Implicit and explicit categorization: a tale of four species

Abstract

Categorization is essential for survival, and it is a widely studied cognitive adaptation in humans and animals. An influential neuroscience perspective differentiates in humans an explicit, rule-based categorization system from an implicit system that slowly associates response outputs to different regions of perceptual space. This perspective is being extended to study categorization in other vertebrate species, using category tasks that have a one-dimensional, rule-based solution or a two-dimensional, information-integration solution. Humans, macaques, and capuchin monkeys strongly dimensionalize perceptual stimuli and learn rule-based tasks more quickly. In sharp contrast, pigeons learn these two tasks equally quickly. Pigeons represent a cognitive system in which the commitment to dimensional analysis and category rules was not strongly made. Their results may reveal the character of the ancestral vertebrate categorization system from which that of primates emerged. The primate results establish continuity with human cognition, suggesting that nonhuman primates share aspects of humans' capacity for explicit cognition. The emergence of dimensional analysis and rule learning could have been an important step in primates' cognitive evolution.

Figure 1

Illustrating rule-based and information-integration category structures. The stimuli are sine-wave disks varying in bar spatial frequency and orientation. For each task, three illustrative Category A and Category B stimuli are provided. In addition, the plus signs and open circles illustrate the distribution of an experiment's stimuli as represented in an abstract space. The text specifies how these abstract values can be converted into physically realized stimuli. The pluses and circles, respectively, are Category A and Category B exemplars. In the top panel, only variation in bar frequency carries diagnostic category information, so optimal performance would be governed by a one-dimensional, bar-frequency rule (widely vs. narrowly spaced bars). In the lower panel, both bar frequency and orientation carry useful but insufficient category information—information from both dimensions would have to be integrated into category decisions.

Figure 2

Humans performing rule-based (RB) and information-integration (II) tasks. A. Proportion of correct responses in each 10-trial block for 30 humans who performed 600 trials of an RB and II category task in that order. B. Proportion of correct responses in each 10-trial block for 30 humans who performed 600 trials of an II and RB category task in that order.

Figure 3

Macaques performing rule-based (RB) and information-integration (II) tasks A. Proportion of correct responses in each 100-trial block for three macaques who performed 6,000 trials of an RB and II category task in that order. B. Proportion of correct responses in each 100-trial block for three macaques who performed 6,000 trials of an II and RB category task in that order.

Figure 4

Capuchin monkeys performing rule-based (RB) and information-integration (II) tasks A-D. Proportion of correct responses in each trial block for four capuchin monkeys. Filled-square symbols and open-triangle symbols, respectively, denote performance in RB and II tasks.

Figure 5

Pigeons performing rule-based (RB) and information-integration (II) tasks A. Proportion of correct responses in each session from the onset of learning forward for 8 RB-learning pigeons (filled-square symbols) and 8 II-learning pigeons (open-triangle symbols). B. Proportion of correct responses in each session from the criterial block backward for 8 RB-learning pigeons and 8 II-learning pigeons.