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FEB 22

💬 Interview with Künstliche Intelligenze

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.

JAN 06

📄 New paper on recursion out in PBR

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.


🎉 Congrats to Reasoning Lab alumni Hillary Harner and Laura Kelly!

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!


👋🏽 Branden Bio starts his postdoc at NRL!

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!


🎞 ICYMI: CogSci 2021 presentations on time, desire, quantity

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)


🎞 Recent work by the R Lab at ICT 2021

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:


📃 mReasoner reasoning engine detailed in Psych Review

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.


📃 Frontiers paper on theories of omission

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.


📃 Paper on norms and future causation out in Cognitive Science

In a project lead by Paul Henne (Lake Forest College), we recently published a paper in Cognitive Science about how norms affect prospective causal judgments, i.e., judgments about whether a particular situation can cause a future event. Here’s the abstract:

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. Four experiments show that people more frequently select norm-violating factors, relative to norm-conforming ones, as the cause of some future outcome. We show that the abnormal-selection effects are not primarily explained by the perception of agency (Experiment 4). We discuss these results in relation to recent efforts to model causal judgment.

and here’s a link to the paper.


📃 How people assess whether an explanation is “complete”

All explanations are incomplete, but some explanations are more complete than others — this is the central result of our recent work and some other research into explanatory reasoning (e.g., Zemla et al., 2017). Joanna Korman and I describe a new theory of explanatory reasoning now out in Acta Psychologica. Here’s the title:

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. A complete explanation refers to a single integrated model, whereas an incomplete explanation refers to multiple models. The theory predicts that if there exists an unspecified causal relation – a gap – anywhere within an explanation, reasoners must maintain multiple models to handle the gap. They should treat such explanations as less complete than those without a gap. Four experiments provided participants with causal descriptions, some of which yield one explanatory model, e.g., A causes B and B causes C, and some of which demand multiple models, e.g., A causes X and B causes C. Participants across the studies preferred one-model descriptions to multiple-model ones on tasks that implicitly and explicitly required them to assess explanatory completeness. The studies corroborate the theory. They are the first to reveal the mental processes that underlie the assessment of explanatory completeness. We conclude by reviewing the theory in light of extant accounts of causal reasoning.

and here’s the paper.