The influence of sleep on subjective well-being: An experience sampling study.

Previous research has associated sleep with subjective well-being (SWB), but less is known about the underlying within-person processes. In the current study, we investigated how self-reported and actigraphy-measured sleep parameters (sleep onset latency, sleep duration, sleep satisfaction, social jetlag, and sleep efficiency) influence SWB (positive affect [PA], negative affect [NA], and life satisfaction [LS]) at the within- and between-person levels. Multilevel analyses of data from 109 university students who completed a 2-week experience sampling study revealed that higher within-person sleep satisfaction was a significant predictor of all three components of next day's SWB (ps < .005). Higher between-person sleep satisfaction was also related to higher levels of PA and LS (ps < .005), whereas shorter self-reported between-person sleep onset latency was associated with higher PA and LS, and lower NA (ps < .05). However, longer actigraphy-measured within-person sleep onset latency was associated with higher next day's LS (p = .028). When including within- and between-person sleep parameters into the same models predicting SWB, only within- and between-person sleep satisfaction remained a significant predictor of all components of SWB. Additionally, we found an effect of higher self-reported within-person sleep onset latency on PA and of shorter self-reported within-person sleep duration on LS (ps < .05). Our results indicate that the evaluative component of sleep-sleep satisfaction-is most consistently linked with SWB. Thus, sleep interventions that are successful in not only altering sleep patterns but also enhancing sleep satisfaction may stand a better chance at improving students' SWB. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.

Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Judgement of sleep quality of the previous night changes as the day unfolds: A prospective experience sampling study.

How we form judgements of sleep quality is poorly understood. Emerging literature suggests that people infer their sleep quality based on multiple sources of accessible information, raising the possibility that sleep quality judgement may evolve as new relevant information becomes available. This study investigated whether people's rating of sleep quality of the night before changes throughout the following day, and what post-sleep factors are associated with the changes. A prospective experience sampling study of 119 healthy young adults, who completed eight short online surveys interspaced 2 hr apart from 08:00 hours to 22:00 hours. Each survey asked the participants to report total sleep time and sleep quality of the night before, and to provide ratings of current mood, physical and social activity, and pain/discomfort. A memory test was added to the final survey of the day to measure the participants' recall of their first survey responses to sleep quality, as well as total sleep time and mood. The absolute majority (91.1%) of the participants had one or more change in their sleep quality rating across the eight surveys. A similar percentage of change was found for mood rating (100%) but not total sleep time report (20.5%). Memory test in the final survey revealed that the within-person variations in sleep quality rating were not simply memory errors. Instead, positive physical activity post-sleep predicted increases in sleep quality rating. Therefore, judgement of sleep quality of the night before changes as the day unfolds, and post-sleep information can be used by people to infer their sleep quality.

Understanding the structure of cognitive noise.

Human cognition is fundamentally noisy. While routinely regarded as a nuisance in experimental investigation, the few studies investigating properties of cognitive noise have found surprising structure. A first line of research has shown that inter-response-time distributions are heavy-tailed. That is, response times between subsequent trials usually change only a small amount, but with occasional large changes. A second, separate, line of research has found that participants' estimates and response times both exhibit long-range autocorrelations (i.e., 1/f noise). Thus, each judgment and response time not only depends on its immediate predecessor but also on many previous responses. These two lines of research use different tasks and have distinct theoretical explanations: models that account for heavy-tailed response times do not predict 1/f autocorrelations and vice versa. Here, we find that 1/f noise and heavy-tailed response distributions co-occur in both types of tasks. We also show that a statistical sampling algorithm, developed to deal with patchy environments, generates both heavy-tailed distributions and 1/f noise, suggesting that cognitive noise may be a functional adaptation to dealing with a complex world.

Perceptual and Cognitive Judgments Show Both Anchoring and Repulsion.

One of the most robust effects in cognitive psychology is anchoring, in which judgments show a bias toward previously viewed values. However, in what is essentially the same task as used in anchoring research, a perceptual illusion demonstrates the opposite effect of repulsion. Here, we united these two literatures, testing in two experiments with adults (total N = 200) whether prior comparative decisions bias cognitive and perceptual judgments in opposing directions or whether anchoring and repulsion are two domain-general biases whose co-occurrence has so far gone undetected. We found that in both perceptual and cognitive tasks, anchoring and repulsion co-occur. Further, the direction of the bias depends on the comparison value: Distant values attract judgments, whereas nearby values repulse judgments. Because none of the leading theories for either effect account for both biases, theoretical integration is needed. As a starting point, we describe one such joint theory based on sampling models of cognition.

Clarifying the relationship between coherence and accuracy in probability judgments.

Bayesian approaches presuppose that following the coherence conditions of probability theory makes probabilistic judgments more accurate. But other influential theories claim accurate judgments (with high "ecological rationality") do not need to be coherent. Empirical results support these latter theories, threatening Bayesian models of intelligence; and suggesting, moreover, that "heuristics and biases" research, which focuses on violations of coherence, is largely irrelevant. We carry out a higher-power experiment involving poker probability judgments (and a formally analogous urn task), with groups of poker novices, occasional poker players, and poker experts, finding a positive relationship between coherence and accuracy both between groups and across individuals. Both the positive relationship in our data, and past null results, are captured by a sample-based Bayesian approximation model, where a person's accuracy and coherence both increase with the number of samples drawn. Thus, we reconcile the theoretical link between accuracy and coherence with apparently negative empirical results.

REFRESH: A new approach to modeling dimensional biases in perceptual similarity and categorization.

Much categorization behavior can be explained by family resemblance: New items are classified by comparison with previously learned exemplars. However, categorization behavior also shows a variety of dimensional biases, where the underlying space has so-called "separable" dimensions: Ease of learning categories depends on how the stimuli align with the separable dimensions of the space. For example, if a set of objects of various sizes and colors can be accurately categorized using a single separable dimension (e.g., size), then category learning will be fast, while if the category is determined by both dimensions, learning will be slow. To capture these dimensional biases, almost all models of categorization supplement family resemblance with either rule-based systems or selective attention to separable dimensions. But these models do not explain how separable dimensions initially arise; they are presumed to be unexplained psychological primitives. We develop, instead, a pure family resemblance version of the Rational Model of Categorization (RMC), which we term the Rational Exclusively Family RESemblance Hierarchy (REFRESH), which does not presuppose any separable dimensions in the space of stimuli. REFRESH infers how the stimuli are clustered and uses a hierarchical prior to learn expectations about the variability of clusters across categories. We first demonstrate the dimensional alignment of natural-category features and then show how through a lifetime of categorization experience REFRESH will learn prior expectations that clusters of stimuli will align with separable dimensions. REFRESH captures the key dimensional biases and also explains their stimulus-dependence and how they are learned and develop. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Using Occam's razor and Bayesian modelling to compare discrete and continuous representations in numerosity judgements.

Previous research has established that numeric estimates are based not just on perceptual data but also past experience, and so may be influenced by the form of this stored information. It remains unclear, however, how such experience is represented: numerical data can be processed by either a continuous analogue number system or a discrete symbolic number system, with each predicting different generalisation effects. The present paper therefore contrasts discrete and continuous prior formats within the domain of numerical estimation using both direct comparisons of computational models of this process using these representations, as well as empirical contrasts exploiting different predicted reactions of these formats to uncertainty via Occam's razor. Both computational and empirical results indicate that numeric estimates commonly rely on a continuous prior format, mirroring the analogue approximate number system, or 'number sense'. This implies a general preference for the use of continuous numerical representations even where both stimuli and responses are discrete, with learners seemingly relying on innate number systems rather than the symbolic forms acquired in later life. There is however remaining uncertainty in these results regarding individual differences in the use of these systems, which we address in recommendations for future work.

Sampling as a resource-rational constraint.

Resource rationality is useful for choosing between models with the same cognitive constraints but cannot settle fundamental disagreements about what those constraints are. We argue that sampling is an especially compelling constraint, as optimizing accumulation of evidence or hypotheses minimizes the cost of time, and there are well-established models for doing so which have had tremendous success explaining human behavior.

The Bayesian sampler: Generic Bayesian inference causes incoherence in human probability judgments.

Human probability judgments are systematically biased, in apparent tension with Bayesian models of cognition. But perhaps the brain does not represent probabilities explicitly, but approximates probabilistic calculations through a process of sampling, as used in computational probabilistic models in statistics. Naïve probability estimates can be obtained by calculating the relative frequency of an event within a sample, but these estimates tend to be extreme when the sample size is small. We propose instead that people use a generic prior to improve the accuracy of their probability estimates based on samples, and we call this model the Bayesian sampler. The Bayesian sampler trades off the coherence of probabilistic judgments for improved accuracy, and provides a single framework for explaining phenomena associated with diverse biases and heuristics such as conservatism and the conjunction fallacy. The approach turns out to provide a rational reinterpretation of "noise" in an important recent model of probability judgment, the probability theory plus noise model (Costello & Watts, 2014, 2016a, 2017; Costello & Watts, 2019; Costello, Watts, & Fisher, 2018), making equivalent average predictions for simple events, conjunctions, and disjunctions. The Bayesian sampler does, however, make distinct predictions for conditional probabilities and distributions of probability estimates. We show in 2 new experiments that this model better captures these mean judgments both qualitatively and quantitatively; which model best fits individual distributions of responses depends on the assumed size of the cognitive sample. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

A dilution effect without dilution: When missing evidence, not non-diagnostic evidence, is judged inaccurately.

When asked to combine two pieces of evidence, one diagnostic and one non-diagnostic, people show a dilution effect: the addition of non-diagnostic evidence dilutes the overall strength of the evidence. This non-normative effect has been found in a variety of tasks and has been taken as evidence that people inappropriately combine information. In a series of five experiments, we found the dilution effect, but surprisingly it was not due to the inaccurate combination of diagnostic and non-diagnostic information. Because we have objectively correct answers for our task, we could see that participants were relatively accurate in judging diagnostic evidence combined with non-diagnostic evidence, but overestimated the strength of diagnostic evidence alone. This meant that the dilution effect - the gap between diagnostic evidence alone and diagnostic evidence combined with non-diagnostic evidence - was not caused by dilution. We hypothesized that participants were filling in "missing" evidence in a biased fashion when presented with diagnostic evidence alone. This hypothesis best explained the experimental results.

What does the mind learn? A comparison of human and machine learning representations.

We present a brief review of modern machine learning techniques and their use in models of human mental representations, detailing three notable branches: spatial methods, logical methods and artificial neural networks. Each of these branches contains an extensive set of systems, and demonstrate accurate emulations of human learning of categories, concepts and language, despite substantial differences in operation. We suggest that continued applications will allow cognitive researchers the ability to model the complex real-world problems where machine learning has recently been successful, providing more complete behavioural descriptions. This will, however, also require careful consideration of appropriate algorithmic constraints alongside these methods in order to find a combination which captures both the strengths and weaknesses of human cognition.

Identifying category representations for complex stimuli using discrete Markov chain Monte Carlo with people.

With the explosion of "big data," digital repositories of texts and images are growing rapidly. These datasets present new opportunities for psychological research, but they require new methodologies before researchers can use these datasets to yield insights into human cognition. We present a new method that allows psychological researchers to take advantage of text and image databases: a procedure for measuring human categorical representations over large datasets of items, such as arbitrary words or pictures. We call this method discrete Markov chain Monte Carlo with people (d-MCMCP). We illustrate our method by evaluating the following categories over datasets: emotions as represented by facial images, moral concepts as represented by relevant words, and seasons as represented by images drawn from large online databases. Three experiments demonstrate that d-MCMCP is powerful and flexible enough to work with complex, naturalistic stimuli drawn from large online databases.

Deficits in category learning in older adults: Rule-based versus clustering accounts.

Memory research has long been one of the key areas of investigation for cognitive aging researchers but only in the last decade or so has categorization been used to understand age differences in cognition. Categorization tasks focus more heavily on the grouping and organization of items in memory, and often on the process of learning relationships through trial and error. Categorization studies allow researchers to more accurately characterize age differences in cognition: whether older adults show declines in the way in which they represent categories with simple rules or declines in representing categories by similarity to past examples. In the current study, young and older adults participated in a set of classic category learning problems, which allowed us to distinguish between three hypotheses: (a) rule-complexity: categories were represented exclusively with rules and older adults had differential difficulty when more complex rules were required, (b) rule-specific: categories could be represented either by rules or by similarity, and there were age deficits in using rules, and (c) clustering: similarity was mainly used and older adults constructed a less-detailed representation by lumping more items into fewer clusters. The ordinal levels of performance across different conditions argued against rule-complexity, as older adults showed greater deficits on less complex categories. The data also provided evidence against rule-specificity, as single-dimensional rules could not explain age declines. Instead, computational modeling of the data indicated that older adults utilized fewer conceptual clusters of items in memory than did young adults. (PsycINFO Database Record

What Sways People's Judgment of Sleep Quality? A Quantitative Choice-Making Study With Good and Poor Sleepers.

Study objectives: We conceptualized sleep quality judgment as a decision-making process and examined the relative importance of 17 parameters of sleep quality using a choice-based conjoint analysis. Methods: One hundred participants (50 good sleepers; 50 poor sleepers) were asked to choose between 2 written scenarios to answer 1 of 2 questions: "Which describes a better (or worse) night of sleep?". Each scenario described a self-reported experience of sleep, stringing together 17 possible determinants of sleep quality that occur at different times of the day (day before, pre-sleep, during sleep, upon waking, day after). Each participant answered 48 questions. Logistic regression models were fit to their choice data. Results: Eleven of the 17 sleep quality parameters had a significant impact on the participants' choices. The top 3 determinants of sleep quality were: Total sleep time, feeling refreshed (upon waking), and mood (day after). Sleep quality judgments were most influenced by factors that occur during sleep, followed by feelings and activities upon waking and the day after. There was a significant interaction between wake after sleep onset and feeling refreshed (upon waking) and between feeling refreshed (upon waking) and question type (better or worse night of sleep). Type of sleeper (good vs poor sleepers) did not significantly influence the judgments. Conclusions: Sleep quality judgments appear to be determined by not only what happened during sleep, but also what happened after the sleep period. Interventions that improve mood and functioning during the day may inadvertently also improve people's self-reported evaluation of sleep quality.

Types of approximation for probabilistic cognition: Sampling and variational.

A basic challenge for probabilistic models of cognition is explaining how probabilistically correct solutions are approximated by the limited brain, and how to explain mismatches with human behavior. An emerging approach to solving this problem is to use the same approximation algorithms that were been developed in computer science and statistics for working with complex probabilistic models. Two types of approximation algorithms have been used for this purpose: sampling algorithms, such as importance sampling and Markov chain Monte Carlo, and variational algorithms, such as mean-field approximations and assumed density filtering. Here I briefly review this work, outlining how the algorithms work, how they can explain behavioral biases, and how they might be implemented in the brain. There are characteristic differences between how these two types of approximation are applied in brain and behavior, which points to how they could be combined in future research.

Fast and Accurate Learning When Making Discrete Numerical Estimates.

Many everyday estimation tasks have an inherently discrete nature, whether the task is counting objects (e.g., a number of paint buckets) or estimating discretized continuous variables (e.g., the number of paint buckets needed to paint a room). While Bayesian inference is often used for modeling estimates made along continuous scales, discrete numerical estimates have not received as much attention, despite their common everyday occurrence. Using two tasks, a numerosity task and an area estimation task, we invoke Bayesian decision theory to characterize how people learn discrete numerical distributions and make numerical estimates. Across three experiments with novel stimulus distributions we found that participants fell between two common decision functions for converting their uncertain representation into a response: drawing a sample from their posterior distribution and taking the maximum of their posterior distribution. While this was consistent with the decision function found in previous work using continuous estimation tasks, surprisingly the prior distributions learned by participants in our experiments were much more adaptive: When making continuous estimates, participants have required thousands of trials to learn bimodal priors, but in our tasks participants learned discrete bimodal and even discrete quadrimodal priors within a few hundred trials. This makes discrete numerical estimation tasks good testbeds for investigating how people learn and make estimates.

Bayesian Brains without Probabilities.

Bayesian explanations have swept through cognitive science over the past two decades, from intuitive physics and causal learning, to perception, motor control and language. Yet people flounder with even the simplest probability questions. What explains this apparent paradox? How can a supposedly Bayesian brain reason so poorly with probabilities? In this paper, we propose a direct and perhaps unexpected answer: that Bayesian brains need not represent or calculate probabilities at all and are, indeed, poorly adapted to do so. Instead, the brain is a Bayesian sampler. Only with infinite samples does a Bayesian sampler conform to the laws of probability; with finite samples it systematically generates classic probabilistic reasoning errors, including the unpacking effect, base-rate neglect, and the conjunction fallacy.

Testing Bayesian and heuristic predictions of mass judgments of colliding objects.

Mass judgments of colliding objects have been used to explore people's understanding of the physical world because they are ecologically relevant, yet people display biases that are most easily explained by a small set of heuristics. Recent work has challenged the heuristic explanation, by producing the same biases from a model that copes with perceptual uncertainty by using Bayesian inference with a prior based on the correct combination rules from Newtonian mechanics (noisy Newton). Here I test the predictions of the leading heuristic model (Gilden and Proffitt, 1989) against the noisy Newton model using a novel manipulation of the standard mass judgment task: making one of the objects invisible post-collision. The noisy Newton model uses the remaining information to predict above-chance performance, while the leading heuristic model predicts chance performance when one or the other final velocity is occluded. An experiment using two different types of occlusion showed better-than-chance performance and response patterns that followed the predictions of the noisy Newton model. The results demonstrate that people can make sensible physical judgments even when information critical for the judgment is missing, and that a Bayesian model can serve as a guide in these situations. Possible algorithmic-level accounts of this task that more closely correspond to the noisy Newton model are explored.