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• W3132700699 abstract "We define the various forms of matching and discuss their rationality. Total responses or time on each option may not give enough information to distinguish between different behavioural allocations that match; information on bout structure is required to uniquely specify the behaviour associated with matching. Although matching on concurrent variable interval schedules is often taken as an indication of optimal behaviour, it need not be. Some forms of matching may be better than others and allocations that do not match may produce greater rewards. Analysis of real-world contexts, such as sports, indicates that the obtained bias and sensitivity parameters may not have the meanings usually given to them. Neural models that produce matching in some conditions are now helping to make sense of experimental data in various species. Matching describes how behaviour is related to rewards. The matching law holds when the ratio of an individual’s behaviours equals the ratio of the rewards obtained. From its origins in the study of pigeons working for food in the laboratory, the law has been applied to a range of species, both in the laboratory and outside it (e.g., human sporting decisions). Probability matching occurs when the probability of a behaviour equals the probability of being rewarded. Input matching predicts the distribution of individuals across habitats. We evaluate the rationality of the matching law and probability matching, expose the logic of matching in real-world cases, review how recent neuroscience findings relate to matching, and suggest future research directions. Matching describes how behaviour is related to rewards. The matching law holds when the ratio of an individual’s behaviours equals the ratio of the rewards obtained. From its origins in the study of pigeons working for food in the laboratory, the law has been applied to a range of species, both in the laboratory and outside it (e.g., human sporting decisions). Probability matching occurs when the probability of a behaviour equals the probability of being rewarded. Input matching predicts the distribution of individuals across habitats. We evaluate the rationality of the matching law and probability matching, expose the logic of matching in real-world cases, review how recent neuroscience findings relate to matching, and suggest future research directions. behaviour that is adequately described by the generalized generalised matching law. an individual chooses each option with a fixed probability. the additional time involved in switching from one option to another, compared with choosing the same option again. on a concurrent procedure, an individual can choose between two (or more) options, each of which has its own schedule. On concurrent VI VI schedules, for instance, each alternative has a VI schedule of rewards. at each time step, visit the other option with probability ε. If the reward is above a critical level, then stay on this new option, otherwise return to the other option immediately. a version of the matching law with two additional (fitted) parameters; (Box 1). when foraging in an environment containing patches of food, rate of gain is maximised by leaving the current patch when the marginal rate of gain falls to the maximum possible long-term rate of gain, taking into account the time spent moving to other patches. a rule in which the individual chooses the option with the highest probability of yielding a reward. a payoff following an action. For convenience we take it to be equivalent to ‘reward’. a special case of the matching law in which the reward rate from two or more options is equal. If only one option is used, matching holds but RE does not. on an interval schedule, the availability of a reinforcement depends on the time since the last reinforcement was obtained. After a reinforcement is obtained on a VI schedule, the next reinforcement will become available after a delay of a random duration. If the individual is free to respond at any time, then an exponential distribution of delays means that reinforcements are set up at a constant rate. If trials are discrete, then the corresponding distribution of delays is geometric and reinforcements are set up with a constant probability per trial. on a ratio schedule, the probability that a response is rewarded depends on the number of unrewarded responses since the last reinforcement. On a VR schedule, this number is random. We focus attention on the case in which each response has a constant probability of resulting in a reinforcement." @default.
• W3132700699 created "2021-03-01" @default.
• W3132700699 creator A5013210116 @default.
• W3132700699 creator A5072603135 @default.
• W3132700699 creator A5090190596 @default.
• W3132700699 date "2021-05-01" @default.
• W3132700699 modified "2023-09-26" @default.
• W3132700699 title "Matching Behaviours and Rewards" @default.
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