Phil Johnson-Laird and I have a new paper the describes a theory and computational model of how people reason about properties. The theory holds that people construct small-scale mental simulations of entities linked to their properties, and that the more mental simulations they build, the harder a problem will be. A computer model, mReasoner, simulates human reasoning across 200 separate inferences. The theory is now out in Psychological Review, and its abstract is here:

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: All the bankers are architects; Some of the chefs are bankers; What follows? The present theory postulates that such assertions establish relations between properties, which mental models represent in corresponding relations between sets of entities. The theory combines the construction of models with innovative heuristics that scan them to draw conclusions. It explains the processes that can generate a conclusion from premises, decide if a given conclusion is necessary or possible, assess its probability, and evaluate the consistency of a set of assertions. A computer program implementing the theory embodies an intuitive system 1 and a deliberative system 2, and it copes with quantifiers such as more than half the architects. It fit data from over 200 different sorts of inference, including those about the properties of individuals, the properties of a set of individuals, and the properties of several such sets in syllogisms. Another innovation is that the program accounts for differences in reasoning from one individual to another, and from one group of individuals to another: Some tend to reason intuitively but some go beyond intuitions to search for alternative models. The theory extends to inferences about disjunctions of properties, about relations rather than properties, and about the properties of properties.

You can read the full paper here.

Hillary Harner and I have a new theoretical paper out in Cognitive Science on the processes and mental representations people rely on to reason about desire. The paper shows that people think of desires as “counterfactive” — A wants X implies that X isn’t the case by default. It also shows that people separate desires from intentions, and that they make systematic reasoning mistakes concerning the predicate “want”.

The abstract of the paper is here:

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. In particular, A wants P implies by default that P is not the case, because you cannot want what is already true. On further deliberation, reasoners may infer that A believes, but does not know for certain, that P is not the case. The theory makes several empirical predictions about how people interpret, assess the consistency of, and draw conclusions from desire predicates like want. Seven experiments tested and validated the theory’s central predictions. We assess the theory in light of recent proposals of desire predicates.

and it’s available for download here.

I was recently interviewed by Nina Bonderup Dohn and Marco Ragni at KI – Künstliche Intelligenz, a German journal of artificial intelligence. You can check out that interview here.

Phil Johnson-Laird, along with his collaborators Monica Bucciarelli, Robert Mackiewicz, and myself, published a paper in Psychonomic Bulletin & Review that reviewed research into how humans consciously reason about recursive operations. Though the term “recursion” is often used by computer scientists to describe specific types of programs, people without any background or training in computer science can engage in recursive reasoning. Even children do so: such as when they perform and describe repeated loops of operations to solve a problem. The paper describes a simulation-based theory of how people reason about recursion: they construct kinematic simulations to carry out loops of operations, and use those simulations as the basis of their descriptions. It also discusses the computational power needed to carry out recursive reasoning and linguistic operations.

The abstract of the paper is here:

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., referring in part to the function itself. Any function that is computable – and many are not – can be computed in an infinite number of distinct programs. Some of these programs are semi-circular too, but they needn’t be, because repeated loops of instructions can compute any recursive function. Our theory aims to explain how naive individuals devise informal programs in natural language, and is itself implemented in a computer program that creates programs. Participants in our experiments spontaneously simulate loops of instructions in kinematic mental models. They rely on such loops to compute recursive functions for rearranging the order of cars in trains on a track with a siding. Kolmogorov complexity predicts the relative difficulty of abducing such programs – for easy rearrangements, such as reversing the order of the cars, to difficult ones, such as splitting a train in two and interleaving the two resulting halves (equivalent to a faro shuffle). This rearrangement uses both the siding and part of the track as working memories, shuffling cars between them, and so it relies on the power of a linear-bounded computer. Linguistic evidence implies that this power is more than necessary to compose the meanings of sentences in natural language from those of their grammatical constituents.

And the paper is available for download here.

Hillary Harner and Laura Kelly successfully completed their NRC Postdoctoral Fellowships this Fall — congratulations to them both! Throughout their years at NRL, Laura and Hillary made some important discoveries into how people reason about durations and how they reason about desire.

Laura Kelly‘s work in the Reasoning Lab focused on how people reason about durations (Kelly, Prabhakar, & Khemlani, 2019): she discovered that they mentally simulate how events relate to one another, though they tend to reason with only one simulation at a time (Kelly & Khemlani, 2019; Kelly, Khemlani, & Johnson-Laird, 2020). She also found that they exhibit systematic biases when they reason about duration (Kelly & Khemlani, 2020). Likewise, when they detect conflicts in temporal information, they build explanations to resolve those temporal conflicts (Kelly & Khemlani, 2021).

Hillary Harner‘s work in the Reasoning Lab focused on desire: how people understand, comprehend, and reason about the word “want”. She found that people make systematic inferences about the world from knowledge of people’s desires (Harner & Khemlani, 2020). Likewise, she found that people distinguish between desires and intentions (Harner & Khemlani, 2021). And, in her ongoing work, she used corpus analysis methodologies to discover how children’s production of the word “want” develops in early childhood (Harner & Khemlani, under review). Beyond her research on desire, Harner collaborated with Gordon Briggs and others to work on how people construct quantified descriptions of groups (Briggs, Harner, & Khemlani, 2020, 2021), how the construct referring expressions (Briggs & Harner, 2019) and how they reason about omissive causal relations (Briggs, Harner, Wasylyshyn, Bello, & Khemlani, 2019; Khemlani, Bello, Briggs, Harner, & Wasylyshyn, 2021). Hillary joined Altamira Corporation as a Behavioral Scientist.

Best of luck to Laura and Hillary!

I’m extremely excited that Branden Bio began his postdoc at NRL this week! Dr. Bio is coming from Princeton’s Psychology Department, where he worked on studying attention, awareness, and its underlying neural mechanisms. His recent work focuses on how people attribute conscious states to others. He’s published papers in PNAS, eLife, and Cerebral Cortex. At the R Lab, he’ll be working on developing experimental methods to test epistemic reasoning. Welcome, Branden!

At this year’s CogSci 2021, the Reasoning Lab presented recent work, including:

• Laura Kelly’s research on how people build explanations to resolve inconsistencies in temporal premises (paper, video)
• Hillary Harner’s work on how they distinguish between desires and intentions (paper, video)
• Gordon Briggs and Hillary Harner’s work on preferences in people’s quantified descriptions of groups (paper, video)
• A new theory of how people comprehend epistemic relations such as know and think (paper, video)

The Reasoning Lab presented work on how people think and reason about time, durations, causality, bouletics, kinematics, and quantifiers at this year’s International Conference on Thinking 2021. For those who couldn’t make the conference, I’ve included an archive of the presentations here:

• 🎞 Directional biases in durative inference presented by Laura Kelly
• 🎞 The consistency of durative relations presented by Laura Kelly
• 🎞 Temporal explanations help resolve temporal conflicts presented by Laura Kelly
• 🎞 These boots are made for walking: A theory of teleological generics presented by Joanna Korman
• 🎞 Difficulties in children’s creation of informal algorithms presented by Monica Bucciarelli
• 🎞 A cognitively plausible algorithm for causal inference presented by Gordon Briggs
• 🎞 Illusions from thinking about desires as possibilities presented by Hillary Harner
• 🎞 Quantifiers and asymmetric relations: Investigating possibilities in young and older adults by Robert Mackiewicz

Phil Johnson-Laird and I recently published a deep dive into the mReasoner computational cognitive model and the new theory of reasoning about properties that it implements. We describe a new model based theory of reasoning about quantifiers, such as “all”, “some”, and “most”, as well as a series of simulation studies that show how the system implements a number of different reasoning tasks, such as assessing whether a set of statements is possible, consistent, or necessary. The system implements and tests a novel set of heuristics for syllogistic reasoning, and it shows how to stochastically vary the structure of mental models. You can read more from the abstract, here:

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: All the bankers are architects; Some of the chefs are bankers; What follows? The present theory postulates that such assertions establish relations between properties, which mental models represent in corresponding relations between sets of entities. The theory combines the construction of models with innovative heuristics that scan them to draw conclusions. It explains the processes that generate a conclusion from premises, decides if a given conclusion is necessary or possible, assesses its probability, and evaluates the consistency of a set of assertions. A computer program implementing the theory embodies an intuitive system 1 and a deliberative system 2, and it copes with quantifiers such as more than half the architects. It fit data from over 200 different sorts of inference, including those about the properties of individuals, the properties of a set of individuals, and the properties of several such sets in syllogisms. Another innovation is that the program accounts for differences in reasoning from one individual to another, and from one group of individuals to another: some tend to reason intuitively but some go beyond intuitions to search for alternative models. The theory extends to inferences about disjunctions of properties, about relations rather than properties, and about the properties of properties.

and from the paper itself, available for download here.

I published a paper in Frontiers in Psychology with a team of researchers at NRL that includes Paul Bello, Gordon Briggs, Hillary Harner, and Christina Wasylyshyn on how people reason about omissive causations. They tend to reason with iconic possibilities that yield temporal inferences, and they tend to reason with one possibility at a time, two patterns that are best explained by the model theory of causation. Here’s the abstract:

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. One theory states that people represent omissive causes as force vectors; another states that omissions are representations of contrasting counterfactual simulations; a third argues that people think about omissions by representing sets of iconic possibilities – mental models – in a piecemeal fashion. In this paper, we tease apart the empirical predictions of the three theories and describe experiments that run counter to two of them. Experiments 1 and 2 show that reasoners can infer temporal relations from omissive causes – a pattern that contravenes the force theory. Experiment 3 asked participants to list the possibilities consistent with an omissive cause – it found that they tended to list particular privileged possibilities first, most often, and faster than alternative possibilities. The pattern is consistent with the model theory, but inconsistent with the contrast hypothesis. We marshal the evidence and explain why it helps to solve a long-standing debate about how the mind represents omissions.

and the paper is available for download here.