Psychosis: Bending Reality to See Around the Corners

Psychosis: Bending Reality to See Around the Corners
Paul Fletcher | TEDxCambridgeUniversity
Dec 2, 2016

Psychosis is a highly misunderstood condition.
In this talk, Paul illustrates the condition’s complexity, taking apart how our brains perceive reality by reinventing illusions around us.
If perception is just a form of controlled hallucination, what does that make hallucination?

Bayesian inference

related:

https://franzcalvo.wordpress.com/2018/06/10/die-philosophie-des-als-ob

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Bayesian learning (2013)

Bayesian learning and the psychology of rule induction.
Cognition. 2013 May;127(2):159-76.
Endress AD.
http://www.ncbi.nlm.nih.gov/pubmed/23454791 [FREE]

In recent years, Bayesian learning models have been applied to an increasing variety of domains. While such models have been criticized on theoretical grounds, the underlying assumptions and predictions are rarely made concrete and tested experimentally. Here, I use Frank and Tenenbaum’s (2011) Bayesian model of rule-learning as a case study to spell out the underlying assumptions, and to confront them with the empirical results Frank and Tenenbaum (2011) propose to simulate, as well as with novel experiments. While rule-learning is arguably well suited to rational Bayesian approaches, I show that their models are neither psychologically plausible nor ideal observer models. Further, I show that their central assumption is unfounded: humans do not always preferentially learn more specific rules, but, at least in some situations, those rules that happen to be more salient. Even when granting the unsupported assumptions, I show that all of the experiments modeled by Frank and Tenenbaum (2011) either contradict their models, or have a large number of more plausible interpretations. I provide an alternative account of the experimental data based on simple psychological mechanisms, and show that this account both describes the data better, and is easier to falsify. I conclude that, despite the recent surge in Bayesian models of cognitive phenomena, psychological phenomena are best understood by developing and testing psychological theories rather than models that can be fit to virtually any data.

related:
Tuning your priors to the world
Top Cogn Sci. 2013 Jan;5(1):13-34.
http://www.ncbi.nlm.nih.gov/pubmed/23335572

Causality in Thought (2014)

Causality in Thought.
Annu Rev Psychol. 2014 Jul 21.
Sloman SA, Lagnado D.
http://www.ncbi.nlm.nih.gov/pubmed/25061673

Causal knowledge plays a crucial role in human thought, but the nature of causal representation and inference remains a puzzle.
Can human causal inference be captured by relations of probabilistic dependency, or does it draw on richer forms of representation?

This article explores this question by reviewing research in reasoning, decision making, various forms of judgment, and attribution.
We endorse causal Bayesian networks as the best normative framework and as a productive guide to theory building.

However, it is incomplete as an account of causal thinking.
On the basis of a range of experimental work, we identify three hallmarks of causal reasoning-the role of mechanism, narrative, and mental simulation-all of which go beyond mere probabilistic knowledge.
We propose that the hallmarks are closely related.
Mental simulations are representations over time of mechanisms.
When multiple actors are involved, these simulations are aggregated into narratives.
http://www.annualreviews.org/catalog/pubdates.aspx