1,309 research outputs found
A computational model of focused attention meditation and its transfer to a sustained attention task
How does rumination impact cognition? A first mechanistic model.
Rumination is a process of uncontrolled, narrowly-foused neg- ative thinking that is often self-referential, and that is a hall- mark of depression. Despite its importance, little is known about its cognitive mechanisms. Rumination can be thought of as a specific, constrained form of mind-wandering. Here, we introduce a cognitive model of rumination that we devel- oped on the basis of our existing model of mind-wandering. The rumination model implements the hypothesis that rumina- tion is caused by maladaptive habits of thought. These habits of thought are modelled by adjusting the number of memory chunks and their associative structure, which changes the se- quence of memories that are retrieved during mind-wandering, such that during rumination the same set of negative memo- ries is retrieved repeatedly. The implementation of habits of thought was guided by empirical data from an experience sam- pling study in healthy and depressed participants. On the ba- sis of this empirically-derived memory structure, our model naturally predicts the declines in cognitive task performance that are typically observed in depressed patients. This study demonstrates how we can use cognitive models to better un- derstand the cognitive mechanisms underlying rumination and depression
Neural and Behavioral Mechanisms of Interval Timing in the Striatum
To guide behavior and learn from its consequences, the brain must represent
time over many scales. Yet, the neural signals used to encode time in the
seconds to minute range are not known. The striatum is the major input area of
the basal ganglia; it plays important roles in learning, motor function and
normal timing behavior in the range of seconds to minutes. We investigated
how striatal population activity might encode time. To do so, we recorded the
electrical activity from striatal neurons in rats performing the serial fixed interval
task, a dynamic version of the fixed Interval schedule of reinforcement. The
animals performed in conformity with proportional timing, but did not strictly
conform to scalar timing predictions, which might reflect a parallel strategy to
optimize the adaptation to changes in temporal contingencies and
consequently to improve reward rate over the session. Regarding the neural
activity, we found that neurons fired at delays spanning tens of seconds and
that this pattern of responding reflected the interaction between time and the
animals’ ongoing sensorimotor state. Surprisingly, cells rescaled responses in
time when intervals changed, indicating that striatal populations encoded
relative time. Moreover, time estimates decoded from activity predicted trial-bytrial
timing behavior as animals adjusted to new intervals, and disrupting
striatal function with local infusion of muscimol led to a decrease in timing
performance. Because of practical limitations in testing for sufficiency a
biological system, we ran a simple simulation of the task; we have shown that
neural responses similar to those we observe are conceptually sufficient to
produce temporally adaptive behavior. Furthermore, we attempted to explain
temporal processes on the basis of ongoing behavior by decoding temporal
estimates from high-speed videos of the animals performing the task; we could
not explain the temporal report solely on basis of ongoing behavior. These
results suggest that striatal activity forms a scalable population firing rate code
for time, providing timing signals that animals use to guide their actions
Affordances in Psychology, Neuroscience, and Robotics: A Survey
The concept of affordances appeared in psychology during the late 60s as an alternative perspective on the visual perception of the environment. It was revolutionary in the intuition that the way living beings perceive the world is deeply influenced by the actions they are able to perform. Then, across the last 40 years, it has influenced many applied fields, e.g., design, human-computer interaction, computer vision, and robotics. In this paper, we offer a multidisciplinary perspective on the notion of affordances. We first discuss the main definitions and formalizations of the affordance theory, then we report the most significant evidence in psychology and neuroscience that support it, and finally we review the most relevant applications of this concept in robotics
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