Читать книгу Philosophy of Psychology - Lisa Bortolotti - Страница 27

When you attempt a task and fail, one of two things may be happening: either you do not have the capacity to accurately perform the task due to some knowledge-gap, or you have the capacity to accurately perform the task but fail to perform it accurately on a particular occasion due to external factors (such as a distraction).

Example: Gina and Tommaso were asked to calculate the square of four by their teacher. Gina did not answer at all because she did not know that the square of a number is the number multiplied by itself. Gina lacked the competence to answer the question correctly. Tommaso answered ‘twelve’. He knew how to make the square of a number and he was familiar with the four times table, but he was distracted by a sudden noise outside the classroom and gave the wrong answer. Tommaso made a performance error.

When Cohen says that ‘ordinary human reasoning […] cannot be held to be faultily programmed’, he means that human reasoning competence is intact (human agents can apply the rules of good reasoning in ideal conditions), although reasoning performance may be imperfect (human agents may make errors in applying the rules of good reasoning due to external factors).

The standard picture of rationality is accepted by many, perhaps most, philosophers, and reasonably so. After all, there is a key sense of ‘rational’ that describes people who follow the rules of logic, probability, and decision-making when they reason and solve problems. There are other senses of ‘rational’, of course, such as those that describe people who are not overwhelmed by their emotions when they make decisions, or those that describe people who support their arguments with evidence instead of merely stamping their foot in a debate (Bortolotti 2014). But rationality as logicality, as we might call it, is widely accepted in philosophy, economics, and psychology. For instance, Phil Gerrans says that ‘a rational subject is one whose reasoning conforms to procedures, such as logical rules, or Bayesian decision theory, which produce inferentially consistent sets of propositions’ (Gerrans 2001, 161), and Richard Nisbett and Paul Thagard define rational behaviour as ‘what people should do given an optimal set of inferential rules’ (Thagard & Nisbett 1983, 251).

Having said that, not everybody agrees that the standard picture is the best understanding of human rationality. This controversy lies at the heart of the rationality wars between pessimists about human rationality (often appealing to the heuristics and biases programme) and optimists about human rationality (often associated with the ecological rationality programme). We will come back to this in Section 1.5.

There are some technical issues about the standard picture of rationality that we would like to mention briefly here.

First, the standard picture seems to presuppose that there is just one system of logic, one theory of probability, and one set of principles for decision-making. However, there are different formal systems of logic and different interpretations of probability; even the principles of decision-making can be disputed. Some rules of inference that are valid in standard logic (often called classical logic) are not valid in some non-classical logical systems. This raises a question: which system of logic should be adopted in evaluating the reasoning performance of agents? This is especially tricky if reasoning performance is consistent with one system of logic but not with another. Should we adopt the former and say that an agent’s performance is rational? Or should we adopt the latter and say that it is irrational? A similar issue arises when considering interpretations of probability. There are different interpretations of what probabilistic statements (e.g., there is a 80% chance that it will rain tomorrow) actually mean. There are also some probabilistic statements that make sense in some interpretations but not in others. This issue is relevant to the debate between pessimists and optimists. Gigerenzer, the most notable optimist, argues that some probabilistic questions in the heuristics and biases experiments are meaningless in light of his favourite interpretation of probability (which is known as the frequency interpretation). This issue will be discussed in Section 1.5.

Second, the standard picture assumes that our reasoning should be evaluated against the standards of logic, probability, and decision-making. This implies that, if our intuitive answer to a reasoning task is incompatible with a rule of logic, we should conclude that our intuition is at fault. But why can’t we say that it is logic, not intuition, that is at fault? In fact, the development of non-classical logic is sometimes at least partially motivated by some counter-intuitive features of classic logic.

This issue raises a further question: what should we do when facing an apparent discrepancy between logic and intuitive judgment? Should we trust logic and dismiss intuition as irrational? Or should we trust intuition and dismiss logic instead? Jonathan Cohen, another notable optimist, raises a similar issue. If the normative rules, against which our intuitive judgments are evaluated, are themselves evaluated on the basis of our intuitive judgments, then our intuition ‘sets its own standards’ (Cohen 1981, 317). But then how can our intuitive judgment be irrational? How can our intuitive judgment deviate from the standards that are set by itself? Cohen adopts a radical conclusion that human irrationality cannot be proven in principle no matter what psychology shows; ‘ordinary human reasoning – by which I mean the reasoning of adults who have not been systematically educated in any branch of logic or probability theory – cannot be held to be faultily programmed’ (Cohen 1981, 317). In making this claim, Cohen relies on the distinction between reasoning competence and reasoning performance (see Box 1B).

Third, the standard picture presupposes that what matters to rational and successful reasoning is the conformity to the rules of logic, probability, and decision-making. Not everybody agrees. For example, Keith Stanovich (1999) makes a distinction between rationality and intelligence, where rationality is a broader notion than in the standard picture, and intelligence is what the standard picture captures. In common discourse, intelligence and rationality are often conflated, or it is assumed that intelligence comprises rationality. But IQ tests do not measure the capacity for making good judgments and good choices that we commonly regard as a mark of rationality. If we take intelligence to stand for whatever IQ tests measure, then it does not tell us which behaviours are more likely to be conducive to the agent’s well-being in real life. Whereas IQ tests measure the capacity to process and manipulate information quickly and efficiently, they are not sensitive to whether the agent forms beliefs that are well supported by the evidence or whether she can critically evaluate the information she receives. To illustrate this distinction, Stanovich describes famous cases of smart people who acted foolishly, by which he means that people who have high intelligence in some domain made bad judgments and bad choices, thereby behaving irrationally. This does not mean that intelligence is not worth studying, just that there are other things that we value. Intelligence and rationality can be seen as having different domains of applications: Stanovich, for instance, suggests that intelligence maps the efficiency of cognitive functioning at an algorithmic level, whereas his more comprehensive notion of rationality tracks thinking dispositions at a higher level, governs decision-making, and takes into account the agent’s goals and values. Some notions of rationality like Stanovich’s are distinct from the standard picture, where rationality is associated with behaviour that conforms to the rules of logic, probability, and decision-making.