{"id":12016,"date":"2026-04-16T12:03:15","date_gmt":"2026-04-16T04:03:15","guid":{"rendered":"https:\/\/www.invbus.com\/blog\/?p=12016"},"modified":"2026-04-16T12:09:32","modified_gmt":"2026-04-16T04:09:32","slug":"%e7%bb%b4%e5%9f%ba%e7%99%be%e7%a7%91%ef%bc%9a%e8%ae%a4%e7%9f%a5%e5%81%8f%e5%b7%ae%e5%88%97%e8%a1%a8","status":"publish","type":"post","link":"https:\/\/www.invbus.com\/blog\/?p=12016","title":{"rendered":"\u7ef4\u57fa\u767e\u79d1\uff1a\u8ba4\u77e5\u504f\u5dee\u5217\u8868"},"content":{"rendered":"\n

\u8ba4\u77e5\u504f\u5dee<\/a><\/strong>\uff0c\u662f\u5224\u65ad\u4e2d\u7684\u7cfb\u7edf\u6027\u504f\u79bb\u6216\u662f\u76f4\u89c9\u3001\u975e\u7406\u6027\u7684\u601d\u7ef4\u6a21\u5f0f\uff0c\u4f7f\u4eba\u4eec\u5728\u5f97\u5230\u786e\u5207\u7ed3\u679c\u524d\uff0c\u4ee5\u5feb\u901f\u3001\u76f4\u89c9\u3001\u81ea\u52a8\u6216\u7f13\u6162\u3001\u6df1\u601d\u719f\u8651\u3001\u5206\u6790\u6027\u7684\u4e24\u79cd\u4e0d\u540c\u601d\u7ef4\u7cfb\u7edf\u505a\u51fa\u4e3b\u89c2\u7684\u5224\u65ad\u548c\u51b3\u7b56\uff0c\u5c24\u5176\u5728\u8baf\u606f\u91cf\u5e9e\u5927\u3001\u65e0\u660e\u663e\u6700\u4f73\u89e3\u51b3\u65b9\u6848\u3001\u65f6\u95f4\u7d27\u8feb\u7b49\u60c5\u51b5\u4e0b\u3002\u8fd9\u4e9b\u5224\u65ad\u8fc7\u7a0b\u5f71\u54cd\u8303\u56f4\u4e0d\u62d8\u9650\u4e8e\u4e2a\u4eba\uff0c\u4e5f\u56ca\u62ec\u5404\u793e\u4f1a\u5c42\u9762\u4ee5\u53ca\u7ec4\u7ec7\uff0c\u4f8b\u5982\u516c\u53f8\u6cbb\u7406\u3001\u653f\u7b56\u5236\u5b9a\u7b49\u590d\u6742\u51b3\u7b56\u5f53\u4e2d\uff0c\u5e76\u53ef\u80fd\u5bfc\u81f4\u51b3\u7b56\u504f\u79bb\u7406\u6027\u548c\u6700\u4f73\u9009\u62e9\u3002[1]<\/a><\/sup>[2]<\/a><\/sup>\u8be5\u4e3b\u9898\u7ecf\u5e38\u5728\u5fc3\u7406\u5b66<\/a>\u3001\u793e\u4f1a\u5b66<\/a>\u548c\u884c\u4e3a\u7ecf\u6d4e\u5b66<\/a>\u4e2d\u88ab\u8fdb\u884c\u7814\u7a76\u3002<\/p>\n\n\n\n

\u5c3d\u7ba1\u5927\u591a\u8be5\u4e9b\u504f\u89c1\u7684\u73b0\u5b9e\u5df2\u88ab\u53ef\u91cd\u590d\uff08\u82f1\u8bed\uff1areproducible\uff09\u7684\u7814\u7a76\u8bc1\u5b9e\uff0c\u5173\u4e8e\u5982\u4f55\u5bf9\u8fd9\u4e9b\u504f\u89c1\u8fdb\u884c\u5206\u7c7b\u6216\u5982\u4f55\u89e3\u91ca\u5b83\u4eec\u7ecf\u5e38\u5b58\u5728\u4e89\u8bae\u3002\u4e00\u4e9b\u8ba4\u77e5\u504f\u5dee\u7684\u51e0\u4e2a\u7406\u8bba\u539f\u56e0\u662f\u5df2\u77e5\u7684\uff0c\u8fd9\u4e9b\u504f\u5dee\u901a\u8fc7\u5176\u5171\u540c\u7684\u751f\u6210\u673a\u5236\uff08\u4f8b\u5982\u5608\u6742\u7684\u4fe1\u606f\u5904\u7406\uff08\u82f1\u8bed\uff1anoisy information-processing\uff09\uff09\u63d0\u4f9b\u4e86\u5bf9\u504f\u5dee\u7684\u5206\u7c7b\u3002 Gerd Gigerenzer \u66fe\u6279\u8bc4\u5c06\u8ba4\u77e5\u504f\u89c1\u89c6\u4e3a\u5224\u65ad\u9519\u8bef\uff0c\u5e76\u503e\u5411\u4e8e\u5c06\u5176\u89e3\u91ca\u4e3a\u4e0e\u903b\u8f91\u601d\u7ef4\u7684\u7406\u6027\u504f\u5dee\u3002<\/p>\n\n\n\n

\u89e3\u91ca\u5305\u62ec\u4fe1\u606f\u5904\u7406\u89c4\u5219\uff08\u82f1\u8bed\uff1ainformation-processing rules\uff0c\u6216\u79f0\u5fc3\u7406\u6377\u5f84\uff08\u82f1\u8bed\uff1amental shortcuts\uff09\uff09\uff0c\u79f0\u4e3a\u542f\u53d1\u5f0f<\/a>\uff0c\u5927\u8111\u7528\u6765\u4ea7\u751f\u51b3\u5b9a<\/a>\u6216\u5224\u65ad\u3002\u504f\u89c1\u6709\u591a\u79cd\u5f62\u5f0f\uff0c\u8868\u73b0\u4e3a\u8ba4\u77e5\uff08\u201c\u51b7\u201d\uff09\u504f\u89c1\uff0c\u4f8b\u5982\u5fc3\u7406\u566a\u97f3\uff08\u82f1\u8bed\uff1amental noise\uff09\uff0c\u6216\u52a8\u673a\uff08\u201c\u70ed\u201d\uff09\u504f\u89c1\uff0c\u4f8b\u5982\u5f53\u4fe1\u5ff5\u88ab\u4e00\u53a2\u60c5\u613f<\/a>\u626d\u66f2\u65f6\u3002\u4e24\u79cd\u6548\u5e94\u53ef\u4ee5\u540c\u65f6\u5b58\u5728\u3002<\/p>\n\n\n\n

\u5173\u4e8e\u8fd9\u4e9b\u504f\u89c1\u4e2d\u7684\u4e00\u4e9b\u662f\u5426\u88ab\u89c6\u4e3a\u65e0\u7528\u6216\u975e\u7406\u6027\uff08\u82f1\u8bed\uff1airrational\uff09\uff0c\u6216\u8005\u5b83\u4eec\u662f\u5426\u4f1a\u5bfc\u81f4\u6709\u7528\u7684\u6001\u5ea6\u6216\u884c\u4e3a\uff0c\u4e5f\u5b58\u5728\u4e89\u8bae\u3002\u4f8b\u5982\uff0c\u5728\u7ed3\u8bc6\u4ed6\u4eba\u65f6\uff0c\u4eba\u4eec\u503e\u5411\u4e8e\u63d0\u51fa\u5f15\u5bfc\u6027\u95ee\u9898<\/a>\uff0c\u8fd9\u4e9b\u95ee\u9898\u4f3c\u4e4e\u504f\u5411\u4e8e\u786e\u8ba4\u4ed6\u4eec\u5bf9\u8fd9\u4e2a\u4eba\u7684\u5047\u8bbe\u3002\u7136\u800c\uff0c\u8fd9\u79cd\u786e\u8ba4\u504f\u5dee<\/a>\u4e5f\u88ab\u8ba4\u4e3a\u662f\u793e\u4ea4\u6280\u5de7<\/a>\uff08\u82f1\u8bed\uff1asocial skill\uff09\u7684\u4e00\u4e2a\u4f8b\u5b50\u3002\u4e00\u79cd\u4e0e\u4ed6\u4eba\u5efa\u7acb\u8054\u7cfb\u7684\u65b9\u5f0f\u3002<\/p>\n\n\n\n

\u5c3d\u7ba1\u8fd9\u9879\u7814\u7a76\u7edd\u5927\u591a\u6570\u6d89\u53ca\u4eba\u7c7b\u53d7\u8bd5\u8005\uff0c\u4f46\u5728\u975e\u4eba\u7c7b\u52a8\u7269\u4e2d\u4e5f\u53d1\u73b0\u4e86\u4e00\u4e9b\u8868\u660e\u5b58\u5728\u504f\u89c1\u7684\u53d1\u73b0\u3002\u4f8b\u5982\uff0c\u7334\u5b50\u5df2\u7ecf\u8868\u73b0\u51fa\u538c\u6076\u635f\u5931<\/a>\uff0c\u800c\u5728\u8001\u9f20\u3001\u9e3d\u5b50\u548c\u7334\u5b50\u4e2d\u5df2\u7ecf\u89c2\u5bdf\u5230\u53cc\u66f2\u6298\u73b0<\/a>\u7b49\u73b0\u8c61\u3002<\/p>\n\n\n\n

\u51b3\u7b56\u3001\u4fe1\u5ff5\u4e0e\u884c\u4e3a\u504f\u5dee<\/h2>\n\n\n\n

\u8fd9\u4e9b\u504f\u5dee\u591a\u534a\u4f1a\u5f71\u54cd\u4fe1\u5ff5\u7684\u5f62\u6210\u3001\u5546\u4e1a\u4e0e\u7ecf\u6d4e\u51b3\u7b56\u3001\u4ee5\u53ca\u5176\u4ed6\u4e00\u822c\u7684\u4eba\u7c7b\u884c\u4e3a\u5b83\u4eec\u662f\u53ef\u590d\u5236\u4e0e\u518d\u73b0\u7684\uff0c\u9762\u4e34\u7279\u5b9a\u60c5\u5883\u65f6\uff0c\u4e00\u822c\u53ef\u9884\u671f\u4eba\u4eec\u4f1a\u6709\u76f8\u5e94\u7684\u504f\u5dee\u503e\u5411\u3002<\/p>\n\n\n\n

\u540d\u79f0<\/th>\u539f\u6587<\/th>\u63cf\u8ff0<\/th><\/tr><\/thead>
\u4e0d\u660e\u786e\u6027\u6548\u5e94<\/a><\/strong>\u6216\u538c\u6076\u4e0d\u660e\u786e<\/a><\/strong><\/td>Ambiguity effect<\/td>\u51b3\u7b56\u65f6\u503e\u5411\u907f\u5f00\u8d44\u8baf\u4e0d\u8db3\u7684\u9009\u9879\u3002<\/td><\/tr>
\u951a\u5b9a\u6548\u5e94<\/a><\/strong><\/td>Anchoring effect; Focalism<\/td>\u4e3a\u4e0d\u719f\u6089\u4e8b\u7269\u4f30\u503c\u65f6\uff0c\u4f1a\u628a\u719f\u6089\u7684\u7c7b\u4f3c\u4e8b\u7269\u6216\u4e0d\u4e45\u524d\u63a5\u89e6\u5230\u7684\u65e0\u5173\u6570\u503c\u5f53\u505a\u201c\u951a\u201d\uff08\u7ecf\u9a8c<\/a>\uff09\u3002\u4eba\u4eec\u5728\u505a\u51fa\u5224\u65ad\u65f6(\u5c24\u5176\u662f\u6570\u503c\u5224\u65ad)\uff0c\u4f1a\u503e\u5411\u671d\u7740\u63a5\u6536\u5230\u7684\u7b2c\u4e00\u4e2a\u8baf\u606f(\u951a)\u6765\u505a\u8c03\u6574\u3002<\/td><\/tr>
\u4eba\u7c7b\u4e2d\u5fc3\u601d\u7ef4<\/a><\/strong><\/td>Anthropocentric thinking<\/td>\u5728\u5b69\u7ae5\u8eab\u4e0a\u89c2\u5bdf\u5230\u7684\u4e00\u79cd\u503e\u5411\uff0c\u4ee5\u4eba\u7c7b\u5f53\u4f5c\u7c7b\u6bd4\u6765\u63a8\u6d4b\u5176\u4ed6\u4e0d\u719f\u6089\u7684\u751f\u7269\u73b0\u8c61\uff08\u62df\u4eba\u5316<\/a>\uff09\u3002\u6216\u662f\u53cd\u8fc7\u6765\u8ba4\u4e3a\u4eba\u6240\u5177\u6709\u7684\u7279\u5f81\u662f\u5176\u4ed6\u52a8\u7269\u90fd\u6ca1\u6709\u7684\u3002\u8b6c\u5982\u7edd\u5927\u591a\u6570\u5173\u4e8e\u8ba4\u77e5\u504f\u5dee\u7684\u7814\u7a76\u90fd\u662f\u4ee5\u4eba\u7c7b\u4e3a\u4e3b\u4f53\u3002\u9664\u4e86\u4eba\u7c7b\u4ee5\u5916\uff0c\u8fd8\u80fd\u591f\u4ece\u8001\u9f20\u3001\u9e3d\u5b50\u3001\u7334\u5b50\u8eab\u4e0a\u4e5f\u80fd\u89c2\u5bdf\u5230\u53cc\u66f2\u6298\u73b0<\/a>\u73b0\u8c61<\/td><\/tr>
\u6ce8\u610f\u529b\u504f\u5dee<\/a><\/strong><\/td>Attentional bias<\/td>\u5e73\u65f6\u5e38\u60f3\u7684\u4e8b\uff08\u6ce8\u610f\u529b<\/a>\u96c6\u4e2d\u7684\u4e8b\u60c5\uff09\uff0c\u4f1a\u5f71\u54cd\u6211\u4eec\u7684\u611f\u77e5\u3002<\/td><\/tr>
\u81ea\u52a8\u5316\u7684\u504f\u89c1<\/a><\/strong><\/td>Automation bias<\/td>\u503e\u5411\u4e8e\u8fc7\u5ea6\u4f9d\u8d56\u81ea\u52a8\u5316<\/a>\u7cfb\u7edf\uff0c\u8fd9\u53ef\u80fd\u5bfc\u81f4\u9519\u8bef\u7684\u81ea\u52a8\u5316\u4fe1\u606f\u8986\u76d6\u6b63\u786e\u7684\u51b3\u7b56\u3002<\/td><\/tr>
\u53ef\u5f97\u6027\u6377\u601d\u6cd5<\/a><\/strong><\/td>Availability heuristic<\/td>\u4eba\u4eec\u503e\u5411\u900f\u8fc7\u4e8b\u4ef6\u7684\u76f8\u5173\u5b9e\u4f8b\u6d6e\u73b0\u5728\u8111\u6d77\u4e2d\u7684\u5bb9\u6613\u7a0b\u5ea6\u6765\u8bc4\u4f30\u4e8b\u4ef6\u7684\u53ef\u80fd\u6027\u3002\u7136\u800c\u4e00\u4ef6\u4e8b\u662f\u5426\u5bb9\u6613\u88ab\u56de\u5fc6\u8fd8\u53d7\u53d1\u751f\u591a\u4e45\u3001\u6fc0\u8d77\u60c5\u7eea\u7684\u7a0b\u5ea6\u7b49\u56e0\u7d20\u5f71\u54cd\uff0c\u65e0\u6cd5\u53cd\u6620\u5b9e\u9645\u7684\u53d1\u751f\u6982\u7387\u3002<\/td><\/tr>
\u53ef\u83b7\u6027\u5c42\u53e0<\/a><\/strong><\/td>Availability cascade<\/td>\u4e00\u4ef6\u4e8b\u8d8a\u5e38\u88ab\u516c\u5f00\u8c08\u8bba\uff0c\u5c31\u8d8a\u52a0\u76f8\u4fe1\u5176\u771f\u786e\u6027\uff08\u7c7b\u4f3c\u201c\u4e09\u4eba\u6210\u864e<\/a>\u201d\uff09\u3002<\/td><\/tr>
\u9006\u706b\u6548\u5e94<\/a><\/strong><\/td>Backfire effect<\/td>\u9047\u4e0a\u4e0e\u81ea\u8eab\u4fe1\u5ff5<\/a>\u62b5\u89e6\u7684\u89c2\u70b9\u6216\u8bc1\u636e\u65f6\uff0c\u9664\u975e\u5b83\u4eec\u8db3\u4ee5\u5b8c\u5168\u6467\u6bc1\u539f\u4fe1\u5ff5\uff0c\u5426\u5219\u4f1a\u5ffd\u7565\u6216\u53cd\u9a73\u5b83\u4eec\uff0c\u539f\u4fe1\u5ff5\u53cd\u800c\u66f4\u52a0\u5f3a\u5316\u3002<\/td><\/tr>
\u4ece\u4f17\u6548\u5e94<\/a><\/strong><\/td>Bandwagon effect<\/td>\u503e\u5411\u505a\u5f88\u591a\u4eba\u505a\u7684\u4e8b\u6216\u76f8\u4fe1\u5f88\u591a\u4eba\u76f8\u4fe1\u7684\u4e8b\uff08\u793e\u4f1a\u5fc3\u7406\u5b66<\/a>\u4e2d\u4eba\u53d7\u793e\u4f1a\u6240\u5f71\u54cd\uff09\u3002<\/td><\/tr>
\u4fe1\u5ff5\u504f\u5dee<\/a><\/strong><\/td>Belief bias<\/td>\u7531\u4e8e\u76f8\u4fe1\u7ed3\u8bba<\/a>\uff0c\u800c\u8ba4\u4e3a\u63a8\u7406\u51fa\u8be5\u7ed3\u8bba\u7684\u8fc7\u7a0b<\/a>\u662f\u6709\u9053\u7406\u3001\u5408\u903b\u8f91\u7684\u3002<\/td><\/tr>
\u504f\u89c1\u76f2\u70b9<\/a><\/strong><\/td>Bias blind spot<\/td>\u8ba4\u4e3a\u81ea\u5df1\u6bd4\u5176\u4ed6\u4eba\u66f4\u80fd\u8fa8\u8bc6\u8ba4\u77e5\u504f\u5dee\uff0c\u4e5f\u8f83\u4e0d\u6613\u53d7\u5f71\u54cd\u3002<\/td><\/tr>
\u5566\u5566\u961f\u6548\u5e94<\/a><\/strong><\/td>Cheerleader effect<\/td>\u5904\u5728\u4f18\u79c0\u7684\u56e2\u4f53\u4f1a\u6bd4\u5355\u72ec\u770b\u8d77\u6765\u66f4\u4f18\u79c0\u3002<\/td><\/tr>
\u652f\u6301\u9009\u62e9\u504f\u5dee<\/a><\/strong><\/td>Choice-supportive bias<\/td>\u5bf9\u81ea\u5df1\u5148\u524d\u9009\u62e9\u7684\u8bc4\u4ef7\u4f1a\u6bd4\u5b9e\u9645\u4e0a\u66f4\u597d\u3002<\/td><\/tr>
\u96c6\u7fa4\u9519\u89c9<\/a><\/strong><\/td>Clustering illusion<\/td>\u8fc7\u5ea6\u671f\u5f85\u4ece\u5c0f\u6837\u672c\u6216\u5c0f\u578b\u6d4b\u8bd5\u4e2d\u53d1\u73b0\u7684\u89c4\u5f8b\uff0c\u7136\u800c\u8fd9\u4e9b\u5c0f\u6837\u672c\u6216\u5c0f\u578b\u6d4b\u8bd5\u662f\u4ece\u5927\u6837\u672c\u968f\u673a\u62bd\u53d6\uff0c\u5927\u6837\u672c\u5f80\u5f80\u5e76\u65e0\u8fd9\u79cd\u89c4\u5f8b\uff08\u7edf\u8ba1\u5b66<\/a>\u53d6\u6837\u504f\u5dee<\/a>\uff09\u3002<\/td><\/tr>
\u8212\u9002\u533a\u6548\u5e94<\/a><\/strong><\/td>Comfort zone effect<\/td>\u5bf9\u4e8e\u8fc7\u53bb\u5e38\u7528\u7684\u65b9\u6848\uff08\u8212\u9002\u5708<\/a>\uff09\uff0c\u9ad8\u4f30\u6548\u76ca\u6216\u6210\u529f\u673a\u4f1a\uff1b\u5bf9\u4e8e\u8fc7\u53bb\u5c11\u7528\u7684\u65b9\u6848\uff0c\u4f4e\u4f30\u6548\u76ca\u6216\u6210\u529f\u673a\u4f1a\u3002<\/td><\/tr>
\u786e\u8ba4\u504f\u8bef<\/a><\/strong><\/td>Confirmation bias<\/td>\u4eba\u4eec\u4f1a\u504f\u597d\u7b26\u5408\u81ea\u8eab\u56de\u5fc6\u6216\u662f\u652f\u6301\u5148\u524d\u4e2a\u4eba\u4fe1\u5ff5\u7684\u8baf\u606f\uff0c\u5ffd\u7565\u4e0d\u7b26\u5408\u81ea\u8eab\u4fe1\u5ff5\u7684\u8baf\u606f\u3002<\/td><\/tr>
\u76f8\u5408\u6027\u504f\u5dee<\/a><\/strong><\/td>Congruence bias<\/td>\u76f4\u63a5\u68c0\u9a8c\u5047\u8bbe<\/a>\uff0c\u5374\u6ca1\u60f3\u5230\u8981\u68c0\u9a8c\u5176\u4ed6\u53ef\u80fd\u7684\u5047\u8bbe\u3002<\/td><\/tr>
\u4fdd\u5b88\u503e\u5411<\/a><\/strong>\u6216\u9000\u7f29\u504f\u5dee<\/a><\/strong><\/td>Conservatism; Regressive bias<\/td>\u503e\u5411\u4fdd\u5b88\u4e2d\u5eb8<\/a>\uff0c\u4f4e\u4f30\u9ad8\u4ef7\u503c\u9ad8\u6216\u7136\u7387\u7684\u4e8b<\/strong>\u800c\u9ad8\u4f30\u4f4e\u4ef7\u503c\u4f4e\u6216\u7136\u7387\u7684\u4e8b<\/strong>\u3002<\/td><\/tr>
\u4fdd\u5b88\u503e\u5411 (\u8d1d\u53f6\u65af)<\/a><\/strong><\/td>Conservatism (Bayesian)<\/td>\u65b0\u8bc1\u636e<\/a>\u51fa\u73b0\u65f6\uff0c\u5bf9\u65e2\u6709\u4fe1\u5ff5<\/a>\u7684\u4fee\u6b63\u5e45\u5ea6\u4e0d\u8db3\u3002<\/td><\/tr>
\u5bf9\u6bd4\u6548\u5e94<\/a><\/strong><\/td>Contrast effect<\/td>\u611f\u53d7\u7279\u8d28\u7684\u7a0b\u5ea6\u4e3b\u8981\u662f\u53d7\u4e0e\u5176\u4ed6\u76f8\u5173\u4e8b\u7269\u7684\u5bf9\u6bd4\u7a0b\u5ea6\u5f71\u54cd\uff0c\u800c\u975e\u53d7\u5176\u5b9e\u9645\u7a0b\u5ea6\u5f71\u54cd\u3002<\/td><\/tr>
\u77e5\u8bc6\u7684\u8bc5\u5492<\/a><\/strong><\/td>Curse of knowledge<\/td>\u61c2\u5f97\u591a\u7684\u4eba\u975e\u5e38\u96be\u4ee5\u4ece\u61c2\u5f97\u5c11\u7684\u4eba\u7684\u89d2\u5ea6\u601d\u8003\u95ee\u9898\u3002<\/td><\/tr>
\u8bf1\u9975\u6548\u5e94<\/a><\/strong><\/td>Decoy effect<\/td>\u8bc4\u4f30\u5bf9A\u4e0eB\u4e8b\u7269\u7684\u504f\u597d\u65f6\uff0c\u5982\u6709\u4e2aC\u4e0eB\u76f8\u8fd1\u5374\u7565\u900a\u4e00\u7b79\uff0c\u5c31\u4f1a\u89c9\u5f97B\u4e8b\u7269\u66f4\u597d\u3002\uff08\u5373\u4ee5C\u4e3a\u8bf1\u9975\uff09<\/td><\/tr>
\u9ed8\u8ba4\u6548\u5e94<\/a><\/strong><\/td>Default effect<\/td>\u5f53\u5728\u51e0\u4e2a\u9009\u9879\u4e2d\u8fdb\u884c\u9009\u62e9\u65f6\uff0c\u503e\u5411\u4e8e\u9009\u62e9\u9ed8\u8ba4\u9009\u9879\u3002<\/td><\/tr>
\u65e2\u89c6\u611f<\/a><\/strong><\/td>D\u00e9j\u00e0 vu<\/td>\u5bf9\u67d0\u4e9b\u4e8b\u7269\u6709\u5f3a\u70c8\u7684\u719f\u6089\u611f\uff0c\u4f3c\u4e4e\u66fe\u7ecf\u63a5\u89e6\u8fc7\uff0c\u4e14\u80fd\u9884\u5148\u60f3\u5230\u63a5\u4e0b\u6765\u4f1a\u53d1\u751f\u4ec0\u4e48\u4e8b\u3002<\/td><\/tr>
\u9762\u989d\u6548\u5e94<\/a><\/strong><\/td>Denomination effect<\/td>\u5373\u4f7f\u91d1\u94b1\u603b\u989d\u76f8\u540c\uff0c\u5e26\u5c0f\u9762\u989d\uff08\u5982\u591a\u91cf\u786c\u5e01\uff09\u6bd4\u5e26\u7740\u5927\u9762\u989d\uff08\u5982\u5c11\u91cf\u7eb8\u949e\uff09\u66f4\u5bb9\u6613\u6d88\u8d39\u6389\u3002<\/td><\/tr>
\u5dee\u5f02\u504f\u5dee<\/a><\/strong><\/td>Distinction bias<\/td>\u76f8\u8f83\u4e8e\u5206\u5f00\u6bd4\u8f83\uff0c\u4e24\u4ef6\u4e8b\u7269\u653e\u5728\u4e00\u8d77\u6bd4\u8f83\uff0c\u5dee\u5f02\u4f1a\u663e\u5f97\u66f4\u5927\u3002<\/td><\/tr>
\u5904\u5206\u6548\u5e94<\/a><\/strong><\/td>Disposition effect<\/td>\u6295\u8d44\u8005\u503e\u5411\u4fdd\u7559\u8d2c\u503c\u7684\u8d44\u4ea7\uff0c\u5e76\u51fa\u552e\u4ef7\u503c\u4e0a\u5347\u7684\u8d44\u4ea7\u3002<\/td><\/tr>
\u8fc7\u7a0b\u65f6\u95f4\u5ffd\u89c6<\/a><\/strong><\/td>Duration neglect<\/td>\u8bc4\u4ef7\u4e0d\u6109\u5feb\u53ca\u75db\u82e6\u7684\u7ecf\u9a8c\u65f6\uff0c\u5176\u6301\u7eed\u65f6\u95f4\u5f71\u54cd\u4e0d\u5927\u3002\uff08\u53c2\u89c1\u5cf0\u7ec8\u6cd5\u5219<\/a>\uff09<\/td><\/tr>
\u79fb\u60c5\u9694\u9602<\/a><\/strong><\/td>Empathy gap<\/td>\u60c5\u611f\u51b7\u6de1\u65f6\uff0c\u4f4e\u4f30\u4ed6\u4eba\u60c5\u611f\u7684\u5f3a\u70c8\u7a0b\u5ea6\uff1b\u60c5\u611f\u5f3a\u70c8\u65f6\uff0c\u9ad8\u4f30\u4ed6\u4eba\u60c5\u611f\u7684\u5f3a\u70c8\u7a0b\u5ea6\u3002<\/td><\/tr>
\u539f\u8d4b\u6548\u5e94<\/a><\/strong>\u6216\u7980\u8d4b\u6548\u5e94<\/a><\/strong><\/td>Endowment effect<\/td>\u62e5\u6709\u6216\u5373\u5c06\u62e5\u6709\u67d0\u7269\u54c1\u6216\u8d44\u4ea7\u65f6\uff0c\u5bf9\u5176\u4ef7\u503c\u7684\u8bc4\u4f30\u4f1a\u6bd4\u6ca1\u6709\u65f6\u9ad8\u51fa\u8bb8\u591a\uff0c\u56e0\u800c\u4e0d\u613f\u5931\u53bb\u6216\u653e\u5f03\u5b83\u3002<\/td><\/tr>
\u672c\u8d28\u4e3b\u4e49<\/a><\/strong><\/td>Essentialism<\/td>\u8ba4\u4e3a\u4eba\u4e0e\u4e8b\u7269\u6709\u4e00\u4e9b\u4e0d\u53ef\u6216\u7f3a\u7684\u672c\u8d28<\/a>\uff0c\u5e76\u636e\u6b64\u4e3a\u5b83\u4eec\u5206\u7c7b\uff0c\u5176\u4ed6\u7684\u5206\u7c7b\u65b9\u5f0f\u662f\u9519\u8bef\u7684\u3002<\/td><\/tr>
\u6781\u7aef\u9884\u671f<\/a><\/strong><\/td>Exaggerated expectation<\/td>\u5b9e\u9645<\/a>\u4e0a\u7684\u60c5\u51b5\u901a\u5e38\u4e0d\u5982\u6211\u4eec\u6240\u9884\u671f<\/a>\u7684\u6781\u7aef\u3002<\/td><\/tr>
\u529f\u80fd\u56fa\u7740<\/a><\/strong><\/td>Functional fixedness<\/td>\u53d7\u7269\u54c1\u7684\u4e00\u822c\u7528\u9014\u5c40\u9650\uff0c\u65e0\u6cd5\u60f3\u5230\u7528\u7279\u522b\u7684\u65b9\u5f0f\u5229\u7528\u7269\u54c1\u3002\uff08\u65e0\u6cd5\u8df3\u8131\u6846\u67b6<\/a>\uff09<\/td><\/tr>
\u805a\u7126\u6548\u5e94<\/a><\/strong><\/td>Focusing effect<\/td>\u8fc7\u5206\u5173\u6ce8<\/a>\u4e8b\u60c5\u7684\u67d0\u4e9b\u660e\u663e\u9762\u5411<\/strong>\u3001\u5ffd\u7565\u4e0d\u660e\u663e\u9762\u5411<\/strong>\uff0c\u5bfc\u81f4\u4e0d\u6070\u5f53\u7684\u9884\u671f<\/a>\u3002<\/td><\/tr>
\u4f5b\u745e\u6548\u5e94<\/a><\/strong>\u6216\u5df4\u7eb3\u59c6\u6548\u5e94<\/a><\/strong><\/td>Forer effect; Barnum effect<\/td>\u4eba\u4eec\u4f1a\u628a\u4ed6\u4eec\u8ba4\u4e3a\u662f\u4e3a\u81ea\u5df1\u91cf\u8eab\u5b9a\u505a\u7684\u4eba\u683c\u63cf\u8ff0\u8bc4\u4ef7\u4e3a\u9ad8\u5ea6\u51c6\u786e\uff0c\u800c\u8fd9\u4e9b\u63cf\u8ff0\u5f80\u5f80\u5341\u5206\u6a21\u7cca<\/strong>\u53ca\u666e\u904d\uff0c\u80fd\u653e\u8bf8\u56db\u6d77\u7686\u51c6\u9002\u7528\u4e8e\u5f88\u591a\u4eba\u3002<\/td><\/tr>
\u6846\u67b6\u6548\u5e94<\/a><\/strong><\/td>Framing effect<\/td>\u540c\u4e00\u8d44\u8baf\u4ee5\u4e0d\u540c\u65b9\u5f0f\u5448\u73b0\u65b9\u5f0f\u4f1a\u5e26\u6765\u4e0d\u540c\u60f3\u6cd5\uff0c\u4f8b\u5982\u201c\u6709\u5341\u5206\u4e4b\u4e5d\u7684\u5b58\u6d3b\u7387\u201d\u548c\u201c\u6709\u5341\u5206\u4e4b\u4e00\u7684\u6b7b\u4ea1\u7387\u201d\u3002<\/td><\/tr>
\u9891\u7387\u9519\u89c9<\/a><\/strong><\/td>Frequency illusion<\/td>\u56e0\u6700\u8fd1\u6ce8\u610f\u5230\u4e00\u4ef6\u539f\u5148\u6ca1\u6ce8\u610f\u5230\u7684\u4e8b\uff0c\u5c31\u89c9\u5f97\u8fd9\u4ef6\u4e8b\u5230\u5904\u90fd\u5728\u53d1\u751f\u3002\uff08\u53c2\u89c1\u8fd1\u56e0\u9519\u89c9<\/a>\u53ca\u9009\u62e9\u504f\u5dee<\/a>\uff09<\/td><\/tr>
\u96be\u6613\u6548\u5e94<\/a><\/strong><\/td>Hard-easy effect<\/td>\u9ad8\u4f30\u81ea\u8ba4\u56f0\u96be\u4e4b\u4e8b\u7684\u96be\u5ea6\uff0c\u4f4e\u4f30\u81ea\u8ba4\u7b80\u5355\u4e4b\u4e8b\u7684\u96be\u5ea6\u3002<\/td><\/tr>
\u540e\u89c1\u4e4b\u660e\u504f\u8bef<\/a><\/strong><\/td>Hindsight bias<\/td>\u53c8\u79f0\u201c\u6211\u65e9\u5c31\u77e5\u9053\u4e86\u201d\u3001\u201c\u9a6c\u540e\u70ae<\/a>\u201d\u3001\u201c\u4e8b\u540e\u8bf8\u845b<\/a>\u201d\u3002\u4e00\u65e6\u4e8b\u4ef6\u53d1\u751f\u6216\u53d1\u5c55\u8fc7\u540e\uff0c\u4eba\u4eec\u4f1a\u8ba4\u4e3a\u8fd9\u4e9b\u4e8b\u4ef6\u66f4\u5bb9\u6613\u9884\u6d4b\uff0c\u8ba4\u5b9a\u81ea\u5df1\u4e8b\u524d\u5c31\u80fd\u9884\u6d4b\u5176\u53d1\u751f\u4e0e\u53d1\u5c55\u3002<\/td><\/tr>
\u654c\u5bf9\u5a92\u4f53\u6548\u5e94<\/a><\/strong><\/td>Hostile media effect<\/td>\u5bf9\u4e8e\u7acb\u573a\u4e0e\u81ea\u5df1\u4e0d\u540c\u7684\u5a92\u4f53\uff0c\u603b\u8ba4\u4e3a\u5b83\u4eec\u6709\u504f\u89c1\u3001\u4e0d\u5ba2\u89c2\u3002<\/td><\/tr>
\u5f53\u4e0b\u504f\u5dee<\/a><\/strong>\u3001\u73b0\u65f6\u504f\u5dee (\u82f1\u8bed\uff1aPresent bias<\/a> \uff08\u9875\u9762\u5b58\u6863\u5907\u4efd<\/a>\uff0c\u5b58\u4e8e\u4e92\u8054\u7f51\u6863\u6848\u9986<\/a>\uff09)<\/strong>\u3001\u6216\u53cc\u66f2\u6298\u73b0<\/a><\/strong><\/td>Current moment bias; Present-bias<\/a> \uff08\u9875\u9762\u5b58\u6863\u5907\u4efd<\/a>\uff0c\u5b58\u4e8e\u4e92\u8054\u7f51\u6863\u6848\u9986<\/a>\uff09; Hyperbolic discounting<\/td>\u770b\u91cd\u5f53\u4e0b\u5229\u76ca\u3001\u4f4e\u4f30\u957f\u8fdc\u5229\u76ca\u3002\uff08\u77ed\u89c6\u8fd1\u5229<\/a>\uff09\u5f97\u5230\u5229\u76ca\u524d\u7684\u5ef6\u8fdf\u65f6\u95f4\u8d8a\u957f\uff0c\u5bf9\u5229\u76ca\u7684\u4ef7\u503c\u8bc4\u4f30\u4fbf\u6253\u8d8a\u591a\u6298\u6263\uff0c\u4e24\u8005\u5173\u7cfb\u8fd1\u4f3c\u4e8e\u53cc\u66f2\u7ebf<\/a>\u3002<\/td><\/tr>
\u53ef\u8fa8\u8bc6\u53d7\u5bb3\u8005\u6548\u5e94<\/a><\/strong><\/td>Identifiable victim effect<\/td>\u5bf9\u4e8e\u5c11\u6570\u800c\u5bb9\u6613\u8fa8\u8bc6\u7684\u53d7\u5bb3\u8005\u6216\u6f5c\u5728\u53d7\u5bb3\u8005<\/strong>\u53cd\u5e94\u8fc7\u5927\uff0c\u800c\u5bf9\u591a\u6570\u800c\u4e0d\u6613\u8fa8\u8bc6\u7684\u53d7\u5bb3\u8005\u6216\u6f5c\u5728\u53d7\u5bb3\u8005<\/strong>\u53cd\u5e94\u8fc7\u5c0f\u3002<\/td><\/tr>
\u5b9c\u5bb6\u6548\u5e94<\/a><\/strong><\/td>IKEA effect<\/td>\u5bf9\u4e8e\u9700\u8981\u81ea\u884c\u7ec4\u88c5\u7684\u4e1c\u897f\u7ed9\u4e88\u4e0d\u6210\u6bd4\u4f8b\u7684\u9ad8\u8bc4\u4ef7\uff0c\u800c\u4e0d\u7ba1\u5176\u5b9e\u9645\u54c1\u8d28\u3002\u6b64\u79f0\u547c\u51fa\u81ea\u4e8e\u7ecf\u5e38\u8d29\u552e\u7ec4\u88c5\u5bb6\u5177\u7684\u5b9c\u5bb6<\/a>\u3002<\/td><\/tr>
\u63a7\u5236\u7684\u9519\u89c9<\/a><\/strong><\/td>Illusion of control<\/td>\u9ad8\u4f30\u81ea\u5df1\u5bf9\u5916\u5728\u4e8b\u4ef6\u7684\u5f71\u54cd\u529b\uff0c\u8ba4\u4e3a\u4e8b\u60c5\u662f\u53d7\u81ea\u5df1\u63a7\u5236\u6216\u5f71\u54cd\uff0c\u4f46\u5b9e\u9645\u4e0a\u53ef\u80fd\u4e0e\u81ea\u5df1\u6beb\u65e0\u5173\u7cfb\u3002<\/td><\/tr>
\u6548\u5ea6\u7684\u9519\u89c9<\/a><\/strong><\/td>Illusion of validity<\/td>\u9ad8\u4f30\u9762\u8c08\u6216\u76f4\u63a5\u89c2\u5bdf\u7684\u6548\u5ea6\u4e14\u80fd\u7528\u4e8e\u63d0\u4f9b\u9884\u6d4b\uff0c\u5373\u4f7f\u8bc1\u636e\u6307\u51fa\u5b83\u4eec\u5f71\u54cd\u751a\u5fae\u3002<\/td><\/tr>
\u9519\u89c9\u76f8\u5173<\/a><\/strong><\/td>Illusory correlation<\/td>\u8ba4\u4e3a\u4e24\u4ef6\u4e8b\u5e94\u8be5\u6709\u5173\u7cfb\u65f6\uff0c\u4fbf\u4f1a\u5728\u68c0\u89c6\u7ecf\u9a8c\u4e0e\u6570\u636e\u65f6\u89c9\u5f97\u5b83\u4eec\u7ecf\u5e38\u4e00\u8d77\u53d1\u751f\uff0c\u5373\u4f7f\u5b83\u4eec\u4e00\u8d77\u53d1\u751f\u7eaf\u7cb9\u662f\u968f\u673a\u73b0\u8c61\u3002<\/td><\/tr>
\u5f71\u54cd\u529b\u504f\u5dee<\/a><\/strong><\/td>Impact bias<\/td>\u9ad8\u4f30\u611f\u89c9<\/a>\u7684\u5f3a\u5ea6\u6216\u6301\u7eed\u65f6\u95f4\u3002<\/td><\/tr>
\u8d44\u8baf\u504f\u5dee<\/a><\/strong><\/td>Information bias<\/td>\u503e\u5411\u5bfb\u6c42\u66f4\u591a\u8d44\u8baf\u4ee5\u505a\u51fa\u51b3\u7b56\uff0c\u5373\u4f7f\u5bfb\u6c42\u7684\u8d44\u8baf\u5bf9\u51b3\u7b56\u6ca1\u6709\u5e2e\u52a9<\/strong>\u3002<\/td><\/tr>
\u6c89\u6ca1\u6210\u672c\u8c2c\u8bef<\/a><\/strong>\u6216\u4e0d\u7406\u6027\u589e\u503c<\/a><\/strong><\/td>Sunk cost fallacy; Irrational escalation<\/td>\u7531\u4e8e\u5148\u524d\u5df2\u5728\u67d0\u4e8b\u4e0a\u6295\u8d44\u5f88\u591a\uff0c\u5373\u4f7f\u65b0\u8bc1\u636e\u663e\u793a\u90a3\u662f\u4e0d\u597d\u7684\u9009\u62e9\uff0c\u4ecd\u503e\u5411\u4e8e\u52a0\u91cd\u6295\u8d44\u3002<\/td><\/tr>
\u5984\u4e0b\u7ed3\u8bba<\/a><\/strong><\/td>Jumping to conclusions<\/td>\u6839\u636e\u5c11\u8bb8\u7684\u8d44\u8baf\u5373\u505a\u51fa\u5224\u65ad\u4e0e\u51b3\u7b56\u3002\u5982\u8bdb\u5fc3<\/a>\u3001\u9884\u8a00<\/a>\u3001\u8d34\u6807\u7b7e<\/a>\u7b49\u7b49\u3002<\/td><\/tr>
\u516c\u6b63\u4e16\u754c\u7406\u8bba<\/a><\/strong><\/td>Just-world hypothesis<\/td>\u76f8\u4fe1\u4e16\u754c\u662f\u516c\u5e73<\/a>\u7684\uff08\u4e0a\u5e1d<\/a>\u662f\u516c\u5e73\u7684\uff09\uff0c\u53d1\u751f\u5728\u8eab\u4e0a\u7684\u90fd\u662f\u5e94\u5f97\u7684\uff0c\u628a\u96be\u4ee5\u89e3\u91ca\u7684\u4e0d\u516c\u5e73\u5f52\u8d23\u4e8e\u53d7\u5bb3\u8005\u7684\u62a5\u5e94\u6216\u662f\u6e90\u81ea\u4e8e\u53d7\u5bb3\u8005\u5185\u5728\u672c\u8d28\u7684\u7ed3\u679c\u3002\uff08\u57fa\u672c\u5f52\u56e0\u8c2c\u8bef<\/a>\uff09<\/td><\/tr>
\u5c11\u5373\u662f\u597d\u7684\u6548\u5e94<\/a><\/strong><\/td>Less-is-better effect<\/td>\u5206\u5f00\u8bc4\u4f30\u65f6\u503e\u5411\u9009\u62e9\u8f83\u5c0f\u7ec4\u7684\u4e1c\u897f\uff0c\u4e00\u8d77\u8bc4\u4f30\u65f6\u503e\u5411\u9009\u62e9\u8f83\u5927\u7ec4\u7684\u4e1c\u897f\u3002<\/td><\/tr>
\u635f\u5931\u8d8b\u907f<\/a><\/strong><\/td>Loss aversion<\/td>\u8ba4\u4e3a\u653e\u5f03\u4e00\u4ef6\u4e8b\u7269\u7684\u6548\u76ca\u635f\u5931\u5927\u4e8e\u5f97\u5230\u4e00\u4ef6\u4e8b\u7269\u7684\u6548\u76ca\u6240\u5f97\u3002\uff08\u53c2\u89c1\u6c89\u6ca1\u6210\u672c<\/a>\u4e0e\u539f\u8d4b\u6548\u5e94<\/a>\uff09<\/td><\/tr>
\u591a\u770b\u6548\u5e94<\/a><\/strong>\u6216\u5355\u7eaf\u63a5\u89e6\u6548\u5e94<\/a><\/strong><\/td>Mere exposure effect<\/td>\u5bf9\u719f\u6089\u7684\u4eba\u4e0e\u4e8b\u4ea7\u751f\u8fc7\u591a\u7684\u597d\u611f\u3002<\/td><\/tr>
\u8d27\u5e01\u9519\u89c9<\/a><\/strong><\/td>Money illusion<\/td>\u4e13\u6ce8\u4e8e\u8d27\u5e01\u540d\u76ee\u4e0a\uff08\u8868\u9762\u4e0a\uff09\u7684\u4ef7\u503c<\/a>\uff0c\u800c\u975e\u5176\u5b9e\u8d28<\/a>\u8d2d\u4e70\u529b<\/a>\u3002<\/td><\/tr>
\u9053\u5fb7\u8bb8\u53ef\u8bc1\u6548\u5e94<\/a><\/strong><\/td>Moral credential effect<\/td>\u7531\u4e8e\u5f97\u5230\u4e86\u67d0\u4e9b\u9ad8\u9053\u5fb7\u7684\u8bc4\u4ef7\u6216\u8ba4\u8bc1\uff0c\u800c\u8ba4\u4e3a\u81ea\u5df1\u505a\u5f97\u591f\u597d\uff0c\u53cd\u800c\u5728\u5176\u4ed6\u9762\u5411\u505a\u4e86\u76f8\u53cd\u7684\u4e8b\u3002\u4f8b\u5982\u6295\u8eab\u73af\u4fdd\u6d3b\u52a8\u5e76\u53d7\u5230\u8868\u626c\u7684\u4eba\uff0c\u5f80\u5f80\u5ffd\u7565\u4e86\u8bb8\u591a\u81ea\u5df1\u7684\u4e0d\u73af\u4fdd\u884c\u4e3a\u3002<\/td><\/tr>
\u6b63\u9762\u6548\u5e94<\/a><\/strong>\u4e0e\u8d1f\u9762\u6548\u5e94<\/a><\/strong><\/td>Positivity effect; Negativity effect<\/td>\u8bc4\u4ef7\u559c\u6b22\u7684\u4eba\u7684\u884c\u4e3a\u65f6\uff0c\u628a\u4ed6\u4eec\u505a\u597d\u4e8b\u5f52\u56e0\u4e8e\u5185\u5728\u672c\u8d28\uff0c\u800c\u628a\u4ed6\u4eec\u505a\u574f\u4e8b\u5f52\u56e0\u4e8e\u73af\u5883\u56e0\u7d20\u3002\u8bc4\u4ef7\u4e0d\u559c\u6b22\u7684\u4eba\u7684\u884c\u4e3a\u65f6\uff0c\u628a\u4ed6\u4eec\u505a\u597d\u4e8b\u5f52\u56e0\u4e8e\u73af\u5883\u56e0\u7d20\uff0c\u800c\u628a\u4ed6\u4eec\u505a\u574f\u4e8b\u5f52\u56e0\u4e8e\u5185\u5728\u672c\u8d28\u3002\uff08\u57fa\u672c\u5f52\u56e0\u8c2c\u8bef<\/a>\uff09<\/td><\/tr>
\u8d1f\u9762\u504f\u5dee<\/a><\/strong><\/td>Negativity bias<\/td>\u5bb9\u6613\u56de\u60f3\u6d88\u6781\u7684\u56de\u5fc6<\/a>\u800c\u4e0d\u6613\u56de\u60f3\u79ef\u6781\u7684\u3002<\/td><\/tr>
\u5ffd\u7565\u53ef\u80fd\u6027<\/a><\/strong><\/td>Neglect of probability<\/td>\u5bf9\u4e8e\u4e0d\u786e\u5b9a<\/a>\u7684\u4e8b\uff0c\u65e0\u6cd5\u51c6\u786e\u8bc4\u4f30\u5176\u53d1\u751f\u51e0\u7387\uff0c\u4e0d\u662f\u5b8c\u5168\u65e0\u89c6\uff0c\u4fbf\u662f\u8fc7\u5206\u9ad8\u4f30\u3002<\/td><\/tr>
\u6b63\u5e38\u5316\u504f\u5dee<\/a><\/strong><\/td>Normalcy bias<\/td>\u6839\u636e\u8fc7\u53bb\u7684\u7ecf\u9a8c<\/a>\u4e86\u89e3\u60c5\u51b5\uff0c\u4f4e\u4f30\u5927\u707e\u96be\u7684\u53ef\u80fd\u6027\u53ca\u5176\u5f71\u54cd\u529b\uff0c\u56e0\u800c\u5e73\u65f6\u6ca1\u6709\u9884\u505a\u51c6\u5907\uff0c\u6216\u5728\u707e\u96be\u53d1\u751f\u65f6\u8f7b\u5ffd\u4e25\u91cd\u6027\u3001\u7f3a\u4e4f\u5e94\u53d8\u3002<\/td><\/tr>
\u4e0d\u4f5c\u4e3a\u504f\u5dee<\/a><\/strong><\/td>Omission bias<\/td>\u8ba4\u4e3a\u4e3b\u52a8\u4f5c\u4e3a\u5bfc\u81f4\u4f24\u5bb3\u6bd4\u88ab\u52a8\u4e0d\u4f5c\u4e3a\u5bfc\u81f4\u4f24\u5bb3\u66f4\u7cdf\u7cd5\u3001\u66f4\u4e0d\u9053\u5fb7<\/a>\uff0c\u5373\u4f7f\u540e\u8005\u4f24\u5bb3\u4e0e\u524d\u8005\u76f8\u5f53\u6216\u6bd4\u540e\u8005\u66f4\u591a\u3002<\/td><\/tr>
\u4e50\u89c2\u504f\u8bef<\/a><\/strong><\/td>Optimism bias<\/td>\u4f4e\u4f30\u8d1f\u9762\u4e8b\u4ef6\u53d1\u751f\u5728\u81ea\u5df1\u8eab\u4e0a\u7684\u53ef\u80fd\u6027\uff0c\u76f8\u4fe1\u81ea\u5df1\u6bd4\u5176\u4ed6\u4eba\u66f4\u4e0d\u5bb9\u6613\u9047\u5230\u4e0d\u597d\u7684\u4e8b\u3002\uff08\u53c2\u89c1\u4e00\u53a2\u60c5\u613f<\/a>\uff09<\/td><\/tr>
\u9e35\u9e1f\u6548\u5e94<\/a><\/strong><\/td>Ostrich effect<\/td>\u5ffd\u89c6<\/a>\u660e\u663e\u7684\uff08\u8d1f\u9762\uff09\u60c5\u51b5\u3002<\/td><\/tr>
\u7ed3\u679c\u504f\u5dee<\/a><\/strong><\/td>Outcome bias<\/td>\u4eba\u4eec\u5728\u8bc4\u4ef7\u51b3\u7b56\u597d\u574f\u65f6\uff0c\u662f\u6839\u636e\u5176\u6700\u7ec8\u7ed3\u679c<\/a>\u6765\u505a\u5224\u65ad\uff0c\u800c\u975e\u6839\u636e\u4e0b\u51b3\u7b56\u5f53\u65f6\u7684\u51b3\u7b56\u54c1\u8d28<\/a>\u3002<\/td><\/tr>
\u8fc7\u5ea6\u81ea\u4fe1\u6548\u5e94<\/a><\/strong><\/td>Overconfidence effect<\/td>\u4eba\u4eec\u666e\u904d\u503e\u5411\u9ad8\u4f30\u81ea\u8eab\u6210\u529f\u6267\u884c\u7279\u5b9a\u4efb\u52a1\u7684\u80fd\u529b\uff0c\u4f8b\u5982\uff1a\u8fc7\u5ea6\u76f8\u4fe1\u81ea\u5df1\u56de\u7b54<\/a>\u3001\u51b3\u7b56<\/a>\u3001\u5224\u65ad<\/a>\u7684\u6b63\u786e\u6027<\/a>\u3002<\/td><\/tr>
\u7a7a\u60f3\u6027\u9519\u89c6<\/a><\/strong><\/td>Pareidolia<\/td>\u4ece\u6a21\u7cca\u3001\u968f\u673a\u7684\u5f71\u50cf\u4e2d\u770b\u51fa\u6709\u610f\u4e49\u7684\u4e1c\u897f\uff0c\u4f8b\u5982\u4ece\u4e91\u6735\u4e2d\u770b\u51fa\u8138\u5b54\u3002\u6709\u65f6\u4e5f\u5ef6\u4f38\u6307\u542c\u89c9\uff0c\u5982\u5f55\u97f3\u5e26\u5012\u64ad\u65f6\u542c\u51fa\u7279\u522b\u7684\u8baf\u606f\u3002<\/td><\/tr>
\u60b2\u89c2\u504f\u8bef<\/a><\/strong><\/td>Pessimism bias<\/td>\u9ad8\u4f30\u8d1f\u9762\u4e8b\u4ef6\u53d1\u751f\u5728\u81ea\u5df1\u8eab\u4e0a\u7684\u53ef\u80fd\u6027\uff0c\u76f8\u4fe1\u81ea\u5df1\u6bd4\u5176\u4ed6\u4eba\u66f4\u5bb9\u6613\u9047\u5230\u4e0d\u597d\u7684\u4e8b\u3002\u60a3\u6709\u6291\u90c1\u75c7<\/a>\u7684\u4eba\u5c24\u5176\u660e\u663e\u3002\uff08\u53c2\u89c1\u60b2\u89c2\u4e3b\u4e49<\/a>\uff09<\/td><\/tr>
\u89c4\u5212\u8c2c\u8bef<\/a><\/strong><\/td>Planning fallacy<\/td>\u4f4e\u4f30\u5b8c\u6210\u4e00\u4ef6\u4e8b\u9700\u8981\u7684\u65f6\u95f4\u3002<\/td><\/tr>
\u6b63\u9762\u7ed3\u679c\u504f\u5dee<\/a><\/strong>\u6216\u60c5\u4ef7\u6548\u5e94<\/a><\/strong><\/td>Positive outcome bias; Valence effect<\/td>\u8ba4\u4e3a\u597d\u4e8b\u6bd4\u574f\u4e8b\u66f4\u53ef\u80fd\u53d1\u751f\u3002<\/td><\/tr>
\u8d2d\u540e\u5408\u7406\u5316<\/a><\/strong><\/td>Post-purchase rationalization<\/td>\u8d2d\u4e70\u540e\u628a\u4e4b\u524d\u7684\u8d2d\u4e70\u51b3\u5b9a\u5408\u7406\u5316\uff0c\u5373\u4f7f\u4e70\u4e0b\u7684\u4ea7\u54c1\u592a\u8fc7\u6602\u8d35\u6216\u53d1\u73b0\u7455\u75b5\u3002<\/td><\/tr>
\u652f\u6301\u521b\u65b0\u504f\u5dee<\/a><\/strong><\/td>Pro-innovation bias<\/td>\u5bf9\u65b0\u6280\u672f\u8fc7\u5ea6\u4e50\u89c2\u3001\u9ad8\u4f30\u5176\u5b9e\u7528\u6027\uff0c\u5ffd\u89c6\u5176\u9650\u5236\u53ca\u5f31\u70b9\u3002<\/td><\/tr>
\u5047\u786e\u5b9a\u6027\u6548\u5e94<\/a><\/strong><\/td>Pseudocertainty effect<\/td>\u9884\u671f\u7ed3\u679c\u662f\u6b63\u9762\u5c31\u9009\u62e9\u907f\u5f00\u98ce\u9669\uff0c\u9884\u671f\u7ed3\u679c\u662f\u8d1f\u9762\u5c31\u9009\u62e9\u5bfb\u6c42\u98ce\u9669\u3002<\/td><\/tr>
\u5bf9\u6297\u5fc3\u7406<\/a><\/strong>\u6216\u6297\u62d2\u5fc3\u7406<\/a><\/strong><\/td>Reactance<\/td>\u4ed6\u4eba\u8981\u6c42\u505a\u6216\u4e0d\u505a\u67d0\u4e8b\u65f6\uff0c\u6709\u505a\u76f8\u53cd\u4e8b\u7684\u51b2\u52a8\uff0c\u5c24\u5176\u8fd9\u8981\u6c42\u5bf9\u81ea\u7531\u3001\u81ea\u4e3b\u9020\u6210\u5a01\u80c1\u65f6\u3002\uff08\u53c2\u89c1\u9006\u53cd\u5fc3\u7406<\/a> \uff09\u3002<\/td><\/tr>
\u53cd\u52a8\u8d2c\u4f4e<\/a><\/strong><\/td>Reactive devaluation<\/td>\u8d2c\u4f4e\u654c\u5bf9\u8005\u7684\u8981\u6c42\u6216\u65b9\u6848\uff0c\u6216\u5f53\u654c\u5bf9\u8005\u5728\u67d0\u4e8b\u8ba9\u6b65\u65f6\uff0c\u89c9\u5f97\u6b64\u65f6\u4e0d\u518d\u6709\u5438\u5f15\u529b\u3002<\/td><\/tr>
\u65b0\u8bcd\u9519\u89c9<\/a><\/strong><\/td>Recency illusion<\/td>\u611f\u89c9\u67d0\u4e9b\u5b57\u8bcd\u6216\u8bed\u53e5\u662f\u65b0\u53d1\u660e\u7684\uff0c\u7136\u800c\u5b83\u5b9e\u9645\u4e0a\u5386\u53f2\u60a0\u4e45\u3002\u4f8b\u5982\u82f1\u8bed\u7528\u201cthey\u201d\u8868\u8fbe\u5355\u6570\u4e0d\u786e\u5b9a\u6027\u522b\u5bf9\u8c61\u3001\u201cyou and I\u201d\uff08\u800c\u975eyou and me\uff09\u3002<\/td><\/tr>
\u81ea\u5236\u504f\u5dee<\/a><\/strong><\/td>Restraint bias<\/td>\u9ad8\u4f30\u81ea\u5df1\u5bf9\u8bf1\u60d1<\/a>\u7684\u81ea\u5236\u529b<\/a>\u3002<\/td><\/tr>
\u97f5\u5f8b\u5f53\u7406\u7531\u6548\u5e94<\/a><\/strong><\/td>Rhyme as reason effect<\/td>\u8ba4\u4e3a\u6709\u97f5\u5f8b\u7684\u8bed\u53e5\u6bd4\u8f83\u6709\u9053\u7406\u3002\uff08\u62bc\u97f5<\/a>\uff09<\/td><\/tr>
\u98ce\u9669\u4ee3\u507f<\/a><\/strong>\u6216\u4f69\u5179\u66fc\u6548\u5e94<\/a><\/strong><\/td>Risk compensation; Peltzman effect<\/td>\u611f\u89c9\u60c5\u51b5\u5b89\u5168\u65f6\uff0c\u4f1a\u503e\u5411\u5192\u66f4\u5927\u7684\u98ce\u9669\u3002<\/td><\/tr>
\u9009\u62e9\u6027\u6ce8\u610f<\/a><\/strong>\u6216\u9009\u62e9\u6027\u77e5\u89c9<\/a><\/strong><\/td>Selective attention; Selective perception<\/td>\u7531\u4e8e\u5bf9\u4eba\u6216\u4e8b\u6709\u7279\u5b9a\u671f\u5f85\uff0c\u56e0\u800c\u503e\u5411\u4e8e\u6ce8\u610f\u7b26\u5408\u671f\u5f85\u7684\u4e8b\u4ef6\uff0c\u5ffd\u7565\u6216\u5fd8\u5374\u4e0d\u7b26\u5408\u671f\u5f85\u7684\u4e8b\u4ef6\u3002<\/td><\/tr>
\u585e\u9ea6\u5c14\u7ef4\u65af\u53cd\u5c04<\/a><\/strong><\/td>Semmelweis reflex<\/td>\u6761\u4ef6\u53cd\u5c04\u822c\u5730\u5426\u5b9a\u3001\u62d2\u7edd\u65b0\u8bc1\u636e<\/a>\u6216\u65b0\u77e5\u8bc6<\/a>\uff0c\u56e0\u5176\u62b5\u89e6\u73b0\u6709\u7684\u5e38\u89c4<\/a>\u3001\u4fe1\u4ef0<\/a>\u6216\u4ef7\u503c\u89c2<\/a>\uff08\u8ba4\u77e5\u95ed\u5408<\/a>\uff09[34]<\/a><\/sup>\u3002<\/td><\/tr>
\u793e\u4f1a\u6bd4\u8f83\u504f\u5dee<\/a><\/strong><\/td>Social comparison bias<\/td>\u6297\u62d2\u96c7\u7528\u3001\u63d0\u62d4\u4e0e\u81ea\u5df1\u6709\u76f8\u4f3c\u4e13\u957f\u7684\u4eba\u3002<\/td><\/tr>
\u5b89\u4e8e\u73b0\u72b6\u504f\u5dee<\/a><\/strong><\/td>Status quo bias<\/td>\u503e\u5411\u4e8e\u7ef4\u6301\u73b0\u72b6\u3002\uff08\u53c2\u89c1\u635f\u5931\u8d8b\u907f<\/a>\u3001\u539f\u8d4b\u6548\u5e94<\/a>\u3001\u7cfb\u7edf\u6b63\u5f53\u5316<\/a>\uff09<\/td><\/tr>
\u523b\u677f\u5370\u8c61<\/a><\/strong><\/td>Stereotyping<\/td>\u6839\u636e\u4e8b\u7269\u7684\u6240\u5c5e\u7c7b\u522b\u6216\u7fa4\u4f53\u5224\u65ad\u5176\u7279\u8d28\uff0c\u800c\u5ffd\u7565\u5176\u72ec\u7279\u6027\u3002<\/td><\/tr>
\u65af\u5fb7\u54e5\u5c14\u6469\u75c7\u5019\u7fa4<\/a><\/strong><\/td>Stockholm syndrome<\/td>\u88ab\u5bb3\u8005\u8ba4\u540c\u52a0\u5bb3\u8005\u7684\u67d0\u4e9b\u89c2\u70b9\u548c\u60f3\u6cd5\uff0c\u5e76\u89c9\u5f97\u81ea\u5df1\u4e0d\u518d\u53d7\u5230\u5a01\u80c1\u3002<\/td><\/tr>
\u5206\u5f00\u52a0\u603b\u6548\u5e94<\/a><\/strong><\/td>Subadditivity effect<\/td>\u8bc4\u4f30\u53ef\u80fd\u6027\u65f6\uff0c\u5bf9\u6574\u4f53\u76f4\u63a5\u8bc4\u4f30\u4f4e\u4e8e\u5bf9\u5404\u90e8\u5206\u5206\u522b\u8bc4\u4f30\u518d\u52a0\u603b\u3002<\/td><\/tr>
\u4e3b\u89c2\u9a8c\u8bc1<\/a><\/strong>\u6216\u4e3b\u89c2\u786e\u8ba4<\/a><\/strong><\/td>Subjective validation<\/td>\u76f8\u4fe1\u67d0\u4e8b\u662f\u5bf9\u7684\uff0c\u5c31\u611f\u89c9\u5b83\u662f\u5bf9\u7684\u3002\u4e5f\u4f1a\u628a\u5de7\u5408<\/a>\u7684\u4e8b\u5f53\u505a\u6709\u5173\u8054<\/a>\u3002<\/td><\/tr>
\u5e78\u5b58\u8005\u504f\u5dee<\/a><\/strong><\/td>Survivorship bias<\/td>\u4e13\u6ce8\u4e8e\u4ece\u67d0\u4e2a\u8fc7\u7a0b\u4e2d\u5b58\u6d3b\u4e0b\u6765\u7684\u4eba\u6216\u4e8b\u5bfb\u627e\u5f31\u70b9\u610f\u6b32\u8865\u5f3a\uff0c\u5374\u5ffd\u7565\u4e86\u6700\u5927\u7684\u5f31\u70b9\u66f4\u53ef\u80fd\u5728\u672a\u5b58\u6d3b\u7684\u4eba\u6216\u4e8b\u4e4b\u4e2d\u3002<\/td><\/tr>
\u7701\u65f6\u504f\u5dee<\/a><\/strong><\/td>Time-saving bias<\/td>\u4f4e\u901f\u884c\u8fdb\u65f6\uff0c\u4f4e\u4f30\u53ef\u8282\u7701\u7684\u65f6\u95f4\u6216\u9ad8\u4f30\u4f1a\u635f\u5931\u7684\u65f6\u95f4\uff1b\u9ad8\u901f\u884c\u8fdb\u65f6\uff0c\u9ad8\u4f30\u53ef\u8282\u7701\u7684\u65f6\u95f4\u6216\u4f4e\u4f30\u4f1a\u635f\u5931\u7684\u65f6\u95f4\u3002<\/td><\/tr>
\u5355\u4f4d\u504f\u8bef<\/a><\/strong><\/td>Unit bias<\/td>\u8ba4\u4e3a\u8ba1\u91cf\u5355\u4f4d<\/a>\u53cd\u6620\u5408\u7406\u7a0b\u5ea6\u3002\u4f8b\u5982\u8ba4\u4e3a\u4e00\u74f6\u3001\u4e00\u7897\u3001\u4e00\u76d8\u98df\u7269\u662f\u6700\u5408\u7406\u7684\u98df\u7528\u91cf\u3002<\/td><\/tr>
\u719f\u6089\u8def\u7ebf\u6548\u5e94<\/a><\/strong><\/td>Well travelled road effect<\/td>\u4f4e\u4f30\u91c7\u7528\u719f\u6089<\/a>\u8def\u7ebf\u7684\u65f6\u95f4\uff0c\u9ad8\u4f30\u91c7\u7528\u964c\u751f<\/a>\u8def\u7ebf\u7684\u65f6\u95f4\u3002<\/td><\/tr>
\u53ea\u770b\u6574\u4f53\u6548\u5e94<\/a><\/strong><\/td>Whole only effect<\/td>\u9009\u9879\u4e3a\u6574\u5957\u65b9\u6848\u65f6\uff0c\u5ffd\u7565\u4e2a\u522b\u90e8\u5206\u6709\u534f\u5546<\/a>\u7684\u53ef\u80fd\u3002<\/td><\/tr>
\u96f6\u98ce\u9669\u504f\u8bef<\/a><\/strong><\/td>Zero-risk bias<\/td>\u504f\u597d\u628a\u5c0f\u98ce\u9669\u964d\u4e3a\u96f6\uff08\u4f8b\u5982 1% \u2192 0%\uff09\uff0c\u80dc\u8fc7\u628a\u5927\u98ce\u9669\u964d\u4f4e\u66f4\u591a\uff08\u4f8b\u5982 5% \u2192 2%\uff09\u3002<\/td><\/tr>
\u96f6\u548c\u6377\u601d<\/a><\/strong><\/td>Zero-sum heuristic<\/td>\u76f4\u89c9\u5224\u5b9a\u4e00\u4e2a\u60c5\u51b5\u662f\u96f6\u548c\u7684\uff0c\u4f46\u5b9e\u60c5\u672a\u5fc5\u5982\u6b64\u3002\u96f6\u548c<\/a>\u4e00\u8bcd\u6e90\u4e8e\u535a\u5f08\u8bba<\/a>\uff0c\u662f\u6307\u6210\u529f\u4e0e\u5931\u8d25\u671f\u671b\u503c\u7684\u603b\u548c\u4e3a\u96f6\u3002<\/td><\/tr>
\u5524\u9192\u7684\u9519\u8bef\u5f52\u56e0<\/a><\/strong><\/td>misattribution of arousal<\/td>\u5f53\u4ea7\u751f\u83ab\u540d\u7684\u751f\u7406<\/a>\u53cd\u5e94\u6216\u8ba4\u77e5\u77db\u76fe<\/a>\u65f6\uff0c\u4eba\u4eec\u53ef\u80fd\u4f1a\u4ee5\u4e0d\u76f8\u5e72\u7684\u7406\u7531\u6765\u5408\u7406\u5316\u4e4b\u3002\u53c8\u79f0\u4e3a\u540a\u6865\u6548\u5e94<\/a><\/strong>\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u793e\u4f1a\u504f\u5dee<\/h2>\n\n\n\n

\u8fd9\u4e9b\u504f\u5dee\u5927\u591a\u662f\u7531\u5f52\u56e0\u504f\u5dee<\/a>\u5bfc\u81f4\u3002<\/p>\n\n\n\n

\u540d\u79f0<\/th>\u539f\u6587<\/th>\u63cf\u8ff0<\/th><\/tr><\/thead>
\u884c\u4e3a\u8005-\u89c2\u5bdf\u8005\u504f\u5dee<\/a><\/strong><\/td>Actor-observer bias<\/td>\u5f53\u4eba\u4eec\u8bc4\u4ef7\u81ea\u5df1\u7684\u884c\u4e3a\u65f6\uff0c\u4e5f\u5c31\u662f\u8eab\u4e3a\u884c\u52a8\u8005\u65f6\uff0c\u4f1a\u5c06\u81ea\u8eab\u7684\u884c\u52a8\u5f52\u56e0\u4e8e\u5916\u90e8\u56e0\u7d20\uff1b\u800c\u8bc4\u4ef7\u4ed6\u4eba\u7684\u884c\u4e3a\u65f6\uff0c\u4e5f\u5c31\u662f\u8eab\u4e3a\u89c2\u5bdf\u8005\u65f6\uff0c\u4f1a\u5c06\u4ed6\u4eba\u7684\u884c\u52a8\u5f52\u56e0\u4e8e\u5185\u5728\u7684\u4e2a\u4eba\u7279\u5f81\uff08\u53c2\u89c1\u57fa\u672c\u5f52\u56e0\u9519\u8bef<\/a>\uff09\u3002<\/td><\/tr>
\u9632\u536b\u6027\u5f52\u56e0<\/a><\/strong><\/td>Defensive attribution hypothesis<\/td>\u628a\u81ea\u5df1\u7684\u6210\u529f\u5f52\u7ed3\u4e8e\u5185\u5728\u56e0\u7d20\uff0c\u5982\u81ea\u5df1\u7684\u80fd\u529b\u3001\u54c1\u8d28\u3001\u52aa\u529b\u7b49\uff1b\u628a\u81ea\u5df1\u7684\u5931\u8d25\u5f52\u7ed3\u4e8e\u5916\u5728\u56e0\u7d20\uff0c\u5982\u8fd0\u6c14\u574f\u3001\u73af\u5883\u6076\u52a3\u3001\u4efb\u52a1\u56f0\u96be\u7b49\u3002<\/td><\/tr>
\u8fbe\u514b\u6548\u5e94<\/a><\/strong><\/td>Dunning\u2013Kruger effect<\/td>\u80fd\u529b\u8fc7\u5dee\u7684\u4eba\u7531\u4e8e\u65e0\u6cd5\u5206\u8fa8\u80fd\u529b\u597d\u4e0e\u80fd\u529b\u5dee\u7684\u4e0d\u540c\uff0c\u56e0\u800c\u8ba4\u4e3a\u5927\u5bb6\u90fd\u4e00\u6837\u3002<\/td><\/tr>
\u81ea\u6211\u4e2d\u5fc3\u504f\u5dee<\/a><\/strong><\/td>Egocentric bias<\/td>\u8fc7\u4e8e\u4f9d\u8d56\u81ea\u5df1\u7684\u89c2\u70b9\u548c\u5bf9\u81ea\u5df1\u7684\u8bc4\u4ef7\u8fc7\u9ad8\u800c\u4e0d\u5408\u5b9e\u9645\u60c5\u51b5<\/td><\/tr>
\u5916\u5728\u6fc0\u52b1\u504f\u5dee<\/a><\/strong><\/td>Extrinsic incentives bias<\/td>\u8ba4\u4e3a\u4ed6\u4eba\u9700\u8981\u5916\u5728\u6fc0\u52b1\u624d\u4f1a\u505a\u597d\uff0c\u800c\u81ea\u5df1\u53ef\u4ee5\u9760\u5185\u5728\u6fc0\u52b1\u505a\u597d\u3002\u57fa\u672c\u5f52\u56e0\u9519\u8bef<\/a>\u7684\u53cd\u4f8b\u3002<\/td><\/tr>
\u9519\u8bef\u5171\u8bc6\u6548\u5e94<\/a><\/strong><\/td>False consensus effect<\/td>\u9ad8\u4f30\u522b\u4eba\u5bf9\u81ea\u5df1\u7684\u8ba4\u540c<\/a>\u7a0b\u5ea6\uff0c\u8ba4\u4e3a\u6240\u6709\u4eba\u4ee5\u540c\u4e00\u65b9\u5f0f\u601d\u8003\u3002<\/td><\/tr>
\u57fa\u672c\u5f52\u56e0\u9519\u8bef<\/a><\/strong><\/td>Fundamental attribution error<\/td>\u89e3\u91ca\u4ed6\u4eba\u884c\u4e3a\u65f6\uff0c\u5f52\u56e0\u4e8e\u5185\u5728\u7279\u8d28\uff08\u4e00\u5b9a\u662f\u4ed6\u6709\u8fd9\u6837\u7684\u4eba\u683c\uff0c\u624d\u505a\u51fa\u8fd9\u6837\u7684\u884c\u4e3a\uff09\uff0c\u800c\u975e\u5916\u5728\u60c5\u5883\uff08\u4e5f\u8bb8\u662f\u60c5\u52bf\u6240\u8feb\uff0c\u6216\u8fd9\u4e2a\u573a\u6240\u6709\u7279\u6b8a\u7684\u6f5c\u89c4\u5219\uff09\u3002<\/td><\/tr>
\u7fa4\u4f53\u5f52\u56e0\u9519\u8bef<\/a><\/strong><\/td>Group attribution error<\/td>\u628a\u4e2a\u4f53\u7684\u7279\u8d28\u5f53\u4f5c\u5176\u6240\u5c5e\u7fa4\u4f53\u7684\u7279\u8d28\uff0c\u6216\u628a\u4e2a\u4f53\u7684\u884c\u4e3a\u5f53\u505a\u5176\u6240\u5c5e\u7fa4\u4f53\u7684\u504f\u597d\u3002\uff08\u767d\u9a6c\u975e\u9a6c<\/a>\uff1a\u5c40\u90e8\u4e0d\u53ef\u4ee5\u4ee3\u8868\u6574\u4f53\u3002\uff09<\/td><\/tr>
\u56e2\u4f53\u8ff7\u601d<\/a><\/strong><\/td>Groupthink<\/td>\u53c8\u79f0\u96c6\u4f53\u9519\u89c9\u3002\u56e2\u4f53\u5728\u51b3\u7b56\u8fc7\u7a0b\u4e2d\uff0c\u7531\u4e8e\u6210\u5458\u503e\u5411\u8ba9\u81ea\u5df1\u7684\u89c2\u70b9\u4e0e\u56e2\u4f53\u4e00\u81f4\uff0c\u56e0\u800c\u4ee4\u6574\u4e2a\u56e2\u4f53\u7f3a\u4e4f\u4e0d\u540c\u7684\u601d\u8003\u89d2\u5ea6\uff0c\u4e0d\u80fd\u8fdb\u884c\u5ba2\u89c2\u5206\u6790\u3002<\/td><\/tr>
\u6708\u6655\u6548\u5e94<\/a><\/strong>\u6216\u6655\u8f6e\u6548\u5e94<\/a><\/strong><\/td>Halo effect<\/td>\u8bc4\u4ef7\u4ed6\u4eba\u65f6\uff0c\u4ee5\u6574\u4f53\u89c2\u611f\u8bc4\u65ad\u7279\u5b9a\u7684\u80fd\u529b\u3001\u7d20\u8d28\u3002\uff08\u53c2\u89c1\u523b\u677f\u5370\u8c61<\/a>\uff09<\/td><\/tr>
\u8ba4\u77e5\u4e0d\u5bf9\u79f0\u7684\u9519\u89c9<\/a><\/strong><\/td>Illusion of asymmetric insight<\/td>\u8ba4\u4e3a\u81ea\u5df1\u6bd4\u5176\u4ed6\u4eba\u61c2\u5f97\u66f4\u591a[80]<\/a><\/sup>\u3002<\/td><\/tr>
\u5916\u90e8\u673a\u6784\u9519\u89c9<\/a><\/strong><\/td>Illusion of external agency<\/td>\u7531\u4e8e\u5bf9\u5916\u90e8\u673a\u6784<\/a>\u6709\u4e3b\u89c2<\/a>\u559c\u597d\uff0c\u800c\u8ba4\u4e3a\u5176\u8d44\u8baf\u662f\u6709\u5f71\u54cd\u529b\u7684\u3001\u6709\u6d1e\u5bdf\u529b<\/a>\u7684\u3001\u4ec1\u6148\u7684\u3002<\/td><\/tr>
\u900f\u660e\u5ea6\u9519\u89c9<\/a><\/strong><\/td>Illusion of transparency<\/td>\u8ba4\u4e3a\u81ea\u5df1\u5f88\u5bb9\u6613\u88ab\u4ed6\u4eba\u770b\u900f\uff0c\u6216\u8ba4\u4e3a\u81ea\u5df1\u5f88\u5bb9\u6613\u770b\u900f\u4ed6\u4eba\u3002<\/td><\/tr>
\u4f18\u4e8e\u5e38\u4eba\u6548\u5e94<\/a><\/strong>\u6216\u4f18\u8d8a\u611f\u504f\u5dee<\/a><\/strong>\u6216<\/td>Better-than-average effect; Superiority bias; Illusory superiority<\/td>\u9ad8\u4f30\u81ea\u5df1\u7684\u4f18\u70b9\u3001\u4f4e\u4f30\u81ea\u5df1\u7684\u7f3a\u70b9\u3002\u662f\u5dee\u4e8e\u5e38\u4eba\u6548\u5e94<\/a>\u7684\u53cd\u4f8b\u3002<\/td><\/tr>
\u7fa4\u5185\u504f\u89c1<\/a><\/strong><\/td>Ingroup bias<\/td>\u8ba4\u4e3a\u5bf9\u65b9\u5c5e\u4e8e\u81ea\u5df1\u56e2\u4f53\uff0c\u4fbf\u7ed9\u4e88\u8f83\u597d\u7684\u5f85\u9047\u3002<\/td><\/tr>
\u9053\u5fb7\u8fd0\u6c14<\/a><\/strong><\/td>Moral luck<\/td>\u8bc4\u4ef7\u4ed6\u4eba\u884c\u4e3a\u7684\u9053\u5fb7\u65f6\uff0c\u6839\u636e\u7ed3\u679c<\/a>\u800c\u975e\u6839\u636e\u884c\u4e3a\u65f6\u7684\u60c5\u5883<\/a>\u3002<\/td><\/tr>
\u7d20\u6734\u72ac\u5112\u4e3b\u4e49<\/a><\/strong><\/td>Na\u00efve cynicism<\/td>\u8ba4\u4e3a\u5176\u4ed6\u4eba\u6bd4\u81ea\u5df1\u66f4\u81ea\u6211\u4e2d\u5fc3<\/a>\u3002<\/td><\/tr>
\u7d20\u6734\u5b9e\u5728\u8bba<\/a><\/strong><\/td>Na\u00efve realism<\/td>\u76f8\u4fe1\u81ea\u5df1\u6240\u89c1\u6240\u95fb\u5373\u662f\u771f\u76f8\u3001\u662f\u5ba2\u89c2\u4e14\u4e0d\u5e26\u504f\u89c1\u7684\uff0c\u8ba4\u4e3a\u8fd9\u662f\u663e\u800c\u6613\u89c1\u7684\u4e8b\u5b9e\uff0c\u7406\u6027\u4eba\u4e00\u5b9a\u4f1a\u540c\u610f\u81ea\u5df1\uff0c\u4e0d\u540c\u610f\u7684\u4eba\u4e00\u5b9a\u662f\u8d44\u8baf\u4e0d\u8db3\u3001\u61d2\u60f0\u3001\u4e0d\u7406\u6027\u6216\u6709\u504f\u89c1\u3002\uff08\u53c2\u89c1\u54f2\u5b66<\/a>\u5b9e\u5728\u8bba<\/a>\uff09<\/td><\/tr>
\u5916\u56e2\u4f53\u540c\u8d28\u6027\u504f\u89c1<\/a><\/strong><\/td>Outgroup homogeneity bias<\/td>\u8ba4\u4e3a\u81ea\u5df1\u7fa4\u4f53\u7684\u6210\u5458\u6bd4\u8f83\u591a\u6837\u5316\uff0c\u800c\u5916\u90e8\u7fa4\u4f53\u7684\u6210\u5458\u662f\u76f8\u5f53\u5355\u4e00\u7684\u3002<\/td><\/tr>
\u6295\u5c04\u504f\u5dee<\/a><\/strong><\/td>Projection bias<\/td>\u4e0d\u81ea\u89c9\u5730\u4ee5\u4e3a\u4ed6\u4eba\uff08\u6216\u672a\u6765\u7684\u81ea\u5df1\uff09\u548c\uff08\u73b0\u5728\u7684\uff09\u81ea\u5df1\u6709\u76f8\u4f3c\u7684\u60c5\u611f<\/a>\u3001\u601d\u60f3<\/a>\u4e0e\u4ef7\u503c\u89c2<\/a>\u3002<\/td><\/tr>
\u81ea\u5229\u504f\u5dee<\/a><\/strong><\/td>Self-serving bias<\/td>\u5c06\u6210\u529f\u5f52\u56e0\u4e8e\u81ea\u5df1\uff0c\u800c\u5c06\u5931\u8d25\u5f52\u56e0\u4e8e\u73af\u5883\u3002\uff08\u53c2\u89c1\u57fa\u672c\u5f52\u56e0\u9519\u8bef<\/a>\uff09<\/td><\/tr>
\u5171\u6709\u8d44\u8baf\u504f\u5dee<\/a><\/strong><\/td>Shared information bias<\/td>\u56e2\u961f\u8ba8\u8bba\u503e\u5411\u82b1\u8f83\u591a\u65f6\u95f4\u4e0e\u7cbe\u529b\u8ba8\u8bba\u6240\u6709\u6210\u5458\u90fd\u77e5\u9053\u7684\u4e8b\uff08\u5373\u5171\u6709\u8d44\u8baf\uff09\uff0c\u800c\u82b1\u8f83\u5c11\u65f6\u95f4\u8ba8\u8bba\u8f83\u5c11\u6210\u5458\u77e5\u9053\u7684\u4e8b\u3002<\/td><\/tr>
\u7cfb\u7edf\u6b63\u5f53\u5316<\/a><\/strong>\u6216\u5236\u5ea6\u6b63\u5f53\u5316<\/a><\/strong><\/td>System justification<\/td>\u4eba\u4eec\u503e\u5411\u4e8e\u559c\u597d\u4e0e\u634d\u536b\u73b0\u6709\u7684\u793e\u4f1a\u3001\u7ecf\u6d4e\u3001\u653f\u6cbb\u5b89\u6392\uff0c\u8d2c\u4f4e\u5176\u4ed6\u65b9\u6848\uff0c\u5373\u4f7f\u8fd9\u6837\u4f1a\u72a0\u7272\u4e2a\u4eba\u6216\u7fa4\u4f53\u5229\u76ca\u3002\uff08\u53c2\u89c1\u5b89\u4e8e\u73b0\u72b6\u504f\u5dee<\/a>\uff09<\/td><\/tr>
\u793e\u4f1a\u8d5e\u8bb8\u504f\u5dee<\/td>Social desirability bias<\/td>\u793e\u4f1a\u8d5e\u8bb8\u504f\u5dee\u662f\u6307\u4eba\u4eec\u4f1a\u8003\u8651\u793e\u4f1a\u7684\u89c2\u611f\uff0c\u56e0\u6b64\u8868\u73b0\u51fa\u7b26\u5408\u793e\u4f1a\u671f\u671b\u7684\u884c\u4e3a\uff0c\u4ee5\u501f\u6b64\u83b7\u5f97\u4ed6\u4eba\u7684\u8ba4\u540c\u3002<\/td><\/tr>
\u6027\u683c\u5f52\u5c5e\u504f\u5dee<\/a><\/strong><\/td>Trait ascription bias<\/td>\u8ba4\u4e3a\u81ea\u5df1\u7684\u4e2a\u6027\u3001\u884c\u4e3a\u3001\u60c5\u7eea\u662f\u591a\u53d8\u7684\uff0c\u800c\u4ed6\u4eba\u662f\u4e00\u6210\u4e0d\u53d8\u4e14\u5bb9\u6613\u9884\u6d4b\u7684\u3002\uff08\u53c2\u89c1\u5916\u56e2\u4f53\u540c\u8d28\u6027\u504f\u89c1<\/a>\uff09<\/td><\/tr>
\u7ec8\u6781\u5f52\u56e0\u9519\u8bef<\/a><\/strong><\/td>Ultimate attribution error<\/td>\u628a\u95ee\u9898\u5f52\u56e0\u4e8e\u6574\u4e2a\u7fa4\u4f53\uff0c\u800c\u975e\u7fa4\u4f53\u4e2d\u7684\u4e2a\u4f53\u3002<\/td><\/tr>
\u5dee\u4e8e\u5e38\u4eba\u6548\u5e94<\/a><\/strong><\/td>Worse-than-average effect<\/td>\u5bf9\u4e8e\u9ad8\u96be\u5ea6\u7684\u4e8b\uff0c\u8ba4\u4e3a\u81ea\u5df1\u6bd4\u4e00\u822c\u4eba\u5dee\u3002\u662f\u4f18\u4e8e\u5e38\u4eba\u6548\u5e94<\/a>\u7684\u53cd\u4f8b\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u8bb0\u5fc6\u9519\u8bef\u4e0e\u504f\u5dee<\/h2>\n\n\n\n

\u5728\u5fc3\u7406\u5b66<\/a>\u548c\u8ba4\u77e5\u79d1\u5b66<\/a>\u4e2d\uff0c\u8bb0\u5fc6\u504f\u5dee<\/strong>\u662f\u4e00\u79cd\u8ba4\u77e5\u504f\u5dee<\/a>\uff0c\u5bfc\u81f4\u8bb0\u5fc6<\/a>\u7684\u5f3a\u5316\u3001\u5f31\u5316\uff08\u5305\u62ec\u80fd\u5426\u60f3\u8d77\u7684\u673a\u4f1a\u3001\u60f3\u8d77\u6240\u9700\u7684\u65f6\u95f4\uff0c\u6216\u4e24\u8005\u7686\u6709\uff09\uff0c\u6216\u8005\u8bb0\u5fc6\u5185\u5bb9\u7684\u6539\u53d8\u3002\u8bb0\u5fc6\u504f\u5dee\u6709\u5f88\u591a\u79cd\uff0c\u5982\u4e0b\uff1a<\/p>\n\n\n\n

\u540d\u79f0<\/th>\u539f\u6587<\/th>\u63cf\u8ff0<\/th><\/tr><\/thead>
\u602a\u5f02\u6548\u5e94<\/a><\/strong><\/td>Bizarreness effect<\/td>\u602a\u5f02\u7684\u4e8b\u6bd4\u5bfb\u5e38\u7684\u4e8b\u66f4\u5bb9\u6613\u8bb0\u4f4f\u3002<\/td><\/tr>
\u652f\u6301\u9009\u62e9\u504f\u5dee<\/a><\/strong><\/td>Choice-supportive bias<\/td>\u56de\u987e\u81ea\u5df1\u505a\u8fc7\u7684\u9009\u62e9<\/a>\u65f6\uff0c\u503e\u5411\u8ba4\u4e3a\u662f\u660e\u667a\u7684\uff08\u53c2\u89c1\u652f\u6301\u9009\u62e9\u504f\u5dee<\/a>\uff09\u3002<\/td><\/tr>
\u6539\u53d8\u504f\u5dee<\/a><\/strong><\/td>Change bias<\/td>\u53c2\u4e0e\u4e00\u4ef6\u4e8b\u7684\u6539\u53d8\u540e\uff0c\u4e0d\u6613\u56de\u5fc6\u8fc7\u53bb\u7684\u60c5\u51b5\u3002<\/td><\/tr>
\u7ae5\u5e74\u5931\u5fc6\u75c7<\/a><\/strong><\/td>Childhood amnesia<\/td>\u96be\u4ee5\u56de\u5fc6\u56db\u5c81\u4ee5\u524d\u7684\u4e8b\u3002<\/td><\/tr>
\u4fdd\u5b88\u503e\u5411<\/a><\/strong>\u6216\u9000\u7f29\u504f\u5dee<\/a><\/strong><\/td>Conservatism; Regressive bias<\/td>\u8bb0\u5fc6\u4f1a\u503e\u5411\u4e2d\u5eb8<\/a>\u4e0d\u6781\u7aef<\/a>\uff0c\u628a\u9ad8\u4ef7\u503c\u9ad8\u6216\u7136\u7387\u7684\u4e8b<\/strong>\u8bb0\u6210\u6bd4\u5b9e\u9645\u4f4e\uff0c\u628a\u4f4e\u4ef7\u503c\u4f4e\u6216\u7136\u7387\u7684\u4e8b<\/strong>\u8bb0\u6210\u6bd4\u5b9e\u9645\u9ad8\u3002<\/td><\/tr>
\u4e00\u81f4\u6027\u504f\u5dee<\/a><\/strong><\/td>Consistency bias<\/td>\u8bb0\u5fc6\u4e2d\u4ed6\u4eba\u8fc7\u53bb\u7684\u6001\u5ea6\u4e0e\u884c\u4e3a\u4f1a\u53d8\u5f97\u50cf\u76ee\u524d\u7684\u6001\u5ea6<\/a>\u4e0e\u884c\u4e3a<\/a>\u3002<\/td><\/tr>
\u60c5\u5883\u6548\u5e94<\/a><\/strong><\/td>Context effect<\/td>\u67d0\u60c5\u5883<\/a>\u4e2d\u7684\u8bb0\u5fc6<\/a>\u5728\u5176\u4ed6\u60c5\u5883\u4e0b\u8f83\u4e0d\u6613\u56de\u60f3<\/a>\u3002<\/td><\/tr>
\u8de8\u79cd\u65cf\u6548\u5e94<\/a><\/strong><\/td>Cross-race effect<\/td>\u96be\u4ee5\u5206\u8fa8\u5176\u4ed6\u79cd\u65cf\u7684\u4eba\u3002<\/td><\/tr>
\u6f5c\u6291\u8bb0\u5fc6<\/a><\/strong><\/td>Cryptomnesia<\/td>\u63a5\u89e6\u540e\u8bb0\u5f97\u67d0\u4e9b\u5f71\u50cf\u6216\u8bed\u53e5\uff0c\u5374\u4e0d\u8bb0\u5f97\u662f\u770b\u6765\u542c\u6765\u7684\uff0c\u751a\u81f3\u4f1a\u8bef\u4ee5\u4e3a\u662f\u81ea\u5df1\u539f\u521b\uff0c\u9020\u6210\u527d\u7a83\u7684\u4e89\u8bae\u3002<\/td><\/tr>
\u81ea\u6211\u4e2d\u5fc3\u504f\u5dee<\/a><\/strong><\/td>Egocentric bias<\/td>\u56de\u5fc6\u4e2d\u7684\u81ea\u5df1\u4f1a\u7f8e\u5316\uff0c\u4f8b\u5982\u8bb0\u5fc6<\/a>\u4e2d\u81ea\u5df1\u7684\u8003\u8bd5\u6210\u7ee9\u6bd4\u5b9e\u9645\u66f4\u597d\u3002<\/td><\/tr>
\u60c5\u611f\u8870\u9000\u504f\u5dee<\/a><\/strong><\/td>Fading affect bias<\/td>\u8d1f\u9762\u3001\u4e0d\u6109\u5feb\u7684\u8bb0\u5fc6<\/a>\u6bd4\u6b63\u9762\u3001\u6109\u5feb\u7684\u8bb0\u5fc6\u66f4\u5feb\u6de1\u5fd8\u3002<\/td><\/tr>
\u865a\u5047\u8bb0\u5fc6<\/a><\/strong>\u6216\u865a\u8c08\u75c7<\/a><\/strong><\/td>False memory; Confabulation<\/td>\u4ea7\u751f\u865a\u6784\u7684\u4e8b\u586b\u8865\u8bb0\u5fc6\u7a7a\u767d\uff0c\u5373\u4f7f\u6ca1\u6709\u6b3a\u9a97\u610f\u56fe\u3002\u4f8b\u5982\u4f2a\u9020\u67d0\u4eba\u7684\u5e7c\u5e74\u7167\u7247\u7ed9\u4ed6\u770b\uff0c\u4ed6\u5f80\u5f80\u80fd\u8bf4\u51fa\u4ee5\u524d\u505a\u90a3\u4e9b\u4e8b\u7684\u8fc7\u7a0b\u4e0e\u56de\u5fc6\uff0c\u4f46\u5b9e\u9645\u4e0a\u4ed6\u6839\u672c\u6ca1\u505a\u8fc7\u3002\u5e38\u89c1\u4e8e\u8bb0\u5fc6\u969c\u788d<\/a>\u7684\u60a3\u8005\u3002<\/td><\/tr>
\u81ea\u6211\u751f\u4ea7\u6548\u5e94<\/a><\/strong><\/td>Self-generation effect<\/td>\u81ea\u5df1\u751f\u4ea7\u7684\u8d44\u8baf\u8bb0\u5f97\u6bd4\u8f83\u6e05\u695a\uff0c\u4ed6\u4eba\u751f\u4ea7\u7684\u5219\u5bb9\u6613\u6de1\u5fd8\u3002<\/td><\/tr>
\u8c37\u6b4c\u6548\u5e94<\/a><\/strong><\/td>Google effect<\/td>\u5bb9\u6613\u4ece\u7f51\u7edc\u4e0a\u641c\u5bfb\u5230\u7684\u8d44\u8baf\u6bd4\u8f83\u5bb9\u6613\u9057\u5fd8\u3002\u672c\u8bcd\u6e90\u81ea\u4e8e\u8457\u540d\u7684\u7f51\u7edc\u641c\u7d22\u5f15\u64ce\u8c37\u6b4c<\/a>\u3002<\/td><\/tr>
\u5e7d\u9ed8\u6548\u5e94<\/a><\/strong><\/td>Humor effect<\/td>\u5e7d\u9ed8\u7684\u4e8b\u6bd4\u4e0d\u5e7d\u9ed8\u7684\u4e8b\u66f4\u5bb9\u6613\u8bb0\u4f4f\uff0c\u53ef\u80fd\u7684\u89e3\u91ca\u662f\u5e7d\u9ed8\u7684\u4e8b\u6709\u8f83\u957f\u7684\u8ba4\u77e5\u5904\u7406\u65f6\u95f4<\/a>\uff0c\u6216\u9020\u6210\u8f83\u591a\u7684\u60c5\u7eea\u6fc0\u53d1<\/a>\uff08\u5904\u7406\u96be\u5ea6\u6548\u5e94<\/a>\uff09\u3002[\u6765\u6e90\u8bf7\u6c42]<\/a><\/sup><\/td><\/tr>
\u771f\u76f8\u9519\u89c9\u6548\u5e94<\/a><\/strong><\/td>Illusion of truth effect<\/td>\u4eba\u4eec\u503e\u5411\u8ba4\u4e3a\u542c\u8fc7\u7684\u662f\u771f\u7684\uff08\u5373\u4f7f\u6ca1\u610f\u8bc6\u5230\u542c\u8fc7\uff09\uff0c\u800c\u4e0d\u7ba1\u8be5\u9648\u8ff0\u5b9e\u9645\u4e0a\u662f\u5426\u6709\u6548\u3002\u6362\u8a00\u4e4b\uff0c\u4eba\u5bb9\u6613\u76f8\u4fe1\u719f\u6089\u7684\u8bdd\u80dc\u8fc7\u964c\u751f\u7684\u3002<\/td><\/tr>
\u9519\u89c9\u76f8\u5173<\/a><\/strong><\/td>Illusory correlation<\/td>\u82e5\u8ba4\u4e3a\u4e24\u4ef6\u4e8b\u76f8\u5173<\/a>\uff0c\u56de\u5fc6\u65f6\u5c31\u4f1a\u89c9\u5f97\u4e24\u8005\u540c\u65f6<\/a>\u53d1\u751f\u7684\u9891\u7387\u8f83\u9ad8\u3002<\/td><\/tr>
\u5ef6\u8fdf\u6548\u5e94<\/a><\/strong><\/td>Lag effect<\/td>\u6bd4\u8d77\u77ed\u5ef6\u8fdf\u65f6\u95f4\u7684\u591a\u6b21\u91cd\u590d\u63a5\u89e6\uff0c\u957f\u5ef6\u8fdf\u65f6\u95f4\u7684\u5c11\u6b21\u63a5\u89e6\u6709\u8f83\u597d\u7684\u8bb0\u5fc6<\/a>\u4e0e\u5b66\u4e60<\/a>\u6548\u5e94\u3002\uff08\u4ea6\u89c1\u95f4\u9694\u6548\u5e94<\/a>\uff09<\/td><\/tr>
\u949d\u5316\u4e0e\u9510\u5316<\/a><\/strong><\/td>Leveling and Sharpening<\/td>\u8bb0\u5fc6\u7ec6\u8282<\/a>\u5728\u6bcf\u6b21\u56de\u5fc6\u4e2d\u626d\u66f2\u7684\u73b0\u8c61\uff0c\u7279\u522b\u91cd\u8981\u7684\u4e8b\u4f1a\u9510\u5316\u6216\u5f97\u5230\u9009\u53d6\u800c\u5fc6\u8d77\uff0c\u4e8b\u4ef6\u7684\u7ec6\u8282\u53ca\u8be6\u60c5\u5219\u4f1a\u949d\u5316\u6216\u9057\u5931\u3002\u65f6\u95f4\u8fc7\u5f97\u8d8a\u4e45\u6216\u56de\u5fc6\u6b21\u6570\u8d8a\u591a\uff0c\u8fd9\u79cd\u504f\u5dee\u5c31\u8d8a\u52a0\u5f3a\u3002<\/td><\/tr>
\u5904\u7406\u6df1\u5ea6\u6548\u5e94<\/a><\/strong><\/td>Levels-of-processing effect<\/td>\u7528\u4e0d\u540c\u65b9\u5f0f\u201c\u7f16\u7801<\/a>\u201d\u7684\u8d44\u8baf\uff0c\u5bb9\u6613\u8bb0\u5fc6\u7684\u7a0b\u5ea6\u4e5f\u4e0d\u76f8\u540c\u3002\u4f8b\u5982\u89c6\u89c9\u3001\u542c\u89c9\u3001\u89e6\u89c9\u3001\u55c5\u89c9\u3002<\/td><\/tr>
\u5217\u8868\u6e05\u5ea6\u6548\u5e94<\/a><\/strong><\/td>List-length effect<\/td>\u5217\u8868\u4e2d\u6709\u8d8a\u591a\u4e1c\u897f\uff0c\u80fd\u8bb0\u4f4f\u7684\u6bd4\u4f8b\u5c31\u8d8a\u5c11\uff0c\u4f46\u80fd\u8bb0\u4f4f\u7684\u9879\u76ee\u603b\u6570\u4f1a\u589e\u52a0\u3002<\/td><\/tr>
\u9519\u8bef\u8baf\u606f\u6548\u5e94<\/a><\/strong><\/td>Misinformation effect<\/td>\u8bb0\u5fc6\u4f1a\u53d7\u5230\u4e8b\u540e\u8d44\u8baf\u5e72\u6270\uff0c\u800c\u53d8\u5f97\u4e0d\u51c6\u786e\u3002\u9519\u8bef\u8baf\u606f\u6548\u5e94\u662f\u9006\u5411\u5e72\u6270<\/a>(retroactive interference)\u7684\u4e3b\u8981\u4f8b\u5b50\u3002<\/td><\/tr>
\u6a21\u6001\u6548\u5e94<\/a><\/strong><\/td>Modality effect<\/td>\u5bf9\u4e8e\u6765\u81ea\u53e3\u8bed\u7684\u8baf\u606f\uff0c\u6700\u540e\u542c\u5230\u7684\u4e1c\u897f\u5370\u8c61\u4f1a\u8f83\u6df1\u523b\uff1b\u5bf9\u4e8e\u6765\u81ea\u6587\u5b57\u7684\u8baf\u606f\u5219\u4e0d\u660e\u663e\u3002<\/td><\/tr>
\u5fc3\u5883\u76f8\u5408\u6027\u8bb0\u5fc6\u504f\u5dee<\/a><\/strong><\/td>Mood-congruent memory bias<\/td>\u4e0e\u5f53\u4e0b\u5fc3\u5883<\/a>\u76f8\u5408\u7684\u8d44\u8baf\u4f1a\u6bd4\u8f83\u5bb9\u6613\u56de\u60f3\u3002<\/td><\/tr>
\u8f6e\u6d41\u53d1\u8a00\u6548\u5e94<\/a><\/strong><\/td>Next-in-line effect<\/td>\u8f6e\u6d41\u53d1\u8a00\u65f6\uff0c\u540e\u53d1\u8a00\u7684\u4eba\u4e0d\u6613\u8bb0\u4f4f\u4e4b\u524d\u7684\u4eba\u8bf4\u8fc7\u7684\u8bdd\u3002<\/td><\/tr>
\u90e8\u5206\u9879\u76ee\u63d0\u793a\u6548\u5e94<\/a><\/strong><\/td>Part-list cueing effect; Part-set cueing effect<\/td>\u8bb0\u5fc6\u7684\u4e1c\u897f\u4e3a\u4e00\u6574\u7ec4\u65f6\uff0c\u63d0\u793a\u90e8\u5206\u9879\u76ee\u4f1a\u4f7f\u56de\u60f3\u540e\u7eed\u9879\u76ee\u66f4\u56f0\u96be\u3002<\/td><\/tr>
\u5cf0\u7ec8\u5b9a\u5f8b<\/a><\/strong><\/td>Peak-end rule<\/td>\u4eba\u4eec\u5bf9\u4e00\u4ef6\u4e8b\u7684\u4f53\u9a8c\u901a\u5e38\u662f\u7531\u6700\u9ad8\u5cf0\u7684\u611f\u89c9\u4e0e\u7ed3\u5c3e\u7684\u611f\u89c9\u51b3\u5b9a\uff0c\u800c\u4e0d\u662f\u7531\u603b\u4f53\u51b3\u5b9a\u3002<\/td><\/tr>
\u521b\u4f24\u7684\u6301\u7eed\u6027<\/a><\/strong><\/td>Persistence of traumatic event<\/td>\u7ecf\u5386\u521b\u4f24\u4e8b\u4ef6<\/a>\u540e\uff0c\u5f80\u5f80\u4f1a\u4e0d\u65ad\u53cd\u590d\u56de\u5fc6\u3002\uff08\u53c2\u89c1\u521b\u4f24\u540e\u538b\u529b\u75c7\u5019\u7fa4<\/a>\uff09<\/td><\/tr>
\u56fe\u7247\u4f18\u52bf\u6548\u5e94<\/a><\/strong><\/td>Picture superiority effect<\/td>\u4ee5\u56fe\u7247\u4f20\u8fbe\u6982\u5ff5\u8f83\u4ee5\u6587\u5b57\u4f20\u8fbe\u6982\u5ff5\u5bb9\u6613\u5b66\u4e60\u4e0e\u56de\u5fc6<\/a>\u3002<\/td><\/tr>
\u6b63\u9762\u6548\u5e94<\/a><\/strong><\/td>Positivity effect<\/td>\u8001\u5e74\u4eba\u56de\u5fc6\u4e2d\u7684\u5185\u5bb9\u5f80\u5f80\u4f1a\u5f80\u6b63\u9762\u626d\u66f2\u3002<\/td><\/tr>
\u521d\u4f4d\u6548\u5e94<\/a><\/strong>\u3001\u8fd1\u65f6\u6548\u5e94<\/a><\/strong>\u3001\u5e8f\u4f4d\u6548\u5e94<\/a><\/strong><\/td>Primacy effect; Recency effect; Serial position effect<\/td>\u5e8f\u5217\u672b\u5c3e\u7684\u4e1c\u897f\u6700\u5bb9\u6613\u56de\u5fc6\uff0c\u5176\u6b21\u662f\u5e8f\u5217\u5f00\u5934\u7684\uff0c\u5e8f\u5217\u4e2d\u95f4\u7684\u8baf\u606f\u6700\u4e0d\u6613\u8bb0\u4f4f\u3002<\/td><\/tr>
\u5904\u7406\u96be\u5ea6\u6548\u5e94<\/a><\/strong><\/td>Processing difficulty effect<\/td>\u5904\u7406\u96be\u5ea6<\/a>\u9ad8\u7684\u8d44\u8baf\uff0c\u7531\u4e8e\u82b1\u8f83\u591a\u65f6\u95f4\u9605\u8bfb\u4e0e\u601d\u8003\uff0c\u56e0\u800c\u8f83\u6613\u8bb0\u4f4f\u3002<\/td><\/tr>
\u6000\u65e7\u9ad8\u5cf0<\/a><\/strong><\/td>Reminiscence bump<\/td>\u56de\u5fc6<\/a>\u4eba\u751f\u4e8b\u4ef6\u65f6\uff0c\u9752\u6625\u671f\u548c\u6210\u5e74\u65e9\u671f\u7684\u4e8b\u7279\u522b\u5bb9\u6613\u60f3\u5230\u3002<\/td><\/tr>
\u73ab\u7470\u8272\u56de\u5fc6<\/a><\/strong><\/td>Rosy retrospection<\/td>\u7f8e\u5316\u8fc7\u53bb\u7684\u56de\u5fc6\uff0c\u5f80\u5f80\u6bd4\u5b9e\u9645\u60c5\u51b5\u66f4\u597d\u3002<\/td><\/tr>
\u81ea\u8eab\u76f8\u5173\u6548\u5e94<\/a><\/strong><\/td>Self-relevance effect<\/td>\u4e0e\u81ea\u8eab\u76f8\u5173\u7684\u8baf\u606f\u8f83\u6613\u56de\u60f3\uff0c\u4e0e\u4ed6\u4eba\u76f8\u5173\u7684\u8f83\u4e0d\u6613\u3002<\/td><\/tr>
\u6765\u6e90\u6df7\u6dc6<\/a><\/strong><\/td>Source confusion<\/td>\u628a\u5076\u53d1\u4e8b\u4ef6\u7684\u8bb0\u5fc6\u548c\u5176\u4ed6\u8d44\u8baf\u6df7\u6dc6\uff0c\u9020\u6210\u626d\u66f2\u7684\u8bb0\u5fc6\u3002<\/td><\/tr>
\u95f4\u9694\u6548\u5e94<\/a><\/strong><\/td>Spacing effect<\/td>\u6bd4\u8d77\u65e0\u95f4\u9694\u7684\u91cd\u590d\u63a5\u89e6\uff0c\u6709\u95f4\u9694\u7684\u91cd\u590d\u63a5\u89e6\u6709\u8f83\u597d\u7684\u8bb0\u5fc6<\/a>\u4e0e\u5b66\u4e60<\/a>\u6548\u5e94\u3002\uff08\u4ea6\u89c1\u5ef6\u8fdf\u6548\u5e94<\/a>\uff09<\/td><\/tr>
\u9541\u5149\u706f\u6548\u5e94<\/a><\/strong><\/td>Spotlight effect<\/td>\u9ad8\u4f30\u81ea\u8eab\u8a00\u884c\u4e3e\u6b62\u53d7\u4ed6\u4eba\u5173\u6ce8\u7684\u7a0b\u5ea6\u3002<\/td><\/tr>
\u540e\u7f00\u6548\u5e94<\/a><\/strong><\/td>Suffix effect<\/td>\u5728\u8d44\u8baf\u5c3e\u7aef\u52a0\u4e0a\u4e0d\u987b\u8bb0\u5fc6\u7684\u9879\u76ee\uff0c\u4f1a\u4f7f\u524d\u9762\u987b\u8bb0\u5fc6\u7684\u9879\u76ee\u53d8\u5f97\u7279\u522b\u4e0d\u6613\u8bb0\u4f4f\u3002\u8fd9\u662f\u8fd1\u65f6\u6548\u5e94<\/a>\u7684\u4e00\u79cd\u5e94\u7528\u3002<\/td><\/tr>
\u53ef\u6697\u793a\u6027<\/a><\/strong><\/td>Suggestibility<\/td>\u56de\u60f3\u4e8b\u60c5\u65f6\uff0c\u5982\u65c1\u4eba\u6697\u793a\u4e86\u67d0\u4e9b\u60f3\u6cd5\uff0c\u60f3\u5230\u7684\u5185\u5bb9\u4f1a\u53d7\u626d\u66f2\u3002<\/td><\/tr>
\u4f38\u7f29\u6548\u5e94<\/a><\/strong><\/td>Telescoping effect<\/td>\u8ba4\u4e3a\u8fd1\u671f\u7684\u4e8b\u6bd4\u5b9e\u9645\u4e45\u8fdc\u4e9b\uff0c\u4e45\u8fdc\u7684\u4e8b\u6bd4\u5b9e\u9645\u8fd1\u671f\u4e9b\u3002<\/td><\/tr>
\u8003\u8bd5\u6548\u5e94<\/a><\/strong><\/td>Testing effect<\/td>\u53cd\u590d\u4e66\u5199\u6bd4\u53cd\u590d\u9605\u8bfb\u66f4\u5bb9\u6613\u8bb0\u4f4f\u8d44\u8baf\u3002<\/td><\/tr>
\u820c\u5c16\u73b0\u8c61<\/a><\/strong><\/td>Tip of the tongue phenomenon<\/td>\u80fd\u60f3\u8d77\u4e00\u4ef6\u4e8b\u7684\u4e00\u90e8\u5206\u6216\u76f8\u5173\u7684\u4e1c\u897f\uff0c\u5374\u96be\u4ee5\u60f3\u8d77\u5168\u90e8\u3002\u5bfc\u81f4\u5728\u5411\u4ed6\u4eba\u53d9\u8ff0\u65f6\uff0c\u8bf4\u5230\u4e00\u534a\u7a81\u7136\u5361\u4f4f\u3002<\/td><\/tr>
\u9010\u5b57\u6548\u5e94<\/a><\/strong><\/td>Verbatim effect<\/td>\u8bdd\u8bed\u4e2d\u7684\u5927\u610f\u6bd4\u5b8c\u6574\u5b57\u53e5\uff08\u9010\u5b57\uff09\u66f4\u5bb9\u6613\u8bb0\u4f4f\u3002<\/td><\/tr>
\u96f7\u65af\u591a\u592b\u6548\u5e94<\/a><\/strong><\/td>Von Restorff effect<\/td>\u9192\u76ee\u7684\u4e8b\u6216\u7279\u522b\u5f3a\u8c03\u7684\u4e8b\u6bd4\u5176\u4ed6\u7684\u4e8b\u66f4\u5bb9\u6613\u8bb0\u4f4f\u3002<\/td><\/tr>
\u8521\u683c\u5c3c\u6548\u5e94<\/a><\/strong><\/td>Zeigarnik effect<\/td>\u6bd4\u8d77\u5df1\u5b8c\u6210\u7684\u4e8b\uff0c\u672a\u5b8c\u6210\u6216\u4e2d\u65ad\u7684\u4e8b\u60c5\u4f1a\u8bb0\u5f97\u66f4\u6e05\u695a\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u7edf\u8ba1\u4e0e\u51e0\u7387\u504f\u5dee<\/h2>\n\n\n\n

\u51e0\u7387<\/a>\u6027\u63a8\u8bba\u662f\u6307\u6839\u636e\u6709\u5173\u51e0\u7387<\/a>\u7684\u8d44\u8baf\u63a8\u8bba\u4e8b\u60c5\u7684\u53ef\u80fd\u6027\uff0c\u800c\u51e0\u7387\u7684\u4f30\u7b97\u901a\u5e38\u6765\u81ea\u7edf\u8ba1<\/a>\u8d44\u6599\uff0c\u56e0\u6b64\u4e24\u8005\u5bc6\u4e0d\u53ef\u5206\u3002\u4ee5\u4e0b\u5217\u51fa\u4e0d\u719f\u6089\u6216\u8bef\u7528\u51e0\u7387\u9020\u6210\u7684\u9519\u8bef\u8ba4\u77e5\u6216\u63a8\u7406\uff1a<\/p>\n\n\n\n

\u540d\u79f0<\/th>\u539f\u6587<\/th>\u63cf\u8ff0<\/th><\/tr><\/thead>
\u57fa\u672c\u6bd4\u7387\u8c2c\u8bef<\/a><\/strong>\u6216\u57fa\u672c\u6bd4\u7387\u5ffd\u89c6<\/strong><\/td>Base rate fallacy; Base rate neglect<\/td>\u53ea\u5173\u6ce8\u9488\u5bf9\u6027\u7684\u8d44\u8baf\uff0c\u5ffd\u7565\u4e00\u822c\u6027\u7684\u8d44\u8baf\uff08\u57fa\u672c\u6bd4\u7387\uff09\uff0c\u5bfc\u81f4\u4e0d\u6070\u5f53\u7684\u8ba4\u77e5\u3002\u4f8b\u5982\u56e0\u4e3a\u9152\u9a7e\u8f83\u5bb9\u6613\u8087\u4e8b\uff0c\u5c31\u8ba4\u4e3a\u8087\u4e8b\u8005\u591a\u534a\u662f\u9152\u9a7e\uff0c\u7136\u800c\u7531\u4e8e\u9152\u9a7e\u8005\u5360\u6240\u6709\u9a7e\u9a76\u8005\u7684\u6bd4\u7387\u751a\u5c0f\uff0c\u8087\u4e8b\u8005\u4e4b\u4e2d\u9152\u9a7e\u8005\u7684\u6bd4\u4f8b\u4e0d\u4f1a\u90a3\u4e48\u9ad8\u3002<\/td><\/tr>
\u5408\u53d6\u8c2c\u8bef<\/a><\/strong><\/td>Conjunction fallacy<\/td>\u8ba4\u4e3a\u8f83\u7279\u6b8a\u6027\u7684\u60c5\u51b5\u6bd4\u8f83\u4e00\u822c\u6027\u7684\u60c5\u51b5\u66f4\u53ef\u80fd\u53d1\u751f\u3002\u4f8b\u5982\u5bf9\u67d0\u4eba\u7684\u63cf\u8ff0\u5f88\u50cf\u5973\u6027\u4e3b\u4e49\u8005\u4f1a\u505a\u7684\u4e8b\u65f6\uff0c\u8ba4\u4e3a\u67d0\u4eba\u662f\u66ff\u5987\u5973\u8fa9\u62a4\u7684\u5f8b\u5e08\u7684\u53ef\u80fd\u6027\u6bd4\u67d0\u4eba\u662f\u5f8b\u5e08\u7684\u53ef\u80fd\u6027\u66f4\u9ad8\u3002<\/td><\/tr>
\u8d4c\u5f92\u8c2c\u8bef<\/a><\/strong><\/td>Gambler’s fallacy<\/td>\u8ba4\u4e3a\u67d0\u4e8b\u591a\u6b21\u53d1\u751f\u5219\u672a\u6765\u53d1\u751f\u7684\u51e0\u7387\u4f1a\u8f83\u5c0f\uff0c\u6216\u591a\u6b21\u672a\u53d1\u751f\u5219\u672a\u6765\u53d1\u751f\u7684\u51e0\u7387\u4f1a\u8f83\u5927\u3002<\/td><\/tr>
\u9006\u8d4c\u5f92\u8c2c\u8bef<\/a><\/strong><\/td>Inverse gambler’s fallacy<\/td>\u8ba4\u4e3a\u51e0\u7387\u5f88\u5c0f\u7684\u4e8b\u53d1\u751f\u4e86\uff0c\u4e00\u5b9a\u662f\u505a\u4e86\u5f88\u591a\u6b21\u3002<\/td><\/tr>
\u70ed\u624b\u8c2c\u8bef<\/a><\/strong><\/td>Hot Hand Fallacy<\/td>\u8ba4\u4e3a\u67d0\u4e8b\u591a\u6b21\u53d1\u751f\u5219\u672a\u6765\u53d1\u751f\u7684\u51e0\u7387\u4f1a\u8f83\u5927\u3002\u4e00\u822c\u662f\u5728\u67d0\u4eba\u8fd1\u6765\u505a\u67d0\u4e8b\u5f88\u6210\u529f\u65f6\uff0c\u8ba4\u4e3a\u4ed6\u672a\u6765\u505a\u540c\u4e00\u4ef6\u4e8b\u4e5f\u4f1a\u5f88\u6210\u529f\uff0c\u800c\u5ffd\u7565\u5176\u6210\u529f\u53ef\u80fd\u53ea\u662f\u968f\u673a<\/a>\u4e0e\u5de7\u5408<\/a>\u5bfc\u81f4\u3002<\/td><\/tr>
\u56de\u5f52\u8c2c\u8bef<\/a><\/strong><\/td>Regression fallacy<\/td>\u975e\u5e38\u6001\u7684\u7532\u4e8b\u53d1\u751f\u4ee5\u540e\uff0c\u7528\u4e59\u63aa\u65bd\u5904\u7406\u540e\u7532\u4e8b\u626d\u8f6c\uff0c\u4fbf\u65ad\u5b9a\u4e59\u63aa\u65bd\u53ef\u626d\u8f6c\u7532\u4e8b\u3002\u7136\u800c\u975e\u5e38\u6001\u7684\u4e8b\u53d1\u751f\u540e\uff0c\u672c\u6765\u5c31\u6bd4\u8f83\u5bb9\u6613\u53d1\u751f\u8f83\u63a5\u8fd1\u5e38\u6001\u7684\u4e8b\u3002<\/td><\/tr>
\u68c0\u5bdf\u5b98\u8c2c\u8bef<\/a><\/strong><\/td>Prosecutor’s fallacy<\/td>\u6cdb\u6307\u591a\u79cd\u6839\u636e\u4e0d\u76f8\u5173<\/a>\u8d44\u8baf\u8ba4\u5b9a\u88ab\u544a\u201c\u65e0\u8f9c\u7684\u51e0\u7387\u201d\u5f88\u5c0f\u7684\u60c5\u51b5\u3002<\/td><\/tr>
\u8fa9\u62a4\u4eba\u8c2c\u8bef<\/a><\/strong><\/td>Defendant’s fallacy<\/td>\u6cdb\u6307\u591a\u79cd\u6839\u636e\u4e0d\u76f8\u5173\u8d44\u8baf\u8ba4\u5b9a\u88ab\u544a\u201c\u72af\u7f6a\u7684\u51e0\u7387\u201d\u5f88\u5c0f\u7684\u60c5\u51b5\u3002<\/td><\/tr>
\u591a\u91cd\u6bd4\u8f83\u8c2c\u8bef<\/a><\/strong><\/td>Multiple Comparisons Fallacy<\/td>\u5e7f\u6cdb\u6bd4\u8f83\u4e8c\u4e2a\u7fa4\u4f53\u7684\u5404\u79cd\u7279\u5f81\uff0c\u4ece\u4e2d\u627e\u51fa\u6709\u660e\u663e\u5dee\u5f02\u7684\u51e0\u4e2a\uff0c\u5ba3\u79f0\u5b83\u5c31\u662f\u9020\u6210\u4e8c\u4e2a\u7fa4\u4f53\u4e0d\u540c\u7684\u539f\u56e0\u3002<\/td><\/tr>
\u5fb7\u5dde\u795e\u67aa\u624b\u8c2c\u8bef<\/a><\/strong><\/td>Texas sharpshooter fallacy<\/td>\u5373\u201c\u5148\u5c04\u7bad\u518d\u753b\u9776\u201d\uff0c\u5e38\u6307\u7edf\u8ba1\u7814\u7a76\u505a\u51fa\u7ed3\u679c\u540e\uff0c\u628a\u5176\u4e2d\u7684\u96c6\u7fa4\u5f53\u505a\u6709\u7edf\u8ba1\u610f\u4e49\uff0c\u7136\u800c\u5b9e\u9645\u4e0a\u6b64\u96c6\u7fa4\u66f4\u53ef\u80fd\u662f\u968f\u673a\u4ea7\u751f\u3002<\/td><\/tr>
\u5bf9\u6837\u672c\u6570\u4e0d\u654f\u9510<\/a><\/strong><\/td>Insensitivity to sample size<\/td>\u8bc4\u4f30\u7edf\u8ba1\u6570\u636e\u65f6\uff0c\u672a\u8003\u8651\u5c0f\u6837\u672c\u6bd4\u5927\u6837\u672c\u66f4\u5bb9\u6613\u89c2\u5bdf\u5230\u6781\u7aef\u7ed3\u679c\u3002<\/td><\/tr>
\u620f\u5c40\u8c2c\u8bef<\/a><\/strong><\/td>Ludic fallacy<\/td>\u8fc7\u5ea6\u4f7f\u7528\u7edf\u8ba1\u4e0e\u51e0\u7387\u9884\u6d4b\u672a\u6765\u3002<\/td><\/tr>
\u9ea6\u7eb3\u9a6c\u62c9\u8c2c\u8bef<\/a><\/strong><\/td>McNamara fallacy<\/td>\u8fc7\u5ea6\u76f8\u4fe1\u6570\u636e\u3001\u4f9d\u8d56\u6570\u636e\u8bc4\u4f30\u4e8b\u60c5\uff0c\u5ffd\u7565\u96be\u4ee5\u91cf\u5316<\/a>\u7684\u4e8b\u3002<\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u5b9e\u9a8c\u4e0e\u7814\u7a76\u504f\u5dee<\/h2>\n\n\n\n

\u8fd9\u662f\u4e00\u4e9b\u79d1\u5b66\u5b9e\u9a8c\u6216\u7edf\u8ba1\u7814\u7a76\u5bb9\u6613\u53d1\u751f\u7684\u504f\u5dee\u3002<\/p>\n\n\n\n

\u540d\u79f0<\/th>\u539f\u6587<\/th>\u63cf\u8ff0<\/th><\/tr><\/thead>
\u9009\u62e9\u504f\u5dee<\/a><\/strong><\/td>Selection bias<\/td><\/td><\/tr>
\u53d6\u6837\u504f\u5dee<\/a><\/strong><\/td>Sampling bias<\/td>\u4ea6\u4f5c\u4ee5\u504f\u6982\u5168<\/a>\uff0c\u5373\u4ee5\u5c40\u90e8\u4e8b\u4f8b\u89e3\u91ca\u6574\u4f53\u539f\u56e0\u3002\uff08\u767d\u9a6c\u975e\u9a6c<\/a>\uff1a\u5c40\u90e8\u4e0d\u80fd\u4ee3\u8868\u6574\u4f53\u3002\uff09<\/td><\/tr>
\u81ea\u6211\u9009\u62e9\u504f\u5dee<\/a><\/strong><\/td>Self-selection bias<\/td><\/td><\/tr>
\u8c31\u5e26\u504f\u5dee<\/a><\/strong><\/td>Spectrum bias<\/td><\/td><\/tr>
\u9884\u7b5b\u9009\u504f\u5dee<\/a><\/strong><\/td>Pre-screening bias<\/td>\u7b5b\u9009\u6837\u672c\u65f6\u9884\u5148\u6392\u9664\u4e86\u67d0\u4e9b\u4e0d\u5e94\u6392\u9664\u7684\u6837\u672c\u3002<\/td><\/tr>
\u6392\u9664\u504f\u5dee<\/a><\/strong><\/td>Exclusion bias<\/td>\u7814\u7a76\u8fdb\u884c\u65f6\u4e0d\u5f53\u6392\u9664\u67d0\u4e9b\u770b\u4f3c\u4e0d\u7b26\u9884\u671f\u7684\u6837\u672c\u3002<\/td><\/tr>
\u9009\u62e9\u6027\u89c2\u5bdf\u504f\u5dee<\/a><\/strong><\/td>Observation selection bias<\/td>\u89c2\u5bdf\u65f6\u4e0d\u53ef\u907f\u514d\u53d7\u5230\u524d\u7f6e\u6761\u4ef6\u7684\u9650\u5236\u800c\u7b5b\u9009\u4e86\u6837\u672c\uff0c\u56e0\u800c\u5f97\u51fa\u4e0d\u9002\u5f53\u7684\u7ed3\u8bba\u3002\u4f8b\u5982\u95ee\u5377\u8c03\u67e5\u5230\u7684\u4eba\u4e00\u5b9a\u662f\u4e2a\u6027\u70ed\u5fc3\u3001\u613f\u610f\u586b\u95ee\u5377\u7684\u4eba\uff0c\u56e0\u800c\u5176\u7ed3\u679c\u672a\u5fc5\u80fd\u53cd\u6620\u4e0d\u70ed\u5fc3\u3001\u4e0d\u613f\u610f\u586b\u95ee\u5377\u7684\u4eba\u7684\u60f3\u6cd5\u3002<\/td><\/tr>
\u89c2\u5bdf\u8005\u671f\u671b\u6548\u5e94<\/a><\/strong><\/td>Observer-expectancy effect<\/td>\u7531\u4e8e\u7814\u7a76\u8005\u671f\u5f85\u67d0\u79cd\u7ed3\u679c\uff0c\u56e0\u800c\u4e0b\u610f\u8bc6\u4e2d\u4e0d\u9002\u5f53\u5730\u64cd\u4f5c\u5b9e\u9a8c\u6216\u8be0\u91ca\u8d44\u6599\uff08\u53c2\u89c1\u53d7\u8bd5\u8005\u671f\u671b\u6548\u5e94<\/a>\uff09\u3002<\/td><\/tr>
\u53d7\u8bd5\u8005\u671f\u671b\u6548\u5e94<\/a><\/strong><\/td>Subject-expectancy effect<\/td>\u7531\u4e8e\u53d7\u8bd5\u8005\u671f\u5f85\u67d0\u79cd\u7ed3\u679c\uff0c\u56e0\u800c\u4e0b\u610f\u8bc6\u5730\u626d\u66f2\u4e86\u56de\u62a5\u5185\u5bb9\u3002\u5e38\u89c1\u7684\u4f8b\u5b50\u662f\u5b89\u6170\u5242\u6548\u5e94<\/a>\u3002<\/td><\/tr>
\u5b89\u6170\u5242\u6548\u5e94<\/a><\/strong>\u6216\u4f2a\u836f\u6548\u5e94<\/a><\/strong><\/td>Placebo effect<\/td>\u7ed9\u4e88\u65e0\u6548\u7684\u836f\u7269\u6216\u6cbb\u7597\uff0c\u75c5\u4eba\u5374\u76f8\u4fe1\u6216\u89c9\u5f97\u60c5\u51b5\u6539\u5584\u3002<\/td><\/tr>
\u53cd\u5b89\u6170\u5242\u6548\u5e94<\/a><\/strong>\u6216\u53cd\u4f2a\u836f\u6548\u5e94<\/a><\/strong><\/td>Nocebo effect<\/td>\u7ed9\u4e88\u6709\u6548\u7684\u836f\u7269\u6216\u6cbb\u7597\uff0c\u75c5\u4eba\u5374\u76f8\u4fe1\u6216\u89c9\u5f97\u60c5\u51b5\u6076\u5316\u3002<\/td><\/tr>
\u5b9e\u9a8c\u8005\u504f\u5dee<\/a><\/strong>\u6216\u671f\u671b\u504f\u5dee<\/a><\/strong><\/td>Experimenter’s bias; Expectation bias<\/td>\u5b9e\u9a8c\u8005\u503e\u5411\u76f8\u4fe1\u3001\u8ba4\u53ef\u3001\u53d1\u5e03\u7b26\u5408\u4ed6\u4eec\u671f\u671b\u7684\u7ed3\u679c\uff0c\u800c\u4e0d\u4fe1\u3001\u8d2c\u4f4e\u3001\u5f03\u7f6e\u4e0d\u7b26\u5408\u7684\u3002<\/td><\/tr>
\u5faa\u73af\u5206\u6790<\/a><\/strong><\/td>Circular analysis<\/td><\/td><\/tr>
\u9057\u6f0f\u53d8\u6570\u504f\u5dee<\/a><\/strong><\/td>Omitted-variable bias<\/td><\/td><\/tr>
\u7ecf\u8d39\u504f\u5dee<\/a><\/strong><\/td>Funding bias<\/td>\u9009\u62e9\u7814\u7a76\u65b9\u6cd5\u6216\u8be0\u91ca\u7814\u7a76\u7ed3\u679c\u65f6\uff0c\u503e\u5411\u8fce\u5408\u7ecf\u8d39\u63d0\u4f9b\u8005\u7684\u7acb\u573a\u3002<\/td><\/tr>
\u901a\u62a5\u504f\u5dee<\/a><\/strong><\/td>Reporting bias<\/td><\/td><\/tr>
\u793e\u4f1a\u671f\u8bb8\u504f\u5dee<\/a><\/strong><\/td>Social desirability bias<\/td>\u8bf4\u660e\u81ea\u5df1\u7684\u770b\u6cd5\u65f6\uff0c\u503e\u5411\u4e8e\u591a\u62a5\u7b26\u5408\u793e\u4f1a\u671f\u8bb8\u7684\u610f\u613f\u6216\u5b9e\u60c5\uff0c\u800c\u5c11\u62a5\u4e0d\u7b26\u5408\u7684\u3002\u4f8b\u5982\u8be2\u53ca\u662f\u5426\u6709\u4e0d\u826f\u55dc\u597d\u3001\u6027\u4f34\u4fa3\u6570\u91cf\u65f6\uff0c\u4f1a\u6709\u610f\u6216\u65e0\u610f\u5730\u9690\u7792\u6216\u6de1\u5316\u5b9e\u60c5\u3002<\/td><\/tr>
\u8d44\u6599\u63a2\u52d8\u504f\u5dee<\/a><\/strong><\/td>Data-snooping bias<\/td><\/td><\/tr>
\u524d\u5bfc\u65f6\u95f4\u504f\u5dee<\/a><\/strong><\/td>Lead-time bias<\/td><\/td><\/tr>
\u65f6\u8ddd\u504f\u5dee<\/a><\/strong><\/td>Length-time bias<\/td><\/td><\/tr>
\u8fc7\u5ea6\u8bca\u65ad\u504f\u5dee<\/a><\/strong><\/td>Overdiagnosis bias<\/td><\/td><\/tr><\/tbody><\/table><\/figure>\n\n\n\n

\u6210\u56e0\u7406\u8bba<\/h2>\n\n\n\n

\u4ee5\u4e0b\u5217\u51fa\u89e3\u91ca\u8ba4\u77e5\u504f\u5dee\u6210\u56e0\u7684\u5e38\u89c1\u7406\u8bba\uff1a<\/p>\n\n\n\n