52 research outputs found
Transfer of Metacognitive Skills and Hint Seeking in Monkeys
Metacognition is knowledge that can be expressed as confidence judgments about what we know (monitoring) and by strategies for learning what we don’t know (control). Although a substantial literature exists on cognitive processes in animals, little is known about their metacognitive abilities. Here we show that rhesus macaques, trained previously to make retrospective confidence judgments about their performance on perceptual tasks, transferred that ability immediately to a new perceptual task and to a working memory task. In a second experiment we show that monkeys can also learn to request “hints” when they are given problems that they would otherwise have to solve by trial and error. This shows, for the first time, that non-human primates share with humans the ability to monitor and transfer their metacognitive ability both within and between different cognitive tasks, and to seek new knowledge on a need to know basis.
Positional Inference in Rhesus Macaques
Understanding how organisms make transitive inferences is critical to understanding their general ability to learn serial relationships. In this context, transitive inference (TI) can be understood as a specific heuristic that applies broadly to many different serial learning tasks, which have been the focus of hundreds of studies involving dozens of species. In the present study, monkeys learned the order of 7-item lists of photographic stimuli by trial and error, and were then tested on “derived” lists. These derived lists combined stimuli from multiple training lists in ambiguous ways. We found that subjects displayed strong preferences when presented with novel test pairs. These preferences were helpful when test pairs had an ordering congruent with their ranks during training, but yielded consistently below-chance performance when pairs had an incongruent order relative to training. This behavior can be explained by the joint contributions of transitive inference and another heuristic that we refer to as “positional inference.” Positional inferences play a complementary role to transitive inferences in facilitating choices between novel pairs of stimuli. The theoretical framework that best explains both transitive and positional inferences is a spatial model that represents both the position and uncertainty of each stimulus. A computational implementation of this framework yields accurate predictions about both correct responses and errors for derived lists
The category chain procedure
<b>(A)</b>. Four consecutive
trials of the Category Chain task. Each trial presents one stimulus from each
of the four categories, but the specific photographs change and the stimulus
positions change randomly from one trial to the next.<b> (B)</b>. An example of a correct trial. The dashed lines indicate
initial touch, and the arrows indicate subsequent touch (neither dashed lines
or arrows were visible to subjects).<b> (C-D)</b>.
Two examples of incorrect trials. In the first case, the initial touch was to
the wrong stimulus, so the trial ended immediately. In the second case, the
first two responses were correct but the third was incorrect. <b>(E)</b>. Two examples each of the four
photographic stimulus categories.<b> (F)</b>.
Two examples each of the four painting stimulus categories
Photographic stimulus pixel entropy
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<p>Kernel density estimates of pixel entropy in the four categories used
for Experiment 1. The median image is indicated by the solid blue line, while
the first and third quartiles are indicated by the blue dashed lines. </p>
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Monkey reaction times to painting stimuli on a log scale
Violin
plots show the distribution of log reaction times (in gray) and the credible
interval for the mean reaction time (in white) for each monkey
Painterly stimulus pixel entropy
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<p>Kernel density estimates of pixel entropy in the four categories used
for Experiment 1. The median image is indicated by the solid blue line, while
the first and third quartiles are indicated by the blue dashed lines. </p>
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