Categorization Decisions Research Articles

Complementary Brain Signals for Categorical Decisions.

Apples come in various shapes, colors, and sizes, but humans can nonetheless easily distinguish them from other fruit (e.g., peaches) or other round objects (e.g., tennis balls). This capacity to assign complex and variable stimuli into discrete and meaningful categories is an essential cognitive

Pinter's World: Relationships, Obsessions, and Artistic Endeavours

Pinter's World: Relationships, Obsessions, and Artistic Endeavours

Revealing the information contents of memory within the stimulus information representation framework.

The information contents of memory are the cornerstone of the most influential models in cognition. To illustrate, consider that in predictive coding, a prediction implies that specific information is propagated down from memory through the visual hierarchy. Likewise, recognizing the input implies that sequentially accrued sensory evidence is successfully matched with memorized information (categorical knowledge). Although the existing models of prediction, memory, sensory representation and categorical decision are all implicitly cast within an information processing framework, it remains a challenge to precisely specify what this information is, and therefore where, when and how the architecture of the brain dynamically processes it to produce behaviour. Here, we review a framework that addresses these challenges for the studies of perception and categorization–stimulus information representation (SIR). We illustrate how SIR can reverse engineer the information contents of memory from behavioural and brain measures in the context of specific cognitive tasks that involve memory. We discuss two specific lessons from this approach that generally apply to memory studies: the importance of task, to constrain what the brain does, and of stimulus variations, to identify the specific information contents that are memorized, predicted, recalled and replayed.This article is part of the Theo Murphy meeting issue ‘Memory reactivation: replaying events past, present and future’.

Is this my group or not? The role of ensemble coding of emotional expressions in group categorization.

When exposed to others' emotional responses, people often make rapid decisions as to whether these others are members of their group or not. These group categorization decisions have been shown to be extremely important to understanding group behavior. Yet, despite their prevalence and importance, we know very little about the attributes that shape these categorization decisions. To address this issue, we took inspiration from ensemble coding research and developed a task designed to reveal the influence of the mean and variance of group members' emotions on participants' group categorization. In Study 1, we verified that group categorization decreases when the group's mean emotion is different from the participant's own emotional response. In Study 2, we established that people identify a group's mean emotion more accurately when its variance is low rather than high. In Studies 3 and 4, we showed that participants were more likely to self-categorize as members of groups with low emotional variance, even if their own emotions fell outside of the range of group emotions they saw, and that this preference is seen for judgments of both positive and negative group emotions. In Study 5, we showed that this unique preference for low group emotional variance is special to group categorization and does not appear in a more basic face categorization task. Our studies reveal unexplored and important tendencies in group categorization based on group emotions. (PsycINFO Database Record (c) 2020 APA, all rights reserved).

What Makes a Panther a Panther?

Species categorizations can involve both scientific input and conservation questions about what should be preserved and how. We present a case study exploring the social construction of species categories using a real-life example of a cougar subspecies (Puma concolor stanleyana) purposefully introduced into Florida to prevent the functional extinction of a related subspecies of panther (P. c. coryi). Participants in an online sample (n = 500) were asked to make categorization decisions and then reflect on those decisions in an open format. Analysis of coded responses suggest people may experience “species” as both a social and biological construct, and that the question of what species people think an animal belongs to cannot be answered in isolation from questions about how that animal fits into larger social and biological systems.

Decoding of single-trial EEG reveals unique states of functional brain connectivity that drive rapid speech categorization decisions

Objective. Categorical perception (CP) is an inherent property of speech perception. The response time (RT) of listeners’ perceptual speech identification is highly sensitive to individual differences. While the neural correlates of CP have been well studied in terms of the regional contributions of the brain to behavior, functional connectivity patterns that signify individual differences in listeners’ speed (RT) for speech categorization is less clear. In this study, we introduce a novel approach to address these questions. Approach. We applied several computational approaches to the EEG, including graph mining, machine learning (i.e., support vector machine), and stability selection to investigate the unique brain states (functional neural connectivity) that predict the speed of listeners’ behavioral decisions. Main results. We infer that (i) the listeners’ perceptual speed is directly related to dynamic variations in their brain connectomics, (ii) global network assortativity and efficiency distinguished fast, medium, and slow RTs, (iii) the functional network underlying speeded decisions increases in negative assortativity (i.e., became disassortative) for slower RTs, (iv) slower categorical speech decisions cause excessive use of neural resources and more aberrant information flow within the CP circuitry, (v) slower responders tended to utilize functional brain networks excessively (or inappropriately) whereas fast responders (with lower global efficiency) utilized the same neural pathways but with more restricted organization. Significance. Findings show that neural classifiers (SVM) coupled with stability selection correctly classify behavioral RTs from functional connectivity alone with over 92% accuracy (AUC = 0.9). Our results corroborate previous studies by supporting the engagement of similar temporal (STG), parietal, motor, and prefrontal regions in CP using an entirely data-driven approach.

How readers process syntactic input depends on their goals

During reading, the recognition of words is influenced by the syntactic compatibility of surrounding words: a sentence-superiority effect. However, when the goal is to make syntactic categorization decisions about single target words, these decisions are influenced by the syntactic congruency rather than compatibility of surrounding words. Although both these premises imply that readers can extract syntactic information from multiple words in parallel, they also suggest that how the brain organizes syntactic input—and consequently how surrounding stimuli affect word recognition—depends on the reader's top-down goals. The present study provides a direct test of this conception. Participants were offered nouns and verbs amidst a grammatical context (‘this horse fell’) and ungrammatical context (‘fell horse this’). Using a conditional task setup, we manipulated the amount of emphasis put on respectively sentences and single words. In two blocks readers were instructed to make sentence grammaticality judgments only if the middle word was respectively noun or verb; in two other blocks readers were instructed to syntactically categorize the middle word only if the sentence was respectively correct or incorrect. We established an interaction effect whereby the impact of grammatical correctness on syntactic categorization decisions was greater than the effect of grammatical correctness per se. This first sentence-superiority effect in the categorization of single words, combined with the absence of this effect in prior flanker studies, leads us to surmise that word-to-word syntactic constraints only operate if the reader is engaged in sentence processing.

The Limits of Marketplace Fee Discrimination

Platforms often use fee discrimination within their marketplace (e.g., Amazon, eBay, and Uber specify a variety of merchant fees). To better understand the impact of marketplace fee discrimination, we develop a model that allows us to determine equilibrium fee and category decisions that depend on the extent of fee discrimination available to the platform and we highlight how our fee discrimination strategies can be derived in practice using data from airbnb.com. In addition, we find that greater fee discrimination allows the platform to serve more markets in its marketplace but also increases fees in high surplus markets. However, if the platform enters into retail, then the platform reduces its fees and generates greater retail competition. These effects mitigate distortions from fee discrimination and improve welfare. In terms of policy, we show that (1) banning fee discrimination and platform entry is detrimental to welfare, (2) a vertical merger within a retail market mitigates fee distortions but is often worse than an equilibrium with platform entry into retail, and (3) taxing the platform in retail (not merchants) levels the retail playing field and can generate a Pareto improvement upon a policy that bans platform retail entry.

Visual word recognition: Evidence for a serial bottleneck in lexical access

Reading is a demanding task, constrained by inherent processing capacity limits. Do those capacity limits allow for multiple words to be recognized in parallel? In a recent study, we measured semantic categorization accuracy for nouns presented in pairs. The words were replaced by post-masks after an interval that was set to each subject’s threshold, such that with focused attention they could categorize one word with ~80% accuracy. When subjects tried to divide attention between both words, their accuracy was so impaired that it supported a serial processing model: on each trial, subjects could categorize one word but had to guess about the other. In the experiments reported here, we investigated how our previous result generalizes across two tasks that require lexical access but vary in the depth of semantic processing (semantic categorization and lexical decision), and across different masking stimuli, word lengths, lexical frequencies and visual field positions. In all cases, the serial processing model was supported by two effects: (1) a sufficiently large accuracy deficit with divided compared to focused attention; and (2) a trial-by-trial stimulus processing tradeoff, meaning that the response to one word was more likely to be correct if the response to the other was incorrect. However, when the task was to detect colored letters, neither of those effects occurred, even though the post-masks limited accuracy in the same way. Altogether, the results are consistent with the hypothesis that visual processing of words is parallel but lexical access is serial.

Food in the corner and money in the cashews: Semantic activation of embedded stems in the presence or absence of a morphological structure.

In visual word identification, readers automatically access word internal information: they recognize orthographically embedded words (e.g., HAT in THAT) and are sensitive to morphological structure (DEAL-ER, BASKET-BALL). The exact mechanisms that govern these processes, however, are not well established yet - how is this information used? What is the role of affixes in this process? To address these questions, we tested the activation of meaning of embedded word stems in the presence or absence of a morphological structure using two semantic categorization tasks in Italian. Participants made category decisions on words (e.g., is CARROT a type of food?). Some no-answers (is CORNER a type of food?) contained category-congruent embedded word stems (i.e., CORN-). Moreover, the embedded stems could be accompanied by a pseudo-suffix (-er in CORNER) or a non-morphological ending (-ce in PEACE) - this allowed gauging the role of pseudo-suffixes in stem activation. The analyses of accuracy and response times revealed that words were harder to reject as members of a category when they contained an embedded word stem that was indeed category-congruent. Critically, this was the case regardless of the presence or absence of a pseudo-suffix. These findings provide evidence that the lexical identification system activates the meaning of embedded word stems when the task requires semantic information. This study brings together research on orthographic neighbors and morphological processing, yielding results that have important implications for models of visual word processing.

Attention-aware perceptual enhancement nets for low-resolution image classification

Classifying low-resolution (LR) images is notoriously challenging because of their noisy representation and limited information. Existing approaches mainly solve this challenge by training carefully designed architectures on LR datasets or by employing an image-resizing algorithm in a straightforward manner. However, the performance improvements of these methods are usually limited or even trivial in the case of LR images. In this work, we address the LR image classification problem by developing an end-to-end architecture that internally elevates representations of an LR image to “super-resolved” ones. This approach imparts characteristics similar to those of high-resolution (HR) images and is thus more discriminative and representative for image classification. For this purpose, we propose an innovative unified framework, named Attention-aware Perceptual Enhancement Nets (APEN), which integrates perceptual enhancement and an attention mechanism in an end-to-end manner for LR image classification. Specifically, the framework includes a perceptual enhancement network to generate super-resolved images from LR images. In addition, a novel attention mechanism is presented to highlight informative regions, while restricting the semantic deviation of super-resolved images. Additionally, we designed a feature rectification strategy to promote the adaptability of category decision. Experiments conducted on publicly available datasets demonstrate the superiority of our method against state-of-the-art methods on both LR and HR datasets.

Mental Effort and Information-Processing Costs Are Inversely Related to Global Brain Free Energy During Visual Categorization.

Mental effort is a neurocognitive process that reflects the controlled expenditure of psychological information-processing resources during perception, cognition, and action. There is a practical need to operationalize and measure mental effort in order to minimize detrimental effects of mental fatigue on real-world human performance. Previous research has identified several neurocognitive indices of mental effort, but these indices are indirect measures that are also sensitive to experimental demands or general factors such as sympathetic arousal. The present study investigated a potential direct neurocognitive index of mental effort based in theories where bounded rational decision makers (realized as embodied brains) are modeled as generalized thermodynamic systems. This index is called free energy, an information-theoretic system property of the brain that reflects the difference between the brain’s current and predicted states. Theory predicts that task-related differences in a decision makers’ free energy are inversely related to information-processing costs related to task decisions. The present study tested this prediction by quantifying global brain free energy from electroencephalographic (EEG) measures of human brain function. EEG signals were recorded while participants engaged in two visual categorization tasks in which categorization decisions resulted from the allocation of different levels of mental information processing resources. A novel method was developed to quantify brain free energy from machine learning classification of EEG trials. Participant information-processing resource costs were estimated via computational analysis of behavior, whereas the subjective expression of mental effort was estimated via participant ratings of mental workload. Following theoretical predictions, task-related differences in brain free energy negatively correlated with increased allocation of information-processing resource costs. These brain free energy differences were smaller for the visual categorization task that required a greater versus lesser allocation of information-processing resources. Ratings of mental workload were positively correlated with information-processing resource costs, and negatively correlated with global brain free energy differences, only for the categorization task requiring the larger amount of information-processing resource costs. These findings support theoretical thermodynamic approaches to decision making and provide the first empirical evidence of a relationship between mental effort, brain free energy, and neurocognitive information-processing.

Does surface dyslexia/dysgraphia relate to semantic deficits in the semantic variant of primary progressive aphasia?

The semantic variant of primary progressive aphasia (sv-PPA) is a degenerative condition which causes surface dyslexia/dysgraphia, resulting in reading/writing errors of irregular words with non-transparent grapheme-to-phoneme correspondences (e.g., ‘plaid’) as opposed to regular words (e.g., ‘cat’). According to connectionist models, most authors have attributed this deficit to semantic impairments, but this assumption is at odds with symbolic models, such as the DRC account, stating that the reading/writing of irregulars relies on the mental lexicon. Our study investigated whether sv-PPA affects the lexicon in addition to the semantic system, and whether semantic or lexical deficits cause surface dyslexia/dysgraphia, while challenging the two major models of written language.We explored a cohort of 12 sv-PPA patients and 25 matched healthy controls using a reading and writing task, a semantic task (category decision: living vs. non-living), and a lexical task (lexical decision: word vs. no-neighbor non-word). Correlation analyses were conducted to assess the relationship between reading/writing scores of irregulars and semantic vs. lexical performance. Furthermore, item-by-item analyses explored the consistency of reading/writing errors with item-specific semantic and lexical errors.Results showed that sv-PPA patients are impaired at reading and writing irregular words, and that they have impaired performance in both the semantic and the lexical task. Reading/writing scores with irregulars correlated significantly with performance in the lexical but not the semantic task. Item-by-item analyses revealed that failure in the lexical task on a given irregular word is a good predictor of reading/writing errors with that item (positive predictive value: 77.5%), which was not the case for the semantic task (positive predictive value: 42.5%).Our findings show that sv-PPA is not restricted to semantic damage but that it also comprises damage to the mental lexicon, which appears to be the major factor for surface dyslexia/dysgraphia. Our data support symbolic models whereas they challenge connectionist accounts.

Condition/Decision Duality and the Internal Logic of Extensive Restriction Categories

In flowchart languages, predicates play an interesting double role. In the textual representation, they are often presented as conditions, i.e., expressions which are easily combined with other conditions (often via Boolean combinators) to form new conditions, though they only play a supporting role in aiding branching statements choose a branch to follow. On the other hand, in the graphical representation they are typically presented as decisions, intrinsically capable of directing control flow yet mostly oblivious to Boolean combination.While categorical treatments of flowchart languages are abundant, none of them provide a treatment of this dual nature of predicates. In the present paper, we argue that extensive restriction categories are precisely categories that capture such a condition/decision duality, by means of morphisms which, coincidentally, are also called decisions. Further, we show that having these categorical decisions amounts to having an internal logic: Analogous to how subobjects of an object in a topos form a Heyting algebra, we show that decisions on an object in an extensive restriction category form a De Morgan quasilattice, the algebraic structure associated with the (three-valued) weak Kleene logicK3w. Full classical propositional logic can be recovered by restricting to total decisions, yielding extensive categories in the usual sense, and confirming (from a different direction) a result from effectus theory that predicates on objects in extensive categories form Boolean algebras.As an application, since (categorical) decisions are partial isomorphisms, this approach provides naturally reversible models of classical propositional logic and weak Kleene logic.

MapReduce based improved quick reduct algorithm with granular refinement using vertical partitioning scheme

In the last few decades, rough sets have evolved to become an essential technology for feature subset selection by way of reduct computation in categorical decision systems. In recent years with the proliferation of MapReduce for distributed/parallel algorithms, several scalable reduct computation algorithms have been developed in this field for large-scale decision systems using MapReduce. The existing MapReduce based reduct computation approaches use horizontal partitioning (division in object space) of the dataset into the nodes of the cluster, requiring a complicated shuffle and sort phase. In this work, we propose an algorithm MR_IQRA_VP which is designed using vertical partitioning (division in attribute space) of the dataset with a simplified shuffle and sort phase of the MapReduce framework. MR_IQRA_VP is a distributed/parallel implementation of the Improved Quick Reduct Algorithm (IQRA_IG) and is implemented using iterative MapReduce framework of Apache Spark. We have done an extensive comparative study through experimentation on benchmark decision systems using existing horizontal partitioning based reduct computation algorithms. Through experimental analysis, along with theoretical validation, we have established that MR_IQRA_VP is suitable and scalable to datasets of larger size attribute space and moderate object space prevalent in the areas of Bioinformatics and Web mining.

Application of likelihood ratios and optimal decision thresholds in fire debris analysis based on a partial least squares discriminant analysis (PLS-DA) model

A set of 9000 computationally generated (in-silico) total ion spectra comprising samples containing ignitable liquids from ASTM E1618-14 classes mixed with pyrolyzed substrates (class IL) and samples containing a mixture of pyrolyzed substrates without ignitable liquid contribution (class SUB) was used to generate a partial least squares discriminant analysis (PLS-DA) model. The training data set was balanced in IL and SUB class composition and the IL class samples were composed of a uniform distribution of ignitable liquids from the ASTM E1618-14 defined classes. The number of components in the optimized PLS-DA model, 13, was determined by optimizing the area under the receiver operating characteristic curve (ROC AUC) as a function of the number of components. Ten cross validation folds had an average ROC AUC of 0.930 ± 0.006. The PLS-DA model was further challenged with a uniformly distributed and balanced IL/SUB validation set of 1000 computationally mixed total ion spectra, giving an ROC AUC 0f 0.93. The model which had been validated on computationally generated total ion spectra was tested on a set of “real” fire debris samples from large-scale burn tests that had been independently evaluated by an “informed analyst”. The results from this study demonstrate the application of a chemometric model to the forensic analysis of fire debris data and emphasize the connection between quantified strength of the evidence and categorical decisions based on a defined operational decision point on the ROC curve. The success of the PLS-DA model demonstrates the feasibility of applying chemometric methods to the analysis of fire debris as a means of introducing probabilistic statements of evidentiary value to the court.

Posterior parietal cortex plays a causal role in perceptual and categorical decisions.

Posterior parietal cortex (PPC) activity correlates with monkeys' decisions during visual discrimination and categorization tasks. However, recent work has questioned whether decision-correlated PPC activity plays a causal role in such decisions. That study focused on PPC's contribution to motor aspects of decisions (deciding where to move), but not sensory evaluation aspects (deciding what you are looking at). We employed reversible inactivation to compare PPC's contributions to motor and sensory aspects of decisions. Inactivation affected both aspects of behavior, but preferentially impaired decisions when visual stimuli, rather than motor response targets, were in the inactivated visual field. This demonstrates a causal role for PPC in decision-making, with preferential involvement in evaluating attended task-relevant sensory stimuli compared with motor planning.

Cultural Structures of Knowledge from Wikipedia Networks of First Links

Knowledge is useless without structure. While the classification of knowledge has been an enduring philosophical enterprise, it recently found applications in computer science, notably for artificial intelligence. The availability of large databases allowed for complex ontologies to be built automatically, for example by extracting structured content from Wikipedia. However, this approach is subject to manual categorization decisions made by online editors. Here we show that an implicit classification hierarchy emerges spontaneously on Wikipedia. We study the network of first links between articles, and find that it centers on a core cycle involving concepts of fundamental classifying importance. We argue that this structure is rooted in cultural history. For European languages, articles like Philosophy and Science are central, whereas Human and Earth dominate for East Asian languages. This reflects the differences between ancient Greek thought and Chinese tradition. Our results reveal the powerful influence of culture on the intrinsic architecture of complex data sets.

Quantifying the Confidence in fMRI-Based Language Lateralisation Through Laterality Index Deconstruction.

In epilepsy patients, language lateralisation is an important part of the presurgical diagnostic process. Using task-based fMRI, language lateralisation can be determined by visual inspection of activity patterns or by quantifying the difference in left- and right-hemisphere activity using variations of a basic formula [(L–R)/(L+R)]. However, the values of this laterality index (LI) depend on the choice of activity thresholds and regions of interest. The diagnostic utility of the LI also depends on how its continuous values are translated into categorical decisions about a patient's language lateralisation. Here, we analysed fMRI data from 712 epilepsy patients who performed a verbal fluency task. Each fMRI data set was evaluated by a trained human rater as depicting left-sided, right-sided, or bilateral lateralisation or as being inconclusive. We used data-driven methods to define the activity thresholds and regions of interest used for LI computation and to define a classification scheme that allowed us to translate the LI values into categorical decisions. By deconstructing the LI into measures of laterality (L–R) and strength (L+R), we also modelled the relationship between activation strength and conclusiveness of a data set. In a held-out data set, predictions reached 91% correct when using only conclusive data and 82% when inconclusive data were included. Although only trained on human evaluations of fMRIs, the approach generalised to the prediction of language Wada test results, allowing for significant above-chance accuracies. Compared against different existing methods of LI-computation, our approach improved the identification and exclusion of inconclusive cases and ensured that decisions for the remaining data could be made with consistently high accuracies. We discuss how this approach can support clinicians in assessing fMRI data on a single-case level, deciding whether lateralisation can be determined with sufficient certainty or whether additional information is needed.

Optimal distinctiveness, strategic categorization, and product market entry on the Google Play app platform

Research SummaryNew entrants often face uncertainty regarding how to optimally position themselves within product markets. We suggest that new entrants can use two important schemas to strategically categorize themselves to gain a competitive advantage in platform markets: category exemplars and category prototypes. Using a unique dataset of more than 83,000 new Google Play developers and more than 139,000 apps, we find that the optimally distinct entry point is at a high level of exemplar similarity and a low level of prototype similarity. We find that greater alignment of an entrant with the prototype corresponds to a weaker benefit of exemplar similarity. These findings have important implications for understanding competitive dynamics within product markets, strategic positioning at entry, and the interdependence of strategic categorization decisions. Managerial SummaryEntrepreneurial startups often find it difficult to know how to optimally position their products among a large number of rivals in highly competitive platform markets. Our study suggests that these startups can draw on two reference points to help determine the optimal positioning for their products: category exemplars and category prototypes. Exemplars include the most successful products in a market category while prototypes represent the most common products in a category. Drawing on a large dataset obtained from the Google Play app store, we find that developers can substantially increase the installs of their first app by crafting an app text description that is as similar as possible to the description of a category exemplar and as different as possible from the category's prototypical description.