases)<\/h3>\n\n\n\n<p><strong>\u60c5\u611f\u8870\u9000\u504f\u8bef<\/strong>\u00a0(Fading affect bias)\uff1a\u4e00\u79cd\u504f\u8bef\uff0c\u5176\u4e2d\u4e0e\u4e0d\u6109\u5feb\u8bb0\u5fc6\u76f8\u5173\u7684\u60c5\u611f\u6bd4\u4e0e\u79ef\u6781\u4e8b\u4ef6\u76f8\u5173\u7684\u60c5\u611f\u8870\u9000\u5f97\u66f4\u5feb\u3002<\/p>\n\n\n\n<p><strong>\u8fd1\u671f\u6548\u5e94<\/strong>\u00a0(Recency effect)\uff1a\u4e00\u79cd\u5e8f\u5217\u4f4d\u7f6e\u6548\u5e94\uff0c\u5217\u8868\u672b\u5c3e\u7684\u9879\u66f4\u5bb9\u6613\u88ab\u56de\u5fc6\u8d77\u3002\u6b64\u6548\u5e94\u53ef\u88ab\u540e\u7f00\u6548\u5e94\u5e72\u6270\u3002\u53c2\u89c1\u9996\u56e0\u6548\u5e94 (Primacy effect)\u3002<\/p>\n\n\n\n<p><strong>\u5217\u8868\u957f\u5ea6\u6548\u5e94<\/strong>\u00a0(List-length effect)\uff1a\u8f83\u957f\u5217\u8868\u4e2d\u8bb0\u4f4f\u7684\u9879\u6240\u5360\u767e\u5206\u6bd4\u8f83\u5c0f\uff0c\u4f46\u968f\u7740\u5217\u8868\u957f\u5ea6\u7684\u589e\u52a0\uff0c\u8bb0\u4f4f\u7684\u9879\u7684\u7edd\u5bf9\u6570\u91cf\u4e5f\u4f1a\u589e\u52a0\u3002<\/p>\n\n\n\n<p><strong>\u8bb0\u5fc6\u6291\u5236<\/strong>\u00a0(Memory inhibition)\uff1a\u5c55\u793a\u67d0\u4e9b\u5217\u8868\u9879\u4f1a\u4f7f\u56de\u5fc6\u5176\u4ed6\u9879\u53d8\u5f97\u66f4\u52a0\u56f0\u96be\uff08\u4f8b\u5982\uff0cSlamecka\uff0c1968\uff09\u3002<\/p>\n\n\n\n<p><strong>\u602a\u5f02\u6548\u5e94<\/strong>\u00a0(Bizarreness effect)\uff1a\u602a\u5f02\u6750\u6599\u6bd4\u666e\u901a\u6750\u6599\u66f4\u5bb9\u6613\u8bb0\u4f4f\u3002<\/p>\n\n\n\n<p><strong>\u8a00\u4e4b\u4fe1\u6548\u5e94<\/strong>\u00a0(Saying is believing effect)\uff1a\u5411\u89c2\u4f17\u4f20\u9012\u7b26\u5408\u793e\u4f1a\u671f\u671b\u7684\u6d88\u606f\u53ef\u80fd\u4f1a\u5bfc\u81f4\u5c06\u8be5\u6d88\u606f\u89c6\u4e3a\u81ea\u5df1\u60f3\u6cd5\u7684\u504f\u8bef\u3002<\/p>\n\n\n\n<p><strong>\u540e\u7f00\u6548\u5e94<\/strong>\u00a0(Suffix effect)\uff1a\u7531\u4e8e\u5728\u5217\u8868\u672b\u5c3e\u6dfb\u52a0\u4e86\u4e0d\u9700\u8981\u56de\u5fc6\u7684\u58f0\u97f3\u9879\uff0c\u5bfc\u81f4\u8fd1\u671f\u6548\u5e94\u7684\u51cf\u5f31\u3002\u4e00\u79cd\u5e8f\u5217\u4f4d\u7f6e\u6548\u5e94\u3002\u53c2\u89c1\u8fd1\u671f\u6548\u5e94\u548c\u9996\u56e0\u6548\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u53ef\u5f97\u6027\u504f\u8bef<\/strong>\u00a0(Availability bias)\uff1a\u66f4\u53ef\u80fd\u56de\u5fc6\u8d77\u6700\u8fd1\u3001\u9644\u8fd1\u6216\u5176\u4ed6\u7acb\u5373\u53ef\u5f97\u7684\u793a\u4f8b\uff0c\u5e76\u4e14\u503e\u5411\u4e8e\u8d4b\u4e88\u8fd9\u4e9b\u793a\u4f8b\u76f8\u5bf9\u4e8e\u5176\u4ed6\u793a\u4f8b\u66f4\u9ad8\u7684\u91cd\u89c6\u3002<\/p>\n\n\n\n<p><strong>\u865a\u5047\u5173\u8054<\/strong>\u00a0(Illusory correlation)\uff1a\u9519\u8bef\u5730\u770b\u5230\u4e24\u4e2a\u5076\u7136\u76f8\u5173\u7684\u4e8b\u4ef6\u4e4b\u95f4\u7684\u5173\u7cfb\u3002<\/p>\n\n\n\n<p><strong>\u4e00\u81f4\u6027\u504f\u8bef<\/strong>\u00a0(Consistency bias)\uff1a\u9519\u8bef\u5730\u8bb0\u4f4f\u8fc7\u53bb\u7684\u6001\u5ea6\u548c\u884c\u4e3a\u4e0e\u73b0\u5728\u7684\u6001\u5ea6\u548c\u884c\u4e3a\u76f8\u4f3c\u3002<\/p>\n\n\n\n<p><strong>\u9519\u8bef\u4fe1\u606f\u6548\u5e94<\/strong>\u00a0(Misinformation effect)\uff1a\u7531\u4e8e\u4e8b\u4ef6\u540e\u4fe1\u606f\u7684\u5e72\u6270\uff0c\u8bb0\u5fc6\u53d8\u5f97\u4e0d\u51c6\u786e\u3002 \u53c2\u89c1\u6301\u7eed\u5f71\u54cd\u6548\u5e94\uff0c\u5373\u5c3d\u7ba1\u9519\u8bef\u4fe1\u606f\u5728\u4e4b\u540e\u88ab\u7ea0\u6b63\uff0c\u4f46\u4ecd\u7ee7\u7eed\u5f71\u54cd\u4e8b\u4ef6\u8bb0\u5fc6\u3002<\/p>\n\n\n\n<p><strong>\u523b\u677f\u5370\u8c61\u504f\u8bef<\/strong>\u00a0(Stereotype bias)\uff1a\u8bb0\u5fc6\u53d7\u5230\u523b\u677f\u5370\u8c61\uff08\u4f8b\u5982\u79cd\u65cf\u6216\u6027\u522b\uff09\u7684\u626d\u66f2\u3002<\/p>\n\n\n\n<p><strong>\u5e73\u8861\u548c\u9510\u5316<\/strong>\u00a0(Leveling and sharpening)\uff1a\u8bb0\u5fc6\u626d\u66f2\uff0c\u5728\u56de\u5fc6\u8fc7\u7a0b\u4e2d\uff0c\u7531\u4e8e\u7ec6\u8282\u7684\u4e27\u5931\uff0c\u901a\u5e38\u4f34\u968f\u7740\u5bf9\u67d0\u4e9b\u7ec6\u8282\u7684\u9510\u5316\u6216\u9009\u62e9\u6027\u56de\u5fc6\uff0c\u8fd9\u4e9b\u7ec6\u8282\u5728\u56de\u5fc6\u8fc7\u7a0b\u4e2d\u663e\u5f97\u5938\u5927\u3002\u968f\u7740\u65f6\u95f4\u7684\u63a8\u79fb\u548c\u53cd\u590d\u56de\u5fc6\u6216\u91cd\u8ff0\uff0c\u8fd9\u4e24\u79cd\u504f\u8bef\u53ef\u80fd\u4f1a\u5f97\u5230\u5f3a\u5316\u3002<\/p>\n\n\n\n<p><strong>\u6301\u7eed\u5f71\u54cd\u6548\u5e94<\/strong>\u00a0(Continued influence effect)\uff1a\u5c3d\u7ba1\u9519\u8bef\u4fe1\u606f\u5df2\u88ab\u7ea0\u6b63\uff0c\u4f46\u9519\u8bef\u4fe1\u606f\u7ee7\u7eed\u5f71\u54cd\u4e8b\u4ef6\u7684\u8bb0\u5fc6\u548c\u63a8\u7406\u3002 \u53c2\u89c1\u9519\u8bef\u4fe1\u606f\u6548\u5e94\uff0c\u5176\u4e2d\u539f\u59cb\u8bb0\u5fc6\u4f1a\u53d7\u5230\u540e\u6765\u83b7\u5f97\u7684\u9519\u8bef\u4fe1\u606f\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n<p><strong>\u7279\u62c9\u7ef4\u65af\u7efc\u5408\u75c7<\/strong>\u00a0(Travis syndrome)\uff1a\u9ad8\u4f30\u5f53\u524d\u4e8b\u4ef6\u7684\u91cd\u8981\u6027\u3002 \u5b83\u4e0e\u65f6\u95f4\u50b2\u6162\u76f8\u5173\uff0c\u53ef\u80fd\u6d89\u53ca\u5bf9\u65b0\u9896\u6027\u903b\u8f91\u8c2c\u8bef\u7684\u504f\u8bef\u3002<\/p>\n\n\n\n<p><strong>\u865a\u5047\u771f\u7406\u6548\u5e94<\/strong>\u00a0(Illusory truth effect)\uff1a\u4eba\u4eec\u66f4\u5bb9\u6613\u8ba4\u4e3a\u81ea\u5df1\u4ee5\u524d\u542c\u8fc7\u7684\u9648\u8ff0\u662f\u771f\u5b9e\u7684 (\u5373\u4f7f\u4ed6\u4eec\u4e0d\u80fd\u81ea\u89c9\u5730\u8bb0\u5f97\u66fe\u542c\u8fc7) \uff0c\u800c\u4e0d\u7ba1\u9648\u8ff0\u7684\u5b9e\u9645\u6709\u6548\u6027\u5982\u4f55\u3002\u6362\u53e5\u8bdd\u8bf4\uff0c\u4eba\u4eec\u66f4\u503e\u5411\u4e8e\u76f8\u4fe1\u719f\u6089\u7684\u9648\u8ff0\uff0c\u800c\u975e\u4e0d\u719f\u6089\u7684\u9648\u8ff0\u3002\u53c2\u89c1\u771f\u7406\u504f\u8bef\u3002<\/p>\n\n\n\n<p><strong>\u6d88\u6781\u504f\u8bef<\/strong>\u00a0(Negativity bias) \u6216\u6d88\u6781\u6548\u5e94 (Negativity effect)\uff1a\u4eba\u7c7b\u66f4\u5bb9\u6613\u56de\u5fc6\u8d77\u4e0d\u6109\u5feb\u7684\u8bb0\u5fc6\uff0c\u800c\u4e0d\u662f\u79ef\u6781\u7684\u8bb0\u5fc6\u3002\u53c2\u89c1\u884c\u4e3a\u8005-\u89c2\u5bdf\u8005\u504f\u8bef\u3001\u7fa4\u4f53\u5f52\u56e0\u9519\u8bef\u3001\u79ef\u6781\u6548\u5e94\u3001\u6d88\u6781\u6548\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u81ea\u6211\u4e2d\u5fc3\u504f\u8bef<\/strong>\u00a0(Egocentric bias)\uff1a\u4ee5\u81ea\u5229\u7684\u65b9\u5f0f\u56de\u5fc6\u8fc7\u53bb\uff0c\u4f8b\u5982\uff0c\u8bb0\u5f97\u81ea\u5df1\u7684\u8003\u8bd5\u6210\u7ee9\u6bd4\u5b9e\u9645\u66f4\u597d\uff0c\u6216\u8bb0\u5f97\u6355\u5230\u7684\u9c7c\u6bd4\u5b9e\u9645\u66f4\u5927\u3002<\/p>\n\n\n\n<p><strong>\u8fb9\u754c\u6269\u5c55<\/strong>\u00a0(Boundary extension)\uff1a\u8bb0\u5fc6\u4e2d\u56fe\u50cf\u7684\u80cc\u666f\u88ab\u8bb0\u5f97\u6bd4\u524d\u666f\u66f4\u5927\u6216\u66f4\u5e7f\u9614\u3002<\/p>\n\n\n\n<p><strong>\u4fdd\u5b88\u504f\u8bef<\/strong>\u00a0(Conservatism or Regressive bias)\uff1a\u503e\u5411\u4e8e\u8bb0\u4f4f\u9ad8\u503c\u548c\u9ad8\u6982\u7387\u7684\u4e8b\u4ef6\u8f83\u4f4e\u7684\u503c\u548c\u6982\u7387\uff0c\u4f4e\u503c\u4e8b\u4ef6\u88ab\u8bb0\u5f97\u6bd4\u5b9e\u9645\u66f4\u9ad8\u3002\u57fa\u4e8e\u8bc1\u636e\uff0c\u8bb0\u5fc6\u901a\u5e38\u4e0d\u591f\u6781\u7aef\u3002<\/p>\n\n\n\n<p><strong>\u4f4d\u7f6e\u504f\u8bef<\/strong>\u00a0(Placement bias)\uff1a\u503e\u5411\u4e8e\u8bb0\u4f4f\u81ea\u5df1\u5728\u67d0\u4e9b\u4efb\u52a1\u4e0a\u7684\u8868\u73b0\u4f18\u4e8e\u4ed6\u4eba\uff0c\u7279\u522b\u662f\u5728\u8ba4\u4e3a\u81ea\u5df1\u4f18\u4e8e\u5e73\u5747\u6c34\u5e73\u7684\u4efb\u52a1\u4e0a\uff08\u4e5f\u79f0\u4e3a\u865a\u5047\u4f18\u8d8a\u611f\u6216\u4f18\u4e8e\u5e73\u5747\u6548\u5e94\uff09\uff0c\u4ee5\u53ca\u5728\u8ba4\u4e3a\u81ea\u5df1\u4f4e\u4e8e\u5e73\u5747\u6c34\u5e73\u7684\u4efb\u52a1\u4e0a\uff0c\u503e\u5411\u4e8e\u8bb0\u5f97\u81ea\u5df1\u8868\u73b0\u8f83\u5dee\uff08\u4e5f\u79f0\u4e3a\u6bd4\u5e73\u5747\u6c34\u5e73\u5dee\u6548\u5e94\uff09\u3002<\/p>\n\n\n\n<p><strong>\u51af\u00b7\u96f7\u65af\u6258\u592b\u6548\u5e94<\/strong>\u00a0(von Restorff effect)\uff1a\u7a81\u51fa\u7684\u9879\u76ee\u6bd4\u5176\u4ed6\u9879\u76ee\u66f4\u5bb9\u6613\u88ab\u8bb0\u4f4f\u3002<\/p>\n\n\n\n<p><strong>\u90e8\u5206\u5217\u8868\u63d0\u793a\u6548\u5e94<\/strong>\u00a0(Part-list cueing effect)\uff1a\u5c55\u793a\u67d0\u4e9b\u5217\u8868\u9879\u5e76\u968f\u540e\u63d0\u53d6\u4e00\u4e2a\u9879\uff0c\u4f1a\u4f7f\u56de\u5fc6\u5176\u4ed6\u9879\u53d8\u5f97\u66f4\u52a0\u56f0\u96be\u3002<\/p>\n\n\n\n<p><strong>\u60c5\u5883\u6548\u5e94<\/strong>\u00a0(Context effect)\uff1a\u8ba4\u77e5\u548c\u8bb0\u5fc6\u4f9d\u8d56\u4e8e\u60c5\u5883\uff0c\u56e0\u6b64\u8131\u79bb\u60c5\u5883\u7684\u8bb0\u5fc6\u6bd4\u5728\u60c5\u5883\u4e2d\u7684\u8bb0\u5fc6\u66f4\u96be\u4ee5\u56de\u5fc6\uff08\u4f8b\u5982\uff0c\u5de5\u4f5c\u76f8\u5173\u7684\u8bb0\u5fc6\u5728\u5bb6\u56de\u5fc6\u65f6\u7684\u65f6\u95f4\u548c\u51c6\u786e\u6027\u4f1a\u8f83\u4f4e\uff0c\u53cd\u4e4b\u4ea6\u7136\uff09\u3002<\/p>\n\n\n\n<p><strong>\u52a0\u5de5\u5c42\u6b21\u6548\u5e94<\/strong>\u00a0(Levels-of-processing effect)\uff1a\u4e0d\u540c\u7684\u4fe1\u606f\u7f16\u7801\u65b9\u6cd5\u5bf9\u8bb0\u5fc6\u7684\u6709\u6548\u6027\u6709\u4e0d\u540c\u7684\u5f71\u54cd\u3002<\/p>\n\n\n\n<p><strong>\u5e7d\u9ed8\u6548\u5e94<\/strong>\u00a0(Humor effect)\uff1a\u5e7d\u9ed8\u7684\u5185\u5bb9\u6bd4\u975e\u5e7d\u9ed8\u7684\u5185\u5bb9\u66f4\u5bb9\u6613\u8bb0\u4f4f\uff0c\u8fd9\u53ef\u4ee5\u901a\u8fc7\u5e7d\u9ed8\u7684\u72ec\u7279\u6027\u3001\u7406\u89e3\u5e7d\u9ed8\u6240\u9700\u7684\u66f4\u591a\u8ba4\u77e5\u52a0\u5de5\u65f6\u95f4\u6216\u5e7d\u9ed8\u5f15\u8d77\u7684\u60c5\u7eea\u5524\u8d77\u6765\u89e3\u91ca\u3002<\/p>\n\n\n\n<p><strong>\u95f4\u9694\u6548\u5e94<\/strong>\u00a0(Spacing effect)\uff1a\u4fe1\u606f\u5728\u8f83\u957f\u65f6\u95f4\u8de8\u5ea6\u5185\u91cd\u590d\u66b4\u9732\u6bd4\u5728\u77ed\u65f6\u95f4\u5185\u91cd\u590d\u66b4\u9732\u66f4\u5bb9\u6613\u88ab\u8bb0\u4f4f\u3002<\/p>\n\n\n\n<p><strong>\u5904\u7406\u96be\u5ea6\u6548\u5e94<\/strong>\u00a0(Processing difficulty effect)\uff1a\u9700\u8981\u66f4\u591a\u65f6\u95f4\u9605\u8bfb\u4e14\u601d\u8003\u66f4\u56f0\u96be\u7684\u4fe1\u606f\uff08\u7ecf\u8fc7\u66f4\u96be\u5904\u7406\u7684\uff09\u66f4\u5bb9\u6613\u8bb0\u4f4f\u3002 \u53c2\u89c1\u52a0\u5de5\u5c42\u6b21\u6548\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u5e8f\u5217\u4f4d\u7f6e\u6548\u5e94<\/strong>\u00a0(Serial position effect)\uff1a\u5e8f\u5217\u672b\u5c3e\u7684\u9879\u6700\u5bb9\u6613\u56de\u5fc6\uff0c\u5176\u6b21\u662f\u5e8f\u5217\u5f00\u5934\u7684\u9879\uff1b\u5e8f\u5217\u4e2d\u95f4\u7684\u9879\u6700\u4e0d\u5bb9\u6613\u88ab\u8bb0\u4f4f\u3002 \u53c2\u89c1\u8fd1\u671f\u6548\u5e94\u3001\u9996\u56e0\u6548\u5e94\u548c\u540e\u7f00\u6548\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u81ea\u6211\u76f8\u5173\u6548\u5e94<\/strong>\u00a0(Self-relevance effect)\uff1a\u4e0e\u81ea\u6211\u76f8\u5173\u7684\u8bb0\u5fc6\u6bd4\u4e0e\u4ed6\u4eba\u76f8\u5173\u7684\u7c7b\u4f3c\u4fe1\u606f\u66f4\u5bb9\u6613\u88ab\u8bb0\u4f4f\u3002<\/p>\n\n\n\n<p><strong>\u6a21\u6001\u6548\u5e94<\/strong>\u00a0(Modality effect)\uff1a\u5f53\u5217\u8868\u9879\u901a\u8fc7\u8bed\u8a00\u63a5\u6536\u65f6\uff0c\u6700\u540e\u51e0\u9879\u6bd4\u901a\u8fc7\u4e66\u9762\u65b9\u5f0f\u63a5\u6536\u65f6\u66f4\u5bb9\u6613\u56de\u5fc6\u3002<\/p>\n\n\n\n<p><strong>\u79ef\u6781\u6548\u5e94<\/strong>\u00a0(Positivity effect) \uff08\u793e\u4f1a\u60c5\u611f\u9009\u62e9\u7406\u8bba\uff09\uff1a\u8001\u5e74\u4eba\u66f4\u503e\u5411\u4e8e\u5728\u8bb0\u5fc6\u4e2d\u504f\u597d\u79ef\u6781\u4fe1\u606f\u800c\u975e\u6d88\u6781\u4fe1\u606f\u3002\u53c2\u89c1\u6109\u60a6\u56de\u5fc6\u3002<\/p>\n\n\n\n<p><strong>\u9ad8\u5cf0\u2013\u7ed3\u675f\u6cd5\u5219<\/strong>\u00a0(Peak\u2013end rule)\uff1a\u4eba\u4eec\u4f3c\u4e4e\u611f\u77e5\u7684\u4e0d\u662f\u6574\u4e2a\u7ecf\u5386\u7684\u603b\u548c\uff0c\u800c\u662f\u5b83\u7684\u9ad8\u5cf0\u90e8\u5206\uff08\u5982\u6109\u5feb\u6216\u4e0d\u6109\u5feb\uff09\u548c\u5b83\u7684\u7ed3\u5c3e\u90e8\u5206\u7684\u5e73\u5747\u503c\u3002<\/p>\n\n\n\n<p><strong>\u751f\u6210\u6548\u5e94<\/strong>\u00a0(Generation effect) \uff08\u81ea\u6211\u751f\u6210\u6548\u5e94\uff09\uff1a\u81ea\u6211\u751f\u6210\u7684\u4fe1\u606f\u6700\u5bb9\u6613\u88ab\u8bb0\u4f4f\u3002\u4f8b\u5982\uff0c\u4eba\u4eec\u66f4\u5bb9\u6613\u56de\u5fc6\u8d77\u4ed6\u4eec\u81ea\u5df1\u751f\u6210\u7684\u9648\u8ff0\uff0c\u800c\u4e0d\u662f\u5176\u4ed6\u4eba\u751f\u6210\u7684\u7c7b\u4f3c\u9648\u8ff0\u3002<\/p>\n\n\n\n<p><strong>\u9010\u5b57\u6548\u5e94<\/strong>\u00a0(Verbatim effect)\uff1a\u4eba\u4eec\u66f4\u5bb9\u6613\u8bb0\u4f4f\u522b\u4eba\u8bf4\u8bdd\u7684\u201c\u5927\u610f\u201d\uff0c\u800c\u4e0d\u662f\u9010\u5b57\u8bb0\u4f4f\u4ed6\u4eec\u6240\u8bf4\u7684\u5185\u5bb9\u3002 \u8fd9\u662f\u56e0\u4e3a\u8bb0\u5fc6\u662f\u8868\u5f81\uff0c\u800c\u975e\u7cbe\u786e\u7684\u590d\u5236\u3002<\/p>\n\n\n\n<p><strong>\u6cfd\u683c\u5c3c\u514b\u6548\u5e94<\/strong>\u00a0(Zeigarnik effect)\uff1a\u672a\u5b8c\u6210\u6216\u4e2d\u65ad\u7684\u4efb\u52a1\u6bd4\u5b8c\u6210\u7684\u4efb\u52a1\u66f4\u5bb9\u6613\u88ab\u8bb0\u4f4f\u3002<\/p>\n\n\n\n<p><strong>\u6d4b\u8bd5\u6548\u5e94<\/strong>\u00a0(Testing effect)\uff1a\u901a\u8fc7\u6539\u5199\u800c\u975e\u91cd\u8bfb\u4fe1\u606f\uff0c\u4eba\u4eec\u66f4\u5bb9\u6613\u56de\u5fc6\u8d77\u6240\u8bfb\u8fc7\u7684\u5185\u5bb9\u3002\u7ecf\u5e38\u6027\u5730\u6d4b\u8bd5\u5df2\u8bb0\u4f4f\u7684\u6750\u6599\u6709\u52a9\u4e8e\u63d0\u9ad8\u8bb0\u5fc6\u56de\u5fc6\u3002<\/p>\n\n\n\n<p><strong>\u60c5\u7eea\u4e00\u81f4\u8bb0\u5fc6\u504f\u8bef<\/strong>\u00a0(Mood-congruent memory bias) \uff08\u72b6\u6001\u4f9d\u8d56\u8bb0\u5fc6\uff09\uff1a\u4e0e\u5f53\u524d\u60c5\u7eea\u4e00\u81f4\u7684\u4fe1\u606f\u66f4\u5bb9\u6613\u88ab\u56de\u5fc6\u3002<\/p>\n\n\n\n<p><strong>\u540e\u89c1\u504f\u8bef<\/strong>\u00a0(Hindsight bias) \uff08\u201c\u6211\u65e9\u5c31\u77e5\u9053\u4e86\u201d\u6548\u5e94\uff09\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u8fc7\u53bb\u7684\u4e8b\u4ef6\u672c\u53ef\u4ee5\u9884\u6d4b\u5230\u3002<\/p>\n\n\n\n<p><strong>\u56fe\u7247\u4f18\u8d8a\u6548\u5e94<\/strong>\u00a0(Picture superiority effect)\uff1a\u901a\u8fc7\u89c2\u770b\u56fe\u7247\u5b66\u4e60\u7684\u6982\u5ff5\u6bd4\u901a\u8fc7\u9605\u8bfb\u6587\u5b57\u5f62\u5f0f\u5b66\u4e60\u7684\u6982\u5ff5\u66f4\u5bb9\u6613\u4e14\u66f4\u9891\u7e41\u5730\u88ab\u56de\u5fc6\u3002<\/p>\n\n\n\n<p><strong>\u6ede\u540e\u6548\u5e94<\/strong>\u00a0(Lag effect)\uff1a\u5f53\u5b66\u4e60\u5206\u6563\u5728\u4e00\u6bb5\u65f6\u95f4\u5185\u65f6\uff0c\u5b66\u4e60\u6548\u679c\u66f4\u4f73\uff0c\u800c\u4e0d\u662f\u5728\u4e00\u6b21\u5b66\u4e60\u8fc7\u7a0b\u4e2d\u5b66\u4e60\u76f8\u540c\u7684\u65f6\u95f4\u3002\u53c2\u89c1\u95f4\u9694\u6548\u5e94\u3002<\/p>\n\n\n\n<p><strong>\u56de\u5fc6\u9ad8\u5cf0<\/strong>\u00a0(Reminiscence bump)\uff1a\u6bd4\u8d77\u5176\u4ed6\u4eba\u751f\u65f6\u671f\uff0c\u9752\u5c11\u5e74\u548c\u65e9\u671f\u6210\u5e74\u671f\u7684\u4e2a\u4eba\u4e8b\u4ef6\u66f4\u5bb9\u6613\u88ab\u56de\u5fc6\u3002<\/p>\n\n\n\n<p><strong>\u73ab\u7470\u8272\u56de\u987e<\/strong>\u00a0(Rosy retrospection)\uff1a\u5c06\u8fc7\u53bb\u8bb0\u5f97\u6bd4\u5b9e\u9645\u66f4\u7f8e\u597d\u3002<\/p>\n\n\n\n<p><strong>\u513f\u7ae5\u9057\u5fd8\u75c7<\/strong>\u00a0(Childhood amnesia)\uff1a\u5bf9\u56db\u5c81\u4e4b\u524d\u7684\u8bb0\u5fc6\u4fdd\u7559\u8f83\u5c11\u3002<\/p>\n\n\n\n<p><strong>\u76ee\u51fb\u8005\u8bb0\u5fc6\u4e2d\u7684\u6027\u522b\u5dee\u5f02<\/strong>\u00a0(Gender differences in eyewitness memory)\uff1a\u76ee\u51fb\u8005\u503e\u5411\u4e8e\u8bb0\u4f4f\u66f4\u591a\u5173\u4e8e\u540c\u4e00\u6027\u522b\u4eba\u7684\u7ec6\u8282\u3002<\/p>\n\n\n\n<p><strong>\u8de8\u79cd\u65cf\u6548\u5e94<\/strong>\u00a0(Cross-race effect)\uff1a\u4e00\u4e2a\u79cd\u65cf\u7684\u4eba\u8f83\u96be\u8bc6\u522b\u53e6\u4e00\u4e2a\u79cd\u65cf\u7684\u4eba\u3002<\/p>\n\n\n\n<p><strong>\u6109\u60a6\u56de\u5fc6<\/strong>\u00a0(Euphoric recall)\uff1a\u4eba\u4eec\u503e\u5411\u4e8e\u4ee5\u79ef\u6781\u7684\u65b9\u5f0f\u8bb0\u4f4f\u8fc7\u53bb\u7684\u7ecf\u5386\uff0c\u540c\u65f6\u5ffd\u89c6\u4e0e\u8be5\u4e8b\u4ef6\u76f8\u5173\u7684\u8d1f\u9762\u7ecf\u5386\u3002<\/p>\n\n\n\n<p><strong>\u671b\u8fdc\u955c\u6548\u5e94<\/strong>\u00a0(Telescoping effect)\uff1a\u503e\u5411\u4e8e\u5c06\u8fd1\u671f\u4e8b\u4ef6\u5012\u9000\u5230\u8fc7\u53bb\uff0c\u800c\u5c06\u8fdc\u671f\u4e8b\u4ef6\u63a8\u524d\uff0c\u4f7f\u5f97\u8fd1\u671f\u4e8b\u4ef6\u663e\u5f97\u66f4\u9065\u8fdc\uff0c\u800c\u8fdc\u671f\u4e8b\u4ef6\u663e\u5f97\u66f4\u8fd1\u3002<\/p>\n\n\n\n<p><strong>\u5b50\u52a0\u6027\u6548\u5e94<\/strong>\u00a0(Subadditivity effect)\uff1a\u503e\u5411\u4e8e\u4f30\u8ba1\u8bb0\u4f4f\u7684\u4e8b\u4ef6\u7684\u53ef\u80fd\u6027\u4f4e\u4e8e\u5176\uff08\u4e24\u4e2a\u4ee5\u4e0a\uff09\u4e92\u65a5\u90e8\u5206\u7684\u603b\u548c\u3002<\/p>\n\n\n\n<p><strong>\u8c37\u6b4c\u6548\u5e94<\/strong>\u00a0(Google effect)\uff1a\u503e\u5411\u4e8e\u5fd8\u8bb0\u53ef\u4ee5\u901a\u8fc7\u4e92\u8054\u7f51\u641c\u7d22\u5f15\u64ce\u8f7b\u677e\u627e\u5230\u7684\u4fe1\u606f\u3002<\/p>\n\n\n\n<p><strong>\u805a\u5149\u706f\u6548\u5e94<\/strong>\u00a0(Spotlight effect)\uff1a\u503e\u5411\u4e8e\u9ad8\u4f30\u4ed6\u4eba\u6ce8\u610f\u5230\u81ea\u5df1\u5916\u8c8c\u6216\u884c\u4e3a\u7684\u7a0b\u5ea6\u3002<\/p>\n\n\n\n<p><strong>\u9009\u62e9\u652f\u6301\u504f\u8bef<\/strong>\u00a0(Choice-supportive bias)\uff1a\u503e\u5411\u4e8e\u8ba4\u4e3a\u81ea\u5df1\u7684\u9009\u62e9\u6bd4\u5b9e\u9645\u66f4\u597d\u3002<\/p>\n\n\n\n<p><strong>\u786e\u8ba4\u504f\u8bef<\/strong>\u00a0(Confirmation bias)\uff1a\u503e\u5411\u4e8e\u4ee5\u7b26\u5408\u81ea\u5df1\u4fe1\u5ff5\u6216\u5047\u8bbe\u7684\u65b9\u5f0f\u641c\u7d22\u3002<\/p>\n\n\n\n<p>\u53d1\u8868\u4e8e\u00a02025-02-07<br><a href=\"https:\/\/wuhaojerry-github-io.pages.dev\/%E8%AE%A4%E7%9F%A5%E5%81%8F%E8%AF%AF%E5%88%97%E8%A1%A8\/\">https:\/\/wuhaojerry-github-io.pages.dev\/%E8%AE%A4%E7%9F%A5%E5%81%8F%E8%AF%AF%E5%88%97%E8%A1%A8\/<\/a><\/p>\n","protected":false},"excerpt":{"rendered":"<p>\u951a\u5b9a\u504f\u8bef (Anchoring bias) \u951a\u5b9a\u504f\u8bef (Anchoring bias) \uff0c\u6216\u79f0\u805a\u7126\u6548\u5e94 (fo [&hellip;]<\/p>\n","protected":false},"author":1,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"footnotes":""},"categories":[1],"tags":[],"class_list":["post-12055","post","type-post","status-publish","format-standard","hentry","category-uncategorized"],"_links":{"self":[{"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=\/wp\/v2\/posts\/12055","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcomments&post=12055"}],"version-history":[{"count":0,"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=\/wp\/v2\/posts\/12055\/revisions"}],"wp:attachment":[{"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fmedia&parent=12055"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Fcategories&post=12055"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/www.invbus.com\/blog\/index.php?rest_route=%2Fwp%2Fv2%2Ftags&post=12055"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}