The Cognition/Metacognition Trade-Off.

Integration to boundary is an optimal decision algorithm that accumulates evidence until the posterior reaches a decision boundary, resulting in the fastest decisions for a target accuracy. Here, we demonstrated that this advantage incurs a cost in metacognitive accuracy (confidence), generating a cognition/metacognition trade-off. Using computational modeling, we found that integration to a fixed boundary results in less variability in evidence integration and thus reduces metacognitive accuracy, compared with a collapsing-boundary or a random-timer strategy. We examined how decision strategy affects metacognitive accuracy in three cross-domain experiments, in which 102 university students completed a free-response session (evidence terminated by the participant's response) and an interrogation session (fixed number of evidence samples controlled by the experimenter). In both sessions, participants observed a sequence of evidence and reported their choice and confidence. As predicted, the interrogation protocol (preventing integration to boundary) enhanced metacognitive accuracy. We also found that in the free-response sessions, participants integrated evidence to a collapsing boundary-a strategy that achieves an efficient compromise between optimizing choice and metacognitive accuracy.

The formation of preference in risky choice.

A key question in decision-making is how people integrate amounts and probabilities to form preferences between risky alternatives. Here we rely on the general principle of integration-to-boundary to develop several biologically plausible process models of risky-choice, which account for both choices and response-times. These models allowed us to contrast two influential competing theories: i) within-alternative evaluations, based on multiplicative interaction between amounts and probabilities, ii) within-attribute comparisons across alternatives. To constrain the preference formation process, we monitored eye-fixations during decisions between pairs of simple lotteries, designed to systematically span the decision-space. The behavioral results indicate that the participants' eye-scanning patterns were associated with risk-preferences and expected-value maximization. Crucially, model comparisons showed that within-alternative process models decisively outperformed within-attribute ones, in accounting for choices and response-times. These findings elucidate the psychological processes underlying preference formation when making risky-choices, and suggest that compensatory, within-alternative integration is an adaptive mechanism employed in human decision-making.

Attentional Selection Mediates Framing and Risk-Bias Effects.

Humans display a number of puzzling choice patterns that contradict basic principles of rationality. For example, they show preferences that change as a result of task framing or of adding irrelevant alternatives into the choice set. A recent theory has proposed that such choice and risk biases arise from an attentional mechanism that increases the relative weighting of goal-consistent information and protects the decision from noise after the sensory stage. Here, using a divided-attention method based on the dot-probe technique, we showed that attentional selection toward values congruent with the task goal takes place while participants make choices between alternatives that consist of payoff sequences. Moreover, we demonstrated that the magnitude of this attentional selection predicts risk attitudes, indicating a common underlying cognitive process. The results highlight the dynamic interplay between attention and choice mechanisms in producing framing effects and risk biases.

Attentional capture by irrelevant emotional distractor faces is contingent on implicit attentional settings.

Although expressions of facial emotion hold a special status in attention relative to other complex objects, whether they summon our attention automatically and against our intentions remains a debated issue. Studies supporting the strong view that attentional capture by facial expressions of emotion is entirely automatic reported that a unique (singleton) emotional face distractor interfered with search for a target that was also unique on a different dimension. Participants could therefore search for the odd-one out face to locate the target and attentional capture by irrelevant emotional faces might be contingent on the adoption of an implicit set for singletons. Here, confirming this hypothesis, an irrelevant emotional face captured attention when the target was the unique face with a discrepant orientation, both when this orientation was unpredictable and when it remained constant. By contrast, no such capture was observed when the target could not be found by monitoring displays for a discrepant face and participants had to search for a face with a specific orientation. Our findings show that attentional capture by emotional faces is not purely stimulus driven and thereby resolve the apparent inconsistency that prevails in the literature on the automaticity of attentional capture by emotional faces.

Exposure to temporal variability promotes subsequent adaptation to new temporal regularities.

Noise is intuitively thought to interfere with perceptual learning; However, human and machine learning studies suggest that, in certain contexts, variability may reduce overfitting and improve generalizability. Whereas previous studies have examined the effects of variability in learned stimuli or tasks, it is hitherto unknown what are the effects of variability in the temporal environment. Here, we examined this question in two groups of adult participants (N = 40) presented with visual targets at either random or fixed temporal routines and then tested on the same type of targets at a new nearly-fixed temporal routine. Findings reveal that participants of the random group performed better and adapted quicker following a change in the timing routine, relative to participants of the fixed group. Corroborated with eye-tracking and computational modeling, these findings suggest that prior exposure to temporal variability promotes the formation of new temporal expectations and enhances generalizability in a dynamic environment. We conclude that noise plays an important role in promoting perceptual learning in the temporal domain: rather than interfering with the formation of temporal expectations, noise enhances them. This counterintuitive effect is hypothesized to be achieved through eliminating overfitting and promoting generalizability.

The effect of perceptual organization on numerical and preference-based decisions shows inter-subject correlation.

Individual differences in cognitive processing have been the subject of intensive research. One important type of such individual differences is the tendency for global versus local processing, which was shown to correlate with a wide range of processing differences in fields such as decision making, social judgments and creativity. Yet, whether these global/local processing tendencies are correlated within a subject across different domains is still an open question. To address this question, we develop and test a novel method to quantify global/local processing tendencies, in which we directly set in opposition the local and global information instead of instructing subjects to specifically attend to one processing level. We apply our novel method to two different domains: (1) a numerical cognition task, and (2) a preference task. Using computational modeling, we accounted for classical effects in choice and numerical-cognition. Global/local tendencies in both tasks were quantified using a salience parameter. Critically, the salience parameters extracted from the numerical cognition and preference tasks were highly correlated, providing support for robust perceptual organization tendencies within an individual.

Evidence integration and decision confidence are modulated by stimulus consistency.

Evidence integration is a normative algorithm for choosing between alternatives with noisy evidence, which has been successful in accounting for vast amounts of behavioural and neural data. However, this mechanism has been challenged by non-integration heuristics, and tracking decision boundaries has proven elusive. Here we first show that the decision boundaries can be extracted using a model-free behavioural method termed decision classification boundary, which optimizes choice classification based on the accumulated evidence. Using this method, we provide direct support for evidence integration over non-integration heuristics, show that the decision boundaries collapse across time and identify an integration bias whereby incoming evidence is modulated based on its consistency with preceding information. This consistency bias, which is a form of pre-decision confirmation bias, was supported in four cross-domain experiments, showing that choice accuracy and decision confidence are modulated by stimulus consistency. Strikingly, despite its seeming sub-optimality, the consistency bias fosters performance by enhancing robustness to integration noise.

Abstract thinking facilitates aggregation of information.

Many situations in life (such as considering which stock to invest in, or which people to befriend) require averaging across series of values. Here, we examined predictions derived from construal level theory, and tested whether abstract compared with concrete thinking facilitates the process of aggregating values into a unified summary representation. In four experiments, participants were induced to think more abstractly (vs. concretely) and performed different variations of an averaging task with numerical values (Experiments 1-2 and 4), and emotional faces (Experiment 3). We found that the induction of abstract, compared with concrete thinking, improved aggregation accuracy (Experiments 1-3), but did not improve memory for specific items (Experiment 4). In particular, in concrete thinking, averaging was characterized by increased regression toward the mean and lower signal-to-noise ratio, compared with abstract thinking. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Extracting Summary Statistics of Rapid Numerical Sequences.

We examine the ability of observers to extract summary statistics (such as the mean and the relative-variance) from rapid numerical sequences of two digit numbers presented at a rate of 4/s. In four experiments (total N = 100), we find that the participants show a remarkable ability to extract such summary statistics and that their precision in the estimation of the sequence-mean improves with the sequence-length (subject to individual differences). Using model selection for individual participants we find that, when only the sequence-average is estimated, most participants rely on a holistic process of frequency based estimation with a minority who rely on a (rule-based and capacity limited) mid-range strategy. When both the sequence-average and the relative variance are estimated, about half of the participants rely on these two strategies. Importantly, the holistic strategy appears more efficient in terms of its precision. We discuss implications for the domains of two pathways numerical processing and decision-making.

Integration to boundary in decisions between numerical sequences.

Integration-to-boundary is a prominent normative principle used in evidence-based decisions to explain the speed-accuracy trade-off and determine the decision-time. Despite its prominence, however, the decision boundary is not directly observed, but rather is theoretically assumed, and there is still an ongoing debate regarding its form: fixed vs. collapsing. The aim of this study is to show that the integration-to-boundary process extends to decisions between rapid pairs of numerical sequences (2 Hz rate), and to determine the boundary type by directly monitoring the noisy accumulated evidence. In a set of two experiments (supplemented by computational modelling), we demonstrate that integration to a collapsing-boundary takes place in such tasks, ruling out non-integration heuristic strategies. Moreover, we show that participants can adaptively adjust their boundaries in response to reward contingencies. Finally, we discuss the implications to decision optimality and the nature of processes and representations in numerical cognition.

Fast and effective: Intuitive processes in complex decisions.

Is it possible to carry out complex multi-attribute decisions (which require an estimation of the weighted average) intuitively, without resorting to simplifying heuristics? Over the course of 600 trials, 26 participants had to choose the better-suiting job-candidate, a task requiring comparison of two alternatives over three/four/five dimensions with specified importance weights, with a time constraint forcing intuitive decisions. Participants performed the task fast (mean reaction time (RT) ~ 1.5 s) and with high accuracy (~86%). The participants were classified as users of one of three strategies: Weighted Additive Utility (WADD), Equal Weight rule and Take-The-Best heuristic (TTB). Fifty-nine percent of the participants were classified as users of the compensatory WADD strategy and 29% as users of the non-compensatory TTB. Moreover, the WADD users achieved higher task accuracy without showing time costs. The results provide support for the existence of an automatic compensatory mechanism in weighted average estimations.