Cognitive Psychology ePosters

A common way of quantifying learning behavior is the estimation of learning rates from subjects’ responses. To this end responses are usually averaged across subjects when estimating learning rates for a whole group or averaged over a sliding window when estimating learning rates for single subjects. However, these…

Systematic biases in perception can be either attractive or repulsive with respect to the immediate spatial or temporal context. There is an extensive amount of literature [1,2,3] on how to explain these differences in biases with a variety of mechanisms, ranging from sensory adaptation over efficient coding to…

This study explores the decision-making processes involved in trajectory extrapolation tasks. Vertical paths were disturbed in the horizontal direction by noise generated by Random Walks (RDW) or Independent and Identically Distributed (IID) noise. In the Experiment, participants (n = 28) extrapolated if a moving…

In order to survive, animals constantly face decisions between competing interpretations for noisy and sparse sensory data. In statistics, multiple formal frameworks exist to tackle this problem, known as Model Selection (MS). However, despite the abundance of theoretical methods, the way in which MS is performed by…

People often choose immediate certain outcomes over delayed uncertain ones, even when the latter are more beneficial. This suggests that delay and uncertainty diminish the perceived value of a reward - a phenomenon known as delayed probabilistic discounting (DPD). Studying discounting mechanisms is critical to…

Our brains predict future sensory input based on their current beliefs about the world around us, but interpreting prediction errors can be challenging in a volatile environment because they can be caused by stochastic sensory noise or by outdated predictions. Noisy signals should be integrated with prior beliefs to…

Integrating information is particularly crucial when decisions contain complex information. In decision neuroscience, it has been widely studied how overall decision value is encoded in the brain, but how information is integrated prior to encoding overall value remains largely unclear. Recently, our neuroimaging…

Cognitive maps have been proposed as an essential relational structure for human memory and thought (Tolman, 1948; Constantinescu et al., 2016). In this format, remembered entities are embedded in space and their relationships are encoded via concrete angles and Euclidean distances. Although much of higher human…

Metacognition encompasses the capability to monitor and control one’s cognitive processes. According to brain imaging, prospective and retrospective second-order judgments recruit widespread portions of prefrontal cortex (PFC), thus leaving the neuronal architecture underlying metacognitive knowledge poorly…

The World Economic Forum in its latest report elects disinformation as the most significant short-term global risk to tackle in modern society. This report more specifically mentions that misinformation is capable of disrupting elections and exacerbating societal polarization. Since photojournalistic images are…

Working memory is inherently limited in capacity and duration, which may hamper learning, including in online learning environments. Because working memory capacity varies among individuals, cognitive load in online learning may not have the same impact for neurotypical and neurodivergent students. This study…

Navigation towards a goal in previously unknown environments likely involves storing and replaying sequences of relevant spatial information. Previous studies on spatial memory adopted “cartography” paradigms in which subjects, typically moving in 2D, could remember and then rely on salient cues, such as landmarks or…

It is well known that humans and other animals can exploit the probabilities of events occurring at different spatial locations to covertly guide attention. However, attentional shifts observed before saccadic eye movements seem to rely on different mechanisms than those observed during covert spatial attention. In…

Perception is transformed by both stable and time varying (contextual) expectations. This influence is observable in the domain of orientation processing as cardinal bias (repulsion from more common vertical and horizontal orientations) and serial dependance (attraction towards recent stimuli) [1,2]. These biases…

Contemporary Bayesian theories of the mind highlight the role of prediction errors as a driving force in learning processes and the updating of our models of the world and of ourselves. This process is postulated to manifest both at explicit levels (e.g., subjective responses) and at implicit levels (e.g., eye…

Memory reconsolidation is the mechanism by which consolidated memories are updated in strength and/or content. Thus, memory reactivation can enhance the retention of reactivated items at long delays. An underlying hypothesis to these findings suggests that reconsolidation would be “reactivation specific”, implying…

Influential models of confidence generation postulate that confidence computation is Bayesian, that is, confidence reflects the posterior probability that a decision is correct. Although several recent studies have found evidence against a strictly Bayesian account in the context of relatively complex tasks, the…

Visual working memory (WM) is the cognitive function that temporarily keeps sensory information online. There is a debate on WM's limited ability: holding a fixed small amount of items or a limited resource allocated to all items stored. The current study tries to solve the debate by model comparison, involving the…

Current studies suggest that individuals estimate the value of their choices based on observed feedback. Here, we ask whether individuals update the value of their unchosen actions, even when the associated feedback remains unknown. Two hundred and three individuals completed a multi-armed bandit task, making choices…

The success of neural networks derives from the fact that they can learn useful representations of observed inputs. However, the dynamics of when and how fast a network will discover good representations are not well understood, even when network training is governed by gradient descent. The potential complexity of…