Probability Matching, Fast and Slow

Task switching with probabilistic reward schemes

Previous research has shown that transitions in reward prospect influence (voluntary) task switching behavior. Specifically, an increase in reward prospect appears to enhance flexibility, as indicated by a higher voluntary switch rate (VSR), compared to situations where the reward prospect remains high. In contrast, when participants are randomly rewarded in the previous task, they tend to stick with this task, resulting in a lower VSR. The present study further explores the impact of probabilistic reward schemes on task switching. Two tasks were associated with distinct probabilities of receiving a reward for correct responses (high vs. low probability). This design allows for more refined predictions regarding VSR based on the results summarized above. In three experiments with voluntary and cued task switching, we observed that participants switched tasks less frequently when they were rewarded on the previous trial, regardless of whether the task had a high or low reward probability. This pattern suggests the use of a win-stay, lose-shift (WSLS) strategy, where participants are more likely to repeat their choice after receiving a reward. However, reward had no impact on switch costs. These results are discussed in the broader context of decision-making research, particularly in relation to strategies like WSLS and possibly different levels of cognitive processes affected by our manipulation and that of studies investigating transitions of reward prospect.

Beyond risk preferences in sequential decision-making: How probability representation, sequential structure and choice perseverance bias optimal search

Sequential decision-making, where choices are made one after the other, is an important aspect of our daily lives. For example, when searching for a job, an apartment, or deciding when to buy or sell a stock, people often have to make decisions without knowing what future opportunities might arise. These situations, which are known as optimal stopping problems, involve a risk associated with the decision to either stop or continue searching. However, previous research has not consistently found a clear connection between individuals' search behavior in these tasks and their risk preferences as measured in controlled experimental settings. In this paper, we explore how particular characteristics of optimal stopping tasks affect people's choices, extending beyond their stable risk preferences. We find that (1) the way the underlying sampling distribution is presented (whether it is based on experience or description), (2) the sequential presentation of options, and (3) the unequal frequencies of choices to reject versus to accept significantly bias people choices. These results shed light on the complex nature of decisions that unfold sequentially and emphasize the importance of incorporating context factors when studying human decision behavior.

On the Disposition to Think Analytically: Four Distinct Intuitive-Analytic Thinking Styles

Many measures have been developed to index intuitive versus analytic thinking. Yet it remains an open question whether people primarily vary along a single dimension or if there are genuinely different types of thinking styles. We distinguish between four distinct types of thinking styles: Actively Open-minded Thinking, Close-Minded Thinking, Preference for Intuitive Thinking, and Preference for Effortful Thinking. We discovered strong predictive validity across several outcome measures (e.g., epistemically suspect beliefs, bullshit receptivity, empathy, moral judgments), with some subscales having stronger predictive validity for some outcomes but not others. Furthermore, Actively Open-minded Thinking, in particular, strongly outperformed the Cognitive Reflection Test in predicting misperceptions about COVID-19 and the ability to discern between vaccination-related true and false news. Our results indicate that people do, in fact, differ along multiple dimensions of intuitive-analytic thinking styles and that these dimensions have consequences for understanding a wide range of beliefs and behaviors.

Maximizing as satisficing: On pattern matching and probability maximizing in groups and individuals

Distinguishing meaningful structure from unpredictable randomness is a key challenge in many domains of life. We examined whether collaborating three-person groups (n = 81) outperform individuals (n = 81) in facing this challenge with a two-part repeated choice task, where outcomes were either serially independent (probabilistic part) or fixed in a particular sequence (pattern part). Groups performed as well as the best individuals in the probabilistic part but groups' accuracy did not credibly exceed that of the average individual in the pattern part. Qualitative coding of group discussion data revealed that failures to identify existing patterns were related to groups accepting probability maximizing as a "good enough" strategy rather than expending effort to search for patterns. These results suggest that probability maximizing can arise via two routes: recognizing that probabilistic processes cannot be outdone (maximizing as optimizing) or settling for an imperfect but easily implementable strategy (maximizing as satisficing).

Exploration and recency as the main proximate causes of probability matching: a reinforcement learning analysis

Research has not yet reached a consensus on why humans match probabilities instead of maximise in a probability learning task. The most influential explanation is that they search for patterns in the random sequence of outcomes. Other explanations, such as expectation matching, are plausible, but do not consider how reinforcement learning shapes people's choices. We aimed to quantify how human performance in a probability learning task is affected by pattern search and reinforcement learning. We collected behavioural data from 84 young adult participants who performed a probability learning task wherein the majority outcome was rewarded with 0.7 probability, and analysed the data using a reinforcement learning model that searches for patterns. Model simulations indicated that pattern search, exploration, recency (discounting early experiences), and forgetting may impair performance. Our analysis estimated that 85% (95% HDI [76, 94]) of participants searched for patterns and believed that each trial outcome depended on one or two previous ones. The estimated impact of pattern search on performance was, however, only 6%, while those of exploration and recency were 19% and 13% respectively. This suggests that probability matching is caused by uncertainty about how outcomes are generated, which leads to pattern search, exploration, and recency.

Benchmarking Aided Decision Making in a Signal Detection Task

OBJECTIVE

A series of experiments examined human operators' strategies for interacting with highly (93%) reliable automated decision aids in a binary signal detection task.

BACKGROUND

Operators often interact with automated decision aids in a suboptimal way, achieving performance levels lower than predicted by a statistically ideal model of information integration. To better understand operators' inefficient use of decision aids, we compared participants' automation-aided performance levels with the predictions of seven statistical models of collaborative decision making.

METHOD

Participants performed a binary signal detection task that asked them to classify random dot images as either blue or orange dominant. They made their judgments either unaided or with assistance from a 93% reliable automated decision aid that provided either graded (Experiments 1 and 3) or binary (Experiment 2) cues. We compared automation-aided performance with the predictions of seven statistical models of collaborative decision making, including a statistically optimal model and Robinson and Sorkin's contingent criterion model.

RESULTS AND CONCLUSION

Automation-aided sensitivity hewed closest to the predictions of the two least efficient collaborative models, well short of statistically ideal levels. Performance was similar whether the aid provided graded or binary judgments. Model comparisons identified potential strategies by which participants integrated their judgments with the aid's.

APPLICATION

Results lend insight into participants' automation-aided decision strategies and provide benchmarks for predicting automation-aided performance levels.

Scalar utility theory and proportional processing: What does it actually imply?

Scalar Utility Theory (SUT) is a model used to predict animal and human choice behaviour in the context of reward amount, delay to reward, and variability in these quantities (risk preferences). This article reviews and extends SUT, deriving novel predictions. We show that, contrary to what has been implied in the literature, (1) SUT can predict both risk averse and risk prone behaviour for both reward amounts and delays to reward depending on experimental parameters, (2) SUT implies violations of several concepts of rational behaviour (e.g. it violates strong stochastic transitivity and its equivalents, and leads to probability matching) and (3) SUT can predict, but does not always predict, a linear relationship between risk sensitivity in choices and coefficient of variation in the decision-making experiment. SUT derives from Scalar Expectancy Theory which models uncertainty in behavioural timing using a normal distribution. We show that the above conclusions also hold for other distributions, such as the inverse Gaussian distribution derived from drift-diffusion models. A straightforward way to test the key assumptions of SUT is suggested and possible extensions, future prospects and mechanistic underpinnings are discussed.

Of matchers and maximizers: How competition shapes choice under risk and uncertainty

In a world of limited resources, scarcity and rivalry are central challenges for decision makers-animals foraging for food, corporations seeking maximal profits, and athletes training to win, all strive against others competing for the same goals. In this article, we establish the role of competitive pressures for the facilitation of optimal decision making in simple sequential binary choice tasks. In two experiments, competition was introduced with a computerized opponent whose choice behavior reinforced one of two strategies: If the opponent probabilistically imitated participant choices, probability matching was optimal; if the opponent was indifferent, probability maximizing was optimal. We observed accurate asymptotic strategy use in both conditions irrespective of the provision of outcome probabilities, suggesting that participants were sensitive to the differences in opponent behavior. An analysis of reinforcement learning models established that computational conceptualizations of opponent behavior are critical to account for the observed divergence in strategy adoption. Our results provide a novel appraisal of probability matching and show how this individually 'irrational' choice phenomenon can be socially adaptive under competition.