风险决策和跨期决策的过程比较: 基于概率和时间的等量转换范式

doi:10.3724/SP.J.1041.2026.1213

心理学报 ›› 2026, Vol. 58 ›› Issue (6): 1213-1236.doi: 10.3724/SP.J.1041.2026.1213 cstr: 32110.14.2026.1213

• 研究报告 • 上一篇

风险决策和跨期决策的过程比较: 基于概率和时间的等量转换范式

周蕾¹, 李立统¹, 梁竹苑²^,³(), 李纾⁴, 惠青山¹, 张磊⁵^,⁶^,⁷^,⁸

¹ 广东工业大学管理学院, 广州 510520
² 中国科学院心理研究所, 认知科学与心理健康全国重点实验室, 北京 100101
³ 中国科学院大学心理学系, 北京 100049
⁴ 福州大学人文社会科学学院应用心理学系, 福州 350108
⁵ 英国伯明翰大学心理学院, 伯明翰 B15 2TT
⁶ 英国伯明翰大学心理健康研究所, 伯明翰 B15 2TT
⁷ 英国伯明翰大学人类脑健康中心, 伯明翰 B15 2TT
⁸ 英国伯明翰大学发展科学中心, 伯明翰 B15 2TT

收稿日期:2025-03-20 发布日期:2026-04-28 出版日期:2026-06-25
通讯作者: 梁竹苑, E-mail: liangzy@psych.ac.cn
基金资助:
国家自然科学基金委面上项目(72271066);国家自然科学基金委面上项目(71471171);国家自然科学基金委面上项目(72271230);广州市科技计划基础与应用基础研究专题项目(2025A04J5384)

Comparison of risky and intertemporal choice processes: An equivalent conversion paradigm of probability and time

ZHOU Lei¹, LI Litong¹, LIANG Zhuyuan²^,³(), LI Shu⁴, HUI Qingshan¹, ZHANG Lei⁵^,⁶^,⁷^,⁸

¹ School of Management, Guangdong University of Technology, Guangzhou 510520, China
² State Key Laboratory of Cognitive Science and Mental Health, Institute of Psychology, Chinese Academy of Sciences, Beijing 100101, China
³ Department of Psychology, University of Chinese Academy of Sciences, Beijing 100049, China
⁴ Department of Applied Psychology, School of Humanities and Social Sciences, Fuzhou University, Fuzhou, 350108, China
⁵ School of Psychology, University of Birmingham, Birmingham, B15 2TT, UK
⁶ Institute for Mental Health, University of Birmingham, Birmingham, B15 2TT, UK
⁷ Centre for Human Brain Health, University of Birmingham, Birmingham, B15 2TT, UK
⁸ Centre for Developmental Sciences, University of Birmingham, Birmingham, B15 2TT, UK

Received:2025-03-20 Online:2026-04-28 Published:2026-06-25

摘要/Abstract

摘要：

风险与跨期决策对人类生存发展至关重要。两类决策在理论、行为和过程上具有多重相似性, 但已有研究缺乏对两类决策过程的系统性直接比较, 且忽略了概率与时间的等量关系问题及其中的个体差异。本研究设计了自适应设计优化的概率−时间等量转换任务新范式, 基于个体层面测量其概率和时间的等量转换值, 据此在个性化的单结果(研究1)和双结果(研究2)风险和跨期决策眼动实验中, 针对两类模型检验的核心规则(补偿性/非补偿性、基于选项/基于维度), 基于多层次指标(行为、局部和整体过程特征、认知建模), 全面比较两类决策的异同。结果表明: 自适应设计优化的概率与时间等量转换范式有效, 个体可对概率与时间进行有效等量转换; 两类决策均更遵循非补偿性和基于维度的规则, 但二者在行为、过程及机制层面均存在特异性。该结果为未来两类决策的比较研究提供了可靠有效的工具, 有助于构建和发展普适性决策模型, 并为该模型提供了精细化参数及基于心理学解释的基础证据。

关键词: 风险决策, 跨期决策, 等量转换, 眼动追踪, 分层贝叶斯模型

Abstract:

Risky choice (RC) and intertemporal choice (IC) are two fundamental decision-making categories essential to people’s daily life. The former involves selecting among outcomes with varying probabilities, whereas the latter requires making decisions across different time points. These domains share similarities regarding theoretical developments, behavioral effects, and neural bases. A critical challenge persists because, although previous studies have revealed that RC and IC involve similar cognitive processes, findings remain inconsistent regarding their precise underlying mechanisms. Examining the similarities and differences between RC and IC from a decision process perspective contributes to the development of a generalized decision-making framework and clarifies the boundaries of its applicability. However, existing studies lack direct comparisons and converging process evidence between these two decision types. Given that probability and time parameters influence decision preferences and processes, ensuring their comparability is essential when comparing RC and IC. Previous research has often used fixed parameters, neglecting the conversion between probability and time, as well as individual differences; such an approach potentially introduces biases in experimental results due to parameter effects and individual variability.

To address these limitations, the present study initially developed a novel paradigm that subjectively equates probability to time and generates a unique set of parameters for each participant. Then, by incorporating eye-tracking technology, the research systematically investigated the cognitive mechanisms underlying RC and IC during single-outcome (Study 1) and dual-outcome (Study 2) tasks. Each study consisted of two phases. In Study 1 (N = 41, M_age = 27.14), each participant first generated pairs of approximately equivalent RC and IC options. Following the adaptive design optimization method, participants made choices between an RC and IC option possessing similar payoffs. The IC option was fixed, whereas the probability of the RC option was adjusted according to user responses until reaching an indifference point. Second, the study used these equivalent options to construct single-outcome RC and IC tasks and examined their underlying processes via eye-tracking technology. In Study 2 (N = 37, M_age = 26.31), the equivalent conversion paradigm operated in the opposite manner. That is, the RC option remained fixed, whereas the time parameter of the IC option was adjusted. The research then extended these findings by constructing dual-outcome options, focusing on compensatory versus noncompensatory and alternative-based versus attribute-basedrules. By integrating eye-tracking and hierarchical Bayesian modeling, the analysis examined local and holistic decision processes.

The entire set of analyses aimed to (1) determine whether the decision processes of RC and IC are similar and (2) identify the computational model most suitable for both decisions. Regarding the first aim, results indicated that RC and IC share equivalent conversion points and comparable local decision processes, which reflect noncompensatoryand attribute-based rules. However, RC and IC differ in holistic process characteristics, as IC undergoes a relatively more deliberate and deeper fashion than RC. Furthermore, as task complexity increased from single-outcome to dual-outcome scenarios, the process similarity between RC and IC increased, suggesting the adoption of more parallelized and simplified decision strategies. Regarding the second aim, computational modeling of process characteristics suggests that both types of decisions are consistent with nondiscounting models. Altogether, these results reveal that participants more likely follow the noncompensatory, attribute-based rules rather than the compensatory, alternative-basedrules when deciding for RC and IC.

To conclude, the present study demonstrates several key findings. (1) The equivalent conversion paradigm confirmed the existence of subjective equivalent options between probability and time. (2) After equivalent conversion, despite process-level differences, RC and IC exhibited consistency in core cognitive mechanisms. In both decision types, and contrary to classic discounting models, individuals seem not to follow compensatory, alternative-based rules, which undergo a “weighting and summing” or “time discounting” process. Instead, they more likely use simple heuristic rules hypothesized by nondiscounting models. (3) RC and IC demonstrated distinct behavioral preferences, process characteristics, and underlying mechanisms, such as differences in processing complexity and overall eye-movement dynamics. Overall, the research provides new perspectives on theoretical and methodological comparisons across different decision-making tasks and offers empirical support for the development of a more unified decision-making theory.

Key words: risky choice, intertemporal choice, equivalent conversion, eye-tracking, hierarchical Bayesian modeling

中图分类号:

B849:C91

周蕾, 李立统, 梁竹苑, 李纾, 惠青山, 张磊. (2026). 风险决策和跨期决策的过程比较: 基于概率和时间的等量转换范式. 心理学报, 58(6), 1213-1236.

ZHOU Lei, LI Litong, LIANG Zhuyuan, LI Shu, HUI Qingshan, ZHANG Lei. (2026). Comparison of risky and intertemporal choice processes: An equivalent conversion paradigm of probability and time. Acta Psychologica Sinica, 58(6), 1213-1236.

图/表 27

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模型类别	拟合模型	风险决策			跨期决策
模型类别	拟合模型	elpd (SE)	elpd-diff(SE)	预测准确率	elpd (SE)	elpd-diff(SE)	预测准确率
折扣模型	指数模型	−1253.6 (31.2)	−602.3 (59.2)	69.98%	−903.8 (56.2)	−237.5 (37.7)	70.31%
折扣模型	双曲线模型	−1439.9 (10.1)	−788.5 (72.6)	72.54%	−824.8 (55.4)	−158.5 (28.5)	71.18%
非折扣模型	ITCH启发式模型	−651.4 (68.4)	0 (0)	86.83%	−666.3 (43.1)	0 (0)	81.75%

模型类别	拟合模型	风险决策			跨期决策
模型类别	拟合模型	elpd (SE)	elpd-diff(SE)	预测准确率	elpd (SE)	elpd-diff(SE)	预测准确率
折扣模型	指数模型	−1253.6 (31.2)	−602.3 (59.2)	69.98%	−903.8 (56.2)	−237.5 (37.7)	70.31%
折扣模型	双曲线模型	−1439.9 (10.1)	−788.5 (72.6)	72.54%	−824.8 (55.4)	−158.5 (28.5)	71.18%
非折扣模型	ITCH启发式模型	−651.4 (68.4)	0 (0)	86.83%	−666.3 (43.1)	0 (0)	81.75%

因变量	自变量	b	SE	z	p	95% CI
风险决策: 选择LH选项的比例	截距	0.97	0.22	4.33	< 0.001	[0.53, 1.41]
	决策时间	0.02	0.08	0.31	0.760	[−0.13, 0.18]
	单个注视点平均时长	0.12	0.12	0.96	0.339	[−0.12, 0.35]
	长注视点比例	−0.06	0.11	−0.59	0.556	[−0.28, 0.15]
	SM值	−0.21	0.07	−3.06	0.002	[−0.35, −0.08]
	注视量百分比	0.12	0.07	1.85	0.064	[−0.01, 0.25]
跨期决策: 选择LL选项的比例	截距	−1.32	0.29	−4.57	< 0.001	[−1.89, −0.76]
	决策时间	0.20	0.08	2.37	0.018	[0.04, 0.37]
	单个注视点平均时长	0.03	0.13	0.25	0.802	[−0.22, 0.28]
	长注视点比例	−0.25	0.13	−2.02	0.044	[−0.50, −0.01]
	SM值	−0.04	0.09	−0.44	0.658	[−0.22, 0.14]
	注视量百分比	−0.06	0.10	−0.65	0.516	[−0.25, 0.12]

因变量	自变量	b	SE	z	p	95% CI
风险决策: 选择LH选项的比例	截距	0.97	0.22	4.33	< 0.001	[0.53, 1.41]
	决策时间	0.02	0.08	0.31	0.760	[−0.13, 0.18]
	单个注视点平均时长	0.12	0.12	0.96	0.339	[−0.12, 0.35]
	长注视点比例	−0.06	0.11	−0.59	0.556	[−0.28, 0.15]
	SM值	−0.21	0.07	−3.06	0.002	[−0.35, −0.08]
	注视量百分比	0.12	0.07	1.85	0.064	[−0.01, 0.25]
跨期决策: 选择LL选项的比例	截距	−1.32	0.29	−4.57	< 0.001	[−1.89, −0.76]
	决策时间	0.20	0.08	2.37	0.018	[0.04, 0.37]
	单个注视点平均时长	0.03	0.13	0.25	0.802	[−0.22, 0.28]
	长注视点比例	−0.25	0.13	−2.02	0.044	[−0.50, −0.01]
	SM值	−0.04	0.09	−0.44	0.658	[−0.22, 0.14]
	注视量百分比	−0.06	0.10	−0.65	0.516	[−0.25, 0.12]

因变量	自变量	b	SE	z	p	95% CI
风险决策: LH选项	截距	−1.57	0.61	−2.55	0.011	[−2.77, −0.36]
	决策时间	−0.10	0.15	−0.69	0.492	[−0.40, 0.19]
	单个注视点平均时长	−0.24	0.16	−1.46	0.143	[−0.56, 0.08]
	长注视点比例	0.05	0.15	0.33	0.740	[−0.24, 0.34]
	SM值	−0.18	0.14	−1.27	0.205	[−0.45, 0.10]
	注视量百分比	−0.28	0.13	−2.16	0.031	[−0.53, −0.03]
跨期决策: LL选项	截距	0.49	0.43	1.14	0.256	[−0.36, 1.34]
	决策时间	−0.16	0.13	−1.25	0.212	[−0.40, 0.09]
	单个注视点平均时长	−0.25	0.14	−1.81	0.071	[−0.52, 0.02]
	长注视点比例	0.11	0.13	0.89	0.375	[−0.13, 0.36]
	SM值	0.11	0.11	0.99	0.322	[−0.11, 0.33]
	注视量百分比	−0.04	0.10	−0.43	0.670	[−0.24, 0.15]

风险决策和跨期决策的过程比较: 基于概率和时间的等量转换范式

Comparison of risky and intertemporal choice processes: An equivalent conversion paradigm of probability and time

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检验层级	决策结果/过程	分析指标	结果
检验层级	决策结果/过程	分析指标	差异性	检验规则
行为结果	决策时间	决策时间	R < I	—
	决策时间	决策时间	R = I	—
	选择偏好	选择LL/LH选项的比例	R > I	—
		选择LL/LH选项的比例	R < I	—
		分层贝叶斯模型拟合(研究1)	R=I	基于维度
局部过程	加工复杂程度	单个注视点平均时长	R<I	非补偿
	加工复杂程度	长注视点比例	R=I	非补偿
	加工深度	注视量百分比	R=I	非补偿
	加工深度	注视量百分比	R>I	非补偿
	加工方向	SM值	R<I	无占优规则
	加工方向	SM值	R>I	基于选项
整体过程	整体动态眼动过程	眼动轨迹	R≠I	无占优规则
	整体动态眼动过程	眼动轨迹	R≠I	基于选项(R), 基于维度(I)
	所有局部过程指标	广义线性混合模型	R≠I	—

决策规则	决策类型	核心假设	代表性模型	参考文献
补偿性/ 基于选项	风险决策	基于数学期望(mathematical expectation), 遵循“加权求和”期望法则, 假设人们通过各选项发生概率对结果加权求和得出期望值, 并选择最大期望值的选项。	期望价值理论(expected value, EV); 期望效用理论(expected utility, EU); 预期理论(prospect theory, PT); 累计预期理论(cumulative prospect theory, CPT)	Pascal, 1670; von Neumann & Morgenstern, 1944; Kahneman & Tversky, 1979; Tversky & Kahneman, 1992
补偿性/ 基于选项	跨期决策	遵循“折扣求和计算”的规则, 假设人们按时间折扣率(discounting rate)对未来各时间点的效用进行折扣求和, 并选择总效用最大的选项。	效用模型(discounted utility model, DU); 双曲线模型(hyperbolic discounting model)	Samuelson, 1937; Ainslie, 1975
非补偿性/ 基于维度	风险决策	决策者仅关注部分关键维度(如“最坏结果”、“最坏结果概率”、“最好结果”等), 并在选项间比较这些维度以做出决策。	极大极小启发式模型(minimax heuristic model); 齐当别模型(equate-to- differentiate model, ETD); 占优启发式模型(priority heuristic model)	Thorngate, 1980; Li, 2004; Brandstätter, Gigerenzer, & Hertwig, 2006
非补偿性/ 基于维度	跨期决策	决策者时间间隔和结果差异的权衡, 并依据差异更大的维度进行决策。	相似性判断模型(similarity judgments model); 权衡模型(tradeoff model); 跨期决策启发式模型(intertemporal choice heuristics model, ITCH)	Leland, 2002; Scholten & Read, 2010; Scholten et al., 2024; Marzilli Ericson et al., 2015

维度	决策属性	分析指标	研究假设
维度	决策属性	分析指标	H₂: 两类决策过程相似	H₄: 两类决策过程更遵循非补偿性/基于维度的规则
行为特征		决策时间	H_2a: 决策时间无差异
		LL/LH选项选择比例	H_2b: 选择偏好无差异
		分层贝叶斯模型拟合	H_2c: 二者可被同类决策模型拟合	H_4a: 相较于折扣模型, 二者能够更好地被非折扣模型拟合
局部过程特征	加工复杂程度	单个注视点平均时长	H_2d: 单个注视点平均时长无差异
	加工复杂程度	长注视点比例	H_2e: 长注视点比例无差异	H_4b: 长注视点比例显著小于50%, 符合非补偿性规则
	加工深度	决策前注视量百分比	H_2f: 二者在决策前的注视信息量百分比无差异	H_4c: 在决策前无需注视所有选项特征, 符合非补偿性规则
	加工方向	SM值	H_2g: 二者基于选项的眼跳和基于维度的眼跳的频数分布无差异	H_4d: 采用基于维度的策略进行决策, 即在反映决策信息搜索方向的SM值指标上均小于零, 符合基于维度加工规则
整体过程特征	整体动态眼动过程	眼动轨迹	H_2h: 眼动轨迹无差异	H_4e: 根据典型试次定性观察到更多基于维度加工模式, 符合基于维度加工规则

风险任务				跨期任务
选项A		选项B		选项A		选项B
概率(%)	金额(元)	概率(%)	金额(元)	时间(天)	金额(元)	时间(天)	金额(元)
78	500	90	200	180	200	60	100
24	100	74	50	700	500	20	20
60	500	90	200	700	50	20	20
78	500	82	50	700	500	20	200
82	500	90	200	700	50	360	20
53	100	82	50	360	100	20	50
10	50	30	20	360	500	20	50
60	50	76	20	700	50	60	20
53	100	76	20	360	100	60	50
60	500	76	20	700	500	20	100
26	200	82	50	180	200	20	50
26	200	82	100	700	500	20	50
60	500	84	200	360	200	180	100
26	50	60	20	360	500	20	100
10	50	49	20	700	200	20	50
53	100	60	20	700	500	60	100
60	50	60	20	180	100	20	20
80	200	82	50	700	100	20	20
76	20	10	50	700	200	60	100
82	100	82	200	700	500	60	200
82	100	60	500	700	200	20	20
76	20	80	200	360	100	60	20
82	100	82	500	700	100	20	50
53	100	26	200	700	200	20	100
84	200	78	500	180	50	60	20
69	100	80	200	20	100	180	200
74	50	26	200	20	100	180	500
60	20	10	50	360	100	700	200
74	50	53	100	20	20	360	200
69	100	82	200	20	20	360	100
58	100	26	200	20	100	360	200
82	100	80	200	60	50	700	100
69	100	26	200	60	50	700	200
76	20	26	50	60	200	360	500
82	50	24	100	20	200	360	500
82	50	58	100	60	100	360	200
82	100	78	500	180	100	700	200
74	50	80	200	20	200	180	500
58	100	80	200	20	20	180	50
69	100	60	500	60	50	360	200
60	20	24	100	60	20	700	200
76	20	58	100	60	20	700	100
82	50	82	200	60	20	360	50
76	20	26	200	180	20	700	50
76	20	24	100	20	50	360	200
82	50	60	500	20	50	180	100
30	20	26	200	20	20	360	50

变异来源	F(df₁, df₂)	p	η_p²
年龄	F(9, 13) = 0.48	0.863	0.25
金额	F(2.28, 29.61) = 12.67	< 0.001	0.494
时间	F(1.22, 15.88) = 41.01	< 0.001	0.759
年龄*金额	F(20.50, 29.61) = 0.69	0.806	0.324
年龄*时间	F(11.00, 15.88) = 0.52	0.863	0.264
金额*时间	F(16, 208) = 3.59	< 0.001	0.216
年龄金额时间	F(144, 208) = 1.05	0.371	0.421
性别	F(1, 21) = 3.25	0.086	0.134
金额	F(2.57, 54.04) = 23.00	< 0.001	0.523
时间	F(1.51, 31.61) = 86.05	< 0.001	0.804
性别*金额	F(2.57, 54.04) = 0.56	0.62	0.026
性别*时间	F(1.51, 31.61) = 4.24	0.033	0.168
金额*时间	F(5.76, 120.91) = 5.77	< 0.001	0.216
性别金额时间	F(5.76, 120.91) = 0.79	0.574	0.036
专业	F(4, 18) = 1.09	0.392	0.195
金额	F(2.47, 44.41) = 11.99	< 0.001	0.4
时间	F(1.48, 26.61) = 27.31	< 0.001	0.603
专业*金额	F(9.87, 44.41) = 1.19	0.325	0.209
专业*时间	F(5.91, 26.61) = 0.74	0.623	0.141
金额*时间	F(5.33, 96.02) = 1.85	0.106	0.093
专业金额时间	F(21.34, 96.02) = 0.95	0.535	0.174
学历	F(2, 20) = 0.26	0.777	0.025
金额	F(2.51, 50.26) = 6.82	< 0.001	0.254
时间	F(1.41, 28.25) = 22.64	< 0.001	0.531
学历*金额	F(5.03, 50.26) = 0.83	0.533	0.077
学历*时间	F(2.82, 28.25) = 0.22	0.872	0.021
金额*时间	F(5.61, 112.27) = 2.88	0.014	0.126
学历金额时间	F(11.23, 112.27) = 1.01	0.443	0.092
收入	F(3, 19) = 0.99	0.419	0.135
金额	F(2.40, 45.59) = 9.68	< 0.001	0.337
时间	F(1.37, 26.09) = 30.20	< 0.001	0.614
收入*金额	F(7.20, 45.59) = 0.62	0.737	0.09
收入*时间	F(4.12, 26.09) = 0.18	0.952	0.027
金额*时间	F(5.47, 103.94) = 2.59	0.026	0.12
收入金额时间	F(16.41, 103.94) = 0.81	0.669	0.114

变异来源	F(df₁, df₂)	p	η_p²
RP	F(1, 21) = 0.60	0.445	0.028
金额	F(2.70, 56.66) = 9.50	< 0.001	0.311
时间	F(1.43, 30.09) = 44.96	< 0.001	0.682
RP *金额	F(2.70, 56.66) = 1.19	0.32	0.054
RP *时间	F(1.43, 30.09) = 0.39	0.613	0.018
金额*时间	F(5.71, 119.82) = 2.48	0.029	0.106
RP 金额时间	F(5.71, 119.82) = 1.67	0.137	0.074
BIS	F(1, 21) = 0.11	0.742	0.005
金额	F(2.35, 49.36) = 24.18	< 0.001	0.535
时间	F(1.46, 30.64) = 71.74	< 0.001	0.774
BIS*金额	F(2.35, 49.36) = 2.17	0.117	0.094
BIS*时间	F(1.46, 30.64) = 0.70	0.462	0.032
金额*时间	F(5.83, 122.37) = 5.47	< 0.001	0.207
BIS金额时间	F(5.83, 122.37) = 0.68	0.665	0.031
NS	F(1, 21) = 0.60	0.445	0.028
金额	F(2.70, 56.66) = 9.50	< 0.001	0.311
时间	F(1.43, 30.09) = 44.96	< 0.001	0.682
NS*金额	F(2.70, 56.66) = 1.19	0.32	0.054
NS*时间	F(1.43, 30.09) = 0.39	0.613	0.018
金额*时间	F(5.71, 119.82) = 2.48	0.029	0.106
NS金额时间	F(5.71, 119.82) = 1.67	0.137	0.074
CRT	F(1, 21) = 0.24	0.629	0.011
金额	F(2.74, 57.47) = 19.11	< 0.001	0.476
时间	F(1.43, 30.08) = 63.11	< 0.001	0.75
CRT*金额	F(2.74, 57.47) = 1.34	0.271	0.06
CRT*时间	F(1.43, 30.08) = 0.09	0.853	0.004
金额*时间	F(5.89, 123.74) = 4.60	< 0.001	0.18
CRT金额时间	F(5.89, 123.74) = 0.56	0.755	0.026

变异来源	F(df₁, df₂)	p	η_p²
RP	F(1, 30) = 0.04	0.846	0.001
金额	F(1.61, 48.43) = 47.52	< 0.001	0.613
概率	F(1.60, 48.08) = 28.48	< 0.001	0.487
RP *金额	F(1.61, 48.43) = 0.49	0.573	0.016
RP *概率	F(1.60, 48.08) = 0.15	0.819	0.005
金额*概率	F(6.97, 209.19) = 3.86	< 0.001	0.114
RP 金额概率	F(6.97, 209.19) = 0.87	0.532	0.028
BIS	F(1, 30) = 4.85	0.036	0.139
金额	F(1.79, 53.63) = 58.14	< 0.001	0.660
概率	F(1.58, 47.48) = 28.74	< 0.001	0.489
BIS*金额	F(1.79, 53.63) = 7.31	0.002	0.196
BIS*概率	F(1.58, 47.48) = 0.42	0.611	0.014
金额*概率	F(7.12, 213.59) = 3.97	< 0.001	0.117
BIS金额概率	F(7.12, 213.59) = 1.78	0.092	0.056
NS	F(1, 30) = 1.86	0.182	0.058
金额	F(1.60, 48.07) = 11.62	< 0.001	0.279
概率	F(1.60, 48.06) = 9.69	< 0.001	0.244
NS*金额	F(1.60, 48.07) = 1.14	0.318	0.037
NS*概率	F(1.60, 48.06) = 0.11	0.849	0.004
金额*概率	F(7.09, 212.67) = 3.54	0.001	0.106
NS金额概率	F(7.09, 212.67) = 1.33	0.236	0.043
CRT	F(1, 30) = 0.50	0.485	0.016
金额	F(1.59, 47.79) = 45.76	< 0.001	0.593
概率	F(1.64, 49.24) = 23.30	< 0.001	0.437
CRT*金额	F(1.59, 47.79) = 0.30	0.694	0.01
CRT*概率	F(1.64, 49.24) = 1.76	0.188	0.055
金额*概率	F(6.96, 208.72) = 3.29	0.002	0.099
CRT金额概率	F(6.96, 208.72) = 0.85	0.543	0.028

决策结果/ 过程	分析指标	决策类型	M		SE		t(30)		p
决策结果/ 过程	分析指标	决策类型	剔除	不剔除	剔除	不剔除	剔除	不剔除	剔除	不剔除
决策时间	决策时间	RC	2.56	2.68	0.12	0.15	−2.80	−1.99	0.01	0.06
决策时间	决策时间	IC	2.85	2.93	0.16	0.18	−2.80	−1.99	0.01	0.06
选择偏好	选择LL/LH选项的比例	RC	67.68%	67.16%	3.66%	3.61%	8.97	9.08	< 0.001	< 0.001
选择偏好	选择LL/LH选项的比例	IC	28.32%	28.64%	4.46%	4.44%	8.97	9.08	< 0.001	< 0.001
加工复杂程度	单个注视点平均时长	RC	208.64	210	5.39	5.44	−2.56	−2.20	0.02	0.04
	单个注视点平均时长	IC	216.53	216.93	6.35	6.33	−2.56	−2.20	0.02	0.04
	长注视点比例	RC	14.67%	14.98%	1.44%	1.46%	−2.42	−2.06	0.02	0.05
		IC	16.94%	17.00%	1.75%	1.75%	−2.42	−2.06	0.02	0.05
		RC与0.5单尾t检验	/	/	/	/	−24.53	−24.03	< 0.001	< 0.001
		IC与0.5单尾t检验	/	/	/	/	−18.84	−18.87	< 0.001	< 0.001
加工深度	决策前注视量比例	RC	98.91%	99.29%	0.47%	0.33%	1.08	1.06	0.29	0.3
		IC	97.92%	98.67%	0.92%	0.59%	1.08	1.06	0.29	0.3
		RC与1单尾t检验	/	/	/	/	−2.33	−2.17	0.01	0.02
		IC与1单尾t检验	/	/	/	/	−2.25	−2.26	0.02	0.02
加工方向	SM值	RC	−0.04	−0.04	0.06	0.06	−2.48	−2.48	0.02	0.02
		IC	0.19	0.19	0.11	0.11	−2.48	−2.48	0.02	0.02
		RC与0单尾t检验	/	/	/	/	−0.69	−0.69	0.25	0.25
		IC与0单尾t检验	/	/	/	/	1.68	1.66	0.10	0.05

决策结果/过程	分析指标	分析类型	Cauchy (r = 0.707)	Normal (0, 0.5)	Normal (0, 1)
决策时间	决策时间	RC vs. IC	4.96	5.48	4.16
选择偏好	选择LL/LH选项的比例	RC vs. IC	> 100	> 100	> 100
加工复杂程度	单个注视点平均时长	RC vs. IC	3.03	3.43	2.49
		RC结果维度 vs. IC结果维度	20.15	20.84	17.79
		RC概率维度 vs. IC时间维度	0.45	0.57	0.34
	长注视点比例	RC vs. IC	2.31	2.66	1.88
		RC vs. 0.5	> 100	> 100	> 100
		IC vs. 0.5	> 100	> 100	> 100
加工深度	决策前注视量比例	RC vs. IC	0.33	0.41	0.23
		RC vs. 1	1.98	1.67	1.12
		IC vs. 1	1.69	1.93	1.32
加工方向	SM值	RC vs. IC	2.58	2.95	2.1
		RC vs. 0	0.24	0.32	0.18
		IC vs. 0	0.67	0.83	0.52

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决策结果/过程	分析指标	决策类型	M		SE		t(31)		p
决策结果/过程	分析指标	决策类型	剔除	不剔除	剔除	不剔除	剔除	不剔除	剔除	不剔除
决策时间	决策时间	RC	4.47	4.59	0.23	0.24	0.34	0.37	0.74	0.71
决策时间	决策时间	IC	4.4	4.5	0.26	0.29	0.34	0.37	0.74	0.71
选择偏好	选择LL/LH选项的比例	RC	35.81%	36.00%	4.82%	4.84%	−3.19	−3.12	0.003	0.004
选择偏好	选择LL/LH选项的比例	IC	56.26%	56.18%	4.88%	4.88%	−3.19	−3.12	0.003	0.004
加工复杂程度	单个注视点平均时长	RC	198.84	199.25	3.55	3.52	−0.61	−0.54	0.54	0.60
	单个注视点平均时长	IC	200.6	200.84	4.08	4.13	−0.61	−0.54	0.54	0.60
	长注视点比例	RC	10.72%	10.83%	0.83%	0.81%	−0.40	−0.30	0.69	0.77
		IC	11.01%	11.06%	1.03%	1.04%	−0.40	−0.30	0.69	0.77
		RC与0.5单尾t检验	/	/	/	/	−47.58	−48.35	< 0.001	< 0.001
		IC与0.5单尾t检验	/	/	/	/	−37.79	−37.45	< 0.001	< 0.001
加工深度	决策前注视量比例	RC	88.17%	88.48%	1.39%	1.35%	3.15	3.41	0.004	0.002
		IC	84.20%	84.23%	1.52%	1.52%	3.15	3.41	0.004	0.002
		RC与1单尾t检验	/	/	/	/	−8.53	−8.53	< 0.001	< 0.001
		IC与1单尾t检验	/	/	/	/	−10.41	−10.38	< 0.001	< 0.001
加工方向	SM值	RC	3.72	3.77	0.17	0.17	0.55	4.4	< 0.001	< 0.001
		IC	3.07	3.08	0.18	0.19	0.55	4.4	< 0.001	< 0.001
		RC与0单尾t检验	/	/	/	/	18.39	22.23	< 0.001	< 0.001
		IC与0单尾t检验	/	/	/	/	16.69	16.46	< 0.001	< 0.001

决策结果/过程	分析指标	决策类型	M		SE		t(31)		p
决策结果/过程	分析指标	决策类型	剔除	不剔除	剔除	不剔除	剔除	不剔除	剔除	不剔除
决策时间	决策时间	RC	4.47	4.59	0.23	0.24	0.34	0.37	0.74	0.71
决策时间	决策时间	IC	4.4	4.5	0.26	0.29	0.34	0.37	0.74	0.71
选择偏好	选择LL/LH选项的比例	RC	35.81%	36.00%	4.82%	4.84%	−3.19	−3.12	0.003	0.004
选择偏好	选择LL/LH选项的比例	IC	56.26%	56.18%	4.88%	4.88%	−3.19	−3.12	0.003	0.004
加工复杂程度	单个注视点平均时长	RC	198.84	199.25	3.55	3.52	−0.61	−0.54	0.54	0.60
	单个注视点平均时长	IC	200.6	200.84	4.08	4.13	−0.61	−0.54	0.54	0.60
	长注视点比例	RC	10.72%	10.83%	0.83%	0.81%	−0.40	−0.30	0.69	0.77
		IC	11.01%	11.06%	1.03%	1.04%	−0.40	−0.30	0.69	0.77
		RC与0.5单尾t检验	/	/	/	/	−47.58	−48.35	< 0.001	< 0.001
		IC与0.5单尾t检验	/	/	/	/	−37.79	−37.45	< 0.001	< 0.001
加工深度	决策前注视量比例	RC	88.17%	88.48%	1.39%	1.35%	3.15	3.41	0.004	0.002
		IC	84.20%	84.23%	1.52%	1.52%	3.15	3.41	0.004	0.002
		RC与1单尾t检验	/	/	/	/	−8.53	−8.53	< 0.001	< 0.001
		IC与1单尾t检验	/	/	/	/	−10.41	−10.38	< 0.001	< 0.001
加工方向	SM值	RC	3.72	3.77	0.17	0.17	0.55	4.4	< 0.001	< 0.001
		IC	3.07	3.08	0.18	0.19	0.55	4.4	< 0.001	< 0.001
		RC与0单尾t检验	/	/	/	/	18.39	22.23	< 0.001	< 0.001
		IC与0单尾t检验	/	/	/	/	16.69	16.46	< 0.001	< 0.001