DOI: 10.5061/dryad.ksn02v7gh
title: Data from: Do goldfish like to be informed?

description: Like humans, several mammalian and avian species prefer foretold over
 unsignalled future events, even if the information is costly and confers
 no direct benefit. It is unclear whether this is an epiphenomenon of basic
 associative learning mechanisms or whether these preferences reflect a
 derived form of information-seeking that is reminiscent of human
 curiosity. We investigate whether a fish that shares basic reinforcement
 learning mechanisms with birds and mammals, also shows such a preference,
 with the aim of elucidating whether widely shared conditioning processes
 are sufficient to explain paradoxical preferences resulting in unusable
 information. Goldfish (Carassius auratus) chose between two alternatives,
 both resulting in a 5s delay and 50% reward chance. The ‘informative’
 option immediately produced a stimulus correlated with the trial’s
 forthcoming outcome (reward/no reward). Choosing the ‘non-informative’
 option instead triggered an uncorrelated stimulus. Goldfish discriminated
 between the different contingencies, but did not develop a preference for
 the informative option, suggesting that in goldfish, associative learning
 mechanisms are not sufficient to generate preferences between alternatives
 differing only in outcome predictability. These results challenge the
 notion that informative preferences are a by-product of ubiquitous
 associative processes, and are consistent with the possibility that
 derived information-seeking mechanisms have evolved in some vertebrate
 species.
description: # Do goldfish like to be informed? Dataset DOI:
 [10.5061/dryad.ksn02v7gh](10.5061/dryad.ksn02v7gh) #### 1. **Data and File
 Overview** **1.1 Dataset files by figure/panel (all in
 *Annotated_data.zip*):** * Fig2a_InfoPreference_all.csv *
 Fig2b_ForcedLatencies.csv * Fig3aTOP_fulltracks.csv *
 Fig3aTOP_SplusTracks.csv           * Fig3aTOP_noInfoTracks.csv *
 Fig3aTOP_SminusTracks.csv * Fig3a_BOTTOM.csv * Fig3b.csv *
 Fig3c_MovementMetric.csv * FigS3_TerminalChoices_all.csv *
 processingMetaData_tracks.csv **1.2. Source_data.zip (See 3. Raw data for
 details)** **1.2.1     Phase 1** Includes individual animal/session event
 data files   **1.2.2     Phase 2** Includes individual animal/session
 event and tracking data files **1.3  Code (all in Code.zip):** * figures.m
 * dataProcessing.m * parse_choices_and_latencies_phase_1.m *
 parse_choices_phase_2.m *  parseTracks.m *
 Statistical_analysis_do_goldfish_like_to_be_informed.R #### 2.  Annotated
 data and accompanying code **2.1. Annotated data descriptions by figure**
 **Figure 2a** Filename: Fig2a_InfoPreference_all.csv Number of variables:
 3 Variable list: * Choice_proportion: proportion of choices made for the
 Info option over the NoInfo option on a given day for a given individual
 (number of Info choice /number of Info choices + number of Noinfo
 choices). * Subject: number identifying a given individual goldfish in the
 study (1-8). * Day: denotes the testing day in consecutive order (1-30).
 **Figure 2b** Filename: Fig2b_ForcedLatencies.csv Number of variables: 4
 Variable list: * Latency: median across trials of time taken in seconds to
 respond to the presentation of either Info or NoInfo in forced trials
 where only one option is presented. * Option: indicates whether data is
 from Info or NoInfo  forced trials. * Subject: number identifying a given
 individual goldfish in the study (1-8). * Day: denotes the testing day in
 consecutive order (1-30). **Figure 3a top panel** Filename:
 Fig3aTOP_fulltracks.csv Number of Variables: 2 Variable list: * Xpos:
 normalised x co-ordinates of an example subject’s (BuBu) centroid from an
 experimental session presented in sequence. * Ypos: normalised y
 co-ordinates of an example subject’s (BuBu) centroid from an experimental
 session presented in sequence. Filename: Fig3aTOP_SplusTracks.csv    
          Number of Variables: 10          Variable list: * t1_x * t1_y *
 t2_x * t2_y * t3_x * t3_y * t4_x * t4_y * t5_x * t5_y The data shows x and
 y co-ordinates of the centroid of an example subject (BuBu) during the
 post-choice delay period in trials where the Info stimulus for reward (S+)
 is presented. Corresponding x and y co-ordinates for 5 example trials are
 present. Column headings denote the trial and co-ordinate type for each
 sequence. For example, ‘t1_x’ is a column of x-coordinates taken from
 trial 1 during the post choice delay when (S+) is presented, while, ‘t1_y’
 is the column of y-coordinates taken from trial 1 during the post choice
 delay when (S+) is presented.  Filename: Fig3aTOP_noInfoTracks.csv  Number
 of Variables: 20  Variable list: * t1_x * t1_y * t2_x * t2_y * t3_x * t3_y
 * t4_x * t4_y * t5_x * t5_y * t6_x * t6_y * t7_x * t7_y * t8_x * t8_y *
 t9_x * t9_y * t10_x * t10_y The data shows x and y co-ordinates of the
 centroid of an example subject (BuBu) during the post-choice delay period
 in trials where the stimuli in the non-informative option (N1 or N2) are
 presented. Corresponding x and y co-ordinates for 10 example trials are
 present. Column headings denote the trial and co-ordinate type for each
 sequence. For example, ‘t1_x’ is a column of x-coordinates taken from
 trial 1 during the post choice delay when either N1 or N2 is presented,
 while, ‘t1_y’ is the column of y-coordinates taken from trial 1 during the
 post choice delay when either N1 or N2  is presented. Filename:
 Fig3aTOP_SminusTracks.csv  Number of Variables: 18 Variable list: * t1_x *
 t1_y * t2_x * t2_y * t3_x * t3_y * t4_x * t4_y * t5_x * t5_y * t6_x * t6_y
 * t7_x * t7_y * t8_x * t8_y * t9_x * t9_y The data shows x and y
 co-ordinates of the centroid of an example subject (BuBu) during the
 post-choice delay period in trials where the Info stimulus for no reward
 (S-) is presented. Corresponding x and y co-ordinates for 9 example trials
 are present. Column headings denote the trial and co-ordinate type for
 each sequence. For example, ‘t1_x’ is a column of x-coordinates taken from
 trial 1 during the post choice delay when (S-) is presented, while, ‘t1_y’
 is the column of y-coordinates taken from trial 1 during the post choice
 delay when (S-) is presented.  **Figure 3a bottom panel**  Filename:
 Fig3a_BOTTOM.csv  Number of Variables: 4  Variable list: * occupancy
 probability: probability of animal being present in a given tank region.
 The probabilities are organised within a 10 by 20 matrix which represents
 the total area of the experimental tank. * subject: identifies
 participating animal. * condition: denotes whether occupancy histograms
 are derived from subject’s position when S+, N1/N2 or S- are presented.
 The data comprises three 10 by 20 matrices (one for each stimulus type)
 showing occupancy probabilities for tank regions during stimulus
 presentation. All data are taken from an example session from the same
 example subject (Bubu). **Figure 3b** Filename: Fig3b.csv  Number of
 Variables: 4  Variable list: * occupancy probability: probability of
 animal being present in a given tank region. The probabilities are
 organised within a 10 by 20 matrix which represents the total area of the
 experimental tank. The probability shown for each region is the average
 across the last 5 experimental days for each subject. * subject:
 identifies participating animal. * condition: denotes whether occupancy
 histograms are derived from subject’s position when S+, N1/N2 or S- are
 presented.  For each subject, the data comprises three 10 by 20 matrices
 (one for each stimulus type) showing averaged occupancy probabilities for
 tank regions during stimulus presentation.  **Figure 3c**  Filename:
 Fig3c_MovementMetric.csv  Number of Variables: 3  Variable list: *
  Entropy: movement metric computed as the entropy of subjects’ spatial
 distributions during the post-choice delay when various anticipatory
 stimuli are presented, split by stimulus type. Larger values correspond to
 more movement during these periods. * subject: identifies participating
 animal (1-8). * Stimulus: Denotes the reward predictive stimulus that was
 presented S+ (100% reward), N1/N2 (50%) or S- (0%), during the post-choice
 delay. **Figure S1** Same data as in Figure 2a and Figure 2b (see above).
 **Figure S2b,c** Same  data as in Figure 3b (see above). **Figure S3**
 Filename: FigS3_TerminalChoices_all.csv  Number of Variables: 4  Variable
 list: *  choice_proportion: proportion of choices for S+ stimulus over
 N1/N2 stimuli or proportion of choices for S- stimulus over N1/N2 stimuli.
 *  Stimulus: shows whether S+ or S- was available to choose. - Subject:
 identifies participating animal (1-8). * Day: denotes the testing day in
 consecutive order (1-5). **2.2. Code to generate figures from annotated
 data** All code required to generate all figures is included in
 &lt;&lt;*figures.m*&gt;&gt;, with separate annotated sections for each
 relevant figure and panel combinations. **2.3. R code for statistical
 analysis** All R code for statistical analyses is included in file
 &lt;&gt;
 with separate sections for each relevant figure panel combination. #### 3.
 Raw data files (source data) Above we reference code to reproduce figures
 and statistical analyses using annotated data which is pre-processed. Here
 we list source data files and code that were used to produce the annotated
 data. The experiment was fully controlled using a custom *Bonsai* workflow
 (see Ajuwon et at. 2024 for details). This workflow simultaneously
 controlled task contingencies and tracked subjects’ centroid in real time
 (see *Apparatus and task control*) and saved the resulting data. For every
 subject and session, two different file types were saved, one — the
 “*events.csv”* file — that included the task’s events (and accompanying
 timestamps and outcome, if relevant) and another — the “*tracks.csv”* file
 (**Figure 1a**) — that saved the x,y coordinates of each subject’s
 centroid and the luminance value of a specific screen location. #### 4.
 Parsing choices and response latencies from event data from phase 1
 Choices proportions and response latencies were obtained for every trial,
 session and subject from
 &lt;&lt;“{subject}*events*{timestamp}.csv”&gt;&gt; files (see example
 snippets in **Appendix figure 1a**) following the routine in
 &lt;&lt;*parse_choices_and_latencies_phase_1.m*&gt;&gt;. The source data
 can be found in &lt;&gt;. The
 resulting data is compiled and annotated in
 &lt;&lt;‘Fig2a_InfoPreference_all.csv’*&gt;&gt;  and
 &lt;&lt;‘Fig2b_ForcedLatencies.csv’*&gt;&gt; (see 1.1. *Annotated data
 descriptions by figure* for details). **Appendix figure 1.** Example
 snippet of an *“event.csv”* file (a) and a “*track.csv”* file (b). **a.**
 Every trial started with an inter-trial-interval (ITI) onset and ended
 with its corresponding trial number. Other events were ITI offset time,
 trial initiation, choice, terminal stimulus onset and offset times and
 outcome. **b.** For every frame in the video (rows) screen luminance (at a
 specific fixed location), x and y position values were stored - columns.
 #### 5. Combining event and track data All code that combines and parses
 events and tracking data, as well as normalizes and transforms tracks can
 be found in &lt;&lt;*parseTracks.m*&gt;&gt;. The accompanying source data
 can be found in &lt;&gt; **5.1.
 Event detection from luminance traces** The luminance of a circular area
 (0.75 cm radius) located in the top and center of was continuously
 recorded (see **Appendix Figure 1b** for example raw file and **Appendix
 Figure 2a** for example session). Its hue would change from black, during
 the ITI, to grey when a trial was available to white following a choice.
 Thresholding of this signal allowed for the trial-by-trial detection of
 these events (**Appendix Figure 2b**). Due to slight variations of
 lighting conditions and camera position across animals, these thresholds
 were pre-set on an individual-by-individual basis, and stored for easy
 access and analyses calls in a metadata file —
 &lt;&lt;*processingMetaData_tracks.csv&gt;&gt;*.  The combination of
 events detected from luminance profiles with those same events logged into
 the event files (**Appendix Figure 2c**), allowed to pinpoint onto the
 animals movement trajectories every relevant task condition and epoch,
 especially during the post-choice pre-outcome period (**Figure 3a top**).
 **Appendix figure 2.** Example session luminance trace. **a.** Raw
 luminance traces. **b.** ITI offset and choice detection based on
 luminance values. **c.** Choice detection combined with event data,
 depicting in different colours, info/no-info choices and corresponding
 terminal stimulus presented.  **5.2. Generating normalized occupancy
 histograms/plots** Animals were tested in two experimental tanks. These
 tanks were, in all respects identical, other than the camera orientation
 that was flipped. Side of conditions and colour allocation of the terminal
 stimuli were counterbalanced across subjects. The metadata file
 (&lt;&lt;*processingMetaData_tracks.csv&gt;&gt;*) includes, in addition to
 thresholding values for luminance detection, information regarding the
 experimental tank, counterbalance and if the tracking data is not
 corrupted (see **Supplementary Table 2**). Using this information, all
 trajectories coordinates were normalized [-1,1], the info side arbitrarily
 set to the left (i.e., bottom left of trajectory and occupancy plots).
 Occupancy 2D-histograms, also normalized (i.e., the number of elements in
 each bin relative to the total number of elements in the input data was at
 most 1) were built for every session and subject and then averaged
 (**Supplementary Figure 2** and &lt;&lt;*Fig3b.csv&gt;&gt;*). #### 6.
 Movement metric calculation Normalized histograms of animals’ occupancy
 (data in &lt;&lt;*Fig3b.csv&gt;&gt;*) were converted into a movement
 metric (for details see *Data analyses* section) score using the function
 &lt;&lt;*entropy()&gt;&gt;* by Will Dwinnell that returns entropy (in
 bits). Example code for this analysis can be found in
 &lt;&lt;*dataProcessing.m&gt;&gt;.* #### 7. Parsing choices from event
 data from phase 2 Choices during phase 2 for every trial, session and
 subject were obtained from “{subject}*terminal_events*{timestamp}.csv”
 files following the routine in
 &lt;&lt;*parse_choices_phase_2.m*&gt;&gt;.The source data can be found in
 &lt;&gt;. The resulting data are compiled
 and annotated in &lt;&lt; &lt;&lt;‘FigS3_TerminalChoices_all.csv’&gt;&gt;
 (see 1.1. *Annotated data descriptions by figure*, for details).