Decision-related Information Research Articles

Selective Attention and Decision-Making Have Separable Neural Bases in Space and Time.

Attention and decision-making processes are fundamental to cognition. However, they are usually experimentally confounded, making it difficult to link neural observations to specific processes. Here we separated the effects of selective attention from the effects of decision-making on brain activity obtained from human participants (both sexes), using a two-stage task where the attended stimulus and decision were orthogonal and separated in time. Multivariate pattern analyses of multimodal neuroimaging data revealed the dynamics of perceptual and decision-related information coding through time with magnetoencephalography (MEG), through space with functional magnetic resonance imaging (fMRI), and their combination (MEG-fMRI fusion). Our MEG results showed an effect of attention before decision-making could begin, and fMRI results showed an attention effect in early visual and frontoparietal regions. Model-based MEG-fMRI fusion suggested that attention boosted stimulus information in the frontoparietal and early visual regions before decision-making was possible. Together, our results suggest that attention affects neural stimulus representations in the frontoparietal regions independent of decision-making.

Learning enhances representations of taste-guided decisions in the mouse gustatory insular cortex

Learning to discriminate overlapping gustatory stimuli that predict distinct outcomes-a feat known as discrimination learning-can mean the difference between ingesting a poison or a nutritive meal. Despite the obvious importance of this process, very little is known about the neural basis of taste discrimination learning. In other sensory modalities, this form of learning can be mediated by either the sharpening of sensory representations or the enhanced ability of "decision-making" circuits to interpret sensory information. Given the dual role of the gustatory insular cortex (GC) in encoding both sensory and decision-related variables, this region represents an ideal site for investigating how neural activity changes as animals learn a novel taste discrimination. Here, we present results from experiments relying on two-photon calcium imaging of GC neural activity in mice performing a taste-guided mixture discrimination task. The task allows for the recording of neural activity before and after learning induced by training mice to discriminate increasingly similar pairs of taste mixtures. Single-neuron and population analyses show a time-varying pattern of activity, with early sensory responses emerging after taste delivery and binary, choice-encoding responses emerging later in the delay before a decision is made. Our results demonstrate that, while both sensory and decision-related information is encoded by GC in the context of a taste mixture discrimination task, learning and improved performance are associated with a specific enhancement of decision-related responses.

A robust incomplete large-scale group decision-making model for metaverse metro operations and maintenance

The metaverse, constructed through digital technology, serves as a virtual realm intertwining with reality. Within this context, the challenge of evaluating data from diverse sources arises, and the application of large-scale group decision-making (LSGDM) methods emerges as a viable solution. Handling incomplete information and reducing dimensionality for large-scale decision-makers (DMs) is crucial in addressing complex decision-making problems. Moreover, addressing missing data is a fundamental and pivotal concern in tackling real-world decision challenges, given the ubiquitous presence of information gaps that cannot be straightforwardly integrated into decision models. Besides, the intricacies of LSGDM amplify this challenge by introducing a wealth of DMs, thereby augmenting the complexity and diversity of decision-related information. This paper proposes an approach to supplement missing data by double-dimensions. This paper explores various facets of similarity relationships within the data to enhance data completeness. Additionally, this paper categorizes DMs into clusters based on their relevance and establishes a two-stage consensus-reaching process (CRP) that takes into account both group sizes and individual consensus contributions. These CRPs play a crucial role in enhancing the overall consistency and consensus within the decision group. Subsequently, this paper applies a robust decision-making method rooted in MULTIMOORA (Multi-Objective Optimization by Ratio Analysis plus the complete MULTIplicative form) to rank decision objects. Finally, this paper employs this proposed methodology in a practical case study that involves evaluating the operational status of a metaverse’s urban construction metro system. Following these considerations, a comprehensive stability analysis of relevant parameters is conducted to guarantee the robustness and reliability of the decision-making process.

Nudging Public Health Behaviors to Prevent COVID-19: A Systematic Review

ABSTRACT Many countries have implemented strict preventive measures and mandatory policies to curb virus transmission during the COVID-19 pandemic. Some have adopted softer approaches, such as nudge-based intervention, to influence public health behavior. This systematic review, conducted following Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) 2020 guidelines, aims to determine if the nudge-based intervention can effectively influence people’s preventive behavior during the early period of the COVID-19 pandemic. The review indicated an overall positive outcome, but results were mixed as nudge-based interventions substantially depended on the situational context. While the review found that the nudging technique that presents and conveys decision-related information was essential to nudging people, a secondary nudge would often applied to deliver the interventions. In addition, there was no indication of an ideal nudge technique that would be effective in most situations. Conversely, our findings indicate that the nudge would likely suffer from habituation after repeated intervention or backfire due to inappropriate use of nudges. Also, the ceiling effect would inhibit any nudge influences regardless of the technique(s) used. In sum, the results and the applicability of nudge-based interventions were mixed, highlighting the need for further research to advance the theory and practical developments.

A decision-making system based on case-based reasoning for predicting stroke rehabilitation demands in heterogeneous information environment

Recently, the increasing number of stroke survivors has created a greater demand for rehabilitation services. How to predict rehabilitation demands is important for policy makers in various countries. The demand prediction is essentially a multi-attribute decision-making problem. The diverse forms of information or data are often neglected or simplified in previous studies of decision-making problems. In this paper, heterogeneous multi-attributes including crisp numbers, interval numbers, and linguistic variables are defined to represent decision-relevant information concerning the patient's condition in cases when applied to real-life scenarios. Moreover, a deviation minimization optimization model combining CRITIC method and H2TLWMSM operator is proposed to assign the attribute weights with dual information. The WHMACBR system can be used to predict stroke rehabilitation demands for making the diagnosis and treatment decisions. The proposed decision-making system based on the CBR approach simulates the operations of human memory and reasoning, thus assisting physicians in addressing the ever-increasing demand for rehabilitation services of stroke survivors. Finally, the effectiveness and applicability of the case retrieval process of the presented CBR model are demonstrated through a stroke rehabilitation case study (top 3 cases ranked by similarity to the target case are C6, C2, and C3) and comparison analysis with relevant existing CBR models. This indicates that the proposed decision-making system, by considering heterogeneous multi-attributes and their weights, is capable of providing more comprehensive and rational decision-related information in the field of rehabilitation medicine.

Joint encoding of stimulus and decision in monkey primary visual cortex.

We investigated whether neurons in monkey primary visual cortex (V1) exhibit mixed selectivity for sensory input and behavioral choice. Parallel multisite spiking activity was recorded from area V1 of awake monkeys performing a delayed match-to-sample task. The monkeys had to make a forced choice decision of whether the test stimulus matched the preceding sample stimulus. The population responses evoked by the test stimulus contained information about both the identity of the stimulus and with some delay but before the onset of the motor response the forthcoming choice. The results of subspace identification analysis indicate that stimulus-specific and decision-related information coexists in separate subspaces of the high-dimensional population activity, and latency considerations suggest that the decision-related information is conveyed by top-down projections.

Consensus reaching process using personalized modification rules in large-scale group decision-making

Managing complex decision-making scenarios often hinges on the effectiveness of large-scale group decision-making (LSGDM). When confronted with a significant number of decision-makers (DMs) in LSGDM, each contributing unique backgrounds and perspectives, addressing the issues of reducing dimensionality and fostering consensus becomes a crucial aspect of the decision-making process. This paper addresses these challenges through several innovative approaches. First, we employ clustering methods to reduce the dimensionality of DMs. We introduce a novel fuzzy c-means clustering method that takes into account both the evaluation values and ranking of alternatives. This reduction in dimensionality serves to simplify the decision complexity and enhance the coherence of decision-related information among DMs placed within the same cluster. Once the clustering phase is complete, we propose a weight solution method for DMs within each group. This method combines the consensus level with the Spearman correlation coefficient of DMs, providing an effective means to determine the weights. Additionally, we introduce a weight solution method for each group based on the average consensus level and the number of DMs it contains. In the consensus reaching process (CRP), we implement a personalized modification rule. This rule takes into consideration the evolving consensus levels and the regret psychology exhibited by different DMs at different points in time. This dynamic approach significantly reduces both the cost and time required for consensus modifications. Finally, to validate the applicability of the proposed method, we apply it to a real-life case. Comprehensive qualitative and quantitative comparative analyses are conducted to evaluate the proposed method, along with a stability analysis of the parameters involved.

Weak evidence for neural correlates of task-switching in macaque V1.

A central goal of systems neuroscience is to understand how populations of sensory neurons encode and relay information to the rest of the brain. Three key quantities of interest are 1) how mean neural activity depends on the stimulus (sensitivity), 2) how neural activity (co)varies around the mean (noise correlations), and 3) how predictive these variations are of the subject's behavior (choice probability). Previous empirical work suggests that both choice probability and noise correlations are affected by task training, with decision-related information fed back to sensory areas and aligned to neural sensitivity on a task-by-task basis. We used Utah arrays to record activity from populations of primary visual cortex (V1) neurons from two macaque monkeys that were trained to switch between two coarse orientation-discrimination tasks. Surprisingly, we find no evidence for significant trial-by-trial changes in noise covariance between tasks, nor do we find a consistent relationship between neural sensitivity and choice probability, despite recording from well-tuned task-sensitive neurons, many of which were histologically confirmed to be in supragranular V1, and despite behavioral evidence that the monkeys switched their strategy between tasks. Thus our data at best provide weak support for the hypothesis that trial-by-trial task-switching induces changes to noise correlations and choice probabilities in V1. However, our data agree with a recent finding of a single "choice axis" across tasks. They also raise the intriguing possibility that choice-related signals in early sensory areas are less indicative of task learning per se and instead reflect perceptual learning that occurs in highly overtrained subjects.NEW & NOTEWORTHY Converging evidence suggests that decision processes affect sensory neural activity, and this has informed numerous theories of neural processing. We set out to replicate and extend previous results on decision-related information and noise correlations in V1 of macaque monkeys. However, in our data, we find little evidence for a number of expected effects. Our null results therefore call attention to differences in task training, stimulus design, recording, and analysis techniques between our and prior studies.

Application of ABB in environmental management accounting: Incorporating MFCA into the budget process

Traditional management accounting methods are difficult to provide the necessary information for environmental economic management decisions. In response to the increasingly urgent need for decision-related information, a new branch of accounting, environmental management accounting, has emerged and is receiving increasing attention. Material Flow Cost Accounting (MFCA) is a useful tool for managing complex resource and waste streams. However, MFCA is mainly used for ex-post accounting and reporting and no efforts are made to use it for forecasting. In this study, we introduce MFCA method into the budgeting process of manufacturing firms, and thus construct an MFCA-ABB (Activity-Based Budget) model. This model is applied to JLC Company which is a fragrant liquor manufacturer in China, in order to forecast and plan for its resource consumption, positive product output, and negative product generation. Based on the forecasts of involved material flows, inefficiencies in the company’s liquor production process are identified; scenario analysis is then conducted to determine the optimal process and the technology adopted. The proposed MFCA-ABB model turns a pure operating budget into an environmental-economic budget, thus achieving both environmental and economic benefits for the company. Besides, this study makes an attempt to apply ABB in environmental management accounting, which suggests the possibility of applying the conventional management accounting tools, after modified, to the environmental-economic management of manufacturing firms in the future.

ExAID: A multimodal explanation framework for computer-aided diagnosis of skin lesions

Background and objectives: One principal impediment in the successful deployment of Artificial Intelligence (AI) based Computer-Aided Diagnosis (CAD) systems in everyday clinical workflows is their lack of transparent decision-making. Although commonly used eXplainable AI (XAI) methods provide insights into these largely opaque algorithms, such explanations are usually convoluted and not readily comprehensible. The explanation of decisions regarding the malignancy of skin lesions from dermoscopic images demands particular clarity, as the underlying medical problem definition is ambiguous in itself. This work presents ExAID (Explainable AI for Dermatology), a novel XAI framework for biomedical image analysis that provides multi-modal concept-based explanations, consisting of easy-to-understand textual explanations and visual maps, to justify the predictions.Methods: Our framework relies on Concept Activation Vectors to map human-understandable concepts to those learned by an arbitrary Deep Learning (DL) based algorithm, and Concept Localisation Maps to highlight those concepts in the input space. This identification of relevant concepts is then used to construct fine-grained textual explanations supplemented by concept-wise location information to provide comprehensive and coherent multi-modal explanations. All decision-related information is presented in a diagnostic interface for use in clinical routines. Moreover, the framework includes an educational mode providing dataset-level explanation statistics as well as tools for data and model exploration to aid medical research and education processes.Results: Through rigorous quantitative and qualitative evaluation of our framework on a range of publicly available dermoscopic image datasets, we show the utility of multi-modal explanations for CAD-assisted scenarios even in case of wrong disease predictions. We demonstrate that concept detectors for the explanation of pre-trained networks reach accuracies of up to 81.46%, which is comparable to supervised networks trained end-to-end.Conclusions: We present a new end-to-end framework for the multi-modal explanation of DL-based biomedical image analysis in Melanoma classification and evaluate its utility on an array of datasets. Since perspicuous explanation is one of the cornerstones of any CAD system, we believe that ExAID will accelerate the transition from AI research to practice by providing dermatologists and researchers with an effective tool that they can both understand and trust. ExAID can also serve as the basis for similar applications in other biomedical fields.

Post-interval EEG activity is related to task-goals in temporal discrimination.

Studies investigating the neural mechanisms of time perception often measure brain activity while participants perform a temporal task. However, several of these studies are based exclusively on tasks in which time is relevant, making it hard to dissociate activity related to decisions about time from other task-related patterns. In the present study, human participants performed a temporal or color discrimination task of visual stimuli. Participants were informed which magnitude they would have to judge before or after presenting the two stimuli (S1 and S2) in different blocks. Our behavioral results showed, as expected, that performance was better when participants knew beforehand which magnitude they would judge. Electrophysiological data (EEG) was analysed using Linear Discriminant Contrasts (LDC) and a Representational Similarity Analysis (RSA) approach to investigate whether and when information about time and color was encoded. During the presentation of S1, we did not find consistent differences in EEG activity as a function of the task. On the other hand, during S2, we found that temporal and color information was encoded in a task-relevant manner. Taken together, our results suggest that task goals strongly modulate decision-related information in EEG activity.

Anatomically and functionally distinct thalamocortical inputs to primary and secondary mouse whisker somatosensory cortices

Subdivisions of mouse whisker somatosensory thalamus project to cortex in a region-specific and layer-specific manner. However, a clear anatomical dissection of these pathways and their functional properties during whisker sensation is lacking. Here, we use anterograde trans-synaptic viral vectors to identify three specific thalamic subpopulations based on their connectivity with brainstem. The principal trigeminal nucleus innervates ventral posterior medial thalamus, which conveys whisker-selective tactile information to layer 4 primary somatosensory cortex that is highly sensitive to self-initiated movements. The spinal trigeminal nucleus innervates a rostral part of the posterior medial (POm) thalamus, signaling whisker-selective sensory information, as well as decision-related information during a goal-directed behavior, to layer 4 secondary somatosensory cortex. A caudal part of the POm, which apparently does not receive brainstem input, innervates layer 1 and 5A, responding with little whisker selectivity, but showing decision-related modulation. Our results suggest the existence of complementary segregated information streams to somatosensory cortices.

Influences of Complexity on Decision Making in Young and Older Adults.

Leading theory hypothesizes that age deficits in decision making may rise as the complexity of decision-related information increases. This suggests that older adults would benefit relative to young adults from simplification of information used to inform decision making. Participants indicated political, nutritional and medical preferences and then chose between politicians, foods and medicines. The amount of information presented was systematically varied but age differences were largely similar for simple and complex trials. Paradoxically, the data showed that decisions based on simpler information could be less aligned with participant’s preferences than decisions based on more complex information. Further analyses suggested that participants may have been responding purely on the basis of their most valued preferences and that when information about those preferences was not presented, decision making became poorer. Contrary to our expectations, simplification of information by exclusion may therefore hinder decision making and may not particularly help older adults.

Age differences in the decision information search: The roles of task complexity and task relevance

A high task complexity may reduce or prompt people’s decision to search for information, and a high task relevance might stimulate a systematic search, but whether these factors interactive and whether their effects vary by age remain unknown. Therefore, we used a process-tracing program to investigate information searches by 58 younger and 60 older adults related to high- and low-relevance decisions of varying complexity. Older adults searched for decision-related information more thoroughly than younger adults, suggesting that increasing age may not be linked with information-minimizing searches. When the task complexity increased, both age groups showed greater search engagement and more systematic searches, suggesting that people evaluate task demands and allocate more effort to address increasing task complexity. People preferred systematic searches for high-relevance decisions, whereas older, but not younger, adults maintained this pattern for low-relevance decisions. Task complexity and task relevance function independently in the decision-related information search.

Predicting Perceptual Decisions Using Visual Cortical Population Responses and Choice History.

Our understanding of the neural basis of perceptual decision making has been built in part on relating co-fluctuations of single neuron responses to perceptual decisions on a trial-by-trial basis. The strength of this relationship is often compared across neurons or brain areas, recorded in different sessions, animals, or variants of a task. We sought to extend our understanding of perceptual decision making in three ways. First, we measured neuronal activity simultaneously in early [primary visual cortex (V1)] and midlevel (V4) visual cortex while macaque monkeys performed a fine orientation discrimination perceptual task. This allowed a direct comparison of choice signals in these two areas, including their dynamics. Second, we asked how our ability to predict animals' decisions would be improved by considering small simultaneously-recorded neuronal populations rather than individual units. Finally, we asked whether predictions would be improved by taking into account the animals' choice and reward histories, which can strongly influence decision making. We found that responses of individual V4 neurons were weakly predictive of decisions, but only in a brief epoch between stimulus offset and the indication of choice. In V1, few neurons showed significant decision-related activity. Analysis of neuronal population responses revealed robust choice-related information in V4 and substantially weaker signals in V1. Including choice- and reward-history information improved performance further, particularly when the recorded populations contained little decision-related information. Our work shows the power of using neuronal populations and decision history when relating neuronal responses to the perceptual decisions they are thought to underlie.SIGNIFICANCE STATEMENT Decades of research has provided a rich description of how visual information is represented in the visual cortex. Yet how cortical responses relate to visual perception remains poorly understood. Here we relate fluctuations in small neuronal population responses, recorded simultaneously in primary visual cortex (V1) and area V4 of monkeys, to perceptual reports in an orientation discrimination task. Choice-related signals were robust in V4, particularly late in the behavioral trial, but not in V1. Models that include both neuronal responses and choice-history information were able to predict a substantial portion of decisions. Our work shows the power of integrating information across neurons and including decision history in relating neuronal responses to perceptual decisions.

Post-decision processing in primate prefrontal cortex influences subsequent choices on an auditory decision-making task.

Perceptual decisions do not occur in isolation but instead reflect ongoing evaluation and adjustment processes that can affect future decisions. However, the neuronal substrates of these across-decision processes are not well understood, particularly for auditory decisions. We measured and manipulated the activity of choice-selective neurons in the ventrolateral prefrontal cortex (vlPFC) while monkeys made decisions about the frequency content of noisy auditory stimuli. As the decision was being formed, vlPFC activity was not modulated strongly by the task. However, after decision commitment, vlPFC population activity encoded the sensory evidence, choice, and outcome of the current trial and predicted subject-specific choice biases on the subsequent trial. Consistent with these patterns of neuronal activity, electrical microstimulation in vlPFC tended to affect the subsequent, but not current, decision. Thus, distributed post-commitment representations of graded decision-related information in prefrontal cortex can play a causal role in evaluating past decisions and biasing subsequent ones.

Directed forgetting in organisations: the positive effects of decision support systems on mental resources and well-being

Decision makers in organisations are often overtaxed by huge amounts of information in daily business processes. As a potential support strategy, this study examined ‘directed forgetting’ (Bjork, 1970) in a simulated sales planning scenario. We assumed that the availability of a computer-based decision support system (DSS) triggers the forgetting of decision-related background information. Such directed forgetting should not only release memory capacities for additional tasks but also enhance decision quality and decrease strain of decision makers. Assumptions were tested in an experimental study with N = 90 participants. Consistent with our assumptions, results revealed a higher recall of decision-unrelated information, higher decision quality and higher well-being when participants could use a DSS as compared to two Control conditions without a DSS. Moreover, directed forgetting effects were qualified by participants’ trust in the DSS. This study provides the first evidence for directed forgetting effects cued by information systems in a business context.Practitioner summary: Information overload is an increasing challenge in modern business organisations. Extending findings from basic memory research, this study shows that availability of a computer-based decision support system triggers forgetting of decision-related background information, which in turn increases users’ mental resources for additional tasks, decision quality, and well-being.

Collecting Survey and Smartphone Sensor Data With an App: Opportunities and Challenges Around Privacy and Informed Consent

The new European General Data Protection Regulation (GDPR) imposes enhanced requirements on digital data collection. This article reports from a 2018 German nationwide population-based probability app study in which participants were asked through a GDPR compliant consent process to share a series of digital trace data, including geolocation, accelerometer data, phone and text messaging logs, app usage, and access to their address books. With about 4,300 invitees and about 650 participants, we demonstrate (1) people were just as willing to share such extensive digital trace data as they were in studies with far more limited requests; (2) despite being provided more decision-related information, participants hardly differentiated between the different data requests made; and (3) once participants gave consent, they did not tend to revoke it. We also show (4) evidence for a widely-held belief that explanations regarding data collection and data usage are often not read carefully, at least not within the app itself, indicating the need for research and user experience improvement to adequately inform and protect participants. We close with suggestions to the field for creating a seal of approval from professional organizations to help the research community promote the safe use of data.

Distributed neural representation of saliency controlled value and category during anticipation of rewards and punishments

An extensive literature from cognitive neuroscience examines the neural representation of value, but interpretations of these existing results are often complicated by the potential confound of saliency. At the same time, recent attempts to dissociate neural signals of value and saliency have not addressed their relationship with category information. Using a multi-category valuation task that incorporates rewards and punishments of different nature, we identify distributed neural representation of value, saliency, and category during outcome anticipation. Moreover, we reveal category encoding in multi-voxel blood-oxygen-level-dependent activity patterns of the vmPFC and the striatum that coexist with value signals. These results help clarify ambiguities regarding value and saliency encoding in the human brain and their category independence, lending strong support to the neural “common currency” hypothesis. Our results also point to potential novel mechanisms of integrating multiple aspects of decision-related information.

Data-Driven Methods to Create Knowledge Maps for Decision Making in Academic Contexts

Knowledge is the primary asset of today’s organisations; thus, knowledge management has been focused on discovery, representation, modification, transformation, and creation of knowledge within an enterprise. A knowledge map is a knowledge management tool that makes organisational processes more visible, feasible, and practicable. It is a graphical representation of decision-related information. What happens, how various events can be managed, and why they happened: all can be demonstrated very precisely by a well-designed knowledge map. There are diverse knowledge-related roles; for example, each university dean’s office — as an instance of a knowledge-based organisation — usually relies upon their institutional memory to make daily decisions. However, utilising a knowledge map greatly facilitates any individual’s or group’s decision-making process, by proposing or establishing key required information. In this study, two important managerial roles — Associate Deans of Research and Education — were selected; then we reviewed their key managerial decisions and proposed three different techniques for supporting their decisions. The chief superiority of the approach offered here was in the creation of role-based knowledge maps, including an expertness map and a collaboration map for the Associate Dean of Research, which were formed using clustering, taxonomy formation, and information retrieval methods. A third map was created for the Associate Dean of Education, including a Bayesian reasoning map based on an Improved PC (IPC) algorithm, which learned the structure and the parameters of a Bayesian network to describe decision-making in the domain of education. To evaluate the proposed approaches, structural and functional evaluation measures and standard datasets (in the available cases) were chosen. The results found that the approaches were comparable to the selected benchmarks within the real data; even after considering the challenging nature of the real data, which included problems such as incomplete and unclean data extracted from the University of Tehran’s education and research management information systems.