Learning dynamics depend on internal and contextual multifactors among which the interaction between motivational context and task demands plays an important role. Spatial navigation provides a powerful framework to study these interactions as it integrates cognitive, motivational, and motor components. To examine how reward modality and task complexity modulate learning dynamics in male mice, we used a sequential navigation task in which the number of decision points progressively increased across training, while preserving the same geometrical context and goal location. We compared three versions of the same task, in which the fixed goal location was rewarded by either escape from water (aquatic version), food pellets, or medial forebrain bundle (MFB) stimulation associated with different levels of complexity. At low task complexity, the aquatic group achieved a higher level of performance. However, this effect does not extend to the acquisition of the most complex sequence, as all groups ultimately converge toward comparable abilities to perform direct trials. Furthermore, reward type appears to modulate motivational engagement and speed in all training conditions. Together, these data show that, during spatial navigation, reward modality influences not only the level of learning but also its behavioral execution.

More measurements don’t eliminate blind spots when each view captures only part of the device.

Read the full paper: Early Spatial and Contextual Coding Deficits in Hippocampal CA1 Precede Performance Decline in an Alzheimer's Disease Model

We show that valid spatial cues directing internal attention during long-term memory retrieval boost recall, while invalid cues incur costs. Our work offers a flexible experimental framework to study ...

The global Augmented Reality (AR) Software Market is experiencing a major transformation, propelled by the rapid integration of immersive digital overlay technology across commercial and industrial enterprise operations. Augmented reality software enables real-time overlay of digital information such as 3D visual models, contextual data, and spatial spatial annotations onto physical real-world environments via mobile devices,…

Two-photon calcium imaging in behaving 5xFAD mice reveals early CA1 coding deficits before behavioral decline. Task-related place cell activity and splitter cell trajectory coding are impaired, with ....

Contextual information, such as spatial locations and social groups, are known to propagate reward-seeking behavior including binge drinking. Animal models suggest that context-evoked drug and alcoho....

Cortical pyramidal neurons project a long apical dendrite to layer 1 (L1), to receive synaptic inputs that are thought to provide contextual and possibly suppressive modulation. In visual cortex, thes...

As artificial intelligence systems are deployed in open-ended, high-stakes settings, a critical dimension remains unmeasured: how perceived risk is translated into action. We test whether large language models (LLMs) exhibit systematic and consistent risk attitudes under uncertainty. We introduce a cross-domain framework that decouples contextual risk belief from categorical decision, and apply it to six representative LLMs and 100 human participants across spatial navigation, clinical triage, and financial allocation tasks. Using regression models, we extract each agents belief-to-decision mapping and quantify risk sensitivity and risk attitude bias. We find that most tested LLMs exhibit (i) robust intra-task consistency, indicating stable mappings from contextual belief to risk decision within a fixed task domain; (ii) cross-domain rank-order stability, preserving relative risk posture across tasks; and (iii) a convergence toward a restricted risk-attitude distribution relative to the broader human baseline. These results reveal risk attitude as a stable and previously uncharacterized dimension of LLM behavior, establishing a foundation for evaluating and aligning AI systems in open-ended decision-making and motivating further investigation into the origins of these intrinsic behavioral dispositions.

We show that valid spatial cues directing internal attention during long-term memory retrieval boost recall, while invalid cues incur costs. Our work offers a flexible experimental framework to study ...

Alzheimer's disease (AD) is characterized by progressive memory decline, yet how hippocampal representations change at early AD stages remains largely unknown. In this study, we utilized a real-world ...

Tel Aviv, Israel--(Newsfile Corp. - June 9, 2026) - Kardome, the leader in spatial and contextual voice AI software, has been named the winner of the In-Vehicle Experience Award at the AutoTech Awards 2026. The recognition marks a significant milestone for the company's In-Vehicle Voice AI solution, an on-device platform purpose-built to give AI genuine contextual intelligence inside the cabin, solving the industry's persistent challenge of accurately isolating and understanding individual...

At WWDC 2026 on Monday, Apple introduced a slew of Siri and Apple Intelligence features like contextual awareness and Spatial Reframing for adjusting photo

Background. Facial expression recognition depends on how visual information is sampled across the face over time. Static area-of-interest (AOI) measures describe where observers look but provide limited information about the sequential organization of gaze. This study examined how gaze is organized during facial expression recognition and whether this organization remains comparable across two conditions differing in the temporal order of contextual and facial stimuli. Methods. Eye-tracking data were collected from 27 participants performing a facial expression recognition task. Fixations on faces were mapped onto three AOIs: Upper Facial Zone (UFZ), Central Facial Zone (CFZ), and Lower Facial Zone (LFZ). Gaze organization was examined using first- and second-order Markov models, entropy estimates, spatial repositioning measures, and a gaze stability index. Results. Gaze transitions showed a structured, non-random organization centered on the CFZ. In the first-order Markov model, transitions from both the UFZ and LFZ were directed primarily toward the CFZ, and within-zone transitions were also most likely in the CFZ. Entropy was lower for the CFZ than for the upper and lower regions, indicating lower transition uncertainty in the central region. The second-order model showed an influence of recent fixation history while preserving the predominance of the CFZ. Spatial repositioning varied across facial zones in both conditions. However, mixed-effects analyses showed no effect of condition on gaze stability. Conclusions. Facial expression recognition was associated with a pattern of exploration in which the central facial region emerged as the most likely fixation destination, with limited evidence of condition-related differences in gaze organization.