Parametric Representation of Tactile Numerosity in Working Memory

Estimated numerosity perception is processed in an approximate number system (ANS) that resembles the perception of a continuous magnitude. The ANS consists of a right lateralized frontoparietal network comprising the lateral prefrontal cortex (LPFC) and the intraparietal sulcus. Although the ANS has been extensively investigated, only a few studies have focused on the mental representation of retained numerosity estimates. Specifically, the underlying mechanisms of estimated numerosity working memory (WM) is unclear. Besides numerosities, as another form of abstract quantity, vibrotactile WM studies provide initial evidence that the right LPFC takes a central role in maintaining magnitudes. In the present fMRI multivariate pattern analysis study, we designed a delayed match-to-numerosity paradigm to test what brain regions retain approximate numerosity memoranda. In line with parametric WM results, our study found numerosity-specific WM representations in the right LPFC as well as in the supplementary motor area and the left premotor cortex extending into the superior frontal gyrus, thus bridging the gap in abstract quantity WM literature

Population-level neural coding for higher cognition

Higher cognition encompasses advanced mental processes that enable complex thinking, decision-making, problem-solving, and abstract reasoning. These functions involve integrating information from multiple sensory modalities and organizing action plans based on the abstraction of past information. The neural activity underlying these functions is often complex, and the contribution of single neurons in supporting population-level representations of cognitive variables is not yet clear. In this thesis, I investigated the neural mechanisms underlying higher cognition in higher-order brain regions with single-neuron resolution in human and non-human primates performing working memory tasks. I aimed to understand how representations are arranged and how neurons contribute to the population code. In the first manuscript, I investigated the population-level neural coding for the maintenance of numbers in working memory within the parietal association cortex. By analyzing intra-operative intracranial micro-electrode array recording data, I uncovered distinct representations for numbers in both symbolic and nonsymbolic formats. In the second manuscript, I delved deeper into the neuronal organizing principles of population coding to address the ongoing debate surrounding memory maintenance mechanisms. I unveiled sparse structures in the neuronal implementation of representations and identified biologically meaningful components that can be directly communicated to downstream neurons. These components were linked to subpopulations of neurons with distinct physiological properties and temporal dynamics, enabling the active maintenance of working memory while resisting distraction. Lastly, using an artificial neural network model, I demonstrated that the sparse implementation of temporally modulated working memory representations is preferred in recurrently connected neural populations such as the prefrontal cortex. In summary, this thesis provides a comprehensive investigation of higher cognition in higher-order brain regions, focusing on working memory tasks involving numerical stimuli. By examining neural population coding and unveiling sparse structures in the neuronal implementation of representations, our findings contribute to a deeper understanding of the mechanisms underlying working memory and higher cognitive functions

Common and Distinct Brain Regions Support Numerical and Non-numerical Magnitude Processing: A Functional Neuroimaging Meta-Analysis

A current debate is whether number is processed using a number-specific system or a general magnitude processing system used for non-numerical magnitudes such as space. Activation likelihood estimation (ALE) was used to conduct the first quantitative meta-analysis of 20 empirical neuroimaging papers examining neural activation during numerical and non-numerical magnitude processing. Foci were compiled to generate probabilistic maps of activation for symbolic numerical magnitudes, nonsymbolic numerical magnitudes and non-numerical magnitudes. Conjunction analyses revealed overlapping activation for symbolic, nonsymbolic and non-numerical magnitudes in frontal and parietal lobes. Contrast analyses revealed specific activation in the left superior parietal lobule (SPL) and right inferior parietal lobule (IPL) for symbolic numerical magnitudes. In contrast, anterior right IPL was specifically activated for nonsymbolic numerical magnitudes. No parietal regions were activated for non-numerical that were not also activated for numerical magnitudes. Therefore, numbers are processed using both a generalized magnitude system and format specific number regions

Processing of quantitative information, investigated with fMRI.

Ever since the discovery of the ‘number neurons’, the neural representation of quantity in the brain has been thought of as a number-selective coding system. In such a system, the neuron is activated by a specific quantity but numerically close quantities also activate the neuron. Recent fMRI studies also confirmed the existence of a number-selective system in humans. Several computational modelling studies predicted a number-sensitive coding stage as a necessary preceding stage to the number-selective neurons (Verguts & Fias, 2004). In this coding scheme, the coding is analogous to the number it represents. This can be implemented by neurons that respond monotonically to number (e.g., more strongly for larger numbers). Recently, the biological reality of such a system has been demonstrated by use of single-cell recording, in the lateral intraparietal area (LIP) of the macaque monkey. In this thesis, we searched for evidence of number-sensitive coding in humans. Using a priming paradigm, we found behavioural evidence for a number-sensitive system in humans for small non-symbolic numerosities (1 to 5). Using event-related fMRI, we showed number-sensitive activation in the human LIP area in the same number range. Remarkably, we could not extend these results for larger numerosities (2 to 64). Whereas the lack of results in the behavioural priming experiment could be due to an insensitivity of the method, this was not a plausible explanation in the fMRI experiment, as the activity measured in human LIP significantly decreased for numerosities larger than 8. We therefore concluded that the number-sensitive system is liable to a capacity limit for higher numerosities, which could be caused by the use of lateral inhibition. We further suggest that the implementation of this lateral inhibition is dependent on the particular task set, and that the capacity limit is not present (or less stringent) when numerosity is not behaviourally relevant. This could explain the finding of number-sensitive neurons for larger numerosities in monkeys. Finally, we suggest that a different mechanism is employed when numerical value of large numerosities is relevant. This leads to the conclusion that dot patterns in the small and large number range are processed differently

Cortical Representation Underlying the Semantic Processing of Numerical Symbols: Evidence from Adult and Developmental Studies

Humans possess the remarkable ability to process numerical information using numerical symbols such as Arabic digits. A growing body of neuroimaging work has provided new insights into the neural correlates associated with symbolic numerical magnitude processing. However, little is known about the cortical specialization underlying the representation of symbolic numerical magnitude in adults and children. To constrain our current knowledge, I conducted a series of functional Magnetic Resonance Imaging (fMRI) studies that aimed to better understand the functional specialization of symbolic numerical magnitudes representation in the human brain. Using a number line estimation task, the first study contrasted the brain activation associated with processing symbolic numerical magnitude against the brain activation associated with non-numerical magnitude (brightness) processing. Results demonstrated a right lateralized parietal network that was commonly engaged when magnitude dimensions were processed. However, the left intraparietal sulcus (IPS) was additionally activated when symbolic numerical magnitudes were estimated, suggesting that number is a special category amongst magnitude dimensions and that the left hemisphere plays a critical role in representing number. The second study tested a child friendly version of an fMRI-adaptation paradigm in adults. For this participant’s brain response was habituated to a numerical value (i.e., 6) and signal recovery in response to the presentation of numerical deviants was investigated. Across two different brain normalization procedures results showed a replication of previous findings demonstrating that the brain response of the IPS is modulated by the semantic meaning of numbers in the absence of overt response selection. The last study aimed to unravel developmental changes in the cortical representation of symbolic numerical magnitudes in children. Using the paradigm tested in chapter 2, results demonstrated an increase in the signal recovery with age in the left IPS as well as an age-independent signal recovery in the right IPS. This finding indicates that the left IPS becomes increasingly specialized for the representation of symbolic numerical magnitudes over developmental time, while the right IPS may play a different and earlier role in symbolic numerical magnitude representation. Findings of these studies are discussed in relation to our current knowledge about symbolic numerical magnitude representation

Multiple faces elicit augmented neural activity

How do our brains respond when we are being watched by a group of people? Despite the large volume of literature devoted to face processing, this question has received very little attention. Here we measured the effects on the face-sensitive N170 and other ERPs to viewing displays of one, two and three faces in two experiments. In Experiment 1, overall image brightness and contrast were adjusted to be constant, whereas in Experiment 2 local contrast and brightness of individual faces were not manipulated. A robust positive-negative-positive (P100-N170-P250) ERP complex and an additional late positive ERP, the P400, were elicited to all stimulus types. As the number of faces in the display increased, N170 amplitude increased for both stimulus sets, and latency increased in Experiment 2. P100 latency and P250 amplitude were affected by changes in overall brightness and contrast, but not by the number of faces in the display per se. In Experiment 1 when overall brightness and contrast were adjusted to be constant, later ERP (P250 and P400) latencies showed differences as a function of hemisphere. Hence, our data indicate that N170 increases its magnitude when multiple faces are seen, apparently impervious to basic low-level stimulus features including stimulus size. Outstanding questions remain regarding category-sensitive neural activity that is elicited to viewing multiple items of stimulus categories other than faces

Visual Enumeration and Estimation: Brain mechanisms, Attentional demands and Number representations.

The work presented in this thesis explored the roles of attention and number awareness in visual enumeration and estimation through a variety of methods. First, a distinction was made between different attentional modes underlying estimation and enumeration in an in-depth single case study of a patient with simultagnosia. Subsequently I demonstrated that, in visual enumeration, subitizing and counting are dissociable processes and they rely on different brain structures. This was done through a neuropsychological single case study as well as through the first large sample neuropsychological group study using a voxel-based correlation method. Following this, behavioural methods were used to examine the relations between subitizing and estimation. I found that, under conditions encouraging estimation, subitizing is an automatic process and may lead to the exact representation of small numbers, which contrasts with approximate representations for larger numerosities. Finally, a functional MRI study was conducted to highlight the brain regions that are activated for subitizable numerosities, but not for larger numerosities under distributed attention conditions. The imaging study provided converging evidence for automatic subitizing leading to an exact number representation. The last chapter discusses the implications of the contrast between subitization and counting for understanding numerical processing

Number-related Brain Potentials Are Differentially Affected by Mapping Novel Symbols on Small versus Large Quantities in a Number Learning Task

The nature of the mapping process that imbues number symbols with their numerical meaning-known as the "symbolgrounding process"-remains poorly understood and the topic of much debate. The aim of this study was to enhance insight into how the nonsymbolic-symbolic number mapping process and its neurocognitive correlates might differ between small (1-4; subitizing range) and larger (6-9) numerical ranges. Hereto, 22 young adults performed a learning task in which novel symbols acquired numerical meaning by mapping them onto nonsymbolic magnitudes presented as dot arrays (range 1-9). Learning-dependent changes in accuracy and RT provided evidence for successful novel symbol quantity mapping in the subitizing (1-4) range only. Corroborating these behavioral results, the number processing related P2p component was only modulated by the learning/mapping of symbols representing small numbers 1-4. The symbolic N1 amplitude increased with learning independent of symbolic numerical range but dependent on the set size of the preceding dot array; it only occurred when mapping on one to four item dot arrays that allow for quick retrieval of a numeric value, on the basis of which, with learning, one could predict the upcoming symbol causing perceptual expectancy violation when observing a different symbol. These combined results suggest that exact nonsymbolic-symbolic mapping is only successful for small quantities 1-4 from which one can readily extract cardinality. Furthermore, we suggest that the P2p reflects the processing stage of first access to or retrieval of numeric codes and might in future studies be used as a neural correlate of nonsymbolic-symbolic mapping/symbol learning