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When given unrestricted time to process an image, people are faster and more accurate at making categorical decisions about a depicted object (e.g., Labrador) if it is close in sensorimotor and linguistic distributional experience to its target category concept (e.g., dog). In this preregistered study, we examined whether sensorimotor and linguistic distributional information affect object categorisation differently as a function of time available for perceptual processing. Using an ultrarapid categorisation paradigm with backwards masking, we systematically varied onset timing (SOA) of a post-stimulus mask (17-133ms) following a briefly displayed (17ms) object. Preliminary results suggest that linguistic distributional distance between concept and category (e.g., Labrador → dog), but not sensorimotor distance, affects categorisation accuracy and RT even in rapid categorisation, and that these effects do not vary systematically by SOA. These findings support the role of a linguistic shortcut (i.e., using linguistic distributional instead of sensorimotor information) in rapid object categorisation.

When given unrestricted time to process an image, people are faster and more accurate at making categorical decisions about a depicted object (e.g., Labrador) if it is close in sensorimotor and linguistic distributional experience to its target category concept (e.g., dog). In this preregistered study, we examined whether sensorimotor and linguistic distributional information affect object categorisation differently as a function of time available for perceptual processing. Using an ultrarapid categorisation paradigm with backwards masking, we systematically varied onset timing (SOA) of a post-stimulus mask (17-133ms) following a briefly displayed (17ms) object. Preliminary results suggest that linguistic distributional distance between concept and category (e.g., Labrador → dog), but not sensorimotor distance, affects categorisation accuracy and RT even in rapid categorisation, and that these effects do not vary systematically by SOA. These findings support the role of a linguistic shortcut (i.e., using linguistic distributional instead of sensorimotor information) in rapid object categorisation.

How do people know what categories objects belong to? Traditional accounts of categorisation typically assume that concepts comprise perceptual or functional features. By contrast, recent accounts of conceptual structure emphasise the dual role of sensorimotor (i.e., perception-action experience of the world) and linguistic distributional information (i.e., statistical distribution of words in language). This thesis contains a literature review and four empirical papers describing pre-registered experiments, which explore how the degree of sensorimotor-linguistic representational overlap between category- and member-concepts may drive object categorisation. Chapter 1 presents a review of the literature, covering relevant theories of categorisation, as well as sensorimotor-linguistic theories of conceptual processing. Chapter 2 presents a study which explored the role of sensorimotor and linguistic distributional information in processing advantages. This study found that overlap in sensorimotor and linguistic distributional representations between category (e.g., dog) and member (e.g., Labrador) concepts reliably predicted performance (accuracy, RT) in a speeded picture category verification task. Chapter 3 reports a study that contrasted the traditional prediction of a basic-level advantage with the sensorimotor-linguistic prediction that representational overlap, not taxonomic level, is more important to categorisation. In a forced-choice categorisation task using labels only, participants decided between a basic- (e.g., dog) and superordinate-level label (e.g., animal) for a target object label (e.g., Labrador). While basic-level labels were overall chosen faster and more frequently, an exploratory analysis suggested that basic-level categorisation was slowed down when sensorimotor-linguistic overlap was greater between the target object label and the superordinate label. Chapter 4 describes the collection of a normed set of 800 photographs of 200 natural objects

How do people know what categories objects belong to? Traditional accounts of categorisation typically assume that concepts comprise perceptual or functional features. By contrast, recent accounts of conceptual structure emphasise the dual role of sensorimotor (i.e., perception-action experience of the world) and linguistic distributional information (i.e., statistical distribution of words in language). This thesis contains a literature review and four empirical papers describing pre-registered experiments, which explore how the degree of sensorimotor-linguistic representational overlap between category- and member-concepts may drive object categorisation. Chapter 1 presents a review of the literature, covering relevant theories of categorisation, as well as sensorimotor-linguistic theories of conceptual processing. Chapter 2 presents a study which explored the role of sensorimotor and linguistic distributional information in processing advantages. This study found that overlap in sensorimotor and linguistic distributional representations between category (e.g., dog) and member (e.g., Labrador) concepts reliably predicted performance (accuracy, RT) in a speeded picture category verification task. Chapter 3 reports a study that contrasted the traditional prediction of a basic-level advantage with the sensorimotor-linguistic prediction that representational overlap, not taxonomic level, is more important to categorisation. In a forced-choice categorisation task using labels only, participants decided between a basic- (e.g., dog) and superordinate-level label (e.g., animal) for a target object label (e.g., Labrador). While basic-level labels were overall chosen faster and more frequently, an exploratory analysis suggested that basic-level categorisation was slowed down when sensorimotor-linguistic overlap was greater between the target object label and the superordinate label. Chapter 4 describes the collection of a normed set of 800 photographs of 200 natural objects

The basic-level advantage is one of the best-known effects in human categorisation. Traditional accounts argue that basic-level categories present a maximally informative or entry-level into a taxonomic organisation of concepts in semanticmemory. However, these explanations are not fully compatible with most recent views on the structure of the concep-tual system, which emphasise the role of sensorimotor (i.e., perception-action experience of the world) and linguisticinformation (i.e., statistical distribution of words in language) in conceptual processing. In a pre-registered wordpicturecategorisation study, we hypothesised that our novel measures of sensorimotor and linguistic distance would contribute tocategorical decision making, and would outperform traditional taxonomic levels (i.e., subordinate, basic, superordinate)in predicting the basic-level advantage. Results showed that, overall, our measures predicted the basic-level advantage atleast as well as taxonomic level. Sensorimotor information best explained processing speed, whereas taxonomic level bestexplained participants choices.

High immersion in virtual reality is often hypothesized to improve learning and memory. This immersion benefit is frequently attributed to presence, the user’s feeling of being present inside the computer-generated environment. Obtaining learning gains due to high immersion may however be difficult, as is evidenced by the null results of multiple studies in this area. In the current study we investigated the role of presence in low- and high-immersion virtual reality settings. No differences in performance in object location and spatial memory were found between low- and high-immersive conditions. Yet, when considering self-report measures of presence, performance improvements in the high immersive condition did become apparent. The finding of the importance to consider the role of presence in virtual reality highlights the complexity of immersion effects in simulated environments