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).

Assessing the distortions introduced when calculating d': A simulation approach.

The discriminability measure d ' is widely used in psychology to estimate sensitivity independently of response bias. The conventional approach to estimate d ' involves a transformation from the hit rate and the false-alarm rate. When performance is perfect, correction methods must be applied to calculate d ' , but these corrections distort the estimate. In three simulation studies, we show that distortion in d ' estimation can arise from other properties of the experimental design (number of trials, sample size, sample variance, task difficulty) that, when combined with application of the correction method, make d ' distortion in any specific experiment design complex and can mislead statistical inference in the worst cases (Type I and Type II errors). To address this problem, we propose that researchers simulate d ' estimation to explore the impact of design choices, given anticipated or observed data. An R Shiny application is introduced that estimates d ' distortion, providing researchers the means to identify distortion and take steps to minimize its impact.

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.

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).

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.

Evaluating the performance of the quick CSF method in detecting contrast sensitivity function changes.

The contrast sensitivity function (CSF) has shown promise as a functional vision endpoint for monitoring the changes in functional vision that accompany eye disease or its treatment. However, detecting CSF changes with precision and efficiency at both the individual and group levels is very challenging. By exploiting the Bayesian foundation of the quick CSF method (Lesmes, Lu, Baek, & Albright, 2010), we developed and evaluated metrics for detecting CSF changes at both the individual and group levels. A 10-letter identification task was used to assess the systematic changes in the CSF measured in three luminance conditions in 112 naïve normal observers. The data from the large sample allowed us to estimate the test-retest reliability of the quick CSF procedure and evaluate its performance in detecting CSF changes at both the individual and group levels. The test-retest reliability reached 0.974 with 50 trials. In 50 trials, the quick CSF method can detect a medium 0.30 log unit area under log CSF change with 94.0% accuracy at the individual observer level. At the group level, a power analysis based on the empirical distribution of CSF changes from the large sample showed that a very small area under log CSF change (0.025 log unit) could be detected by the quick CSF method with 112 observers and 50 trials. These results make it plausible to apply the method to monitor the progression of visual diseases or treatment effects on individual patients and greatly reduce the time, sample size, and costs in clinical trials at the group level.

A hierarchical Bayesian approach to adaptive vision testing: A case study with the contrast sensitivity function.

Measurement efficiency is of concern when a large number of observations are required to obtain reliable estimates for parametric models of vision. The standard entropy-based Bayesian adaptive testing procedures addressed the issue by selecting the most informative stimulus in sequential experimental trials. Noninformative, diffuse priors were commonly used in those tests. Hierarchical adaptive design optimization (HADO; Kim, Pitt, Lu, Steyvers, & Myung, 2014) further improves the efficiency of the standard Bayesian adaptive testing procedures by constructing an informative prior using data from observers who have already participated in the experiment. The present study represents an empirical validation of HADO in estimating the human contrast sensitivity function. The results show that HADO significantly improves the accuracy and precision of parameter estimates, and therefore requires many fewer observations to obtain reliable inference about contrast sensitivity, compared to the method of quick contrast sensitivity function (Lesmes, Lu, Baek, & Albright, 2010), which uses the standard Bayesian procedure. The improvement with HADO was maintained even when the prior was constructed from heterogeneous populations or a relatively small number of observers. These results of this case study support the conclusion that HADO can be used in Bayesian adaptive testing by replacing noninformative, diffuse priors with statistically justified informative priors without introducing unwanted bias.

Putative mechanisms mediating tolerance for audiovisual stimulus onset asynchrony.

Audiovisual (AV) speech perception is robust to temporal asynchronies between visual and auditory stimuli. We investigated the neural mechanisms that facilitate tolerance for audiovisual stimulus onset asynchrony (AVOA) with EEG. Individuals were presented with AV words that were asynchronous in onsets of voice and mouth movement and judged whether they were synchronous or not. Behaviorally, individuals tolerated (perceived as synchronous) longer AVOAs when mouth movement preceded the speech (V-A) stimuli than when the speech preceded mouth movement (A-V). Neurophysiologically, the P1-N1-P2 auditory evoked potentials (AEPs), time-locked to sound onsets and known to arise in and surrounding the primary auditory cortex (PAC), were smaller for the in-sync than the out-of-sync percepts. Spectral power of oscillatory activity in the beta band (14-30 Hz) following the AEPs was larger during the in-sync than out-of-sync perception for both A-V and V-A conditions. However, alpha power (8-14 Hz), also following AEPs, was larger for the in-sync than out-of-sync percepts only in the V-A condition. These results demonstrate that AVOA tolerance is enhanced by inhibiting low-level auditory activity (e.g., AEPs representing generators in and surrounding PAC) that code for acoustic onsets. By reducing sensitivity to acoustic onsets, visual-to-auditory onset mapping is weakened, allowing for greater AVOA tolerance. In contrast, beta and alpha results suggest the involvement of higher-level neural processes that may code for language cues (phonetic, lexical), selective attention, and binding of AV percepts, allowing for wider neural windows of temporal integration, i.e., greater AVOA tolerance.

Long-term temporal tracking of speech rate affects spoken-word recognition.

Humans unconsciously track a wide array of distributional characteristics in their sensory environment. Recent research in spoken-language processing has demonstrated that the speech rate surrounding a target region within an utterance influences which words, and how many words, listeners hear later in that utterance. On the basis of hypotheses that listeners track timing information in speech over long timescales, we investigated the possibility that the perception of words is sensitive to speech rate over such a timescale (e.g., an extended conversation). Results demonstrated that listeners tracked variation in the overall pace of speech over an extended duration (analogous to that of a conversation that listeners might have outside the lab) and that this global speech rate influenced which words listeners reported hearing. The effects of speech rate became stronger over time. Our findings are consistent with the hypothesis that neural entrainment by speech occurs on multiple timescales, some lasting more than an hour.

A hierarchical adaptive approach to optimal experimental design.

Experimentation is at the core of research in the behavioral and neural sciences, yet observations can be expensive and time-consuming to acquire (e.g., MRI scans, responses from infant participants). A major interest of researchers is designing experiments that lead to maximal accumulation of information about the phenomenon under study with the fewest possible number of observations. In addressing this challenge, statisticians have developed adaptive design optimization methods. This letter introduces a hierarchical Bayes extension of adaptive design optimization that provides a judicious way to exploit two complementary schemes of inference (with past and future data) to achieve even greater accuracy and efficiency in information gain. We demonstrate the method in a simulation experiment in the field of visual perception.

The influence of amplitude envelope information on resolving lexically ambiguous spoken words.

Prior studies exploring the contribution of amplitude envelope information to spoken word recognition are mixed with regard to the question of whether amplitude envelope alone, without spectral detail, can aid isolated word recognition. Three experiments show that the amplitude envelope will aid word identification only if two conditions are met: (1) It is not the only information available to the listener and (2) lexical ambiguity is not present. Implications for lexical processing are discussed.

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).

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.

Alpha activity marking word boundaries mediates speech segmentation.

This study examined the neurophysiological mechanisms of speech segmentation, the process of parsing the continuous speech signal into isolated words. Individuals listened to sequences of two monosyllabic words (e.g. gas source) and non-words (e.g. nas sorf). When these phrases are spoken, talkers usually produce one continuous s-sound, not two distinct s-sounds, making it unclear where one word ends and the next one begins. This ambiguity in the signal can also result in perceptual ambiguity, causing the sequence to be heard as one word (failed to segment) or two words (segmented). We compared listeners' electroencephalogram activity when they reported hearing one word or two words, and found that bursts of fronto-central alpha activity (9-14 Hz), following the onset of the physical /s/ and end of phrase, indexed speech segmentation. Left-lateralized beta activity (14-18 Hz) following the end of phrase distinguished word from non-word segmentation. A hallmark of enhanced alpha activity is that it reflects inhibition of task-irrelevant neural populations. Thus, the current results suggest that disengagement of neural processes that become irrelevant as the words unfold marks word boundaries in continuous speech, leading to segmentation. Beta activity is likely associated with unifying word representations into coherent phrases.

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).

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.

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 biospectroscopic interrogation of fine needle aspirates points towards segregation between graded categories: an initial study towards diagnostic screening.

Fine needle aspirates (FNAs) of suspicious breast lesions are often used to aid the diagnosis of female breast cancer. Biospectroscopy tools facilitate the acquisition of a biochemical cell fingerprint representative of chemical bonds present in a biological sample. The mid-infrared (IR; 4,000-400 cm(-1)) is absorbed by the chemical bonds present, allowing one to derive an absorbance spectrum. Complementary to IR spectroscopy, Raman spectroscopy measures the scattering by chemical bonds following excitation by a laser to generate an intensity spectrum. Our objective was to apply these methods to determine whether a biospectroscopy approach could objectively segregate different categories of FNAs. FNAs of breast tissue were collected (n = 48) in a preservative solution and graded into categories by a cytologist as C1 (non-diagnostic), C2 (benign), C3 (suspicious, probably benign) or C5 (malignant) [or C4 (suspicious, probably malignant); no samples falling within this category were identified during the collection period of the study]. Following washing, the cellular material was transferred onto BaF(2) (IR-transparent) slides for interrogation by Raman or Fourier-transform IR (FTIR) microspectroscopy. In some cases where sufficient material was obtained, this was transferred to low-E (IR-reflective) glass slides for attenuated total reflection-FTIR spectroscopy. The spectral datasets produced from these techniques required multivariate analysis for data handling. Principal component analysis followed by linear discriminant analysis was performed independently on each of the spectral datasets for only C2, C3 and C5. The resulting scores plots revealed a marked overlap of C2 with C3 and C5, although the latter pair were both significantly segregated (P < 0.001) in the Raman spectra. Good separation was observed between C3 and C5 in all three spectral datasets. Analysis performed on the average spectra showed the presence of three distinct cytological groups. Our findings suggest that biospectroscopy tools coupled with multivariate analysis may support the current FNA tests whilst increasing the sensitivity and associated reliability for improved diagnostics.