Subjective probability plays an increasingly important role in many fields concerned with human cognition and behavior. Yet there have been significant criticisms of the idea that probabilities could actually be represented in the mind. This paper presents and elaborates a view of subjective probability as a kind of sampling propensity associated with internally represented generative models. The resulting view answers to some of the most well known criticisms of subjective probability, and is also supported by empirical work in neuroscience and (...) behavioral psychology. The repercussions of the view for how we conceive of many ordinary instances of subjective probability, and how it relates to more traditional conceptions of subjective probability, are discussed in some detail. (shrink)

The past 50 years have seen an accumulation of evidence suggesting that associative learning depends on high-level cognitive processes that give rise to propositional knowledge. Yet, many learning theorists maintain a belief in a learning mechanism in which links between mental representations are formed automatically. We characterize and highlight the differences between the propositional and link approaches, and review the relevant empirical evidence. We conclude that learning is the consequence of propositional reasoning processes that cooperate with the unconscious processes involved (...) in memory retrieval and perception. We argue that this new conceptual framework allows many of the important recent advances in associative learning research to be retained, but recast in a model that provides a firmer foundation for both immediate application and future research. (shrink)

This article proposes a new model of human concept learning that provides a rational analysis of learning feature‐based concepts. This model is built upon Bayesian inference for a grammatically structured hypothesis space—a concept language of logical rules. This article compares the model predictions to human generalization judgments in several well‐known category learning experiments, and finds good agreement for both average and individual participant generalizations. This article further investigates judgments for a broad set of 7‐feature concepts—a more natural setting in several (...) ways—and again finds that the model explains human performance. (shrink)

Bayesian reverse-engineering is a research strategy for developing three-level explanations of behavior and cognition. Starting from a computational-level analysis of behavior and cognition as optimal probabilistic inference, Bayesian reverse-engineers apply numerous tweaks and heuristics to formulate testable hypotheses at the algorithmic and implementational levels. In so doing, they exploit recent technological advances in Bayesian artificial intelligence, machine learning, and statistics, but also consider established principles from cognitive psychology and neuroscience. Although these tweaks and heuristics are highly pragmatic in character and (...) are often deployed unsystematically, Bayesian reverse-engineering avoids several important worries that have been raised about the explanatory credentials of Bayesian cognitive science: the worry that the lower levels of analysis are being ignored altogether; the challenge that the mathematical models being developed are unfalsifiable; and the charge that the terms ‘optimal’ and ‘rational’ have lost their customary normative force. But while Bayesian reverse-engineering is therefore a viable and productive research strategy, it is also no fool-proof recipe for explanatory success. (shrink)

The authors examined cue competition effects in young children using the blicket detector paradigm, in which objects are placed either singly or in pairs on a novel machine and children must judge which objects have the causal power to make the machine work. Cue competition effects were found in a 5- to 6-year-old group but not in a 4-year-old group. Equivalent levels of forward and backward blocking were found in the former group. Children's counterfactual judgments were subsequently examined by asking (...) whether or not the machine would have gone off in the absence of I of 2 objects that had been placed on it as a pair. Cue competition effects were demonstrated only in 5- to 6-year-olds using this mode of assessing causal reasoning. (shrink)

The breadth-first search adopted by Bayesian researchers to map out the conceptual space and identify what the framework can do is beneficial for science and reflective of its collaborative and incremental nature. Theoretical pluralism among researchers facilitates refinement of models within various levels of analysis, which ultimately enables effective cross-talk between different levels of analysis.

Sequence learning is fundamental to a wide range of cognitive functions. Explaining how sequences—and the relations between the elements they comprise—are learned is a fundamental challenge to cognitive science. However, although hundreds of articles addressing this question are published each year, the actual learning mechanisms involved in the learning of sequences are rarely investigated. We present three experiments that seek to examine these mechanisms during a typing task. Experiments 1 and 2 tested learning during typing single letters on each trial. (...) Experiment 3 tested for “chunking” of these letters into “words.” The results of these experiments were used to examine the mechanisms that could best account for them, with a focus on two particular proposals: statistical transitional probability learning and discriminative error-driven learning. Experiments 1 and 2 showed that error-driven learning was a better predictor of response latencies than either n-gram frequencies or transitional probabilities. No evidence for chunking was found in Experiment 3, probably due to interspersing visual cues with the motor response. In addition, learning occurred across a greater distance in Experiment 1 than Experiment 2, suggesting that the greater predictability that comes with increased structure leads to greater learnability. These results shed new light on the mechanism responsible for sequence learning. Despite the widely held assumption that transitional probability learning is essential to this process, the present results suggest instead that the sequences are learned through a process of discriminative learning, involving prediction and feedback from prediction error. (shrink)

A word often expresses many different morphological functions. Which part of a word contributes to which part of the overall meaning is not always clear, which raises the question as to how such functions are learned. While linguistic studies tacitly assume the co-occurrence of cues and outcomes to suffice in learning these functions (Baer-Henney, Kügler, & van de Vijver, 2015; Baer-Henney & van de Vijver, 2012), error-driven learning suggests that contingency rather than contiguity is crucial (Nixon, 2020; Ramscar, Yarlett, Dye, (...) Denny, & Thorpe, 2010). In error-driven learning, cues gain association strength if they predict a certain outcome, and they lose strength if the outcome is absent. This reduction of association strength is called unlearning. So far, it is unclear if such unlearning has consequences for cue–outcome associations beyond the ones that get reduced. To test for such consequences of unlearning, we taught participants morphophonological patterns in an artificial language learning experiment. In one block, the cues to two morphological outcomes—plural and diminutive—co-occurred within the same word forms. In another block, a single cue to only one of these two outcomes was presented in a different set of word forms. We wanted to find out, if participants unlearn this cue's association with the outcome that is not predicted by the cue alone, and if this allows the absent cue to be associated with the absent outcome. Our results show that if unlearning was possible, participants learned that the absent cue predicts the absent outcome better than if no unlearning was possible. This effect was stronger if the unlearned cue was more salient. This shows that unlearning takes place even if no alternative cues to an absent outcome are provided, which highlights that learners take both positive and negative evidence into account—as predicted by domain general error-driven learning. (shrink)

The field of causal learning and reasoning (largely overlooked in the target article) provides an illuminating case study of how the modern Bayesian framework has deepened theoretical understanding, resolved long-standing controversies, and guided development of new and more principled algorithmic models. This progress was guided in large part by the systematic formulation and empirical comparison of multiple alternative Bayesian models.

This thesis consists of five main chapters including three independent studies, focusing on reward-based distractor interference and reward-association. In particular, the thesis addresses at which attentional processing stages the reward-based distractor interference takes place, as well as whether and how the reward association is learned on different levels. In the first chapter, I introduced a general background of attention, associative learning, and relations between reward associative learning and attention. In the end, I highlighted the open issues that this thesis aimed (...) to address. Chapter II aimed to answer the question at which processing stage the reward-based distractor interferes with the ongoing task. The study adopted the visual additional singleton search task but extended it to visual-tactile search. A classical finding of the reward-based distractor interference is that a reward-associated color feature, which appears as a task-irrelevant distractor, albeit it is not salient, captures attention and impedes task-relevant response. However, it is unclear at which attentional processing stage the reward-driven capture effects occur. To identify the distractor interference at the pre-attentional searching stage or post/focal- attentional decision-making stage, three experiments were conducted. Instead of a circular search display used in a standard attentional capture paradigm, this study used a horizontal search display to maximize potential attentional capture and disengagement. The results revealed that the reward-based distractor interference was only observed when the target and reward-based distractor were on the same side, indicating the interference likely occurred at the post/focal- attentional decision-making stage. To confirm this, Experiments 2 and 3 adopted the crossmodal search paradigm, in which the search stage and post-selective identification stage took place in different modalities. Such design enabled us to distinguish whether the interference effect occurs at the searching stage or the post/focal-attentional decision-making stage. The results further corroborated that the reward-driven capture effects occur at the post/focal-attention stage. Chapter III focused on the question related to the level of reward associative learning. Most previous studies on reward-based attentional capture implicitly used feature-based reward association, particularly on the feature level, e.g., color-reward association. This leaves the question open regarding whether the associative reward could be established at the level of response mapping or task-set mapping. Two experiments were designed to address this question. In Experiment 1, high/low rewards were associated with both the two colors and the left/right response in the training phase, and the reward-associated color became one of the task-irrelevant distractors in the test phase. We failed to find any evidence on the reward-response association. However, the results showed the distractor with previously reward-associated features facilitated search, indicating a better distractor handling in the test phase. In Experiment 2, the reward was associated with task-sets in the training phase and the task-sets kept the same in the test phase, while the search task was changed. The results suggested that reward-associated learning facilitated distractor handling and task-set learning. Chapter IV tackled the open research question - whether reward associative learning could take place at the multi-conjunction level. The question is important, given that previous studies on reward-based attentional capture only investigated associative learning of a single feature. As reward association in daily life is usually complex, Study 3 aims to explore whether reward learning can be established through reward assignments with conjunction features and whether the interference of distractors with previously reward-associated features can be observed in a similar way as previous studies shown on single-feature-based reward-driven capture effects. Reward learning was found locally trial-by-trial throughout the whole learning process, however, it was hard to establish a reliable conjunction reward association. Furthermore, there was no reward-based distractor interference in the test phase. The findings suggest that conjunction reward-association is difficult, though a short-lived trial-to-trial conjunction reward learning can be established. The final Chapter V discussed the findings of these three studies and their inter-relations. And I summarized how these findings could contribute to the debate of reward-based distractor interference. In summary, this thesis helps to improve our knowledge about reward-based distractor interference, particularly on the identification of the processing stage of distractor interference and what can be associated with reward in reward associative learning. (shrink)