Probability matching involves rule-generating ability: A neuropsychological mechanism dealing with probabilities

Face and context integration in emotion inference is limited and variable across categories and individuals

The ability to make nuanced inferences about other people's emotional states is central to social functioning. While emotion inferences can be sensitive to both facial movements and the situational context that they occur in, relatively little is understood about when these two sources of information are integrated across emotion categories and individuals. In a series of studies, we use one archival and five empirical datasets to demonstrate that people could be integrating, but that emotion inferences are just as well (and sometimes better) captured by knowledge of the situation alone, while isolated facial cues are insufficient. Further, people integrate facial cues more for categories for which they most frequently encounter facial expressions in everyday life (e.g., happiness). People are also moderately stable over time in their reliance on situational cues and integration of cues and those who reliably utilize situation cues more also have better situated emotion knowledge. These findings underscore the importance of studying variability in reliance on and integration of cues.

Evaluating the Bayesian causal inference model of intentional binding through computational modeling

Intentional binding refers to the subjective compression of the time interval between an action and its consequence. While intentional binding has been widely used as a proxy for the sense of agency, its underlying mechanism has been largely veiled. Bayesian causal inference (BCI) has gained attention as a potential explanation, but currently lacks sufficient empirical support. Thus, this study implemented various computational models to describe the possible mechanisms of intentional binding, fitted them to individual observed data, and quantitatively evaluated their performance. The BCI models successfully isolated the parameters that potentially contributed to intentional binding (i.e., causal belief and temporal prediction) and generally better explained an observer's time estimation than traditional models such as maximum likelihood estimation. The estimated parameter values suggested that the time compression resulted from an expectation that the actions would immediately cause sensory outcomes. Furthermore, I investigated the algorithm that realized this BCI and found probability-matching to be a plausible candidate; people might heuristically reconstruct event timing depending on causal uncertainty rather than optimally integrating causal and temporal posteriors. The evidence demonstrated the utility of computational modeling to investigate how humans infer the causal and temporal structures of events and individual differences in that process.

Pupil dilation and response slowing distinguish deliberate explorative choices in the probabilistic learning task

This study examined whether pupil size and response time would distinguish directed exploration from random exploration and exploitation. Eighty-nine participants performed the two-choice probabilistic learning task while their pupil size and response time were continuously recorded. Using LMM analysis, we estimated differences in the pupil size and response time between the advantageous and disadvantageous choices as a function of learning success, i.e., whether or not a participant has learned the probabilistic contingency between choices and their outcomes. We proposed that before a true value of each choice became known to a decision-maker, both advantageous and disadvantageous choices represented a random exploration of the two options with an equally uncertain outcome, whereas the same choices after learning manifested exploitation and direct exploration strategies, respectively. We found that disadvantageous choices were associated with increases both in response time and pupil size, but only after the participants had learned the choice-reward contingencies. For the pupil size, this effect was strongly amplified for those disadvantageous choices that immediately followed gains as compared to losses in the preceding choice. Pupil size modulations were evident during the behavioral choice rather than during the pretrial baseline. These findings suggest that occasional disadvantageous choices, which violate the acquired internal utility model, represent directed exploration. This exploratory strategy shifts choice priorities in favor of information seeking and its autonomic and behavioral concomitants are mainly driven by the conflict between the behavioral plan of the intended exploratory choice and its strong alternative, which has already proven to be more rewarding.

Probability matching is not the default decision making strategy in human and non-human primates

Probability matching has long been taken as a prime example of irrational behaviour in human decision making; however, its nature and uniqueness in the animal world is still much debated. In this paper we report a set of four preregistered experiments testing adult humans and Guinea baboons on matched probability learning tasks, manipulating task complexity (binary or ternary prediction tasks) and reinforcement procedures (with and without corrective feedback). Our findings suggest that probability matching behaviour within primate species is restricted to humans and the simplest possible binary prediction tasks; utility-maximising is seen in more complex tasks for humans as pattern-search becomes more effortful, and we observe it across the board in baboons, altogether suggesting that it is a cognitively less demanding strategy. These results provide further evidence that neither human nor non-human primates default to probability matching; however, unlike other primates, adult humans probability match when the cost of pattern search is low.

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).

Probability Matching on a Simple Simulated Foraging Task: The Effects of Reward Persistence and Accumulation on Choice Behavior

Over a series of decisions between two or more probabilistically rewarded options, humans have a tendency to diversify their choices, even when this will lead to diminished overall reward. In the extreme case of probability matching, this tendency is expressed through allocation of choices in proportion to their likelihood of reward. Research suggests that this behaviour is an instinctive response, driven by heuristics, and that it may be overruled through the application of sufficient deliberation and self-control. However, if this is the case, then how and why did this response become established? The present study explores the hypothesis that diversification of choices, and potentially probability matching, represents an overextension of a historically normative foraging strategy. This is done through examining choice behaviour on a simple simulated foraging task, designed to model the natural process of accumulation of unharvested resources over time. Behaviour was then directly compared with that observed on a standard fixed probability task (cf. Ellerby & Tunney, 2017). Results indicated a convergence of choice patterns on the simulated foraging task, between participants who acted intuitively and those who took a more strategic approach. These findings are also compared with those of another similarly motivated study (Schulze, van Ravenzwaaij, & Newell, 2017).

The Effects of Heuristics and Apophenia on Probabilistic Choice

Given a repeated choice between two or more options with independent and identically distributed reward probabilities, overall pay-offs can be maximized by the exclusive selection of the option with the greatest likelihood of reward. The tendency to match response proportions to reward contingencies is suboptimal. Nevertheless, this behaviour is well documented. A number of explanatory accounts have been proposed for probability matching. These include failed pattern matching, driven by apophenia, and a heuristic-driven response that can be overruled with sufficient deliberation. We report two experiments that were designed to test the relative effects on choice behaviour of both an intuitive versus strategic approach to the task and belief that there was a predictable pattern in the reward sequence, through a combination of both direct experimental manipulation and post-experimental self-report. Mediation analysis was used to model the pathways of effects. Neither of two attempted experimental manipulations of apophenia, nor self-reported levels of apophenia, had a significant effect on proportions of maximizing choices. However, the use of strategy over intuition proved a consistent predictor of maximizing, across all experimental conditions. A parallel analysis was conducted to assess the effect of controlling for individual variance in perceptions of reward contingencies. Although this analysis suggested that apophenia did increase probability matching in the standard task preparation, this effect was found to result from an unforeseen relationship between self-reported apophenia and perceived reward probabilities. A Win-Stay Lose-Shift (WSLS ) analysis indicated no reliable relationship between WSLS and either intuition or strategy use.

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.

Single- and Dual-Process Models of Biased Contingency Detection

Decades of research in causal and contingency learning show that people's estimations of the degree of contingency between two events are easily biased by the relative probabilities of those two events. If two events co-occur frequently, then people tend to overestimate the strength of the contingency between them. Traditionally, these biases have been explained in terms of relatively simple single-process models of learning and reasoning. However, more recently some authors have found that these biases do not appear in all dependent variables and have proposed dual-process models to explain these dissociations between variables. In the present paper we review the evidence for dissociations supporting dual-process models and we point out important shortcomings of this literature. Some dissociations seem to be difficult to replicate or poorly generalizable and others can be attributed to methodological artifacts. Overall, we conclude that support for dual-process models of biased contingency detection is scarce and inconclusive.

Taking the easy way out? Increasing implementation effort reduces probability maximizing under cognitive load

Cognitive load has previously been found to have a positive effect on strategy selection in repeated risky choice. Specifically, whereas inferior probability matching often prevails under single-task conditions, optimal probability maximizing sometimes dominates when a concurrent task competes for cognitive resources. We examined the extent to which this seemingly beneficial effect of increased task demands hinges on the effort required to implement each of the choice strategies. Probability maximizing typically involves a simple repeated response to a single option, whereas probability matching requires choice proportions to be tracked carefully throughout a sequential choice task. Here, we flipped this pattern by introducing a manipulation that made the implementation of maximizing more taxing and, at the same time, allowed decision makers to probability match via a simple repeated response to a single option. The results from two experiments showed that increasing the implementation effort of probability maximizing resulted in decreased adoption rates of this strategy. This was the case both when decision makers simultaneously learned about the outcome probabilities and responded to a dual task (Exp. 1) and when these two aspects were procedurally separated in two distinct stages (Exp. 2). We conclude that the effort involved in implementing a choice strategy is a key factor in shaping repeated choice under uncertainty. Moreover, highlighting the importance of implementation effort casts new light on the sometimes surprising and inconsistent effects of cognitive load that have previously been reported in the literature.

Striving for perfection and falling short: The influence of goals on probability matching

Probability matching in sequential prediction tasks is argued to occur because participants implicitly adopt the unrealistic goal of perfect prediction of sequences. Biases in the understanding of randomness then lead them to generate mixed rather than pure sequences of predictions in attempting to achieve this goal. In Study 1, N = 350 participants predicted 100 trials of a binary-outcome event. Two factors were manipulated: probability bias (the outcomes were equiprobable or distributed with a 75%-25% bias), and goal type-namely, whether single-trial predictions or the perfect prediction of four-trial sequences was emphasized and rewarded. As we hypothesized, predicting sequences led to more probability-matching behavior than did predicting single trials, for both the bias and no-bias conditions. In Study 1B, we added a control condition to distinguish the effects of the grouped presentation of trials from the effects of sequence-level perfect-prediction rewards. The results supported goal type rather than presentation format as the cause of the Study 1 differences in matching between the sequence and single-trial conditions. In Study 2, all participants (N = 300) predicted the outcomes for five-trial sequences, but with different goal levels being rewarded: 60%, 80%, or 100% correct predictions. The 100% goal resulted in the most probability matching, as hypothesized. Paradoxically, using the inferior strategy of probability matching may be triggered by adopting an unrealistic perfect-prediction goal.

A simple artificial life model explains irrational behavior in human decision-making

Although praised for their rationality, humans often make poor decisions, even in simple situations. In the repeated binary choice experiment, an individual has to choose repeatedly between the same two alternatives, where a reward is assigned to one of them with fixed probability. The optimal strategy is to perseverate with choosing the alternative with the best expected return. Whereas many species perseverate, humans tend to match the frequencies of their choices to the frequencies of the alternatives, a sub-optimal strategy known as probability matching. Our goal was to find the primary cognitive constraints under which a set of simple evolutionary rules can lead to such contrasting behaviors. We simulated the evolution of artificial populations, wherein the fitness of each animat (artificial animal) depended on its ability to predict the next element of a sequence made up of a repeating binary string of varying size. When the string was short relative to the animats' neural capacity, they could learn it and correctly predict the next element of the sequence. When it was long, they could not learn it, turning to the next best option: to perseverate. Animats from the last generation then performed the task of predicting the next element of a non-periodical binary sequence. We found that, whereas animats with smaller neural capacity kept perseverating with the best alternative as before, animats with larger neural capacity, which had previously been able to learn the pattern of repeating strings, adopted probability matching, being outperformed by the perseverating animats. Our results demonstrate how the ability to make predictions in an environment endowed with regular patterns may lead to probability matching under less structured conditions. They point to probability matching as a likely by-product of adaptive cognitive strategies that were crucial in human evolution, but may lead to sub-optimal performances in other environments.

Glucose promotes controlled processing: Matching, maximizing, and root beer

Participants drank either regular root beer or sugar-free diet root beer before working on a probability-learning task in which they tried to predict which of two events would occur on each of 200 trials. One event (E1) randomly occurred on 140 trials, the other (E2) on 60. In each of the last two blocks of 50 trials, the regular group matched prediction and event frequencies. In contrast, the diet group predicted E1 more often in each of these blocks. After the task, participants were asked to write down rules they used for responding. Blind ratings of rule complexity were inversely related to E1 predictions in the final 50 trials. Participants also took longer to advance after incorrect predictions and before predicting E2, reflecting time for revising and consulting rules. These results support the hypothesis that an effortful controlled process of normative rule-generation produces matching in probability-learning experiments, and that this process is a function of glucose availability.

Alterations in choice behavior by manipulations of world model

How to compute initially unknown reward values makes up one of the key problems in reinforcement learning theory, with two basic approaches being used. Model-free algorithms rely on the accumulation of substantial amounts of experience to compute the value of actions, whereas in model-based learning, the agent seeks to learn the generative process for outcomes from which the value of actions can be predicted. Here we show that (i) "probability matching"-a consistent example of suboptimal choice behavior seen in humans-occurs in an optimal Bayesian model-based learner using a max decision rule that is initialized with ecologically plausible, but incorrect beliefs about the generative process for outcomes and (ii) human behavior can be strongly and predictably altered by the presence of cues suggestive of various generative processes, despite statistically identical outcome generation. These results suggest human decision making is rational and model based and not consistent with model-free learning.

Probability matching as a computational strategy used in perception

The question of which strategy is employed in human decision making has been studied extensively in the context of cognitive tasks; however, this question has not been investigated systematically in the context of perceptual tasks. The goal of this study was to gain insight into the decision-making strategy used by human observers in a low-level perceptual task. Data from more than 100 individuals who participated in an auditory-visual spatial localization task was evaluated to examine which of three plausible strategies could account for each observer's behavior the best. This task is very suitable for exploring this question because it involves an implicit inference about whether the auditory and visual stimuli were caused by the same object or independent objects, and provides different strategies of how using the inference about causes can lead to distinctly different spatial estimates and response patterns. For example, employing the commonly used cost function of minimizing the mean squared error of spatial estimates would result in a weighted averaging of estimates corresponding to different causal structures. A strategy that would minimize the error in the inferred causal structure would result in the selection of the most likely causal structure and sticking with it in the subsequent inference of location-"model selection." A third strategy is one that selects a causal structure in proportion to its probability, thus attempting to match the probability of the inferred causal structure. This type of probability matching strategy has been reported to be used by participants predominantly in cognitive tasks. Comparing these three strategies, the behavior of the vast majority of observers in this perceptual task was most consistent with probability matching. While this appears to be a suboptimal strategy and hence a surprising choice for the perceptual system to adopt, we discuss potential advantages of such a strategy for perception.

Impulsivity and executive functions in polysubstance-using rave attenders

OBJECTIVES

Rave parties are characterized by high levels of drug use and polysubstance-using patterns that may be especially harmful for psychological and neuropsychological functioning. The aim of this study was to conduct a comprehensive assessment of different aspects of impulsivity and executive functions in a sample of polysubstance-using rave attenders.

METHODS

We collected data from two groups: rave attenders (RvA, n = 25) and drug-free healthy comparison individuals (HCI, n = 27). RvA were regular users of cannabis, cocaine, methampethamine, hallucinogens, and alcohol. The assessment protocol included a drug-taking interview, the UPPS-P Impulsive Behavior Scale, the delay-discounting questionnaire and a set of neuropsychological tests taxing different aspects of executive functions: response speed, working memory, reasoning, response inhibition and switching, self-regulation, decision making, and emotion perception.

RESULTS

For impulsivity measures, RvA had significantly elevated scores on lack of perseverance and positive and negative urgency, but did not differ from controls on lack of premeditation or sensation seeking. For neuropsychological functioning, RvA had significantly poorer performance on indices of analogical reasoning, processing speed, working memory, inhibition/switching errors, and decision making, but performed similar to controls on indices of self-regulation, reversal learning, and emotion processing. Peak and binge alcohol and drug use were positively correlated with positive urgency, and negatively correlated with performance on executive indices.

CONCLUSION

Rave attenders have selective alterations of impulsive personality and executive functions. These findings can contribute to delineate the neuropsychological profiles that distinguish recreational polysubstance use from substance dependence.

Aging and decision making under uncertainty: behavioral and neural evidence for the preservation of decision making in the absence of learning in old age

Decision making under uncertainty is an essential component of everyday life. Recent psychological studies suggest that older adults, despite age-related neurological decline, can make advantageous decisions when information about the contingencies of the outcomes is available. In this study, a two-choice prediction paradigm has been used, in conjunction with functional magnetic resonance imaging (fMRI), to investigate the effects of normal aging on neural substrates underlying uncertain decision making in the absence of learning that have not been addressed in previous neuroimaging studies. Neuroimaging results showed that both the healthy older and young adults recruited a network of brain regions comprising the right dorsolateral prefrontal cortex, bilateral inferior parietal lobule, medial frontal cortex, and right lateral orbitofrontal cortex during the prediction task. As was hypothesized, the performance of older adults in the prediction task was not impaired compared to young adults. Although no significant age-related increases in brain activity have been found, we observed an age-related decrease in activity in the right inferior parietal lobule. We speculate that the observed age-related decrease in parietal activity could be explained by age-related differences in decision making behavior revealed by questionnaire results and maximizing scores. Together, this study demonstrates behavioral and neural evidence for the preservation of decision making in older adults when information about the contingencies of the outcome is available.

Probability matching in choice under uncertainty: intuition versus deliberation

Gaissmaier and Schooler (2008) [Gaissmaier, W., & Schooler, L. J. (2008). The smart potential behind probability matching. Cognition, 109, 416-422] argue that probability matching, which has traditionally been viewed as a decision making error, may instead reflect an adaptive response to environments in which outcomes potentially follow predictable patterns. In choices involving monetary stakes, we find that probability matching persists even when it is not possible to identify or exploit outcome patterns and that many "probability matchers" rate an alternative strategy (maximizing) as superior when it is described to them. Probability matching appears to reflect a mistaken intuition that can be, but often is not, overridden by deliberate consideration of alternative choice strategies.

The smart potential behind probability matching

Probability matching is a classic choice anomaly that has been studied extensively. While many approaches assume that it is a cognitive shortcut driven by cognitive limitations, recent literature suggests that it is not a strategy per se, but rather another outcome of people's well-documented misperception of randomness. People search for patterns even in random sequences, which results in probability matching at the outcome level. Previous studies have supported this by the finding that distracting people with a secondary verbal working memory task presumably prevents the pattern search, resulting in more maximizing behavior that is considered more rational. The current paper demonstrates with two experiments that there is actually truth in both accounts. For some participants, probability matching indeed seems to be the result of a cognitive shortcut, a simple "win-stay, lose-shift" strategy, and in one experiment identified these as participants low in working memory capacity. For others, however, a potentially smart pattern search strategy underlies probability matching. These probability matchers (who still look irrational in the absence of patterns) actually have a higher chance of finding a pattern if one exists. Contrary to the almost uniformly negative perception of probability matching, we therefore conclude that there can be a potentially smart strategy behind probability matching.

An ecological perspective to cognitive limits: Modeling environment-mind interactions with ACT-R

Contrary to the common belief that more information is always better, Gigerenzer et al. (1999) showed that simple decision strategies which rely on little information can be quite successful. The success of simple strategies depends both on bets about the structure of the environment and on the core capacities of the human mind, such as recognition memory (Gigerenzer, 2004). However, the interplay between the environment and the mind’s core capacities has rarely been precisely modeled. We illustrate how these environment-mind interactions could be formally modeled within the cognitive architecture ACT-R (J. R. Anderson et al., 2004). ACT-R is an integrated theory of mind that is tuned to the statistical structure of the environment, and it can account for a variety of phenomena such as learning, problem solving, and decision making. Here, we focus on studying decision strategies and show how the success of theses strategies in particular environments depends on characteristics of core cognitive capacities, such as recognition and short term memory.