A detailed description of the simultaneous type, serial token (ST2) model is presented. ST2 is a model of temporal attention and working memory that encapsulates 5 principles: (a) M. M. Chun and M. C. Potter's (1995) 2-stage model, (b) a Stage 1 salience filter, (c) N. G. Kanwisher's (1987, 1991) types-tokens distinction, (d) a transient attentional enhancement, and (e) a mechanism for associating types with tokens called the binding pool. The authors instantiate this theoretical position in a connectionist implementation, called neural-ST2, which they illustrate by modeling temporal attention results focused on the attentional blink (AB). They demonstrate that the ST2 model explains a spectrum of AB findings. Furthermore, they highlight a number of new temporal attention predictions arising from the ST2 theory, which are tested in a series of behavioral experiments. Finally, the authors review major AB models and theories and compare them with ST2.

How do we navigate a deeply structured world? Why are you reading this sentence first - and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating - and neuronal process theories - to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.

How do we navigate a deeply structured world? Why are you reading this sentence first - and did you actually look at the fifth word? This review offers some answers by appealing to active inference based on deep temporal models. It builds on previous formulations of active inference to simulate behavioural and electrophysiological responses under hierarchical generative models of state transitions. Inverting these models corresponds to sequential inference, such that the state at any hierarchical level entails a sequence of transitions in the level below. The deep temporal aspect of these models means that evidence is accumulated over nested time scales, enabling inferences about narratives (i.e., temporal scenes). We illustrate this behaviour with Bayesian belief updating - and neuronal process theories - to simulate the epistemic foraging seen in reading. These simulations reproduce perisaccadic delay period activity and local field potentials seen empirically. Finally, we exploit the deep structure of these models to simulate responses to local (e.g., font type) and global (e.g., semantic) violations; reproducing mismatch negativity and P300 responses respectively.

Neural oscillations are important for memory formation in the brain. The desynchronization of alpha (10 Hz) oscillations in the neocortex has been shown to predict successful memory encoding and retrieval. However, when engaging in learning, it has been found that the hippocampus synchronizes in theta (4 Hz) oscillations, and that learning is dependent on the phase of theta. This inconsistency as to whether synchronization is “good” for memory formation leads to confusion over which oscillations we should expect to see and where during learning paradigm experiments. This paper seeks to respond to this inconsistency by presenting a neural network model of how a well functioning learning system could exhibit both of these phenomena, i.e., desynchronization of alpha and synchronization of theta during successful memory encoding.We present a spiking neural network (the Sync/deSync model) of the neocortical and hippocampal system. The simulated hippocampus learns through an adapted spike-time dependent plasticity rule, in which weight change is modulated by the phase of an extrinsically generated theta oscillation. Additionally, a global passive weight decay is incorporated, which is also modulated by theta phase. In this way, the Sync/deSync model exhibits theta phase-dependent long-term potentiation and long-term depression. We simulated a learning paradigm experiment and compared the oscillatory dynamics of our model with those observed in single-cell and scalp-EEG studies of the medial temporal lobe. Our Sync/deSync model suggests that both the desynchronization of neocortical alpha and the synchronization of hippocampal theta are necessary for successful memory encoding and retrieval.SIGNIFICANCE STATEMENTA fundamental question is the role of rhythmic activation of neurons, i.e., how and why their firing oscillates between high and low rates. A particularly important question is how oscillatory dynamics between the neocortex and hippocampus support memory formation. We present a spiking neural-network model of such memory formation, with the central ideas that (1) in neocortex, neurons need to break out of an alpha oscillation to represent a stimulus (i.e., alpha desynchronizes), whereas (2) in hippocampus, the firing of neurons at theta facilitates formation of memories (i.e., theta synchronizes). Accordingly, successful memory formation is marked by reduced neocortical alpha and increased hippocampal theta. This pattern has been observed experimentally and gives our model its name—the Sync/deSync model.

Masked priming experiments have revealed a precise set of facilitatory and inhibitory visuomotor control processes. Most notably, inhibitory effects have been identified in which prime-target compatibility induces performance costs and prime-target incompatibility induces performance benefits. We argue that this profile of data is commensurate with an “emergency braking mechanism”, whereby responses can be retracted as a result of changing sensory evidence. The main contribution of this paper is to provide a neural network-based explanation of this phenomenon. This is obtained through the use of feedforward inhibition to implement backward masking, lateral inhibition to implement response competition, and opponent processing mechanisms to implement response retraction. Although the model remains simple, it does a very good job of reproducing the available masked priming data. For example, it reproduces a large spectrum of reaction time data across a number of different experimental conditions. Perhaps most notably, however, it also reproduces lateralized readiness potentials that have been recorded while subjects perform different conditions. In addition, it provides a concrete set of testable predictions.

We propose a novel deception detection system based on Rapid Serial Visual Presentation (RSVP). One motivation for the new method is to present stimuli on the fringe of awareness, such that it is more difficult for deceivers to confound the deception test using countermeasures. The proposed system is able to detect identity deception (by using the first names of participants) with a 100% hit rate (at an alpha level of 0.05). To achieve this, we extended the classic Event-Related Potential (ERP) techniques (such as peak-to-peak) by applying Randomisation, a form of Monte Carlo resampling, which we used to detect deception at an individual level. In order to make the deployment of the system simple and rapid, we utilised data from three electrodes only: Fz, Cz and Pz. We then combined data from the three electrodes using Fisher's method so that each participant was assigned a single p-value, which represents the combined probability that a specific participant was being deceptive. We also present subliminal salience search as a general method to determine what participants find salient by detecting breakthrough into conscious awareness using EEG.