Categorization Decisions Research Articles

Primate superior colliculus is causally engaged in abstract higher-order cognition.

The superior colliculus is an evolutionarily conserved midbrain region that is thought to mediate spatial orienting, including saccadic eye movements and covert spatial attention. Here, we reveal a role for the superior colliculus in higher-order cognition, independent of its role in spatial orienting. We trained rhesus macaques to perform an abstract visual categorization task that involved neither instructed eye movements nor differences in covert attention. We compared neural activity in the superior colliculus and the posterior parietal cortex, a region previously shown to causally contribute to abstract category decisions. The superior colliculus exhibits robust encoding of learned visual categories, which is stronger than in the posterior parietal cortex and arises at a similar latency in the two areas. Moreover, inactivation of the superior colliculus markedly impaired animals' category decisions. These results demonstrate that the primate superior colliculus mediates abstract, higher-order cognitive processes that have traditionally been attributed to the neocortex.

Retail category management under shelf-space dependent demand: The effectiveness of category captainship

Category captainship is a retail category management practice where the retailer delegates certain category decisions to a vendor, known as the category captain, to leverage the vendor’s market expertise and reduce the retailer’s workload in managing the category. We examine the effectiveness of category captainship compared to traditional retailer category management, focusing on scenarios where shelf space capacity decisions are delegated to the category captain, and demand is dependent on shelf space capacity. Our study considers two competing manufacturers, each selling a substitutable product through a common retailer. We find that when the retailer’s profit-sharing ratio is sufficiently but not excessively high, category captainship can enhance profits for both the retailer and the captain manufacturer. These profit improvements are more pronounced at intermediate values of shelf space elasticity of demand. Although the non-captain manufacturer may suffer from competitive exclusion under certain conditions, “win-win-win” outcomes in profits and sales occur when the products are highly differentiated by price or shelf-space elasticity. The retailer benefits from increasing demand elasticity of shelf space and prefers to designate the manufacturer with the higher elasticity as the category captain. When the profit-sharing ratio is determined through a retailer-led bargaining framework, the retailer benefits most by negotiating sequentially with the manufacturers, inducing the manufacturer with lower bargaining power to take on the category captain role. Our findings provide valuable insights into developing category management strategies under shelf-space elastic demand.

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.

Choice-dependent delta-band neural trajectory during semantic category decision making in the human brain

Recent human brain imaging studies have identified widely distributed cortical areas that represent information about the meaning of language. Yet, the dynamic nature of widespread neural activity as a correlate of the semantic information processing remains poorly explored. Our state space analysis of electroencephalograms (EEGs) recorded during semantic match-to-category task show that depending on the semantic category and decision path chosen by participants, whole-brain delta-band dynamics follow distinct trajectories that are correlated with participants' response time on a trial-by-trial basis. Especially, the proximity of the neural trajectory to category decision-specific region in the state space was predictive of participants' decision-making reaction times. We also found that posterolateral regions primarily encoded word categories while postero-central regions encoded category decisions. Our results demonstrate the role of neural dynamics embedded in the evolving multivariate delta-band activity patterns in processing the semantic relatedness of words and the semantic category-based decision-making.

A Computational Decision-Tree Approach to Inform Post-Conviction Intake Decisions

How might data analytic tools support intake decisions? When faced with a request for post-conviction assistance, innocence organizations’ intake staff must determine (1) whether the applicant can be shown to be factually innocent, and (2) whether the organization has the resources to help. These difficult categorization decisions are often made with incomplete information (Weintraub, 2022). We explore data from the National Registry of Exonerations (NRE; 4/26/2023, N = 3,284 exonerations) to inform such decisions, using patterns of features associated with successful prior cases. We first reproduce Berube et al. (2023)’s latent class analysis, identifying four underlying categories across cases. We then apply a second technique to increase transparency, decision tree analysis (WEKA, Frank et al., 2013). Decision trees can decompose complex patterns of data into ordered flows of variables, with the potential to guide intermediate steps that could be tailored to the particular organization’s limitations, areas of expertise, and resources.

The autocorrelated Bayesian sampler: A rational process for probability judgments, estimates, confidence intervals, choices, confidence judgments, and response times.

Normative models of decision-making that optimally transform noisy (sensory) information into categorical decisions qualitatively mismatch human behavior. Indeed, leading computational models have only achieved high empirical corroboration by adding task-specific assumptions that deviate from normative principles. In response, we offer a Bayesian approach that implicitly produces a posterior distribution of possible answers (hypotheses) in response to sensory information. But we assume that the brain has no direct access to this posterior, but can only sample hypotheses according to their posterior probabilities. Accordingly, we argue that the primary problem of normative concern in decision-making is integrating stochastic hypotheses, rather than stochastic sensory information, to make categorical decisions. This implies that human response variability arises mainly from posterior sampling rather than sensory noise. Because human hypothesis generation is serially correlated, hypothesis samples will be autocorrelated. Guided by this new problem formulation, we develop a new process, the Autocorrelated Bayesian Sampler (ABS), which grounds autocorrelated hypothesis generation in a sophisticated sampling algorithm. The ABS provides a single mechanism that qualitatively explains many empirical effects of probability judgments, estimates, confidence intervals, choice, confidence judgments, response times, and their relationships. Our analysis demonstrates the unifying power of a perspective shift in the exploration of normative models. It also exemplifies the proposal that the "Bayesian brain" operates using samples not probabilities, and that variability in human behavior may primarily reflect computational rather than sensory noise. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Cognitive Economy and Product Categorization

In mediated markets, the categorization of products by mediators is critical to efficient interaction between producers and consumers. As organizational research tends to focus on the consequences of categorization rather than its antecedents, however, we know relatively little about why mediators assign one category label or another to a product. In this study, we argue that two informational properties of labels, specificity and distinctiveness, determine the outcomes of mediators’ categorization decisions. Our analysis of product categorization decisions made by members of an online music community, 2000–2020, supports this argument. We find that a label’s odds of being assigned to a product increase (a) if this label encodes information that is neither too similar nor too different from that which is encoded by a superordinate label, that is, it has moderate specificity; and (b) if it encodes information that differs as much as possible from that which is encoded by horizontally related labels, that is, it has maximal distinctiveness. These findings persist after controlling for other possible determinants of mediators’ categorization decisions, including producers’ claims to labels, products’ typicality, and mediators’ expertise. Funding: M. Piazzai received funding from the Community of Madrid [EPUC3M12, VPRICIT] and the Spanish Ministry of Science and Innovation [RYC2021-032325-I]. M. Liu received funding from Durham University Business School. Supplemental Material: The e-companion is available at https://doi.org/10.1287/orsc.2021.15751 .

Part-Object Progressive Refinement Network for Zero-Shot Learning.

Zero-shot learning (ZSL) recognizes unseen images by sharing semantic knowledge transferred from seen images, encouraging the investigation of associations between semantic and visual information. Prior works have been devoted to the alignment of global visual features with semantic information, i.e., attribute vectors, or further mining the local part regions related to each attribute and then simply concatenating them for category decisions. Although effective, these works ignore intrinsic interactions between local parts and the whole object, which enables a more discriminative and representative knowledge transfer for ZSL. In this paper, we propose a Part-Object Progressive Refinement Network (POPRNet), where discriminative and transferable semantics are progressively refined by the cooperation between parts and the whole object. Specifically, POPRNet incorporates discriminative part semantics and object-centric semantics guided by semantic intensity to improve cross-domain transferability. To achieve part-object learning, a semantic-augment transformer (SaT) is proposed to model the part-object relation at the part-level via an encoder and at the object-level via a decoder, generating a comprehensive semantic representation to boost discriminability and transferability. By introducing the prototype updating module embedded with the prototype selection layers, the discriminative ability of the updated category prototype is enhanced to further improve the recognition performance of ZSL. Extensive experiments are conducted to demonstrate the superiority and competitiveness of our proposed POPRNet method on three public benchmark datasets. The code is available at https://github.com/ManLiuCoder/POPRNet.

Quantifying Levels of Influence and Causal Responsibility in Dynamic Decision Making Events

Intelligent systems support human operators’ decision-making processes, many of which are dynamic and involve temporal changes in the decision-related parameters. As we increasingly depend on automation, it becomes imperative to understand and quantify its influence on the operator’s decisions and to evaluate its implications for the human’s causal responsibility for outcomes. Past studies proposed a model for human responsibility in static decision-making processes involving intelligent systems. We present a model for dynamic, non-stationary decision-making events based on the concept of causation strength. We apply it to a test case of a dynamic binary categorization decision. The results show that for automation to influence humans significantly, it must have high detection sensitivity. However, this condition is insufficient since it is unlikely that automation, irrespective of its sensitivity, will sway humans with high detection sensitivity away from their original position. Specific combinations of automation and human detection sensitivities are required for automation to have a major influence. Moreover, the automation influence and the human causal responsibility that can be derived from it are sensitive to possible changes in the human’s detection capabilities due to fatigue or other factors, creating a “Responsibility Cliff.” This should be considered during system design and when policies and regulations are defined. This model constitutes a basis for further analyses of complex events in which human and automation sensitivity levels change over time and for evaluating human involvement in such events.

Coherent category training enhances generalization in prototype-based categories.

A major question for the study of learning and memory is how to tailor learning experiences to promote knowledge that generalizes to new situations. In two experiments, we used category learning as a representative domain to test two factors thought to influence the acquisition of conceptual knowledge: the number of training examples (set size) and the similarity of training examples to the category average (set coherence). Across participants, size and coherence of category training sets were varied in a fully crossed design. After training, participants demonstrated the breadth of their category knowledge by categorizing novel examples varying in their distance from the category center. Results showed better generalization following more coherent training sets, even when categorizing items furthest from the category center. Training set size had limited effects on performance. We also tested the types of representations underlying categorization decisions by fitting formal prototype and exemplar models. Prototype models posit abstract category representations based on the category's central tendency, whereas exemplar models posit that categories are represented by individual category members. In Experiment 1, low coherence training led to fewer participants relying on prototype representations, except when training length was extended. In Experiment 2, low coherence training led to chance performance and no clear representational strategy for nearly half of the participants. The results indicate that highlighting commonalities among exemplars during training facilitates learning and generalization and may also affect the types of concept representations that individuals form. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

Outlier detection for partially labeled categorical data based on conditional information entropy

Labeling a large amount of data is exceptionally costly and practically infeasible, and thus available data may have missing labels. In this article, we investigate outlier detection for partially labeled categorical data based on conditional information entropy. Firstly, the equivalence class in a partially labeled categorical decision information system (p-CDIS) is introduced, so that the missing labels can be predicted by use of conditional probability. Then, conditional information entropy in a p-CDIS is calculated, which provides a more comprehensive measure of uncertainty. Additionally, the relative information entropy and relative cardinality in a p-CDIS are proposed. Next, the degree of outlierness and the weight function are presented to find outlier factors. Finally, an outlier detection method in a p-CDIS based on conditional information entropy is proposed, and a corresponding conditional information entropy algorithm (CEOF) is designed. To evaluate the stability of the CEOF algorithm, experiments are performed on ten UCI Machine Learning Repository datasets. Compared with five other algorithms, the proposed method is shown to have good effectiveness and adaptability for categorical data.

Impact of aversive affect on neural mechanisms of categorization decisions.

Many theories contend that evidence accumulation is a critical component of decision-making. Cognitive accumulation models typically interpret two main parameters: a drift rate and decision threshold. The former is the rate of accumulation, based on the quality of evidence, and the latter is the amount of evidence required for a decision. Some studies have found neural signals that mimic evidence accumulators and can be described by the two parameters. However, few studies have related these neural parameters to experimental manipulations of sensory data or memory representations. Here, we investigated the influence of affective salience on neural accumulation parameters. High affective salience has been repeatedly shown to influence decision-making, yet its effect on neural evidence accumulation has been unexamined. The current study used a two-choice object categorization task of body images (feet or hands). Half the images in each category were high in affective salience because they contained highly aversive features (gore and mutilation). To study such quick categorization decisions with a relatively slow technique like functional magnetic resonance imaging, we used a gradual reveal paradigm to lengthen cognitive processing time through the gradual "unmasking" of stimuli. Because the aversive features were task-irrelevant, high affective salience produced a distractor effect, slowing decision time. In visual accumulation regions of interest, high affective salience produced a longer time to peak activation. Unexpectedly, the later peak appeared to be the product of changes to both drift rate and decision threshold. The drift rate for high affective salience was shallower, and the decision threshold was greater. To our knowledge, this is the first demonstration of an experimental manipulation of sensory data or memory representations that changed the neural decision threshold. These findings advance our knowledge of the neural mechanisms underlying affective responses in general and the influence of high affective salience on object representations and categorization decisions.

Metric error monitoring as a component of metacognitive processing.

Metacognitive processing constitutes one of the contemporary target domains in consciousness research. Error monitoring (the ability to correctly report one's own errors without feedback) is considered one of the functional outcomes of metacognitive processing. Error monitoring is traditionally investigated as part of categorical decisions where choice accuracy is a binary construct (choice is either correct or incorrect). However, recent studies revealed that this ability is characterized by metric features (i.e., direction and magnitude) in temporal, spatial, and numerical domains. Here, we discuss methodological approaches to investigating metric error monitoring in both humans and non-human animals and review their findings. The potential neural substrates of metric error monitoring measures are also discussed. This new scope of metacognitive processing can help improve our current understanding of conscious processing from a new perspective. Thus, by summarizing and discussing the perspectives, findings, and common applications in the metric error monitoring literature, this paper aims to provide a guideline for future research.

A multidimensional examination of children's endorsement of gender stereotypes

AbstractThe present research applied a multidimensional framework to the study of gender stereotypes by investigating whether elementary school children display different levels of endorsement when considering distinct gender stereotype constructs (ability, category, and interest) and feminine versus masculine stereotypes. Study 1 (N = 403) compared children's ability and category beliefs using a set of gender‐neutral skill items. Study 2 (N = 539) extended this research by examining whether children showed different patterns of ability and category decisions for feminine versus masculine occupational items. Study 3 (N = 974) furthered our understanding of the construct dimension by comparing children's interest and ability decisions within the STEM domain. Findings revealed that older elementary school children endorsed ability stereotypes more strongly than category stereotypes and, across all age groups, children endorsed interest stereotypes more strongly than ability stereotypes. Findings also revealed age differences in how children think about masculine versus feminine stereotypes. For masculine stereotypes, younger children showed stronger endorsement than older children; however, for feminine stereotypes, the reverse pattern was found such that older children showed more stereotyped thinking than younger children. The present study illustrates the benefits of employing a multidimensional framework to gain a more nuanced understanding of how children apply their increasing knowledge of gender stereotypes.

Should the fox guard the henhouse? Category captainship arrangement as a strategic information transmission mechanism

Retailers may collaborate with key suppliers to manage some specified categories. This collaboration is often formalized as a “captainship arrangement” between the retailer and her leading supplier in the category. The designated “category captain” assists the retailer with category management decisions. We show that captainship arrangements have the potential to generate a valuable information effect for the retailer: nonprice decisions by the category captain can reveal information that allows the retailer to improve profit through better retail pricing decisions. We also show that captainship arrangements have the greatest potential to make the retailer better off in categories where the retailer is highly uncertain about the impact of category resources on brands' demand. Moreover, the more substitutable the brands in the category, the more valuable a captainship arrangement is to the retailer. The information effect of category captainship is robust to a variety of arrangements. It is present under delegation arrangements (where the retailer delegates the task of category resources deployment to the captain), as well as under advisory arrangements (where the retailer retains control over category decisions, relying on the captain for advice). Interestingly, we find that delegation has a “better” potential to transmit information than advisory. Furthermore, in case of an advisory arrangement, the retailer is better off keeping the captain's advice confidential rather than sharing it with the other suppliers.

Puzzle—Integer Linear Programming: Spreadsheet Solver Excellence Without Excel

Because of the limitations on problem size with the built-in optimization solver in Microsoft Excel, other software applications are required to illustrate the power of integer linear programming in spreadsheets. The built-in linear optimization solver in LibreOffice Calc allows much larger problems. In this paper, we build a spreadsheet in LibreOffice Calc to solve sudoku problems and a simple generalization that further illustrates the power of integer linear programming in spreadsheets. The generalization we consider, n-doku, also introduces an interesting puzzle that includes, as a special case, a union of multiple sudoku puzzles. Besides further demonstrating the power of integer linear programming in spreadsheets, we use the n-doku solutions to develop optimization intuition and to, more generally, foster critical thinking. We use this spreadsheet in the classroom to transition from continuous to categorical decisions, emphasizing the idea of one-hot encoding. We also illustrate multiple optima and add preferences to sudoku puzzles as a way introduce multiobjective optimization. Supplemental Material: The online supplement is available at https://doi.org/10.1287/ited.2022.0068 .

Attentional focusing and filtering in multisensory categorization.

Selective attention refers to the ability to focus on goal-relevant information while filtering out irrelevant information. In a multisensory context, how do people selectively attend to multiple inputs when making categorical decisions? Here, we examined the role of selective attention in cross-modal categorization in two experiments. In a speed categorization task, participants were asked to attend to visual or auditory targets and categorize them while ignoring other irrelevant stimuli. A response-time extended multinomial processing tree (RT-MPT) model was implemented to estimate the contribution of attentional focusing on task-relevant information and attentional filtering of distractors. The results indicated that the role of selective attention was modality-specific, with differences found in attentional focusing and filtering between visual and auditory modalities. Visual information could be focused on or filtered out more effectively, whereas auditory information was more difficult to filter out, causing greater interference with task-relevant performance. The findings suggest that selective attention plays a critical and differential role across modalities, which provides a novel and promising approach to understanding multisensory processing and attentional focusing and filtering mechanisms of categorical decision-making.

Instance theory predicts categorization decisions in the absence of categorical structure: A computational analysis of artificial grammar learning without a grammar.

Theories of categorization have historically focused on the stimulus characteristics to which people are sensitive. Artificial grammar learning (AGL) provides a clear example of this phenomenon, with theorists debating between knowledge of rules, fragments, whole strings, and so on as the basis of categorization decisions (i.e., stimulus-driven explanations). We argue that this focus loses sight of the more important question of how participants make categorization decisions on a mechanistic level (i.e., process-driven explanations). To address the problem, we derived predictions from an instance-based model of human memory in a pseudo-AGL task in which all study and test strings were generated randomly, a task that stimulus-driven explanations of AGL would have difficulty accommodating. We conducted a standard AGL experiment with human participants using the same strings. The model's predictions corresponded to participants' decisions well, even in the absence of any experimenter-generated structure and regardless of whether test stimuli contained any incidental structure. We argue that theories of categorization ought to continue shifting towards the goal of modeling categorization at the level of cognitive processes rather than primarily attempting to identify the stimulus characteristics to which participants are drawn.

Multisensory perception constrains the formation of object categories: a review of evidence from sensory-driven and predictive processes on categorical decisions.

Although object categorization is a fundamental cognitive ability, it is also a complex process going beyond the perception and organization of sensory stimulation. Here we review existing evidence about how the human brain acquires and organizes multisensory inputs into object representations that may lead to conceptual knowledge in memory. We first focus on evidence for two processes on object perception, multisensory integration of redundant information (e.g. seeing and feeling a shape) and crossmodal, statistical learning of complementary information (e.g. the 'moo' sound of a cow and its visual shape). For both processes, the importance attributed to each sensory input in constructing a multisensory representation of an object depends on the working range of the specific sensory modality, the relative reliability or distinctiveness of the encoded information and top-down predictions. Moreover, apart from sensory-driven influences on perception, the acquisition of featural information across modalities can affect semantic memory and, in turn, influence category decisions. In sum, we argue that both multisensory processes independently constrain the formation of object categories across the lifespan, possibly through early and late integration mechanisms, respectively, to allow us to efficiently achieve the everyday, but remarkable, ability of recognizing objects. This article is part of the theme issue 'Decision and control processes in multisensory perception'.

Phenomenological Fragments of Trinitarian Discourse: Being, Having, Relating

Among the important tasks of Trinitarian theology today is the need to rethink its basic conceptual coordinates. This contribution is a proposal for a phenomenological and existential approach to Trinitarian theology. The starting point is the analysis of three essential existential operators, by means of which the depth of the filial experience of Jesus is expressed. These operators are: being, having, and entering into relations. Their analysis in light of the data of the Gospel narrative allows us to create an interesting conceptual framework for a new articulation of the Trinitarian discourse. The article builds on the conviction that the concrete shapes and modalities of Jesus’ life are essential moments of the revelation of the Trinity. Before it is simultaneously closed and opened in concrete historical forms of discourse and in concrete metaphysical concepts, it is accomplished in the categorical decisions, actions, and words of Jesus, in which his filial consciousness is revealed. The ambition of the text is to reintroduce metaphysics into theology, however, from a different perspective than was conducted, for example, by classical scholasticism. It is about the existential recovery of metaphysical potential in theology. Revelation takes place in history and in the concrete of life. The metaphysics that theology needs must realize this and, above all, be up to the task of pointing to the living, historical center of Revelation and all theology. The article argues that such an existential deepening of metaphysics for Trinitarian theology can be conducted through collaboration with phenomenology. In such a perspective, the fragments of Jesus’ life, especially his way of being, having, and entering into relations, are ways in which the Trinity reveals itself in history. In this way, Trinitarian theology ceases to be a mere intellectual puzzle, becoming an existential paradigm, and the fragments of Revelation reveal an impressive structure in which speculation and life become integral paths toward the Mystery. On the formal side, the text argues for the integration and use of both metaphysics and phenomenology in Trinitarian theology to enhance its existential impact. This in turn implies a rethinking of how metaphysics, phenomenology, and theology itself are usually understood as well.