The roles of intrinsic motivation and capability-related factors in cognitive effort-based decision-making.

Individual differences in cognitive effort-based decision-making can be used to reveal human motivations to invest effort into a given task. Preferences among options that differ by dimensions related to demand levels (i.e., the interaction of task characteristics and performance measures) are also heavily influenced by how likely a person can succeed at a given option. However, most existing cognitive effort-based research has focused primarily on demand-related factors, leading to confounding inferences about the motivation behind these choices. This study used an adaptive algorithm to adjust relative demand levels for three cognitive tasks to investigate general and individual differences in demand preferences. The results highlight an overall pattern of individual differences in intrinsic motivation to perform challenging tasks, supporting research that found cognitive effort aversive to some but attractive to others. These results suggest that relative demand levels and intrinsic task factors drive the motivation to select an action.

The effect of ostracism on prospective memory in problem solving.

Successfully generating plans, while seemingly straightforward, can be riddled with external and internal interferences. One important possible source of interference is ostracism, which has been consistently shown to induce negative psychological effects in various executive functions. Therefore, understanding the impact of unforeseen ostracism on planning is vital to a broad spectrum of the population, from university students, whose self-esteem partly derives from social acceptance, to healthcare professionals, whose performance oftentimes relies on peer feedback. An individual's ability to navigate through intended actions is an evaluation of their prospective memory (PM), which is traditionally divided into three consecutive phases: (1) planning, (2) recall, and (3) performance. This study primarily focused on the impacts of ostracism via Cyberball simulation on the first two phases of PM in the Tower of London (TOL), an assessment of executive functioning designed specifically to test planning ability during problem solving. Using Bayesian analysis, the study found substantial evidence of there being no difference in planning success between social exclusion and inclusion conditions. However, an individual's sex had significant effects on their planning success at baseline (i.e., inclusion condition). Surprisingly, there was no difference in performance between male participants and female participants when excluded, suggesting that ostracism may play an equalizing role. In addition, male participants both listed more moves at planning and recalled more moves, which led to no difference between sexes in terms of recall percentage. This study underscores a need to consider various factors such as sex and differing perceptions of ostracism when analyzing and addressing problem solving performance.

The role of posterior parietal cortex in detecting changes in feedback contingency.

Well-practiced or learned behaviors are extremely resilient. For example, it is extremely difficult for a trained typist to forget how to use a keyboard configuration that they are familiar with. While they can be trained on a new keyboard configuration, the original skill quickly comes back when the old keyboard configuration is used again. This resiliency of learned skills is both a blessing and a curse. It makes useful skills durable, but it also makes maladaptive behaviors difficult to extinguish. Crossley et al. (2013) proposed a computational model and behavioral paradigm aimed at unlearning skills using various feedback contingency manipulations during an extinction phase. They showed that partially-valid feedback during extinction removed evidence for fast reacquisition, which they interpreted as evidence for unlearning. In this article, we replicated the Crossley et al. paradigm using fMRI. Univariate analyses showed differences in BOLD signals between the different experiment phases in the frontoparietal attention network. The superior and inferior parietal lobules (SPL and IPL, respectively) showed the largest cluster differences both between experimental phases and between extinction conditions. In contrast, the prefrontal cortex only showed differences in cluster of activities between extinction conditions. Multivariate pattern analysis was also used with seeds in the SPL and IPL. The results showed that these brain areas were critical in detecting changes in experimental phases. Overall, the fMRI results found mixed evidence for the Crossley et al. model and suggest that while unlearning prevents fast reacquisition, the absence of fast reacquisition does not necessarily implies that unlearning occurred.

A computational model of prefrontal and striatal interactions in perceptual category learning.

Work on multiple-system theories of cognition mostly focused on the systems themselves, while limited work has been devoted to understanding the interactions between systems. Generally, multiple-system theories include a model-based decision system supported by the prefrontal cortex and a model-free decision system supported by the striatum. Here we propose a neurobiological model to describe the interactions between model-based and model-free decision systems in category learning. The proposed model used spiking neurons to simulate activity of the hyperdirect pathway of the basal ganglia. The hyperdirect pathway acts as a gate for the response signal from the model-free system located in the striatum. We propose that the model-free system's response is inhibited when the model-based system is in control of the response. The new model was used to simulate published data from young adults, people with Parkinson's disease, and aged-matched older adults. The simulation results further suggest that system-switching ability may be related to individual differences in executive function. A new behavioral experiment tested this model prediction. The results show that an updating score predicts the ability to switch system in a categorization task. The article concludes with new model predictions and implications of the results for research on system interactions.

Cognitive flexibility assessment with a new Reversal learning task paradigm compared with the Wisconsin card sorting test exploring the moderating effect of gender and stress.

Cognitive flexibility plays a crucial role in psychological health and this research aimed to investigate its assessment. We developed a novel Reversal learning task (RLT) paradigm adding pure reward (+ 100 points, 0) and punishment (- 100 points, 0) conditions to the classic reward-punishment condition (+ 100, - 100); we also analyzed the RLT convergent validity with approach-avoidance questionnaires (BIS-BAS and Approach-Avoidance Temperament questionnaire) and the Wisconsin card sorting test (WCST) scores through a Principal component analysis. In a sample of 374 participants, we found that these three conditions differently assess flexibility and that high RLT reward sensitivity in the punishment condition (0; - 100) is related with high BAS reward responsiveness. Moreover, we found that RLT and WCST flexibility scores, although associated, detect different facets of cognitive flexibility. Finally, in a second sample (N = 172), we explored the impact of stress, moderated by gender, on RLT and WCST. Whereas, WCST was not impacted by these variables, in RLT stressed women showed increased perseverative errors in punishment condition (- 100, 0) and reduced punishment sensitivity in reward condition (+ 100, 0).Overall, our newly developed RLT paradigm and the WCST seem to provide different ways to assess cognitive flexibility and to be differently affected by moderators, such as gender and stress.

The effect of mode of presentation on Tower of Hanoi problem solving.

The Tower of Hanoi (TOH) is a classic problem that can be solved via multiple strategies. This study used TOH to examine how mode of presentation of a problem influences strategy use and transfer. Undergraduate students (Experiment 1) or Prolific workers (Experiment 2) completed two TOH problems of varying difficulty (4-disk/5-disk). They were randomly assigned to different conditions in which problems were either high in internal representation (mental) or high in external representation (computer). Participants were better able to complete problems successfully when external representations were available but completed problems in fewer moves when relying on internal representations. In addition, participants spent more time between moves when solving problems mentally, suggesting that external representations encourage speed while internal representations promote accuracy when solving recursion problems. Lastly, both experiments provide evidence that first solving a problem mentally encouraged participants to use strategies similar to goal recursion on a second problem.

When is Psychology Research Useful in Artificial Intelligence? A Case for Reducing Computational Complexity in Problem Solving.

A problem is a situation in which an agent seeks to attain a given goal without knowing how to achieve it. Human problem solving is typically studied as a search in a problem space composed of states (information about the environment) and operators (to move between states). A problem such as playing a game of chess has 10 120 $10^{120}$ possible states, and a traveling salesperson problem with as little as 82 cities already has more than 10 120 $10^{120}$ different tours (similar to chess). Biological neurons are slower than the digital switches in computers. An exhaustive search of the problem space exceeds the capacity of current computers for most interesting problems, and it is fairly clear that humans cannot in their lifetime exhaustively search even small fractions of these problem spaces. Yet, humans play chess and solve logistical problems of similar complexity on a daily basis. Even for simple problems humans do not typically engage in exploring even a small fraction of the problem space. This begs the question: How do humans solve problems on a daily basis in a fast and efficient way? Recent work suggests that humans build a problem representation and solve the represented problem-not the problem that is out there. The problem representation that is built and the process used to solve it are constrained by limits of cognitive capacity and a cost-benefit analysis discounting effort and reward. In this article, we argue that better understanding the way humans represent and solve problems using heuristics can help inform how simpler algorithms and representations can be used in artificial intelligence to lower computational complexity, reduce computation time, and facilitate real-time computation in complex problem solving.

The impact of training methodology and representation on rule-based categorization: An fMRI study.

Hélie, Shamloo, & Ell (2017) showed that regular classification learning instructions (A/B) promote between-category knowledge in rule-based categorization whereas conceptual learning instructions (YES/NO) promote learning within-category knowledge with the same categories. Here we explore how these tasks affect brain activity using fMRI. Participants learned two sets of two categories. Computational models were fit to the behavioral data to determine the type of knowledge learned by each participant. fMRI contrasts were computed to compare BOLD signal between the tasks and between the types of knowledge. The results show that participants in the YES/NO task had more activity in the pre-supplementary motor area, prefrontal cortex, and the angular/supramarginal gyrus. These brain areas are related to working memory and part of the dorsal attention network, which showed increased task-based functional connectivity with the medial temporal lobes. In contrast, participants in the A/B task had more activity in the thalamus and caudate. These results suggest that participants in the YES/NO task used bivalent rules and may have treated each contextual question as a separate task, switching task each time the question changed. Activity in the A/B condition was more consistent with participants applying direct Stimulus → Response rules. With regards to knowledge representation, there was a large shared network of brain areas, but participants learning between-category information showed additional posterior parietal activity, which may be related to the inhibition of incorrect motor programs.

A neurocomputational theory of how rule-guided behaviors become automatic.

This article introduces a biologically detailed computational model of how rule-guided behaviors become automatic. The model assumes that initially, rule-guided behaviors are controlled by a distributed neural network centered in the prefrontal cortex, and that in addition to initiating behavior, this network also trains a faster and more direct network that includes projections from sensory association cortex directly to rule-sensitive neurons in the premotor cortex. After much practice, the direct network is sufficient to control the behavior, without prefrontal involvement. The model is implemented as a biologically detailed neural network constructed from spiking neurons and displaying a biologically plausible form of Hebbian learning. The model successfully accounts for single-unit recordings and human behavioral data that are problematic for other models of automaticity. (PsycInfo Database Record (c) 2021 APA, all rights reserved).

A Study of Individual Differences in Categorization with Redundancy.

Humans and other animals are constantly learning new categories and making categorization decisions in their everyday life. However, different individuals may focus on different information when learning categories, which can impact the category representation and the information that is used when making categorization decisions. This article used computational modeling of behavioral data to take a closer look at this possibility in the context of a categorization task with redundancy. Iterative decision bomid modeling and drift diffusion models were used to detect individual differences in human categorization performance. The results show that participants differ in terms of what stimulus features they learned and how they use the learned features. For example, while some participants only learn one stimulus dimension (which is sufficient for perfect accuracy), others learn both stimulus dimensions (which is not required for perfect accuracy). Among participants that learned both dimensions, some used both dimensions, while others show error and RT patterns suggesting the use of only one of the dimensions. The diversity of obtained results is problematic for existing categorization models and suggests that each categorization model may be able to account for the performance of some but not all participants.

3D shape estimation in a constraint optimization neural network.

One of the most important aspects of visual perception is the inference of 3D shape from a 2D retinal image of the outside world. The existence of several valid mapping functions from object to data makes this inverse problem ill-posed and therefore computationally difficult. In human vision, the retinal image is a 2D projection of the 3D world. The visual system imposes certain constraints on the family of solutions in order to uniquely and efficiently solve this inverse problem. This work specifically focused on the minimization of standard deviations of 3D angles (MSDA) for 3D perception. Our goal was to use a Deep Convolutional Neural Network based on biological principles derived from visual area V4 to achieve 3D reconstruction using constrained minimization of MSDA. We conducted an experiment using novel shapes with human subjects to collect data and test the model. The performance of the network largely agreed with how humans estimated novel 3D shapes. The results show that the constraint of MSDA in 3D shape can be implemented in a neural network and produce human-like results. Additional visual constraints can be added to the network in the future to fully test the theory of visual constraints as a basis of 3D shape perception.

Whole-brain R1 predicts manganese exposure and biological effects in welders.

Manganese (Mn) is a neurotoxicant that, due to its paramagnetic property, also functions as a magnetic resonance imaging (MRI) T1 contrast agent. Previous studies in Mn toxicity have shown that Mn accumulates in the brain, which may lead to parkinsonian symptoms. In this article, we trained support vector machines (SVM) using whole-brain R1 (R1 = 1/T1) maps from 57 welders and 32 controls to classify subjects based on their air Mn concentration ([Mn]Air), Mn brain accumulation (ExMnBrain), gross motor dysfunction (UPDRS), thalamic GABA concentration (GABAThal), and total years welding. R1 was highly predictive of [Mn]Air above a threshold of 0.20 mg/m3 with an accuracy of 88.8% and recall of 88.9%. R1 was also predictive of subjects with GABAThal having less than or equal to 2.6 mM with an accuracy of 82% and recall of 78.9%. Finally, we used an SVM to predict age as a method of verifying that the results could be attributed to Mn exposure. We found that R1 was predictive of age below 48 years of age with accuracies ranging between 75 and 82% with recall between 94.7% and 76.9% but was not predictive above 48 years of age. Together, this suggests that lower levels of exposure (< 0.20 mg/m3 and < 18 years of welding on the job) do not produce discernable signatures, whereas higher air exposures and subjects with more total years welding produce signatures in the brain that are readily identifiable using SVM.

Learning and generalization of within-category representations in a rule-based category structure.

The task requirements during the course of category learning are critical for promoting within-category representations (e.g., correlational structure of the categories). Recent data suggest that for unidimensional rule-based structures, only inference training promotes the learning of within-category representations, and generalization across tasks is limited. It is unclear if this is a general feature of rule-based structures, or a limitation of unidimensional rule-based structures. The present work reports the results of three experiments further investigating this issue using an exclusive-or rule-based structure where successful performance depends upon attending to two stimulus dimensions. Participants were trained using classification or inference and were tested using inference. For both the classification and inference training conditions, within-category representations were learned and could be generalized at test (i.e., from classification to inference) and this result was dependent upon a congruence between local and global regions of the stimulus space. These data further support the idea that the task requirements during learning (i.e., a need to attend to multiple stimulus dimensions) are critical determinants of the category representations that are learned and the utility of these representations for supporting generalization in novel situations.

A unified model of rule-set learning and selection.

The ability to focus on relevant information and ignore irrelevant information is a fundamental part of intelligent behavior. It not only allows faster acquisition of new tasks by reducing the size of the problem space but also allows for generalizations to novel stimuli. Task-switching, task-sets, and rule-set learning are all intertwined with this ability. There are many models that attempt to individually describe these cognitive abilities. However, there are few models that try to capture the breadth of these topics in a unified model and fewer still that do it while adhering to the biological constraints imposed by the findings from the field of neuroscience. Presented here is a comprehensive model of rule-set learning and selection that can capture the learning curve results, error-type data, and transfer effects found in rule-learning studies while also replicating the reaction time data and various related effects of task-set and task-switching experiments. The model also factors in many disparate neurological findings, several of which are often disregarded by similar models.

The impact of training methodology and category structure on the formation of new categories from existing knowledge.

Categorization decisions are made thousands of times every day, and a typical adult knows tens of thousands of categories. It is thus relatively rare that adults learn new categories without somehow reorganizing pre-existing knowledge. Yet, most perceptual categorization research has investigated the ability to learn new categories without considering they relation to existing knowledge. In this article, we test the ability of young adults to merge already known categories into new categories as a function of training methodology and category structures using two experiments. Experiment 1 tests participants' ability to merge rule-based or information-integration categories that are either contiguous, semi-contiguous, or non-contiguous in perceptual space using a classification paradigm. Experiment 2 is similar Experiment 1 but uses a YES/NO learning paradigm instead. The results of both experiments suggest a strong effect of the contiguity of the merged categories in perceptual space that depends on the type of category representation that is learned. The type of category representation that is learned, in turn, depends on a complex interaction of the category structures and training task. We conclude by discussing the relevance of these results for categorization outside the laboratory.

Can categorical knowledge be used in visual search?

Smith, Redford, Gent, and Washburn (2005) have proposed a new categorization paradigm called the visual-search categorization task to study how display size affects categorization performance. Their results show that, in a wide range of conditions, category knowledge collapses as soon as multiple stimuli are simultaneously displayed in a scene. This result is surprising and important considering that humans parse and categorize objects from complex scenes on a daily basis. However, Smith et al. only studied one kind of category structure. This article presents the results of three experiments exploring the effect of display size on perceptual categorization as a function of category structure. We show that rule-based and information-integration categories are differently affected by display size in the visual search categorization task. For rule-based structures, target-present and target-absent trials are not much affected by display size. However, the effect of display size is bigger for information-integration category structures, and much more pronounced for target-absent trials than for target-present trials. A follow-up experiment shows that target redundancy (i.e., having more than one target in the display) does not improve performance with information-integration category structures. These results suggest that categories may be learned differently depending on their underlying structure, and that the resulting category representation may influence performance in the visual search categorization task.

Creative Sparks or Paralysis Traps? The Effects of Contradictions on Creative Processing and Creative Products.

Paradoxes are an unavoidable part of work life. The unusualness of attempting to simultaneously satisfy contradictory imperatives can result in creative outcomes that simultaneously satisfy both imperatives by inducing search for, and selection of, novel and useful solutions. Likewise, extant research suggests that paradoxes can also result in anxiety, defensiveness, and persistence of old ways of doing things. However, there is little work attempting to describe how paradoxes affect cognition and when it results in higher or lower creativity. To tackle this issue, a theory of paradoxical creativity is developed. Paradoxical creativity is the attempt by an individual to creatively resolve a contradiction by simultaneously achieving competing demands. The theory is implemented into a computational model and a simulation is used to describe how paradoxes affect creative cognitive process and how these processes in turn result in higher or lower degrees of creativity. The results show that creative output is enhanced when paradoxes have a balanced effect on the cognitive processes responsible for an individual's capacity to search for new information and willingness to tolerate new ideas. Hence, individuals with high baseline levels of creative cognition are more likely to suffer negative creative performance consequences resulting from contradictory demands. For those individuals, contradictory demands may produce more alternatives, which increases uncertainty and time to insight (if insight is ever reached). This suggests that incentives or rewards to resolve contradictions may have the unintentional effect of reducing creative output in some circumstance.

A New Computational Model for Astrocytes and Their Role in Biologically Realistic Neural Networks.

Recent studies in neuroscience show that astrocytes alongside neurons participate in modulating synapses. It led to the new concept of "tripartite synapse", which means that a synapse consists of three parts: presynaptic neuron, postsynaptic neuron, and neighboring astrocytes. However, it is still unclear what role is played by the astrocytes in the tripartite synapse. Detailed biocomputational modeling may help generate testable hypotheses. In this article, we aim to study the role of astrocytes in synaptic plasticity by exploring whether tripartite synapses are capable of improving the performance of a neural network. To achieve this goal, we developed a computational model of astrocytes based on the Izhikevich simple model of neurons. Next, two neural networks were implemented. The first network was only composed of neurons and had standard bipartite synapses. The second network included both neurons and astrocytes and had tripartite synapses. We used reinforcement learning and tested the networks on categorizing random stimuli. The results show that tripartite synapses are able to improve the performance of a neural network and lead to higher accuracy in a classification task. However, the bipartite network was more robust to noise. This research provides computational evidence to begin elucidating the possible beneficial role of astrocytes in synaptic plasticity and performance of a neural network.

Categorization system-switching deficits in typical aging and Parkinson's disease.

OBJECTIVE: Numerous studies documenting cognitive deficits in Parkinson's disease (PD) revealed impairment in a variety of tasks related to memory, learning, and attention. One ubiquitous task that has not received much attention, is categorization system-switching. Categorization system-switching is a form of task-switching requiring participants to switch between different categorization systems. In this article, we explore whether older adults and people with PD show deficits in categorization system-switching. METHOD: Twenty older adults diagnosed with PD, 20 neurologically intact older adults, and 67 young adults participated in this study. Participants were first trained in rule-based (RB) and later information-integration (II) categorization separately. After training on the tasks, participants performed a block of trial-by-trial switching where the RB and II trials were randomly intermixed. Finally, the last block of trials also intermixed RB and II trials were randomly but additionally changed the location of the response buttons. RESULTS: Contrary to our hypothesis, the results show no difference in accuracy between older adults and people with PD during the intermixed trial block, as well as no difference in response time (RT) switch cost. However, both groups were less accurate during intermixed trial blocks and had a higher RT switch cost when compared with young adults. In addition, the proportion of participants able to switch systems was smaller in people with PD than in young adults. CONCLUSIONS: The results suggest that older adults and people with PD have impaired categorization system-switching ability, and that this ability may be related to a decrease in tonic dopamine (DA) levels associated with normal aging and PD. (PsycINFO Database Record