Introspection often creates the perception that our decisions are driven by the evaluation of each alternative, and as a consequence we assume that animals also choose by evaluating alternatives. If this were true, choosing would take information-processing effort and time: more options, more time. However, in starlings, this pattern does not hold. The Sequential Choice Model (SCM) was proposed to deal with this curious finding. Its main feature is that it predicts behaviour in choice situations using data from no-choice encounters with each alternative. Its premises are: (1) When an animal faces a single option, it doesn’t take it immediately (the “latency”). Each alternative faced on its own elicits a specific probability density function of latencies. Latencies are not reaction times: they exceed RTs duration by an order of magnitude and have different properties; (2) Latencies to take single options are decreasing functions of the improvement in state-dependent fitness that the decision maker expects from that option relative to the context; (3) Expectations about each option depend on both the subject’s state and the average properties of the environment during learning; (4) When more than one option is met simultaneously, each elicits a sample from its original distribution of latencies. The shortest sample is expressed as a choice. There is no comparative evaluation at choice time: each option elicits a candidate latency just as in sequential encounters. This cross-censorship between latency distributions means that latencies for each option are shorter when picked out of a choice than when picked in the absence of alternatives. The SCM was proposed for a system with pairs of options, where its predictive performance was very successful. To investigate its generality, I will now test it in a wide variety of choice paradigms, including multi-alternative choice, the time-left procedure, risky choice and comparative valuation scenarios.
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