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A long-standing question in neuroscience is how perceptual processes select stimuli for encoding and later retrieval by memory processes. Using a functional magnetic resonance imaging study with human participants, we report the discovery of a global, stimulus-driven processing stream that we call memorability. Memorability automatically tags the statistical distinctiveness of stimuli for later encoding, and shows separate neural signatures from both low-level perception (memorability shows no signal in early visual cortex) and classical subsequent memory based on individual memory. Memorability and individual subsequent memory show dissociable neural substrates: first, memorability effects consistently emerge in the medial temporal lobe (MTL), whereas individual subsequent memory effects emerge in the prefrontal cortex (PFC). Second, memorability effects remain consistent even in the absence of memory (i.e., for forgotten images). Third, the MTL shows higher correlations with memorability-based patterns, while the PFC shows higher correlations with individual memory voxels patterns. Taken together, these results support a reformulated framework of the interplay between perception and memory, with the MTL determining stimulus statistics and distinctiveness to support later memory encoding, and the PFC comparing stimuli to specific individual memories. As stimulus memorability is a confound present in many previous memory studies, these findings should stimulate a revisitation of the neural streams dedicated to perception and memory. •Memorability is an intrinsic perceptual property with dedicated neural signals.•Ventral visual stream, medial temporal lobe sensitive to face, scene memorability.•Early visual cortex and attention regions show no memorability sensitivity.•Memorability effects exist even in the absence of memory (i.e., forgotten images).•Dissociation of neural patterns and loci between memorability and memory.

Suitable for readers studying or working in the fields of computer graphics and visualization, cognitive science, and visual neuroscience, this book provides an introduction to human visual perception. It focuses on how computer graphics images are generated, rather than solely on the visual system's organization, so the text provides a more direct tie between image generation and the resulting perceptual phenomena. It covers topics such as illumination, action, and perception of factors including material properties, pictorial space, image statistics, and spatial cognition.

Electrocorticography (ECoG) based Brain-Computer Interfaces (BCIs) have been proposed as a way to restore and replace motor function or communication in severely paralyzed people. To date, most motor-based BCIs have either focused on the sensorimotor cortex as a whole or on the primary motor cortex (M1) as a source of signals for this purpose. Still, target areas for BCI are not confined to M1, and more brain regions may provide suitable BCI control signals. A logical candidate is the primary somatosensory cortex (S1), which not only shares similar somatotopic organization to M1, but also has been suggested to have a role beyond sensory feedback during movement execution. Here, we investigated whether four complex hand gestures, taken from the American sign language alphabet, can be decoded exclusively from S1 using both spatial and temporal information. For decoding, we used the signal recorded from a small patch of cortex with subdural high-density (HD) grids in five patients with intractable epilepsy. Notably, we introduce a new method of trial alignment based on the increase of the electrophysiological response, which virtually eliminates the confounding effects of systematic and non-systematic temporal differences within and between gestures execution. Results show that S1 classification scores are high (76%), similar to those obtained from M1 (74%) and sensorimotor cortex as a whole (85%), and significantly above chance level (25%). We conclude that S1 offers characteristic spatiotemporal neuronal activation patterns that are discriminative between gestures, and that it is possible to decode gestures with high accuracy from a very small patch of cortex using subdurally implanted HD grids. The feasibility of decoding hand gestures using HD-ECoG grids encourages further investigation of implantable BCI systems for direct interaction between the brain and external devices with multiple degrees of freedom. •Primary somatosensory cortex offers discriminative patterns between four gestures.•Spatiotemporal information is robust and reliable for fine movement decoding.•A new trial alignment method to reduce decoding temporal jitter is introduced.•Optimal coverage and consistent execution are essential for accurate decoding.

The estimation of the correct number of dimensions is a long-standing problem in psychometrics. Several methods have been proposed, such as parallel analysis (PA), Kaiser-Guttman's eigenvalue-greater-than-one rule, multiple average partial procedure (MAP), the maximum-likelihood approaches that use fit indexes as BIC and EBIC and the less used and studied approach called very simple structure (VSS). In the present paper a new approach to estimate the number of dimensions will be introduced and compared via simulation to the traditional techniques pointed above. The approach proposed in the current paper is called exploratory graph analysis (EGA), since it is based on the graphical lasso with the regularization parameter specified using EBIC. The number of dimensions is verified using the walktrap, a random walk algorithm used to identify communities in networks. In total, 32,000 data sets were simulated to fit known factor structures, with the data sets varying across different criteria: number of factors (2 and 4), number of items (5 and 10), sample size (100, 500, 1000 and 5000) and correlation between factors (orthogonal, .20, .50 and .70), resulting in 64 different conditions. For each condition, 500 data sets were simulated using lavaan. The result shows that the EGA performs comparable to parallel analysis, EBIC, eBIC and to Kaiser-Guttman rule in a number of situations, especially when the number of factors was two. However, EGA was the only technique able to correctly estimate the number of dimensions in the four-factor structure when the correlation between factors were .7, showing an accuracy of 100% for a sample size of 5,000 observations. Finally, the EGA was used to estimate the number of factors in a real dataset, in order to compare its performance with the other six techniques tested in the simulation study.

Visual reasoning is essential for building intelligent agents that understand the world and perform problem-solving beyond perception. Differentiable forward reasoning has been developed to integrate reasoning with gradient-based machine learning paradigms. However, due to the memory intensity, most existing approaches do not bring the best of the expressivity of first-order logic, excluding a crucial ability to solve visual reasoning , where agents need to perform reasoning by using analogies on abstract concepts in different scenarios. To overcome this problem, we propose NEUro-symbolic Message-pAssiNg reasoNer (NEUMANN) , which is a graph-based differentiable forward reasoner, passing messages in a memory-efficient manner and handling structured programs with functors. Moreover, we propose a computationally-efficient structure learning algorithm to perform explanatory program induction on complex visual scenes. To evaluate, in addition to conventional visual reasoning tasks, we propose a new task, visual reasoning behind-the-scenes , where agents need to learn abstract programs and then answer queries by imagining scenes that are not observed. We empirically demonstrate that NEUMANN solves visual reasoning tasks efficiently, outperforming neural, symbolic, and neuro-symbolic baselines.

Many researchers in the behavioral sciences depend on research software that presents stimuli, and records response times, with sub-millisecond precision. There are a large number of software packages with which to conduct these behavioral experiments and measure response times and performance of participants. Very little information is available, however, on what timing performance they achieve in practice. Here we report a wide-ranging study looking at the precision and accuracy of visual and auditory stimulus timing and response times, measured with a Black Box Toolkit. We compared a range of popular packages: PsychoPy, E-Prime®, NBS Presentation®, Psychophysics Toolbox, OpenSesame, Expyriment, Gorilla, jsPsych, Lab.js and Testable. Where possible, the packages were tested on Windows, macOS, and Ubuntu, and in a range of browsers for the online studies, to try to identify common patterns in performance. Among the lab-based experiments , Psychtoolbox, PsychoPy, Presentation and E-Prime provided the best timing, all with mean precision under 1 millisecond across the visual, audio and response measures. OpenSesame had slightly less precision across the board, but most notably in audio stimuli and Expyriment had rather poor precision. Across operating systems , the pattern was that precision was generally very slightly better under Ubuntu than Windows, and that macOS was the worst, at least for visual stimuli, for all packages. Online studies did not deliver the same level of precision as lab-based systems, with slightly more variability in all measurements. That said, PsychoPy and Gorilla, broadly the best performers, were achieving very close to millisecond precision on several browser/operating system combinations. For response times (measured using a high-performance button box), most of the packages achieved precision at least under 10 ms in all browsers, with PsychoPy achieving a precision under 3.5 ms in all. There was considerable variability between OS/browser combinations, especially in audio-visual synchrony which is the least precise aspect of the browser-based experiments. Nonetheless, the data indicate that online methods can be suitable for a wide range of studies, with due thought about the sources of variability that result. The results, from over 110,000 trials, highlight the wide range of timing qualities that can occur even in these dedicated software packages for the task. We stress the importance of scientists making their own timing validation measurements for their own stimuli and computer configuration.

Differences in audiovisual integration are commonly observed in autism. Temporal binding windows (TBWs) of audiovisual speech can be trained (i.e., narrowed) in non-autistic adults; this study evaluated a computer-based perceptual training in autistic youth and assessed whether treatment outcomes varied according to individual characteristics. Thirty autistic youth aged 8–21 were randomly assigned to a brief perceptual training (n = 15) or a control condition (n = 15). At post-test, the perceptual training group did not differ, on average, on TBWs for trained and untrained stimuli and perception of the McGurk illusion compared to the control group. The training benefited youth with higher language and nonverbal IQ scores; the training caused widened TBWs in youth with co-occurring cognitive and language impairments.

Brain representations of visual space are predominantly eye-centred (retinotopic) yet our experience of the world is largely world-centred (spatiotopic). A long-standing question is how the brain creates continuity between these reference frames across successive eye movements (saccades). Here we use functional magnetic resonance imaging (fMRI) to address whether spatially specific repetition suppression (RS) is evident during trans-saccadic perception. We presented two successive Gabor patches (S1 and S2) in either the upper or lower visual field, left or right of fixation. Spatial congruency was manipulated by having S1 and S2 occur in the same or different upper/lower visual field. On half the trials, a saccade was cued between S1 and S2, placing spatiotopic and retinotopic reference frames in opposition. Equivalent RS was observed in the posterior parietal cortex and frontal eye fields when S1-S2 were spatiotopically congruent, irrespective of whether retinotopic and spatiotopic coordinates were in accord or were placed in opposition by a saccade. Additionally the post-saccadic response to S2 demonstrated spatially-specific RS in retinotopic visual regions, with stronger RS in extrastriate than striate cortex. Collectively, these results are consistent with a robust trans-saccadic spatial updating mechanism for object position that directly influences even the earliest levels of visual processing. •Repetition suppression (RS) was measured using fMRI.•Trans-saccadic RS occurs when the world centred stimulus location is the same.•This spatially-specific RS was found across saccades in early visual cortex.•Oculomotor regions showed similar RS with and without saccades.•Findings are consistent with trans-saccadic spatial updating.

Humans and many animals can distinguish between stimuli that differ in numerosity, the number of objects in a set. Human and macaque parietal lobes contain neurons that respond to changes in stimulus numerosity. However, basic non-numerical visual features can affect neural responses to and perception of numerosity, and visual features often co-vary with numerosity. Therefore, it is debated whether numerosity or co-varying low-level visual features underlie neural and behavioral responses to numerosity. To test the hypothesis that non-numerical visual features underlie neural numerosity responses in a human parietal numerosity map, we analyze responses to a group of numerosity stimulus configurations that have the same numerosity progression but vary considerably in their non-numerical visual features. Using ultra-high-field (7T) fMRI, we measure responses to these stimulus configurations in an area of posterior parietal cortex whose responses are believed to reflect numerosity-selective activity. We describe an fMRI analysis method to distinguish between alternative models of neural response functions, following a population receptive field (pRF) modeling approach. For each stimulus configuration, we first quantify the relationships between numerosity and several non-numerical visual features that have been proposed to underlie performance in numerosity discrimination tasks. We then determine how well responses to these non-numerical visual features predict the observed fMRI responses, and compare this to the predictions of responses to numerosity. We demonstrate that a numerosity response model predicts observed responses more accurately than models of responses to simple non-numerical visual features. As such, neural responses in cognitive processing need not reflect simpler properties of early sensory inputs. •We quantitatively test whether numerosity-tuned neural response reflect other visual features.•We quantify how non-numerical features change in common numerosity stimuli.•Parietal fMRI voxels respond to changes in stimulus numerosity.•Non-numerical stimulus feature changes predict these responses less well than numerosity.•Cognitive neural responses need not reflect low-level stimulus properties.