Numerous experiments have suggested that extreme outcomes are disproportionately influential when we make decisions involving risk, but there is less consensus on what it actually means to be extreme. Existing accounts broadly fall into two categories: those that suggest that the best and worst outcomes are uniquely influential and those that suggest that outcomes become more influential with increasing deviation from the centre of the distribution. We conducted two experiments that aimed to tease apart these explanations. Although there was some evidence that the distance from the centre influences memory, neither account was able to fully explain the choices made by participants. This finding has implications for the viability of these explanations as well as for the generalisability of the effect and the interpretation of the method used to assess memory.
People tend to place value on information even when it does not affect the outcome of a decision. Two competing accounts offer explanations for such non-instrumental information seeking. One account foregrounds the role of anticipation and the other focusses on uncertainty aversion. Both accounts make similar predictions for short cueoutcome delays and when outcomes are positively valenced, but they differ in their explanation of information preference at long delays with negative outcomes. We present a series of experiments involving both primary and secondary reinforcers that pit these accounts against each other. The results indicate a consistent preference for non-instrumental information even at long cue-outcome delays and no evidence for information avoidance with negative outcomes. This pattern appears to provide more support for the uncertainty-aversion account than one based on anticipation
How do people solve the explore-exploit trade-off in a changing environment? In this paper we present experimental evidence in an “observe or bet” task, comparing human behavior in a changing environment to their behavior in an unchanging one. We present a Bayesian analysis of the observe or bet task and show that human judgments are consistent with that analysis. However, we find that people’s behavior is most consistent with a Bayesian model that assumes a rate of change that is higher than the true rate in the task. We argue that this tendency is the result of asymmetric consequences: assuming that the world changes more often than it really does is not very costly, whereas assuming a too-low rate of change can carry much more severe consequences.
In most everyday decisions we learn about the outcomes of alternative courses of action through experience: a sampling process. Current models of these decisions from experience do not explain how the sample outcomes are used to form a representation of the distribution of outcomes. We overcome this limitation by developing a new and simple model, the Exemplar Confusion (ExCon) model. In a novel experiment, the model predicted participants’ choices and their knowledge of outcome probabilities, when choosing among multiple-outcome gambles in sampling and feedback versions of the task. The model also performed at least as well as other leading choice models when evaluated against benchmark data from the Technion Prediction Tournament. Our approach advances current understanding by proposing a psychological mechanism for how probability estimates arise rather than using estimates solely as inputs to choice models.
A basic challenge in decision-making is to know how long to search for information, and how to adapt searchprocesses as performance, goals, and the nature of the task environment vary. We consider human performance on two experiments involving a sequence of simple multiple-cue decision-making trials, which allow search to be measured, and provide feedback on decision accuracy. In both experiments, the nature of the trials changes, unannounced, several times. Initially minimal search is required, then more extensive search is required, and finally only minimal search is again required to achieve decision accuracy. We find that people, considered both on aggregate, and as individuals, are sensitive to all of these changes. We discuss the theoretical implications of these findings for modeling search and decision-making, and emphasize that they show adaptation to an external error signal must be accompanied by some sort of internal self-regulation in any satisfactory account of people’s behavior.
An experiment examined two aspects of performance in a multi-attribute inference task: i) the effect of stimulus presentation format (image or text) on the adoption of decision strategies; and ii) the ability of an evidence accumulation model, which unifies take-the-best (TTB) and rational(RAT) strategies, to explain participants’ judgments. Presentation format had no significant effect on strategy adoption at a group level. Individual level analysis revealed large intraparticipant consistency, including some participants who consistently changed the amount of evidence considered for a decision as a function of format, but wide inter-participant differences. A unified model captured these individual differences and was preferred to the TTB or RAT models on the basis of the minimum description length model selection criterion.
The extent to which a low probability event can be imagined appears to increase the weight attached to the possibility of that event occurring. Two experiments tested contrasting accounts of how this ‘imagability’ of events is enhanced. The experiments used negative (e.g. suffering the side effect of a vaccine) and positive (e.g. winning a lottery) low probability events. Both experiments found strong support for the frequency format account, whereby imagability is enhanced through the use of frequency formats for conveying statistical information (e.g., 20 out of 2000). However, only limited support was found for ‘exemplar-cuing theory’ (J.J. Koehler & L. Macchi, 2004), which proposes two distinct mechanisms for the generation of instances. Overall, the results support the claim that the imagability of outcomes plays a role in thinking about low probability events, but question the underlying mechanisms specified by exemplar cuing theory for mediating such effects.
Previous studies suggest improved learning when participants actively intervene rather than passively observe the stimuli in a judgment task. In two experiments the authors investigate if this improvement generalizes to multiple cue judgment tasks where judgments may be formed from abstract knowledge of cue-criterion relations or exemplar memory. More specific hypotheses were that intervention in learning should improve performance over observation, and that improvement should be associated with a relative shift from exemplar memory to cue abstraction. In contrast to previous studies, in a multiplecue judgment task with binary cues and continuous criterion, there was poorer learning with intervention than observation, and participants actively experimenting more produced poorer judgments. The results suggest that intervention may distract from efficient exemplar encoding and improvement may be limited to tasks efficiently addressed by cue-abstraction.