In 1974 an article appeared in Science magazine with the dry-sounding title “Judgment Under Uncertainty: Heuristics and Biases” by a pair of psychologists who were not well known outside their discipline of decision theory. In it Amos Tversky and Daniel Kahneman introduced the world to Prospect Theory, which mapped out how humans actually behave when faced with decisions about gains and losses, in contrast to how economists assumed that people behave. Prospect Theory turned Economics on its head by demonstrating through a series of ingenious experiments that people are much more concerned with losses than they are with gains, and that framing a choice from one perspective or the other will result in decisions that are exactly the opposite of each other, even if the outcomes are monetarily the same. Prospect Theory led cognitive psychology in a new direction that began to uncover other human biases in thinking that are probably not learned but are part of our brain’s wiring.

Thinking fast and slow.

Emotion and Adaptation

The hippocampus as a cognitive map

Thank you very much for downloading elements of episodic memory. Maybe you have knowledge that, people have look numerous times for their chosen novels like this elements of episodic memory, but end up in malicious downloads. Rather than reading a good book with a cup of coffee in the afternoon, instead they cope with some infectious virus inside their computer. elements of episodic memory is available in our book collection an online access to it is set as public so you can download it instantly. Our book servers spans in multiple countries, allowing you to get the most less latency time to download any of our books like this one. Merely said, the elements of episodic memory is universally compatible with any devices to read.

Elements Of Episodic Memory

(2001). A Universe of Consciousness: How Matter Becomes Imagination. Neuropsychoanalysis: Vol. 3, No. 2, pp. 245-248.

A Universe of Consciousness: How Matter Becomes Imagination

We describe a cognitive architecture learning intelligent distribution agent (LIDA) that affords attention, action selection and human-like learning intended for use in controlling cognitive agents that replicate human experiments as well as performing real-world tasks. LIDA combines sophisticated action selection, motivation via emotions, a centrally important attention mechanism, and multimodal instructionalist and selectionist learning. Empirically grounded in cognitive science and cognitive neuroscience, the LIDA architecture employs a variety of modules and processes, each with its own effective representations and algorithms. LIDA has much to say about motivation, emotion, attention, and autonomous learning in cognitive agents. In this paper, we summarize the LIDA model together with its resulting agent architecture, describe its computational implementation, and discuss results of simulations that replicate known experimental data. We also discuss some of LIDA's conceptual modules, propose nonlinear dynamics as a bridge between LIDA's modules and processes and the underlying neuroscience, and point out some of the differences between LIDA and other cognitive architectures. Finally, we discuss how LIDA addresses some of the open issues in cognitive architecture research.

LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning

We propose that human cognition consists of cascading cycles of recurring brain events. Each cognitive cycle senses the current situation, interprets it with reference to ongoing goals, and then selects an internal or external action in response. While most aspects of the cognitive cycle are unconscious, each cycle also yields a momentary “ignition” of conscious broadcasting. Neuroscientists have independently proposed ideas similar to the cognitive cycle, the fundamental hypothesis of the LIDA model of cognition. High-level cognition, such as deliberation, planning, etc., is typically enabled by multiple cognitive cycles. In this paper we describe a timing model LIDA's cognitive cycle. Based on empirical and simulation data we propose that an initial phase of perception (stimulus recognition) occurs 80–100 ms from stimulus onset under optimal conditions. It is followed by a conscious episode (broadcast) 200–280 ms after stimulus onset, and an action selection phase 60–110 ms from the start of the conscious phase. One cognitive cycle would therefore take 260–390 ms. The LIDA timing model is consistent with brain evidence indicating a fundamental role for a theta-gamma wave, spreading forward from sensory cortices to rostral corticothalamic regions. This posteriofrontal theta-gamma wave may be experienced as a conscious perceptual event starting at 200–280 ms post stimulus. The action selection component of the cycle is proposed to involve frontal, striatal and cerebellar regions. Thus the cycle is inherently recurrent, as the anatomy of the thalamocortical system suggests. The LIDA model fits a large body of cognitive and neuroscientific evidence. Finally, we describe two LIDA-based software agents: the LIDA Reaction Time agent that simulates human performance in a simple reaction time task, and the LIDA Allport agent which models phenomenal simultaneity within timeframes comparable to human subjects. While there are many models of reaction time performance, these results fall naturally out of a biologically and computationally plausible cognitive architecture.

/pdf/the-timing-of-the-cognitive-cycle-3xt6txvxf3.pdf

The Timing of the Cognitive Cycle

http://www.cs.man.ac.uk/~kechen/publication/nn2015.pdf

Computational cognitive models of spatial memory in navigation space

Over a decade in the making and described in some seventy-five published papers, the LIDA cognitive model is comprehensive, complex, and hard to “wrap one’s head around”. Here we offer, in tutorial fashion, a current, relatively complete and somewhat detailed, description of the conceptual LIDA model, with pointers to more complete accounts of individual processes in the literature. These descriptions also include some features of the workings of the LIDA model that have not been published previously. The tutorial begins with several short sections designed to ease the reader into the LIDA model. These are followed by an account of the conceptual commitments of the LIDA model. We also include a brief introduction to the LIDA computational model via the LIDA Framework, with pointers to its own tutorial. This is followed by sketches of several of the LIDA based agents developed with the help of the Framework. The tutorial ends with a section on current research activity, which includes a table showing which aspects of the LIDA conceptual model have currently been implemented computationally.

A LIDA cognitive model tutorial

Accurate spatial localization requires a mechanism that corrects for errors, which might arise from inaccurate sensory information or neuronal noise. In this paper, we propose that Hippocampal place cells might implement such an error correction mechanism by integrating different sources of information in an approximately Bayes-optimal fashion. We compare the predictions of our model with physiological data from rats. Our results suggest that useful predictions regarding the firing fields of place cells can be made based on a single underlying principle, Bayesian cue integration, and that such predictions are possible using a remarkably small number of model parameters.

/pdf/bayesian-integration-of-information-in-hippocampal-place-1nvys65m5f.pdf

Tamas Madl

Papers

LIDA: A Systems-level Architecture for Cognition, Emotion, and Learning

The Timing of the Cognitive Cycle

Computational cognitive models of spatial memory in navigation space

A LIDA cognitive model tutorial

Bayesian integration of information in hippocampal place cells.