The 2-feature Simultaneous Type/ Serial Token Model of temporal feature conjunctions: Successes are merely fortunate conjunctions
Year: 2026
Type: preprint
Abstract: Resolving the binding problem, i.e., how we integrate perceptual features in space and time to perceive objects, people and events as coherent wholes instead of masses of free-floating features, has been a central goal of cognitive scientists for decades. Conjunction errors occur whenever features are mis-bound leading to illusory percepts that were not actually presented. A specific kind of Rapid Serial Visual Presentation experiment induces temporal illusory conjunctions, called distractor intrusion errors. Despite these errors being well-established, current theories and models provide limited explanations. We present the 2-feature Simultaneous Type/Serial Token (2f-ST 2 ) Model, the latest addition to the family of ST 2 -models, which simulates the most important behavioural and electrophysiological distractor intrusion findings. These include response distributions, reaction time patterns, and evoked electrophysiology responses. Importantly, we simulate these results across diverse empirical paradigms, implementing a range of stimulus sets, temporal dynamics, and involving multiple responses after each trial, without needing to modify our model’s architecture or core parameters. Our findings are discussed in relation to theories of selective attention and working-memory binding. We argue that the 2f-ST 2 model is more parsimonious than the leading theory of distractor-intrusions (the focussed-attention/sophisticated-guessing model), since it uses only a single binding mechanism, implying that correct bindings are simply fortunate conjunctions. The 2f-ST 2 model further makes predictions for stimulus-competition, subjective-visibility and human electrophysiology. In summary, the ST 2 -model is a general framework for understanding how perception, temporal attention and working-memory interact, and the 2f-ST 2 model extends the framework to distractor intrusions, thus explaining temporal feature binding.
Language: English
Subfield: Cognitive Neuroscience
Field: Neuroscience
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7212425625