About

Laurence Thomas Maloney is a Professor of Psychology and Neural Science at New York University. His research focuses on visual perception, decision making, and movement planning, with a particular interest in how organisms gather information and act on it. Maloney's central research interest is the comparison of human performance to models of performance based on mathematical statistics, physics, and mathematics. He completed his undergraduate studies in mathematics at Yale University in 1973, where he also took courses in computer science and automata theory. After graduation, he spent six years as a systems programmer designing operating systems before pursuing a Ph.D. in Psychology at Stanford University, which he obtained in 1985. During his time at Stanford, he studied color vision, issues of representation and measurement, and signal detection theory and statistical modeling. His doctoral dissertation concerned surface color perception and color constancy. Maloney has held academic positions at the University of Michigan, Ann Arbor, and has been a member of the vision group at New York University since then. His work has been recognized with numerous awards, including the Humboldt Research Award, the Troland Research Award, a Fulbright Scholar Award, a Guggenheim Fellowship, and fellowships from several scientific societies. His research and contributions have significantly advanced understanding in the fields of visual perception and cognitive modeling.

Research topics

• Computer Science
• Artificial Intelligence
• Psychology
• Cognitive psychology
• Mathematics
• Econometrics
• Statistics
• Social psychology
• Economics
• Physics
• Biology

Selected publications

• Dissecting Bayes: Using influence measures to test normative use of probability density information derived from a sample

  PLoS Computational Biology · 2024-05-01 · 3 citations

  articleOpen accessSenior author

  Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the possible outcomes of actions are associated with rewards or penalties. The resulting normative models specify how decision makers should encode and combine information about uncertainty and value-step by step-in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples. We break the BDT model into a series of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample-accuracy, additivity and influence. Influence measures allow us to assess how much weight each point in the sample is assigned in making decisions and allow us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual's use of sample information is markedly different from the predictions of BDT. We will show that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. An alternative model basing decisions on a single extreme sample point provided a better account for participants' data than the normative BDT model.

  PublisherDOI

• Lightness constancy in reality, in virtual reality, and on flat-panel displays

  Behavior Research Methods · 2024-03-05 · 4 citations

  articleOpen access
  PublisherOA PDFDOI

• A comparison of human and GPT-4 use of probabilistic phrases in a coordination game

  Scientific Reports · 2024-03-21 · 3 citations

  articleOpen access1st authorCorresponding

  English speakers use probabilistic phrases such as likely to communicate information about the probability or likelihood of events. Communication is successful to the extent that the listener grasps what the speaker means to convey and, if communication is successful, individuals can potentially coordinate their actions based on shared knowledge about uncertainty. We first assessed human ability to estimate the probability and the ambiguity (imprecision) of twenty-three probabilistic phrases in a coordination game in two different contexts, investment advice and medical advice. We then had GPT-4 (OpenAI), a Large Language Model, complete the same tasks as the human participants. We found that GPT-4's estimates of probability both in the Investment and Medical Contexts were as close or closer to that of the human participants as the human participants' estimates were to one another. However, further analyses of residuals disclosed small but significant differences between human and GPT-4 performance. Human probability estimates were compressed relative to those of GPT-4. Estimates of probability for both the human participants and GPT-4 were little affected by context. We propose that evaluation methods based on coordination games provide a systematic way to assess what GPT-4 and similar programs can and cannot do.

  PublisherOA PDFDOI

• Use of probabilistic phrases in a coordination game: human versus GPT-4

  arXiv (Cornell University) · 2023-10-16

  preprintOpen access1st authorCorresponding

  English speakers use probabilistic phrases such as likely to communicate information about the probability or likelihood of events. Communication is successful to the extent that the listener grasps what the speaker means to convey and, if communication is successful, individuals can potentially coordinate their actions based on shared knowledge about uncertainty. We first assessed human ability to estimate the probability and the ambiguity (imprecision) of twenty-three probabilistic phrases in a coordination game in two different contexts, investment advice and medical advice. We then had GPT4 (OpenAI), a Large Language Model, complete the same tasks as the human participants. We found that the median human participant and GPT4 assigned probability estimates that were in good agreement (proportions of variance accounted for close to .90). GPT4's estimates of probability both in the investment and Medical contexts were as close or closer to that of the human participants as the human participants' estimates were to one another. Estimates of probability for both the human participants and GPT4 were little affected by context. In contrast, human and GPT4 estimates of ambiguity were not in such good agreement.

  PublisherOA PDFDOI

• Use of probabilistic phrases in a coordination game: Human versus GPT-4

  Research Square · 2023-12-25

  preprintOpen access1st authorCorresponding

  Abstract English speakers use probabilistic phrases such as likely to communicate information about the probability or likelihood of events. Communication is successful to the extent that the listener grasps what the speaker means to convey and, if communication is successful, individuals can potentially coordinate their actions based on shared knowledge about uncertainty. We first assessed human ability to estimate the probability and the ambiguity (imprecision) of twenty-three probabilistic phrases in a coordination game in two different contexts, investment advice and medical advice. We then had GPT-4 (OpenAI), a Large Language Model, complete the same tasks as the human participants. We found that GPT-4’s estimates of probability both in the investment and Medical contexts were as close or closer to that of the human participants as the human participants’ estimates were to one another. However, further analyses of residuals disclosed small but significant differences between human and GPT-4 performance. In particular, human probability estimates were compressed relative to those of GPT-4. Estimates of probability for both the human participants and GPT-4 were little affected by context. We propose that evaluation methods based on coordination games provide a systematic way to assess what GPT-4 and similar programs can and cannot do.

  PublisherOA PDFDOI

• Using influence measures to test normative use of probability density information derived from a sample

  bioRxiv (Cold Spring Harbor Laboratory) · 2023-02-05

  preprintOpen accessSenior author

  Abstract Bayesian decision theory (BDT) is frequently used to model normative performance in perceptual, motor, and cognitive decision tasks where the outcome of each trial is a reward or penalty that depends on the subject’s actions. The resulting normative models specify how decision makers should encode and use information about uncertainty and value – step by step – in order to maximize their expected reward. When prior, likelihood, and posterior are probabilities, the Bayesian computation requires only simple arithmetic operations: addition, etc. We focus on visual cognitive tasks where Bayesian computations are carried out not on probabilities but on (1) probability density functions and (2) these probability density functions are derived from samples . We break the BDT model into a serries of computations and test human ability to carry out each of these computations in isolation. We test three necessary properties of normative use of pdf information derived from a sample – accuracy , additivity and influence . Influence measures allows us to assess how much weight each point in the sample is assigned in making decisions and allows us to compare normative use (weighting) of samples to actual, point by point. We find that human decision makers violate accuracy and additivity systematically but that the cost of failure in accuracy or additivity would be minor in common decision tasks. However, a comparison of measured influence for each sample point with normative influence measures demonstrates that the individual’s use of sample information is markedly different from the predictions of BDT. We demonstrate that the normative BDT model takes into account the geometric symmetries of the pdf while the human decision maker does not. A heuristic model basing decisions on a single extreme sample point provided a better account for participants’ data than the normative BDT model. Author Summary Bayesian decision theory (BDT) is used to model human performance in tasks where the decision maker must compensate for uncertainty in order to to gain rewards and avoid losses. BDT prescribes how the decision maker can combine available data, prior knowledge, and value to reach a decision maximizing expected winnings. Do human decision makers actually use BDT in making decisions? Researchers typically compare overall human performance (total winnings) to the predictions of BDT but we cannot conclude that BDT is an adequate model for human performance based on just overall performance. We break BDT down into elementary operations and test human ability to execute such operations. In two of the tests human performance deviated only slightly (but systematically) from the predictions of BDT. In the third test we use a novel method to measure the influence of each sample point provided to the human decision maker and compare it to the influence predicted by BDT. When we look at what human decision makers do – in detail – we find that they use sensory information very differently from what the normative BDT observer does. We advance an alternative non-Bayesian model that better predicts human performance.

  PublisherOA PDFDOI

• Detecting visual texture patterns in binary sequences through pattern features

  Journal of Vision · 2023-11-01 · 2 citations

  articleOpen accessSenior authorCorresponding

  We measured human ability to detect texture patterns in a signal detection task. Observers viewed sequences of 20 blue or yellow tokens placed horizontally in a row. They attempted to discriminate sequences generated by a random generator ("a fair coin") from sequences produced by a disrupted Markov sequence (DMS) generator. The DMSs were generated in two stages: first a sequence was generated using a Markov chain with probability, pr = 0.9, that a token would be the same color as the token to its left. The Markov sequence was then disrupted by flipping each token from blue to yellow or vice versa with probability, pd-the probability of disruption. Disruption played the role of noise in signal detection terms. We can frame what observers are asked to do as detecting Markov texture patterns disrupted by noise. The experiment included three conditions differing in pd (0.1, 0.2, 0.3). Ninety-two observers participated, each in only one condition. Overall, human observers' sensitivities to texture patterns (d' values) were markedly less than those of an optimal Bayesian observer. We considered the possibility that observers based their judgments not on the entire texture sequence but on specific features of the sequences such as the length of the longest repeating subsequence. We compared human performance with that of multiple optimal Bayesian classifiers based on such features. We identify the single- and multiple-feature models that best match the performance of observers across conditions and develop a pattern feature pool model for the signal detection task considered.

  PublisherOA PDFDOI

• Lightness constancy in reality, in virtual reality, and on flat-panel displays

  Journal of Vision · 2022-12-05 · 1 citations

  articleOpen access

  Virtual reality (VR) technology is being used in an increasing number of applications. However, research has shown that we often perceive surface properties differently in real and virtual environments. To evaluate how well virtual platforms support realistic lightness perception, we measured lightness constancy in a physical scene, in VR, and on a 2D flat-panel display. Twelve observers participated in three conditions. In the physical condition, observers performed a lightness matching task on a custom-built apparatus where adjustable reflectance patches were visible through two 2-degree apertures. On each trial, the reference aperture was set to one of three reflectances (0.18, 0.39, 0.55). The match aperture had one of five illumination levels, between 1.25 and 3.05 times the illuminance at the reference aperture. Observers adjusted the reflectance at the match aperture until it appeared to match the reflectance at the reference aperture. In the VR condition, observers viewed an apparatus and room that replicated the physical condition, rendered in Unity, on an Oculus Rift S headset. In the flat-panel condition, observers viewed an apparatus like the one in the physical condition, rendered on an LCD screen using Unity. Thouless ratios for lightness constancy were significantly higher (indicating greater constancy) in the physical condition (mean and 95% confidence interval 0.87 ± 0.04) than in the flat-panel condition (0.79 ± 0.08). Thouless ratios were not significantly different in the VR condition (0.83 ± 0.08) than in the physical condition or the flat-panel condition. Thus in the simple scenes considered here, lightness constancy is moderately lower in virtual environments than in physical environments. This discrepancy should be considered when developing applications where realistic performance is critical, but our results suggest that VR can be a flexible alternative to flat panel displays and a reasonable proxy for real environments.

  PublisherDOI

• Two sources of uncertainty independently modulate temporal expectancy

  Proceedings of the National Academy of Sciences · 2021 · 45 citations

  • Computer Science
  • Artificial Intelligence
  • Computer Science

  The environment is shaped by two sources of temporal uncertainty: the discrete probability of whether an event will occur and-if it does-the continuous probability of when it will happen. These two types of uncertainty are fundamental to every form of anticipatory behavior including learning, decision-making, and motor planning. It remains unknown how the brain models the two uncertainty parameters and how they interact in anticipation. It is commonly assumed that the discrete probability of whether an event will occur has a fixed effect on event expectancy over time. In contrast, we first demonstrate that this pattern is highly dynamic and monotonically increases across time. Intriguingly, this behavior is independent of the continuous probability of when an event will occur. The effect of this continuous probability on anticipation is commonly proposed to be driven by the hazard rate (HR) of events. We next show that the HR fails to account for behavior and propose a model of event expectancy based on the probability density function of events. Our results hold for both vision and audition, suggesting independence of the representation of the two uncertainties from sensory input modality. These findings enrich the understanding of fundamental anticipatory processes and have provocative implications for many aspects of behavior and its neural underpinnings.

  PublisherOA PDFDOI

• Heuristic Feature Models for Detection of Disrupted Markov Patterns

  Journal of Vision · 2021-09-01

  articleOpen accessSenior author

  A challenge for the visual system is to go beyond immediately visible patterns to identify the scene processes that generated them. One sees the stripes, one infers the zebra. We asked observers to judge whether each of 100 binary sequences of 20 blue and yellow squares were generated by one of two generators. Each sequence was equally likely to be the outcome of a random generator with probability of repetition 0.5 ("a fair coin") or a two-state Markov generator, with a probability of repetition 0.9, tending to generate long, repeating sequences of yellow or blue. In addition for the sequences generated by the Markov generator, each square in each sequence could be independently disrupted (flipped from blue to yellow or vice versa). There were three experimental conditions with probabilities of disruption 0.1, 0.2, and 0.3, respectively. Each observer received extensive training with both generators and disruption. We first compared human performance to that of an ideal model derived from Bayesian decision theory (BDT). Ratios between the accuracy of observers and that of the BDT model were 0.83, 0.97, and 0.95 in the three conditions. Human observers were markedly suboptimal but (surprisingly) the relative advantage of the Bayesian model decreased with increasing disruption. We then compared human performance to that of several different heuristic feature models (HFMs). Each HFM based its judgment on a specific visual feature of a sequence such as the length of the longest repeating subsequence or the number of subsequences. No HFM performed as well as the Bayesian but some feature models outperformed the median human observer. Two HFMs ("length of longest subsequence" and "total number of repetition") matched the pattern of responses for roughly half the observers. Human performance is better captured by simple heuristic feature models than a model based on Bayesian decision theory.

  PublisherDOI

Recent grants

• Surface color perception and illuminant cues in dynamic three-dimensional scenes

  NSF · $324k · 2011–2014

• NIH Grant R01EY019889

  NIH · $889k · 2015

Frequent coauthors

• Michael S. Landy

  New York University

  40 shared

• Kenneth Knoblauch

  University of South-Eastern Norway

  38 shared

• G Ander

  Ames Research Center

  36 shared

• Gordon L. Shulman

  36 shared

• Lincoln G. Craton

  Stonehill College

  36 shared

• Hang Zhang

  Center for Life Sciences

  36 shared

• Corrine M. Wickens

  Northern Illinois University

  36 shared

• Mary K. Kaiser

  36 shared

Awards & honors

• Humboldt Research Award of the Alexander von Humboldt Founda…
• Troland Research Award of the National Academy of Sciences (…
• Fulbright Scholar Award, Hungary (2014-2015)
• Guggenheim Fellowship, John Simon Guggenheim Foundation (201…
• Fellow, American Association for the Advancement of Science…