ABOUT AMLAB

Our lab, led by Dr. Benchi Wang, who earned his PhD in 2019 in Amsterdam and is currently a tenured associate professor at South China Normal University, is well-supported by multiple national grants, providing ample resources for research within our designated topics. Our diverse team consists of members who share a keen interest in cognition and its neural mechanisms. They are not only friendly but also enthusiastic about engaging in discussions on any related questions.

Research Interests:

1. Salience and Cognition

Salience—the property that makes stimuli stand out—fundamentally shapes human cognition. It captures attention rapidly, sometimes overriding top-down goals, and influences what information is stored and retrieved in memory. My research examines both the benefits of salience, such as facilitating encoding and prioritization of important events, and its costs, such as distraction by irrelevant but striking stimuli. Using behavioral paradigms, eye-tracking, fMRI, and EEG/iEEG, I investigate how the brain represents salient information, and how learning and context modulate these effects. This direction highlights salience as a central principle in linking perception, attention, and memory within a unified cognitive system.

2. Unified Memory System

Traditional theories separate memory into distinct systems—working memory, long-term memory, episodic, semantic. I propose instead that these reflect different modes of operation within a single integrated memory system. Evidence from my work shows that long-term memory retrieval can bypass working memory, even when capacity is saturated, challenging the classical multi-store view. Neural data from the hippocampus and cortex suggest shared substrates across short-term buffering and long-term consolidation. This perspective reframes memory as a unified process with flexible mechanisms, better reflecting the dynamic demands of cognition.

3. Learning and Neural Plasticity

Learning is not only the acquisition of knowledge but also a process that reshapes neural activity and reorganizes cognitive systems. My research spans multiple forms of learning—perceptual, statistical, sequence, associative, and automatic—to understand how they modify brain representations. Statistical learning studies show that people extract environmental regularities to form priority maps that guide attention. Perceptual and sequence learning enhance sensitivity and anticipation, while associative learning builds flexible links between stimuli and outcomes. Automatic learning illustrates how effortful processes become streamlined, supported by neural efficiency gains. Together, these studies reveal learning as a unifying force that sculpts cognition across domains.

4. Brain Stimulation and Interfaces

Beyond observation, I seek to understand cognition through intervention and translation. Brain stimulation techniques, both invasive and non-invasive, provide causal tests of how neural circuits implement attention, suppression, and memory. A major translational goal is the development of brain–machine interfaces capable of restoring or augmenting human functions, with particular focus on generating artificial sensations. To ground these technologies, I emphasize cross-species research, aligning human iEEG findings with animal electrophysiology to uncover conserved mechanisms. This approach bridges basic neuroscience with applied innovation, advancing both theory and clinical application.

All four directions are united by a single guiding vision: to understand how human cognition emerges from the interaction of salience, learning, and memory, and to translate this knowledge into practical applications.
1. Salience provides the entry point: it determines what captures attention and leaves lasting memory traces.
2. The unified memory system ensures that these traces are not handled by isolated modules but by a flexible, integrated architecture.
3. Learning processes reshape this architecture over time, altering neural activity and transforming effortful control into automatic responses.
4. Brain stimulation and interfaces allow us to test these mechanisms causally, extend them through technology, and validate them across species.
Together, these directions form a continuum: from fundamental principles (salience, memory, learning) to causal tests and translation (stimulation, BMIs)