Intensity effects in human cue-outcome learning

Journal Pre-proof Intensity effects in human cue-outcome learning Paula Balea, James Byron Nelson

PII:

S0376-6357(19)30354-7

DOI:

https://doi.org/10.1016/j.beproc.2019.104015

Reference:

BEPROC 104015

To appear in:

Behavioural Processes

Received Date:

19 August 2019

Revised Date:

26 October 2019

Accepted Date:

25 November 2019

Please cite this article as: Balea P, Nelson JB, Intensity effects in human cue-outcome learning, Behavioural Processes (2019), doi: https://doi.org/10.1016/j.beproc.2019.104015

This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2019 Published by Elsevier.

Intensity effects 1 Running head: INTENSITY EFFECTS

Intensity effects in human cue-outcome learning

Paula Balea*, James Byron Nelson

ro of

University of the Basque Country (UPV/EHU)

University of the Basque Country, Sarriena, s/n 48940 – Leioa, Spain.

-p

* Corresponding author at: Departamento Procesos Psicológicos Básicos y su Desarrollo. Avenida de Tolosa, 70, San Sebastián, 20018, España.

re

E-mail addresses: [email protected] (P. Balea), [email protected] (J. B. Nelson)

Human conditioning was unaffected by the intensity of a visual CS. Intensity altered the normal (symmetrical) shape of the generalization gradient. Results are explained by assuming that intense stimuli include less intense ones.

Jo

ur

na

  

lP

Highlights:

Intensity effects 2 Abstract Literature on conditioned stimulus intensity effects is briefly reviewed and one experiment presented with human subjects and a video-game method. The intensity (Bright or Dim) or color (Red or Blue) of a cue that predicted the appearance of a spaceship was manipulated. Testing was conducted with either the alternate brightness or the alternate color. Responding to the cue was unaffected by its intensity in training. During testing, a downshift in brightness decreased responding while an upshift had no effect, suggesting an asymmetrical intensity gradient. Red tended to condition better than Blue in the first phase, but the same participants conditioned better in the second phase to Blue. The results are discussed with respect to prior demonstrations of intensity effects using within-subject designs and favor an explanation based on stimulus-sampling theory.

Jo

ur

na

lP

re

-p

ro of

Keywords: Generalization; Human conditioning; Stimulus intensity dynamism.

Intensity effects 3 1. Introduction The physical intensity of a conditioned stimulus (CS) is said to affect conditioning (e.g., Rescorla and Wagner, 1972). This relationship was termed by Hull (1949) stimulus intensity dynamism (SID), and refers to the idea that high-intensity stimuli have an energizing effect on the conditioned response (CR). Despite that CS intensity has received considerable research, its effect in conditioning and generalization procedures is still unclear, particularly in the case of humans.

ro of

Overall, literature in non-human animals strongly supports a positive relationship between CS intensity and the magnitude of the CR. For instance, Kamin and Schaub (1963, E1) using a conditioned suppression procedure with rats, found acquisition of barpress suppression to be faster with higher intensities (volume) of a white-noise CS paired with shock (for other examples see Barnes, 1956; Birkimer and Drane, 1968; Gray, 1965; Imada et al., 1981; Kamin and Brimer, 1963; Kamin and Schaub, 1963; Kessen, 1953; Leonard and Monteau, 1971; Rogers, 1973; Scavio and Gormezano, 1974; Welker and Wheatley, 1977; Woodard, 1966; but see, Heyman, 1957; Miller and Greene, 1954; Mourant and Pennypacker, 1968; Paschall and Davis, 2002).

lP

re

-p

The picture is different when the effect of CS intensity has been assessed in humans. Using between-subjects designs, where each participant is exposed to a single stimulus intensity, the SID effect has not been demonstrated consistently. Among four experiments that reported eyeblink measures (Carter, 1941; Grice and Hunter, 1964, E1; Mattson and Moore, 1964; Walker, 1960), a positive effect was found in only two of them (Mattson and Moore, 1964; Walker, 1960). Among four studies that used the galvanic skin response (GSR) (Cermak, 1967; Champion, 1962, E1; Kimmel, 1959; Kimmel et al., 1962), there is a single positive report by Champion (1962, E1). Finally, Kimmel et al. (1962) reported a null result with a fingerwithdrawal measure.

Jo

ur

na

In contrast to the scarce positive results found in humans with between-subjects assessments, the SID effect has been consistently found when intensity has been manipulated within subjects. Grice and Hunter (1964, E1; see also E2) explicitly compared both types of assessments. Participants received tones paired with air puffs to the eye. Groups L (loud) and S (soft) received conditioning with a single stimulus, a 100-dB or 50-dB tone, respectively. Groups LS-L and LS-S received conditioning with both tones in a quasi-random manner. Their results showed an interaction between the CS intensity and the experimental design. The 100dB tone elicited more eyeblink responding than the 50-dB tone when participants had been trained with both stimulus intensities (LS groups), but when a single tone had been trained in each group (S vs. L comparison) responses were similar. Positive results with within-subjects designs were obtained in four other experiments using an eyeblink procedure (Beck, 1963; Grice et al., 1966, E1, E2; Lipkin and Moore, 1966) and one using the GSR (Cermak, 1967). It is noteworthy that all human studies mentioned thus far used auditory CSs: There are no reports (positive or negative) with visual stimuli. Intensity can also be thought of as just a stimulus dimension, such as wavelength, along which generalization can occur. Usually, presenting a novel stimulus that differs from the trained CS on some dimension(s) produces a decrease in responding (generalization

Intensity effects 4 decrement) proportional to the amount of difference between the two stimuli (e.g., Guttman and Kalish, 1956). Since that proportionality applies equally to stimuli that fall above and below the CS, the generalization gradient tends to be symmetrical. The study by Kamin and Schaub (1963) mentioned above illustrates this point with regards to intensity. Following acquisition, the groups trained with the weakest (W) or the strongest (S) CS were tested in extinction either with the same or with the alternate stimulus intensity. As expected, the groups that had a stimulus shift in the test (W-S and S-W groups) showed larger response decrements than those trained and tested with the same intensity (W-W and S-S groups). Importantly, there were no differences between the W-S and S-W groups; the decrement was symmetrical, with similar decrements to both the more and less intense stimulus.

-p

ro of

The results of Kamin and Schaub (1963; see also, Mourant and Pennypacker, 1968) contrast with other animal studies where testing high-intensity stimuli seemed to alter the shape of the gradient. Miller and Greene (1954), for instance, found no response decrements when rats trained with a 93-dB buzzer in a signaled-shock avoidance procedure were tested at either 99 or 108 dB. Although the up-shift did not increase the response over the training stimulus, as some reports have shown (Hearst, 1969; Rohrbaugh et al., 1971; Scavio and Gormezano, 1974; Woodard, 1966), it appeared to prevent the usual decrement in the right half of the gradient.

na

lP

re

Asymmetries along the intensity gradient have also been reported in humans (Hall and Prokasy, 1961; Hovland, 1937; Spiker, 1956; but see Grant and Schneider, 1948, 1949; Walker, 1960). Hall and Prokasy (1961), for instance, measured the GSR to a tone of either 30 or 80 dB paired with a shock. After conditioning, the two groups were tested in extinction at 30, 54, or 80 dB. The results showed a response decrement in the groups tested at lower intensities (8054 and 80-30 groups, but an upward generalization curve in the those tested at higher intensities (30-54 and 30-80 groups). Presumably, the SID effect was so large that it not only compensated for any generalization decrement, but increased responding to the test stimuli above the trained one. A similar upward curve was shown by Hovland (1937). In other cases, a decrement has been found at both sides of the gradient, but the decay was steeper on the left side than on the right side (Spiker, 1956).

Jo

ur

To summarize, non-symmetrical intensity gradients have been attributed to the energizing properties of more intense stimuli in the right half of the gradient. If the SID effect is enough to surpass the generalization decrement produced by the stimulus change, the gradient will increase monotonically (Hall and Prokasy, 1961; Hearst, 1969; Hovland, 1937; Rohrbaugh et al., 1971; Scavio and Gormezano, 1974; Woodard, 1966); otherwise, it will simply be flattened (Miller and Greene, 1954), or shallower on the right side (Heyman, 1957; Spiker, 1956). The study presented here assesses intensity effects in conditioning and generalization using modern techniques and in the visual modality, where intensity effects have not been tested before in humans. Additionally, given the antiquity of the literature on intensity effects, studies often lack adequate statistical treatments in the case of null results, due to the unavailability of either the techniques or practical methods by which they can be implemented. In this study, we supplement analyses where results could indicate that intensity

Intensity effects 5 has no effect with Bayesian analysis (i.e., Wagenmakers, 2007) to determine the support for the null that the data offer. We first trained participants to shoot at a spaceship by pressing a key on the keyboard in order to use it later as the “unconditioned stimulus” (US) (Arcediano et al., 1996; Franssen et al., 2010; Ivanov-Smolensky, 1927, for conceptually similar procedures and rationale). During conditioning, the spaceship was preceded by a sensor light (the CS). Importantly, activating the weapon required initial pressing (charging the weapon) before it began to fire. Therefore, if conditioning occurred, the sensor light was used by the participants as a signal to start charging. The “CR” was the number of presses per second during the CS-US interval.

ro of

In phase 1 the participants were conditioned with a bright or dim sensor, which could be red or blue, counterbalanced (see Figure 1). If the stimulus intensity has an effect on conditioning, the Bright group should show a faster learning rate or a larger CR at asymptote than the Dim group. Phase 1 will also inform as to whether the colors have a differential effect on conditioning. Intuitively speaking, for human participants a red warning signal could be inherently more indicative of danger than a blue one and, thus, be more easily conditioned. [Insert Figure 1 about here]

Jo

ur

na

lP

re

-p

In phase 2, the phase-1 groups were divided into halves depending on the dimension that changed with respect to phase 1. The Intensity Changed groups were conditioned with a CS of the same color, but used the alternate intensity brightness; the Color Changed groups were conditioned with the alternate color but the same brightness as in phase 1. The very first trial with the new stimulus served as a generalization test. The Color Changed groups are expected to show a response decrement due to the wavelength change (e.g., Guttman and Kalish, 1956). This decrement should be similar regardless of the direction of the change (from red to blue or vice versa). The Intensity Changed groups will inform whether the stimulus intensity has any effect on the generalization gradient. If intensity gradients have symmetrical shapes, these groups should show a similar decrement (e.g., Kamin and Schaub, 1963). Otherwise, differences are expected. In the groups going from bright to dim (top half of Figure 1), the joint effect of the stimulus change and the loss in intensity should clearly produce a response decrement. However, in the groups going from dim to bright (bottom half of Figure 1), the intensity of the test stimulus could compensate for the decrement that the stimulus change alone produces, yielding to a smaller decay in the response, or even to an increase. Finally, an analyses of the entire phase 2 will assess the intensity effect after participants have had experience with the two different intensities, something that has been shown to enhance SID effects (Cermak, 1967; Grice and Hunter, 1964, see also Frey, 1969; Murray, 1970). In addition to the possibility of stimulus intensity dynamism affecting rates of acquisition and the shape of the generalization gradients, other explanations are possible that either appeal to the establishment of thresholds for responding (Grice and Hunter, 1964), or incorporate stimulus sampling theory into the Rescorla-Wagner model (Rescorla, 1976). Both accounts will be discussed further in the discussion section.

Intensity effects 6 2. Method 2.1 Participants Participants were recruited through announcements in the university campus and compensated with 5 euros for their collaboration. For proper counterbalancing, group sizes were planned at n = 8 (N=32). No volunteer who showed for his/her appointment was turned away resulting in 52 students (79% women) taking part in this study. The mean age was 21.35, ranging from 18 to 56 years old. Participants were randomly assigned to groups (ns = 26). 2.2 Apparatus

-p

ro of

This section has been adapted from Balea et al., (2018) with permission from Elsevier. The video game used was that of Nelson et al. (2014), and most visual details described below are pictured there. A download is available at http://drjbn.wordpress.com/the-learning-gamedownload-links/. The game was played on four Dell OptiPlex computers with 22-inch monitors with an aspect ratio of 1.6 (width / height). The resolution was set at 1280 × 800 pixels. A trapezoidal box constructed of black foam board with rectangular ends and the front face uncovered was placed over the monitor and keyboard. The opening was 70 by 70 cm and the back wall was 70 by 50 cm (width × height), the overall length of the side walls was 1 m. The front opening allowed participants to sit at the table with their head and shoulders just inside the box, isolating each participant.

ur

na

lP

re

Participants played a three-dimensional first-person spaced-themed video game. Their view was as if they were inside of a spaceship looking out of a viewscreen. The viewscreen contained a crescent-shaped panel near the bottom that contained two rows of oval, canistershaped devices. There were five on the upper row, and three on the lower row. In this experiment, illumination of the middle canister of the top row served as the CS for half the participants, and illumination of the middle canister of the bottom row as CS for the other half. The CS was the 20-s flashing of a colored light at rate of three cycles per second. Two different intensities and two different colors were used. Bright red was created by setting the red component of the color in the RGB system to its maximum (i.e., 255), and dim red setting the component to 29.4% of the maximum (i.e., 75), with the green and blue values set at zero in both cases. The bright and dim blue colors used the same values described above (255 and 75, respectively) in the blue component, with the red and green values set at zero. The diameter of each canister was 50 pixels when lit.

Jo

Environments were visible through the view screen. The first was a “training environment” which appeared as if the participant’s craft was inside of a large, green wireframe cube with square grid lines on each wall. The second, referred to as “Nicholosia”, was a colorful star system consisting of a green ringed planet surrounded by stars and a yellow gaseous nebula. There was a large 3D spiral-shaped rotating space station present near the participants’ center of view and a custom-made music track looping in the background. Instructions were presented in yellow Arial font on a black translucent panel that rose from the bottom of the screen, and also through the headphones in a pre-recorded voice. Four spaceships were available to use as outcomes, and each one could be repelled by a different weapon. All four were used in an initial “response training” phase, described below.

Intensity effects 7 Afterwards, a single spaceship (the “Learian”) was used. The Learian was a blue saucer-shaped craft and was repelled by a weapon in the upper right of the screen named the “SOP Cannon” that fired glowing green balls. The other three ships and associated weapons were as described in Nelson et al., (2014). Each weapon was activated by pressing a different key on the keyboard. A weapon became active once 15 keypresses at a rate of at least 3 per second had been accumulated. From that moment, every other keypress resulted in the weapon firing at the spaceship, but only when it was present and a response occurred at least every 0.75 s. The backspace key was used to activate the SOP Canon. 2.2 Procedure

2.2.1

ro of

The participants entered the experimental room and provided their informed consent. Then, they sat at the computer desk and were told to wear the headphones and press the B key when they were ready to start. Response Training

na

lP

re

-p

This phase was conducted exactly as in Balea et al., (2018) and is reproduced here with permission from Elsevier. Participants were instructed that they must learn to activate weapons to repel invading spaceships and received practice trials with all four of the different ships. On the first trial with a particular ship the instructions informed the participant of the name of the ship, the weapon used to repel it, and the key to press to activate the weapon. They were instructed that the key must be pressed rapidly and repeatedly. The participant was then left to press the key and discover the effort necessary to activate the weapon. The ship was repelled after firing 8 shots. An instruction screen then appeared congratulating the participants and reminding them of the weapon to use on that ship. On subsequent appearances of the ship, no further instructions were provided; the ship simply remained on the screen until the participant repelled it. Participants were trained to respond to the four different spaceships (five trials each) in the manner described in the “response training” phase of Experiment 2 in Nelson et al., (2014).

Jo

ur

After the final response training trial, participants were informed that they were ready for patrol. The final instructions encouraged participants to have the weapons ready if they thought invaders were going to appear so that they might attack the invader upon its arrival before it attacked the space station. They were told that invaders might appear, or that they may pass their patrol enjoying “the beauty of the galaxies and music beamed from the stations” without invaders. They were then virtually transported to the galaxy where the experimental manipulations took place. A single spaceship (the Learian) was used in the remainder of the experiment.

2.2.2

Phase 1

The Bright and Dim groups (see Figure 1) received eight conditioning trials with a bright or a dim light, respectively. Based on our experience with this method, near asymptotic responding appears around trial 8 (see, e.g., Nelson et al., 2014). With this amount of training we wanted the participants responding to be clearly above a floor, so as to be able to observe

Intensity effects 8 performance decrements (e.g., after a stimulus change), while not pushing the responding so far into an asymptote that small increments could not be detected (e.g., after an increase in the stimulus intensity in phase 2). A conditioning trial began with the light onset. The total CS duration was 20 seconds. Five seconds after the CS onset, the spaceship (US) appeared and remained during the last 15 seconds of the CS. At second 20 both the CS and the US disappeared regardless of whether the participant pressed the corresponding key or not. In the latter case, the weapon fired once (with no participant input) at the programmed end of the trial, and both the CS and US disappeared. The inter-trial interval followed a randomly determined sequence across trials and phases averaging 16.25 s across the whole experiment, with a range from 10 to 25 s. Phase 2

ro of

2.2.3

lP

re

-p

Phase 2 followed phase 1 uninterrupted and unannounced. During phase 2, participants received eight conditioning trials with a different stimulus. The Dim and Bright groups were split into halves (see Figure 1) depending on whether the phase-2 stimulus differed from the phase-1 stimulus in intensity or color. The Intensity Changed groups received eight conditioning trials with the alternate stimulus intensity. Thus, the Bright-Intensity Changed group was conditioned with a dim stimulus, and the Dim-Intensity Changed group was conditioned with the bright intensity. The color (red or blue) was kept constant with respect to phase 1 in these groups. The Color Changed groups were conditioned with the same stimulus intensity as in phase 1 (dim or bright), but the color of the light changed; those who had conditioning with a red light in phase 1 were conditioned with a blue one in this phase, and vice versa. 2.3 Data analysis

Jo

ur

na

Data were analyzed with analysis of variance (ANOVA). Simple effects were conducted using error terms appropriately derived from the overall analysis (Howell, 1987). As we were eight participants short of having the counterbalancing completely balanced with respect to the sub-grouping, the analysis included the color counterbalancing variable and used the standard Type III sums of squares so as to avoid weighting by the sample sizes.

3. Results

3.1 Phase 1

A Change Type (either the color or intensity to be changed in phase 2) × Training Color (Red or Blue) × Training Intensity (Bright or Dim) × Trials × Seconds ANOVA was run on the phase-1 trials. The Change Type variable was included to determine any potential pre-existing differences between the phase-2 groups (i.e., sampling error). The analysis revealed effects of Trials F(7,336) = 36.91, p < .0001, η2p = .43, and a Trials × Training Color interaction, F(7,336) = 2.39, p = .02, η2p = .048. The interaction indicates that the effect of Trials, which was clearly reliable as indicated by Figure 2, was somewhat larger with the red color than with the blue (bottom panel of Figure 2). Those trained with blue appeared to start slightly higher and finish

Intensity effects 9 slightly lower. Despite the interaction, simple effect tests of Training Color on each trial found no differences, Fs(1,117) ≤ 1.89, ps ≥ .17. There was an effect of Seconds, with responding increasing within trials, which was superseded by the Trials × Seconds interaction, F(28,1344) = 8.86, p < .0001, η2p = .156, as the increase during a trial grew more prominent as conditioning proceeded. There were no other reliable effects, ps ≤ 0.24. Of interest for the effect of the intensity manipulation (top panel of Figure 2), there was no effect of Training Intensity, F < 1 and it did not interact with any other variable, ps ≤ 0.24.

ro of

Although we have a null result regarding the effects of the intensity manipulation between-subjects, it does appear to be meaningful. Bayesian analysis (Wagenmakers, 2007) indicated that the odds favored the null hypothesis with respect to a lack of effect of intensity. Regarding the main effect of Training Intensity, the odds favored the null 6.33 to 1. With respect to the Training Intensity × Trials and Training Intensity × Trials × Seconds interactions, either of which would indicate differences in the rate of acquisition, the odds overwhelmingly favored the null (873652 to 1 and 6.1 × 1023 to 1, respectively). [Insert Figure 2 about here] 3.2 Generalization test

lP

re

-p

Generalization was assessed by comparing the last trial of phase 1 with the first trial of phase 2. The data were analyzed with a Change (last phase 1 vs. first phase 2 trial) × Change Type (Intensity or Color) × Test Color (Red or Blue in phase 2) × Test Intensity (Bright or Dim in phase 2) × Seconds ANOVA. There was a Change × Change Type interaction, F(1,44)= 21.71, p < .0001, η2p = .33. As indicated in Figure 3 without further analysis, the effect of changing the color between phases (right two panels) was much greater than any effect of changing brightness (left two panels). There was an effect of Seconds, F(4,176) = 48.07, p < .0001, η2p = .52, as responding increased over seconds.

na

[Insert Figure 3 about here]

Jo

ur

The analyses yielded also a Change × Change Type × Test Color × Seconds interaction, F(4,176) = 2.94, p = .022, η2p = .06. This particular breakdown is not shown in the figure, but is entirely sensible. In the groups receiving changing intensity across phases, the Test Color variable collapses over whether or not the shift was Dim to Bright or Bright to Dim. As we will analyze below, the effect of brightness change was not symmetric, thus, the average change was almost flat and there were no effects of the Change variable or interactions with the Test Color, Fs(1,22 ) ≤ 3.47, ps ≥ .08 (trial 1) variable at this level of the Change Type variable. In the groups receiving the color manipulation there was an effect of the shift on every second, Fs(1,22) ≥ 6.34, ps ≤ .02, η2p ≥ .22, as the color changed between phases, with no effects involving intensity of the color, Fs ≤ 1.71, ps ≥ .21. The interaction suggests that the change in color produced an asymmetric decrement. The decrement seemed to be smaller when the shift was from Blue (last trial mean = 3.14) to Red (mean = .72) than when the shift was from Red (last trial mean = 4.77) to Blue (mean = .57). Nevertheless, the groups did not differ reliably on either the last training F(1,22) = 3.68, p = .068, despite the trend for responding to red to be greater, nor or the test trial, F < 1. The interaction shows that the

Intensity effects 10 difference from Red to Blue (4.2 responses) was greater than the difference from Blue to Red (2.42 responses). However, to observe the same size difference in the shift from Blue to Red would require a negative response (-1.06) on test. Any cause of asymmetry that might be inferred here is confounded with a floor effect at testing.

ro of

Of most interest for the effect of the intensity manipulation is the Change × Change Type × Test Intensity × Seconds interaction, which was at the cusp of traditional significance, F(4,176) = 2.42, p = .050, η2p = .052, and is shown in Figure 3. Here the analysis suggests that the effect of the Test Intensity depends on whether intensity or color was manipulated between phases and whether it was early or late in the trial. Change × Test Intensity analyses within each Change Type (Intensity changes on the left of Figure 3, Color changes on the right of Figure 3) on each second revealed a Change × Test Intensity interaction on the first second when the Intensity was being manipulated across phases, F(1,22) = 5.29, p = .03, η2p = .194. There was an effect of shifting from Bright to Dim, F(1,22) = 6.51, p = .01, η2p = .228, but not when shifting from Dim to Bright, F < 1. As the figure indicates, there was clear room in both the ceiling and floor to observe an effect. On the remaining seconds there were no effects of Change or interactions with Test Intensity, ps ≥ .15. As reported above, in the Color Changed groups the Change was reliable on every second.

-p

3.3 Phase 2

lP

re

Since the groups that received the intensity manipulation differed considerably from those receiving the color manipulation on the first trial of phase 2, an analysis of all the trials of phase 2 will produce a clear interaction with the Change Type variable. Thus, we analyzed each Change Type separately, focusing on the effects of the color and intensity of the Phase 2 stimulus.

Jo

ur

na

A Test Intensity × Test Color × Trials × Seconds ANOVA was conducted on the groups that received the intensity change (top panel of Figure 4). The analysis showed effects of Trials and Seconds, ps ≤ .0036. Of most importance there was a Test Intensity × Trials interaction, F(7,154) = 3.87, p = .007, η2p = .15. No other effects in the overall ANOVA were notable, ps ≥ .08 (Intensity × Trials × Seconds). Simple effects of Test Intensity on each trial, however, failed to find any differences at p < .05, Fs(1,39) ≤ 3.27, ps ≥ .08 (trial 7). As shown in the analysis of the last training vs. first test trial earlier, those shifted from Bright to Dim showed a small generalization decrement, leaving greater room for an increment in responding. They showed a significant, within-subject, effect of Trials, F(7,154) = 6.40, p < .0001, η2p = .348, whereas those shifted from Dim to Bright showed no evidence of a generalization decrement, and their responding did not increase further F < 1, η2p = .019. [Insert Figure 4 about here]

A Test Color × Test Intensity × Trials × Seconds ANOVA was conducted on the groups that received the color manipulation. There were effects of Trials, Seconds, and Trials × Seconds, ps ≤ .0001. Of most importance there was a Test Color × Trials interaction, F(7,154) = 2.96, p = .006, η2p = .119, and no other effects, Fs < 1. Simple effects of Test Color on each trial showed a difference on trial 2, F(1,50) = 9.88, p = .003, η2p = .30, and none other, ps ≥ .17. Unlike the trend observed in phase 1, these data suggest that Blue conditioned more rapidly

Intensity effects 11

4. Discussion

ro of

than did Red in phase 2. The data showing the color manipulation in phase 1 collapsed over the groups that would have the color changed at test (i.e., those being analyzed here) and those who received the intensity manipulation, analyzed in the previous paragraph. However, as mentioned four paragraphs earlier, the examination of the phase-1 data of only the two groups that received the color change showed that the participants being conditioned with red was trending to respond more than those conditioned with blue at the end of phase 1. The observation that the same participants, those who tended to respond more in phase 1, also tended to respond more in phase 2 questions whether the Training Color × Trials interaction reported in phase 1 was anything other than sampling error. Alternatively, this pattern could be explained by assuming that greater conditioning with red resulted in a greater generalization to the blue color in phase 2, but that was not apparent on the first trial where only generalization can affect the data.

re

-p

The intensity of the conditioned stimulus has been shown to affect acquisition of the CR in animals, but the results are less clear with humans. Intensity also seems to have a role when it varies along untrained-test stimuli in generalization studies. In this respect, there are several reports that intensity gradients tend towards monotonicity (e.g., Ghirlanda and Enquist, 2003; Hall and Prokasy, 1961; Miller and Greene, 1954; Spiker, 1956), but symmetric intensity gradients have been also observed (e.g., Kamin and Schaub, 1963; Mourant and Pennypacker, 1968).

na

lP

The present paper provides an empirical assessment of the effects of intensity on conditioning and generalization in humans using modern methodology and stimuli from a previously unassessed sensory domain. We assessed whether lights of different intensity brightness’s produced different amounts of conditioning in a videogame task, and whether a change in their intensity vs. color yielded different generalization gradients. Overall, the results indicate that intensity did not have an effect on conditioning, but it did have an effect on the generalization gradient.

Jo

ur

In phase 1, where the effects of intensity were assessed between groups, there was no differential responding to a bright vs. a dim light. This result is in agreement with most between-subjects assessments in human subjects, that have been done with the eyeblink (Carter, 1941; Grice and Hunter, 1964, E1) and GSR (Cermak, 1967; Kimmel, 1959; Kimmel et al., 1962) procedures. Complimentary to these previous findings, the results reported here have been obtained with a visual stimulus and a considerably different procedure. With respect to the color, it was reasonable to expect that people would treat “Red” as a better warning signal than “Blue”. Indeed, the red color tended to condition slightly more rapidly than did blue. But, on test, that pattern reversed where the participants being conditioned with blue did so more rapidly than those conditioned with red. There are two possible explanations for this result. First, it could be that the phase-1 differences were simply due to sampling error, as those conditioned with red in phase 1 were those conditioned with blue in phase 2. Second, it is possible that greater conditioning with the red light in phase 1 resulted in a greater generalization to blue in phase 2, though that difference was not present on trial 1 of

Intensity effects 12 testing where only generalization could affect the participants’ response. Whatever the case, the result does indicate that the procedure could detect small differences regardless of their source, and none were observed with the intensity manipulation. Moreover, Bayesian analysis provided strong support for the idea that there were no differences between the different intensity conditions.

-p

ro of

Studies have shown that measuring the intensity effect within-subjects can increase the intensity effect (e.g., Cermak, 1967; Grice and Hunter, 1964). In our experiment, when participants were exposed to a new stimulus, differences appeared between the groups depending on the intensity to which they were switched. The group that was tested with a lower intensity showed a generalization decrement. If the gradient were symmetrical, with the same differences between the stimuli we should have observed a similar decrement in the group receiving a dim-to-bright change. However, the group tested with a higher intensity showed no decrement. This result is in line with other studies where the SID effect produced by intense stimuli compensates for the generalization decrement that occurs due to the stimulus change (e.g., Miller and Greene, 1954). Consistent with the generalization decrement observed in shifting from bright to dim, that group showed conditioning along phase 2, while the dim-to-bright condition showed strong responding throughout. Any advantage that the dim-to-bright condition might have due to an SID effect in phase 2 could be obscured by a response ceiling.

na

lP

re

As just described, intensity effects were only detected after the participants had the opportunity to experience both intensities. According to Grice and Hunter (1964), during training, the participants adopt an adaptation level (AL) for responding. The placement of that AL is such that it guarantees responding to all the stimuli present during training. When a single stimulus is used, the AL would be set at the intensity value of that stimulus. Thus, whether that stimulus is bright or dim would be irrelevant; the response would be maximal to both without differences between the groups. However, when the participant is exposed to two different intensities in an intermixed manner the AL “…might be expected to lie between the two values. With the weak stimulus below AL and the strong above, an exaggerated intensity effect would be expected” (Grice and Hunter, 1964, p. 252).

Jo

ur

In our experiment, with the two stimuli being presented in a sequence, the theory would have implications for the effect of the shift. Participants that were trained with a bright stimulus should have established their AL to a relatively high brightness, provoking low responses to the dim stimulus initially, until the AL is adjusted to the new intensity. To the contrary, the relatively low location of the AL in the participants trained with a dim stimulus in phase 1 will produce a vigorous response to a brighter stimulus in phase 2. Our results partially confirmed this prediction. The group shifted from bright to dim showed a decay in responding. However, given that such decrement was most prevalent during the first second of the presentation of the new stimulus, it should be assumed that a single second is enough for the AL to be adjusted to the new stimulus. Additionally, the theory would predict an increase in responding over the training value to the bright stimulus in the dim-to-bright group, but that result was not obtained.

Intensity effects 13 The lack of an effect of shifting up in intensity that we observed (see also Miller and Greene, 1954) is accommodated by assuming that the SID effect is offset by a generalization decrement. Thus, accounts of generalization should be incorporated into any explanation of the effect. An account proposed by Rescorla (1976) may provide a particularly good starting point. His experiments involved pairing tones of different intensities with shock. He assumed that a high tone (e.g., 1800-hz) and a low tone (e.g., 250-hz) share a certain number of elements (X), and also that each have elements that are not contained by the other (e.g., H & L). Following asymptotic conditioning with the high tone, one group continued to receive conditioning with the same stimulus while the other received conditioning with the low tone. Then, both groups were tested in extinction with the high tone. The group that had received additional conditioning with the low tone showed greater suppression than did the group receiving conditioning with the high tone throughout.

re

-p

ro of

Rescorla’s explanation for this effect combines stimulus sampling theory (e.g., Estes, 1955) with the Rescorla-Wagner model (Rescorla and Wagner, 1972). As the high tone (HX) is assumed to encompass some elements contained in the low tone (LX), both the particular (H) and common (X) elements would be conditioned in phase 1 such that, combined, they reach asymptote (i.e., H + X = λ) and no further conditioning can take place in phase 2. However, in the group that is shifted to the low tone, the asymptote is disrupted by the loss of the high elements, reducing responding to that controlled by the X elements (e.g., X = .5 λ), and allowing the low tone, to receive further conditioning until their combination reaches asymptote (i.e., X = .75λ, L = .25 λ). Afterwards, when the high tone is tested, the H elements would be recombined with stronger common elements (X) and a greater response is observed (i.e., H + X = 1.25 λ).

Jo

ur

na

lP

Such an explanation can be applied to studies where two stimuli of different intensities are intermixed in training, where SID effects are more apparent (e.g., Beck, 1963; Cermak, 1967; Grice and Hunter, 1964; Grice et al., 1966; Lipkin and Moore, 1966). Although a straightforward application of this account would predict that conditioning with each stimulus reinforces portions of the alternate stimulus, the explanation can effectively work if it assumed that there is very little that is unique to the lower-intensity stimulus. If the stimuli are represented more as HL and L, rather than HX and XL, responding to L will be consistently lower than to HL due to the removal of the H elements. Rescorla’s explanation would be also effective if, in line with the Rescorla-Wagner model, the learning rate alpha for the elements that are unique to the low intensity stimulus is lower than that for the high intensity elements. Whereas the stimulus sampling account predicts larger within-subject effects than between, the pure Rescorla-Wagner approach to SID involving alpha, however, predicts an effect in phase 1 in our experiment that we did not observe (see also, Carter, 1941; Cermak, 1967; Grice and Hunter, 1964; Kimmel, 1959; Kimmel et al., 1962). Rescorla’s (1976) account can also be applied to the non-symmetric gradient that we found during the stimulus shift. In the case of training with the bright light and shifting to the dim, we can assume that the dim light is a subset to the left of the elements centered around the bright light. When the bright light is conditioned in phase 1 the dim and bright elements receive conditioning. On the generalization test with a dim light the loss of the bright elements should produce a straight-forward generalization decrement, which is what we observed. In

Intensity effects 14 the case of training with a dim light, the elements centered around the dim value receive conditioning, but it is reasonable to assume that other elements towards the bright direction may be inconsistently sampled and conditioned as well. Then, when tested with the bright light, the actual presentation of those inconsistently sampled conditioned elements is expected to increase responding. However, the inclusion of the additional bright elements (those not sampled during conditioning of the dim stimulus) can produce generalization decrement through a variety of mechanisms (McLaren and Mackintosh, 2000; Wagner, 2003), something that could limit the response increase producing little net change in responding.

-p

ro of

Assuming that less intense stimuli are encompassed by more intense stimuli explains examples where conditioning with different intensity stimuli, within subjects, reveals that intense stimuli condition more rapidly than less-intense ones. That explanation, however, does not apply to demonstrations of between-subject effects where intensity is manipulated in animals (Imada et al., 1981, E1; Kamin and Brimer, 1963; Kamin and Schaub, 1963; Kessen, 1953, E2; Myers, 1962; Rogers, 1973; Scavio and Gormezano, 1974; Welker and Wheatley, 1977, E4; Woodard, 1966) or the few cases where it has been observed in humans (Champion, 1962, E1; Mattson and Moore, 1964; Walker, 1960). Those cases would need to appeal to a parameter reflecting salience, such as alpha, in the Rescorla-Wagner (1972) model, which then begs the question as to why we did not observe any effect of intensity between subjects in training.

ur

na

lP

re

A possibility for the lack of between-subjects difference in our experiment is that the intensities of the stimuli used in this study may not be different enough so as to provoke different alpha parameters without comparison. As discussed in the introduction, our null result is not an exception, but a trend in between-subject assessments with humans. The failure to observe between-subject differences contrasts with the observation that such differences are better obtained in animal research. Interestingly, both might be related to the effective salience of the stimuli used. The situations used to motivate animals to respond (e.g., food or water deprivation) accompanied by the types of outcomes used in conditioning (e.g., shock or food) make the stimuli and their predictors much more biologically significant to the survival of the organism. Significance might facilitate stimulus processing and make differences in CS intensity more apparent and effective as compared to the relatively innocuous stimuli used in human studies, such as this one, or others that are adjusted to be merely annoying (e.g., Kimmel, 1959).

Jo

In summary, we found that the physical brightness of a CS in a behavioral task affects responding in humans. Changes in brightness did not appear to follow predictions based on simple stimulus intensity dynamism, where brighter stimuli should produce greater responding in both training and testing. The differences emerged only in testing, when participants had experienced both stimulus dimensions. That observation is in line with multiple reports that show what appears to be an SID effect in within-subject designs and favors the idea that brightness is represented along a continuum of stimulus values where the less-bright stimulus is contained within the brighter one.

Intensity effects 15 Declarations of interest: none.

Acknowledgments

ro of

The current experiment is related to the first author dissertation. It was prepared and carried out with support from the Ministerio de Economía, Industria y Competitividad (Ministry of Economy, Industry and Competitiveness) of Spain (grants BES-2015-074309 and PSI201452263-C2-2-P). Its preparation for publication was made possible by grant PGC2018-097769-BC21 from the Ministerio de Ciencia, Innovación y Universidades (Ministry of Science, Innovation, and Universities) of Spain and grant IT1341-19 from the Basque Government.

References

-p

Arcediano, F., Ortega, N., Matute, H., 1996. A behavioural preparation for the study of human Pavlovian conditioning. Q. J. Exp. Psychol. 49, 270–283. https://doi.org/10.1080/713932633

re

Balea, P., Sanjuan, M. del C., Nelson, J.B., 2018. Learning to learn in conditioning and extinction in humans. Behav. Processes 157, 148–160. https://doi.org/10.1016/J.BEPROC.2018.09.005

lP

Barnes, G.W., 1956. Conditioned stimulus intensity and temporal factors in spaced-trial classical conditioning. J. Exp. Psychol. 51, 192–198. https://doi.org/10.1037/h0047728

na

Beck, S.B., 1963. Eyelid conditioning as a function of CS intensity, UCS intensity, and manifest anxiety scale score. J. Exp. Psychol. 66, 429–438. https://doi.org/10.1037/h0042820

ur

Birkimer, J.C., Drane, D.L., 1968. Stimulus intensity dynamism with stimuli equal decibel distances above and below background. Psychon. Sci. 12, 213–214. https://doi.org/10.3758/BF03331275 Carter, L.F., 1941. Intensity of conditioned stimulus and rate of conditioning. J. Exp. Psychol. 28, 481–490. https://doi.org/10.1037/h0060478

Jo

Cermak, L.S., 1967. CS intensity as a function of the experimental design. Psychon. Sci. 8, 151– 152. https://doi.org/10.3758/BF03331594 Champion, R.A., 1962. Stimulus-intensity effects in response evocation. Psychol. Rev. 69, 428– 449. https://doi.org/10.1037/h0047258 Estes, W.K., 1955. Statistical theory of spontaneous recovery and regression. Psychol. Rev. 62, 145–154. https://doi.org/10.1037/h0048509 Franssen, M., Clarysse, J., Beckers, T., van Vooren, P.R., Baeyens, F., 2010. A free software package for a human online-conditioned suppression preparation. Behav. Res. Methods 42, 311–317. https://doi.org/10.3758/BRM.42.1.311

Intensity effects 16 Frey, P.W., 1969. Within- and between-session CS intensity performance effects in rabbit eyelid conditioning. Psychon. Sci. 17, 1–2. https://doi.org/10.3758/BF03329135 Ghirlanda, S., Enquist, M., 2003. A century of generalization. Anim. Behav. 66, 15–36. https://doi.org/10.1006/anbe.2003.2174 Grant, D.A., Schneider, D.E., 1949. Intensity of the conditioned stimulus and strength of conditioning: II. The conditioned galvanic skin response to an auditory stimulus. J. Exp. Psychol. 39, 35–40. https://doi.org/10.1037/h0062367 Grant, D.A., Schneider, D.E., 1948. Intensity of the conditioned stimulus and strength of conditioning: I. The conditioned eyelid response to light. J. Exp. Psychol. 38, 690–696. https://doi.org/10.1037/h0056739

ro of

Gray, J.A., 1965. Relation between stimulus intensity and operant response rate as a function of discrimination training and drive. J. Exp. Psychol. 69, 9–24. https://doi.org/10.1037/h0021626

Grice, G.R., Hunter, J.J., 1964. Stimulus intensity effects depend upon the type of experimental design. Psychol. Rev. 71, 247–256. https://doi.org/10.1037/h0047547

-p

Grice, G.R., Masters, L., Kohfeld, D.L., 1966. Classical conditioning without discrimination training: A test of the generalization theory of CS intensity effects. J. Exp. Psychol. 72, 510–513. https://doi.org/10.1037/h0023761

re

Guttman, N., Kalish, H.I., 1956. Discriminability and stimulus generalization. J. Exp. Psychol. 51, 79–88. https://doi.org/10.1037/h0046219 Hall, J.F., Prokasy, W.F., 1961. Stimulus Generalization to Absolutely Discriminable Tones. Percept. Mot. Skills 12, 175–178. https://doi.org/10.2466/pms.1961.12.2.175

lP

Hearst, E., 1969. Stimulus intensity dynamism and auditory generalization for approach and avoidance behavior in rats. J. Comp. Physiol. Psychol. 68, 111–117. https://doi.org/10.1037/h0027680

na

Heyman, W., 1957. Certain relationships between stimulus intensity and stimulus generalization. J. Exp. Psychol. 53, 239–248. https://doi.org/10.1037/h0047221

ur

Hovland, C.I., 1937. The generalization of conditioned responses: II. The sensory generalization of conditioned responses with varying intensities of tone. Pedagog. Semin. J. Genet. Psychol. 51, 279–291. https://doi.org/10.1080/08856559.1937.10532503 Howell, D.C., 1987. Statistical methods for psychology. Duxbury Press, Boston.

Jo

Hull, C.L., 1949. Stimulus instensity dynamism (V) and stimulus generalization. Psychol. Rev. 56, 67–76. https://doi.org/10.1037/h0058051 Imada, H., Yamazaki, A., Morishita, M., 1981. The effects of signal intensity upon conditioned suppression: Effects upon responding during signals and intersignal intervals. Anim. Learn. Behav. 9, 269–274. https://doi.org/10.3758/BF03197830 Ivanov-Smolensky, A.G., 1927. On the methods of examining the conditioned food reflexes in children and in mental disorders. Brain 50, 138–141. https://doi.org/10.1093/brain/50.2.138 Kamin, L.J., Brimer, C.J., 1963. The effects of intensity of conditioned and unconditioned stimuli

Intensity effects 17 on a conditioned emotional response. Can. J. Psychol. 17, 194–200. Kamin, L.J., Schaub, R.E., 1963. Effects of conditioned stimulus intensity on the conditioned emotional response. J. Comp. Physiol. Psychol. 56, 502–507. https://doi.org/10.1037/h0046616 Kessen, W., 1953. Response strength and conditioned stimulus intensity. J. Exp. Psychol. 45, 82–86. https://doi.org/10.1037/h0057700 Kimmel, H.D., 1959. Amount of conditioning and intensity of conditioned stimulus. J. Exp. Psychol. 58, 283–288. https://doi.org/10.1037/h0048529 Kimmel, H.D., Hill, F.A., Morrow, M.C., 1962. Strength of GSR and avoidance conditioning as a function of CS intensity. Psychol. Rep. 11, 103–109. https://doi.org/10.2466/pr0.1962.11.1.103

ro of

Leonard, D.W., Monteau, J.E., 1971. Does CS intensity determine CR amplitude? Psychon. Sci. 23, 369–370. https://doi.org/10.3758/BF03336157 Lipkin, S.G., Moore, J.W., 1966. Eyelid trace conditioning, CS intensity, CS-UCS interval, and a correction for “spontaneous” blinking. J. Exp. Psychol. 72, 216–220. https://doi.org/10.1037/h0023448

-p

Mattson, M., Moore, J.W., 1964. Intertrial responding and CS intensity in classical eyelid conditioning. J. Exp. Psychol. 68, 396–401. https://doi.org/10.1037/h0042371

re

McLaren, I.P.L., Mackintosh, N.J., 2000. An elemental model of associative learning: I. Latent inhibition and perceptual learning. Anim. Learn. Behav. 28, 211–246. https://doi.org/10.3758/BF03200258

lP

Miller, W.C., Greene, J.E., 1954. Generalization of an avoidance response to varying intensities of sound. J. Comp. Physiol. Psychol. 47, 136–139. https://doi.org/10.1037/h0060367

na

Mourant, R.R., Pennypacker, H.S., 1968. The effects of CS intensity upon learning and performance of the conditioned eyelid response in monkeys. Psychon. Sci. 12, 81–82. https://doi.org/10.3758/BF03331203 Murray, H.G., 1970. Stimulus intensity and reaction time: Evaluation of a decision-theory model. J. Exp. Psychol. 84, 383–391. https://doi.org/10.1037/h0029284

ur

Myers, A.K., 1962. Effects of CS intensity and quality in avoidance conditioning. J. Comp. Physiol. Psychol. 55, 57–61. https://doi.org/10.1037/h0041373

Jo

Nelson, J.B., Navarro, A., Sanjuan, M. del C., 2014. Presentation and validation of “The Learning Game,” a tool to study associative learning in humans. Behav. Res. Methods 46, 1068– 1078. https://doi.org/10.3758/s13428-014-0446-2 Paschall, G.Y., Davis, M., 2002. Olfactory-mediated fear-potentiated startle. Behav. Neurosci. 116, 4–12. https://doi.org/10.1037/0735-7044.116.1.4 Rescorla, R.A., 1976. Stimulus generalization: Some predictions from a model of Pavlovian conditioning. J. Exp. Psychol. Anim. Behav. Process. 2, 88–96. https://doi.org/10.1037/0097-7403.2.1.88 Rescorla, R.A., Wagner, A.R., 1972. A theory of Pavlovian conditioning: Variations in the effectiveness of reinforcement and nonreinforcement., in: Black, A.H., Prokasy, W.F.

Intensity effects 18 (Eds.), Classical Conditioning: Current Research and Theory. Appleton-Century-Crofts., New York, NY, pp. 64–99. Rogers, J.D., 1973. Stimulus intensity and trace intervals in sensory preconditioning using the CER. Bull. Psychon. Soc. 1, 107–109. https://doi.org/10.3758/BF03334309 Rohrbaugh, M., Brennan, J.F., Riccio, D.C., 1971. Control of two-way shuttle avoidance in rats by auditory frequency and intensity. J. Comp. Physiol. Psychol. 75, 324–330. https://doi.org/10.1037/h0030828 Scavio, M.J., Gormezano, I., 1974. CS Intensity effects on rabbit nictitating membrane conditioning, extinction and generalization. Pavlov. J. Biol. Sci. Off. J. Pavlov. 9, 25–34. https://doi.org/10.1007/bf03000500

ro of

Spiker, C.C., 1956. The stimulus generalization gradient as a function of the intensity of stimulus lights. Child Dev. 27, 85–98. Thomas, D.R., Hiss, R.H., 1963. A test of the “units hypothesis” employing wave-length generalization in human subjects. J. Exp. Psychol. 65, 59–62.

Wagenmakers, E.-J., 2007. A practical solution to the pervasive problems of p values. Psychon. Bull. Rev. 14, 779–804. https://doi.org/10.3758/BF03194105

-p

Wagner, A.R., 2003. Context-sensitive elemental theory. Q. J. Exp. Psychol. Sect. B 56, 7–29. https://doi.org/10.1080/02724990244000133

re

Walker, E.G., 1960. Eyelid conditioning as a function of intensity of conditioned and unconditioned stimuli. J. Exp. Psychol. 59, 303–311. https://doi.org/10.1037/h0048058

lP

Welker, R.L., Wheatley, K.L., 1977. Differential acquisition of conditioned suppression in rats with increased and decreased luminance levels as CS + s. Learn. Motiv. 8, 247–262. https://doi.org/10.1016/0023-9690(77)90009-1

Jo

ur

na

Woodard, W.T., 1966. The effect of conditioned stimulus intensity in classical conditioning of the common carp. University of Hawaii, Ann Arbor, Michigan.

Intensity effects 19 Figure captions

Figure 1. Experimental design. The Left column represents the phase-1 grouping. Participants were conditioned with a Bright or a Dim light, that could be either red (R) or blue (B), counterbalanced. The columns under the phase-2 groups heading represent how participants were divided in phase 2, where the phase-1 groups were tested with a different intensity (middle column) or with a different color (right column).

ro of

Figure 2. Presses per second during phase 1. The top panel shows responding in the two phase-1 groups based on the intensity of the CS. In the bottom panel, data from the same participants are split according to the color counterbalancing. Bars represent the standard error of the mean.

-p

Figure 3. Presses per second during the last phase-1 trial (dashed lines) and the generalization test (solid lines). The phase-1 bright (solid triangles) and dim (open triangles) groupings are divided here depending on whether they had an intensity change (left half) or a color change (right half) in phase 2. In the Color Changed groups, the figure collapses across the color counterbalancing variable (Red vs. Blue). Bars represent the standard error of the mean.

Jo

ur

na

lP

re

Figure 4. Presses per second during phase 2 in the Intensity Changed groups (top panel) and Color Changed groups (bottom panel). Bars represent the standard error of the mean.

Intensity effects 20 Figure 1

Jo

ur

na

lP

re

-p

ro of

(Color should be used in this figure)

Intensity effects 21 Figure 2

6

Bright

Dim

4

ro of

Responses per second

5

3 2

1 0 1

2

3

4

-p

12345 12345 12345 12345 12345 12345 12345 12345 5

6

7

8

6

Red

Blue

lP

4

2 1

0

na

3

ur

Responses per second

5

re

Seconds × Trials

12345 12345 12345 12345 12345 12345 12345 12345

Jo

1

2

3

4

5

Seconds × Trials

6

7

8

Intensity effects 22

ro of

Figure 3

7

Dashed lines = Last training trial Solid lines = First test trial

-p

5 4

re

3 2 1 0 2

3

4

5

1

2

na

1

lP

Responses per second

6

Jo

ur

Bright - Intensity changed

3

4

Dim - Intensity changed

5

1

2

3

4

Bright - Color changed

5

1

2

3

4

Dim - Color changed

5

Intensity effects 23 Figure 4

7

Bright

Dim

5

ro of

Responses per second

6

4 3 2

1

-p

0

12345 12345 12345 12345 12345 12345 12345 12345 1

2

3

4

5

6

7

8

7

Red

Blue

lP

5 3 2 1 0

na

4

ur

Responses per second

6

re

Seconds × Trials

12345 12345 12345 12345 12345 12345 12345 12345

Jo

1

2

3

4

5

Seconds × Trials

6

7

8