Encoding of temporal probabilities in the human brain

Anticipating the timing of future events is a necessary precursor to preparing actions and allocating resources to sensory processing. This requires elapsed time to be represented in the brain and used to predict the temporal probability of upcoming events. While neuropsychological, imaging, magnetic stimulation studies, and single-unit recordings implicate the role of higher parietal and motor-related areas in temporal estimation, the role of earlier, purely sensory structures remains more controversial. Here we demonstrate that the temporal probability of expected visual events is encoded not by a single area but by a wide network that importantly includes neuronal populations at the very earliest cortical stages of visual processing. Moreover, we show that activity in those areas changes dynamically in a manner that closely accords with temporal expectations

Evaluation of a load cell model for dynamic calibration of the rotor systems research aircraft

The Rotor Systems Research Aircraft uses load cells to isolate the rotor/transmission system from the fuselage. An analytical model of the relationship between applied rotor loads and the resulting load cell measurements is derived by applying a force-and-moment balance to the isolated rotor/transmission system. The model is then used to estimate the applied loads from measured load cell data, as obtained from a ground-based shake test. Using nominal design values for the parameters, the estimation errors, for the case of lateral forcing, were shown to be on the order of the sensor measurement noise in all but the roll axis. An unmodeled external load appears to be the source of the error in this axis

Stochastic satisficing account of confidence in uncertain value-based decisions

Every day we make choices under uncertainty; choosing what route to work or which queue in a supermarket to take, for example. It is unclear how outcome variance, e.g. uncertainty about waiting time in a queue, affects decisions and confidence when outcome is stochastic and continuous. How does one evaluate and choose between an option with unreliable but high expected reward, and an option with more certain but lower expected reward? Here we used an experimental design where two choices’ payoffs took continuous values, to examine the effect of outcome variance on decision and confidence. We found that our participants’ probability of choosing the good (high expected reward) option decreased when the good or the bad options’ payoffs were more variable. Their confidence ratings were affected by outcome variability, but only when choosing the good option. Unlike perceptual detection tasks, confidence ratings correlated only weakly with decisions’ time, but correlated with the consistency of trial-by-trial choices. Inspired by the satisficing heuristic, we propose a “stochastic satisficing” (SSAT) model for evaluating options with continuous uncertain outcomes. In this model, options are evaluated by their probability of exceeding an acceptability threshold, and confidence reports scale with the chosen option’s thus-defined satisficing probability. Participants’ decisions were best explained by an expected reward model, while the SSAT model provided the best prediction of decision confidence. We further tested and verified the predictions of this model in a second experiment. Our model and experimental results generalize the models of metacognition from perceptual detection tasks to continuous-value based decisions. Finally, we discuss how the stochastic satisficing account of decision confidence serves psychological and social purposes associated with the evaluation, communication and justification of decision-making

Post-decisional accounts of biases in confidence

Most models of decision-making suggest that confidence, the 'feeling of knowing' that accompanies our choices, is constructed as the decision unfolds. However, more recent studies have noted that processes occurring after we commit to a particular choice also affect this subjective belief. This leads to the following question: when are we better judges of ourselves? If, after a decision, evidence continues to accumulate in an unbiased manner, then our confidence judgements should improve. Conversely, if post-decisional information processing is biased, our sense of confidence could be distorted, and so our confidence judgements should degrade with time. We briefly discuss recently proposed models of post-decisional evidence accumulation, and explore whether, and how, biases in confidence could arise

Are collapse models testable with quantum oscillating systems? The case of neutrinos, kaons, chiral molecules

Collapse models provide a theoretical framework for understanding how classical world emerges from quantum mechanics. Their dynamics preserves (practically) quantum linearity for microscopic systems, while it becomes strongly nonlinear when moving towards macroscopic scale. The conventional approach to test collapse models is to create spatial superpositions of mesoscopic systems and then examine the loss of interference, while environmental noises are engineered carefully. Here we investigate a different approach: We study systems that naturally oscillate --creating quantum superpositions-- and thus represent a natural case-study for testing quantum linearity: neutrinos, neutral mesons, and chiral molecules. We will show how spontaneous collapses affect their oscillatory behavior, and will compare them with environmental decoherence effects. We will show that, contrary to what previously predicted, collapse models cannot be tested with neutrinos. The effect is stronger for neutral mesons, but still beyond experimental reach. Instead, chiral molecules can offer promising candidates for testing collapse models.Comment: accepted by NATURE Scientific Reports, 12 pages, 1 figures, 2 table

The effects of Anethum graveolens essence on scopolamine-induced memory impairment in mice

Since Anethum graveolens (Dill) has phytoestrogenic compounds and it is proven that estrogens exert beneficial effects on cognition; the aim of this study was to understand if this plant can improve memory performance. Male Balb/c mice weighing 25-30 g were used in this study and memory was assessed by the novel object recognition task. In this method, the difference in the exploration time between a familiar object and a novel object is taken as an index of memory performance (recognition index, RI). Scopolamine significantly reduced memory index (RI = -15.5% ± 3.0). Dill essence (100 mg/kg, ip) prevented the harmful effects of scopolamine on memory (RI = 40% ± 5.5), thus RI did not differ with control animals (RI = 50% ± 5.8). In addition, 17-β estradiol also prevented memory impairment in animals (0.2 mg/kg, ip; RI = 35.8% ± 6.5). Nevertheless, the beneficial effects of dill essence were antagonized by prior injection of tamoxifen (1 mg/kg, ip; RI = -30% ± 7.8). Although phytoesrogens are not steroids, the beneficial effect of dill on memory, at least in part, may have been achieved by estrogenic receptors present in the brain. Thus dill essence could be promising in improving memory and cognition, mainly in postmenopausal women

Metacognitive Deficiency in a Perceptual but Not a Memory Task in Methadone Maintenance Patients

Drug addiction has been associated with lack of insight into one's own abilities. However, the scope of metacognition impairment among drug users in general and opiate dependent individuals in particular is not fully understood. Investigating the impairments of metacognitive ability in Substance Dependent Individuals (SDIs) in different cognitive tasks could contribute to the ongoing debate over whether metacognition has domain-general or domain-specific neural substrates. We compared metacognitive self-monitoring ability of a group of SDIs during methadone maintenance treatment (n = 23) with a control group (n = 24) in a memory and a visual perceptual task. Post decision self judgements of probability of correct choice were obtained through trial by trial confidence ratings and were used to compute metacognitive ability. Results showed that despite comparable first order performance in the perceptual task, SDIs had lower perceptual metacognition than the control group. However, although SDIs had poorer memory performance, their metacognitive judgements in the memory task were as accurate as the control group. While it is commonly believed that addiction causes pervasive impairment in cognitive functions, including metacognitive ability, we observed that the impairment was only significant in one specific task, the perceptual task, but not in the memory task

Doubly Bayesian Analysis of Confidence in Perceptual Decision-Making.

Humans stand out from other animals in that they are able to explicitly report on the reliability of their internal operations. This ability, which is known as metacognition, is typically studied by asking people to report their confidence in the correctness of some decision. However, the computations underlying confidence reports remain unclear. In this paper, we present a fully Bayesian method for directly comparing models of confidence. Using a visual two-interval forced-choice task, we tested whether confidence reports reflect heuristic computations (e.g. the magnitude of sensory data) or Bayes optimal ones (i.e. how likely a decision is to be correct given the sensory data). In a standard design in which subjects were first asked to make a decision, and only then gave their confidence, subjects were mostly Bayes optimal. In contrast, in a less-commonly used design in which subjects indicated their confidence and decision simultaneously, they were roughly equally likely to use the Bayes optimal strategy or to use a heuristic but suboptimal strategy. Our results suggest that, while people's confidence reports can reflect Bayes optimal computations, even a small unusual twist or additional element of complexity can prevent optimality