Visual category-driven differences in memory

Categorizing classes of stimuli in the real-world is thought to underlie features of general intelligence, including our ability to infer identities of new objects, environments, and people never encountered before. Our understanding of human categorization, and the neural mechanisms that underlie this ability, was initially described in the context of visual perception. It is now broadly accepted that a network of high-level visual areas on the ventral and lateral surfaces of the brain exhibit some level of ‘domain (or category)-selective’ activity: preferential neural responses to visual stimuli of one category more than another (e.g., larger responses to faces compared to scenes or manipulable objects). Inspired by this robust and intuitive organization, recent studies have begun investigating the extent to which human memory systems also exhibit a category-selective organization. Surprisingly, this work has revealed strong evidence for the existence of category-selective areas in swaths of cortex previously considered to be domain-general. These results suggest that category-selectivity is a general organizing principle not only of visual cortex, but also for higher-level cortical areas involved in memory. In this chapter we review the evidence for the manifestation of visual category preferences in memory systems, and how this relates to the well-established category-selectivity exhibited within visual cortex.

A step forward in understanding pas: the post-verbal negator in Old Occitan from the perspective of communication and rhetorical strategizing

In recent years, within the cognitive linguistics approach there has been a trend of scholarly research committed to exploring the motivation for language change. The way in which people use language in communication, together with principles of human categorization, are the locus where language change and innovations are to be found. Interactional contexts in particular, seen as playing a crucial role in bringing about syntactic change (Traugott 2010b), have figured prominently in recent contributions on diachronic micro-changes, bringing to the fore the role played by dialogue as both the manifestation of the participants’ own voices and the realization of the constant negotiation that characterizes human communication. Against this background, this contribution focuses on a particular use of the Occitan post-verbal negator pas in negative rhetorical questions, which was very productive in fifteenth-century collections of religious theatrical texts. It is claimed that these dialogic contexts allowed a polyphonic use of pas, crucially restricted to this post-verbal negator, which is key to identifying the reasons behind the eventual establishment of pas as the generalized sentential negator in the modern language.

Capturing human categorization of natural images by combining deep networks and cognitive models

Human categorization is one of the most important and successful targets of cognitive modeling, with decades of model development and assessment using simple, low-dimensional artificial stimuli. However, it remains unclear how these findings relate to categorization in more natural settings, involving complex, high-dimensional stimuli. Here, we take a step towards addressing this question by modeling human categorization over a large behavioral dataset, comprising more than 500,000 judgments over 10,000 natural images from ten object categories. We apply a range of machine learning methods to generate candidate representations for these images, and show that combining rich image representations with flexible cognitive models captures human decisions best. We also find that in the high-dimensional representational spaces these methods generate, simple prototype models can perform comparably to the more complex memory-based exemplar models dominant in laboratory settings.

Extending the ALCOVE model of category learning to featural stimulus domains

The ALCOVE model of category learning, despite its considerable success in accounting for human performance across a wide range of empirical tasks, is limited by its reliance on spatial stimulus representations. Some stimulus domains are better suited to featural representation, characterizing stimuli in terms of the presence or absence of discrete features, rather than as points in a multidimensional space. We report on empirical data measuring human categorization performance across a featural stimulus domain and show that ALCOVE is unable to capture fundamental qualitative aspects of this performance. In response, a featural version of the ALCOVE model is developed, replacing the spatial stimulus representations that are usually generated by multidimensional scaling with featural representations generated by additive clustering. We demonstrate that this featural version of ALCOVE is able to capture human performance where the spatial model failed, explaining the difference in terms of the contrasting representational assumptions made by the two approaches. Finally, we discuss ways in which the ALCOVE categorization model might be extended further to use “hybrid” representational structures combining spatial and featural components.

Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence

Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision-making. Here we show that, in a task in which uncertainty is relevant for performance, human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer’s attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer’s decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.

Disability History and Greco-Roman Antiquity

To obtain a historical perspective on disability, we need to know what questions people of the past asked about each other and thus how they grouped human types. This effort involves removing the carapace of modern forms of classification and avoiding their imposition on the primary sources of an era so distant from our own (“retrospective diagnosis”). At least three major forms are identifiable: (1) the post-Cartesian divide between mind and body; (2) the tightening of forms of human categorization in general since the late Middle Ages; and (3) the thoroughly modern divide between the scientific/medical and the social. Human disparities and putative disabilities, ranging widely from the ancient era to the start of the Middle Ages and including the body, the senses, cognition, speech, social behavior, and sexual make-up, are discussed. These may or may not correspond with modern categorizations.

Humans incorporate attention-dependent uncertainty into perceptual decisions and confidence

Perceptual decisions are better when they take uncertainty into account. Uncertainty arises not only from the properties of sensory input but also from cognitive sources, such as different levels of attention. However, it is unknown whether humans appropriately adjust for such cognitive sources of uncertainty during perceptual decision making. Here we show that human categorization and confidence decisions take into account uncertainty related to attention. We manipulated uncertainty in an orientation categorization task from trial to trial using only an attentional cue. The categorization task was designed to disambiguate decision rules that did or did not depend on attention. Using formal model comparison to evaluate decision behavior, we found that category and confidence decision boundaries shifted as a function of attention in an approximately Bayesian fashion. This means that the observer’s attentional state on each trial contributed probabilistically to the decision computation. This responsiveness of an observer’s decisions to attention-dependent uncertainty should improve perceptual decisions in natural vision, in which attention is unevenly distributed across a scene.