Abstract:
This dissertation investigates how the brain of non-human primates encodes, retains
and recognizes numerical information, combining behavioral and neuronal approaches.
Understanding how numerical cognition emerges from distributed neural processing
offers a unique opportunity to explore the interaction between working memory,
recognition memory and decision-making – three core components of higher cognition.
Electrophysiological recordings from the dorsolateral prefrontal (dlPFC) and posterior
parietal (PPC) cortices of rhesus monkeys performing a sequential delayed match-to-numerosity task revealed that single neurons encode recognition memory for numerical
quantities. During comparisons of the sample stimulus with up to three test stimuli, the
neurons exhibited distinct yet interacting patterns of familiarity modulation, including
repetition suppression and match enhancement. These results suggest that recognition
memory encompasses abstract, non-symbolic representations, and that the ability to
detect familiarity is distributed across prefrontal and parietal networks.
At the population level, the interaction between dlPFC and PPC was further
characterized by analyzing simultaneously recorded neuronal activity using canonical
correlation analysis (CCA). This approach revealed dynamic, direction-specific
coupling between both regions that varied across task phases. During stimulus
encoding, information flow was predominantly feedforward from parietal to prefrontal
areas, whereas maintenance periods were marked by more balanced interactions.
These results suggest that numerical recognition arises from the flexible coordination
of the frontoparietal network as a whole, rather than from the isolated processing of a
single brain region.
Extending these findings to a comparative perspective, behavioral performance in the
same task was examined across species, revealing that rhesus monkeys and carrion
crows show comparable accuracy and numerical effects, such as distance and size
effects. Despite these similarities, the temporal dynamics of decision-making differed
systematically between species. Monkeys exhibited increasing reaction times across
successive test phases, whereas crows responded progressively faster. Machinelearning
analyses further demonstrated that reaction times carried information about numerosity and task phase in monkeys but not in crows, suggesting species-specific
strategies in numerical recognition and decision control.
Together, these studies provide a comprehensive, multi-level account of numerical
cognition, covering from single-neuron encoding and network dynamics to behavior
across species. By integrating neurophysiological and behavioral evidence, this thesis
sheds light on abstract cognitive functions, such as number recognition.
