Figure 2 - uploaded by Peter Kok
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Data analysis (A) Visualization of the selected anatomical V1 ROI (light gray) on a mean functional image of an example participant. Overlaid red and yellow lines represent coregistered anatomical WM (yellow) and pial surface (red) boundaries to the mean functional image, showing voxels that were significantly active against baseline to the presented stimuli in the functional localizer task (green). (B) A mean functional image overlaid with distributions of voxels in superficial (green), middle (blue), and deep (red) layers of the cortex. (C) A schematic representing the level-set approach used to determine the volume distribution of a selected voxel (e.g., red square) over the superficial, middle, and deep cortical layers. 19,20 (D) A schematic of the decoding approach adopted here. Voxel proportions across the three layer bins in (C) were used to separate voxels according to the majority layer and formed layer masks for V1. Linear classifiers (SVMs) were trained on CW and CCW stimuli from the localizer task and tested on Gabors from the main task. The procedure was repeated separately for expected and unexpected time courses and in each V1 layer mask.
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... design orthogonalized the Gabor orientation presentation from the response. Linear support vector machines (SVMs) were trained to discriminate Gabor orientations from V1 activation during a localizer and were tested on the main task, 18 separately for expected and unexpected events (Figure 2). Under the predictive processing account outlined above, an interaction is hypothesized between expectation and layer in decoding accuracy. ...
Context 2
... these active voxel masks were used to design a matrix of distributed voxels across each layer bin using the level-set definition described earlier. 20 These participant-specific design matrices specified the proportion of each active voxel across the 5 layer bins specified above (3GM, WM, CSF), where each voxel was binned into one of the three GM layer bins according to its majority proportion ( Figure 2D; see online materials for a complementary univariate analysis approach). For example, a voxel that was spatially located 7% in superficial, 76% in middle and 17% in deep layers would be labelled as a middle layer voxel and selected to contribute to the voxels in the middle layer mask. ...
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... 3 However, the methodological, more particular matter of the violation-of-expectation paradigm (see the general review by Margoni et al., 2023) will not be discussed here. 4 Nowadays it is known that unexpected events can only be connected to superficial layers of the visual primary area, while expected events are also connected to the deeper levels of that area- Thomas et al. (2024)-, and, thus, it is possible to suspect that expectations are coded in the brain in a different format than perceptions. (This publication studied human adults. ...
Is there a qualitative difference between apes’ and humans ‘ability to estimate others’ mental states’, a.k.a. ‘Theory-of-Mind’? After opting for the idea that expectations are empty profiles that recognize a particular content when it arrives, I apply the same description to ‘vicarious expectations’—very probably present in apes. Thus, (empty) vicarious expectations and one’s (full) contents are distinguished without needing meta-representation. Then, I propose: First, vicarious expectations are enough to support apes’ Theory-of-Mind (including ‘spontaneous altruism’). Second, since vicarious expectations require a profile previously built in the subject that activates them, this subject cannot activate any vicarious expectation of mental states that are intrinsically impossible for him. Third, your mental states that think of me as a distal individual are intrinsically impossible states for me, and therefore, to estimate them, I must estimate your mental contents. This ability (the original nucleus of the human Theory-of-Mind) is essential in the human lifestyle. It is involved in unpleasant and pleasant self-conscious emotions, which respectively contribute to ‘social order’ and to cultural innovations. More basically, it makes possible human (prelinguistic or linguistic) communication, since it originally made possible the understanding of others’ mental states as states that are addressed to me, and that are therefore impossible for me.
Keywords: human lifestyle; language evolution; mentalese; self-conscious emotions; Theory-of-Mind; vicarious expectations
... The use of UHF fMRI has yielded insights into the specific contributions of cortical layers in predictive processing. A wealth of studies has centered on the visual [25][26][27][28] and the somatosensory cortex [29] and have broadly confirmed the hypotheses regarding the mesoscopic implementation of PC. ...
... [20]. In humans, the relevance of the mesoscopic architecture to the processing of errors is so-far supported by a study conducted in the visual domain demonstrating that deviant stimuli are only decodable in superficial layers of V1 [28]. Our results extend this research and provide, for the first time in the human auditory cortex, evidence of the involvement of superficial cortical layers in processing prediction errors. ...
... We also found a robust effect in deep layers in response to mispredicted compared to predictable stimuli, which cannot be ascribed to local prediction differences, as the local context is the same across conditions. This deep layer neuronal modulation to mispredicted stimuli, was not reported in a previous laminar fMRI studies contrasting expected and unexpected stimuli in the visual domain [28]. ...
In our dynamic environments, predictive processing is vital for auditory perception and its associated behaviors. Predictive coding formalizes inferential processes by implementing them as information exchange across cortical layers and areas. With laminar-specific blood oxygenation level dependent we measured responses to a cascading oddball paradigm, to ground predictive auditory processes on the mesoscopic human cortical architecture. We show that the violation of predictions are potentially hierarchically organized and associated with responses in superficial layers of the planum polare and middle layers of the lateral temporal cortex. Moreover, we relate the updating of the brain’s internal model to changes in deep layers. Using a modeling approach, we derive putative changes in neural dynamics while accounting for draining effects. Our results support the role of temporal cortical architecture in the implementation of predictive coding and highlight the ability of laminar fMRI to investigate mesoscopic processes in a large extent of temporal areas.
... Empirical evidence for supporting PP in early visual processing primarily arises from studies observing early visually evoked responses in the absence of bottom-up input, across both deep and superficial layers of primary visual cortex (V1) (Aitken et al., 2020;Kok et al., 2016;Muckli et al., 2015). Ultra-high field fMRI studies showed, prior expectations selectively trigger stimulusspecific activity in the deep layers of the V1 (Aitken et al., 2020), while unexpected events invoke responses in superficial layers of V1 (Thomas et al., 2024). These findings support the PP laminar specification of prediction and prediction error in deep and superficial layers of early visual processing, respectively. ...
In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs and creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.
... However, we acknowledge that the current data cannot rule out a strategic decision effect, and we discuss future directions that can help establish this. Specifically, using neuroimaging we would be able to investigate whether expectation effects on orientation and confidence reports are reflected in the visual cortex (Aitken, Turner, & Kok, 2020;Thomas et al., 2024). ...
... In previous work where participants were unaware of the purpose of the cues, we found that content cues did not necessarily induce false percepts. However, in these studies false percepts still arose through stimulus-like signals reflecting the falsely perceived orientation Haarsma et al., 2024). One possibility is that explicit content cues induce sensory-like activity (Kok, Mostert, & De Lange, 2017;Aitken, Turner, & Kok, 2020) resulting in false percepts. ...
... Empirical evidence for supporting PP in early visual processing primarily arises from studies observing early visually evoked responses in the absence of bottom-up input, across both deep and superficial layers of primary visual cortex (V1) (Aitken et al., 2020;Kok et al., 2016;Muckli et al., 2015). Ultra-high field fMRI studies showed, prior expectations selectively trigger stimulusspecific activity in the deep layers of the V1 (Aitken et al., 2020), while unexpected events invoke responses in superficial layers of V1 (Thomas et al., 2024). These findings support the PP laminar specification of prediction and prediction error in deep and superficial layers of early visual processing, respectively. ...
In response to shortcomings of the current classification system in translating discoveries from basic science to clinical applications, NIMH offers a new framework for studying mental health disorders called Research Domain Criteria (RDoC). This framework holds a multidimensional outlook on psychopathologies focusing on functional domains of behavior and their implementing neural circuits. In parallel, the Predictive Processing (PP) framework stands as a leading theory of human brain function, offering a unified explanation for various types of information processing in the brain. While both 1 Corresponding author: Anahita Khorrami Banarak, Institute for Cognitive Science Studies, KHORRAMIAANAHITA@GMAIL.COM frameworks share an interest in studying psychopathologies based on pathophysiology, their integration still needs to be explored. Here, we argued in favor of the explanatory power of PP to be a groundwork for the RDoC matrix in validating its constructs, creating testable hypotheses about mechanistic interactions between molecular biomarkers and clinical traits, and finally, considering developmental trajectories and environmental factors in psychopathologies. Together, predictive processing may serve as a foundation for achieving the goals of the RDoC framework.
