Artificial intelligence plays an increasingly important role in legal disputes, influencing not only the reality outside the court but also the judicial decision-making process itself. While it is clear why judges may generally benefit from technology as a tool for reducing effort costs or increasing accuracy, the presence of technology in the judicial process may also affect the public perception of the courts. In particular, if individuals are averse to adjudication that involves a high degree of automation, particularly given fairness (...) concerns, then judicial technology may yield lower benefits than expected. However, the degree of aversion may well depend on how technology is used, i.e., on the timing and strength of judicial reliance on algorithms. Using an exploratory survey, we investigate whether the stage in which judges turn to algorithms for assistance matters for individual beliefs about the fairness of case outcomes. Specifically, we elicit beliefs about the use of algorithms in four different stages of adjudication: (i) information acquisition, (ii) information analysis, (iii) decision selection, and (iv) decision implementation. Our analysis indicates that individuals generally perceive the use of algorithms as fairer in the information acquisition stage than in other stages. However, individuals with a legal profession also perceive automation in the decision implementation stage as less fair compared to other individuals. Our findings, hence, suggest that individuals do care about how and when algorithms are used in the courts. (shrink)

Mental representations remain the central posits of psychology after many decades of scrutiny. However, there is no consensus about the representational format(s) of biological cognition. This paper provides a survey of evidence from computational cognitive psychology, perceptual psychology, developmental psychology, comparative psychology, and social psychology, and concludes that one type of format that routinely crops up is the language-of-thought (LoT). We outline six core properties of LoTs: (i) discrete constituents; (ii) role-filler independence; (iii) predicate–argument structure; (iv) logical operators; (v) inferential (...) promiscuity; and (vi) abstract content. These properties cluster together throughout cognitive science. Bayesian computational modeling, compositional features of object perception, complex infant and animal reasoning, and automatic, intuitive cognition in adults all implicate LoT-like structures. Instead of regarding LoT as a relic of the previous century, researchers in cognitive science and philosophy-of-mind must take seriously the explanatory breadth of LoT-based architectures. We grant that the mind may harbor many formats and architectures, including iconic and associative structures as well as deep-neural-network-like architectures. However, as computational/representational approaches to the mind continue to advance, classical compositional symbolic structures – that is, LoTs – only prove more flexible and well-supported over time. (shrink)

Recent work in cognitive modelling has found that most of the data that has been cited as evidence for the dual-process theory (DPT) of reasoning is best explained by non-linear, “monotonic” one-process models (Stephens et al., 2018, 2019). In this paper, I consider an important caveat of this research: it uses models that are committed to unrealistic assumptions about how effectively task conditions can isolate Type-1 and Type-2 reasoning. To avoid this caveat, I develop a coordinated theoretical, experimental, and modelling (...) strategy to better test DPT. First, I propose that Type-1 and Type-2 reasoning are defined as reasoning that precedes and follows metacognitive control, respectively. Second, I argue that reasoning that precedes and follows metacognitive control can be effectively isolated using debiasing paradigms that manipulate metacognitive heuristics (e.g., processing fluency) to prevent or trigger metacognitive control, respectively. Third, I argue that monotonic modelling can allow us to decisively test DPT only when we use them to analyse data from this particular kind of debiasing paradigm. (shrink)

Does avoiding biased responding to reasoning problems and grasping the ­correct solution require engaging in effortful deliberation or can such solution insight be acquired more intuitively? In this study we set out to test the impact of deliberation on the efficiency of a de-bias training in which the problem logic was explained to participants. We focused on the infamous bat-and-ball problem and varied the degree of possible deliberation during the training session by manipulating time constraints and cognitive load. The results (...) show that the less constrained the deliberation, the more participants improve. However, even under extremely stringent conditions (high time-pressure and dual task load), participants still show a significant improvement. Critically, this “intuitive” insight effect persists over two months. This suggests that deliberation helps reasoners benefit from the training, but it is not indispensable. We discuss critical applied and theoretical implications. (shrink)

Recent reasoning accounts suggest that people can process elementary logical principles intuitively. These controversial “logical intuitions” are believed to result from a learning process in which developing reasoners automatize their application. To verify this automatization hypothesis, we contrasted the reasoning performance of younger (7th grade) and older (12th grade) reasoners with a two-response paradigm. Participants initially responded with the first intuitive response that came to mind and subsequently were allowed to deliberate on classic “bias” problems (base-rate problems and syllogisms). Results (...) showed that in addition to showing less deliberate correction of an initial erroneous response, younger reasoners were specifically less likely to generate the correct response from the outset. The findings lend credence to the role of a developmental automatization process and indicate that developmental improvements in reasoning accuracy are at least partially driven by an improvement in the accuracy of our intuitions. (shrink)

One of the most famous problems in the decision-making literature is the “bat and a ball” problem from the cognitive reflection test (CRT; Frederick, 2005). The problem goes as follows: „A bat and...

Research on implicit conflict detection suggests that people are sensitive to violations of logical principles. When they make reasoning errors, their epistemic radar presumably detects an anomaly....

A key component of the susceptibility to cognitive biases is the ability to monitor for conflict between intuitively cued “heuristic” answers and logical principles. While there is evidence that pe...

Although reasoning has been characterized as the essence of our being, it is often prone to cognitive biases. Decades of research in the reasoning and decision making fields have shown that when fa...

The logical intuition hypothesis proposes a difference in the strength between logical and heuristic intuitions. The labels of logical and heuristic intuitions are exclusive to conventional reasoning research. This paper reports the result of testing intuition strength using the dot-probe methodology in a novel multiplication paradigm. Here, “logical intuition” and “heuristic intuition” were relabeled as “weaker intuition” (-1 × 5 = 5) and “stronger intuition” (1 × 5 = 5), respectively, to assess the assumptions about the difference in the strength (...) of “Chinese multiplication mnemonics” intuitions. The dot-probe paradigm was utilized in the following experiments to measure attentional bias. This was assessed by calculating the difference in reaction time between incongruent (the target location is opposite to the probe location) and congruent (the target location is the same as the probe location) conditions. Experiment 1 found attentional bias directed attention to the correct answer for both weaker and stronger intuitions. Experiment 2 replicated and extended Experiment 1 with a secondary task to manipulate high and low cognitive loads. Consequently, stronger intuitions were unaffected by cognitive load and participants were biased toward the correct answer, whereas attentional bias for weaker intuitions only occurred in low load conditions. These results revealed that although both intuitions can guide attention, stronger intuition may guide attention bias in a high-load situation due to its robust automation. Our findings extend the logical intuition hypothesis that proposes a difference in the automated strengths of intuitions to mathematical and analytic thinking. (shrink)

Human reasoning is often conceived as an interplay between a more intuitive and deliberate thought process. In the last 50 years, influential fast-and-slow dual-process models that capitalize on this distinction have been used to account for numerous phenomena – from logical reasoning biases, over prosocial behavior, to moral decision making. The present paper clarifies that despite the popularity, critical assumptions are poorly conceived. My critique focuses on two interconnected foundational issues: the exclusivity and switch feature. The exclusivity feature refers to (...) the tendency to conceive intuition and deliberation as generating unique responses such that one type of response is assumed to be beyond the capability of the fast-intuitive processing mode. I review the empirical evidence in key fields and show that there is no solid ground for such exclusivity. The switch feature concerns the mechanism by which a reasoner can decide to shift between more intuitive and deliberate processing. I present an overview of leading switch accounts and show that they are conceptually problematic – precisely because they presuppose exclusivity. I build on these insights to sketch the groundwork for a more viable dual-process architecture and illustrate how it can set a new research agenda to advance the field in the coming years. (shrink)