The latent scope bias: Robust and replicable.

People appear to prefer explanations that minimize unobserved effects, a pattern known as the latent scope bias in explanatory reasoning. A recent set of studies published in Cognition argues that the bias can be elicited only in certain narrow conditions and with certain tasks, such as a forced-choice task (Stephan, 2023). This commentary assesses the robustness of the bias in two ways: it weighs the most recent discoveries against previous research, and it presents two new studies using the most general possible elicitation task, i.

Human verifications: Computable with truth values outside logic.

Cognitive scientists treat verification as a computation in which descriptions that match the relevant situation are true, but otherwise false. The claim is controversial: The logician Gödel and the physicist Penrose have argued that human verifications are not computable. In contrast, the theory of mental models treats verification as computable, but the two truth values of standard logics, and , as insufficient.

Iconicity bias and duration.

Descriptions of durational relations can be ambiguous, for example, the description "one meeting happened during another" could mean that one meeting started before the other ended, or it could mean that the meetings started and ended simultaneously. A recent theory posits that people mentally simulate descriptions of durational events by representing their starts and ends along a spatial axis, that is, an iconic representation of time. To draw conclusions from this iconic mental model, reasoners consciously scan it in the direction of earlier to later timepoints.

Building a transdisciplinary expert consensus on the cognitive drivers of performance under pressure: An international multi-panel Delphi study.

Introduction: The ability to perform optimally under pressure is critical across many occupations, including the military, first responders, and competitive sport. Despite recognition that such performance depends on a range of cognitive factors, how common these factors are across performance domains remains unclear. The current study sought to integrate existing knowledge in the performance field in the form of a transdisciplinary expert consensus on the cognitive mechanisms that underlie performance under pressure.

Temporal explanations.

People can explain phenomena by appealing to temporal relations, for example, you might explain a colleague's absence at a meeting by inferring that their prior meeting ended late. Previous explanatory reasoning research shows that people construct causal explanations to resolve causal conflicts. Accordingly, temporal explanations may help reasoners resolve temporal conflicts, and we describe four experimental tests of the hypothesis ( = 240).

Reasoning About Want.

No present theory explains the inferences people draw about the real world when reasoning about "bouletic" relations, that is, predicates that express desires, such as want in "Lee wants to be in love". Linguistic accounts of want define it in terms of a relation to a desirer's beliefs, and how its complement is deemed desirable. In contrast, we describe a new model-based theory that posits that by default, desire predicates such as want contrast desires against facts.

Understanding Is a Process.

How do we gauge understanding? Tests of understanding, such as Turing's imitation game, are numerous; yet, attempts to achieve a state of understanding are not satisfactory assessments. Intelligent agents designed to pass one test of understanding often fall short of others. Rather than approaching understanding as a system state, in this paper, we argue that understanding is a process that changes over time and experience.

Recursion in programs, thought, and language.

This article presents a theory of recursion in thinking and language. In the logic of computability, a function maps one or more sets to another, and it can have a recursive definition that is semi-circular, i.e.

Reasoning about properties: A computational theory.

We present a theory of how people reason about properties. Such inferences have been studied since Aristotle's invention of Western logic. But, no previous psychological theory gives an adequate account of them, and most theories do not go beyond syllogistic inferences, such as: ? The present theory postulates that such assertions establish relations between properties, which mental models represent in corresponding relations between sets of entities.

Much Ado About Nothing: The Mental Representation of Omissive Relations.

When the absence of an event causes some outcome, it is an instance of omissive causation. For instance, not eating lunch may cause you to be hungry. Recent psychological proposals concur that the mind represents causal relations, including omissive causal relations, through mental simulation, but they disagree on the form of that simulation.

Norms Affect Prospective Causal Judgments.

People more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some outcome. Until recently, this abnormal-selection effect has been studied using retrospective vignette-based paradigms. We use a novel set of video stimuli to investigate this effect for prospective causal judgments-that is, judgments about the cause of some future outcome.

Causal conflicts produce domino effects.

Inconsistent beliefs call for revision-but which of them should individuals revise? A long-standing view is that they should make minimal changes that restore consistency. An alternative view is that their primary task is to explain how the inconsistency arose. Hence, they are likely to violate minimalism in two ways: they should infer more information than is strictly necessary to establish consistency and they should reject more information than is strictly necessary to establish consistency.

Reasoning about Durations.

A set of assertions is consistent provided they can all be true at the same time. Naive individuals could prove consistency using the formal rules of a logical calculus, but it calls for them to fail to prove the negation of one assertion from the remainder in the set. An alternative procedure is for them to use an intuitive system (System 1) to construct a mental model of all the assertions.

Explanatory completeness.

All explanations are incomplete, but reasoners think some explanations are more complete than others. To explain this behavior, we propose a novel theory of how people assess explanatory incompleteness. The account assumes that reasoners represent explanations as causal mental models - iconic representations of possible arrangements of causes and effects.

Teleological generics.

Certain "generic" generalizations concern functions and purposes, e.g., cars are for driving.

Updating and reasoning: Different processes, different models, different functions.

Two issues should be addressed to refine and extend the distinction between temporal updating and reasoning advocated by Hoerl & McCormack. First, do the mental representations constructed during updating differ from those used for reasoning? Second, are updating and reasoning the only two processes relevant to temporal thinking? If not, is a dual-systems framework sensible? We address both issues below.

Mental models and omissive causation.

Some causal relations refer to causation by commission (e.g., "A gunshot causes death"), and others refer to causation by omission (e.

Facts and Possibilities: A Model-Based Theory of Sentential Reasoning.

This article presents a fundamental advance in the theory of mental models as an explanation of reasoning about facts, possibilities, and probabilities. It postulates that the meanings of compound assertions, such as conditionals (if) and disjunctions (or), unlike those in logic, refer to conjunctions of epistemic possibilities that hold in default of information to the contrary. Various factors such as general knowledge can modulate these interpretations.

Episodes, events, and models.

We describe a novel computational theory of how individuals segment perceptual information into representations of events. The theory is inspired by recent findings in the cognitive science and cognitive neuroscience of event segmentation. In line with recent theories, it holds that online event segmentation is automatic, and that event segmentation yields mental simulations of events.

Logic, probability, and human reasoning.

This review addresses the long-standing puzzle of how logic and probability fit together in human reasoning. Many cognitive scientists argue that conventional logic cannot underlie deductions, because it never requires valid conclusions to be withdrawn - not even if they are false; it treats conditional assertions implausibly; and it yields many vapid, although valid, conclusions. A new paradigm of probability logic allows conclusions to be withdrawn and treats conditionals more plausibly, although it does not address the problem of vapidity.

Immediate inferences from quantified assertions.

We propose a theory of immediate inferences from assertions containing a single quantifier, such as: All of the artists are bakers; therefore, some of the bakers are artists. The theory is based on mental models and is implemented in a computer program, mReasoner. It predicts three main levels of increasing difficulty: (a) immediate inferences in which the premise and conclusion have identical meanings; (b) those in which the initial mental model of the premise yields the correct conclusion; and (c) those in which only an alternative to the initial model establishes the correct conclusion.

Causal reasoning with mental models.

This paper outlines the model-based theory of causal reasoning. It postulates that the core meanings of causal assertions are deterministic and refer to temporally-ordered sets of possibilities: A causes B to occur means that given A, B occurs, whereas A enables B to occur means that given A, it is possible for B to occur. The paper shows how mental models represent such assertions, and how these models underlie deductive, inductive, and abductive reasoning yielding explanations.

Naive Probability: Model-Based Estimates of Unique Events.

We describe a dual-process theory of how individuals estimate the probabilities of unique events, such as Hillary Clinton becoming U.S. President.

The negations of conjunctions, conditionals, and disjunctions.

How do reasoners understand and formulate denials of compound assertions, such as conjunctions and disjunctions? A theory based on mental models postulates that individuals enumerate models of the various possibilities consistent with the assertions. It therefore predicts a novel interaction: in affirmations, conjunctions, A and B, which refer to one possibility, should be easier to understand than disjunctions, A or B, which refer to more than one possibility; in denials, conjunctions, not(A and B), which refer to more than one possibility, should be harder to understand than disjunctions, not(A or B), which do not. Conditionals are ambiguous and they should be of intermediate difficulty.