Validation of the Virtual Reality Everyday Assessment Lab (VR-EAL): An Immersive Virtual Reality Neuropsychological Battery with Enhanced Ecological Validity.

OBJECTIVE: The assessment of cognitive functions such as prospective memory, episodic memory, attention, and executive functions benefits from an ecologically valid approach to better understand how performance outcomes generalize to everyday life. Immersive virtual reality (VR) is considered capable of simulating real-life situations to enhance ecological validity. The present study attempted to validate the Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive VR neuropsychological battery, against an extensive paper-and-pencil neuropsychological battery. METHODS: Forty-one participants (21 females) were recruited: 18 gamers and 23 non-gamers who attended both an immersive VR and a paper-and-pencil testing session. Bayesian Pearson's correlation analyses were conducted to assess construct and convergent validity of the VR-EAL. Bayesian t-tests were performed to compare VR and paper-and-pencil testing in terms of administration time, similarity to real-life tasks (i.e., ecological validity), and pleasantness. RESULTS: VR-EAL scores were significantly correlated with their equivalent scores on the paper-and-pencil tests. The participants' reports indicated that the VR-EAL tasks were significantly more ecologically valid and pleasant than the paper-and-pencil neuropsychological battery. The VR-EAL battery also had a shorter administration time. CONCLUSION: The VR-EAL appears as an effective neuropsychological tool for the assessment of everyday cognitive functions, which has enhanced ecological validity, a highly pleasant testing experience, and does not induce cybersickness.

An ecologically valid examination of event-based and time-based prospective memory using immersive virtual reality: the effects of delay and task type on everyday prospective memory.

Recent research has focused on assessing either event- or time-based prospective memory (PM) using laboratory tasks. Yet, the findings pertaining to PM performance on laboratory tasks are often inconsistent with the findings on corresponding naturalistic experiments. Ecologically valid neuropsychological tasks resemble the complexity and cognitive demands of everyday tasks, offer an adequate level of experimental control, and allow a generalisation of the findings to everyday performance. The Virtual Reality Everyday Assessment Lab (VR-EAL), an immersive virtual reality neuropsychological battery with enhanced ecological validity, was implemented to comprehensively assess everyday PM (i.e., focal and non-focal event-based, and time-based). The effects of the length of delay between encoding and initiating the PM intention and the type of PM task on everyday PM performance were examined. The results revealed that everyday PM performance was affected by the length of delay rather than the type of PM task. The effect of the length of delay differentially affected performance on the focal, non-focal, and time-based tasks and was proportional to the PM cue focality (i.e., semantic relationship with the intended action). This study also highlighted methodological considerations such as the differentiation between functioning and ability, distinction of cue attributes, and the necessity of ecological validity.

A mechanism for the cortical computation of hierarchical linguistic structure.

Biological systems often detect species-specific signals in the environment. In humans, speech and language are species-specific signals of fundamental biological importance. To detect the linguistic signal, human brains must form hierarchical representations from a sequence of perceptual inputs distributed in time. What mechanism underlies this ability? One hypothesis is that the brain repurposed an available neurobiological mechanism when hierarchical linguistic representation became an efficient solution to a computational problem posed to the organism. Under such an account, a single mechanism must have the capacity to perform multiple, functionally related computations, e.g., detect the linguistic signal and perform other cognitive functions, while, ideally, oscillating like the human brain. We show that a computational model of analogy, built for an entirely different purpose-learning relational reasoning-processes sentences, represents their meaning, and, crucially, exhibits oscillatory activation patterns resembling cortical signals elicited by the same stimuli. Such redundancy in the cortical and machine signals is indicative of formal and mechanistic alignment between representational structure building and "cortical" oscillations. By inductive inference, this synergy suggests that the cortical signal reflects structure generation, just as the machine signal does. A single mechanism-using time to encode information across a layered network-generates the kind of (de)compositional representational hierarchy that is crucial for human language and offers a mechanistic linking hypothesis between linguistic representation and cortical computation.

A theory of relation learning and cross-domain generalization.

People readily generalize knowledge to novel domains and stimuli. We present a theory, instantiated in a computational model, based on the idea that cross-domain generalization in humans is a case of analogical inference over structured (i.e., symbolic) relational representations. The model is an extension of the Learning and Inference with Schemas and Analogy (LISA; Hummel & Holyoak, 1997, 2003) and Discovery of Relations by Analogy (DORA; Doumas et al., 2008) models of relational inference and learning. The resulting model learns both the content and format (i.e., structure) of relational representations from nonrelational inputs without supervision, when augmented with the capacity for reinforcement learning it leverages these representations to learn about individual domains, and then generalizes to new domains on the first exposure (i.e., zero-shot learning) via analogical inference. We demonstrate the capacity of the model to learn structured relational representations from a variety of simple visual stimuli, and to perform cross-domain generalization between video games (Breakout and Pong) and between several psychological tasks. We demonstrate that the model's trajectory closely mirrors the trajectory of children as they learn about relations, accounting for phenomena from the literature on the development of children's reasoning and analogy making. The model's ability to generalize between domains demonstrates the flexibility afforded by representing domains in terms of their underlying relational structure, rather than simply in terms of the statistical relations between their inputs and outputs. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

A computational account of children's analogical reasoning: balancing inhibitory control in working memory and relational representation.

Theories accounting for the development of analogical reasoning tend to emphasize either the centrality of relational knowledge accretion or changes in information processing capability. Simulations in LISA (Hummel & Holyoak, 1997, 2003), a neurally inspired computer model of analogical reasoning, allow us to explore how these factors may collaboratively contribute to the development of analogy in young children. Simulations explain systematic variations in United States and Hong Kong children's performance on analogies between familiar scenes (Richland, Morrison & Holyoak, 2006; Richland, Chang, Morrison & Au, 2010). Specifically, changes in inhibition levels in the model's working-memory system explain the developmental progression in US children's ability to handle increases in relational complexity and distraction from object similarity during analogical reasoning. In contrast, changes in how relations are represented in the model best capture cross-cultural differences in performance between children of the same ages (3-4 years) in the United States and Hong Kong. We use these results and simulations to argue that the development of analogical reasoning in children may best be conceptualized as an equilibrium between knowledge accretion and the maturation of information processing capability.

Multiple dimensions of semantic and perceptual similarity contribute to mnemonic discrimination for pictures.

People often misrecognize objects that are similar to those they have previously encountered. These mnemonic discrimination errors are attributed to shared memory representations (gist) typically characterized in terms of meaning. In two experiments, we investigated multiple semantic and perceptual relations that may contribute: at the concept level, a feature-based measure of concept confusability quantified each concept's tendency to activate other similar concepts via shared features; at the item level, rated item exemplarity indexed the degree to which the specific depicted objects activated their particular concepts. We also measured visual confusability over items using a computational model of vision, and an index of color confusability. Participants studied single (Experiment 1, N = 60) or multiple (Experiment 2, N = 60) objects for each basic-level concept, followed by a recognition memory test including studied items, similar lures, and novel items. People were less likely to recognize studied items with high concept confusability, and less likely to falsely recognize their lures. This points to weaker basic-level semantic gist representations for objects with more confusable concepts because of greater emphasis on coarse processing of shared features relative to fine-grained processing of individual concepts. In contrast, people were more likely to misrecognize lures that were better exemplars of their concept, suggesting that enhanced basic-level semantic gist processing increased errors due to gist across items. False recognition was also more frequent for more visually confusable lures. The results implicate semantic similarity at multiple levels and highlight the importance of perceptual as well as semantic relations. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Tensors and compositionality in neural systems.

Neither neurobiological nor process models of meaning composition specify the operator through which constituent parts are bound together into compositional structures. In this paper, we argue that a neurophysiological computation system cannot achieve the compositionality exhibited in human thought and language if it were to rely on a multiplicative operator to perform binding, as the tensor product (TP)-based systems that have been widely adopted in cognitive science, neuroscience and artificial intelligence do. We show via simulation and two behavioural experiments that TPs violate variable-value independence, but human behaviour does not. Specifically, TPs fail to capture that in the statements fuzzy cactus and fuzzy penguin, both cactus and penguin are predicated by fuzzy(x) and belong to the set of fuzzy things, rendering these arguments similar to each other. Consistent with that thesis, people judged arguments that shared the same role to be similar, even when those arguments themselves (e.g., cacti and penguins) were judged to be dissimilar when in isolation. By contrast, the similarity of the TPs representing fuzzy(cactus) and fuzzy(penguin) was determined by the similarity of the arguments, which in this case approaches zero. Based on these results, we argue that neural systems that use TPs for binding cannot approximate how the human mind and brain represent compositional information during processing. We describe a contrasting binding mechanism that any physiological or artificial neural system could use to maintain independence between a role and its argument, a prerequisite for compositionality and, thus, for instantiating the expressive power of human thought and language in a neural system. This article is part of the theme issue 'Towards mechanistic models of meaning composition'.

Technological Competence Is a Pre-condition for Effective Implementation of Virtual Reality Head Mounted Displays in Human Neuroscience: A Technological Review and Meta-Analysis.

Immersive virtual reality (VR) emerges as a promising research and clinical tool. However, several studies suggest that VR induced adverse symptoms and effects (VRISE) may undermine the health and safety standards, and the reliability of the scientific results. In the current literature review, the technical reasons for the adverse symptomatology are investigated to provide suggestions and technological knowledge for the implementation of VR head-mounted display (HMD) systems in cognitive neuroscience. The technological systematic literature indicated features pertinent to display, sound, motion tracking, navigation, ergonomic interactions, user experience, and computer hardware that should be considered by the researchers. Subsequently, a meta-analysis of 44 neuroscientific or neuropsychological studies involving VR HMD systems was performed. The meta-analysis of the VR studies demonstrated that new generation HMDs induced significantly less VRISE and marginally fewer dropouts. Importantly, the commercial versions of the new generation HMDs with ergonomic interactions had zero incidents of adverse symptomatology and dropouts. HMDs equivalent to or greater than the commercial versions of contemporary HMDs accompanied with ergonomic interactions are suitable for implementation in cognitive neuroscience. In conclusion, researchers' technological competency, along with meticulous methods and reports pertinent to software, hardware, and VRISE, are paramount to ensure the health and safety standards and the reliability of neuroscientific results.

Validation of the Virtual Reality Neuroscience Questionnaire: Maximum Duration of Immersive Virtual Reality Sessions Without the Presence of Pertinent Adverse Symptomatology.

There are major concerns about the suitability of immersive virtual reality (VR) systems (i.e., head-mounted display; HMD) to be implemented in research and clinical settings, because of the presence of nausea, dizziness, disorientation, fatigue, and instability (i.e., VR induced symptoms and effects; VRISE). Research suggests that the duration of a VR session modulates the presence and intensity of VRISE, but there are no suggestions regarding the appropriate maximum duration of VR sessions. The implementation of high-end VR HMDs in conjunction with ergonomic VR software seems to mitigate the presence of VRISE substantially. However, a brief tool does not currently exist to appraise and report both the quality of software features and VRISE intensity quantitatively. The Virtual Reality Neuroscience Questionnaire (VRNQ) was developed to assess the quality of VR software in terms of user experience, game mechanics, in-game assistance, and VRISE. Forty participants aged between 28 and 43 years were recruited (18 gamers and 22 non-gamers) for the study. They participated in 3 different VR sessions until they felt weary or discomfort and subsequently filled in the VRNQ. Our results demonstrated that VRNQ is a valid tool for assessing VR software as it has good convergent, discriminant, and construct validity. The maximum duration of VR sessions should be between 55 and 70 min when the VR software meets or exceeds the parsimonious cut-offs of the VRNQ and the users are familiarized with the VR system. Also, the gaming experience does not seem to affect how long VR sessions should last. Also, while the quality of VR software substantially modulates the maximum duration of VR sessions, age and education do not. Finally, deeper immersion, better quality of graphics and sound, and more helpful in-game instructions and prompts were found to reduce VRISE intensity. The VRNQ facilitates the brief assessment and reporting of the quality of VR software features and/or the intensity of VRISE, while its minimum and parsimonious cut-offs may appraise the suitability of VR software for implementation in research and clinical settings. The findings of this study contribute to the establishment of rigorous VR methods that are crucial for the viability of immersive VR as a research and clinical tool in cognitive neuroscience and neuropsychology.

A theory of the discovery and predication of relational concepts.

Relational thinking plays a central role in human cognition. However, it is not known how children and adults acquire relational concepts and come to represent them in a form that is useful for the purposes of relational thinking (i.e., as structures that can be dynamically bound to arguments). The authors present a theory of how a psychologically and neurally plausible cognitive architecture can discover relational concepts from examples and represent them as explicit structures (predicates) that can take arguments (i.e., predicate them). The theory is instantiated as a computer program called DORA (Discovery Of Relations by Analogy). DORA is used to simulate the discovery of novel properties and relations, as well as a body of empirical phenomena from the domain of relational learning and the development of relational representations in children and adults.

Individual Differences in Relational Learning and Analogical Reasoning: A Computational Model of Longitudinal Change.

Children's cognitive control and knowledge at school entry predict growth rates in analogical reasoning skill over time; however, the mechanisms by which these factors interact and impact learning are unclear. We propose that inhibitory control (IC) is critical for developing both the relational representations necessary to reason and the ability to use these representations in complex problem solving. We evaluate this hypothesis using computational simulations in a model of analogical thinking, Discovery of Relations by Analogy/Learning and Inference with Schemas and Analogy (DORA/LISA; Doumas et al., 2008). Longitudinal data from children who solved geometric analogy problems repeatedly over 6 months show three distinct learning trajectories though all gained somewhat: analogical reasoners throughout, non-analogical reasoners throughout, and transitional - those who start non-analogical and grew to be analogical. Varying the base level of top-down lateral inhibition in DORA affected the model's ability to learn relational representations, which, in conjunction with inhibition levels used in LISA during reasoning, simulated accuracy rates and error types seen in the three different learning trajectories. These simulations suggest that IC may not only impact reasoning ability but may also shape the ability to acquire relational knowledge given reasoning opportunities.

Representing composed meanings through temporal binding.

A key feature of human thought and language is compositionality, the ability to bind pre-existing concepts or word meanings together in order to express new ideas. Here we ask how newly composed complex concepts are mentally represented and matched to the outside world, by testing whether it is harder to verify if a picture matches the meaning of a phrase, like big pink tree, than the meaning of a single word, like tree. Five sentence-picture verification experiments provide evidence that, in fact, the meaning of a phrase can often be checked just as fast as the meaning of one single word (and sometimes faster), indicating that the phrase's constituent concepts can be represented and checked in parallel. However, verification times were increased when matched phrases had more complex modification structures, indicating that it is costly to represent structural relations between constituent concepts. This pattern of data can be well-explained if concepts are composed together using two different mechanisms, binding by synchrony and binding by asynchrony, which have been suggested as solutions to the "binding problem" faced in both vision science and higher-level cognition. Our results suggest that they can also explain aspects of compositional language processing.

Shaping relations: Exploiting relational features for visuospatial priming.

Although relational reasoning has been described as a process at the heart of human cognition, the exact character of relational representations remains an open debate. Symbolic-connectionist models of relational cognition suggest that relations are structured representations, but that they are ultimately grounded in feature sets; thus, they predict that activating those features can affect the trajectory of the relational reasoning process. The present work points out that such models do not necessarily specify what those features are though, and endeavors to show that spatial information is likely a part of it. To this end, it presents 2 experiments that used visuospatial priming to affect the course of relational reasoning. The first is a relational category-learning experiment in which this type of priming was shown to affect which spatial relation was learned when multiple were possible. The second used crossmapping analogy problems, paired with this same type of priming, to show that visuospatial cues can make participants more likely to map analogs based on relational roles, even with short presentation times. (PsycINFO Database Record

Varying variation: the effects of within- versus across-feature differences on relational category learning.

Learning of feature-based categories is known to interact with feature-variation in a variety of ways, depending on the type of variation (e.g., Markman and Maddox, 2003). However, relational categories are distinct from feature-based categories in that they determine membership based on structural similarities. As a result, the way that they interact with feature variation is unclear. This paper explores both experimental and computational data and argues that, despite its reliance on structural factors, relational category-learning should still be affected by the type of feature variation present during the learning process. It specifically suggests that within-feature and across-feature variation should produce different learning trajectories due to a difference in representational cost. The paper then uses the DORA model (Doumas et al., 2008) to discuss how this account might function in a cognitive system before presenting an experiment aimed at testing this account. The experiment was a relational category-learning task and was run on human participants and then simulated in DORA. Both sets of results indicated that learning a relational category from a training set with a lower amount of variation is easier, but that learning from a training set with increased within-feature variation is significantly less challenging than learning from a set with increased across-feature variation. These results support the claim that, like feature-based category-learning, relational category-learning is sensitive to the type of feature variation in the training set.

Comparison and mapping facilitate relation discovery and predication.

Relational concepts play a central role in human perception and cognition, but little is known about how they are acquired. For example, how do we come to understand that physical force is a higher-order multiplicative relation between mass and acceleration, or that two circles are the same-shape in the same way that two squares are? A recent model of relational learning, DORA (Discovery of Relations by Analogy; Doumas, Hummel & Sandhofer, 2008), predicts that comparison and analogical mapping play a central role in the discovery and predication of novel higher-order relations. We report two experiments testing and confirming this prediction.

A window of perception when diverting attention? Enhancing recognition for explicitly presented, unattended, and irrelevant stimuli by target alignment.

Research has demonstrated that irrelevant suprathreshold motion stimuli that are aligned with attended targets in a separate task, fail to illicit inhibitory control in a subsequent motion direction discrimination task (Tsushima, Seitz, & Watanabe, 2008). We extended these findings to conditions involving higher exposure levels to a more salient stimulus (written words) in an inattentional blindness paradigm. Across three experiments, participants were required to respond to immediate picture repetitions in a stream of serially presented line drawings, while at the same time ignore a simultaneously presented stream of superimposed words. Immediately following, a surprise test was given that measured recognition for the unattended words. Words that had appeared simultaneously with a repeated picture in the repetition detection task were not inhibited, but instead recognized significantly more often than words that had appeared with nonrepeating pictures. These findings support the notion that when attention is taxed, recognition for target-aligned task-irrelevant semantic items can be enhanced in a subsequent recognition task. This indicates a learning effect for frequently exposed, high-level irrelevant-stimuli that were temporally aligned with a relevant item in a separate task.

Conditions of directed attention inhibit recognition performance for explicitly presented target-aligned irrelevant stimuli.

The fate of irrelevant and overtly presented stimuli that was temporally aligned with an attended target in a separate task was explored. Seitz and Watanabe (2003) demonstrated that if an irrelevant motion stimulus was implicit (i.e., subthreshold), a later facilitation for the same motion direction was observed if the previously presented implicit motion (of the same direction) was temporally aligned with the presence of an attended target. Later research, however, demonstrated that if the motion stimulus aligned with the attended target was explicit (i.e., suprathreshold), a later inhibition was observed (Tsushima, Seitz, & Watanabe, 2008). The current study expands on this by using more salient stimuli (words and pictures) in an inattentional blindness paradigm, and suggests that when attention is depleted, recognition for target-aligned task-irrelevant items is impaired in a subsequent recognition task. Participants were required to respond to either immediate picture, or word, repetitions in a stream of simultaneously presented line drawings and written words, and later given a surprise recognition test that measured recognition for the words or the pictures. When analyzing word recognition performance after attention had been directed to the pictures, words that had appeared simultaneously with a picture repetition in the repetition detection task were recognized at levels significantly below chance. The same inhibition was mirrored when testing for picture recognition after having attended to the words in the repetition detection task. These data suggest an inhibitory mechanism that is exhibited in later recognition tests for salient information that was previously unattended and had been simultaneously being presented with an attended target in a different task.

A computational account of the development of the generalization of shape information.

Abecassis, Sera, Yonas, and Schwade (2001) showed that young children represent shapes more metrically, and perhaps more holistically, than do older children and adults. How does a child transition from representing objects and events as undifferentiated wholes to representing them explicitly in terms of their attributes? According to RBC (Recognition-by-Components theory; Biederman, 1987), objects are represented as collections of categorical geometric parts ("geons") in particular categorical spatial relations. We propose that the transition from holistic to more categorical visual shape processing is a function of the development of geon-like representations via a process of progressive intersection discovery. We present an account of this transition in terms of DORA (Doumas, Hummel, & Sandhofer, 2008), a model of the discovery of relational concepts. We demonstrate that DORA can learn representations of single geons by comparing objects composed of multiple geons. In addition, as DORA is learning it follows the same performance trajectory as children, originally generalizing shape more metrically/holistically and eventually generalizing categorically.