Uncertainty limits the use of power analysis.

The calculation of statistical power has been taken up as a simple yet informative tool to assist in designing an experiment, particularly in justifying sample size. A difficulty with using power for this purpose is that the classical power formula does not incorporate sources of uncertainty (e.g., sampling variability) that can impact the computed power value, leading to a false sense of precision and confidence in design choices. We use simulations to demonstrate the consequences of adding two common sources of uncertainty to the calculation of power. Sampling variability in the estimated effect size (Cohen's d) can introduce a large amount of uncertainty (e.g., sometimes producing rather flat distributions) in power and sample-size determination. The addition of random fluctuations in the population effect size can cause values of its estimates to take on a sign opposite the population value, making calculated power values meaningless. These results suggest that calculated power values or use of such values to justify sample size add little to planning a study. As a result, researchers should put little confidence in power-based choices when planning future studies. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Dual-process modeling of sequential decision making in the balloon analogue risk task.

People are often faced with repeated risky decisions that involve uncertainty. In sequential risk-taking tasks, like the Balloon Analogue Risk Task (BART), the underlying decision process is not yet fully understood. Dual-process theory proposes that human cognition involves two main families of processes, often referred to as System 1 (fast and automatic) and System 2 (slow and conscious). We cross models of the BART with different architectures of the two systems to yield a pool of computational dual-process models that are evaluated on multiple performance measures (e.g., parameter identifiability, model recovery, and predictive accuracy). Results show that the best-performing model configuration assumes the two systems are competitively connected, an evaluation process based on the Scaled Target Learning model of the BART, and an assessment rate that incorporates sensitivity to the trial number, pumping opportunity, and bias to engage in System 1. Findings also shed light on how modeling choices and response times in a dual-process framework can benefit our understanding of sequential risk-taking behavior.

Implementation of an online spacing flanker task and evaluation of its test-retest reliability using measures of inhibitory control and the distribution of spatial attention.

The flanker task (Eriksen & Eriksen, Perception & Psychophysics, 16(1), 143-149, 1974) has been highly influential and widely used in studies of visual attention. Its simplicity has made it popular to include it in experimental software packages and online platforms. The spacing flanker task (SFT), in which the distance between the target and flankers varies, is useful for studying the distribution of attention across space as well as inhibitory control. Use of the SFT requires that the viewing environment (e.g., stimulus size and viewing distance) be controlled, which is a challenge for online delivery. We implement and evaluate an online version of the SFT that includes two calibration pretests to provide the necessary control. Test-retest and split-half reliability of the online version was compared with a laboratory version on measures of inhibitory control and measures of the distribution of attention across space. Analyses show that the online SFT is comparable to laboratory testing on all measures. Results also identify two measures with good test-retest reliability that hold promise for studying performance in the SFT: the mean flanker effect (ICC = 0.745) and RTs on incongruent trials across distances (ICC = 0.65-0.71).

Target voice probability influences enhancement in auditory selective attention.

Auditory selective attention is thought to consist of two mechanisms: an enhancement mechanism that boosts the target signal, and a suppression mechanism that attenuates concurrent distracting signals. The current study explored the conditions necessary to observe enhancement of predictable auditory objects. Participants heard scenes consisting of three voices and a distracting noise. They were asked to find the gender singleton (target) and report whether it was saying even or odd numbers. One of the voices appeared as the high-probability target (70%) across trials. We expected responses to be faster when the high-probability target was in the scene, and results from Experiment 1 supported that prediction. However, this target enhancement effect was substantially weakened when a distracting noise was also in the scene, suggesting that the distractor captured attention and interfered with enhancement. Experiment 2 tested the hypothesis that distractor predictability modulates target enhancement by varying the probability of the distractor. Although this hypothesis was not supported, the results of Experiment 1 were replicated. Findings support the existence of an easily disruptable enhancement mechanism that boosts the representation of highly probable target objects.

Probabilistic Decision-Making in Children With Dyslexia.

Background: Neurocognitive mechanisms underlying developmental dyslexia (dD) remain poorly characterized apart from phonological and/or visual processing deficits. Assuming such deficits, the process of learning complex tasks like reading requires the learner to make decisions (i.e., word pronunciation) based on uncertain information (e.g., aberrant phonological percepts)-a cognitive process known as probabilistic decision making, which has been linked to the striatum. We investigate (1) the relationship between dD and probabilistic decision-making and (2) the association between the volume of striatal structures and probabilistic decision-making in dD and typical readers. Methods: Twenty four children diagnosed with dD underwent a comprehensive evaluation and MRI scanning (3T). Children with dD were compared to age-matched typical readers (n = 11) on a probabilistic, risk/reward fishing task that utilized a Bayesian cognitive model with game parameters of risk propensity (γ+) and behavioral consistency (ß), as well as an overall adjusted score (average number of casts, excluding forced-fail trials). Volumes of striatal structures (caudate, putamen, and nucleus accumbens) were analyzed between groups and associated with game parameters. Results: dD was associated with greater risk propensity and decreased behavioral consistency estimates compared to typical readers. Cognitive model parameters associated with timed pseudoword reading across groups. Risk propensity related to caudate volumes, particularly in the dD group. Conclusion: Decision-making processes differentiate dD, associate with the caudate, and may impact learning mechanisms. This study suggests the need for further research into domain-general probabilistic decision-making in dD, neurocognitive mechanisms, and targeted interventions in dD.

A number-line task with a Bayesian active learning algorithm provides insights into the development of non-symbolic number estimation.

To characterize numerical representations, the number-line task asks participants to estimate the location of a given number on a line flanked with zero and an upper-bound number. An open question is whether estimates for symbolic numbers (e.g., Arabic numerals) and non-symbolic numbers (e.g., number of dots) rely on common processes with a common developmental pathway. To address this question, we explored whether well-established findings in symbolic number-line estimation generalize to non-symbolic number-line estimation. For exhaustive investigations without sacrificing data quality, we applied a novel Bayesian active learning algorithm, dubbed Gaussian process active learning (GPAL), that adaptively optimizes experimental designs. The results showed that the non-symbolic number estimation in participants of diverse ages (5-73 years old, n = 238) exhibited three characteristic features of symbolic number estimation.

Individual differences in selective attention reveal the nonmonotonicity of visual spatial attention and its association with working memory capacity.

Visual spatial attention is typically thought to have a facilitatory effect on processing that monotonically decreases with the distance from the center of attention (Posner, 1980). Some studies suggest that the distribution of attention across space is nonmonotonic, with suppression around the target object (Cutzu & Tsotsos, 2003; Müller et al., 2005). We show in two flanker-task experiments that discrepancies in past work can be unified by a surround inhibition account in which the shape of the attentional distribution is determined by individual differences in selective attention. The distance from the target at which flanker interference was locally suppressed differed greatly among participants and correlated negatively with working memory capacity. The results suggest that attentional control modulates the breadth of the attentional distribution, constrained by limited cognitive capacity, to enhance target identification. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

The scaled target learning model: Revisiting learning in the balloon analogue risk task.

The Balloon Analogue Risk Task (BART) is a sequential decision making paradigm that assesses risk-taking behavior. Several computational models have been proposed for the BART that characterize risk-taking propensity. An aspect of task performance that has proven challenging to model is the learning that develops from experiencing wins and losses across trials, which has the potential to provide further insight into risky decision making. We developed the Scaled Target Learning (STL) model for this purpose. STL describes learning as adjustments to an individual's strategy in reaction to outcomes in the task, with the size of adjustments reflecting an individual's sensitivity to wins and losses. STL is shown to be sensitive to the learning elicited by experimental manipulations. In addition, the model matches or bests the performance of three competing models in traditional model comparison tests (e.g., parameter recovery performance, predictive accuracy, sensitivity to risk-taking propensity). Findings are discussed in the context of the learning process involved in the task. By characterizing the extent to which people are willing to adapt their strategies based on past experience, STL is a step toward a complete depiction of the psychological processes underlying sequential risk-taking behavior.

Distractor probability influences suppression in auditory selective attention.

Auditory selective attention is thought to facilitate listening to the sound of interest (e.g., voice or music) in a noisy environment. One mechanism thought to underlie this ability is suppression of distracting stimuli. However, little is known about its operation or characteristics. We tested whether suppression in auditory selective attention capitalizes on statistical regularities in the environment to facilitate attention. Participants listened to seven-second scenes consisting of several voices speaking sequences of numbers and a distractor, which occurred more (70%) or less (30%) frequently across trials. Participants had to find the voice that was a gender singleton and report whether it was saying even or odd numbers. If suppression is an active component of auditory selective attention, task performance was expected to be better when the more frequent distractor was present. Results across the experiment and three replications revealed significantly shorter RTs when the high-probability distractor was in the scene relative to the low-probability distractor. Results are suggestive of a suppression mechanism that mitigates the detrimental influence of a frequently occurring distracting sound.

Data-driven experimental design and model development using Gaussian process with active learning.

Interest in computational modeling of cognition and behavior continues to grow. To be most productive, modelers should be equipped with tools that ensure optimal efficiency in data collection and in the integrity of inference about the phenomenon of interest. Traditionally, models in cognitive science have been parametric, which are particularly susceptible to model misspecification because their strong assumptions (e.g. parameterization, functional form) may introduce unjustified biases in data collection and inference. To address this issue, we propose a data-driven nonparametric framework for model development, one that also includes optimal experimental design as a goal. It combines Gaussian Processes, a stochastic process often used for regression and classification, with active learning, from machine learning, to iteratively fit the model and use it to optimize the design selection throughout the experiment. The approach, dubbed Gaussian process with active learning (GPAL), is an extension of the parametric, adaptive design optimization (ADO) framework (Cavagnaro, Myung, Pitt, & Kujala, 2010). We demonstrate the application and features of GPAL in a delay discounting task and compare its performance to ADO in two experiments. The results show that GPAL is a viable modeling framework that is noteworthy for its high sensitivity to individual differences, identifying novel patterns in the data that were missed by the model-constrained ADO. This investigation represents a first step towards the development of a data-driven cognitive modeling framework that serves as a middle ground between raw data, which can be difficult to interpret, and parametric models, which rely on strong assumptions.

ADOpy: a python package for adaptive design optimization.

Experimental design is fundamental to research, but formal methods to identify good designs are lacking. Advances in Bayesian statistics and machine learning offer algorithm-based ways to identify good experimental designs. Adaptive design optimization (ADO; Cavagnaro, Myung, Pitt, & Kujala, 2010; Myung, Cavagnaro, & Pitt, 2013) is one such method. It works by maximizing the informativeness and efficiency of data collection, thereby improving inference. ADO is a general-purpose method for conducting adaptive experiments on the fly and can lead to rapid accumulation of information about the phenomenon of interest with the fewest number of trials. The nontrivial technical skills required to use ADO have been a barrier to its wider adoption. To increase its accessibility to experimentalists at large, we introduce an open-source Python package, ADOpy, that implements ADO for optimizing experimental design. The package, available on GitHub, is written using high-level modular-based commands such that users do not have to understand the computational details of the ADO algorithm. In this paper, we first provide a tutorial introduction to ADOpy and ADO itself, and then illustrate its use in three walk-through examples: psychometric function estimation, delay discounting, and risky choice. Simulation data are also provided to demonstrate how ADO designs compare with other designs (random, staircase).

Rapid, precise, and reliable measurement of delay discounting using a Bayesian learning algorithm.

Machine learning has the potential to facilitate the development of computational methods that improve the measurement of cognitive and mental functioning. In three populations (college students, patients with a substance use disorder, and Amazon Mechanical Turk workers), we evaluated one such method, Bayesian adaptive design optimization (ADO), in the area of delay discounting by comparing its test-retest reliability, precision, and efficiency with that of a conventional staircase method. In all three populations tested, the results showed that ADO led to 0.95 or higher test-retest reliability of the discounting rate within 10-20 trials (under 1-2 min of testing), captured approximately 10% more variance in test-retest reliability, was 3-5 times more precise, and was 3-8 times more efficient than the staircase method. The ADO methodology provides efficient and precise protocols for measuring individual differences in delay discounting.

Efficient Closed-loop Maximization of Carbon Nanotube Growth Rate using Bayesian Optimization.

A major technological challenge in materials research is the large and complex parameter space, which hinders experimental throughput and ultimately slows down development and implementation. In single-walled carbon nanotube (CNT) synthesis, for instance, the poor yield obtained from conventional catalysts is a result of limited understanding of input-to-output correlations. Autonomous closed-loop experimentation combined with advances in machine learning (ML) is uniquely suited for high-throughput research. Among the ML algorithms available, Bayesian optimization (BO) is especially apt for exploration and optimization within such high-dimensional and complex parameter space. BO is an adaptive sequential design algorithm for finding the global optimum of a black-box objective function with the fewest possible measurements. Here, we demonstrate a promising application of BO in CNT synthesis as an efficient and robust algorithm which can (1) improve the growth rate of CNT in the BO-planner experiments over the seed experiments up to a factor 8; (2) rapidly improve its predictive power (or learning); (3) Consistently achieve good performance regardless of the number or origin of seed experiments; (4) exploit a high-dimensional, complex parameter space, and (5) achieve the former 4 tasks in just over 100 hundred experiments (~8 experimental hours) - a factor of 5× faster than our previously reported results.

A test of linguistic influences in the perceptual organization of speech.

Research in speech perception has explored how knowledge of a language influences phonetic perception. The current study investigated whether such linguistic influences extend to the perceptual (sequential) organization of speech. Listeners heard sinewave analogs of word pairs (e.g., loose seam, which contains a single [s] frication but is perceived as two /s/ phonemes) cycle continuously, which causes the stimulus to split apart into foreground and background percepts. They had to identify the foreground percept when the stimuli were heard as nonspeech and then again when heard as speech. Of interest was how grouping changed across listening condition when [s] was heard as speech or as a hiss. Although the section of the signal that was identified as the foreground differed little across listening condition, a strong bias to perceive [s] as forming the onset of the foreground was observed in the speech condition (Experiment 1). This effect was reduced in Experiment 2 by increasing the stimulus repetition rate. Findings suggest that the sequential organization of speech arises from the interaction of auditory and linguistic processes, with the former constraining the latter.

Mechanisms underlying the spacing effect in learning: A comparison of three computational models.

The spacing effect is one of the most widely replicated results in experimental psychology: Separating practice repetitions by a delay slows learning but enhances retention. The current study tested the suitability of the underlying, explanatory mechanism in three computational models of the spacing effect. The relearning of forgotten material was measured, as the models differ in their predictions of how the initial study conditions should affect relearning. Participants learned Japanese-English paired associates presented in a massed or spaced manner during an acquisition phase. They were tested on the pairs after retention intervals ranging from 1 to 21 days. Corrective feedback was given during retention tests to enable relearning. The results of 2 experiments showed that spacing slowed learning during the acquisition phase, increased retention at the start of tests, and accelerated relearning during tests. Of the 3 models, only 1, the predictive performance equation (PPE), was consistent with the finding of spacing-accelerated relearning. The implications of these results for learning theory and educational practice are discussed. (PsycINFO Database Record

Neurophysiology underlying influence of stimulus reliability on audiovisual integration.

We tested the predictions of the dynamic reweighting model (DRM) of audiovisual (AV) speech integration, which posits that spectrotemporally reliable (informative) AV speech stimuli induce a reweighting of processing from low-level to high-level auditory networks. This reweighting decreases sensitivity to acoustic onsets and in turn increases tolerance to AV onset asynchronies (AVOA). EEG was recorded while subjects watched videos of a speaker uttering trisyllabic nonwords that varied in spectrotemporal reliability and asynchrony of the visual and auditory inputs. Subjects judged the stimuli as in-sync or out-of-sync. Results showed that subjects exhibited greater AVOA tolerance for non-blurred than blurred visual speech and for less than more degraded acoustic speech. Increased AVOA tolerance was reflected in reduced amplitude of the P1-P2 auditory evoked potentials, a neurophysiological indication of reduced sensitivity to acoustic onsets and successful AV integration. There was also sustained visual alpha band (8-14 Hz) suppression (desynchronization) following acoustic speech onsets for non-blurred vs. blurred visual speech, consistent with continuous engagement of the visual system as the speech unfolds. The current findings suggest that increased spectrotemporal reliability of acoustic and visual speech promotes robust AV integration, partly by suppressing sensitivity to acoustic onsets, in support of the DRM's reweighting mechanism. Increased visual signal reliability also sustains the engagement of the visual system with the auditory system to maintain alignment of information across modalities.

A model-based analysis of decision making under risk in obsessive-compulsive and hoarding disorders.

Attitudes towards risk are highly consequential in clinical disorders thought to be prone to "risky behavior", such as substance dependence, as well as those commonly associated with excessive risk aversion, such as obsessive-compulsive disorder (OCD) and hoarding disorder (HD). Moreover, it has recently been suggested that attitudes towards risk may serve as a behavioral biomarker for OCD. We investigated the risk preferences of participants with OCD and HD using a novel adaptive task and a quantitative model from behavioral economics that decomposes risk preferences into outcome sensitivity and probability sensitivity. Contrary to expectation, compared to healthy controls, participants with OCD and HD exhibited less outcome sensitivity, implying less risk aversion in the standard economic framework. In addition, risk attitudes were strongly correlated with depression, hoarding, and compulsion scores, while compulsion (hoarding) scores were associated with more (less) "rational" risk preferences. These results demonstrate how fundamental attitudes towards risk relate to specific psychopathology and thereby contribute to our understanding of the cognitive manifestations of mental disorders. In addition, our findings indicate that the conclusion made in recent work that decision making under risk is unaltered in OCD is premature.

Generalization from newly learned words reveals structural properties of the human reading system.

Connectionist accounts of quasiregular domains, such as spelling-sound correspondences in English, represent exception words (e.g., pint) amid regular words (e.g., mint) via a graded "warping" mechanism. Warping allows the model to extend the dominant pronunciation to nonwords (regularization) with minimal interference (spillover) from the exceptions. We tested for a behavioral marker of warping by investigating the degree to which participants generalized from newly learned made-up words, which ranged from sharing the dominant pronunciation (regulars), a subordinate pronunciation (ambiguous), or a previously nonexistent (exception) pronunciation. The new words were learned over 2 days, and generalization was assessed 48 hr later using nonword neighbors of the new words in a tempo naming task. The frequency of regularization (a measure of generalization) was directly related to degree of warping required to learn the pronunciation of the new word. Simulations using the Plaut, McClelland, Seidenberg, and Patterson (1996) model further support a warping interpretation. These findings highlight the need to develop theories of representation that are integrally tied to how those representations are learned and generalized. (PsycINFO Database Record

Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach.

Experimentation is at the heart of scientific inquiry. In the behavioral and neural sciences, where only a limited number of observations can often be made, it is ideal to design an experiment that leads to the rapid accumulation of information about the phenomenon under study. Adaptive experimentation has the potential to accelerate scientific progress by maximizing inferential gain in such research settings. To date, most adaptive experiments have relied on myopic, one-step-ahead strategies in which the stimulus on each trial is selected to maximize inference on the next trial only. A lingering question in the field has been how much additional benefit would be gained by optimizing beyond the next trial. A range of technical challenges has prevented this important question from being addressed adequately. This study applies dynamic programming (DP), a technique applicable for such full-horizon, "global" optimization, to model-based perceptual threshold estimation, a domain that has been a major beneficiary of adaptive methods. The results provide insight into conditions that will benefit from optimizing beyond the next trial. Implications for the use of adaptive methods in cognitive science are discussed.

Revisiting Neil Armstrongs Moon-Landing Quote: Implications for Speech Perception, Function Word Reduction, and Acoustic Ambiguity.

Neil Armstrong insisted that his quote upon landing on the moon was misheard, and that he had said one small step for a man, instead of one small step for man. What he said is unclear in part because function words like a can be reduced and spectrally indistinguishable from the preceding context. Therefore, their presence can be ambiguous, and they may disappear perceptually depending on the rate of surrounding speech. Two experiments are presented examining production and perception of reduced tokens of for and for a in spontaneous speech. Experiment 1 investigates the distributions of several acoustic features of for and for a. The results suggest that the distributions of for and for a overlap substantially, both in terms of temporal and spectral characteristics. Experiment 2 examines perception of these same tokens when the context speaking rate differs. The perceptibility of the function word a varies as a function of this context speaking rate. These results demonstrate that substantial ambiguity exists in the original quote from Armstrong, and that this ambiguity may be understood through context speaking rate.