因果与权重：决策的眼动模型

结合眼动注视的漂移扩散模型可很好描述个体的决策行为,但尚存在两个问题未得到解决：注视与决策的因果关系以及决策过程中累积证据的权重问题。本研究采用基于注视的操纵范式考察了基于价值的决策中注视与决策的关系,发现操纵被试对选项的注视时间可影响其选择,注视操纵主要影响决策后期时程,且模型参数估计结果更支持近因模型。研究结果支持了漂移扩散模型的近因假设,为今后的模型发展指明了新方向。     

Loss aversion and inhibition in dynamical models of multialternative choice

The roles of loss aversion and inhibition among alternatives are examined in models of the similarity, compromise, and attraction effects that arise in choices among 3 alternatives differing on 2 attributes. R. M. Roe, J. R. Busemeyer, and J. T. Townsend (2001) have proposed a linear model in which effects previously attributed to loss aversion (A. Tversky & D. Kahneman, 1991) arise from attention switching between attributes and similarity-dependent inhibitory interactions among alternatives. However, there are several reasons to maintain loss aversion in a theory of choice. In view of this, an alternative theory is proposed, integrating loss aversion and attention switching into a nonlinear model (M. Usher & J. L. McClelland, 2001) that relies on inhibition independent of similarity among alternatives. The model accounts for the 3 effects and makes testable predictions contrasting with those of the Roe et al. (2001) model.     

Simple matrix methods for analyzing diffusion models of choice probability, choice response time, and simple response time

Diffusion processes (e.g., Wiener process, Ornstein-Uhlenbeck process) are powerful approaches to model human information processes in a variety of psychological tasks. Lack of mathematical tractability, however, has prevented broad applications of these models to empirical data. This tutorial explains step by step, using a matrix approach, how to construct these models, how to implement them on a computer, and how to calculate the predictions made by these models. In particular, we present models for binaries choices for unidimensional and multiattribute choice alternatives; for simple reaction time tasks; and for three alternatives choice problems.     

The psychophysics of uneconomical choice: non-linear reward evaluation by a nectar feeder

Uneconomical choices by humans or animals that evaluate reward options challenge the expectation that decision-makers always maximize the return currency. One possible explanation for such deviations from optimality is that the ability to sense differences in physical value between available alternatives is constrained by the sensory and cognitive processes for encoding profitability. In this study, we investigated the capacity of a nectarivorous bat species (Glossophaga commissarisi) to discriminate between sugar solutions with different concentrations. We conducted a two-alternative free-choice experiment on a population of wild electronically tagged bats foraging at an array of computer-automated artificial flowers that recorded individual choices. We used a Bayesian approach to fit individual psychometric functions, relating the strength of preferring the higher concentration option to the intensity of the presented stimulus. Psychometric analysis revealed that discrimination ability increases non-linearly with respect to intensity. We combined this result with a previous psychometric analysis of volume perception. Our theoretical analysis of choice for rewards that vary in two quality dimensions revealed regions of parameter combinations where uneconomic choice is expected. Discrimination ability may be constrained by non-linear perceptual and cognitive encoding processes that result in uneconomical choice.     

Evaluating the reliance on past choices in adaptive learning models

Adaptive learning models are used to predict behavior in repeated choice tasks. Predictions can be based on previous payoffs or previous choices of the player. The current paper proposes a new method for evaluating the degree of reliance on past choices, called equal payoff series extraction (EPSE). Under this method a simulated player has the same exact choices as the player but receives equal constant payoffs from all of the alternatives. Success in predicting the next choice ahead for this simulated player therefore relies strictly on mimicry of previous choices of the actual player. This allows determining the marginal fit of predictions that are not based on the actual task payoffs. To evaluate the reliance on past choices under different models, an experiment was conducted in which 48 participants completed a three-alternative choice task in four task conditions. Two different learning rules were evaluated: an interference rule and a decay rule. The results showed that while the predictions of the decay rule relied more on past choices, only the reliance on past payoffs was associated with improved parameter generality. Moreover, we show that the Equal Payoff Series can be used as a criterion for optimizing parameters resulting in better parameter generalizability.     

Context effects in multi-alternative decision making: empirical data and a Bayesian model

For decisions between many alternatives, the benchmark result is Hick's Law: that response time increases log-linearly with the number of choice alternatives. Even when Hick's Law is observed for response times, divergent results have been observed for error rates-sometimes error rates increase with the number of choice alternatives, and sometimes they are constant. We provide evidence from two experiments that error rates are mostly independent of the number of choice alternatives, unless context effects induce participants to trade speed for accuracy across conditions. Error rate data have previously been used to discriminate between competing theoretical accounts of Hick's Law, and our results question the validity of those conclusions. We show that a previously dismissed optimal observer model might provide a parsimonious account of both response time and error rate data. The model suggests that people approximate Bayesian inference in multi-alternative choice, except for some perceptual limitations.     

Do humans produce the speed–accuracy trade-off that maximizes reward rate?

In this paper we investigate trade-offs between speed and accuracy that are produced by humans when confronted with a sequence of choices between two alternatives. We assume that the choice process is described by the drift diffusion model, in which the speed–accuracy trade-off is primarily controlled by the value of the decision threshold. We test the hypothesis that participants choose the decision threshold that maximizes reward rate, defined as an average number of rewards per unit of time. In particular, we test four predictions derived on the basis of this hypothesis in two behavioural experiments. The data from all participants of our experiments provide support only for some of the predictions, and on average the participants are slower and more accurate than predicted by reward rate maximization. However, when we limit our analysis to subgroups of 30–50% of participants who earned the highest overall rewards, all the predictions are satisfied by the data. This suggests that a substantial subset of participants do select decision thresholds that maximize reward rate. We also discuss possible reasons why the remaining participants select thresholds higher than optimal, including the possibility that participants optimize a combination of reward rate and accuracy or that they compensate for the influence of timing uncertainty, or both.     

Is a Single‐Bladed Knife Enough to Dissect Human Cognition? Commentary on Griffiths et al.

Griffiths, Christian, and Kalish (this issue) present an iterative-learning paradigm applying a Bayesian model to understand inductive biases in categorization. The authors argue that the paradigm is useful as an exploratory tool to understand inductive biases in situations where little is known about the task. It is argued that a theory developed only at the computational level is much like a single-bladed knife that is only useful in highly idealized situations. To be useful as a general tool that cuts through the complex fabric of cognition, we need at least two-bladed scissors that combine both computational and psychological constraints to characterize human behavior. To temper its sometimes expansive claims, it is time to show what a Bayesian model cannot explain. Insight as to how human reality may differ from the Bayesian predictions may shed more light on human cognition than the simpler focus on what the Bayesian approach can explain. There remains much to be done in terms of integrating Bayesian approaches and other approaches in modeling human cognition.     

A neural autopilot theory of habit: Evidence from consumer purchases and social media use

This article applies a two-process “neural autopilot” model to field data. The autopilot model hypothesizes that habitual choice occurs when the reward from a behavior has low numerical “doubt” (i.e., reward prediction errors are small). The model toggles between repeating a previous choice (habit) when doubt is low and making a goal-directed choice when doubt is high. The model has ingredients established in animal learning and cognitive neuroscience and is simple enough to make nonobvious predictions. In two empirical applications, we fit the model to field data on purchases of canned tuna and posting on the Chinese social media site Weibo. This style of modeling is called “structural” because there is a theoretical model of how different variables influence choices by agents (the “structure”), which tightly restricts how hidden variables lead to observed choices. There is empirical support for the model, more strongly for tuna purchases than for Weibo posting, relative to a baseline “reduced-form” model in which current choices are correlated with past choices without a mechanistic (structural) explanation. An interesting set of predictions can also be derived about how consumers react to different kinds of changes in prices and qualities of goods (this is called “counterfactual analysis”).     

Choices and preferences: Evidence from implicit choices and response times

We present a new experimental paradigm where choice-induced preference change is measured for alternatives which are never compared directly, but rather confronted with other alternatives in a way which keeps choices predictable without exogenously manipulating them. This implicit-choice design improves on the free-choice paradigm, avoiding the recently criticized selection bias. Rating and ranking spreads in two experiments show that preference-based choices feed back into and alter preferences even if choices are not directly among similarly evaluated alternatives. In agreement with recent brain-imaging evidence, response time measurements for direct choice pairs in our experiments indicate that reappraisal processes are already triggered during decision making, with larger post-choice spreads (sharper attitude change) being associated to quicker decisions.     

What are the Bayesian constraints in the Bayesian reader? Reply to Norris and Kinoshita (2010)

In this brief reply, I argue that the Bayesian reader can account for any pattern of data (including those not actually observed) because the predictions of the model are largely independent of any Bayesian principles. It is a good thing that the model is flexible, as the implemented model has been falsified by existing data.     

Take The First: Option-generation and resulting choices

Experimental decision-making research often uses a task in which participants are presented with alternatives from which they must choose. Although tasks of this type may be useful in determining measures (e.g., preference) related to explicitly stated alternatives, they neglect an important aspect of many real-world decision-making environments—namely, the option-generation process. The goal of the present research is to extend previous literature that fills this void by presenting a model that attempts to describe the link between the use of different strategies and the subsequent option-generation process, as well as the resulting choice characteristics. Specifically, we examine the relationship between strategy use, number and order of generated options, choice quality, and dynamic inconsistency. “Take The First” is presented as a heuristic that operates in ill-defined tasks, based on our model assumptions. An experiment involving a realistic (sports) situation was conducted on suitable participants (athletes) to test the predictions of the model. Initial results support the model’s key predictions: strategies producing fewer generated options result in better and more consistent decisions.     

Cueing and Cognitive Conflict in Judge-Advisor Decision Making

The Judge-Advisor paradigm (Sniezek & Buckley, 1989) allows for the study of decision making by groups with differentiated roles. This paper reports a study using this approach to investigate the impact of advice vis-à-vis the judge′s own initial choice. Teams consisting of one judge and two advisors (business students randomly assigned to these roles) were given a choice task (concerning business events) composed of 70 items with two alternatives each. The judges provided final team choices and confidence assessments under one of three conditions: Dependent (judge has no basis for own choice), Cued (judge chooses only after being advised), or Independent (judges makes own tentative choice prior to being advised as well as subsequent final choice). Results showed that this manipulation affected the judge′s final choice accuracy and confidence, leading to the best performance by Independent judges and the poorest by Dependent judges. These data are discussed with respect to theory and data on the cueing effect (Sniezek, Paese, & Switzer, 1990) for individual choice. In addition, the effects on the decision making process of the judge from (a) advisors′ confidence and (b) conflict between advisors′ recommendations are examined in detail. Finally, issues concerning the potential contribution of the Judge-Advisor paradigm to the understanding of social decision making are addressed.     

On the reduction of choice reaction times with practice

Information theory concepts have been discussed in relation to data from choice reaction time experiments. Specifically, it has been stated that choice reaction time is proportional to the logarithm of the number of randomly appearing alternative stimuli. It is suggested that the reported increase in choice reaction times with an increased number of alternatives is the result of insufficient practice. Data are provided to show that, with sufficient practice, this increase does not occur between two and four choices.     

汉语为二语学习者言语产生中的跨语言干扰

采用跨语言长时竞争启动范式探讨汉语为二语学习者言语产生中的跨语言干扰。实验1的被试选择18名俄语为母语者, 实验2选择18名日语为母语者。自变量是命名语言(一语和二语)和学习条件(学过一致、学过不一致和未学过), 因变量是测验阶段被试图片命名的反应时和正确率。实验分为学习阶段和测验阶段, 被试分别用一语和二语命名图片, 观察被试在测验阶段学过一致与学过不一致两种条件下是否都能产生促进效应。实验结果发现, 无论俄语为母语者还是日语为母语者, 被试只有在学过一致条件下的反应时显著快于未学过条件, 而学过不一致条件下的反应时跟未学过没有显著差异。本研究结果表明, 汉语为二语学习者言语产生中存在跨语言干扰, 且语言间书写特征的差异对跨语言干扰没有影响。     

Bayesian Fundamentalism or Enlightenment? On the explanatory status and theoretical contributions of Bayesian models of cognition

The prominence of Bayesian modeling of cognition has increased recently largely because of mathematical advances in specifying and deriving predictions from complex probabilistic models. Much of this research aims to demonstrate that cognitive behavior can be explained from rational principles alone, without recourse to psychological or neurological processes and representations. We note commonalities between this rational approach and other movements in psychology - namely, Behaviorism and evolutionary psychology - that set aside mechanistic explanations or make use of optimality assumptions. Through these comparisons, we identify a number of challenges that limit the rational program's potential contribution to psychological theory. Specifically, rational Bayesian models are significantly unconstrained, both because they are uninformed by a wide range of process-level data and because their assumptions about the environment are generally not grounded in empirical measurement. The psychological implications of most Bayesian models are also unclear. Bayesian inference itself is conceptually trivial, but strong assumptions are often embedded in the hypothesis sets and the approximation algorithms used to derive model predictions, without a clear delineation between psychological commitments and implementational details. Comparing multiple Bayesian models of the same task is rare, as is the realization that many Bayesian models recapitulate existing (mechanistic level) theories. Despite the expressive power of current Bayesian models, we argue they must be developed in conjunction with mechanistic considerations to offer substantive explanations of cognition. We lay out several means for such an integration, which take into account the representations on which Bayesian inference operates, as well as the algorithms and heuristics that carry it out. We argue this unification will better facilitate lasting contributions to psychological theory, avoiding the pitfalls that have plagued previous theoretical movements.