Darwin and Tinbergen represent two enduring contrasts in comparative cognitive psychology: in the types of behaviors studied and the kinds of explanations sought. Darwin encouraged the search for human-like behaviors in animals as evidence for evolutionary continuity of mental processes. Tinbergen encouraged the careful causal analysis of animal behaviors as such and eschewed interpretations in terms of anthropomorphic processes. The Darwinian program has reemerged in contemporary research on comparative cognition. Its development and relationship to other areas of behavioral biology are traced. In using behavior as a window onto the animal mind, it is important to remember the lessons of Tinbergen and like-minded behaviorists in psychology. Several of the challenges that arise in attempting to show that other species share complex cognitive processes with humans are discussed in the light of the contrast represented by Darwin and Tinbergen, as are examples of how these approaches are being productively integrated.
K. Cheng (1986) suggested that learning the geometry of enclosing surfaces takes place in a geometric module blind to other spatial information. Failures to find blocking or overshadowing of geometry learning by features near a goal seem consistent with this view. The authors present an operant model in which learning spatial features competes with geometry learning, as in the Rescorla-Wagner model. Relative total associative strength of cues at a location determines choice of that location and thus the frequencies of reward paired with each cue. The model shows how competitive learning of local features and geometry can appear to result in potentiation, blocking, or independence, depending on enclosure shape and kind of features. The model reproduces numerous findings from dry arenas and water mazes.
Spatial judgments and actions are often based on multiple cues. The authors review a multitude of phenomena on the integration of spatial cues in diverse species to consider how nearly optimally animals combine the cues. Under the banner of Bayesian perception, cues are sometimes combined and weighted in a near optimal fashion. In other instances when cues are combined, how optimal the integration is might be unclear. Only 1 cue may be relied on, or cues may seem to compete with one another. The authors attempt to bring some order to the diversity by taking into account the subjective discrepancy in the dictates of multiple cues. When cues are too discrepant, it may be best to rely on 1 cue source. When cues are not too discrepant, it may be advantageous to combine cues. Such a dual principle provides an extended Bayesian framework for understanding the functional reasons for the integration of spatial cues.
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