Expertise-based differences in search and option-generation strategies

The current work builds on option-generation research using experts of various skill levels in a realistic task. We extend previous findings that relate an athlete's performance strategy to generated options and subsequent choices in handball. In a 2-year longitudinal study, we present eye-tracking data to independently verify decision strategies previously inferred from patterns of generated options. A verbal protocol identified the option-generation process for each individual prior to an allocation decision. Although athletes of varying expertise generated the same number of options on average, these options differed in quality between expert, near-expert, and nonexpert athletes for both their initial and final choices. These and other key results are formalized to elaborate a model of option generation, deliberation, and selection.     

跨期决策中的维度差异偏好：眼动证据

在跨期决策研究领域,虽然基于维度的跨期模型得到了一些源自结果检验和过程检验的证据支持,但此类模型所假设的维度间差异比较的心理过程尚缺乏直接的过程证据。本研究通过两个眼动实验,系统考察了相关眼动指标对维度差异偏好的预测效应。结果发现,根据基于维度的权衡模型可有效拟合出个体在跨期决策中的维度差异偏好,并且反应时、眼跳注视熵和静态注视熵等指标均与维度差异偏好负相关,而基于维度的注意分配与维度差异判断正相关。这些研究发现支持了本研究所提出的跨期眼动模型的相关假设,证实了维度差异偏好与跨期决策的认知加工过程之间的联系,为基于维度的跨期模型提供了更直接的过程证据,并为今后跨期决策的眼动模型发展指明了新方向。     

Dynamic response-by-response models of matching behavior in rhesus monkeys

We studied the choice behavior of 2 monkeys in a discrete-trial task with reinforcement contingencies similar to those Herrnstein (1961) used when he described the matching law. In each session, the monkeys experienced blocks of discrete trials at different relative-reinforcer frequencies or magnitudes with unsignalled transitions between the blocks. Steady-state data following adjustment to each transition were well characterized by the generalized matching law; response ratios undermatched reinforcer frequency ratios but matched reinforcer magnitude ratios. We modelled response-by-response behavior with linear models that used past reinforcers as well as past choices to predict the monkeys' choices on each trial. We found that more recently obtained reinforcers more strongly influenced choice behavior. Perhaps surprisingly, we also found that the monkeys' actions were influenced by the pattern of their own past choices. It was necessary to incorporate both past reinforcers and past choices in order to accurately capture steady-state behavior as well as the fluctuations during block transitions and the response-by-response patterns of behavior. Our results suggest that simple reinforcement learning models must account for the effects of past choices to accurately characterize behavior in this task, and that models with these properties provide a conceptual tool for studying how both past reinforcers and past choices are integrated by the neural systems that generate behavior.     

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。     

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.     

Some probabilistic models of best, worst, and best-worst choices

Over the past decade or so, a choice design in which a person is asked to select both the best and the worst option in an available set of options has been gaining favor over more traditional designs, such as where the person is asked, for instance, to: select the best option; select the worst option; rank the options; or rate the options. In this paper, we develop theoretical results for three overlapping classes of probabilistic models for best, worst, and best-worst choices, with the models in each class proposing specific ways in which such choices might be related. The models in these three classes are called random ranking and random utility, joint and sequential, and ratio scale. We include some models that belong to more than one class, with the best known being the maximum-difference (maxdiff) model, summarize estimation issues related to the models, and formulate a number of open theoretical problems.     

Stimulus exposure and gaze bias: A further test of the gaze cascade model

We tested predictions derived from the gaze cascade model of preference decision making (Shimojo, Simion, Shimojo, & Scheier, 2003; Simion & Shimojo, 2006, 2007). In each trial, participants’ eye movements were monitored while they performed an eight-alternative decision task in which four of the items in the array were preexposed prior to the trial. Replicating previous findings, we found a gaze bias toward the chosen item prior to the response. However, contrary to the prediction of the gaze cascade model, preexposure of stimuli decreased, rather than increased, the magnitude of the gaze bias in preference decisions. Furthermore, unlike the prediction of the model, preexposure did not affect the likelihood of an item being chosen, and the pattern of looking behavior in preference decisions and on a nonpreference control task was remarkably similar. Implications of the present findings in multistage models of decision making are discussed.     

Integrating Cognitive Process and Descriptive Models of Attitudes and Preferences

Discrete choice experiments—selecting the best and/or worst from a set of options—are increasingly used to provide more efficient and valid measurement of attitudes or preferences than conventional methods such as Likert scales. Discrete choice data have traditionally been analyzed with random utility models that have good measurement properties but provide limited insight into cognitive processes. We extend a well‐established cognitive model, which has successfully explained both choices and response times for simple decision tasks, to complex, multi‐attribute discrete choice data. The fits, and parameters, of the extended model for two sets of choice data (involving patient preferences for dermatology appointments, and consumer attitudes toward mobile phones) agree with those of standard choice models. The extended model also accounts for choice and response time data in a perceptual judgment task designed in a manner analogous to best–worst discrete choice experiments. We conclude that several research fields might benefit from discrete choice experiments, and that the particular accumulator‐based models of decision making used in response time research can also provide process‐level instantiations for random utility models.     

Using cognitive models to map relations between neuropsychological disorders and human decision-making deficits

Findings from a complex decision-making task (the Iowa gambling task) show that individuals with neuropsychological disorders are characterized by decision-making deficits that lead to maladaptive risk-taking behavior. This article describes a cognitive model that distills performance in this task into three different underlying psychological components: the relative impact of rewards and punishments on evaluations of options, the rate that the contingent payoffs are learned, and the consistency between learning and responding. Findings from 10 studies are organized by distilling the observed decision deficits into the three basic components and locating the neuropsychological disorders in this component space. The results reveal a cluster of populations characterized by making risky choices despite high attention to losses, perhaps because of difficulties in creating emotive representations. These findings demonstrate the potential contribution of cognitive models in building bridges between neuroscience and behavior.     

Generality, repetition, and the role of descriptive learning models

Previous studies of learning in certain classes of games appear to lead to inconsistent conclusions. Studies that focus on the observed sequential changes in behavior support models that imply high-action inertia and allow for different parameters in different games. Studies that use simulation-based analysis and focus on the prediction of behavior in new games support models that imply little-action inertia, and demonstrate the value of models that assume general parameters over certain classes of games. We show that this apparent inconsistency emerges even when analyzing a large data set with a single model. We then show that the inconsistency between the two analyses can be a product of the tendency by subjects to repeat past choices.     

Instance‐based Learning: A General Model of Repeated Binary Choice

A common practice in cognitive modeling is to develop new models specific to each particular task. We question this approach and draw on an existing theory, instance‐based learning theory (IBLT), to explain learning behavior in three different choice tasks. The same instance‐based learning model generalizes accurately to choices in a repeated binary choice task, in a probability learning task, and in a repeated binary choice task within a changing environment. We assert that, although the three tasks are different, the source of learning is equivalent and therefore, the cognitive process elicited should be captured by one single model. This evidence supports previous findings that instance‐based learning is a robust learning process that is triggered in a wide range of tasks from the simple repeated choice tasks to the most dynamic decision making tasks. Copyright © 2010 John Wiley & Sons, Ltd.     

Linking patterns of infant eye movements to a neural network model of the ventral stream using representational similarity analysis

Little is known about the development of higher-level areas of visual cortex during infancy, and even less is known about how the development of visually guided behavior is related to the different levels of the cortical processing hierarchy. As a first step toward filling these gaps, we used representational similarity analysis (RSA) to assess links between gaze patterns and a neural network model that captures key properties of the ventral visual processing stream. We recorded the eye movements of 4- to 12-month-old infants (N = 54) as they viewed photographs of scenes. For each infant, we calculated the similarity of the gaze patterns for each pair of photographs. We also analyzed the images using a convolutional neural network model in which the successive layers correspond approximately to the sequence of areas along the ventral stream. For each layer of the network, we calculated the similarity of the activation patterns for each pair of photographs, which was then compared with the infant gaze data. We found that the network layers corresponding to lower-level areas of visual cortex accounted for gaze patterns better in younger infants than in older infants, whereas the network layers corresponding to higher-level areas of visual cortex accounted for gaze patterns better in older infants than in younger infants. Thus, between 4 and 12 months, gaze becomes increasingly controlled by more abstract, higher-level representations. These results also demonstrate the feasibility of using RSA to link infant gaze behavior to neural network models. A video abstract of this article can be viewed at https://youtu.be/K5mF2Rw98Is     

Comparison of basic assumptions embedded in learning models for experience-based decision making

The present study examined basic assumptions embedded in learning models for predicting behavior in decisions based on experience. In such decisions, the probabilities and payoffs are initially unknown and are learned from repeated choice with payoff feedback. We examined combinations of two rules for updating past experience with new payoff feedback and of two choice rule assumptions for mapping experience onto choices. The combination of these assumptions produced four classes of models that were systematically compared. Two methods were employed to evaluate the success of learning models for approximating players’ choices: One was based on estimating parameters from each person’s data to maximize the prediction of choices one step ahead, conditioned by the observed past history of feedback. The second was based on making a priori predictions for the entire sequence of choices using parameters estimated from a separate experiment. The results indicated the advantage of a class of models incorporating decay of previous experience, whereas the ranking of choice rules depended on the evaluation method used.     

The effect of foregone outcomes on choices from experience

We examined the relative weight given to obtained and foregone outcomes (i.e., outcomes from the non-chosen options) in repeated choices using cognitive modeling. Previous modeling studies have yielded mixed results. When participants' choices are analyzed by models that predict the next choice ahead in a sequence of decisions, the results imply that people give less weight to foregone than to obtained outcomes. In contrast, in simulation models of n trials ahead, the results imply that, on average, people give equal weight to foregone and obtained outcomes. Using datasets of experience-based binary choices with fixed (stationary) payoff distributions (Erev & Haruvy, in press) and dynamic (nonstationary) payoff distributions (Rakow & Miler, 2009), we employed generalization tests at the individual level to examine whether the findings derived from the one-step-ahead method are due to overfitting. The results of trial-ahead model fitting implied that for the nonstationary tasks only, foregone outcomes received lower weight. However, when this dataset was assessed via generalization criteria at the individual level, equal weighting of foregone and obtained outcomes was the best assumption. This implies that overfitting is implicated in the superior fit of models that assume discounting of foregone outcomes.     

Emotion,Decision, and Risk: Betting on Gambles versus Betting on People

We examined the effects of two emotions, fear and anger, on risk‐taking behavior in two types of tasks: Those in which uncertainty is generated by a randomizing device (“lottery risk”) and those in which it is generated by the uncertain behavior of another person (“person‐based risk”). Participants first completed a writing task to induce fear or anger. They then made choices either between lotteries (Experiment 1) or between actions in risky two‐person decisions (Experiments 2 and 3). The experiments involved substantial real‐money payoffs. Replicating earlier studies (which used hypothetical rewards), Experiment 1 showed that fearful participants were more risk‐averse than angry participants in lottery‐risk tasks. However—the key result of this study—fearful participants were substantially less risk‐averse than angry participants in a two‐person task involving person‐based risk (Experiment 2). Experiment 3 offered options and payoffs identical to those of Experiment 2 but with lottery‐type risk. Risk‐taking returned to the pattern of Experiment 1. The impact of incidental emotions on risk‐taking appears to be contingent on the class of uncertainty involved. For lottery risk, fear increased the frequency of risk‐averse choices and anger reduced it. The reverse pattern was found when uncertainty in the decision was person‐based. Further, the effect was specifically on differences in willingness to take risks rather than on differences in judgments of how much risk was present. The impact of different emotions on risk‐taking or risk‐avoiding behavior is thus contingent on the type, as well as the degree, of uncertainty the decision maker faces. Copyright © 2010 John Wiley & Sons, Ltd.     

A Survey of Dynamic Principles Governing Memory

A triadic coalition game was structured as an achievement-oriented situation by inclusion of a skill-based game task. Deductions regarding coalition partner choices were derived from Atkinsons' model of achievement motivation with the expectation that achievement-oriented players (Ms < Maf) would prefer moderate-risk strategy options while failure-avoidant players (Maf < Ms) would avoid moderate-risk strategies.

Observation was made of the coalition partner preferences of 126 college sophomore males and females who played the game in same-sex triads. The Ss were categorized into motive configuration groups on the basis of scores derived from the Test of Insight and the Test Anxiety Questionnaire.

The results were supportive of the hypotheses, since the Ms < Maf group chose moderate-risk coalition options more frequently than did the Maf < Ms group. By contrast the Maf < Ms group chose low-risk strategies and tended to avoid moderate-risk coalition options. While other coalition-game studies have shown differences in strategy behavior between males and females, sex differences did not occur in the present game. The lack of sex differences was explained by the high level of concern for winning among members of both sexes generated by the achievement nature of the game task.     

Gaze behavior in audiovisual speech perception: the influence of ocular fixations on the McGurk effect

We conducted three experiments in order to examine the influence of gaze behavior and fixation on audiovisual speech perception in a task that required subjects to report the speech sound they perceived during the presentation of congruent and incongruent (McGurk) audiovisual stimuli. Experiment 1 showed that the subjects' natural gaze behavior rarely involved gaze fixations beyond the oral and ocular regions of the talker's face and that these gaze fixations did not predict the likelihood of perceiving the McGurk effect. Experiments 2 and 3 showed that manipulation of the subjects' gaze fixations within the talker's face did not influence audiovisual speech perception substantially and that it was not until the gaze was displaced beyond 10 degrees - 20 degrees from the talker's mouth that the McGurk effect was significantly lessened. Nevertheless, the effect persisted under such eccentric viewing conditions and became negligible only when the subject's gaze was directed 60 degrees eccentrically. These findings demonstrate that the analysis of high spatial frequency information afforded by direct oral foveation is not necessary for the successful processing of visual speech information.     

Instance-based learning: integrating sampling and repeated decisions from experience

In decisions from experience, there are 2 experimental paradigms: sampling and repeated-choice. In the sampling paradigm, participants sample between 2 options as many times as they want (i.e., the stopping point is variable), observe the outcome with no real consequences each time, and finally select 1 of the 2 options that cause them to earn or lose money. In the repeated-choice paradigm, participants select 1 of the 2 options for a fixed number of times and receive immediate outcome feedback that affects their earnings. These 2 experimental paradigms have been studied independently, and different cognitive processes have often been assumed to take place in each, as represented in widely diverse computational models. We demonstrate that behavior in these 2 paradigms relies upon common cognitive processes proposed by the instance-based learning theory (IBLT; Gonzalez, Lerch, & Lebiere, 2003) and that the stopping point is the only difference between the 2 paradigms. A single cognitive model based on IBLT (with an added stopping point rule in the sampling paradigm) captures human choices and predicts the sequence of choice selections across both paradigms. We integrate the paradigms through quantitative model comparison, where IBLT outperforms the best models created for each paradigm separately. We discuss the implications for the psychology of decision making.     

Integrative processing of perception and reward in an auditory localization paradigm

Under natural conditions, human beings routinely have to choose among multiple alternatives which are associated with specific outcomes of varying desirability. Typically, decisions are based upon the processing of perceptual input, which introduces additional noise to the system. Bayesian decision theory (BDT) allows us to formalize decision under risk and to predict statistically optimal choice behavior. In the present study, human observers performed a classification task characterized by an extensive amount of perceptual uncertainty (auditory localization). In addition, a spatial reward function was imposed on the task. We set up a BDT model with no free parameters to serve as a benchmark for statistically optimal choices, and tested it against a purely perceptual model and a hybrid, heuristic model. In addition, we tested these three models with free rather than fixed parameters for the perceptual uncertainty and the peak of the reward function. The log likelihoods of the models given the empirical data were determined by means of Monte Carlo simulations. Bayesian model comparison (BMC) revealed that the BDT model with two free parameters was the most plausible among the tested models. The fitted parameter values for the peak of the reward function were consistently smaller than the actual peak reward communicated to the participants. The results are discussed in the context of an internal underweighting of the reward function.     

Understanding strategy differences in a fault-finding task

This article examines strategy choices for how people find faults in a simple device by using models of several strategies and new data. Diag, a model solving this task, used a single strategy that predicted the behavior of most participants in a previous study with remarkable accuracy. This article explores additional strategies used in this reasoning task that arise when less directive instructions are provided. Based on our observations, five new strategies for the task were identified and described by being modeled. These different strategies, realized in different models, predict the speed of solution while the participant is learning the task, and were validated by comparing their predictions to the observations (r² = .27 to .90). The results suggest that participants not only created different strategies for this simple fault-finding task but that some also, with practice, shifted between strategies. This research provides insights into how strategies are an important aspect of the variability in learning, illustrates the transfer of learning on a problem-by-problem level, and shows that the noisiness that most learning curves show can arise from differential transfer between problems.