Predicting Category Intuitiveness With the Rational Model, the Simplicity Model, and the Generalized Context Model

Naïve observers typically perceive some groupings for a set of stimuli as more intuitive than others. The problem of predicting category intuitiveness has been historically considered the remit of models of unsupervised categorization. In contrast, this article develops a measure of category intuitiveness from one of the most widely supported models of supervised categorization, the generalized context model (GCM). Considering different category assignments for a set of instances, the authors asked how well the GCM can predict the classification of each instance on the basis of all the other instances. The category assignment that results in the smallest prediction error is interpreted as the most intuitive for the GCM—the authors refer to this way of applying the GCM as “unsupervised GCM.” The authors systematically compared predictions of category intuitiveness from the unsupervised GCM and two models of unsupervised categorization: the simplicity model and the rational model. The unsupervised GCM compared favorably with the simplicity model and the rational model. This success of the unsupervised GCM illustrates that the distinction between supervised and unsupervised categorization may need to be reconsidered. However, no model emerged as clearly superior, indicating that there is more work to be done in understanding and modeling category intuitiveness

On the adequacy of current empirical evaluations of formal models of categorization

Categorization is one of the fundamental building blocks of cognition, and the study of categorization is notable for the extent to which formal modeling has been a central and influential component of research. However, the field has seen a proliferation of noncomplementary models with little consensus on the relative adequacy of these accounts. Progress in assessing the relative adequacy of formal categorization models has, to date, been limited because (a) formal model comparisons are narrow in the number of models and phenomena considered and (b) models do not often clearly define their explanatory scope. Progress is further hampered by the practice of fitting models with arbitrarily variable parameters to each data set independently. Reviewing examples of good practice in the literature, we conclude that model comparisons are most fruitful when relative adequacy is assessed by comparing well-defined models on the basis of the number and proportion of irreversible, ordinal, penetrable successes (principles of minimal flexibility, breadth, good-enough precision, maximal simplicity, and psychological focus)

A quantum geometric model of similarity

No other study has had as great an impact on the development of the similarity literature as that of Tversky (1977), which provided compelling demonstrations against all the fundamental assumptions of the popular, and extensively employed, geometric similarity models. Notably, similarity judgments were shown to violate symmetry and the triangle inequality, and also be subject to context effects, so that the same pair of items would be rated differently, depending on the presence of other items. Quantum theory provides a generalized geometric approach to similarity and can address several of Tversky’s (1997) main findings. Similarity is modeled as quantum probability, so that asymmetries emerge as order effects, and the triangle equality violations and the diagnosticity effect can be related to the context-dependent properties of quantum probability. We so demonstrate the promise of the quantum approach for similarity and discuss the implications for representation theory in general

Quantum like modelling of decision making: quantifying uncertainty with the aid of the Heisenberg-Robertson inequality

This paper contributes to quantum-like modeling of decision making (DM) under uncertainty through application of Heisenberg’s uncertainty principle (in the form of the Robertson inequality). In this paper we apply this instrument to quantify uncertainty in DM performed by quantum-like agents. As an example, we apply the Heisenberg uncertainty principle to the determination of mutual interrelation of uncertainties for “incompatible questions” used to be asked in political opinion pools. We also consider the problem of representation of decision problems, e.g., in the form of questions, by Hermitian operators, commuting and noncommuting, corresponding to compatible and incompatible questions respectively. Our construction unifies the two different situations (compatible versus incompatible mental observables), by means of a single Hilbert space and of a deformation parameter which can be tuned to describe these opposite cases. One of the main foundational consequences of this paper for cognitive psychology is formalization of the mutual uncertainty about incompatible questions with the aid of Heisenberg’s uncertainty principle implying the mental state dependence of (in)compatibility of questions

Challenging the classical notion of time in cognition: a quantum perspective

All mental representations change with time. A baseline intuition is that mental representations have specific values at different time points, which may be more or less accessible, depending on noise, forgetting processes etc. We present a radically alternative, motivated by recent research using the mathematics from quantum theory for cognitive modelling. Such cognitive models raise the possibility that certain possibilities or events may be incompatible, so that perfect knowledge of one necessitates uncertainty for the others. In the context of time dependence, in physics, this issue is explored with the so-called temporal Bell (TB) or Leggett-Garg inequalities. We consider in detail the theoretical and empirical challenges involved in exploring the TB inequalities in the context of cognitive systems. One interesting conclusion is that we believe the study of the TB inequalities to be empirically more constrained in psychology, than in physics. Specifically, we show how the TB inequalities, as applied to cognitive systems, can be derived from two simple assumptions, Cognitive Realism and Cognitive Completeness. We discuss possible implications of putative violations of the TB inequalities for cognitive models and our understanding of time in cognition in general. Overall, the paper provides a surprising, novel direction, in relation to how time should be conceptualized in cognition

The automatic nature of habitual goal-state activation in substance use; implications from a dyslexic population

Habitual goal-state activation may automatically elicit effects upon cognition, motivation, and emotion, through influence upon processes operating outside of awareness. For example, alcohol craving may be triggered by environmental cues. This experiment considered whether priming habitual goal-states would have similar effects for adult dyslexics and non-dyslexic controls. Dyslexia may be associated with automatisation deficits, which may affect habitual goal-state response. Dyslexics were compared to non-dyslexics on their reported alcohol cravings, following priming of one of two habitual goal-state conditions; studying or socialising. Within some of the exploratory analyses, a difference between dyslexics and non-dyslexics was demonstrated. However, the difference was not in the anticipated direction, as it was the dyslexics who were more affected by the primes. This suggests that dyslexics may be affected by primes differently to non-dyslexics. This research potentially helps understand the role that habitual goal-states play within substance use

Measuring inhibitory processes for alcohol-related attentional biases: introducing a novel attentional bias measure

Introduction: Attentional biases for alcohol related information (AB) have often been reported for heavy drinkers. These attentional biases have been found to have predictive value regarding relapse in abstaining alcoholics. Similarly impaired inhibitory processes have also been found to be associated with heavy drinkers. This paper describes a new experimental paradigm that can be utilised to investigate attentional bias towards alcohol-related visual stimuli, specifically the ability to inhibit the orientation of initial and sustained attention, towards peripherally appearing stimuli. In this way we hope to study a novel aspect of attentional biases and so how they relate to substance abuse. Methods: We used a novel eye-tracking task which aims to measure inhibitory processes for AB. The experiment utilised a gaze contingency paradigm to measure the compulsion to process or attend to alcohol stimuli. 86 undergraduate participants were recruited (31 males; 55 females), aged 18–49 years (m=20.88;sd=4.52). A ‘break frequency’ variable was computed for each participant. This was number of times participants tried to look at peripheral stimuli. We argue that this variable is a direct measure of how distracting peripheral stimuli were. Results: It was found that reported alcohol use was associated with the eye-tracking break frequency measure of inhibitory control. Thus, heavy drinking may be associated with decreased inhibitory control and increased attentional bias. Conclusions: Results suggest that attentional bias is not just a process of stimuli becoming prioritised, but also stimuli becoming compulsory to attend and process

The fickle nature of similarity change as a result of categorization

Several researchers have reported that learning a particular categorization leads to compatible changes in the similarity structure of the categorized stimuli. The purpose of this study is to examine whether different category structures may lead to greater or less corresponding similarity change. We created six category structures and examined changes in similarity within categories or between categories, as a result of categorization, in between-participant conditions. The best supported hypothesis was that the ease of learning a categorization affects change in within categories similarity, so that greater (within categories) similarity change was observed for category structures which were harder to learn

On the adequacy of Bayesian evaluations of categorization models: Reply to Vanpaemel and Lee (2012)

Vanpaemel and Lee (2012) argue, and we agree, that the comparison of formal models can be facilitated by Bayesian methods. However, Bayesian methods neither precede nor supplant our proposals (Wills & Pothos, 2012), as Bayesian methods can be applied both to our proposals and to their polar opposites. Furthermore, the use of Bayesian methods to control for model complexity can be actively misleading when combined with the consideration of narrow data sets, and significant development work is required before Bayesian methods can be applied to some of the leading formal models of categorization. Even where Bayesian methods can be applied, the use of non-Bayesian methods is sometimes preferable due to their computational simplicity (Vanpaemel & Storms, 2010). We also clarify our position on arbitrarily variable parameters, and on the relationship between ordinal properties and overfitting

Role of prior knowledge in implicit and explicit learning of artificial grammars

Artificial grammar learning (AGL) performance reflects both implicit and explicit processes and has typically been modeled without incorporating any influence from general world knowledge. Our research provides a systematic investigation of the implicit vs. explicit nature of general knowledge and its interaction with knowledge types investigated by past AGL research (i.e., rule- and similarity-based knowledge). In an AGL experiment, a general knowledge manipulation involved expectations being either congruent or incongruent with training stimulus structure. Inconsistent observations paradoxically led to an advantage in structural knowledge and in the use of general world knowledge in both explicit (conscious) and implicit (unconscious) cases (as assessed by subjective measures). The above findings were obtained under conditions of reduced processing time and impaired executive resources. Κey findings from our work are that implicit AGL can clearly be affected by general knowledge, and implicit learning can be enhanced by the violation of expectations