"The eyes are the window to the representation": Linking gaze to memory precision and decision weights in object discrimination tasks.

Humans selectively attend to task-relevant information in order to make accurate decisions. However, selective attention incurs consequences if the learning environment changes unexpectedly. This trade-off has been underscored by studies that compare learning behaviors between adults and young children: broad sampling during learning comes with a breadth of information in memory, often allowing children to notice details of the environment that are missed by their more selective adult counterparts. The current work extends the exemplar-similarity account of object discrimination to consider both the intentional and consequential aspects of selective attention when predicting choice. In a novel direct input approach, we used trial-level eye-tracking data from training and test to replace the otherwise freely estimated attention dynamics of the model. We demonstrate that only a model imbued with gaze correlates of memory precision in addition to decision weights can accurately predict key behaviors associated with (a) selective attention to a relevant dimension, (b) distributed attention across dimensions, and (c) flexibly shifting strategies between tasks. Although humans engage in selective attention with the intention of being accurate in the moment, our findings suggest that its consequences on memory constrain the information that is available for making decisions in the future. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

The Role of Attention in Category Representation.

Numerous studies have found that selective attention affects category learning. However, previous research did not distinguish between the contribution of focusing and filtering components of selective attention. This study addresses this issue by examining how components of selective attention affect category representation. Participants first learned a rule-plus-similarity category structure, and then were presented with category priming followed by categorization and recognition tests. Additionally, to evaluate the involvement of focusing and filtering, we fit models with different attentional mechanisms to the data. In Experiment 1, participants received rule-based category training, with specific emphasis on a single deterministic feature (D feature). Experiment 2 added a recognition test to examine participants' memory for features. Both experiments indicated that participants categorized items based solely on the D feature, showed greater memory for the D feature, were primed exclusively by the D feature without interference from probabilistic features (P features), and were better fit by models with focusing and at least one type of filtering mechanism. The results indicated that selective attention distorted category representation by highlighting the D feature and attenuating P features. To examine whether the distorted representation was specific to rule-based training, Experiment 3 introduced training, emphasizing all features. Under such training, participants were no longer primed by the D feature, they remembered all features well, and they were better fit by the model assuming only focusing but no filtering process. The results coupled with modeling provide novel evidence that while both focusing and filtering contribute to category representation, filtering can also result in representational distortion.

The Pigeon as a Model of Complex Visual Processing and Category Learning.

Over the past 30 years, behavioral, computational, and neuroscientific investigations have yielded fresh insights into how pigeons adapt to the diverse complexities of their visual world. A prime area of interest has been how pigeons categorize the innumerable individual stimuli they encounter. Most studies involve either photorealistic representations of actual objects thus affording the virtue of being naturalistic, or highly artificial stimuli thus affording the virtue of being experimentally manipulable. Together those studies have revealed the pigeon to be a prodigious classifier of both naturalistic and artificial visual stimuli. In each case, new computational models suggest that elementary associative learning lies at the root of the pigeon's category learning and generalization. In addition, ongoing computational and neuroscientific investigations suggest how naturalistic and artificial stimuli may be processed along the pigeon's visual pathway. Given the pigeon's availability and affordability, there are compelling reasons for this animal model to gain increasing prominence in contemporary neuroscientific research.

The pigeon as a machine: Complex category structures can be acquired by a simple associative model.

Never known for its smarts, the pigeon has proven to be a prodigious classifier of complex visual stimuli. What explains its surprising success? Does it possess elaborate executive functions akin to those deployed by humans? Or does it effectively deploy an unheralded, but powerful associative learning mechanism? In a series of experiments, we first confirm that pigeons can learn a variety of category structures - some devised to foil the use of advanced cognitive processes. We then contrive a simple associative learning model to see how effectively the model learns the same tasks given to pigeons. The close fit of the associative model to pigeons' categorization behavior provides unprecedented support for associative learning as a viable mechanism for mastering complex category structures and for the pigeon's using this mechanism to adapt to a rich visual world. This model will help guide future neuroscientific research into the biological substrates of visual cognition.

Explaining the description-experience gap in risky decision-making: learning and memory retention during experience as causal mechanisms.

When making decisions based on probabilistic outcomes, people guide their behavior using knowledge gathered through both indirect descriptions and direct experience. Paradoxically, how people obtain information significantly impacts apparent preferences. A ubiquitous example is the description-experience gap: individuals seemingly overweight low probability events when probabilities are described yet underweight them when probabilities must be experienced firsthand. A leading explanation for this fundamental gap in decision-making is that probabilities are weighted differently when learned through description relative to experience, yet a formal theoretical account of the mechanism responsible for such weighting differences remains elusive. We demonstrate how various learning and memory retention models incorporating neuroscientifically motivated learning mechanisms can explain why probability weighting and valuation parameters often are found to vary across description and experience. In a simulation study, we show how learning through experience can lead to systematically biased estimates of probability weighting when using a traditional cumulative prospect theory model. We then use hierarchical Bayesian modeling and Bayesian model comparison to show how various learning and memory retention models capture participants' behavior over and above changes in outcome valuation and probability weighting, accounting for description and experience-based decisions in a within-subject experiment. We conclude with a discussion of how substantive models of psychological processes can lead to insights that heuristic statistical models fail to capture.

Cognition: The power of simple associative learning.

Associative learning is traditionally considered to be slow and inefficient compared to 'smarter' rule-based learning. New research reveals the remarkable ability of associative learning in acquiring exceedingly complex categories.

The benefits of immature cognitive control: How distributed attention guards against learning traps.

Cognitive control allows one to focus one's attention efficiently on relevant information while filtering out irrelevant information. This ability provides a means of rapid and effective learning, but using this control also brings risks. Importantly, useful information may be ignored and missed, and learners may fall into "learning traps" (e.g., learned inattention) wherein they fail to realize that what they ignore carries important information. Previous research has shown that adults may be more prone to such traps than young children, but the mechanisms underlying this difference are unclear. The current study used eye tracking to examine the role of attentional control during learning in succumbing to these learning traps. The participants, 4-year-old children and adults, completed a category learning task in which an unannounced switch occurred wherein the feature dimensions most relevant to correct categorization became irrelevant and formerly irrelevant dimensions became relevant. After the switch, adults were more likely than children to ignore the new highly relevant dimension and settle on a suboptimal categorization strategy. Furthermore, eye-tracking analyses reveal that greater attentional selectivity during learning (i.e., optimizing attention to focus only on the most relevant sources of information) predicted this tendency to miss important information later. Children's immature cognitive control, leading to broadly distributed attention, appears to protect children from this trap-although at the cost of less efficient and slower learning. These results demonstrate the double-edged sword of cognitive control and suggest that immature control may serve an adaptive function early in development.

The quest for simplicity in human learning: Identifying the constraints on attention.

For better or worse, humans live a resource-constrained existence; only a fraction of physical sensations ever reach conscious awareness, and we store a shockingly small subset of these experiences in memory for later use. Here, we examined the effects of attention constraints on learning. Among models that frame selective attention as an optimization problem, attention orients toward information that will reduce errors. Using this framing as a basis, we developed a suite of models with a range of constraints on the attention available during each learning event. We fit these models to both choice and eye-fixation data from four benchmark category-learning data sets, and choice data from another dynamic categorization data set. We found consistent evidence for computations we refer to as "simplicity", where attention is deployed to as few dimensions of information as possible during learning, and "competition", where dimensions compete for selective attention via lateral inhibition.

As within, so without, as above, so below: Common mechanisms can support between- and within-trial category learning dynamics.

Two fundamental difficulties when learning novel categories are deciding (a) what information is relevant and (b) when to use that information. Although previous theories have specified how observers learn to attend to relevant dimensions over time, those theories have largely remained silent about how attention should be allocated on a within-trial basis, which dimensions of information should be sampled, and how the temporal order of information sampling influences learning. Here, we use the adaptive attention representation model (AARM) to demonstrate that a common set of mechanisms can be used to specify: (a) How the distribution of attention is updated between trials over the course of learning and (b) how attention dynamically shifts among dimensions within a trial. We validate our proposed set of mechanisms by comparing AARM's predictions to observed behavior in four case studies, which collectively encompass different theoretical aspects of selective attention. We use both eye-tracking and choice response data to provide a stringent test of how attention and decision processes dynamically interact during category learning. Specifically, how does attention to selected stimulus dimensions gives rise to decision dynamics, and in turn, how do decision dynamics influence which dimensions are attended to via gaze fixations? (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Distributed Neural Systems Support Flexible Attention Updating during Category Learning.

To accurately categorize items, humans learn to selectively attend to the stimulus dimensions that are most relevant to the task. Models of category learning describe how attention changes across trials as labeled stimuli are progressively observed. The Adaptive Attention Representation Model (AARM), for example, provides an account in which categorization decisions are based on the perceptual similarity of a new stimulus to stored exemplars, and dimension-wise attention is updated on every trial in the direction of a feedback-based error gradient. As such, attention modulation as described by AARM requires interactions among processes of orienting, visual perception, memory retrieval, prediction error, and goal maintenance to facilitate learning. The current study explored the neural bases of attention mechanisms using quantitative predictions from AARM to analyze behavioral and fMRI data collected while participants learned novel categories. Generalized linear model analyses revealed patterns of BOLD activation in the parietal cortex (orienting), visual cortex (perception), medial temporal lobe (memory retrieval), basal ganglia (prediction error), and pFC (goal maintenance) that covaried with the magnitude of model-predicted attentional tuning. Results are consistent with AARM's specification of attention modulation as a dynamic property of distributed cognitive systems.

A regularization method for linking brain and behavior.

In a world of big data and computational resources, there has been a growing interest in further validating computational models of decision making by subjecting them to more rigorous constraints. One prominent area of study is model-based cognitive neuroscience, where measures of neural activity are explained and interpreted through the lens of a cognitive model. Although some early work has developed the statistical framework for exploiting the covariation between brain and behavior through factor analysis linking functions, current methods are still far from providing parsimonious accounts of high-dimensional (e.g., voxel-level) data. In this article, we contribute to this endeavor by investigating the fidelity of regularization methods such as the Lasso. Here, a combination of local and global penalty terms are applied to pressure elements of the factor loading matrix toward zero, reducing the false alarm rate. Such penalties facilitate the emergence of parsimonious network structure in the study of neural activation, giving way to clearer interpretations of high-dimensional data. We show through a set of three simulation studies and one application to real data that the Lasso can be an effective regularization method in the context of linking complex patterns of brain data to theoretical explanations of decisions. Although our analyses are specific to linking brain to behavior, the structure of the model is invariant to the type of high-dimensional data under investigation. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Constraining functional coactivation with a cluster-based structural connectivity network.

In this article, we propose a two-step pipeline to explore task-dependent functional coactivations of brain clusters with constraints from the structural connectivity network. In the first step, the pipeline employs a nonparametric Bayesian clustering method that can estimate the optimal number of clusters, cluster assignments of brain regions of interest (ROIs), and the strength of within- and between-cluster connections without any prior knowledge. In the second step, a factor analysis model is applied to functional data with factors defined as the obtained structural clusters and the factor structure informed by the structural network. The coactivations of ROIs and their clusters can be studied by correlations between factors, which can largely differ by ongoing cognitive task. We provide a simulation study to validate that the pipeline can recover the underlying structural and functional network. We also apply the proposed pipeline to empirical data to explore the structural network of ROIs obtained by the Gordon parcellation and study their functional coactivations across eight cognitive tasks and a resting-state condition.

In this article, we propose a two-step pipeline to explore task-dependent functional coactivations of brain clusters with constraints imposed from structural connectivity networks. In the first step, the pipeline employs a nonparametric Bayesian clustering method that can estimate the optimal number of clusters, cluster assignments of brain regions of interest, and the strength of within- and between-cluster connections without any prior knowledge. In the second step, a factor analysis model is applied to functional data with factors defined as the obtained structural clusters and the factor structure informed by the structural network.

Reconciling similarity across models of continuous selections.

Recently developed models of decision-making have provided accounts of the cognitive processes underlying choice on tasks where responses can fall along a continuum, such as identifying the color or orientation of a stimulus. Even though nearly all of these models seek to extend diffusion decision processes to a continuum of response options, they vary in terms of complexity, tractability, and their ability to predict patterns of data such as multimodal distributions of responses. We suggest that these differences are almost entirely due to differences in how these models account for the similarity among response options. In this theoretical note, we reconcile these differences by characterizing the existing models under a common framework, where the assumptions about psychological representations of similarity, and their implications for behavioral data (e.g., multimodal responses), are made explicit. Furthermore, we implement a simulation-based approach to computing model likelihoods that allows for greater freedom in constructing and implementing continuous response models. The resulting geometric similarity representation (GSR) can supplement approaches like the circular/spherical diffusion models by allowing them to generate multimodal distributions of responses from a single drift, or simplify models like the spatially continuous diffusion model (SCDM) by condensing their representations of similarity and allowing them to generate simulations more efficiently. To illustrate its utility, we apply this approach to multimodal distributions responses, two-dimensional responses (such as locations on a computer screen), and continuous response options with nontrivial, nonlinear similarity relations between response options. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Equal evidence perceptual tasks suggest a key role for interactive competition in decision-making.

The dynamics of decision-making have been widely studied over the past several decades through the lens of an overarching theory called sequential sampling theory (SST). Within SST, choices are represented as accumulators, each of which races toward a decision boundary by drawing stochastic samples of evidence through time. Although progress has been made in understanding how decisions are made within the SST framework, considerable debate centers on whether the accumulators exhibit dependency during the evidence accumulation process; namely, whether accumulators are independent, fully dependent, or partially dependent. To evaluate which type of dependency is the most plausible representation of human decision-making, we applied a novel twist on two classic perceptual tasks; namely, in addition to the classic paradigm (i.e., the unequal-evidence conditions), we used stimuli that provided different magnitudes of equal-evidence (i.e., the equal-evidence conditions). In equal-evidence conditions, response times systematically decreased with increase in the magnitude of evidence, whereas in unequal-evidence conditions, response times systematically increased as the difference in evidence between the two alternatives decreased. We designed a spectrum of models that ranged from independent accumulation to fully dependent accumulation, while also examining the effects of within-trial and between-trial variability (BTV). We then fit the set of models to our two experiments and found that models instantiating the principles of partial dependency provided the best fit to the data. Our results further suggest that mechanisms inducing partial dependency, such as lateral inhibition, are beneficial for understanding complex decision-making dynamics, even when the task is relatively simple. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Quantifying mechanisms of cognition with an experiment and modeling ecosystem.

Although there have been major strides toward uncovering the neurobehavioral mechanisms involved in cognitive functions like memory and decision making, methods for measuring behavior and accessing latent processes through computational means remain limited. To this end, we have created SUPREME (Sensing to Understanding and Prediction Realized via an Experiment and Modeling Ecosystem): a toolbox for comprehensive cognitive assessment, provided by a combination of construct-targeted tasks and corresponding computational models. SUPREME includes four tasks, each developed symbiotically with a mechanistic model, which together provide quantified assessments of perception, cognitive control, declarative memory, reward valuation, and frustrative nonreward. In this study, we provide validation analyses for each task using two sessions of data from a cohort of cognitively normal participants (N = 65). Measures of test-retest reliability (r: 0.58-0.75), stability of individual differences (ρ: 0.56-0.70), and internal consistency (α: 0.80-0.86) support the validity of our tasks. After fitting the models to data from individual subjects, we demonstrate each model's ability to capture observed patterns of behavioral results across task conditions. Our computational approaches allow us to decompose behavior into cognitively interpretable subprocesses, which we can compare both within and between participants. We discuss potential future applications of SUPREME, including clinical assessments, longitudinal tracking of cognitive functions, and insight into compensatory mechanisms.

Testing the factor structure underlying behavior using joint cognitive models: Impulsivity in delay discounting and Cambridge gambling tasks.

Neurocognitive tasks are frequently used to assess disordered decision making, and cognitive models of these tasks can quantify performance in terms related to decision makers' underlying cognitive processes. In many cases, multiple cognitive models purport to describe similar processes, but it is difficult to evaluate whether they measure the same latent traits or processes. In this article, we develop methods for modeling behavior across multiple tasks by connecting cognitive model parameters to common latent constructs. This approach can be used to assess whether 2 tasks measure the same dimensions of cognition, or actually improve the estimates of cognitive models when there are overlapping cognitive processes between 2 related tasks. The approach is then applied to connecting decision data on 2 behavioral tasks that evaluate clinically relevant deficits, the delay discounting task and Cambridge gambling task, to determine whether they both measure the same dimension of impulsivity. We find that the discounting rate parameters in the models of each task are not closely related, although substance users exhibit more impulsive behavior on both tasks. Instead, temporal discounting on the delay discounting task as quantified by the model is more closely related to externalizing psychopathology like aggression, while temporal discounting on the Cambridge gambling task is related more to response inhibition failures. The methods we develop thus provide a new way to connect behavior across tasks and grant new insights onto the different dimensions of impulsivity and their relation to substance use. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

Score-based tests for detecting heterogeneity in linear mixed models.

Cross-level interactions among fixed effects in linear mixed models (also known as multilevel models) can be complicated by heterogeneity stemming from random effects and residuals. When heterogeneity is present, tests of fixed effects (including cross-level interaction terms) are subject to inflated type I or type II error. While the impact of variance change/heterogeneity has been noticed in the literature, few methods have been proposed to detect this heterogeneity in a simple, systematic way. In addition, when heterogeneity among clusters is detected, researchers often wish to know which clusters' variances differed from the others. In this study, we utilize a recently proposed family of score-based tests to distinguish between cross-level interactions and heterogeneity in variance components, also providing information about specific clusters that exhibit heterogeneity. These score-based tests only require estimation of the null model (when variance homogeneity is assumed to hold), and they have been previously applied to psychometric models to detect measurement invariance. In this paper, we extend the tests to linear mixed models, allowing us to use the tests to differentiate between interaction and heterogeneity. We detail the tests' implementation and performance via simulation, provide an empirical example of the tests' use in practice, and provide code illustrating the tests' general application.

Gaussian process linking functions for mind, brain, and behavior.

The link between mind, brain, and behavior has mystified philosophers and scientists for millennia. Recent progress has been made by forming statistical associations between manifest variables of the brain (e.g., electroencephalogram [EEG], functional MRI [fMRI]) and manifest variables of behavior (e.g., response times, accuracy) through hierarchical latent variable models. Within this framework, one can make inferences about the mind in a statistically principled way, such that complex patterns of brain-behavior associations drive the inference procedure. However, previous approaches were limited in the flexibility of the linking function, which has proved prohibitive for understanding the complex dynamics exhibited by the brain. In this article, we propose a data-driven, nonparametric approach that allows complex linking functions to emerge from fitting a hierarchical latent representation of the mind to multivariate, multimodal data. Furthermore, to enforce biological plausibility, we impose both spatial and temporal structure so that the types of realizable system dynamics are constrained. To illustrate the benefits of our approach, we investigate the model's performance in a simulation study and apply it to experimental data. In the simulation study, we verify that the model can be accurately fitted to simulated data, and latent dynamics can be well recovered. In an experimental application, we simultaneously fit the model to fMRI and behavioral data from a continuous motion tracking task. We show that the model accurately recovers both neural and behavioral data and reveals interesting latent cognitive dynamics, the topology of which can be contrasted with several aspects of the experiment.

A model of dynamic, within-trial conflict resolution for decision making.

Growing evidence for moment-to-moment fluctuations in visual attention has led to questions about the impetus and time course of cognitive control. These questions are typically investigated with paradigms like the flanker task, which require participants to inhibit an automatic response before making a decision. Connectionist modeling work suggests that between-trial changes in attention result from fluctuations in conflict-as conflict occurs, attention needs to be upregulated to resolve it. Current sequential sampling models (SSMs) of within-trial effects, however, suggest that attention focuses on a goal-relevant target as a function of time. We propose that within-trial changes in cognitive control and attention are emergent properties of the dynamics of the decision itself. We tested our hypothesis by developing a set of SSMs, each making alternative assumptions about attention modulation and evidence accumulation mechanisms. Combining the SSM framework with likelihood-free Bayesian approximation methods allowed us to conduct quantified comparisons between subject-level fits. Models included either time- or control-based attention mechanisms, and either strongly- (via feedforward inhibition) or weakly correlated (via leak and lateral inhibition) evidence accumulation mechanisms. We fit all models to behavioral data collected in variants of the flanker task, one accompanied by EEG measures. Across three experiments, we found converging evidence that control-based attention processes in combination with evidence accumulation mechanisms governed by leak and lateral inhibition provided the best fits to behavioral data, and uniquely mapped onto observed decision-related signals in the brain. (PsycInfo Database Record (c) 2020 APA, all rights reserved).

Hierarchies improve individual assessment of temporal discounting behavior.

Delay discounting behavior has proven useful in assessing impulsivity across a wide range of populations. As such, accurate estimation of the shape of each individual's temporal discounting profile is paramount when drawing conclusions about how impulsivity relates to clinical and health outcomes such as gambling, addiction, and obesity. Here, we identify an estimation problem with current methods of assessing temporal discounting behavior, and propose a simple solution. First, through a simulation study we identify types of temporal discounting profiles that cannot reliably be estimated. Second, we show how imposing constraints through hierarchical modeling ameliorates these recovery problems. Finally, we apply our solution to a large data set from a temporal discounting task, and illustrate the importance of reliable estimation within patient populations. We conclude with a brief discussion on how hierarchical Bayesian methods can aid in model estimation, compensate for small samples, and improve predictions of externalizing psychopathology.