Maximum saliency bias in binocular fusion

Subjective experience at any instant consists of a single (“unitary”), coherent interpretation of sense data rather than a “Bayesian blur” of alternatives. However, computation of Bayes-optimal actions has no role for unitary perception, instead being required to integrate over every possible action-percept pair to maximise expected utility. So what is the role of unitary coherent percepts, and how are they computed? Recent work provided objective evidence for non-Bayes-optimal, unitary coherent, perception and action in humans; and further suggested that the percept selected is not the maximum a posteriori percept but is instead affected by utility. The present study uses a binocular fusion task first to reproduce the same effect in a new domain, and second, to test multiple hypotheses about exactly how utility may affect the percept. After accounting for high experimental noise, it finds that both Bayes optimality (maximise expected utility) and the previously proposed maximum-utility hypothesis are outperformed in fitting the data by a modified maximum-salience hypothesis, using unsigned utility magnitudes in place of signed utilities in the bias function

Machines Learning - Towards a New Synthetic Autobiographical Memory

Autobiographical memory is the organisation of episodes and contextual information from an individual’s experiences into a coherent narrative, which is key to our sense of self. Formation and recall of autobiographical memories is essential for effective, adaptive behaviour in the world, providing contextual information necessary for planning actions and memory functions such as event reconstruction. A synthetic autobiographical memory system would endow intelligent robotic agents with many essential components of cognition through active compression and storage of historical sensorimotor data in an easily addressable manner. Current approaches neither fulfil these functional requirements, nor build upon recent understanding of predictive coding, deep learning, nor the neurobiology of memory. This position paper highlights desiderata for a modern implementation of synthetic autobiographical memory based on human episodic memory, and proposes that a recently developed model of hippocampal memory could be extended as a generalised model of autobiographical memory. Initial implementation will be targeted at social interaction, where current synthetic autobiographical memory systems have had success

Is Auditory Distraction by Changing-State and Deviant Sounds Underpinned by the Same Mechanism? Evidence from Pupillometry

The mere presence of task-irrelevant auditory stimuli is known to interfere with cognitive functioning. Disruption can be caused by changing auditory distractors (the changing-state effect) or by a sound that deviates from the auditory background (the deviation effect). The unitary account of auditory distraction explains both phenomena in terms of attentional capture whereas the duplex-mechanism account posits that they reflect two fundamentally different forms of distraction in which only the deviation effect is caused by attentional capture. To test these predictions, we exploited a physiological index of attention orienting: the pupillary dilation response (PDR). Participants performed visual serial recall while ignoring sequences of spoken letters. These sequences either comprised repeated or changing letters, and one letter could sometimes be replaced by pink noise (the deviant). Recall was poorer in both changing-state and deviant trials. Interestingly, the PDR was elicited by deviant sounds but not changing-state sounds. This physiological dissociation of the changing-state and the deviation effects suggests they are subtended by distinct mechanisms thereby procuring support for the duplex-mechanism account over the unitary account

Interference Effects in Quantum Belief Networks

Probabilistic graphical models such as Bayesian Networks are one of the most powerful structures known by the Computer Science community for deriving probabilistic inferences. However, modern cognitive psychology has revealed that human decisions could not follow the rules of classical probability theory, because humans cannot process large amounts of data in order to make judgements. Consequently, the inferences performed are based on limited data coupled with several heuristics, leading to violations of the law of total probability. This means that probabilistic graphical models based on classical probability theory are too limited to fully simulate and explain various aspects of human decision making. Quantum probability theory was developed in order to accommodate the paradoxical findings that the classical theory could not explain. Recent findings in cognitive psychology revealed that quantum probability can fully describe human decisions in an elegant framework. Their findings suggest that, before taking a decision, human thoughts are seen as superposed waves that can interfere with each other, influencing the final decision. In this work, we propose a new Bayesian Network based on the psychological findings of cognitive scientists. We made experiments with two very well known Bayesian Networks from the literature. The results obtained revealed that the quantum like Bayesian Network can affect drastically the probabilistic inferences, specially when the levels of uncertainty of the network are very high (no pieces of evidence observed). When the levels of uncertainty are very low, then the proposed quantum like network collapses to its classical counterpart

Backwards is the way forward: feedback in the cortical hierarchy predicts the expected future

Clark offers a powerful description of the brain as a prediction machine, which offers progress on two distinct levels. First, on an abstract conceptual level, it provides a unifying framework for perception, action, and cognition (including subdivisions such as attention, expectation, and imagination). Second, hierarchical prediction offers progress on a concrete descriptive level for testing and constraining conceptual elements and mechanisms of predictive coding models (estimation of predictions, prediction errors, and internal models)

Causal inference in multisensory perception and the brain

To build coherent and veridical multisensory representations of the environment, human observers consider the causal structure of multisensory signals: If they infer a common source of the signals, observers integrate them weighted by their reliability. Otherwise, they segregate the signals. Generally, observers infer a common source if the signals correspond structurally and spatiotemporally. In six projects, the current PhD thesis investigated this causal inference model with the help of audiovisual spatial signals presented to human observers in a ventriloquist paradigm. A first psychophysical study showed that sensory reliability determines causal inference via two mechanisms: Sensory reliability modulates how observers infer the causal structure from spatial signal disparity. Further, sensory reliability determines the weight of audiovisual signals if observers integrate the signals under assumption of a common source. Using multivariate decoding of fMRI signals, three PhD projects revealed that auditory and visual cortical hierarchies jointly implement causal inference. Specific regions of the hierarchies represented constituent spatial estimates of the causal inference model. In line with this model, anterior regions of intraparietal sulcus (IPS) represent audiovisual signals dependent on visual reliability, task-relevance, and spatial disparity of the signals. However, even in case of small signal discrepancies suggesting a common source, reliability-weighting in IPS was suboptimal as compared to a Maximum Estimation Likelihood model. By temporally manipulating visual reliability, the fifth PhD project demonstrated that human observers learn sensory reliability from current and past signals in order to weight audiovisual signals, consistent with a Bayesian learner. Finally, the sixth project showed that if visual flashes were rendered unaware by continuous flash suppression, the visual bias of the perceived auditory location was strongly reduced but still significant. The reduced ventriloquist effect was presumably mediated by the drop of visual reliability accompanying perceptual unawareness. In conclusion, the PhD thesis suggests that human observers integrate multisensory signals according to their causal structure and temporal regularity: They integrate the signals if a common source is likely by weighting them proportional to the reliability which they learnt from the signals’ history. Crucially, specific regions of cortical hierarchies jointly implement these multisensory processes

Processes models, environmental analyses, and cognitive architectures: Quo vadis quantum probability theory?

A lot of research in cognition and decision making suffers from a lack of formalism. The quantum probability program could help to improve this situation, but we wonder whether it would provide even more added value if its presumed focus on outcome models were complemented by process models that are, ideally, informed by ecological analyses and integrated into cognitive architecture