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Cogsci - Coggle Diagram

- - - - Problems with the one factor account
        
        Resistance - Delusions are characterized by a strong resistance to counter evidence. The one factor account seems to explain why a person may adopt a delusion but does not have the scope to explain this resistance.
        
        Specificity : One factor delusions have a difficult time explaining why the monothematic delusion is typically localized to one subject, i.e the person with Capgras only suspecting their husband of being an imposter but not their children. Two factor solutions also face this problem.
        
        Predictivity: Not all people that share an abnormal experience associated with a particular delusion develop said delusion, one factor has does not predict this.
        
        Strong counter cases: Bisach et al 1986 finds hemiplegic patients who are paralyzed in their left leg and leg arm but have anosognosia for only for the arm but not the leg
        
        Contrary to Medical Practice : Reiss, Ankey (2016) 'Modern western medicine makes no presupposition on the number, form or mode of action for a medical outcome, only that there is some cause or set of causes - in approaching delusions in the way described, one and two factor accounts may be paradigmatically limited in that they aren't helpful analyses of delusions because they are ad-hoc and cannot be intervened
      - Arguments in favor of the One factor account
        
        No second factor? Two factor accounts appeal to some notion of cognitive demerit in individuals who develop delusions, Sullivan-Bissett 2018 finds that there the cognitive features of delusional patients fall within normal ranges.
  - - - Problems with the one factor account
        
        Salience : Two factor accounts have a hard time being rigorous about the exact nature of their second factor, 'faulty belief evaluation' is not precise in the way theorists would hope
  - - - Problems with Prediction Error Theory
- - - - further supported by
        
        Wason 1992 When given abstract logic tasks individuals perform poorly, when the same task (in logical structure) is framed in terms of rules and social permissions, success rate increases massively.
        
        Stone et al (2002) R.M presents with orbitofrontal brain damage. R.M performs poorly on social contract problems but has no problems with the same problems formulated abstractly. Implies these two tasks are tackled by different sections of the mind. People with similar damage as R.Ms are recorded to be at high risk of being taken advantage of or exploited for this very lack of social inability.
        
        Suggests Massive modularity
- - - - Tverskey and Kahneman (1986) Deviations from normative model reasoning are too widespread to be ignored, too systematic to be dismissed as random error
      - Against Pessimistic Views
        
        Content Effect Experiments like the Wason selection task show us that we can do just fine on reasoning tasks given the correct context (contrary to pessimist views) Fielder (1988) reframing allows for better results on the linda problem, success rate goes from 9% to 78%
        
        Meaningless Objection Argues for the dismissal of pessimism based on systematic documented failures (a la Heuristics and Biases) because they pertain to meaningless questions about single-event probabilities Gigerenzer (1994): On a frequentist interpretation of probability, these types of questions are ill formed and thus meaningless.
        
        Two formulations of the meaningless objection
        
        Factual : Frequentism is the philosophically sound interpretation of probability, probabilistic questions in the heuristics and biases literature are objectively non-sense
        
        Psychological: Probabilistic information formulated in the mind are formulated as frequencies. Therefore the probabilistic questions in the literature are psychologically nonsense (NOT WIDELY SUPPORTED)
        
        Gigerenzer (2008) Feasibility Objection Reasoning performance should not be evaluated in terms of rules of logic, probability and decision making. These practices 'ignore the constraints imposed on human beings', constraints take the form of limited mental and environmental resources (e.g limited memory = mental resource). Information cost = environmental resource
        
        Harman (1999) Argues for human-relative normative standards, reflecting the capacities and limitations of human beings. Argues that logical consistency is not feasible for human computation , as we do not have the necessary resoures
        
        Objection Stanovich (1999) This claim fails to explain systematic reasoning failures in more relaxed settings, finds that computational constraints and their relation to the human brain are minimal.
        
        Gigerenzer (2008) Social and physical natural environments were the condition in which the Human brain adapted (EEA) . People are thus, 'ecologically rational' in that we can reason about relevant content using knowledge possessed on the subject matter, we can estimate probabilities given that we are familiar with a sequence of events and instances of it.
        
        Pressing worry
        (1) Are we conflating biological adaptiveness and rationality?
        (2) It is possible that adaptive behaviour is ration in the EEA; we are no longer in the EEA and are therefore CURRENTLY not rational Bartolotti and Miyazono 2021
    - - Dual Process Theory (Kahneman 2011) The view that we have two 'systems' of thinking,
        system 1: Fast, parallel, automatic.
        system 2: slow, serial, rule-governed.
  - - - Undermined by <
  - - - Contains a huge amount of empirical evidence that we are poor at assessing probability, inefficient/unreliable memory recollection, frequently violate utility theory, do not calibrate degrees of belief correctly, choices effected by irrelevant context, biases and ignorance of alternate expectations
- - - - Idealized exemplar : Classification into concepts involves the comparison with the stimulus to a mentally represented 'best exemplar' that statistically summarizes what already exists of that concept.
        
        Potential Issue Rosch (2011) prototypes range across a spectrum of concreteness (e.g musical instruments, impossible to give a visual best exemplar prototype).
        
        leads to
      - Unordered Weighted Feature List (Hampton 1979) Takes prototypes to be a list of unordered and weighted features; where a stimulus is compared by each feature in the concept to the weighted list, if the sum of these values exceeds a 'categorization threshold' then membership is affirmed