7.1. Stimulus strength
Infants exhibit elevated levels of looking to complex stimuli (Brown, 1974; Caron & Caron, 1969; Cohen et al., 1975; Greenberg et al., 1973) and, in some cases, more quickly learn about stimuli that are dynamic (Horst, Oakes, & Madole, 2005; Robinson & Sloutsky, 2004). One hypothesis for these findings is that complex and dynamic stimuli recruit and require more information processing resources. In DNFs, the strength of activation is an index of the processing of stimuli (Schöner, 2009), which is influenced by stimulus strength. To illustrate this concept, Schöner and Thelen (2006) showed that intense stimulation produced more looking time across trials but, interestingly, did not affect habituation rate.
In our DNF model, strong stimulation produces similar results. Fig. 12 shows the looking time (A) and distribution of trials on which a stable WM peak was formed (B) for the standard, young infant model when the stimulus strength was increased from 17 (black line) to 19 (blue line). Stronger stimulation led to an overall increase in looking time but no dramatic change in habituation rate. The model also formed a stable WM peak earlier when strength was increased to 19 (blue bars). Although a stable WM peak emerges early in habituation, looking time does not show a sharp decrease over trials because strong excitation in PF counteracts the inhibitory contribution from WM. We also tested the model with a weaker stimulus of strength 15, which led to low levels of looking (green line in A) and a spread distribution of WM peak formation (green bars in B). These results are consistent with Hunter and Ames’s (1988) multifactor model, which posits that the time course of memory formation is affected by the stimulus context.
Figure 12. The looking time (A) and distribution of trials on which a stable WM peak was formed (B) for the standard (young infant) model presented with three different stimulus strengths. When the stimulus strength was increased from 17 (black line and circles) to 19 (blue line and diamonds), looking time was elevated across trials but habituation occurred at the same rate. When the stimulus strength was decreased to 15, looking time was reduced across trials and the model did not exhibit any habituation (green line and squares). The stronger stimulus strength increased the rate at which a stable WM peak was formed (blue bars) relative to the standard young infant model memory (black bars), and the weaker stimulus strength slowed the rate at which a stable WM peak was formed (green bars). For simplicity, error bars are not shown. SD (in s) for strength 15 during the first block of three trials was 4.34, 3.69, and 3.06; for strength 17 was 3.16, 2.75, and 2.91; for strength 19 was 2.99, 3.04, and 3.45. SD for strength 15 during the last block of three trials was 2.53, 2.55, and 2.57; for strength 17 was 3.11, 2.68, and 2.25; for strength 19 was 5.13, 4.69, and 5.11.
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7.2. Inter-stimulus interval
In our model, long ISIs attenuate habituation because the formation of a stable WM peak is slowed or prevented. This has two sources. First, activation in HWM decays over long delays, which provides little support for WM as the model repeatedly encounters a stimulus across trials. Second, WM peaks can be destabilized during long delays in the presence of noise. Fig. 13A shows the looking time of the standard model with ISIs at 5 s (black), 30 s (green), 60 s (blue), and 120 s (red). Habituation was slowed with an ISI of 30 s, slowed even more with an ISI of 60 s, and the model did not exhibit any habituation with an ISI of 120 s. The differences in habituation rate mirror the rate at which the model formed a stable WM peak across the different ISIs (see Fig. 13B). At the longest ISI, the model rarely formed a stable WM peak. These results are, once again, consistent with both Schöner and Thelen’s (2006) model and Hunter and Ames’s (1988) multifactor model which posit that task factors such as the ISI affect the time course of memory formation.
Figure 13. The looking time (A) and distribution of trials on which a stable WM peak was formed (B) for the standard young infant model for four different ISI lengths. As the ISI was increased from the standard 5 s (black line and circles) to 30 (green line and squares) and 60 (blue line and diamonds), the model exhibited relatively little habituation. When the ISI was increased to 120 s (red line and triangles), the model exhibited no habituation. As the ISI was increased, the model acquired a stable WM peak increasingly later in the habituation phase. The model was rarely able to form a working memory when ISIs were set to 60 s (blue bars) and 120 s (red bars). For simplicity, error bars are not shown. SD (in s) for ISI 5 s during the first block of three trials was 3.16, 2.75, and 2.91; for ISI 30 s was 3.28, 2.85, and 2.80; for ISI 60 s was 3.08, 2.92, and 2.78; for ISI 120 s was 3.34, 2.84, and 2.76. SD for ISI 5 s during the last block of three trials was 3.11, 2.68, and 2.25; for ISI 30 s was 2.59, 2.88, and 2.47; for ISI 60 s was 2.41, 2.73, and 2.96; for ISI 120 s was 2.24, 2.14, and 2.44.
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7.3. Capturing familiarity and novelty over development
Familiarity preferences have long been observed, but the mechanism that underlies such preferences is still poorly understood. Familiarity preferences are more pronounced early in learning and early in development (for a review, see Rose, Feldman, & Jankowski, 2004, 2007). Familiarity preferences are typically studied in the visual paired comparison procedure, in which infants’ preference to look at a familiar stimulus over a novel one is assumed to reflect active encoding and initial memory formation. Why infants would be biased to look at a familiar stimulus over a novel one in single presentation tasks, however, is not immediately obvious. Indeed, it is rather striking that infants will sometimes exhibit relatively less looking to a novel stimulus on one trial relative to a familiar item on a previous trial. Schöner and Thelen (2006) were able to capture familiarity preferences in a single presentation task. Here, we probe whether our model can also capture familiarity preferences in a single presentation task as well as capture a familiarity-to-novelty shift over development. In doing so, we provide an explanation for why familiarity preferences are more prevalent early in development.
To investigate these issues, we asked whether the DNF model could quantitatively capture the only existing empirical data set examining a familiarity-to-novelty shift over development in a single presentation habituation task (Wetherford & Cohen, 1973). This study was particularly intriguing because it examined habituation in very young infants—between 6 and 12 weeks—who have not been the focus of previous formal theories of infant habituation. Results showed a dramatic developmental transition from a lack of habituation and familiarity preferences to rapid habituation and novelty preferences across a 2-week period. This developmental period is also important because there are significant changes in the control of fixation. For example, during this period, there is increased control over continuous visual tracking, orienting, and disengaging (for a review, see Johnson, 2002), and it is during this period that movements of the body and shifts of gaze become tightly coupled (Robertson et al., 2001b).
Wetherford and Cohen (1973) habituated 6-, 8-, 10-, and 12-week-olds to a two-dimensional stimulus that consisted of one shape and one color across 17 trials. On trials 2, 9, and 16, they measured infants’ stimulus preferences using a different novel stimulus for each of the three trials. An example of the experimental design is shown in Fig. 14A. Infants’ looking time across blocks of two trials is shown in 14B. Blocks consisted of the average looking time on adjacent trials, excluding the novel stimulus (e.g., trials 1 and 3, 4 and 5, and so on). Six- and 8-week-olds did not exhibit any evidence of habituation, 10-week-olds showed habituation late in the habituation phase, and 12-week-olds rapidly habituated. Infants’ preference scores are shown in Fig. 14C. Six- and 8-week-olds showed a familiarity preference on the second and third tests, while 10-week-olds showed a novelty preference on the third test. The 12-week-olds also showed a novelty preference after the first block of trials. Thus, across only a 2-week period, there was a developmental transition from familiarity-to-novelty preferences late in learning.
Figure 14. (A) Experimental design from Wetherford and Cohen (1973). Six-, 8-, 10-, and 12-week-old infants were habituated to a single stimulus (shown as gray star) across seventeen 15-s trials with ISIs of 8 s. On trials 2, 9, and 16, a different novel stimulus was presented. A preference to look at the novel stimulus was calculated by subtracting looking time to the novel stimulus from looking time on the preceding trial with the familiar, habituation stimulus. (B–E) The (estimated) empirical and model results of Wetherford and Cohen (1973). (B) Looking time across seven blocks of two trials from 6-, 8-, 10-, and 12-week-old infants. Six- (blue lines and diamonds) and 8-week-old (red lines and triangles) infants exhibited no evidence of habituation. At 10 weeks of age (green lines and squares), infants exhibited a decline in looking late in habituation, and at 12 weeks of age (black lines and circles), infants rapidly habituated. (C) Infants’ change in looking to the novel stimuli on trials 2, 9, and 16 relative to the preceding trial. Six-week-old showed a trend toward a familiarity preference on the third novel test, and 8-week-olds tended to exhibit a familiarity preference on the second and third novel tests. Ten- and 12-week-olds tended to exhibit a novelty preference on the second and third novel tests. The model effectively produced the same looking behavior across trials (D) and preferences at test (E). For simplicity, error bars are not shown. SD (in s) during block 1 for 6-week model was 1.16; 8-week model was 2.48; 10-week model was 1.76; 12-week model was 1.38. SD during block 7 for 6-week model was 1.21; for 8-week model was 1.35; for 10-week model was 0.99; for 12-week model was 0.50. SD on preference scores for 6-week model on test 1 was 3.16, test 2 was 2.57, and test 3 was 2.72; SD on preference scores for 8-week model on test 1 was 4.40, test 2 was 4.44, and test 3 was 3.89; SD on preference scores for 10-week model on test 1 was 3.52, test 2 was 2.94, and test 3 was 3.60; SD on preference scores for 12-week model on test 1 was 2.94, test 2 was 3.40, and test 3 was 2.99.
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To examine whether the DNF model could capture this rapid developmental transition, we tested the model with Wetherford and Cohen’s procedure. The metric similarity of the stimuli used by Wetherford and Cohen is unknown. Thus, we assumed a moderate amount of similarity among items by setting the metric distance between items to 30 neurons, half the distance between the close and far test items from the habituation stimulus in the simulations described previously. The first novel test was 60 neurons from the familiar stimulus, and the second and third novel tests were positioned 30 neurons on either side of the familiar stimulus.
We created 6-, 8-, 10-, and 12-week-old infant models by implementing the SPH (see Table 2). Pilot simulations revealed that changes to only the SPH parameters, in isolation, were not sufficient to capture the looking behavior of 6- and 8-week-olds. Given the dramatic changes in the control of fixation during this period, we added a second type of developmental change—we made the fixation dynamics less stable early in development by implementing the SPH on the fixation system. Specifically, we weakened the excitatory connection to PF from the fixation system (cui) and from PF to the fixation system (ciu), as well as the self-excitation of the fixation system (cii). These changes slowed the transitions from the looking away state to the looking state and the tendency of the fixation system to reenter the looking away state once fixated. Finally, we increased the noise in PF, which affects the model’s ability to form a working memory for a stimulus. Noise in PF was set to .4 for the 6-week-old model, decreased to .2 for the 8-week-old model, and returned to its base value of .12 for the 10- and 12-week-old models.
As can be seen in Fig. 14D and 14E, the model produced the same pattern of looking as infants. Simulations of 6- and 8-week-olds did not exhibit habituation, 10-week-old simulations exhibited a decline in looking during the last blocks of the habituation phase, and 12-week-old simulations exhibited a rapid decrease in looking, showing minimal looking by block 4. The novelty preferences for each age group on the three novel tests are shown in 14E. The 6-week-old model exhibited a slow increase in the strength of its familiarity preference across trials. The 8-week-old model, in contrast, exhibited a rapid increase from a null preference on the first novel test to a strong familiarity preference on the second and third novel test. Both the 10- and 12-week-old models exhibited a novelty preference on the second novel test, but only the 10-week-old model exhibited a stronger novelty preference on the third novel test.
To illustrate the dynamics that underlie this rapid developmental transition, Fig. 15 shows the state of PF and WM at the onset of the three novel tests for the 8-week-old (A-C) and 10-week-old (D–F) models. Eight-week-olds exhibit an increasing familiarity preference across trials. How might such a behavior arise? When the 8-week-old infant model encounters the first novel test on trial 2, HPF has accumulated little activity (A; gray line, right y-axis). The model has yet to accumulate any bias to look at the familiar stimulus on previous trials and, therefore, cannot exhibit a decline in looking to what amounts to an equally novel stimulus. Across trials, activity in HPF accumulates, leading to an increase in looking to the familiar stimulus. In contrast, when the model encounters the novel stimulus on trials Fig. 9B and Fig. 16C, the lack of HPF activity associated with these novel feature values leads to little looking. At 8 weeks of age, then, familiarity preferences are driven by the neural dynamics associated with perceptual encoding. Note that neural interactions are too weak to support stable WM peak formation (see circle in C); consequently, no habituation occurs.
Figure 15. The mechanisms by which looking fails to habituate at 8 weeks of age (A–C) and habituates at 10 weeks of age (D–F). The state of PF and WM is shown at the onset of the first novel test (A and D), second novel test (B and E), and third novel test (C and F). The placement of the familiar stimulus and three novel tests along a metrically organized feature dimension is shown at the top. The 8-week-old infant model acquires strong activation in HPF across trials (see gray line, right y-axis in B and C), which biases looking to the familiar over the novel stimulus. The 10-week-old infant model establishes a stable WM peak late in habituation (F), which leads to a decline in looking and an increase in looking to the novel stimulus.
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Figure 16. Looking time of the standard young infant model as it was initialized with an increasing strength of HPF and HWM from 0% to 35% of that accumulated in the simulations shown in Fig. 7. As initialization strength increased, the model habituated more quickly. This mimics robust long-term memory after only short delays between first and second exposures to the habituation stimulus.
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The picture is very different just 2 weeks later. Although the 10-week-old model is comparable to the 8-week-old model on the first novel test (trial 2, see D), some simulations have started to form a WM peak by the second novel test on trial Fig. 9E. This leads to a small decline in looking across simulations. By the third novel test on trial Fig. 16F, the model has established a stable WM peak, inhibition in PF is strong, and looking has declined. Consequently, the model exhibits a strong relative increase in looking to the novel stimulus.
These simulations show that small, quantitative increases in the strength of neural interactions lead to a qualitative shift in memory formation over development (for discussion of related issues, see Spencer & Perone, 2008). Interestingly, this developmental shift mirrors the qualitative transition from encoding to working memory formation that occurs over learning in the young and old infant models (see Fig. 8G and 8H). These simulations also demonstrate that the DNF model can quantitatively capture the details of infants’ performance within a specific task context. It is noteworthy that the model was able to capture this data set in particular. To our knowledge, these are the first quantitative simulations of looking data from infants this young. It is also noteworthy that quantitative fits required implementing changes to the fixation system (and increasing noise). This provides a point of convergence between our modeling efforts and empirical work showing dramatic changes in fixation dynamics during this early period.
7.4. Integration of learning with real-time process
Learning in Schöner and Thelen’s (2006) model did not involve excitatory memory. Our model, in contrast, learns via an excitatory Hebbian process that builds a long-term learning history as the model looks and looks away in real time. Several studies on delayed recognition have shown that infants’ long-term memory for a stimulus contributes to their subsequent looking. For example, Martin (1975) found that infants habituated to a stimulus more quickly during a second experimental session after habituating to the same stimulus during the first experimental session (for related results, see Fagan, 1973; for a review, see Rose et al., 2007). Similarly, Bahrick and Pickens (1995) found that infants spent more time looking to a familiar stimulus as the delay between initial exposure and test increased. Here, we test whether learning from one session in the DNF model produces these behavioral patterns when that learning is carried forward to a subsequent session.
To probe this, we initialized the standard, young infant model with the HPF and HWM accumulated from the simulations described previously (Fig. 7). To account for the decay and interference processes that occur over varying time delays, we modulated the strength of HPF and HWM from 0% to 35%. Fig. 16 shows the looking time across trials as the strength of HPF and HWM was increased from 0% to 35%. The model, like infants, habituated more quickly when it has a learning history with the stimulus (Martin, 1975). Importantly, no learning history with the stimulus (i.e., initialization 0, black line) or a weak history (i.e., initialization .15, green line) induced more looking early than did a relatively strong history (i.e., initializations .25 and 35, blue and red lines; Bahrick & Pickens, 1995). These simulations show that very little retention (<35%) is needed to have a large impact on subsequent behavior. Our Hebbian learning mechanism accounts for fast and flexible task-specific learning well, and it also accounts for the time-dependent decline that a long-term learning history has on infants’ behavior. It is important to point out, however, that it is unclear whether our Hebbian learning mechanism in its current form can be applied to time delays on the order of weeks, months, or years between initial learning and subsequent testing. We are currently probing this issue in our laboratory.