Answering hastily retards learning

Abstract

Appropriate decisions involve at least two aspects: the speed of the decision and the correctness of the decision. Although a quick and correct decision is generally believed to work favorably, these two aspects may be interdependent in terms of overall task performance. In this study, we scrutinized learning behaviors in an operant task in which rats were required to poke their noses into either of two holes by referring to a light cue. All 22 rats reached the learning criterion, an 80% correct rate, within 4 days of testing, but they were diverse in the number of sessions spent to reach the learning criterion. Individual analyses revealed that the mean latency for responding was negatively correlated with the number of sessions until learning, suggesting that the rats that responded more rapidly to the cues learned the task more slowly. For individual trials, the mean latency for responding in correct trials (LC) was significantly longer than that in incorrect trials (LI), suggesting that, on average, long deliberation times led to correct answers in the trials. The success ratio before learning was not correlated with the learning speed. Thus, deliberative decision-making, rather than overall correctness, is critical for learning.

Fig 1. Nose-poke behavior test.

(a) Experimental procedure. The behavior test consisted of a training phase (Days 1–2) and a test phase (Days 3–6). (b) Illustration of the operant chamber with the two nose-poke holes (left). In the training phase, a rat was rewarded whenever the nose was poked into either nose-poke hole. In the test phase, however, the rat could gain a reward pellet only when it poked the nose into the hole that was not illuminated by a green light (right).

Fig 2. Summarized data of behavioral performance during the training and test phases of the operant task.

(a) Time changes in the mean correct rates in the training phase (left) and the test phase (right). (b) Same as a, but for the mean omission rates, i.e., a percentage of trials in which the rats did not respond within the time limit. (c) Same as a, but for the mean latencies to respond. Error bars represent SEMs for 22 rats.

Fig 3. Individual differences in learning curves.

(a) Time courses of the correct rates for 22 rats. Reaching the correct rate of 80% was defined as the criterion for completion of learning. Red dots indicate the first session in which the rats met the criterion. (b) Distribution of the sessions spent to reach the criterion.

Fig 4. Behavioral parameters before, during, and after learning.

(a) Left: time changes in the correct rates for 22 individual rats (gray) were aligned to the first session that reached the criterion. The blue line indicates the mean value. Right: training represents data from the last 4 sessions on Day 2 in the training phase. Four sessions immediately before and 5-to-8 sessions after reaching the criterion were defined as the during-learning and post-learning periods, respectively. These periods are shown by the black bars in the left panel. The mean correct rate in the during-learning period was significantly lower than the mean correct rates in the training period and the post-learning period. Training versus during-learning: P = 9.56 × 10⁻¹⁰, Q_3,63 = 28.0; during-learning versus post-learning: P = 9.56 × 10⁻¹⁰, Q_3,63 = 24.0; P = 1.14 × 10⁻²⁹, F_2,63 = 230; Tukey's test after one-way ANOVA. (b) Same as a, but for the rates of omission trials. Training versus during-learning: P = 0.034, Q_3,63 = 3.62; during-learning versus post-learning: P = 6.91 × 10⁻⁴, Q_3,63 = 5.51; P = 9.12 × 10⁻⁴, F_2,63 = 7.84. (c) Same as a, but for the latencies to respond. Training versus during-learning: P = 0.984, Q_3,63 = 0.235; during-learning versus post-learning: P = 0.092, Q_3,63 = 3.01; P = 0.044, F_2,63 = 3.28.

Fig 5. Relationship between nose-poke latencies in the during-learning period and task performance.

(a) The number of sessions spent to reach the criterion is plotted against the latency to respond. Each dot indicates data from a single rat. The blue line is the best-fit line determined by the least-squares method, and its 95% confidence intervals are shown by two broken lines. As a whole, rats with shorter latencies reached the criterion more slowly (P = 0.024, R = -0.48, Pearson's correlation test, n = 22 rats). (b) The latencies of individual trials are separately plotted for correct trials (L_C) and incorrect trials (L_I). L_I was significantly shorter than L_C (P = 0.016, bootstrap resampling test). Error bars represent SEMs for 22 rats. (c) Same as a, but against the latency ratio L_I/L_C. Rats with a smaller L_I/L_C ratio learned more slowly (P = 0.012, R = -0.52). (d) Same as a, but against the mean correct-trial ratio from the beginning of the test phase to the first session that reached the criterion (P = 0.197, R = -0.29).

Fig 6. Preference bias during the training and test phases.

(a) Time changes in the preference bias of individual 22 rats (gray lines) in the training phase (left) and the test phase (right). The yellow line indicates the means ± SEMs of 22 rats. (b) Same as (a), but they were aligned to the first session that reached the criterion. (c) Comparisons of the mean ± SEM preference biases during the training period, the during-learning period, and the post-learning period. The mean preference bias in the during-learning period was significantly lower than that in the training period and was significantly higher than that in the post-learning period. Training versus during-learning: P = 4.16 × 10⁻⁵, Q_3,63 = 6.66; during-learning versus post-learning: P = 5.59 × 10⁻⁶, Q_3,63 = 7.43; P = 1.12 × 10⁻¹³, F_2,63 = 49.7; Tukey's test after one-way ANOVA.